PBRM1 BIOMARKERS PREDICTIVE OF ANTI-IMMUNE CHECKPOINT RESPONSE

Information

  • Patent Application
  • 20190338369
  • Publication Number
    20190338369
  • Date Filed
    January 03, 2018
    6 years ago
  • Date Published
    November 07, 2019
    5 years ago
Abstract
The present invention is based on the identification of novel biomarkers predictive of responsiveness to anti-immune checkpoint therapies.
Description
BACKGROUND OF THE INVENTION

Immune checkpoint inhibitors, including monoclonal antibodies targeting programmed cell death-1 (PD-1) and its ligand PD-L1, have yielded durable clinical benefit for patients with a range of tumor types, including metastatic melanoma, non-small cell lung cancer (NSCLC), and bladder cancer (Topalian et al. (2015) Cancer cell 27:450-461). Renal cell carcinoma (RCC) has been known to be immunotherapy-responsive for more than 20 years, with high-dose interleukin-2 yielding durable complete responses in a small minority of patient. Recently, immune checkpoint blockade has been shown to be remarkably effective in RCC, yielding superior rates of objective response compared to everolimus (Motzer et al. (2015) N. Engl. J. Med. 373:1803-1813). However, responses only occurred in about a quarter of patients, and immunohistochemistry for PD-L1 was not predictive of treatment response (Choueiri et al. (2016) Clin. Cancer Res. 22:5461-5471; Motzer et al. (2015), supra), making identification of pre-treatment predictors of patient benefit a clinical priority.


Studies featuring somatic genetic analysis of patients receiving immune checkpoint therapies for metastatic melanoma, non-small cell lung cancer, and colorectal cancer have demonstrated that pre-treatment tumor mutational load, neoantigen burden, microsatellite instability, gene expression signatures, and neoantigen clonality can influence likelihood of response (Hugo et al. (2016) Cell 165:35-44; Le et al. (2015) N. Engl. J. Med. 372:2509-2520; McGranahan et al. (2016) Science 351:1463-1469; Rizvi et al. (2015) Science 348:124-128; Snyder et al. (2014) N. Engl. J. Med. 371:2189-2199; and Van Allen et al. (2015) Science 350:207-211). These studies support the concept that highly mutated tumors generate tumor-specific antigens (neoantigens) that mediate a strong immune response to cancer cells after the administration of immune checkpoint therapies that disrupt immunosuppression in the tumor microenvironment.


In contrast to melanoma, non-small cell lung cancer, and microsatellite-unstable colorectal cancer, which commonly harbor more than 10 to 400 mutations per megabase (Mb), clear cell renal cell carcinoma (ccRCC) has an average of 1.1 nonsynonymous mutations/Mb, without significant outliers (Cancer Genome Atlas Research (2013) Nature 499:43-49), while ranking among the highest across multiple tumor types in cytolytic activity (Rooney et al. (2015) Cell 160:48-61), immune infiltration score, and T cell infiltration score (Sȩnbabaoğlu et al. (2016) Genome Biol. 17:231), suggesting that distinct molecular mechanisms other than mutational burden or neoantigen burden may underlie its T-cell enriched microenvironment and responsiveness to immune checkpoint therapy. RCC is also characterized by frequent alterations in von Hippel Lindau protein (VHL), a tumor suppressor that regulates the transcription factor hypoxia inducible factor 1a (HIF1A) central to controlling angiogenesis. Alterations in SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complex are also common. SWI/SNF subunits commonly mutated in ccRCC include polybromo 1 (PBRM1), AT-Rich Interaction Domain 1A (ARID1A), and Transcription activator BRG1 (SMARCA4) are also common. Other commonly mutated genes included the histone deubiquitinase BRCA1 Associated Protein 1 (BAP1), and the histone methyltransferase SET domain containing 2 (SETD2). The genes encoding VHL, PBRM1, BAP1, and SETD2 are all clustered in the small arm of chromosome 3 (chr3p), and arm-level deletions of chr3p are exceedingly common in ccRCC (>90% of samples; TCGA (2013) Nature 499:43-49). While the relationship between these DNA-level alterations affecting chromatin remodeling, angiogenesis, and response to hypoxia and the enrichment in immune cell infiltration in ccRCC is still not fully understood, experimental studies aiming to characterize the functional impact of PBRM1 loss have identified upregulation of the interleukin-6-mediated signaling pathway as one effect of re-expressing PBRM1 in PBRM1-deficient RCC cell lines (Chowdhury et al. (2016) PLoS One 11:e0153718).


In clinical studies of patients receiving anti-PD-1 therapy for metastatic RCC, whole genome microarray characterization of pre-treatment tumors from 11 patients revealed that nonresponders had higher expression of genes related to cell metabolism and solute transport, while responders overexpressed immune markers (Ascierto et al. (2016) Cancer Immunol Res. 4:726-733). Germline variants in STAT3, a transcription factor associated with immune function, have also previously been linked to response to immunotherapy with high-dose interferon (Eto et al. (2013) Eur. Urol. 63:745-752). However, no study has yet examined pre-treatment tumor whole exome and whole transcriptome sequencing with matched germline whole exome sequencing in well-annotated cohorts of renal cell carcinoma patients treated with immune checkpoint inhibitor therapy (e.g., anti-PD1 therapy used to treat metastatic RCC) to discover alterations in specific genes, transcriptional profiles, and immunological features that may predict response to immune checkpoint therapy. Accordingly, there remains a great need in the art to identify biomarkers predictive of response to immune checkpoint therapy for improved clinical stratification and enhanced understanding of the mechanism of these drugs.


SUMMARY OF THE INVENTION

The present invention is based, at least in part, on the discovery that loss-of-function mutation of PBRM1 is a highly specific biomarker for prediction of clinical outcomes (e.g., improved clinical outcomes such as tumor shrinkage and prolonged survival) in renal cell carcinoma patients treated with immune checkpoint therapies, such as those comprising an anti-PD-1 therapeutic (e.g., PD-1 blocking antibody).


In one aspect, a method of identifying the likelihood of a cancer in a subject to be responsive to an immune checkpoint therapy, the method comprising a) obtaining or providing a subject sample from a patient having cancer; b) measuring the amount or activity of at least one biomarker listed in Table 1 in the subject sample; and c) comparing said amount or activity of the at least one biomarker listed in Table 1 in a control sample, wherein the absence of or a significantly decreased amount or activity of the at least one biomarker listed in Table 1 in the subject sample and/or the presence of or a significantly increased amount or activity of the at least one biomarker listed in Table 1 having a loss of function mutation in the subject sample, relative to the control sample identifies the cancer as being more likely to be responsive to the immune checkpoint therapy; and wherein the presence of or a significantly increased amount or activity of the at least one biomarker listed in Table 1 in the subject sample and/or the absence of or a decreased amount or activity of the at least one biomarker listed in Table 1 having a loss of function mutation in the subject sample, relative to the control sample identifies the cancer as being less likely to be responsive to the immune checkpoint therapy, is provided.


In another aspect, a method of identifying the likelihood of a cancer in a subject to be responsive to immune checkpoint therapy, the method comprising a) obtaining or providing a subject sample from a patient having cancer, wherein the sample comprises nucleic acid molecules from the subject; b) determining the copy number of at least one biomarker listed in Table 1 in the subject sample; and c) comparing said copy number to that of a control sample, wherein a decreased copy number of the at least one biomarker listed in Table 1 in the in the subject sample and/or an increased copy number of the at least one biomarker listed in Table 1 having a loss of function mutation in the subject sample, relative to the control sample identifies the cancer as being more likely to be responsive to the immune checkpoint therapy; and wherein a wild type or increased copy number of the biomarker in the subject sample and/or or a decreased copy number of the at least one biomarker listed in Table 1 having a loss of function mutation in the sample relative to the control sample identifies the cancer as being less likely to be responsive to the immune checkpoint therapy, is provided.


Numerous embodiments are further provided that can be applied to any aspect of the present invention and/or combined with any other embodiment described herein. For example, in one embodiment, the method provided herein further comprises recommending, prescribing, or administering the immune checkpoint therapy if the cancer is determined likely to be responsive to the immune checkpoint therapy or administering an anti-cancer therapy other than the immune checkpoint therapy if the cancer is determined be less likely to be responsive to the immune checkpoint therapy. The anti-cancer therapy may be, for example, selected from the group consisting of targeted therapy, chemotherapy, radiation therapy, and/or hormonal therapy. In another embodiment, the control sample described herein is determined from a cancerous or non-cancerous sample from either the patient or a member of the same species to which the patient belongs. In still another embodiment, the control sample is a cancerous or non-cancerous sample from the patient obtained from an earlier point in time than the patient sample. In yet another embodiment, the control sample is obtained before the patient has received immune checkpoint therapy and the patient sample is obtained after the patient has received immune checkpoint therapy. In another embodiment, the control sample described herein comprises cells or does not comprise cells. In still another embodiment, the control sample comprises cancer cells known to be responsive or non-responsive to the immune checkpoint therapy.


In another aspect, a method of assessing the efficacy of an agent for treating a cancer in a subject that is unlikely to be responsive to an immune checkpoint therapy, comprising a) detecting in a first subject sample and maintained in the presence of the agent the amount or activity of at least one biomarker listed in Table 1; b) detecting the amount or activity of the at least one biomarker listed in Table 1 in a second subject sample and maintained in the absence of the test compound; and c) comparing the amount or activity of the at least one biomarker listed in Table 1 from steps a) and b), wherein the presence of or a significantly increased amount or activity of the at least one biomarker listed in Table 1 in the first subject sample and/or the absence of or a decreased amount or activity of the at least one biomarker listed in Table 1 having a loss of function mutation in the first subject sample, relative to at least one subsequent subject sample, indicates that the agent treats the cancer in the subject, is provided.


In another aspect, a method of assessing the efficacy of an agent for treating a cancer in a subject or prognosing progression of a cancer in a subject, comprising a) detecting in a subject sample at a first point in time the amount or activity of at least one biomarker listed in Table 1; b) repeating step a) during at least one subsequent point in time after administration of the agent; and c) comparing the expression and/or activity detected in steps a) and b), wherein the presence of or a significantly increased amount or activity of the at least one biomarker listed in Table 1 in the first subject sample and/or the absence of or a decreased amount or activity of the at least one biomarker listed in Table 1 having a loss of function mutation in the first subject sample, relative to at least one subsequent subject sample, indicates that the cancer is unlikely to progress or that the agent treats the cancer in the subject, is provided. In one embodiment, between the first point in time and the subsequent point in time, the subject has undergone treatment, completed treatment, and/or is in remission for the cancer. In another embodiment, the first and/or at least one subsequent sample is selected from the group consisting of ex vivo and in vivo samples. In still another embodiment, the first and/or at least one subsequent sample is obtained from an animal model of the cancer. In yet another embodiment, the first and/or at least one subsequent sample is a portion of a single sample or pooled samples obtained from the subject.


In another aspect, a cell-based assay for screening for agents that have a cytotoxic or cytostatic effect on a cancer cell that is unresponsive to an immune checkpoint therapy comprising, contacting the cancer cell with a test agent, and determining the ability of the test agent to decrease the amount or activity of at least one biomarker listed in Table 1 in the subject sample and/or increase the amount or activity of the at least one biomarker listed in Table 1 having a loss of function mutation, is provided. In one embodiment, the step of contacting occurs in vivo, ex vivo, or in vitro. In another embodiment, the subject sample and/or the control sample has not been contacted with a renal cell cancer treatment or inhibitor of an immune checkpoint. In still another embodiment, the subject has not been administered a renal cell cancer treatment or inhibitor of an immune checkpoint. In yet another embodiment, the method or the cell-based assay provided herein further comprises recommending, prescribing, or administering at least one additional anti-cancer therapeutic agent. In another embodiment, the at least one additional anti-cancer therapeutic agent is nivolumab and/or an anti-PBRM-1 therapeutic agent.


As described above, numerous embodiments are contemplated for any aspect of the present invention described herein. For example, in one embodiment, the subject sample is selected from the group consisting of serum, whole blood, plasma, urine, cells, cell lines, and biopsies. In another embodiment, the amount of the at least one biomarker listed in Table 1 is detected using a reagent which specifically binds with the protein. For example, the reagent may be selected from the group consisting of an antibody, an antibody derivative, and an antibody fragment. In still another embodiment, the at least one biomarker listed in Table 1 is assessed by detecting the presence in the sample of a transcribed polynucleotide or portion thereof. For example, the transcribed polynucleotide may be an mRNA or a cDNA. The transcribed polynucleotide can be detected by identifying a nucleic acid that anneals with the biomarker nucleic acid, or a portion thereof, under stringent hybridization conditions. In yet another embodiment, the step of detecting further comprises amplifying the transcribed polynucleotide. In another embodiment, the at least one biomarker listed in Table 1 is human PBRM-1, or a fragment thereof. In still another embodiment, the immune checkpoint therapy described herein comprises at least one antibody selected from the group consisting of anti-PD-1 antibodies, anti-CTLA-4 antibodies, anti-PD-L1 antibodies, anti-PD-L2 antibodies, and combinations thereof. For example, the immune checkpoint therapy may comprise nivolumab. In yet another embodiment, the likelihood of the cancer in the subject to be responsive to immune checkpoint therapy is the likelihood of at least one criteria selected from the group consisting of cellular proliferation, tumor burden, m-stage, metastasis, progressive disease, clinical benefit rate, survival until mortality, pathological complete response, semi-quantitative measures of pathologic response, clinical complete remission, clinical partial remission, clinical stable disease, recurrence-free survival, metastasis free survival, disease free survival, circulating tumor cell decrease, circulating marker response, and RECIST criteria. In another embodiment, the cancer is a solid tumor. In still another embodiment, the cancer is a renal cell cancer. In yet another embodiment, the renal cell cancer is a clear cell renal cell cancer (ccRcc). In another embodiment, the clear cell renal cell cancer is a metastatic clear cell renal cell carcinoma (mRCC). In still another embodiment, the subject described herein is a mammal. In yet another embodiment, the mammal is an animal model of cancer. In another embodiment, the mammal is a human.





BRIEF DESCRIPTION OF FIGURES


FIG. 1 includes 5 panels, identified as panels A, B, C, D, and E, which show the cohort consolidation and clinical characteristics of the training cohort. Panels A and B summarize the clinical cohort investigated unless otherwise indicated, such as at Panel D. Generally, of the 91 patients who received anti-PD1 monotherapy (nivolumab) as part of CA209-009, 56 had available pre-treatment tumor for whole exome sequencing. After quality control, 34 pre-treatment tumors were processed through standardized analytical pipelines and included in the final analysis cohort (Panel B). Sixteen samples (the leftmost column) were excluded for low sample purity (including patients who had early death on treatment) (Panel C). Patient were classified into clinical response groups based on objective tumor response RECIST classifications (complete response: CR, partial response: PR, stable disease: SD, or progressive disease: PD) (CITE: RECIST) as well as duration of progression-free survival (PFS) (time from starting immune checkpoint therapy to experiencing objective tumor growth). “Extreme responders” had CR or PR by RECIST or SD with objective tumor shrinkage lasting >6 months) while “extreme progressors” experienced PD by RECIST with PFS<3 months). A third group called “intermediate benefit” or “stable disease” had responses to therapy intermediate between the extreme responders and extreme progressors, based on a combination of objective tumor response by RECIST and duration of progression-free survival. Patients' overall survival (OS) following initiation therapy (in years) vs. PFS (in years) and PFS vs. decrease in tumor burden are shown in Panels C and D. One patient with early minor tumor growth followed by sustained tumor shrinkage was classified as an extreme-responder despite short PFS (see FIG. 2).



FIG. 2 includes 4 panels, identified as panels A, B, C, and D, which show the patient response classifications described in FIG. 1. One patient (5_50) had early tumor growth (likely pseudoprogression) in the setting of overall response to therapy followed by sustained tumor response and was classified as an extreme responder despite disease progression by RECIST criteria prior to 6 months. The results shown in Panels A and B versus those of Panels C and D correspond to the clinical cohort described in Panels A and D, respectively, of FIG. 1.



FIG. 3 includes 4 panels, identified as panels A, B, C, and D, which compare the patient survival probability vs. different clinical characteristics, including different groups receiving different dosages of treatment (Panel A), different sexes (Panel B), pre-treatment tumor immunohistochemical staining for the PD-1 ligand PD-L1(Panel C), and response by RECIST criteria (Panel D). Kaplan-Meier analyses showed that baseline clinical characteristics, including pre-treatment PD-L1 immunohistochemistry (Panel C), did not influence overall survival. Objective tumor response by RECIST criteria was strongly associated with overall survival (p=0.00027). Two patients who did not receive staging scans (RECIST not evaluable) following commencement of anti-PD1 therapy were excluded from further analyses.



FIG. 4 includes 5 panels, identified as panels A, B, C, D, and E, which show whole exome features of the training cohort (N=41). Overall number of detected mutations per sample (all changes to the DNA sequence of a gene) and nonsynonymous mutations (mutations that change the amino acid sequence of the resulting protein encoded by a gene) per sample were similar for patients classified as extreme progressors, extreme responders, or intermediate benefit (Panel A). The ratio of clonal to subclonal mutations was not associated with clinical benefit. Nonsynonymous mutational burden, mutations in gene commonly mutated in clear-cell renal cell carcinoma, estimated tumor purity by ABSOLUTE (Carter et al. (202) Nat. Biotechnol. 30:413-421), and outcomes with immune checkpoint blockade are shown in a stacked CoMut plot (Panel B). The five shown genes were selected as the intersection between significantly mutated genes in TCGA clear-cell renal cell carcinoma and 7 genes significantly mutated by MutSigCV (Lawrence et al. (2013) Nature 499:214-218) in this cohort (see Table 2C). Truncating mutations in PBRM1 were significantly more common in extreme responders (8/9) vs. extreme progressors (3/12) (p=0.0037; q=0.026; Pearson's chi-squared, FDR over 7 genes significantly mutated by MutSigCV) (Panel C). Dashed red line indicates p<0.01. Genes in black were significantly mutated across the entire cohort by MutSigCV, while genes in grey were mutated at lower levels. Patients with truncating alterations in PBRM1 had prolonged overall survival compared to those without truncating PBRM1 mutations (p=0.042; Cox proportional hazards) (Panel D). Three patients with truncating alterations in PBRM1 who were “extreme progressors” due to early tumor growth on anti-PD1 monotherapy had longer-than-expected overall survival (9_97: PFS 1.2 months, OS 28.6+ months and 13_96: PFS 1.2 months; OS 19.1+ months), with duration of overall survival being unevaluable in a third due to censoring (5_18: PFS 1.4 months, OS 3.6+ months) (Panel E).



FIG. 5 show genes significantly mutated in extreme responders vs. extreme progressors. Of all 2,285 genes containing at least 1 nonsynonymous mutation in the training cohort, PBRM1 was the only gene mutated significantly more frequently in extreme responders vs. extreme progressors (8/9 extreme responders vs. 4/12 extreme progressors, p=0.011; Pearson's chi-squared) prior to correcting for multiple hypothesis testing. Genes in black were significantly mutated across the entire training cohort according to MutSigCV, while genes in grey were not. Dashed red line indicates p<0.01.



FIG. 6 shows that patients with truncating mutations in PBRM1 had objective decreases in tumor burden and prolonged overall survival on immune checkpoint monotherapy.



FIG. 7 includes 4 panels, identified as panels A, B, C, and D, which show the association between PBRM1 alterations and clinical benefit from immune checkpoint therapies in a validation cohort of patients with clear-cell renal cell carcinoma treated with monoclonal antibodies targeting PD-1 and PD-L1, either alone or in combination with monoclonal antibodies targeting the immune checkpoint cytotoxic T lymphocyte-associated protein 4 (CTLA-4). A clinical cohort of 41 patients treated with immune checkpoint therapy for metastatic renal cell carcinoma was narrowed to 28 patients in the final validation cohort (Panel A). Patients were stratified into extreme responder, extreme progressor, and intermediate benefit groups using the same definitions as in the training cohort (Panel B). Truncating alterations in PBRM1 were significantly more frequent in patients with extreme response to immune checkpoint monotherapy compared to those experiencing extreme progression (8/13 vs. 1/7) (p=0.043; Pearson's chi-squared) (Panel C). Truncating alterations in PBRM1 frequently occurred in the context of heterozygous deletion of chromosome 3p, though 2 patients with frameshift alterations in PBRM1 who were copy-neutral at chromosome 3p also experienced extreme response (Panel D).



FIG. 8 includes 4 panels, identified as panels A, B, C, and D, which show the gene set enrichment analysis (GSEA) (Subramanian et al. (2005) Proc. Natl. Acad. Sci. 102:15545-15550) of gene sets significantly enriched in untreated ccRCC tumors from the TCGA in tumors with truncating alterations in PBRM1 versus those without. GSEA showed that PBRM1-truncated tumors had significantly decreased infiltration of macrophages (Panel A), TH1 cells (Panel B), TH2 cells (Panel C), and T cells (Panel D).



FIG. 9 includes 2 panels, identified as panels A and B, which show that expression of the immune checkpoints PD-L1 and PD-1 as well as CD8A and IFNG were significantly lower in PBRM1-mutant compared to PBRM1-wildtype ccRCC (TCGA). No significant differences were noted in levels of expression of immune checkpoints (CTLA4, PDCD1: encoding PD-1, CD274: encoding PD-L1, PDCD1LG2: encoding the PD-1 ligand PD-L2). Markers of cytolytic activity (GZMA, PRF1), interferon gamma (IFNγ), or CD8 T cells (CD8A) were noted between tumors with truncating mutations in PBRM1 (blue) versus those without (yellow) in pre-treatment patient samples (Panel A). However, analysis in a larger set of TCGA clear-cell RCC reveals significantly lower expression of CD8A (p=0.0093), IFNG (p=0.00105), PD-L2 (p=0.0173), and PD-1 (p=0.0165) in PBRM1-mutant tumors.



FIG. 10 shows a lollipop plot summarizing PBRM1 mutations described in the Examples.



FIG. 11 summarizes the clinical characteristics of RCC CA209-009 patients (N=91).



FIG. 12 compares training cohort (CA209009) with validation cohort (DFCI+MSKCC). Mutations and copy number alterations in B2M, HLA, and other antigen presentation machinery were rare and did not segregate by response status.



FIG. 13 includes 4 panels, identified as panels A, B, C, and D, which describe cohort consolidation and clinical characteristics of the discovery cohort. Panel A shows sample inclusion/exclusion criteria and computational workflow. Panel B shows clinical stratification by degree of objective change in tumor burden (y-axis) and duration of progression-free survival (x-axis). One patient (RCC_99) is not shown due to lack of tumor response data. *Patient RCC_50 was classified as clinical benefit despite PFS<6 months because there was continued tumor shrinkage after an initial period of minor tumor progression (see FIG. 15). Panel C shows the mutation burden in the discovery cohort by response group. Panel D shows the ratio of subclonal to clonal mutations, as estimated by ABSOLUTE, by response group. ns=not significant. Abbreviations: CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease.



FIG. 14 includes 2 panels, identified as panels A and B, which show that clinical characteristics do not differ significantly between samples that passed and failed whole exome sequencing in the discovery cohort (N=35 pass, N=20 fail). Panel A shows a distribution of the best RECIST scores of patients whose samples passed and failed sequencing. CR=complete response; PR=partial response; SD=stable disease; PD=progressive disease; NE=not evaluable. Panel B show the overall survival (OS) and progression-free survival (PFS) distribution between patients with samples that passed sequencing and samples that failed, measured in days from anti-PD-1 treatment initiation.



FIG. 15 includes 2 panels, identified as panels A and B, which shows spider plots of change in tumor burden for discovery cohort (N=35). Panel A shows a spider plot showing change in target tumor size in the discovery cohort over time. Shading of lines corresponds to best response by RECIST: CR=complete response (purple), PR=partial response (pink), SD=stable disease (light green), PD=progressive disease (dark green). * Patient RCC_50 was classified as clinical benefit despite early (prior to 6 months) minor increase in tumor size (likely pseudo-progression), as this was followed by sustained tumor shrinkage. Patient RCC_99 is not shown due to early clinical disease progression and lack of re-staging scans after baseline. Panel B shows a spider plot shaded by response group in this study.



FIG. 16 shows that pre-treatment clinical covariates did not predict response to immune checkpoint therapy. Dose of immune checkpoint therapy, patient sex, and PD-L1 immunohistochemical staining did not predict patient overall survival following anti-PD-1 therapy (p>0.05, log-rank test).



FIG. 17 includes 2 panels, identified as panels A and B, which show alterations in HLA alleles and antigen presentation machinery in the discovery and validation cohorts. Mutations and copy number alterations in discovery cohorts (panel A) (N=35) and the validation cohort (panel B) (N=41; only tumors from the MSKCC and DFCI patients in the validation cohort (41 out of 69 total validation cohort patients) had raw sequencing data available for these analyses) are shown. One clinical benefit patient in the validation cohort had a heterozygous TAP1 nonsense mutation, while two B2M mutations occurred in the no clinical benefit cohort, one missense and one nonsense.



FIG. 18 includes 4 panels, identified as panels A, B, C, and D, which show that tumor genome features in the discovery cohort reveals a correlation between PBRM1 LOF mutations and clinical benefit from anti-PD-1 therapy. Panel A shows mutations in the discovery cohort. Patients are ordered by response category, with tumor mutation burden in decreasing order within each response category. Shown are the genes that were recurrently mutated at a significant frequency, as assessed by MutSig2CV analysis. CNA=copy number alteration. Panel B shows enrichment of truncating mutations in tumors from patients in the CB vs. NCB groups. The top dashed line denotes q<0.1 (Fisher's exact test). Mutations in genes above the lower black dotted line are enriched in tumors of patients with CB from anti-PD-1 therapy and mutations in genes below the line are enriched in tumors of patients with NCB. Panel C shows a Kaplan-Meier curve comparing overall survival of patients treated with anti-PD-1 therapy whose tumors did or did not harbor LOF mutations in PBRM1. See also FIG. 19 for a Kaplan-Meier curve comparing progression-free survival of these patients. Panel D shows a spider plot showing objective decrease in tumor burden in PBRM1-LOF vs. PBRM1-intact tumors. Three patients with early progression on anti-PD-1 therapy and truncating mutations in PBRM1 (darkest shading) had long and/or censored OS.



FIG. 19 shows a Kaplan-Meier curve of discovery cohort patient progression-free survival by PBRM1 mutation status. PBRM1 truncating alterations are associated with increased progression-free survival following anti-PD-1 therapy (p=0.029; log-rank test).



FIG. 20 includes 4 panels, identified as panels A, B, C, and D, which show that PBRM1 LOF mutations correlate with clinical benefit in a validation cohort of ccRCC patients treated with immune checkpoint inhibitors. Panel A shows selection of the validation cohort. Panel B shows clinical outcomes in the validation cohort. Ten patients without post-treatment restaging scans (eight with clinical PD, two with SD, and one with PR) as well as 14 patients with targeted panel sequencing are not shown. Panel C shows the proportion of tumors harboring PBRM1 LOF mutations in patients in the CB vs. NCB groups. Error bars are S.E. *Fisher's exact p<0.05. Panel D shows truncating alterations in PBRM1 and response to anti-PD-(L)1 therapies by sample. Shaded boxes indicate samples with truncating mutations in PBRM1, while light shading denotes samples without PBRM1 truncating mutations. Missense LOF denotes a missense mutation detected by targeted sequencing that was confirmed to be LOF by PBRM1 immunohistochemistry.



FIG. 21 shows a Kaplan-Meier curve of combined discovery and validation cohort patient progression-free survival by PBRM1 LOF mutation status, stratified by therapy line. PBRM1 truncating alterations in patients who received anti-PD-(L) 1 therapy in a setting other than first-line (N=81) were associated with increased progression-free survival (p=0.0087, log-rank test). This association was not observed in patients who received immune checkpoint blockade as first-line therapy (N=17).



FIG. 22 includes 2 panels, identified as panels A and B, which show PBRM1 mutational status in ccRCC influences immune gene expression. Panel A shows the results of GSEA performed on PBAF-deficient (A704BAF180−/− and A704BAF180 wt, BRG1−/−) vs. PBAF-proficient (A704BAF180 wt) kidney cancer cell lines using both Hallmark and corresponding Founder gene sets. GSEA enrichment plot shown for the KEGG cytokine-cytokine receptor interaction gene set in A704BAF180−/− vs. A704BAF180 wt (PBRM1 null vs. wildtype). The enrichment plot is similar for A704BAF180 wt, BRG1−/− vs. A704BAF180 wt (BRG1 null vs. wildtype); see Table 61. Panel B shows the results of GSEA also performed on RNA-seq from pre-treatment tumors in the discovery and validation cohorts of this study (n=18 PBRM1-LOF vs. n=14 PBRM1-intact) using the Hallmark gene sets. Enrichment plots show increased expression of the hypoxia and IL6/JAK-STAT3 gene sets in the PBRM1-LOF tumors.



FIG. 23 includes 3 panels, identified as panels A, B, and C, which show expression of immune genes and PBRM1 in three independent ccRCC cohorts by PBRM1 mutation status. Panel A shows expression of immune checkpoints and immune cell markers in TCGA clear-cell renal cell carcinoma between PBRM1-loss-of-function (LOF) (N=104) and PBRM1-intact (N=288) tumors. Immune inhibitory ligands, including PDCD1, PDCD1LG2, LAG3, TIGIT, and VTCN1 are significantly upregulated in PBRM1-intact versus PBRM1-LOF tumors (*q<0.05, **q<0.01). Panel B shows differential immune gene expression analysis in Sato et al. (N=73 PBRM1-intact vs. N=19 PBRM-LOF) shows significant upregulation of VTCN1 in PBRM1-intact tumors (*p<0.05, **p<0.005). Panel C demonstrates that in N=32 patient tumors, no immune genes were significantly differentially expressed, although PBRM1-LOF tumors trended towards lower expression of most checkpoints (*p<0.05, **p<0.005). All three cohorts show significantly lower expression of PBRM1 in PBRM1-LOF tumors compared to PBRM1-intact tumors (p=0.0027, 0.048, and 0.022, respectively), while tumors with non-truncating mutations in PBRM1 more closely resembled the PBRM1-intact expression phenotype.



FIG. 24 show immune gene expression in TCGA KIRC by VHL mutation status. The presence or absence of truncating mutations in VHL did not correlate with expression levels of immune inhibitory ligands or other immune cell markers.


Note that for every figure containing a histogram, the bars from left to right for each discrete measurement correspond to the figure boxes from top to bottom in the figure legend as indicated.





DETAILED DESCRIPTION OF THE INVENTION

It has been determined herein that PBRM1 is a highly specific biomarker for predicted clinical outcome in cancer patients (e.g., renal cell carcinoma patients) receiving anti-immune checkpoint-based therapy (e.g., anti-PD1/PD-L1 agents alone or in combination with other anti-cancer therapeutics). Accordingly, the present invention relates, in part, to methods for stratifying patients and predicting response of a cancer in a subject to immune checkpoint therapy based upon a determination and analysis of mutations, described herein, of biomarkers, compared to a control. In addition, such analyses can be used in order to provide useful anti-immune checkpoint treatment regimens (e.g., based on predictions of clinical response, subject survival or relapse, timing of adjuvant or neoadjuvant treatment, etc.).


I. Definitions

The articles “a” and “an” are used herein to refer to one or to more than one (i.e. to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element.


The term “altered amount” or “altered level” refers to increased or decreased copy number (e.g., germline and/or somatic) of a biomarker nucleic acid, e.g., increased or decreased expression level in a cancer sample, as compared to the expression level or copy number of the biomarker nucleic acid in a control sample. The term “altered amount” of a biomarker also includes an increased or decreased protein level of a biomarker protein in a sample, e.g., a cancer sample, as compared to the corresponding protein level in a normal, control sample. Furthermore, an altered amount of a biomarker protein may be determined by detecting posttranslational modification such as methylation status of the marker, which may affect the expression or activity of the biomarker protein.


The amount of a biomarker in a subject is “significantly” higher or lower than the normal amount of the biomarker, if the amount of the biomarker is greater or less, respectively, than the normal level by an amount greater than the standard error of the assay employed to assess amount, and preferably at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 300%, 350%, 400%, 500%, 600%, 700%, 800%, 900%, 1000% or than that amount. Such “significance” can be assessed from any desired or known point of comparison, such as a particular post-treatment versus pre-treatment biomarker measurement ratio (e.g., 1-fold, 1.1-fold, 1.2-fold, 1.3-fold, 1.4-fold, 1.5-fold, and the like) or a particular pre-treatment serum biomarker protein measurement (e.g., 2,500 pg/ml, 2,750 pg/ml, 3,000 pg/ml, 3,175 pg/ml, 3,250 pg/ml, 3,500 pg/ml, and the like). Alternately, the amount of the biomarker in the subject can be considered “significantly” higher or lower than the normal amount if the amount is at least about two, and preferably at least about three, four, or five times, higher or lower, respectively, than the normal amount of the biomarker. Such “significance” can also be applied to any other measured parameter described herein, such as for expression, inhibition, cytotoxicity, cell growth, and the like.


The term “altered level of expression” of a biomarker refers to an expression level or copy number of the biomarker in a test sample, e.g., a sample derived from a patient suffering from cancer, that is greater or less than the standard error of the assay employed to assess expression or copy number, and is preferably at least twice, and more preferably three, four, five or ten or more times the expression level or copy number of the biomarker in a control sample (e.g., sample from a healthy subjects not having the associated disease) and preferably, the average expression level or copy number of the biomarker in several control samples. The altered level of expression is greater or less than the standard error of the assay employed to assess expression or copy number, and is preferably at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 300%, 350%, 400%, 500%, 600%, 700%, 800%, 900%, 1000% or more times the expression level or copy number of the biomarker in a control sample (e.g., sample from a healthy subjects not having the associated disease) and preferably, the average expression level or copy number of the biomarker in several control samples.


The term “altered activity” of a biomarker refers to an activity of the biomarker which is increased or decreased in a disease state, e.g., in a cancer sample, as compared to the activity of the biomarker in a normal, control sample. Altered activity of the biomarker may be the result of, for example, altered expression of the biomarker, altered protein level of the biomarker, altered structure of the biomarker, or, e.g., an altered interaction with other proteins involved in the same or different pathway as the biomarker or altered interaction with transcriptional activators or inhibitors.


The term “altered structure” of a biomarker refers to the presence of mutations or allelic variants within a biomarker nucleic acid or protein, e.g., mutations which affect expression or activity of the biomarker nucleic acid or protein, as compared to the normal or wild-type gene or protein. For example, mutations include, but are not limited to substitutions, deletions, or addition mutations. Mutations may be present in the coding or non-coding region of the biomarker nucleic acid.


The term “PBRM1” refers to protein Polybromo-1, which is a subunit of ATP-dependent chromatin-remodeling complexes. PBRM1 functions in the regulation of gene expression as a constituent of the evolutionary-conserved SWI/SNF chromatin remodeling complexes (Euskirchen et al. (2012) J. Biol. Chem. 287:30897-30905). Beside BRD7 and BAF200, PBRM1 is one of the unique components of the SWI/SNF-B complex, also known as polybromo/BRG1-associated factors (or PBAF), absent in the SWI/SNF-A (BAF) complex (Xue et al. (2000) Proc Natl Acad Sci USA. 97:13015-13020; Brownlee et al. (2012) Biochem Soc Trans. 40:364-369). On that account, and because it contains bromodomains known to mediate binding to acetylated histones, PBRM1 has been postulated to target the PBAF complex to specific chromatin sites, therefore providing the functional selectivity for the complex (Xue et al. (2000), supra; Lemon et al. (2001) Nature 414:924-928; Brownlee et al. (2012), supra). Although direct evidence for PBRM1 involvement is lacking, SWI/SNF complexes have also been shown to play a role in DNA damage response (Park et al. (2006) EMBO J. 25:3986-3997). In vivo studies have shown that PBRM1 deletion leads to embryonic lethality in mice, where PBRM1 is required for mammalian cardiac chamber maturation and coronary vessel formation (Wang et al. (2004) Genes Dev. 18:3106-3116; Huang et al. (2008) Dev Biol. 319:258-266). PBRM1 mutations are most predominant in renal cell carcinomas (RCCs) and have been detected in over 40% of cases, placing PBRM1 second (after VHL) on the list of most frequently mutated genes in this cancer (Varela et al. (2011) Nature 469:539-542; Hakimi et al. (2013) Eur Urol. 63:848-854; Pena-Llopis et al. (2012) Nat Genet. 44:751-759; Pawlowski et al. (2013) Int J Cancer. 132:E11-E17). PBRM1 mutations have also been found in a smaller group of breast and pancreatic cancers (Xia et al. (2008) Cancer Res. 68:1667-1674; Shain et al. (2012) Proc Natl Acad Sci USA. 109:E252-E259; Numata et al. (2013) Int J Oncol. 42:403-410). PBRM1 mutations are more common in patients with advanced disease stage (Hakimi et al. (2013), supra), and loss of PBRM1 protein expression has been associated with advanced tumour stage, low differentiation grade and worse patient outcome (Pawlowski et al. (2013), supra). In another study, no correlation between PBRM1 status and tumour grade was found (Pena-Llopis et al. (2012), supra). Although PBRM1-mutant tumours are associated with better prognosis than BAP1-mutant tumours, tumours mutated for both PBRM1 and BAP1 exhibit the greatest aggressiveness (Kapur et al. (2013) Lancet Oncol. 14:159-167). PBRM1 is ubiquitously expressed during mouse embryonic development (Wang et al. (2004), supra) and has been detected in various human tissues including pancreas, kidney, skeletal muscle, liver, lung, placenta, brain, heart, intestine, ovaries, testis, prostate, thymus and spleen (Xue et al. (2000), supra; Horikawa and Barrett (2002) DNA Seq. 13:211-215).


PBRM1 protein localises to the nucleus of cells (Nicolas and Goodwin (1996) Gene 175:233-240). As a component of the PBAF chromatin-remodelling complex, it associates with chromatin (Thompson (2009) Biochimie. 91:309-319), and has been reported to confer the localisation of PBAF complex to the kinetochores of mitotic chromosomes (Xue et al. (2000), supra). Human PBRM1 gene encodes a 1582 amino acid protein, also referred to as BAF180. Six bromodomains (BD1-6), known to recognize acetylated lysine residues and frequently found in chromatin-associated proteins, constitute the N-terminal half of PBRM1 (e.g., six BD domains at amino acid residue no. 44-156, 182-284, 383-484, 519-622, 658-762, and 775-882 of SEQ ID NO:2). The C-terminal half of PBRM1 contains two bromo-adjacent homology (BAH) domains (BAH1 and BAH2, e.g., at amino acid residue no. 957-1049 and 1130-1248 of SE ID NO:2), present in some proteins involved in transcription regulation. High mobility group (HMG) domain is located close to the C-terminus of PBRM1 (e.g., amino acid residue no. 1328-1377 of SEQ ID NO:2). HMG domains are found in a number of factors regulating DNA-dependent processes where HMG domains often mediate interactions with DNA.


The term “PBRM1” is intended to include fragments, variants (e.g., allelic variants), and derivatives thereof. Representative human PBRM1 cDNA and human PBRM1 protein sequences are well-known in the art and are publicly available from the National Center for Biotechnology Information (NCBI). For example, two different human PBRM1 isoforms are known. Human PBRM1 transcript variant 2 (NM_181042.4) represents the longest transcript. Human PBRM1 transcript variant 1 (NM_018313.4, having a CDS from the 115-4863 nucleotide residue of SEQ ID NO:1) differs in the 5′ UTR and uses an alternate exon and splice site in the 3′ coding region, thus encoding a distinct protein sequence (NP 060783.3, as SEQ ID NO:2) of the same length as the isoform (NP 851385.1) encoded by variant 2. Nucleic acid and polypeptide sequences of PBRM1 orthologs in organisms other than humans are well known and include, for example, chimpanzee PBRM1 (XM_009445611.2 and XP 009443886.1, XM_009445608.2 and XP_009443883.1, XM_009445602.2 and XP_009443877.1, XM_016941258.1 and XP_016796747.1, XM_016941256.1 and XP_016796745.1, XM_016941249.1 and XP_016796738.1, XM_016941260.1 and XP_016796749.1, XM_016941253.1 and XP_016796742.1, XM_016941250.1 and XP_016796739.1, XM_016941261.1 and XP_016796750.1, XM_009445605.2 and XP_009443880.1, XM_016941252.1 and XP_016796741.1, XM_009445603.2 and XP_009443878.1, XM_016941263.1 and XP_016796752.1, XM_016941262.1 and XP_016796751.1, XM_009445604.2 and XP_009443879.1, XM_016941251.1 and XP_016796740.1, XM_016941257.1 and XP_016796746.1, XM_016941255.1 and XP_016796744.1, XM_016941254.1 and XP_016796743.1, XM_016941265.1 and XP_016796754.1, XM_016941264.1 and XP_016796753.1, XM_016941248.1 and XP_016796737.1, XM_009445617.2 and XP_009443892.1, XM_009445616.2 and XP_009443891.1, XM_009445619.2 and XP_009443894.1 XM_009445615.2 and XP_009443890.1, XM_009445618.2 and XP_009443893.1, and XM_016941266.1 and XP_016796755.1), rhesus monkey PBRM1 (XM_015130736.1 and XP_014986222.1, XM_015130739.1 and XP_014986225.1, XM_015130737.1 and XP_014986223.1, XM_015130740.1 and XP_014986226.1, XM_015130727.1 and XP_014986213.1, XM_015130726.1 and XP_014986212.1, XM_015130728.1 and XP_014986214.1, XM_015130743.1 and XP_014986229.1, XM_015130731.1 and XP_014986217.1, XM_015130745.1 and XP_014986231.1, XM_015130741.1 and XP_014986227.1, XM_015130734.1 and XP_014986220.1, XM_015130744.1 and XP_014986230.1, XM_015130748.1 and XP_014986234.1, XM_015130746.1 and XP_014986232.1, XM_015130742.1 and XP_014986228.1, XM_015130747.1 and XP_014986233.1, XM_015130730.1 and XP_014986216.1, XM_015130732.1 and XP_014986218.1, XM_015130733.1 and XP_014986219.1, XM_015130735.1 and XP_014986221.1, XM_015130738.1 and XP_014986224.1, and XM_015130725.1 and XP_014986211.1), dog PBRM1 (XM 005632441.2 and XP_005632498.1, XM_014121868.1 and XP_013977343.1, XM_005632451.2 and XP_005632508.1, XM_014121867.1 and XP_013977342.1, XM_005632440.2 and XP_005632497.1, XM_005632446.2 and XP_005632503.1, XM_533797.5 and XP_533797.4, XM_005632442.2 and XP_005632499.1, XM_005632439.2 and XP_005632496.1, XM_014121869.1 and XP_013977344.1, XM_005632448.1 and XP_005632505.1, XM_005632449.1 and XP_005632506.1, XM_005632452.1 and XP_005632509.1, XM_005632445.1 and XP_005632502.1, XM_005632450.1 and XP_005632507.1, XM_005632453.1 and XP_005632510.1, XM_014121870.1 and XP_013977345.1, XM_005632443.1 and XP_005632500.1, XM_005632444.1 and XP_005632501.1, and XM_005632447.2 and XP_005632504.1), cow PBRM1 (XM_005222983.3 and XP_005223040.1, XM_005222979.3 and XP_005223036.1, XM_015459550.1 and XP_015315036.1, XM_015459551.1 and XP_015315037.1, XM_015459548.1 and XP_015315034.1, XM_010817826.1 and XP_010816128.1, XM_010817829.1 and XP_010816131.1, XM_010817830.1 and XP_010816132.1, XM_010817823.1 and XP_010816125.1, XM_010817824.2 and XP_010816126.1, XM_010817819.2 and XP_010816121.1, XM_010817827.2 and XP_010816129.1, XM_010817828.2 and XP_010816130.1, XM_010817817.2 and XP_010816119.1, and XM_010817818.2 and XP_010816120.1), mouse PBRM1 (NM_001081251.1 and NP 001074720.1), chicken PBRM1 (NM_205165.1 and NP 990496.1), tropical clawed frog PBRM1 (XM_018090224.1 and XP_017945713.1), zebrafish PBRM1 (XM_009305786.2 and XP_009304061.1, XM_009305785.2 and XP_009304060.1, and XM_009305787.2 and XP_009304062.1), fruit fly PBRM1 (NM_143031.2 and NP 651288.1), and worm PBRM1 (NM_001025837.3 and NP 001021008.1 and .NM_001025838.2 and NP 001021009.1).


Representative sequences of PBRM1 orthologs are presented below in Table 1. Anti-PBRM1 antibodies suitable for detecting PBRM1 protein are well-known in the art and include, for example, ABE70 (rabbit polyclonal antibody, EMD Millipore, Billerica, Mass.), TA345237 and TA345238 (rabbit polyclonal antibodies, OriGene Technologies, Rockville, Md.), NBP2-30673 (mouse monoclonal) and other polyclonal antibodes (Novus Biologicals, Littleton, Colo.), ab196022 (rabiit mAb, AbCam, Cambridge, Mass.), PAH437Hu01 and PAH437Hu02 (rabbit polyclonal antibodies, Cloud-Clone Corp., Houston, Tex.), GTX100781 (GeneTex, Irvine, Calif.), 25-498 (ProSci, Poway, CA), sc-367222 (Santa Cruz Biotechnology, Dallas, Tex.), etc. In addition, reagents are well-known for detecting PBRM1 expression (see, for example, PBRM1 Hu-Cy3 or Hu-Cy5 SmartFlare™ RNA Detection Probe (EMD Millipore). Moreover, mutilple siRNA, shRNA, CRISPR constructs for reducing PBRM1 expression can be found in the commercial product lists of the above-referenced companies. Ribavirin and PFI 3 are known PBRM1 inhibitors. It is to be noted that the term can further be used to refer to any combination of features described herein regarding PBRM1 molecules. For example, any combination of sequence composition, percentage identify, sequence length, domain structure, functional activity, etc. can be used to describe an PBRM1 molecule of the present invention.


The term “PBRM1 loss of function mutation” refers to any mutation in a PBRM1-related nucleic acid or protein that results in reduced or eliminated PBRM1 protein amounts and/or function. For example, nucleic acid mutations include single-base substitutions, multi-base substitutions, insertion mutations, deletion mutations, frameshift mutations, missesnse mutations, nonsense mutations, splice-site mutations, epigenetic modifications (e.g., methylation, phosphorylation, acetylation, ubiquitylation, sumoylation, histone acetylation, histone deacetylation, and the like), and combinations thereof. In some embodiments, the mutation is a “nonsynonymous mutation,” meaning that the mutation alters the amino acid sequence of PBRM1. Such mutations reduce or eliminate PBRM1 protein amounts and/or function by eliminating proper coding sequences required for proper PBRM1 protein translation and/or coding for PBRM1 proteins that are non-functional or have reduced function (e.g., deletion of enzymatic and/or structural domains, reduction in protein stability, alteration of sub-cellular localization, and the like). Such mutations are well-known in the art. In addition, a representative list describing a wide variety of structural mutations correlated with the functional result of reduced or eliminated PBRM1 protein amounts and/or function is described in Table 1 and the Examples. Without being bound by theory, it is believed that nonsense, frameshift, and splice-site mutations are particularly amenable to PBRM1 loss of function because they are known to be indicative of lack of PBRM1 expression in cell lines harboring such mutations.


Unless otherwise specified here within, the terms “antibody” and “antibodies” broadly encompass naturally-occurring forms of antibodies (e.g. IgG, IgA, IgM, IgE) and recombinant antibodies such as single-chain antibodies, chimeric and humanized antibodies and multi-specific antibodies, as well as fragments and derivatives of all of the foregoing, which fragments and derivatives have at least an antigenic binding site. Antibody derivatives may comprise a protein or chemical moiety conjugated to an antibody.


The term “antibody” as used herein also includes an “antigen-binding portion” of an antibody (or simply “antibody portion”). The term “antigen-binding portion”, as used herein, refers to one or more fragments of an antibody that retain the ability to specifically bind to an antigen (e.g., a biomarker polypeptide or fragment thereof). It has been shown that the antigen-binding function of an antibody can be performed by fragments of a full-length antibody. Examples of binding fragments encompassed within the term “antigen-binding portion” of an antibody include (i) a Fab fragment, a monovalent fragment consisting of the VL, VH, CL and CH1 domains; (ii) a F(ab′)2 fragment, a bivalent fragment comprising two Fab fragments linked by a disulfide bridge at the hinge region; (iii) a Fd fragment consisting of the VH and CH1 domains; (iv) a Fv fragment consisting of the VL and VH domains of a single arm of an antibody, (v) a dAb fragment (Ward et al. (1989) Nature 341:544-546), which consists of a VH domain; and (vi) an isolated complementarity determining region (CDR). Furthermore, although the two domains of the Fv fragment, VL and VH, are coded for by separate genes, they can be joined, using recombinant methods, by a synthetic linker that enables them to be made as a single protein chain in which the VL and VH regions pair to form monovalent polypeptides (known as single chain Fv (scFv); see e.g., Bird et al. (1988) Science 242:423-426; and Huston et al. (1988) Proc. Natl. Acad. Sci. USA 85:5879-5883; and Osbourn et al. 1998, Nature Biotechnology 16: 778). Such single chain antibodies are also intended to be encompassed within the term “antigen-binding portion” of an antibody. Any VH and VL sequences of specific scFv can be linked to human immunoglobulin constant region cDNA or genomic sequences, in order to generate expression vectors encoding complete IgG polypeptides or other isotypes. VH and VL can also be used in the generation of Fab, Fv or other fragments of immunoglobulins using either protein chemistry or recombinant DNA technology. Other forms of single chain antibodies, such as diabodies are also encompassed. Diabodies are bivalent, bispecific antibodies in which VH and VL domains are expressed on a single polypeptide chain, but using a linker that is too short to allow for pairing between the two domains on the same chain, thereby forcing the domains to pair with complementary domains of another chain and creating two antigen binding sites (see e.g., Holliger, P., et al. (1993) Proc. Natl. Acad. Sci. USA 90:6444-6448; Poljak, R. J., et al. (1994) Structure 2:1121-1123).


Still further, an antibody or antigen-binding portion thereof may be part of larger immunoadhesion polypeptides, formed by covalent or noncovalent association of the antibody or antibody portion with one or more other proteins or peptides. Examples of such immunoadhesion polypeptides include use of the streptavidin core region to make a tetrameric scFv polypeptide (Kipriyanov, S. M., et al. (1995) Human Antibodies and Hybridomas 6:93-101) and use of a cysteine residue, biomarker peptide and a C-terminal polyhistidine tag to make bivalent and biotinylated scFv polypeptides (Kipriyanov, S. M., et al. (1994) Mol. Immunol. 31:1047-1058). Antibody portions, such as Fab and F(ab′)2 fragments, can be prepared from whole antibodies using conventional techniques, such as papain or pepsin digestion, respectively, of whole antibodies. Moreover, antibodies, antibody portions and immunoadhesion polypeptides can be obtained using standard recombinant DNA techniques, as described herein.


Antibodies may be polyclonal or monoclonal; xenogeneic, allogeneic, or syngeneic; or modified forms thereof (e.g. humanized, chimeric, etc.). Antibodies may also be fully human. Preferably, antibodies of the present invention bind specifically or substantially specifically to a biomarker polypeptide or fragment thereof. The terms “monoclonal antibodies” and “monoclonal antibody composition”, as used herein, refer to a population of antibody polypeptides that contain only one species of an antigen binding site capable of immunoreacting with a particular epitope of an antigen, whereas the term “polyclonal antibodies” and “polyclonal antibody composition” refer to a population of antibody polypeptides that contain multiple species of antigen binding sites capable of interacting with a particular antigen. A monoclonal antibody composition typically displays a single binding affinity for a particular antigen with which it immunoreacts.


Antibodies may also be “humanized”, which is intended to include antibodies made by a non-human cell having variable and constant regions which have been altered to more closely resemble antibodies that would be made by a human cell. For example, by altering the non-human antibody amino acid sequence to incorporate amino acids found in human germline immunoglobulin sequences. The humanized antibodies of the present invention may include amino acid residues not encoded by human germline immunoglobulin sequences (e.g., mutations introduced by random or site-specific mutagenesis in vitro or by somatic mutation in vivo), for example in the CDRs. The term “humanized antibody”, as used herein, also includes antibodies in which CDR sequences derived from the germline of another mammalian species, such as a mouse, have been grafted onto human framework sequences.


The term “assigned score” refers to the numerical value designated for each of the biomarkers after being measured in a patient sample. The assigned score correlates to the absence, presence or inferred amount of the biomarker in the sample. The assigned score can be generated manually (e.g., by visual inspection) or with the aid of instrumentation for image acquisition and analysis. In certain embodiments, the assigned score is determined by a qualitative assessment, for example, detection of a fluorescent readout on a graded scale, or quantitative assessment. In one embodiment, an “aggregate score,” which refers to the combination of assigned scores from a plurality of measured biomarkers, is determined. In one embodiment the aggregate score is a summation of assigned scores. In another embodiment, combination of assigned scores involves performing mathematical operations on the assigned scores before combining them into an aggregate score. In certain, embodiments, the aggregate score is also referred to herein as the “predictive score.”


The term “biomarker” refers to a measurable entity of the present invention that has been determined to be predictive of immune checkpoint therapy effects on a cancer. Biomarkers can include, without limitation, nucleic acids and proteins, including those shown in Table 1, the Examples, and the Figures.


A “blocking” antibody or an antibody “antagonist” is one which inhibits or reduces at least one biological activity of the antigen(s) it binds. In certain embodiments, the blocking antibodies or antagonist antibodies or fragments thereof described herein substantially or completely inhibit a given biological activity of the antigen(s).


The term “body fluid” refers to fluids that are excreted or secreted from the body as well as fluids that are normally not (e.g. amniotic fluid, aqueous humor, bile, blood and blood plasma, cerebrospinal fluid, cerumen and earwax, cowper's fluid or pre-ejaculatory fluid, chyle, chyme, stool, female ejaculate, interstitial fluid, intracellular fluid, lymph, menses, breast milk, mucus, pleural fluid, pus, saliva, sebum, semen, serum, sweat, synovial fluid, tears, urine, vaginal lubrication, vitreous humor, vomit).


The terms “cancer” or “tumor” or “hyperproliferative” refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features, as well as evading immune destruction (Hanahan and Weinberg (2000) 100:57-70; Hanahan and Weinberg (2011) Cell 144:646-674). In some embodiments, such cells exhibit such characteristics in part or in full due to the expression and activity of immune checkpoint proteins, such as PD-1, PD-L1, and/or CTLA-4. Cancer cells are often in the form of a tumor, but such cells may exist alone within an animal, or may be a non-tumorigenic cancer cell, such as a leukemia cell. As used herein, the term “cancer” includes premalignant as well as malignant cancers. Cancers include, but are not limited to, B cell cancer, e.g., multiple myeloma, Waldenstrom's macroglobulinemia, the heavy chain diseases, such as, for example, alpha chain disease, gamma chain disease, and mu chain disease, benign monoclonal gammopathy, and immunocytic amyloidosis, melanomas, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematologic tissues, and the like. Other non-limiting examples of types of cancers applicable to the methods encompassed by the present invention include human sarcomas and carcinomas, e.g., fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon carcinoma, colorectal cancer, pancreatic cancer, breast cancer, ovarian cancer, prostate cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, liver cancer, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, cervical cancer, bone cancer, brain tumor, testicular cancer, lung carcinoma, small cell lung carcinoma, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, melanoma, neuroblastoma, retinoblastoma; leukemias, e.g., acute lymphocytic leukemia and acute myelocytic leukemia (myeloblastic, promyelocytic, myelomonocytic, monocytic and erythroleukemia); chronic leukemia (chronic myelocytic (granulocytic) leukemia and chronic lymphocytic leukemia); and polycythemia vera, lymphoma (Hodgkin's disease and non-Hodgkin's disease), multiple myeloma, Waldenstrom's macroglobulinemia, and heavy chain disease. In some embodiments, cancers are epithlelial in nature and include but are not limited to, bladder cancer, breast cancer, cervical cancer, colon cancer, gynecologic cancers, renal cancer, laryngeal cancer, lung cancer, oral cancer, head and neck cancer, ovarian cancer, pancreatic cancer, prostate cancer, or skin cancer. In other embodiments, the cancer is breast cancer, prostate cancer, lung cancer, or colon cancer. In still other embodiments, the epithelial cancer is non-small-cell lung cancer, nonpapillary renal cell carcinoma, cervical carcinoma, ovarian carcinoma (e.g., serous ovarian carcinoma), or breast carcinoma. The epithelial cancers may be characterized in various other ways including, but not limited to, serous, endometrioid, mucinous, clear cell, Brenner, or undifferentiated.


In certain embodiments, the cancer encompasses renal cell carcinoma (RCC). The term “renal cell carcinoma” generally refers to a type of kidney cancer that starts in the lining of the proximal convoluted tubule, a part of the very small tubes in the kidney that transport waste molecules from the blood to the urine. RCC is the most common type of kidney cancer in adults, responsible for approximately 90-95% of cases. Renal cell carcinoma is the most common type of kidney cancer in adults. It occurs most often in men 50 to 70 years old. The different types of RCC are generally distinguished by the way that cancer cells appear when viewed under a microscope, such as clear cell RCC (ccRCC), papillary RCC, chromophobe RCC, oncocytoma RCC, collecting duct RCC, and other unclassified RCC. In clear cell RCC or conventional RCC, the cells have a clear or pale appearance. CCRCC classically has apical nuclei, i.e. the nucleus is adjacent to the luminal aspect (Bing and Tomaszewski (2011) Case Rep Transplant. 2011:387645). In most glandular structures the nuclei are usually basally located, i.e. in the cytoplasm adjacent to the basement membrane. They typically stain with CK7 and do not stain with TFE3 and AMACR (Rohan et al. (2011) Mod Pathol. 24:1207-1220). Around 70 to 80 percent of individuals with renal cell cancer have clear cell RCC. The growth of these cells can be either slow or fast. Metastatic renal cell carcinoma (mRCC) is the spread of the primary renal cell carcinoma from the kidney to other organs. About 25-30% of people have this metastatic spread by the time they are diagnosed with renal cell carcinoma. This high proportion is explained by the fact that clinical signs are generally mild until the disease progresses to a more severe state. The most common sites for metastasis are the lymph nodes, lung, bones, liver and brain. mRCC has a poor prognosis compared to other cancers, though average survival times have increased in the last few years due to treatment advances. Average survival time in 2008 for the metastatic form of the disease was under a year and by 2013 this improved to an average of 22 months. Despite this improvement, the 5-year survival rate for mRCC remains under 10%. About 20-25% of suffers remain unresponsive to all treatments and in these cases, the disease has a rapid progression. The known risk factors of kidney cancer include, e.g., smoking, obesity, dialysis treatment, family history of the disease, high blood pressure, horseshoe kidney, long-term use of certain medicines, such as pain pills or water pills (diuretics), polycystic kidney disease, von Hippel-Lindau disease (a hereditary disease that affects blood vessels in the brain, eyes, and other body parts), etc. Symptoms of RCC may include any of the following: abdominal pain and swelling, back pain, blood in the urine, swelling of the veins around a testicle (varicocele), flank pain, weight loss, excessive hair growth in females, pale skin, vision problems, etc. The initial symptoms of RCC often include: blood in the urine (occurring in 40% of affected persons at the time they first seek medical attention), flank pain (40%), a mass in the abdomen or flank (25%), weight loss (33%), fever (20%), high blood pressure (20%), night sweats and generally feeling unwell. When RCC metastasises, it most commonly spreads to the lymph nodes, lungs, liver, adrenal glands, brain or bones. RCC is also associated with a number of paraneoplastic syndromes (PNS) which are conditions caused by either the hormones produced by the tumour or by the body's attack on the tumour and are present in about 20% of those with RCC. These paraneoplastic syndromes most commonly affect tissues which have not been invaded by the cancer. The most common PNSs seen in people with RCC are: high blood calcium levels, polycythaemia (the opposite of anaemia, due to an overproduction of erythropoietin), thrombocytosis (too many platelets in the blood, leading to an increased tendency for blood clotting and bleeds) and secondary amyloidosis. For exam and diagnosis, a physical exam may reveal mass or swelling of the abdomen and/or a varicocele in the male scrotum. Diagnostic tests include, e.g., abdominal CT scan, blood chemistry, complete blood count (CBC), intravenous pyelogram (IVP), liver function tests, renal arteriography, ultrasound of the abdomen and kidney, and urine tests. Tests for detecting spread RCC may include abdominal CT scan, adominal MM, bone scan, chest x-ray or CT scan, and PET scan. Availabe treatment for RCC may include surgery to remove of all or part of the kidney (nephrectomy). This may include removing the bladder, surrounding tissues, or lymph nodes. Chemotherapy or radiation therapy is generally not effective for treating kidney cancer. Current immunotherapies include the immune system medicines interleukin-2 (IL-2) and nivolumab, developed after observing that in some cases there was spontaneous regression (Davar et al. (2013) “Immunotherapy for Renal Cell Carcinoma”. Renal Cell Carcinoma Clinical Management. Humana. pp. 279-302). Other targeted therapies include anti-angiogenesis therapies (e.g., bevacizumab (Avastin®)), tyrosine kinase inhibitors (TKIs) (e.g., cabozantinib (Cabometyx™), pazopanib (Votrient®), sorafenib (Nexavar), axitinib (INLYTA®) and sunitinib (Sutent®)), mTOR inhibitors (e.g., Everolimus (Afinitor®) and temsirolimus))(Torise®), and other inhibitors to growth factors that have been shown to promote the growth and spread of tumours (e.g., lenvatinib (LENVIMA®), also see Santoni et al. (2013) Expert Review of Anticancer Therapy. 13:697-709; Stroup (2013) “Neoadjuvant Targeted Therapy and Consolidative Surgery” Renal Cell Carcinoma Clinical Management. Humana. pp. 219-230).


The term “coding region” refers to regions of a nucleotide sequence comprising codons which are translated into amino acid residues, whereas the term “noncoding region” refers to regions of a nucleotide sequence that are not translated into amino acids (e.g., 5′ and 3′ untranslated regions).


The term “complementary” refers to the broad concept of sequence complementarity between regions of two nucleic acid strands or between two regions of the same nucleic acid strand. It is known that an adenine residue of a first nucleic acid region is capable of forming specific hydrogen bonds (“base pairing”) with a residue of a second nucleic acid region which is antiparallel to the first region if the residue is thymine or uracil. Similarly, it is known that a cytosine residue of a first nucleic acid strand is capable of base pairing with a residue of a second nucleic acid strand which is antiparallel to the first strand if the residue is guanine. A first region of a nucleic acid is complementary to a second region of the same or a different nucleic acid if, when the two regions are arranged in an antiparallel fashion, at least one nucleotide residue of the first region is capable of base pairing with a residue of the second region. Preferably, the first region comprises a first portion and the second region comprises a second portion, whereby, when the first and second portions are arranged in an antiparallel fashion, at least about 50%, and preferably at least about 75%, at least about 90%, or at least about 95% of the nucleotide residues of the first portion are capable of base pairing with nucleotide residues in the second portion. More preferably, all nucleotide residues of the first portion are capable of base pairing with nucleotide residues in the second portion.


The terms “conjoint therapy” and “combination therapy,” as used herein, refer to the administration of two or more therapeutic substances, e.g., combinations of anti-immune checkpoint therapies, multiple inhibitors of an immune checkpoint of interest, combinations of immune checkpoint therapy with an inhibitor of PBRM1, and combinations thereof. The different agents comprising the combination therapy may be administered concomitant with, prior to, or following the administration of one or more therapeutic agents.


The term “control” refers to any reference standard suitable to provide a comparison to the expression products in the test sample. In one embodiment, the control comprises obtaining a “control sample” from which expression product levels are detected and compared to the expression product levels from the test sample. Such a control sample may comprise any suitable sample, including but not limited to a sample from a control cancer patient (can be stored sample or previous sample measurement) with a known outcome; normal tissue or cells isolated from a subject, such as a normal patient or the cancer patient, cultured primary cells/tissues isolated from a subject such as a normal subject or the cancer patient, adjacent normal cells/tissues obtained from the same organ or body location of the cancer patient, a tissue or cell sample isolated from a normal subject, or a primary cells/tissues obtained from a depository. In another preferred embodiment, the control may comprise a reference standard expression product level from any suitable source, including but not limited to housekeeping genes, an expression product level range from normal tissue (or other previously analyzed control sample), a previously determined expression product level range within a test sample from a group of patients, or a set of patients with a certain outcome (for example, survival for one, two, three, four years, etc.) or receiving a certain treatment (for example, standard of care cancer therapy). It will be understood by those of skill in the art that such control samples and reference standard expression product levels can be used in combination as controls in the methods of the present invention. In one embodiment, the control may comprise normal or non-cancerous cell/tissue sample. In another preferred embodiment, the control may comprise an expression level for a set of patients, such as a set of cancer patients, or for a set of cancer patients receiving a certain treatment, or for a set of patients with one outcome versus another outcome. In the former case, the specific expression product level of each patient can be assigned to a percentile level of expression, or expressed as either higher or lower than the mean or average of the reference standard expression level. In another preferred embodiment, the control may comprise normal cells, cells from patients treated with combination chemotherapy, and cells from patients having benign cancer. In another embodiment, the control may also comprise a measured value for example, average level of expression of a particular gene in a population compared to the level of expression of a housekeeping gene in the same population. Such a population may comprise normal subjects, cancer patients who have not undergone any treatment (i.e., treatment naive), cancer patients undergoing standard of care therapy, or patients having benign cancer. In another preferred embodiment, the control comprises a ratio transformation of expression product levels, including but not limited to determining a ratio of expression product levels of two genes in the test sample and comparing it to any suitable ratio of the same two genes in a reference standard; determining expression product levels of the two or more genes in the test sample and determining a difference in expression product levels in any suitable control; and determining expression product levels of the two or more genes in the test sample, normalizing their expression to expression of housekeeping genes in the test sample, and comparing to any suitable control. In particularly preferred embodiments, the control comprises a control sample which is of the same lineage and/or type as the test sample. In another embodiment, the control may comprise expression product levels grouped as percentiles within or based on a set of patient samples, such as all patients with cancer. In one embodiment a control expression product level is established wherein higher or lower levels of expression product relative to, for instance, a particular percentile, are used as the basis for predicting outcome. In another preferred embodiment, a control expression product level is established using expression product levels from cancer control patients with a known outcome, and the expression product levels from the test sample are compared to the control expression product level as the basis for predicting outcome. As demonstrated by the data below, the methods of the present invention are not limited to use of a specific cut-point in comparing the level of expression product in the test sample to the control.


The “copy number” of a biomarker nucleic acid refers to the number of DNA sequences in a cell (e.g., germline and/or somatic) encoding a particular gene product. Generally, for a given gene, a mammal has two copies of each gene. The copy number can be increased, however, by gene amplification or duplication, or reduced by deletion. For example, germline copy number changes include changes at one or more genomic loci, wherein said one or more genomic loci are not accounted for by the number of copies in the normal complement of germline copies in a control (e.g., the normal copy number in germline DNA for the same species as that from which the specific germline DNA and corresponding copy number were determined). Somatic copy number changes include changes at one or more genomic loci, wherein said one or more genomic loci are not accounted for by the number of copies in germline DNA of a control (e.g., copy number in germline DNA for the same subject as that from which the somatic DNA and corresponding copy number were determined).


The “normal” copy number (e.g., germline and/or somatic) of a biomarker nucleic acid or “normal” level of expression of a biomarker nucleic acid or protein is the activity/level of expression or copy number in a biological sample, e.g., a sample containing tissue, whole blood, serum, plasma, buccal scrape, saliva, cerebrospinal fluid, urine, stool, and bone marrow, from a subject, e.g., a human, not afflicted with cancer, or from a corresponding non-cancerous tissue in the same subject who has cancer.


As used herein, the term “costimulate” with reference to activated immune cells includes the ability of a costimulatory molecule to provide a second, non-activating receptor mediated signal (a “costimulatory signal”) that induces proliferation or effector function. For example, a costimulatory signal can result in cytokine secretion, e.g., in a T cell that has received a T cell-receptor-mediated signal. Immune cells that have received a cell-receptor mediated signal, e.g., via an activating receptor are referred to herein as “activated immune cells.”


The term “determining a suitable treatment regimen for the subject” is taken to mean the determination of a treatment regimen (i.e., a single therapy or a combination of different therapies that are used for the prevention and/or treatment of the cancer in the subject) for a subject that is started, modified and/or ended based or essentially based or at least partially based on the results of the analysis according to the present invention. One example is determining whether to provide targeted therapy against a cancer to provide immunotherapy that generally increases immune responses against the cancer (e.g., immune checkpoint therapy). Another example is starting an adjuvant therapy after surgery whose purpose is to decrease the risk of recurrence, another would be to modify the dosage of a particular chemotherapy. The determination can, in addition to the results of the analysis according to the present invention, be based on personal characteristics of the subject to be treated. In most cases, the actual determination of the suitable treatment regimen for the subject will be performed by the attending physician or doctor.


The term “diagnosing cancer” includes the use of the methods, systems, and code of the present invention to determine the presence or absence of a cancer or subtype thereof in an individual. The term also includes methods, systems, and code for assessing the level of disease activity in an individual.


A molecule is “fixed” or “affixed” to a substrate if it is covalently or non-covalently associated with the substrate such that the substrate can be rinsed with a fluid (e.g. standard saline citrate, pH 7.4) without a substantial fraction of the molecule dissociating from the substrate.


The term “expression signature” or “signature” refers to a group of two or more coordinately expressed biomarkers. For example, the genes, proteins, metabolites, and the like making up this signature may be expressed in a specific cell lineage, stage of differentiation, or during a particular biological response. The biomarkers can reflect biological aspects of the tumors in which they are expressed, such as the cell of origin of the cancer, the nature of the non-malignant cells in the biopsy, and the oncogenic mechanisms responsible for the cancer. Expression data and gene expression levels can be stored on computer readable media, e.g., the computer readable medium used in conjunction with a microarray or chip reading device. Such expression data can be manipulated to generate expression signatures.


“Homologous” as used herein, refers to nucleotide sequence similarity between two regions of the same nucleic acid strand or between regions of two different nucleic acid strands. When a nucleotide residue position in both regions is occupied by the same nucleotide residue, then the regions are homologous at that position. A first region is homologous to a second region if at least one nucleotide residue position of each region is occupied by the same residue. Homology between two regions is expressed in terms of the proportion of nucleotide residue positions of the two regions that are occupied by the same nucleotide residue. By way of example, a region having the nucleotide sequence 5′-ATTGCC-3′ and a region having the nucleotide sequence 5′-TATGGC-3′ share 50% homology. Preferably, the first region comprises a first portion and the second region comprises a second portion, whereby, at least about 50%, and preferably at least about 75%, at least about 90%, or at least about 95% of the nucleotide residue positions of each of the portions are occupied by the same nucleotide residue. More preferably, all nucleotide residue positions of each of the portions are occupied by the same nucleotide residue.


The term “immune cell” refers to cells that play a role in the immune response. Immune cells are of hematopoietic origin, and include lymphocytes, such as B cells and T cells; natural killer cells; myeloid cells, such as monocytes, macrophages, eosinophils, mast cells, basophils, and granulocytes.


The term “immune checkpoint” refers to a group of molecules on the cell surface of CD4+ and/or CD8+ T cells that fine-tune immune responses by down-modulating or inhibiting an anti-tumor immune response. Immune checkpoint proteins are well known in the art and include, without limitation, CTLA-4, PD-1, VISTA, B7-H2, B7-H3, PD-L1, B7-H4, B7-H6, 2B4, ICOS, HVEM, PD-L2, CD160, gp49B, PIR-B, KIR family receptors, TIM-1, TIM-3, TIM-4, LAG-3, BTLA, SIRPalpha (CD47), CD48, 2B4 (CD244), B7.1, B7.2, ILT-2, ILT-4, TIGIT, and A2aR (see, for example, WO 2012/177624). The term further encompasses biologically active protein fragment, as well as nucleic acids encoding full-length immune checkpoint proteins and biologically active protein fragments thereof. In some embodiment, the term further encompasses any fragment according to homology descriptions provided herein.


“Immune checkpoint therapy” refers to the use of agents that inhibit immune checkpoint nucleic acids and/or proteins. Inhibition of one or more immune checkpoints can block or otherwise neutralize inhibitory signaling to thereby upregulate an immune response in order to more efficaciously treat cancer. Exemplary agents useful for inhibiting immune checkpoints include antibodies, small molecules, peptides, peptidomimetics, natural ligands, and derivatives of natural ligands, that can either bind and/or inactivate or inhibit immune checkpoint proteins, or fragments thereof; as well as RNA interference, antisense, nucleic acid aptamers, etc. that can downregulate the expression and/or activity of immune checkpoint nucleic acids, or fragments thereof. Exemplary agents for upregulating an immune response include antibodies against one or more immune checkpoint proteins block the interaction between the proteins and its natural receptor(s); a non-activating form of one or more immune checkpoint proteins (e.g., a dominant negative polypeptide); small molecules or peptides that block the interaction between one or more immune checkpoint proteins and its natural receptor(s); fusion proteins (e.g. the extracellular portion of an immune checkpoint inhibition protein fused to the Fc portion of an antibody or immunoglobulin) that bind to its natural receptor(s); nucleic acid molecules that block immune checkpoint nucleic acid transcription or translation; and the like. Such agents can directly block the interaction between the one or more immune checkpoints and its natural receptor(s) (e.g., antibodies) to prevent inhibitory signaling and upregulate an immune response. Alternatively, agents can indirectly block the interaction between one or more immune checkpoint proteins and its natural receptor(s) to prevent inhibitory signaling and upregulate an immune response. For example, a soluble version of an immune checkpoint protein ligand such as a stabilized extracellular domain can binding to its receptor to indirectly reduce the effective concentration of the receptor to bind to an appropriate ligand. In one embodiment, anti-PD-1 antibodies, anti-PD-L1 antibodies, and anti-CTLA-4 antibodies, either alone or in combination, are used to inhibit immune checkpoints.


“Ipilimumab” is a representative example of an immune checkpoint therapy. Ipilimumab (previously MDX-010; Medarex Inc., marketed by Bristol-Myers Squibb as YERVOY™) is a fully human anti-human CTLA-4 monoclonal antibody that blocks the binding of CTLA-4 to CD80 and CD86 expressed on antigen presenting cells, thereby, blocking the negative down-regulation of the immune responses elicited by the interaction of these molecules (see, for example, WO 2013/169971, U.S. Pat. Publ. 2002/0086014, and U.S. Pat. Publ. 2003/0086930.


The term “immune response” includes T cell mediated and/or B cell mediated immune responses. Immune responses can also include B- and T-cell independent and rely on macrophages and NK cells (along with other cell types) instead (innate immunity). Exemplary immune responses include T cell responses, e.g., cytokine production and cellular cytotoxicity. In addition, the term immune response includes immune responses that are indirectly effected by T cell activation, e.g., antibody production (humoral responses) and activation of cytokine responsive cells, e.g., macrophages.


The term “immunotherapeutic agent” can include any molecule, peptide, antibody or other agent which can stimulate a host immune system to generate an immune response to a tumor or cancer in the subject. Various immunotherapeutic agents are useful in the compositions and methods described herein.


The term “inhibit” includes the decrease, limitation, or blockage, of, for example a particular action, function, or interaction. In some embodiments, cancer is “inhibited” if at least one symptom of the cancer is alleviated, terminated, slowed, or prevented. As used herein, cancer is also “inhibited” if recurrence or metastasis of the cancer is reduced, slowed, delayed, or prevented.


The term “interaction”, when referring to an interaction between two molecules, refers to the physical contact (e.g., binding) of the molecules with one another. Generally, such an interaction results in an activity (which produces a biological effect) of one or both of said molecules.


An “isolated protein” refers to a protein that is substantially free of other proteins, cellular material, separation medium, and culture medium when isolated from cells or produced by recombinant DNA techniques, or chemical precursors or other chemicals when chemically synthesized. An “isolated” or “purified” protein or biologically active portion thereof is substantially free of cellular material or other contaminating proteins from the cell or tissue source from which the antibody, polypeptide, peptide or fusion protein is derived, or substantially free from chemical precursors or other chemicals when chemically synthesized. The language “substantially free of cellular material” includes preparations of a biomarker polypeptide or fragment thereof, in which the protein is separated from cellular components of the cells from which it is isolated or recombinantly produced. In one embodiment, the language “substantially free of cellular material” includes preparations of a biomarker protein or fragment thereof, having less than about 30% (by dry weight) of non-biomarker protein (also referred to herein as a “contaminating protein”), more preferably less than about 20% of non-biomarker protein, still more preferably less than about 10% of non-biomarker protein, and most preferably less than about 5% non-biomarker protein. When antibody, polypeptide, peptide or fusion protein or fragment thereof, e.g., a biologically active fragment thereof, is recombinantly produced, it is also preferably substantially free of culture medium, i.e., culture medium represents less than about 20%, more preferably less than about 10%, and most preferably less than about 5% of the volume of the protein preparation.


A “kit” is any manufacture (e.g. a package or container) comprising at least one reagent, e.g. a probe or small molecule, for specifically detecting and/or affecting the expression of a marker of the present invention. The kit may be promoted, distributed, or sold as a unit for performing the methods of the present invention. The kit may comprise one or more reagents necessary to express a composition useful in the methods of the present invention. In certain embodiments, the kit may further comprise a reference standard, e.g., a nucleic acid encoding a protein that does not affect or regulate signaling pathways controlling cell growth, division, migration, survival or apoptosis. One skilled in the art can envision many such control proteins, including, but not limited to, common molecular tags (e.g., green fluorescent protein and beta-galactosidase), proteins not classified in any of pathway encompassing cell growth, division, migration, survival or apoptosis by GeneOntology reference, or ubiquitous housekeeping proteins. Reagents in the kit may be provided in individual containers or as mixtures of two or more reagents in a single container. In addition, instructional materials which describe the use of the compositions within the kit can be included.


The term “neoadjuvant therapy” refers to a treatment given before the primary treatment. Examples of neoadjuvant therapy can include chemotherapy, radiation therapy, and hormone therapy. For example, in treating breast cancer, neoadjuvant therapy can allows patients with large breast cancer to undergo breast-conserving surgery.


The “normal” level of expression of a biomarker is the level of expression of the biomarker in cells of a subject, e.g., a human patient, not afflicted with a cancer. An “over-expression” or “significantly higher level of expression” of a biomarker refers to an expression level in a test sample that is greater than the standard error of the assay employed to assess expression, and is preferably at least 10%, and more preferably 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 times or more higher than the expression activity or level of the biomarker in a control sample (e.g., sample from a healthy subject not having the biomarker associated disease) and preferably, the average expression level of the biomarker in several control samples. A “significantly lower level of expression” of a biomarker refers to an expression level in a test sample that is at least 10%, and more preferably 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 times or more lower than the expression level of the biomarker in a control sample (e.g., sample from a healthy subject not having the biomarker associated disease) and preferably, the average expression level of the biomarker in several control samples.


An “over-expression” or “significantly higher level of expression” of a biomarker refers to an expression level in a test sample that is greater than the standard error of the assay employed to assess expression, and is preferably at least 10%, and more preferably 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 times or more higher than the expression activity or level of the biomarker in a control sample (e.g., sample from a healthy subject not having the biomarker associated disease) and preferably, the average expression level of the biomarker in several control samples. A “significantly lower level of expression” of a biomarker refers to an expression level in a test sample that is at least 10%, and more preferably 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 times or more lower than the expression level of the biomarker in a control sample (e.g., sample from a healthy subject not having the biomarker associated disease) and preferably, the average expression level of the biomarker in several control samples.


The term “pre-determined” biomarker amount and/or activity measurement(s) may be a biomarker amount and/or activity measurement(s) used to, by way of example only, evaluate a subject that may be selected for a particular treatment, evaluate a response to a treatment such as an anti-immune checkpoint inhibitor therapy, and/or evaluate the disease state. A pre-determined biomarker amount and/or activity measurement(s) may be determined in populations of patients with or without cancer. The pre-determined biomarker amount and/or activity measurement(s) can be a single number, equally applicable to every patient, or the pre-determined biomarker amount and/or activity measurement(s) can vary according to specific subpopulations of patients. Age, weight, height, and other factors of a subject may affect the pre-determined biomarker amount and/or activity measurement(s) of the individual. Furthermore, the pre-determined biomarker amount and/or activity can be determined for each subject individually. In one embodiment, the amounts determined and/or compared in a method described herein are based on absolute measurements. In another embodiment, the amounts determined and/or compared in a method described herein are based on relative measurements, such as ratios (e.g., serum biomarker normalized to the expression of a housekeeping or otherwise generally constant biomarker). The pre-determined biomarker amount and/or activity measurement(s) can be any suitable standard. For example, the pre-determined biomarker amount and/or activity measurement(s) can be obtained from the same or a different human for whom a patient selection is being assessed. In one embodiment, the pre-determined biomarker amount and/or activity measurement(s) can be obtained from a previous assessment of the same patient. In such a manner, the progress of the selection of the patient can be monitored over time. In addition, the control can be obtained from an assessment of another human or multiple humans, e.g., selected groups of humans, if the subject is a human. In such a manner, the extent of the selection of the human for whom selection is being assessed can be compared to suitable other humans, e.g., other humans who are in a similar situation to the human of interest, such as those suffering from similar or the same condition(s) and/or of the same ethnic group.


The term “predictive” includes the use of a biomarker nucleic acid and/or protein status, e.g., over- or under-activity, emergence, expression, growth, remission, recurrence or resistance of tumors before, during or after therapy, for determining the likelihood of response of a cancer to anti-immune checkpoint treatment (e.g., therapeutic antibodies against CTLA-4, PD-1, PD-L1, and the like). Such predictive use of the biomarker may be confirmed by, e.g., (1) increased or decreased copy number (e.g., by FISH, FISH plus SKY, single-molecule sequencing, e.g., as described in the art at least at J. Biotechnol., 86:289-301, or qPCR), overexpression or underexpression of a biomarker nucleic acid (e.g., by ISH, Northern Blot, or qPCR), increased or decreased biomarker protein (e.g., by IHC), or increased or decreased activity, e.g., in more than about 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 100%, or more of assayed human cancers types or cancer samples; (2) its absolute or relatively modulated presence or absence in a biological sample, e.g., a sample containing tissue, whole blood, serum, plasma, buccal scrape, saliva, cerebrospinal fluid, urine, stool, or bone marrow, from a subject, e.g. a human, afflicted with cancer; (3) its absolute or relatively modulated presence or absence in clinical subset of patients with cancer (e.g., those responding to a particular immune checkpoint therapy or those developing resistance thereto).


The term “pre-malignant lesions” as described herein refers to a lesion that, while not cancerous, has potential for becoming cancerous. It also includes the term “pre-malignant disorders” or “potentially malignant disorders.” In particular this refers to a benign, morphologically and/or histologically altered tissue that has a greater than normal risk of malignant transformation, and a disease or a patient's habit that does not necessarily alter the clinical appearance of local tissue but is associated with a greater than normal risk of precancerous lesion or cancer development in that tissue (leukoplakia, erythroplakia, erytroleukoplakia lichen planus (lichenoid reaction) and any lesion or an area which histological examination showed atypia of cells or dysplasia.


The terms “prevent,” “preventing,” “prevention,” “prophylactic treatment,” and the like refer to reducing the probability of developing a disease, disorder, or condition in a subject, who does not have, but is at risk of or susceptible to developing a disease, disorder, or condition.


The term “probe” refers to any molecule which is capable of selectively binding to a specifically intended target molecule, for example, a nucleotide transcript or protein encoded by or corresponding to a biomarker nucleic acid. Probes can be either synthesized by one skilled in the art, or derived from appropriate biological preparations. For purposes of detection of the target molecule, probes may be specifically designed to be labeled, as described herein. Examples of molecules that can be utilized as probes include, but are not limited to, RNA, DNA, proteins, antibodies, and organic molecules.


The term “prognosis” includes a prediction of the probable course and outcome of cancer or the likelihood of recovery from the disease. In some embodiments, the use of statistical algorithms provides a prognosis of cancer in an individual. For example, the prognosis can be surgery, development of a clinical subtype of cancer (e.g., solid tumors, such as lung cancer, melanoma, and renal cell carcinoma), development of one or more clinical factors, development of intestinal cancer, or recovery from the disease.


The term “response to immune checkpoint therapy” relates to any response of the hyperproliferative disorder (e.g., cancer) to an immune checkpoint therapy, preferably to a change in tumor mass and/or volume after initiation of neoadjuvant or adjuvant chemotherapy or as prolonged patient survival following treatment compared to patients not receiving the therapy. Hyperproliferative disorder response may be assessed, for example for efficacy or in a neoadjuvant or adjuvant situation, where the size of a tumor after systemic intervention can be compared to the initial size and dimensions as measured by CT, PET, mammogram, ultrasound or palpation. Responses may also be assessed by caliper measurement or pathological examination of the tumor after biopsy or surgical resection. Response may be recorded in a quantitative fashion like percentage change in tumor volume or in a qualitative fashion like “pathological complete response” (pCR), “clinical complete remission” (cCR), “clinical partial remission” (cPR), “clinical stable disease” (cSD), “clinical progressive disease” (cPD) or other qualitative criteria. Assessment of hyperproliferative disorder response may be done early after the onset of neoadjuvant or adjuvant therapy, e.g., after a few hours, days, weeks or preferably after a few months. A typical endpoint for response assessment is upon termination of neoadjuvant chemotherapy or upon surgical removal of residual tumor cells and/or the tumor bed. This is typically three months after initiation of neoadjuvant therapy. In some embodiments, clinical efficacy of the therapeutic treatments described herein may be determined by measuring the clinical benefit rate (CBR). The clinical benefit rate is measured by determining the sum of the percentage of patients who are in complete remission (CR), the number of patients who are in partial remission (PR) and the number of patients having stable disease (SD) at a time point at least 6 months out from the end of therapy. The shorthand for this formula is CBR=CR+PR+SD over 6 months. In some embodiments, the CBR for a particular cancer therapeutic regimen is at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or more. Additional criteria for evaluating the response to cancer therapies are related to “survival,” which includes all of the following: survival until mortality, also known as overall survival (wherein said mortality may be either irrespective of cause or tumor related); “recurrence-free survival” (wherein the term recurrence shall include both localized and distant recurrence); metastasis free survival; disease free survival (wherein the term disease shall include cancer and diseases associated therewith). The length of said survival may be calculated by reference to a defined start point (e.g., time of diagnosis or start of treatment) and end point (e.g., death, recurrence or metastasis). In addition, criteria for efficacy of treatment can be expanded to include response to chemotherapy, probability of survival, probability of metastasis within a given time period, and probability of tumor recurrence. For example, in order to determine appropriate threshold values, a particular cancer therapeutic regimen can be administered to a population of subjects and the outcome can be correlated to biomarker measurements that were determined prior to administration of any cancer therapy. The outcome measurement may be pathologic response to therapy given in the neoadjuvant setting. Alternatively, outcome measures, such as overall survival and disease-free survival can be monitored over a period of time for subjects following cancer therapy for whom biomarker measurement values are known. In certain embodiments, the doses administered are standard doses known in the art for cancer therapeutic agents. The period of time for which subjects are monitored can vary. For example, subjects may be monitored for at least 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 25, 30, 35, 40, 45, 50, 55, or 60 months. Biomarker measurement threshold values that correlate to outcome of a cancer therapy can be determined using well-known methods in the art, such as those described in the Examples section.


The term “resistance” refers to an acquired or natural resistance of a cancer sample or a mammal to a cancer therapy (i.e., being nonresponsive to or having reduced or limited response to the therapeutic treatment), such as having a reduced response to a therapeutic treatment (cessation of tumor shrinkage and development of tumor growth while receiving a given therapy) by 25% or more, for example, 30%, 40%, 50%, 60%, 70%, 80%, or more, to 2-fold, 3-fold, 4-fold, 5-fold, 10-fold, 15-fold, 20-fold or more. The reduction in response can be measured by comparing with the same cancer sample or mammal before the resistance is acquired, or by comparing with a different cancer sample or a mammal who is known to have no resistance to the therapeutic treatment. A typical acquired resistance to chemotherapy is called “multidrug resistance.” The multidrug resistance can be mediated by P-glycoprotein or can be mediated by other mechanisms, or it can occur when a mammal is infected with a multi-drug-resistant microorganism or a combination of microorganisms. The determination of resistance to a therapeutic treatment is routine in the art and within the skill of an ordinarily skilled clinician, for example, can be measured by cell proliferative assays and cell death assays as described herein as “sensitizing.” In some embodiments, the term “reverses resistance” means that the use of a second agent in combination with a primary cancer therapy (e.g., chemotherapeutic or radiation therapy) is able to produce a significant decrease in tumor volume at a level of statistical significance (e.g., p<0.05) when compared to tumor volume of untreated tumor in the circumstance where the primary cancer therapy (e.g., chemotherapeutic or radiation therapy) alone is unable to produce a statistically significant decrease in tumor volume compared to tumor volume of untreated tumor. This generally applies to tumor volume measurements made at a time when the untreated tumor is growing log rhythmically.


The terms “response” or “responsiveness” refers to an anti-cancer response, e.g. in the sense of reduction of tumor size or inhibiting tumor growth. The terms can also refer to an improved prognosis, for example, as reflected by an increased time to recurrence, which is the period to first recurrence censoring for second primary cancer as a first event or death without evidence of recurrence, or an increased overall survival, which is the period from treatment to death from any cause. To respond or to have a response means there is a beneficial endpoint attained when exposed to a stimulus. Alternatively, a negative or detrimental symptom is minimized, mitigated or attenuated on exposure to a stimulus. It will be appreciated that evaluating the likelihood that a tumor or subject will exhibit a favorable response is equivalent to evaluating the likelihood that the tumor or subject will not exhibit favorable response (i.e., will exhibit a lack of response or be non-responsive).


An “RNA interfering agent” as used herein, is defined as any agent which interferes with or inhibits expression of a target biomarker gene by RNA interference (RNAi). Such RNA interfering agents include, but are not limited to, nucleic acid molecules including RNA molecules which are homologous to the target biomarker gene of the present invention, or a fragment thereof, short interfering RNA (siRNA), and small molecules which interfere with or inhibit expression of a target biomarker nucleic acid by RNA interference (RNAi).


“RNA interference (RNAi)” is an evolutionally conserved process whereby the expression or introduction of RNA of a sequence that is identical or highly similar to a target biomarker nucleic acid results in the sequence specific degradation or specific post-transcriptional gene silencing (PTGS) of messenger RNA (mRNA) transcribed from that targeted gene (see Coburn, G. and Cullen, B. (2002) J of Virology 76(18):9225), thereby inhibiting expression of the target biomarker nucleic acid. In one embodiment, the RNA is double stranded RNA (dsRNA). This process has been described in plants, invertebrates, and mammalian cells. In nature, RNAi is initiated by the dsRNA-specific endonuclease Dicer, which promotes processive cleavage of long dsRNA into double-stranded fragments termed siRNAs. siRNAs are incorporated into a protein complex that recognizes and cleaves target mRNAs. RNAi can also be initiated by introducing nucleic acid molecules, e.g., synthetic siRNAs or RNA interfering agents, to inhibit or silence the expression of target biomarker nucleic acids. As used herein, “inhibition of target biomarker nucleic acid expression” or “inhibition of marker gene expression” includes any decrease in expression or protein activity or level of the target biomarker nucleic acid or protein encoded by the target biomarker nucleic acid. The decrease may be of at least 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or 99% or more as compared to the expression of a target biomarker nucleic acid or the activity or level of the protein encoded by a target biomarker nucleic acid which has not been targeted by an RNA interfering agent.


The term “sample” used for detecting or determining the presence or level of at least one biomarker is typically whole blood, plasma, serum, saliva, urine, stool (e.g., feces), tears, and any other bodily fluid (e.g., as described above under the definition of “body fluids”), or a tissue sample (e.g., biopsy) such as a small intestine, colon sample, or surgical resection tissue. In certain instances, the method of the present invention further comprises obtaining the sample from the individual prior to detecting or determining the presence or level of at least one marker in the sample.


The term “sensitize” means to alter cancer cells or tumor cells in a way that allows for more effective treatment of the associated cancer with a cancer therapy (e.g., anti-immune checkpoint, chemotherapeutic, and/or radiation therapy). In some embodiments, normal cells are not affected to an extent that causes the normal cells to be unduly injured by the immune checkpoint therapy. An increased sensitivity or a reduced sensitivity to a therapeutic treatment is measured according to a known method in the art for the particular treatment and methods described herein below, including, but not limited to, cell proliferative assays (Tanigawa N, Kern D H, Kikasa Y, Morton D L, Cancer Res 1982; 42: 2159-2164), cell death assays (Weisenthal L M, Shoemaker R H, Marsden J A, Dill P L, Baker J A, Moran E M, Cancer Res 1984; 94: 161-173; Weisenthal L M, Lippman M E, Cancer Treat Rep 1985; 69: 615-632; Weisenthal L M, In: Kaspers G J L, Pieters R, Twentyman P R, Weisenthal L M, Veerman A J P, eds. Drug Resistance in Leukemia and Lymphoma. Langhorne, P A: Harwood Academic Publishers, 1993: 415-432; Weisenthal L M, Contrib Gynecol Obstet 1994; 19: 82-90). The sensitivity or resistance may also be measured in animal by measuring the tumor size reduction over a period of time, for example, 6 month for human and 4-6 weeks for mouse. A composition or a method sensitizes response to a therapeutic treatment if the increase in treatment sensitivity or the reduction in resistance is 25% or more, for example, 30%, 40%, 50%, 60%, 70%, 80%, or more, to 2-fold, 3-fold, 4-fold, 5-fold, 10-fold, 15-fold, 20-fold or more, compared to treatment sensitivity or resistance in the absence of such composition or method. The determination of sensitivity or resistance to a therapeutic treatment is routine in the art and within the skill of an ordinarily skilled clinician. It is to be understood that any method described herein for enhancing the efficacy of a cancer therapy can be equally applied to methods for sensitizing hyperproliferative or otherwise cancerous cells (e.g., resistant cells) to the cancer therapy.


The term “synergistic effect” refers to the combined effect of two or more anti-immune checkpoint agents can be greater than the sum of the separate effects of the anticancer agents alone.


“Short interfering RNA” (siRNA), also referred to herein as “small interfering RNA” is defined as an agent which functions to inhibit expression of a target biomarker nucleic acid, e.g., by RNAi. An siRNA may be chemically synthesized, may be produced by in vitro transcription, or may be produced within a host cell. In one embodiment, siRNA is a double stranded RNA (dsRNA) molecule of about 15 to about 40 nucleotides in length, preferably about 15 to about 28 nucleotides, more preferably about 19 to about 25 nucleotides in length, and more preferably about 19, 20, 21, or 22 nucleotides in length, and may contain a 3′ and/or 5′ overhang on each strand having a length of about 0, 1, 2, 3, 4, or 5 nucleotides. The length of the overhang is independent between the two strands, i.e., the length of the overhang on one strand is not dependent on the length of the overhang on the second strand. Preferably the siRNA is capable of promoting RNA interference through degradation or specific post-transcriptional gene silencing (PTGS) of the target messenger RNA (mRNA).


In another embodiment, an siRNA is a small hairpin (also called stem loop) RNA (shRNA). In one embodiment, these shRNAs are composed of a short (e.g., 19-25 nucleotide) antisense strand, followed by a 5-9 nucleotide loop, and the analogous sense strand. Alternatively, the sense strand may precede the nucleotide loop structure and the antisense strand may follow. These shRNAs may be contained in plasmids, retroviruses, and lentiviruses and expressed from, for example, the pol III U6 promoter, or another promoter (see, e.g., Stewart, et al. (2003) RNA 9:493-501 incorporated by reference herein).


RNA interfering agents, e.g., siRNA molecules, may be administered to a patient having or at risk for having cancer, to inhibit expression of a biomarker gene which is overexpressed in cancer and thereby treat, prevent, or inhibit cancer in the subject.


The term “subject” refers to any healthy animal, mammal or human, or any animal, mammal or human afflicted with a cancer, e.g., lung, ovarian, pancreatic, liver, breast, prostate, and colon carcinomas, as well as melanoma and multiple myeloma. The term “subject” is interchangeable with “patient.”


The term “survival” includes all of the following: survival until mortality, also known as overall survival (wherein said mortality may be either irrespective of cause or tumor related); “recurrence-free survival” (wherein the term recurrence shall include both localized and distant recurrence); metastasis free survival; disease free survival (wherein the term disease shall include cancer and diseases associated therewith). The length of said survival may be calculated by reference to a defined start point (e.g. time of diagnosis or start of treatment) and end point (e.g. death, recurrence or metastasis). In addition, criteria for efficacy of treatment can be expanded to include response to chemotherapy, probability of survival, probability of metastasis within a given time period, and probability of tumor recurrence.


The term “therapeutic effect” refers to a local or systemic effect in animals, particularly mammals, and more particularly humans, caused by a pharmacologically active substance. The term thus means any substance intended for use in the diagnosis, cure, mitigation, treatment or prevention of disease or in the enhancement of desirable physical or mental development and conditions in an animal or human. The phrase “therapeutically-effective amount” means that amount of such a substance that produces some desired local or systemic effect at a reasonable benefit/risk ratio applicable to any treatment. In certain embodiments, a therapeutically effective amount of a compound will depend on its therapeutic index, solubility, and the like. For example, certain compounds discovered by the methods of the present invention may be administered in a sufficient amount to produce a reasonable benefit/risk ratio applicable to such treatment.


The terms “therapeutically-effective amount” and “effective amount” as used herein means that amount of a compound, material, or composition comprising a compound of the present invention which is effective for producing some desired therapeutic effect in at least a sub-population of cells in an animal at a reasonable benefit/risk ratio applicable to any medical treatment. Toxicity and therapeutic efficacy of subject compounds may be determined by standard pharmaceutical procedures in cell cultures or experimental animals, e.g., for determining the LD50 and the ED50. Compositions that exhibit large therapeutic indices are preferred. In some embodiments, the LD50 (lethal dosage) can be measured and can be, for example, at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900%, 1000% or more reduced for the agent relative to no administration of the agent. Similarly, the ED50 (i.e., the concentration which achieves a half-maximal inhibition of symptoms) can be measured and can be, for example, at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900%, 1000% or more increased for the agent relative to no administration of the agent. Also, Similarly, the IC50 (i.e., the concentration which achieves half-maximal cytotoxic or cytostatic effect on cancer cells) can be measured and can be, for example, at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900%, 1000% or more increased for the agent relative to no administration of the agent. In some embodiments, cancer cell growth in an assay can be inhibited by at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or even 100%. In another embodiment, at least about a 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or even 100% decrease in a solid malignancy can be achieved.


A “transcribed polynucleotide” or “nucleotide transcript” is a polynucleotide (e.g. an mRNA, hnRNA, a cDNA, or an analog of such RNA or cDNA) which is complementary to or homologous with all or a portion of a mature mRNA made by transcription of a biomarker nucleic acid and normal post-transcriptional processing (e.g. splicing), if any, of the RNA transcript, and reverse transcription of the RNA transcript.


As used herein, the term “unresponsiveness” includes refractivity of immune cells to stimulation, e.g., stimulation via an activating receptor or a cytokine. Unresponsiveness can occur, e.g., because of exposure to immunosuppressants or exposure to high doses of antigen. As used herein, the term “anergy” or “tolerance” includes refractivity to activating receptor-mediated stimulation. Such refractivity is generally antigen-specific and persists after exposure to the tolerizing antigen has ceased. For example, anergy in T cells (as opposed to unresponsiveness) is characterized by lack of cytokine production, e.g., IL-2. T cell anergy occurs when T cells are exposed to antigen and receive a first signal (a T cell receptor or CD-3 mediated signal) in the absence of a second signal (a costimulatory signal). Under these conditions, reexposure of the cells to the same antigen (even if reexposure occurs in the presence of a costimulatory polypeptide) results in failure to produce cytokines and, thus, failure to proliferate. Anergic T cells can, however, proliferate if cultured with cytokines (e.g., IL-2). For example, T cell anergy can also be observed by the lack of IL-2 production by T lymphocytes as measured by ELISA or by a proliferation assay using an indicator cell line. Alternatively, a reporter gene construct can be used. For example, anergic T cells fail to initiate IL-2 gene transcription induced by a heterologous promoter under the control of the 5′ IL-2 gene enhancer or by a multimer of the AP1 sequence that can be found within the enhancer (Kang et al. (1992) Science 257:1134).


There is a known and definite correspondence between the amino acid sequence of a particular protein and the nucleotide sequences that can code for the protein, as defined by the genetic code (shown below). Likewise, there is a known and definite correspondence between the nucleotide sequence of a particular nucleic acid and the amino acid sequence encoded by that nucleic acid, as defined by the genetic code.












GENETIC CODE


















Alanine (Ala, A)
GCA, GCC, GCG, GCT



Arginine (Arg, R)
AGA, ACG, CGA, CGC, CGG, CGT



Asparagine (Asn, N)
AAC, AAT



Aspartic acid (Asp, D)
GAC, GAT



Cysteine (Cys, C)
TGC, TGT



Glutamic acid (Glu, E)
GAA, GAG



Glutamine (Gln, Q)
CAA, CAG



Glycine (Gly, G)
GGA, GGC, GGG, GGT



Histidine (His, H)
CAC, CAT



Isoleucine (Ile, I)
ATA, ATC, ATT



Leucine (Leu, L)
CTA, CTC, CTG, CTT, TTA, TTG



Lysine (Lys, K)
AAA, AAG



Methionine (Met, M)
ATG



Phenylalanine (Phe, F)
TTC, TTT



Proline (Pro, P)
CCA, CCC, CCG, CCT



Serine (Ser, S)
AGC, AGT, TCA, TCC, TCG, TCT



Threonine (Thr, T)
ACA, ACC, ACG, ACT



Tryptophan (Trp, W)
TGG



Tyrosine (Tyr, Y)
TAC, TAT



Valine (Val, V)
GTA, GTC, GTG, GTT



Termination signal (end)
TAA, TAG, TGA










An important and well known feature of the genetic code is its redundancy, whereby, for most of the amino acids used to make proteins, more than one coding nucleotide triplet may be employed (illustrated above). Therefore, a number of different nucleotide sequences may code for a given amino acid sequence. Such nucleotide sequences are considered functionally equivalent since they result in the production of the same amino acid sequence in all organisms (although certain organisms may translate some sequences more efficiently than they do others). Moreover, occasionally, a methylated variant of a purine or pyrimidine may be found in a given nucleotide sequence. Such methylations do not affect the coding relationship between the trinucleotide codon and the corresponding amino acid.


In view of the foregoing, the nucleotide sequence of a DNA or RNA encoding a biomarker nucleic acid (or any portion thereof) can be used to derive the polypeptide amino acid sequence, using the genetic code to translate the DNA or RNA into an amino acid sequence. Likewise, for polypeptide amino acid sequence, corresponding nucleotide sequences that can encode the polypeptide can be deduced from the genetic code (which, because of its redundancy, will produce multiple nucleic acid sequences for any given amino acid sequence). Thus, description and/or disclosure herein of a nucleotide sequence which encodes a polypeptide should be considered to also include description and/or disclosure of the amino acid sequence encoded by the nucleotide sequence. Similarly, description and/or disclosure of a polypeptide amino acid sequence herein should be considered to also include description and/or disclosure of all possible nucleotide sequences that can encode the amino acid sequence.


Finally, nucleic acid and amino acid sequence information for the loci and biomarkers of the present invention (e.g., biomarkers listed in Table 1) are well known in the art and readily available on publicly available databases, such as the National Center for Biotechnology Information (NCBI). For example, exemplary nucleic acid and amino acid sequences derived from publicly available sequence databases are provided below.









TABLE 1







SEQ ID NO: 1 Human PBRM1 Transcript Variant 1 cDNA Sequence (NM_018313.4)








   1
gcggccgcgg ccggaggagc aatagcagca gccgtggcgg ccacggggcg gggcgcggcg





  61
gtcggtgacc gcggccgggg ctgcaggcgg cggagcggct ggaagttgga ttccatgggt





 121
tccaagagaa gaagagctac ctccccttcc agcagtgtca gcggggactt tgatgatggg





 181
caccattctg tgtcaacacc aggcccaagc aggaaaagga ggagactttc caatcttcca





 241
actgtagatc ctattgccgt gtgccatgaa ctctataata ccatccgaga ctataaggat





 301
gaacagggca gacttctctg tgagctcttc attagggcac caaagcgaag aaatcaacca





 361
gactattatg aagtggtttc tcagcccatt gacttgatga aaatccaaca gaaactaaaa





 421
atggaagagt atgatgatgt taatttgctg actgctgact tccagcttct ttttaacaat





 481
gcaaagtcct attataagcc agattctcct gaatataaag ccgcttgcaa actctgggat





 541
ttgtaccttc gaacaagaaa tgagtttgtt cagaaaggag aagcagatga cgaagatgat





 601
gatgaagatg ggcaagacaa tcagggcaca gtgactgaag gatcttctcc agcttacttg





 661
aaggagatcc tggagcagct tcttgaagcc atagttgtag ctacaaatcc atcaggacgt





 721
ctcattagcg aactttttca gaaactgcct tctaaagtgc aatatccaga ttattatgca





 781
ataattaagg agcctataga tctcaagacc attgcccaga ggatacagaa tggaagctac





 841
aaaagtattc atgcaatggc caaagatata gatctcctcg caaaaaatgc caaaacttat





 901
aatgagcctg gctctcaagt attcaaggat gcaaattcaa ttaaaaaaat attttatatg





 961
aaaaaggctg aaattgaaca tcatgaaatg gctaagtcaa gtcttcgaat gaggactcca





1021
tccaacttgg ctgcagccag actgacaggt ccttcacaca gtaaaggcag ccttggtgaa





1081
gagagaaatc ccactagcaa gtattaccgt aataaaagag cagtacaagg aggtcgttta





1141
tcagcaatta caatggcact tcaatatggc tcagaaagtg aagaagatgc tgctttagct





1201
gctgcacgct atgaagaggg agagtcagaa gcagaaagca tcacttcctt tatggatgtt





1261
tcaaatcctt tttatcagct ttatgacaca gttaggagtt gtcggaataa ccaagggcag





1321
ctaatagctg aaccttttta ccatttgcct tcaaagaaaa aataccctga ttattaccag





1381
caaattaaaa tgcccatatc actacaacag atccgaacaa aactgaagaa tcaagaatat





1441
gaaactttag atcatttgga gtgtgatctg aatttaatgt ttgaaaatgc caaacgctat





1501
aatgtgccca attcagccat ctacaagcga gttctaaaat tgcagcaagt tatgcaggca





1561
aagaagaaag agcttgccag gagagacgat atcgaggacg gagacagcat gatctcttca





1621
gccacctctg atactggtag tgccaaaaga aaaagtaaaa agaacataag aaagcagcga





1681
atgaaaatct tattcaatgt tgttcttgaa gctcgagagc caggttcagg cagaagactt





1741
tgtgacctat ttatggttaa accatccaaa aaggactatc ctgattatta taaaatcatc





1801
ttggagccaa tggacttgaa aataattgag cataacatcc gcaatgacaa atatgctggt





1861
gaagagggaa tgatagaaga catgaagctg atgttccgga atgccaggca ctataatgag





1921
gagggctccc aggtttataa tgatgcacat atcctggaga agttactcaa ggagaaaagg





1981
aaagagctgg gcccactgcc tgatgatgat gacatggctt ctcccaaact caagctgagt





2041
aggaagagtg gcatttctcc taaaaaatca aaatacatga ctccaatgca gcagaaacta





2101
aatgaggtct atgaagctgt aaagaactat actgataaga ggggtcgccg cctcagtgcc





2161
atatttctga ggcttccctc tagatctgag ttgcctgact actatctgac tattaaaaag





2221
cccatggaca tggaaaaaat tcgaagtcac atgatggcca acaagtacca agatattgac





2281
tctatggttg aggactttgt catgatgttt aataatgcct gtacatacaa tgagccggag





2341
tctttgatct acaaagatgc tcttgttcta cacaaagtcc tgcttgaaac acgcagagac





2401
ctggagggag atgaggactc tcatgtccca aatgtgactt tgctgattca agagcttatc





2461
cacaatcttt ttgtgtcagt catgagtcat caggatgatg agggaagatg ctacagcgat





2521
tctttagcag aaattcctgc tgtggatccc aactttccta acaaaccacc ccttacattt





2581
gacataatta ggaagaatgt tgaaaataat cgctaccgtc ggcttgattt atttcaagag





2641
catatgtttg aagtattgga acgagcaaga aggatgaatc ggacagattc agaaatatat





2701
gaagatgcag tagaacttca gcagtttttt attaaaattc gtgatgaact ctgcaaaaat





2761
ggagagattc ttctttcacc ggcactcagc tataccacaa aacatttgca taatgatgtg





2821
gagaaagaga gaaaggaaaa attgccaaaa gaaatagagg aagataaact aaaacgagaa





2881
gaagaaaaaa gagaagctga aaagagtgaa gattcctctg gtgctgcagg cctctcaggc





2941
ttacatcgca catacagcca ggactgtagc tttaaaaaca gcatgtacca tgttggagat





3001
tacgtctatg tggaacctgc agaggccaac ctacaaccac atatcgtctg tattgaaaga





3061
ctgtgggagg attcagctga aaaagaagtt tttaagagtg actattacaa caaagttcca





3121
gttagtaaaa ttctaggcaa gtgtgtggtc atgtttgtca aggaatactt taagttatgc





3181
ccagaaaact tccgagatga ggatgttttt gtctgtgaat cacggtattc tgccaaaacc





3241
aaatctttta agaaaattaa actgtggacc atgcccatca gctcagtcag gtttgtccct





3301
cgggatgtgc ctctgcctgt ggttcgcgtg gcctctgtat ttgcaaatgc agataaaggt





3361
gatgatgaga agaatacaga caactcagag gacagtcgag ctgaagacaa ttttaacttg





3421
gaaaaggaaa aagaagatgt ccctgtggaa atgtccaatg gtgaaccagg ttgccactac





3481
tttgagcagc tccattacaa tgacatgtgg ctgaaggttg gcgactgtgt cttcatcaag





3541
tcccatggcc tggtgcgtcc tcgtgtgggc agaattgaaa aagtatgggt tcgagatgga





3601
gctgcatatt tttatggccc catcttcatt cacccagaag aaacagagca tgagcccaca





3661
aaaatgttct acaaaaaaga agtatttctg agtaatctgg aagaaacctg ccccatgaca





3721
tgtattctcg gaaagtgtgc tgtgttgtca ttcaaggact tcctctcctg caggccaact





3781
gaaataccag aaaatgacat tctgctttgt gagagccgct acaatgagag cgacaagcag





3841
atgaagaaat tcaaaggatt gaagaggttt tcactctctg ctaaagtggt agatgatgaa





3901
atttactact tcagaaaacc aattgttcct cagaaggagc catcaccttt gctggaaaag





3961
aagatccagt tgctagaagc taaatttgcc gagttagaag gtggagatga tgatattgaa





4021
gagatgggag aagaagatag tgagtctacc ccaaagtctg ccaaaggcag tgcaaagaag





4081
gaaggctcca aacggaaaat caacatgagt ggctacatcc tgttcagcag tgagatgagg





4141
gctgtgatta aggcccaaca cccagactac tctttcgggg agctcagccg cctggtgggg





4201
acagaatgga gaaatcttga gacagccaag aaagcagaat atgaaggcat gatgggtggc





4261
tatccgccag gccttccacc tttgcagggc ccagttgatg gccttgttag catgggcagc





4321
atgcagccac ttcaccctgg ggggcctcca ccccaccatc ttccgccagg tgtgcctggc





4381
ctcccgggca tcccaccacc gggtgtgatg aaccaaggag tggcccctat ggtagggact





4441
ccagcaccag gtggaagtcc atatggacaa caggtgggag ttttggggcc tccagggcag





4501
caggcaccac ctccatatcc cggcccacat ccagctggac cccctgtcat acagcagcca





4561
acaacaccca tgtttgtagc tcccccacca aagacccagc ggcttcttca ctcagaggcc





4621
tacctgaaat acattgaagg actcagtgcg gagtccaaca gcattagcaa gtgggatcag





4681
acactggcag ctcgaagacg cgacgtccat ttgtcgaaag aacaggagag ccgcctaccc





4741
tctcactggc tgaaaagcaa aggggcccac accaccatgg cagatgccct ctggcgcctt





4801
cgagatttga tgctccggga caccctcaac attcgccaag catacaacct agaaaatgtt





4861
taatcacatc attacgtttc ttttatatag aagcataaag agttgtggat cagtagccat





4921
tttagttact gggggtgggg ggaaggaaca aaggaggata atttttattg cattttactg





4981
tacatcacaa ggccattttt atatacggac acttttaata agctatttca atttgtttgt





5041
tatattaagt tgactttatc aaatacacaa agattttttt gcatatgttt ccttcgttta





5101
aaaccagttt cataattggt tgtatatgta gacttggagt tttatctttt tacttgttgc





5161
catggaactg aaaccattag aggtttttgt cttggcttgg ggtttttgtt ttcttggttt





5221
tgggtttttt tatatatata tataaaagaa caaaatgaaa aaaaacacac acacacaaga





5281
gtttacagat tagtttaaat tgataatgaa atgtgaagtt tgtcctagtt tacatcttag





5341
agaggggagt atacttgtgt ttgtttcatg tgcctgaata tcttaagcca ctttctgcaa





5401
aagctgtttc ttacagatga agtgctttct ttgaaaggtg gttatttagg ttttagatgt





5461
ttaatagaca cagcacattt gctctattaa ctcagaggct cactacagaa atatgtaatc





5521
agtgctgtgc atctgtctgc agctaatgta cctcctggac accaggaggg gaaaaagcac





5581
tttttcaatt gtgctgagtt agacatctgt gagttagact atggtgtcag tgatttttgc





5641
agaacacgtg cacaaccctg aggtatgttt aatctaggca ggtacgttta aggatatttt





5701
gatctattta taatgaattc acaatttatg cctataaatt tcagatgatt taaaatttta





5761
aacctgttac attgaaaaac attgaagttc gtcttgaaga aagcattaag gtatgcatgg





5821
aggtgattta tttttaaaca taacacctaa cctaacatgg gtaagagagt atggaactag





5881
atatgagctg tataagaagc ataattgtga acaagtagat tgattgcctt catatacaag





5941
tatgttttag tattccttat ttccttatta tcagatgtat tttttctttt aagtttcaat





6001
gttgttataa ttctcaacca gaaatttaat actttctaaa atatttttta aatttagctt





6061
gtgcttttga attacaggag aagggaatca taatttaata aaacgcttac tagaaagacc





6121
attacagatc ccaaacactt gggtttggtg accctgtctt tcttatatga ccctacaata





6181
aacatttgaa ggcagcatag gatggcagac agtaggaaca ttgtttcact tggcggcatg





6241
tttttgaaac ctgctttata gtaactgggt gattgccatt gtggtagagc ttccactgct





6301
gtttataatc tgagagagtt aatctcagag gatgcttttt tccttttaat ctgctatgaa





6361
tcagtaccca gatgtttaat tactgtactt attaaatcat gagggcaaaa gagtgtagaa





6421
tggaaaaaag tctcttgtat ctagatactt taaatatggg aggcccttta acttaattgc





6481
ctttagtcaa ccactggatt tgaatttgca tcaagtattt taaataatat tgaatttaaa





6541
aaaatgtatt gcagtagtgt gtcagtacct tattgttaaa gtgagtcaga taaatcttca





6601
attcctggct atttgggcaa ttgaatcatc atggactgta taatgcaatc agattatttt





6661
gtttctagac atccttgaat tacaccaaag aacatgaaat ttagttgtgg ttaaattatt





6721
tatttatttc atgcattcat tttatttccc ttaaggtctg gatgagactt ctttggggag





6781
cctctaaaaa aatttttcac tgggggccac gtgggtcatt agaagccaga gctctcctcc





6841
aggctccttc ccagtgccta gaggtgctat aggaaacata gatccagcca ggggcttccc





6901
taaagcagtg cagcaccggc ccagggcatc actagacagg ccctaattaa gtttttttta





6961
aaaagcctgt gtatttattt tagaatcatg tttttctgta tattaacttg ggggatatcg





7021
ttaatattta ggatataaga tttgaggtca gccatcttca aaaaagaaaa aaaaattgac





7081
tcaagaaagt acaagtaaac tatacacctt tttttcataa gttttaggaa ctgtagtaat





7141
gtggcttaga aagtataatg gcctaaatgt tttcaaaatg taagttcctg tggagaagaa





7201
ttgtttatat tgcaaacggg gggactgagg ggaacctgta ggtttaaaac agtatgtttg





7261
tcagccaact gatttaaaag gcctttaact gttttggttg ttgttttttt tttaagccac





7321
tctccccttc ctatgaggaa gaattgagag gggcacctat ttctgtaaaa tccccaaatt





7381
ggtgttgatg attttgagct tgaatgtttt catacctgat taaaacttgg tttattctaa





7441
tttctgtatc atatcatctg aggtttacgt ggtaactagt cttataacat gtatgtatct





7501
tttttttgtt gttcatctaa agctttttaa tccaaataaa tacagagttt gcaaagtgat





7561
ttggattaac caggaaaaaa aaaaaaaaaa aa










SEQ ID NO: 2 Human PBRM1 Variant 1 Amino Acid Sequence (NP_060783.3)








   1
mgskrrrats psssysgdfd dghhsystpg psrkrrrlsn lptvdpiavc helyntirdy





  61
kdeqgrllce lfirapkrrn qpdyyevvsq pidlmkiqqk lkmeeyddvn lltadfqllf





 121
nnaksyykpd speykaackl wdlylrtrne fvqkgeadde dddedgqdnq gtvtegsspa





 181
ylkeileqll eaivvatnps grliselfqk lpskvqypdy yaiikepidl ktiagriqng





 241
syksihamak didllaknak tynepgsqvf kdansikkif ymkkaeiehh emaksslrmr





 301
tpsnlaaarl tgpshskgsl geernptsky yrnkravqgg rlsaitmalq ygseseedaa





 361
laaaryeege seaesitsfm dvsnpfyqly dtvrscrnnq gqliaepfyh lpskkkypdy





 421
yqqikmpisl qqirtklknq eyetldhlec dlnlmfenak rynvpnsaiy krvlklqqvm





 481
qakkkelarr ddiedgdsmi ssatsdtgsa krkskknirk qrmkilfnvv learepgsgr





 541
rlcdlfmvkp skkdypdyyk iilepmdlki iehnirndky ageegmiedm klmfrnarhy





 601
neegsqvynd ahilekllke krkelgplpd dddmaspklk lsrksgispk kskymtpmqq





 661
klnevyeavk nytdkrgrrl saiflrlpsr selpdyylti kkpmdmekir shmmankyqd





 721
idsmvedfvm mfnnactyne pesliykdal vlhkvlletr rdlegdedsh vpnvtlliqe





 781
lihnlfvsvm shqddegrcy sdslaeipav dpnfpnkppl tfdiirknve nnryrrldlf





 841
qehmfevler arrmnrtdse iyedavelqq ffikirdelc kngeillspa lsyttkhlhn





 901
dvekerkekl pkeieedklk reeekreaek sedssgaagl sglhrtysqd csfknsmyhv





 961
gdyvyvepae anlqphivci erlwedsaek evfksdyynk vpvskilgkc vvmfvkeyfk





1021
lcpenfrded vfvcesrysa ktksfkkikl wtmpissvrf vprdvplpvv rvasvfanad





1081
kgddekntdn sedsraednf nlekekedvp vemsngepgc hyfeqlhynd mwlkvgdcvf





1141
ikshglvrpr vgriekvwvr dgaayfygpi fihpeetehe ptkmfykkev flsnleetcp





1201
mtcilgkcav lsfkdflscr pteipendil lcesrynesd kqmkkfkglk rfslsakvvd





1261
deiyyfrkpi vpqkepspll ekkiqlleak faeleggddd ieemgeedse stpksakgsa





1321
kkegskrkin msgyilfsse mravikaqhp dysfgelsrl vgtewrnlet akkaeyegmm





1381
ggyppglppl qgpvdglvsm gsmqplhpgg ppphhlppgv pglpgipppg vmnqgvapmv





1441
gtpapggspy gqqvgvlgpp gqqapppypg phpagppviq qpttpmfvap ppktqrllhs





1501
eaylkyiegl saesnsiskw dqtlaarrrd vhlskeqesr lpshwlkskg ahttmadalw





1561
rlrdlmlrdt lnirqaynle nv










SEQ ID NO: 3 Human PBRM1 Transcript Variant 2 cDNA Sequence (NM_181042.4)








   1
gcggccgggg ctgcaggcgg cggagcggct ggcttgccaa cacttggtgt cacatgtgag





  61
cctcccacat gtattcactc tccattccag ctctgtgatt gaactctgct cttattgact





 121
agggggcagt tgggcaggca tgcctcattc ctggaattga cagtcattcc taataagttg





 181
gattccatgg gttccaagag aagaagagct acctcccctt ccagcagtgt cagcggggac





 241
tttgatgatg ggcaccattc tgtgtcaaca ccaggcccaa gcaggaaaag gaggagactt





 301
tccaatcttc caactgtaga tcctattgcc gtgtgccatg aactctataa taccatccga





 361
gactataagg atgaacaggg cagacttctc tgtgagctct tcattagggc accaaagcga





 421
agaaatcaac cagactatta tgaagtggtt tctcagccca ttgacttgat gaaaatccaa





 481
cagaaactaa aaatggaaga gtatgatgat gttaatttgc tgactgctga cttccagctt





 541
ctttttaaca atgcaaagtc ctattataag ccagattctc ctgaatataa agccgcttgc





 601
aaactctggg atttgtacct tcgaacaaga aatgagtttg ttcagaaagg agaagcagat





 661
gacgaagatg atgatgaaga tgggcaagac aatcagggca cagtgactga aggatcttct





 721
ccagcttact tgaaggagat cctggagcag cttcttgaag ccatagttgt agctacaaat





 781
ccatcaggac gtctcattag cgaacttttt cagaaactgc cttctaaagt gcaatatcca





 841
gattattatg caataattaa ggagcctata gatctcaaga ccattgccca gaggatacag





 901
aatggaagct acaaaagtat tcatgcaatg gccaaagata tagatctcct cgcaaaaaat





 961
gccaaaactt ataatgagcc tggctctcaa gtattcaagg atgcaaattc aattaaaaaa





1021
atattttata tgaaaaaggc tgaaattgaa catcatgaaa tggctaagtc aagtcttcga





1081
atgaggactc catccaactt ggctgcagcc agactgacag gtccttcaca cagtaaaggc





1141
agccttggtg aagagagaaa tcccactagc aagtattacc gtaataaaag agcagtacaa





1201
ggaggtcgtt tatcagcaat tacaatggca cttcaatatg gctcagaaag tgaagaagat





1261
gctgctttag ctgctgcacg ctatgaagag ggagagtcag aagcagaaag catcacttcc





1321
tttatggatg tttcaaatcc tttttatcag ctttatgaca cagttaggag ttgtcggaat





1381
aaccaagggc agctaatagc tgaacctttt taccatttgc cttcaaagaa aaaataccct





1441
gattattacc agcaaattaa aatgcccata tcactacaac agatccgaac aaaactgaag





1501
aatcaagaat atgaaacttt agatcatttg gagtgtgatc tgaatttaat gtttgaaaat





1561
gccaaacgct ataatgtgcc caattcagcc atctacaagc gagttctaaa attgcagcaa





1621
gttatgcagg caaagaagaa agagcttgcc aggagagacg atatcgagga cggagacagc





1681
atgatctctt cagccacctc tgatactggt agtgccaaaa gaaaaagtaa aaagaacata





1741
agaaagcagc gaatgaaaat cttattcaat gttgttcttg aagctcgaga gccaggttca





1801
ggcagaagac tttgtgacct atttatggtt aaaccatcca aaaaggacta tcctgattat





1861
tataaaatca tcttggagcc aatggacttg aaaataattg agcataacat ccgcaatgac





1921
aaatatgctg gtgaagaggg aatgatagaa gacatgaagc tgatgttccg gaatgccagg





1981
cactataatg aggagggctc ccaggtttat aatgatgcac atatcctgga gaagttactc





2041
aaggagaaaa ggaaagagct gggcccactg cctgatgatg atgacatggc ttctcccaaa





2101
ctcaagctga gtaggaagag tggcatttct cctaaaaaat caaaatacat gactccaatg





2161
cagcagaaac taaatgaggt ctatgaagct gtaaagaact atactgataa gaggggtcgc





2221
cgcctcagtg ccatatttct gaggcttccc tctagatctg agttgcctga ctactatctg





2281
actattaaaa agcccatgga catggaaaaa attcgaagtc acatgatggc caacaagtac





2341
caagatattg actctatggt tgaggacttt gtcatgatgt ttaataatgc ctgtacatac





2401
aatgagccgg agtctttgat ctacaaagat gctcttgttc tacacaaagt cctgcttgaa





2461
acacgcagag acctggaggg agatgaggac tctcatgtcc caaatgtgac tttgctgatt





2521
caagagctta tccacaatct ttttgtgtca gtcatgagtc atcaggatga tgagggaaga





2581
tgctacagcg attctttagc agaaattcct gctgtggatc ccaactttcc taacaaacca





2641
ccccttacat ttgacataat taggaagaat gttgaaaata atcgctaccg tcggcttgat





2701
ttatttcaag agcatatgtt tgaagtattg gaacgagcaa gaaggatgaa tcggacagat





2761
tcagaaatat atgaagatgc agtagaactt cagcagtttt ttattaaaat tcgtgatgaa





2821
ctctgcaaaa atggagagat tcttctttca ccggcactca gctataccac aaaacatttg





2881
cataatgatg tggagaaaga gagaaaggaa aaattgccaa aagaaataga ggaagataaa





2941
ctaaaacgag aagaagaaaa aagagaagct gaaaagagtg aagattcctc tggtgctgca





3001
ggcctctcag gcttacatcg cacatacagc caggactgta gctttaaaaa cagcatgtac





3061
catgttggag attacgtcta tgtggaacct gcagaggcca acctacaacc acatatcgtc





3121
tgtattgaaa gactgtggga ggattcagct ggtgaaaaat ggttgtatgg ctgttggttt





3181
taccgaccaa atgaaacatt ccacctggct acacgaaaat ttctagaaaa agaagttttt





3241
aagagtgact attacaacaa agttccagtt agtaaaattc taggcaagtg tgtggtcatg





3301
tttgtcaagg aatactttaa gttatgccca gaaaacttcc gagatgagga tgtttttgtc





3361
tgtgaatcac ggtattctgc caaaaccaaa tcttttaaga aaattaaact gtggaccatg





3421
cccatcagct cagtcaggtt tgtccctcgg gatgtgcctc tgcctgtggt tcgcgtggcc





3481
tctgtatttg caaatgcaga taaaggtgat gatgagaaga atacagacaa ctcagaggac





3541
agtcgagctg aagacaattt taacttggaa aaggaaaaag aagatgtccc tgtggaaatg





3601
tccaatggtg aaccaggttg ccactacttt gagcagctcc attacaatga catgtggctg





3661
aaggttggcg actgtgtctt catcaagtcc catggcctgg tgcgtcctcg tgtgggcaga





3721
attgaaaaag tatgggttcg agatggagct gcatattttt atggccccat cttcattcac





3781
ccagaagaaa cagagcatga gcccacaaaa atgttctaca aaaaagaagt atttctgagt





3841
aatctggaag aaacctgccc catgacatgt attctcggaa agtgtgctgt gttgtcattc





3901
aaggacttcc tctcctgcag gccaactgaa ataccagaaa atgacattct gctttgtgag





3961
agccgctaca atgagagcga caagcagatg aagaaattca aaggattgaa gaggttttca





4021
ctctctgcta aagtggtaga tgatgaaatt tactacttca gaaaaccaat tgttcctcag





4081
aaggagccat cacctttgct ggaaaagaag atccagttgc tagaagctaa atttgccgag





4141
ttagaaggtg gagatgatga tattgaagag atgggagaag aagatagtga ggtcattgaa





4201
cctccttctc tacctcagct tcagaccccc ctggccagtg agctggacct catgccctac





4261
acacccccac agtctacccc aaagtctgcc aaaggcagtg caaagaagga aggctccaaa





4321
cggaaaatca acatgagtgg ctacatcctg ttcagcagtg agatgagggc tgtgattaag





4381
gcccaacacc cagactactc tttcggggag ctcagccgcc tggtggggac agaatggaga





4441
aatcttgaga cagccaagaa agcagaatat gaaggtgtga tgaaccaagg agtggcccct





4501
atggtaggga ctccagcacc aggtggaagt ccatatggac aacaggtggg agttttgggg





4561
cctccagggc agcaggcacc acctccatat cccggcccac atccagctgg accccctgtc





4621
atacagcagc caacaacacc catgtttgta gctcccccac caaagaccca gcggcttctt





4681
cactcagagg cctacctgaa atacattgaa ggactcagtg cggagtccaa cagcattagc





4741
aagtgggatc agacactggc agctcgaaga cgcgacgtcc atttgtcgaa agaacaggag





4801
agccgcctac cctctcactg gctgaaaagc aaaggggccc acaccaccat ggcagatgcc





4861
ctctggcgcc ttcgagattt gatgctccgg gacaccctca acattcgcca agcatacaac





4921
ctagaaaatg tttaatcaca tcattacgtt tcttttatat agaagcataa agagttgtgg





4981
atcagtagcc attttagtta ctgggggtgg ggggaaggaa caaaggagga taatttttat





5041
tgcattttac tgtacatcac aaggccattt ttatatacgg acacttttaa taagctattt





5101
caatttgttt gttatattaa gttgacttta tcaaatacac aaagattttt ttgcatatgt





5161
ttccttcgtt taaaaccagt ttcataattg gttgtatatg tagacttgga gttttatctt





5221
tttacttgtt gccatggaac tgaaaccatt agaggttttt gtcttggctt ggggtttttg





5281
ttttcttggt tttgggtttt tttatatata tatataaaag aacaaaatga aaaaaaacac





5341
acacacacaa gagtttacag attagtttaa attgataatg aaatgtgaag tttgtcctag





5401
tttacatctt agagagggga gtatacttgt gtttgtttca tgtgcctgaa tatcttaagc





5461
cactttctgc aaaagctgtt tcttacagat gaagtgcttt ctttgaaagg tggttattta





5521
ggttttagat gtttaataga cacagcacat ttgctctatt aactcagagg ctcactacag





5581
aaatatgtaa tcagtgctgt gcatctgtct gcagctaatg tacctcctgg acaccaggag





5641
gggaaaaagc actttttcaa ttgtgctgag ttagacatct gtgagttaga ctatggtgtc





5701
agtgattttt gcagaacacg tgcacaaccc tgaggtatgt ttaatctagg caggtacgtt





5761
taaggatatt ttgatctatt tataatgaat tcacaattta tgcctataaa tttcagatga





5821
tttaaaattt taaacctgtt acattgaaaa acattgaagt tcgtcttgaa gaaagcatta





5881
aggtatgcat ggaggtgatt tatttttaaa cataacacct aacctaacat gggtaagaga





5941
gtatggaact agatatgagc tgtataagaa gcataattgt gaacaagtag attgattgcc





6001
ttcatataca agtatgtttt agtattcctt atttccttat tatcagatgt attttttctt





6061
ttaagtttca atgttgttat aattctcaac cagaaattta atactttcta aaatattttt





6121
taaatttagc ttgtgctttt gaattacagg agaagggaat cataatttaa taaaacgctt





6181
actagaaaga ccattacaga tcccaaacac ttgggtttgg tgaccctgtc tttcttatat





6241
gaccctacaa taaacatttg aaggcagcat aggatggcag acagtaggaa cattgtttca





6301
cttggcggca tgtttttgaa acctgcttta tagtaactgg gtgattgcca ttgtggtaga





6361
gcttccactg ctgtttataa tctgagagag ttaatctcag aggatgcttt tttcctttta





6421
atctgctatg aatcagtacc cagatgttta attactgtac ttattaaatc atgagggcaa





6481
aagagtgtag aatggaaaaa agtctcttgt atctagatac tttaaatatg ggaggccctt





6541
taacttaatt gcctttagtc aaccactgga tttgaatttg catcaagtat tttaaataat





6601
attgaattta aaaaaatgta ttgcagtagt gtgtcagtac cttattgtta aagtgagtca





6661
gataaatctt caattcctgg ctatttgggc aattgaatca tcatggactg tataatgcaa





6721
tcagattatt ttgtttctag acatccttga attacaccaa agaacatgaa atttagttgt





6781
ggttaaatta tttatttatt tcatgcattc attttatttc ccttaaggtc tggatgagac





6841
ttctttgggg agcctctaaa aaaatttttc actgggggcc acgtgggtca ttagaagcca





6901
gagctctcct ccaggctcct tcccagtgcc tagaggtgct ataggaaaca tagatccagc





6961
caggggcttc cctaaagcag tgcagcaccg gcccagggca tcactagaca ggccctaatt





7021
aagttttttt taaaaagcct gtgtatttat tttagaatca tgtttttctg tatattaact





7081
tgggggatat cgttaatatt taggatataa gatttgaggt cagccatctt caaaaaagaa





7141
aaaaaaattg actcaagaaa gtacaagtaa actatacacc tttttttcat aagttttagg





7201
aactgtagta atgtggctta gaaagtataa tggcctaaat gttttcaaaa tgtaagttcc





7261
tgtggagaag aattgtttat attgcaaacg gggggactga ggggaacctg taggtttaaa





7321
acagtatgtt tgtcagccaa ctgatttaaa aggcctttaa ctgttttggt tgttgttttt





7381
tttttaagcc actctcccct tcctatgagg aagaattgag aggggcacct atttctgtaa





7441
aatccccaaa ttggtgttga tgattttgag cttgaatgtt ttcatacctg attaaaactt





7501
ggtttattct aatttctgta tcatatcatc tgaggtttac gtggtaacta gtcttataac





7561
atgtatgtat cttttttttg ttgttcatct aaagcttttt aatccaaat










SEQ ID NO: 4 Human PBRM1 Variant 2 Amino Acid Sequence (NP_851385.1)








   1
mgskrrrats psssysgdfd dghhsystpg psrkrrrlsn lptvdpiavc helyntirdy





  61
kdeqgrllce lfirapkrrn qpdyyevvsq pidlmkiqqk lkmeeyddvn lltadfqllf





 121
nnaksyykpd speykaackl wdlylrtrne fvqkgeadde dddedgqdnq gtvtegsspa





 181
ylkeileqll eaivvatnps grliselfqk lpskvqypdy yaiikepidl ktiaqrigng





 241
syksihamak didllaknak tynepgsqvf kdansikkif ymkkaeiehh emaksslrmr





 301
tpsnlaaarl tgpshskgsl geernptsky yrnkravqgg rlsaitmalq ygseseedaa





 361
laaaryeege seaesitsfm dvsnpfyqly dtvrscrnnq gqliaepfyh lpskkkypdy





 421
yqqikmpisl qqirtklknq eyetldhlec dlnlmfenak rynvpnsaiy krvlklqqvm





 481
qakkkelarr ddiedgdsmi ssatsdtgsa krkskknirk qrmkilfnvv learepgsgr





 541
rlcdlfmvkp skkdypdyyk iilepmdlki iehnirndky ageegmiedm klmfrnarhy





 601
neegsqvynd ahilekllke krkelgplpd dddmaspklk lsrksgispk kskymtpmqq





 661
klnevyeavk nytdkrgrrl saiflrlpsr selpdyylti kkpmdmekir shmmankyqd





 721
idsmvedfvm mfnnactyne pesliykdal vlhkvlletr rdlegdedsh vpnvtlliqe





 781
lihnlfvsvm shqddegrcy sdslaeipav dpnfpnkppl tfdiirknve nnryrrldlf





 841
qehmfevler arrmnrtdse iyedavelqq ffikirdelc kngeillspa lsyttkhlhn





 901
dvekerkekl pkeieedklk reeekreaek sedssgaagl sglhrtysqd csfknsmyhv





 961
gdyvyvepae anlqphivci erlwedsage kwlygcwfyr pnetfhlatr kflekevfks





1021
dyynkvpvsk ilgkcvvmfv keyfklcpen frdedvfvce srysaktksf kkiklwtmpi





1081
ssvrfvprdv plpvvrvasv fanadkgdde kntdnsedsr aednfnleke kedvpvemsn





1141
gepgchyfeq lhyndmwlkv gdcvfikshg lvrprvgrie kvwvrdgaay fygpifihpe





1201
eteheptkmf ykkevflsnl eetcpmtcil gkcavlsfkd flscrpteip endillcesr





1261
ynesdkqmkk fkglkrfsls akvvddeiyy frkpivpqke pspllekkiq lleakfaele





1321
ggdddieemg eedseviepp slpqlqtpla seldlmpytp pqstpksakg sakkegskrk





1381
inmsgyilfs semravikaq hpdysfgels rlvgtewrnl etakkaeyeg vmnqgvapmv





1441
gtpapggspy gqqvgvlgpp gqqapppypg phpagppviq qpttpmfvap ppktqrllhs





1501
eaylkyiegl saesnsiskw dqtlaarrrd vhlskeqesr lpshwlkskg ahttmadalw





1561
rlrdlmlrdt lnirqaynle nv










SEQ ID NO: 5 Mouse PBRM1 cDNA Sequence (NM_001081251.1)








   1
ggatttacgg cagcactggg aggggtgagg gcggtgaggg cggcgggtgc cggagagacg





  61
gccgcggcca gaggagcgct agcagccgtg gcggccacgg ggcggggctc ggcggtcggg





 121
gaccgcagcc ggggctgcag gcggcggagc ggcgggcttg ccaacacttg gtgtcacatg





 181
tgagcctccc acatgtgtgc actctccatt ccagctctgt gattgaactc tgctcttatt





 241
gactaggggg cacttgggca ggcatgcttc attcctggag ttgacagtca tttcataaga





 301
agttggattc catgggttcc aagagaagaa gagccacctc tccttccagc agtgtcagtg





 361
gagactttga tgacgggcac cattctgtgc ctacaccagg cccaagcagg aaaaggagaa





 421
gactgtccaa tcttccaact gtagatccta ttgctgtgtg ccatgaactc tataacacca





 481
tccgagacta taaggatgaa cagggcagac tcctctgtga gctgttcatt agggctccaa





 541
agcggagaaa tcaaccagac tattatgaag tggtttctca gcccattgac ttgatgaaaa





 601
tccaacagaa acttaaaatg gaagagtatg atgatgttaa tctactgact gctgacttcc





 661
agctgctttt taacaatgca aaggcctact ataagccaga ttcccctgag tataaagctg





 721
cttgtaaact ctgggatttg taccttcgaa caagaaatga gtttgttcag aaaggagaag





 781
cagacgatga agatgatgac gaagatgggc aagacaatca aggcacactg gctgacggct





 841
cttctccagg ttatctgaag gagatcctgg agcagcttct tgaagccata gttgtagcca





 901
caaatccatc aggacggctc atcagtgaac tttttcagaa actgccttcc aaagtgcaat





 961
atccagacta ttatgcaata attaaggaac ctatagatct caagaccatt gctcagagga





1021
tacagaatgg aagctacaaa agtatacacg caatggccaa agatatagat cttctagcaa





1081
aaaatgccaa aacatacaat gagcctgggt ctcaagtatt caaggatgcc aattcgatta





1141
aaaaaatatt ttatatgaaa aaggcagaaa ttgaacatca tgaaatgact aaatcaagtc





1201
ttcgaataag gactgcatca aatttggctg cagccaggct gacaggtcct tcgcacaata





1261
aaagcagcct tggtgaagaa agaaacccca ctagcaagta ttaccgtaat aaaagagcag





1321
tccaaggggg tcgcttgtca gcaattacca tggcacttca gtatggatca gagagtgaag





1381
aggacgctgc tttagctgct gcacgctatg aagaagggga atctgaagca gagagcatca





1441
cttccttcat ggacgtttcc aacccctttc atcagcttta cgacacagtt aggagctgta





1501
ggaatcacca agggcagctc atagctgaac ctttcttcca tttgccttca aagaaaaaat





1561
acccagatta ttatcagcaa attaaaatgc ccatatcact tcaacagatc agaacaaagc





1621
taaagaacca agaatatgaa actttagatc atttggagtg tgatctgaat ttaatgtttg





1681
aaaatgccaa acgttataac gttcccaatt cagccatcta taagcgagtt ctaaaactgc





1741
agcaagtcat gcaggcaaag aagaaggagc ttgcgaggag agatgacatt gaggacggag





1801
acagcatgat ctcctcagcc acttctgaca ctggtagtgc caaaaggaaa aggaatactc





1861
atgacagtga gatgttgggt ctcaggaggc tatccagtaa aaagaacata agaaaacagc





1921
gaatgaaaat tttattcaat gttgttcttg aagctcgaga gccaggttca ggcagaagac





1981
tttgcgatct atttatggtt aagccatcca agaaggacta tcctgattat tataaaatca





2041
tcttagagcc aatggacctg aaaataattg agcataacat ccgaaatgac aaatatgcag





2101
gtgaagaagg aatgatggaa gacatgaaac tcatgttccg caatgccagg cactacaatg





2161
aggagggctc ccaggtatac aatgatgccc atatcctgga gaagttactc aaagataaaa





2221
ggaaagagct gggccctctg cctgatgatg atgacatggc ttctcccaaa cttaaattga





2281
gtaggaagag tggtgtttct cctaagaaat caaagtacat gactccaatg cagcagaaac





2341
tgaatgaagt gtatgaagct gtaaagaact atactgataa gaggggtcgc cgccttagtg





2401
ctatatttct aagactcccc tctagatcag agctgcctga ctactacctg accattaaaa





2461
agcccatgga catggaaaaa attcgaagtc acatgatggc aaacaagtac caagacatag





2521
attctatggt agaggacttt gtcatgatgt ttaataatgc ctgtacctac aatgaaccag





2581
agtctttgat ctacaaagat gcccttgtac tgcataaagt cctccttgag actcggagag





2641
acctggaggg agatgaggat tctcatgtcc ctaatgtgac gttgctgatt caagagctca





2701
tccataacct ttttgtgtca gtcatgagtc atcaggatga cgaagggagg tgttacagcg





2761
actccttagc agaaattcct gctgtggatc ccaactctcc caataaacct ccccttacat





2821
ttgacattat caggaaaaat gttgaaagta atcggtatcg gcgacttgat ttatttcagg





2881
agcatatgtt tgaagtattg gaacgggcaa gaaggatgaa ccggacagat tccgaaatat





2941
atgaggatgc tgtagaactt cagcagtttt ttattagaat tcgtgatgaa ctctgcaaaa





3001
atggagagat ccttctttct ccagcactca gctataccac aaaacacttg cataacgatg





3061
tggaaaaaga aaaaaaggaa aaattgccta aagaaataga ggaagataaa ctaaaacgcg





3121
aagaagaaaa aagagaagct gaaaaaagtg aagattcctc aggtactaca ggcctctcag





3181
gcttacatcg tacatacagc caggactgca gctttaagaa cagcatgtat catgtcggag





3241
attatgtcta tgttgaacct gcggaggcca atctacaacc acatatagtg tgtattgaga





3301
gactgtggga ggattcagct ggtgaaaaat ggttgtacgg ctgttggttt tatcggccaa





3361
atgaaacatt ccatttggct acacgaaaat ttctagaaaa agaagttttt aagagtgact





3421
actacaataa agtacctgtt agtaaaattc taggcaaatg tgtagtcatg tttgtcaagg





3481
aatactttaa attatgtcca gaaaactttc gcgatgagga tgtttttgtc tgtgaatcga





3541
ggtattctgc caaaaccaaa tcttttaaga aaattaaact gtggaccatg cccatcagtt





3601
cagttagatt tgtccctcgg gatgtgcctt tgcctgtggt ccgagtggcc tctgtgtttg





3661
caaatgcaga taaaggggat gatgagaaga atacagacaa ctcagatgac aatagagctg





3721
aagacaattt taacttggaa aaggaaaaag aagatgttcc tgtggagatg tccaatggtg





3781
agccaggttg ccactacttt gagcagcttc ggtacaatga catgtggctg aaggttggtg





3841
attgtgtctt catcaaatcc cacggcttgg tgcgccctcg tgtgggcaga attgagaaag





3901
tatgggtccg agatggagct gcatattttt atggccctat cttcattcat ccagaagaaa





3961
cagaacatga gcccacaaaa atgttctaca aaaaagaagt gtttctgagt aatctggaag





4021
agacctgccc tatgagttgt attctgggga aatgtgcagt gctgtcattc aaggacttcc





4081
tctcctgcag gccaactgaa ataccagaaa atgacattct gctttgtgag agccgctata





4141
atgagagtga caagcagatg aagaagttca agggtttgaa gaggttttca ctctctgcta





4201
aagttgtaga tgatgaaatc tactacttca gaaaaccaat cattcctcag aaggaaccct





4261
cacctttgtt agaaaagaag atacaattgc tagaagctaa atttgcagag ttagaaggag





4321
gagatgatga tattgaggag atgggagaag aggatagtga agtcattgaa gctccatctc





4381
tacctcaact gcagacaccc ctggccaatg agttggacct catgccctat acacccccac





4441
agtctacccc aaagtctgcc aaaggcagtg caaagaagga aagttctaaa cgaaaaatca





4501
acatgagtgg ctacattttg ttcagcagtg aaatgagagc tgtgattaaa gcccagcacc





4561
cagactactc ttttggggag ctcagcagac tggtggggac agaatggaga aaccttgaaa





4621
cagccaagaa agcagaatat gaagagcggg cagctaaagt tgctgagcag caggagagag





4681
agcgagcagc acagcaacag cagccgagtg cttctccccg agcaggcacc cctgtggggg





4741
ctctcatggg ggtggtgcca ccaccaacac caatggggat gctcaatcag cagttgacac





4801
ctgttgcagg catgatgggt ggctatccgc caggccttcc acctttgcag ggcccagttg





4861
atggccttgt tagcatgggc agcatgcagc cacttcaccc tggggggcct ccacctcacc





4921
atcttccgcc aggtgtgcct ggcctcccag gcatcccacc accgggtgtg atgaatcaag





4981
gagtagcccc catggtaggg actccagcac caggtggaag tccgtatgga caacaggtag





5041
gagttttggg acctccaggg cagcaggcac cacctccata tcctggtcct catccagctg





5101
gcccccctgt catacagcag ccaacaacgc ccatgtttgt ggctccccca ccaaagaccc





5161
aaaggcttct ccactcagag gcctacctga aatacattga aggactcagt gctgaatcca





5221
acagcattag caagtgggac caaactttgg cagctcgaag acgggatgtc catttgtcca





5281
aagaacagga gagccgccta ccttctcact ggctcaaaag taaaggggca cacaccacca





5341
tggcagatgc cctctggcgc ctacgggatt taatgcttcg agacactctc aacatccgac





5401
aggcatacaa cctagaaaat gtttaatcac atcactgttt cttctgtgga agcaaagagt





5461
tgtggagcgg tagccatttt agttactggg gtgggaggga ggaacaaagg atgataattt





5521
ttattgcatt ttattgtaca tcacacagcc atttttatat aaggacactt ttaataagct





5581
atttcaaatt tggttttgtt acattaagtt gactatcaaa tacacaaaag attttttttg





5641
catatgtttc ctttgtttaa aaccagtttc ataattggtt atatatagta atagttttat





5701
ctttacttgt taaaggactt aaatcatcaa aggttttggc ttggcttagg gttttcgttt





5761
tcttttttat aaatatatat tatatatata tacacatata aaagaaaaaa tgaaaaaaaa





5821
gtttacaaat ttaagttgac aatgaaatgt gaagttggtc ctagtttaca tcttagagga





5881
atgtatatgt atgttttaca tgcctaaata tctgcaggtt ttcttacagg taaagcgaag





5941
tgctttgaaa agtttagatt atacatgtgt gacagatgcg gcatatttgc tctattaaca





6001
cagaggctta ctatagaaat ctaaagtcaa tgctgtacat ccatccagtt agtgtaactg





6061
aagggaaatg taactttgtg ctgagttaga catctgtatt gtcagtgatt cttgtagaat





6121
atgtgctcag atctgagtta tatttagttt tggaaggtaa gttgaagagt acttttgatc





6181
agtttatgat tcagtttatg attttagttt ttgccttcat gttatacatt tatgatttga





6241
aactgtacat ctgttacctt gaaaaacatt gaagaaagta ctgaagtgtg catggaggtg





6301
gtttaagcat aatacttaac ccaagaaaga gtgtaagtgg acacaagctg tgcctgcaca





6361
tagctgtgca gggtagactg cctacataca catggccggg attctttatt tccttgttat





6421
caattatagt gctttgtttg tttcagggtt ggaattctca accagaaata atactttcta





6481
aaatatttta aaattcagct tgtgctttgg attatagaag gaaattatac tttaagaaaa





6541
tgttcacaaa aaaaaaaaaa aaaaaaggac tattacagat cccaatactt ggatttggtg





6601
accttgtctt tctttctttt cttgagacat ggtcctacta ccaaccctgg ctggactgga





6661
gctcagtgta tagaccaggc tagtctcaaa ctctgcctct tcctcccaag tgctgggatt





6721
aagggcaggt accatagtgc tcagcaacca caaccctgtc tttccaacac ggccctagcg





6781
taagcactga ggcagtgtgc agtgctcagg cagcagcaaa catttcccgg gggtggtttt





6841
gaacctgctt gggtggttgt gtggtgctga cgctgccact gccctgttgt tcattgagaa





6901
tgattgttaa atgacactct tcctttagaa tataacggat cagtactcat gtttaattgc





6961
catgcttaat aaatcatgag aacaaaagag tatagaatgg aaagcattcc ctggtagcta





7021
ctttaaatac aggagccctg taacttaata ccagtagtca accactggat ctcagttttc





7081
atcaagtatt ttaaataaat aatcttaaat tttaaaatac gtactgcaga gtatgccagt





7141
atcttattgt taaaactgaa tcaaataaat cttcgattcc tggttatttg gaccattgac





7201
tcatcatgga ctatataatg taataagatt cttttctctt aaggtatcct tgaattacac





7261
caaagaacca gaaacttaat tttggttaaa ttatttattt atttcatgca ttaattttct





7321
ttttcttttt aaaggtttag atgaggctcc ttagggagtc tctaaaaccg cttcactatc





7381
agcaaccagg agtactagaa gccagagcac tcttcctcct ggctcctccc cagtgctcta





7441
gtgctgtagg aaccaagagc cagccccagg ttccccgagg cagtaaaaat ccagcacagg





7501
gggctgtgtc cctaaggcaa gccctgatta cctttaaaaa aaaccaaaaa aacaaacaaa





7561
aaaaaaaaac ctaattaact aaagcattta aggcactatt tattttagaa tcatgctttt





7621
gaagagcatc agtgattact tagggtgtaa tatgtaaaga tcagacatct ccaaaaacag





7681
aaaaagtaca agtaaacaac acactttctc atgactttta agaactgtag taatgtggct





7741
taggaaatat aatggcctaa ttgttttcaa aatgtaagtt cctgtgaaga attttgttta





7801
tattgggttg gggacctata ggtttaaaat agaatgtcag tcagctgact taaaaaacat





7861
tggttttact aagtctgcct tccccttcta aggaagaact gagtgggtaa gggacaggtg





7921
tgtaaaatct ccaaatggat gttacagctt tcagcttgaa cgtttgtttc cagacctgat





7981
taaaatttgg tttattctaa tttctgtact atatcatctg aggttttaag tggtaactgg





8041
ttctatacca tgtatgtatc atatgtttgt tcatcaaagc tttttaatcc aaataaaaac





8101
aacagtttgc aaagtga










SEQ ID NO: 6 Mouse PBRM1 Amino Acid Sequence (NP_001074720.1)








   1
mgskrrrats psssysgdfd dghhsvptpg psrkrrrlsn lptvdpiavc helyntirdy





  61
kdeqgrllce lfirapkrrn qpdyyevvsq pidlmkiqqk lkmeeyddvn lltadfqllf





 121
nnakayykpd speykaackl wdlylrtrne fvqkgeadde dddedgqdnq gtladgsspg





 181
ylkeileqll eaivvatnps grliselfqk lpskvqypdy yaiikepidl ktiaqriqng





 241
syksihamak didllaknak tynepgsqvf kdansikkif ymkkaeiehh emtksslrir





 301
tasnlaaarl tgpshnkssl geernptsky yrnkravqgg rlsaitmalq ygseseedaa





 361
laaaryeege seaesitsfm dvsnpfhqly dtvrscrnhq gqliaepffh lpskkkypdy





 421
yqqikmpisl qqirtklknq eyetldhlec dlnlmfenak rynvpnsaiy krvlklqqvm





 481
qakkkelarr ddiedgdsmi ssatsdtgsa krkrnthdse mlglrrlssk knirkqrmki





 541
lfnvvleare pgsgrrlcdl fmvkpskkdy pdyykiilep mdlkiiehni rndkyageeg





 601
mmedmklmfr narhyneegs qvyndahile kllkdkrkel gplpddddma spklklsrks





 661
gvspkkskym tpmqqklnev yeavknytdk rgrrlsaifl rlpsrselpd yyltikkpmd





 721
mekirshmma nkyqdidsmv edfvmmfnna ctynepesli ykdalvlhkv lletrrdleg





 781
dedshvpnvt lliqelihnl fvsvmshqdd egrcysdsla eipavdpnsp nkppltfdii





 841
rknvesnryr rldlfqehmf evlerarrmn rtdseiyeda velqqffiri rdelckngei





 901
llspalsytt khlhndveke kkeklpkeie edklkreeek reaeksedss gttglsglhr





 961
tysqdcsfkn smyhvgdyvy vepaeanlqp hivcierlwe dsagekwlyg cwfyrpnetf





1021
hlatrkflek evfksdyynk vpvskilgkc vvmfvkeyfk lcpenfrded vfvcesrysa





1081
ktksfkkikl wtmpissvrf vprdvplpvv rvasvfanad kgddekntdn sddnraednf





1141
nlekekedvp vemsngepgc hyfeqlrynd mwlkvgdovf ikshglvrpr vgriekvwvr





1201
dgaayfygpi fihpeetehe ptkmfykkev flsnleetcp mscilgkcav lsfkdflscr





1261
pteipendil lcesrynesd kqmkkfkglk rfslsakvvd deiyyfrkpi ipqkepspll





1321
ekkiqlleak faeleggddd ieemgeedse vieapslpql qtplaneldl mpytppqstp





1381
ksakgsakke sskrkinmsg yilfssemra vikaqhpdys fgelsrlvgt ewrnletakk





1441
aeyeeraakv aeqqereraa qqqqpsaspr agtpvgalmg vvppptpmgm lnqqltpvag





1501
mmggyppglp plqgpvdglv smgsmqplhp ggppphhlpp gvpglpgipp pgvmnqgvap





1561
mvgtpapggs pygqqvgvlg ppgqqapppy pgphpagppv iqqpttpmfv apppktqrll





1621
hseaylkyie glsaesnsis kwdqtlaarr rdvhlskeqe srlpshwlks kgahttmada





1681
lwrlrdlmlr dtlnirqayn lenv











    • Included in Table 1 are RNA nucleic acid molecules (e.g., thymines replaced with uredines), nucleic acid molecules encoding orthologs of the encoded proteins, as well as DNA or RNA nucleic acid sequences comprising a nucleic acid sequence having at least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more identity across their full length with the nucleic acid sequence of any SEQ ID NO listed in Table 1, or a portion thereof. Such nucleic acid molecules can have a function of the full-length nucleic acid as described further herein.

    • Included in Table 1 are orthologs of the proteins, as well as polypeptide molecules comprising an amino acid sequence having at least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, or more identity across their full length with an amino acid sequence of any SEQ ID NO listed in Table 1, or a portion thereof. Such polypeptides can have a function of the full-length polypeptide as described further herein.

    • Included in Table 1 is PBRM1, including any PBRM1 cDNA or polypeptide.

    • Included in Table 1 are PBRM1 nucleic acid and/or amino acid sequences encoding or representing PBRM1 having reduced or eliminated PBRM1 function (e.g., truncating PBRM1 mutations causing encoding of incomplete PBRM1 protein). Many of these mutations were found in RCC patients which is insensitive to immune checkpoint therapies. Some exemplary mutations are listed below and are also found in Table 6 filed herewith:

    • 1. Truncating PBRM1 alterations in patients training cohort passing whole exome quality control (N=34; genomic positions in the table can be determined from PBRM1_ENST00000337303.4_Nonsense_Mutation_p.E449*|PBRM|_ENST0000 0394830.3_Nonsense_Mutation_p.E449*1PBRM1 ENST00000409114.3 Nonsense_Mutation_p.E449*|PBRM|_ENST00000409767.1 Nonsense_Mutation_p.E449*| PBRM1 ENST00000410007.1 Nonsense_Mutation_p.E449*|PBRM|_ENST0000 0296302.7_Nonsense_Mutation_p.E449*|PBRM|_ENST00000409057.1_Nonsense_Mutation_p.E449*; NM_018165.4; or uc003der.2)




























PBRM1_



Variant_

Tumor_
Tumor_


patient_
Hugo_
mean_
Chromo-
Start_
End_
Classi-
Reference_
Seq_
Seq_


id
Symbol
coverage
some
position
position
fication
Allele
Allele1
Allele2





9_97
PBRM1
119.39
3
52663008
52663008
Nonsense_
C
C
A








Mutation








9_52
PBRM1
 97.9 
3
52613205
52613205
Frame_
T
T









Shift_Del








9_27
PBRM1
248.99
3
52598081
52598101
In_Frame_
TCA TCA
TCA TCA









Del
TCT ACC
TCT ACC










ACT TTA
ACT TTA










GCA
GCA






9_119
PBRM1
 28.57
3
52682459
52682459
Splice_Site
C
C
G





8_105
NA
146.47
NA
NA
NA
NA








6_39
NA
130.38
NA
NA
NA
NA








5_73
NA
181.65
NA
NA
NA
NA








5_50
PBRM1
135.79
3
52712515
52712515
Splice_Site
C
C
T





5_41
NA
123.22
NA
NA
NA
NA








5_21
PBRM1
125.64
3
52613210
52613210
Frame_
T
T









Shift_Del








5_18
PBRM1
126.07
3
52678748
52678748
Nonsense_
C
C
C








Mutation








5_106
PBRM1
155.18
3
52620610
52620614
Frame_
ATTTT
ATTTT









Shift_Del








5_1
NA
138.81
NA
NA
NA
NA








4_68
NA
100.73
NA
NA
NA
NA








3_15
PBRM1
 94.84
3
52613194
52613194
Nonsense_
C
C
A








Mutation








3_117
PBRM1
146.69
3
52643375
52643375
Nonsense_
G
G
A








Mutation








3_114
PBRM1
111.22
3
52662964
52662964
Frame_
A
A









Shift_Del








2_85
NA
 47.52
NA
NA
NA
NA
NA
NA
NA





2_84
PBRM1
130.86
3
52696272
52696272
Frame_
T
T









Shift_Del








2_58
NA
 95.31
NA
NA
NA
NA








2_102
PBRM1
266.4 
3
52663052
52663052
Splice_Site
C
C
T





13_96
PBRM1
164.39
3
52643489
52643489
Frame_
A
A









Shift_Del








13_90
NA
124.87
NA
NA
NA
NA








12_115
NA
115.51
NA
NA
NA
NA








11_93
PBRM1
173.78
3
52651277
52651277
Splice_Site
C
C
T





11_79
PBRM1
 67.19
3
52621487
52621487
Frame_
T
T









Shift_Del








11_56
NA
221.56
NA
NA
NA
NA








11_25
NA
124.35
NA
NA
NA
NA








11_14
PBRM1
131.63
3
52623201
52623201
Frame_
G
G









Shift_Del








11_11
NA
 62.73
NA
NA
NA
NA








11_10
PBRM1
 89.9 
3
52623120
52623120
Frame_
G
G









Shift_Del








1_62
PBRM1
131.16
3
52613062
52613068
Splice_Site
ACA CTC
ACA CTC










A
A






1_32
NA
120.85
NA
NA
NA
NA








1_20
PBRM1
 28.98
3
52649455
52649456
Frame_


T








Shift_Ins



















patient_
Protein_
Variant_
i_
t_alt_
t_ref_

Indel_



id
Change
Type
tumor_f
count
count
clonal
Caller






9_97
p.E417*
SNP
0.278481
22
 57
1
NA






9_52
p.D1148fs
DEL
0.235955056
21
 68
1
strelka,










indelocator






9_27
p.AKVVDDE1249del
DEL
0.15
14
 77
not
indelocator









evaluable







9_119

SNP
0.666667
10
  5
1
NA






8_105
NA
NA
NA
NA
NA
NA
NA






6_39
NA
NA
NA
NA
NA
NA
NA






5_73
NA
NA
NA
NA
NA
NA
NA






5_50

SNP
0.213592
22
 81
1
NA






5_41
NA
NA
NA
NA
NA
NA
NA






5_21
p.K1146fs
DEL
0.441666667
53
 67
1
strelka,










indelocator






5_18
p.E291*
SNP
0.150943
8
 45
1
NA






5_106
p.KI1087fs
DEL
0.067137809
19
264
0
strelka,










indelocator






5_1
NA
NA
NA
NA
NA
NA
NA






4_68
NA
NA
NA
NA
NA
NA
NA






3_15
p.E1105*
SNP
0.53
53
 47
1
NA






3_117
p.Q809*
SNP
0.288
36
 89
1
NA






3_114
p.N463fs
DEL
0.108695652
10
 82
1
strelka,










indelocator






2_85
NA
NA
NA
NA
NA
NA
NA






2_84
p.K135fs
DEL
0.171428571
12
 58
1
strelka,










indelocator






2_58
NA
NA
NA
NA
NA
NA
NA






2_102

SNP
0.235849
25
 81
1
NA






13_96
p.S818fs
DEL
0.402654867
91
135
1
strelka,










indelocator






13_90
NA
NA
NA
NA
NA
NA
NA






12_115
NA
NA
NA
NA
NA
NA
NA






11_93

SNP
0.12766
 6
 41
1
NA






11_79
p.N1017fs
DEL
0.464285714
13
 15
1
strelka,










indelocator






11_56
NA
NA
NA
NA
NA
NA
NA






11_25
NA
NA
NA
NA
NA
NA
NA






11_14
p.D965fs
DEL
0.25
15
 45
1
strelka,










indelocator






11_11
NA
NA
NA
NA
NA
NA
NA






11_10
p.I992fs
DEL
0.55
55
 45
1
strelka,










indelocator






1_62

DEL
0.17370892
37
176
0
strelka






1_32
NA
NA
NA
NA
NA
NA
NA






1_20
p.H627fs
INS
0.363636364
 8
 14
1
strelka,










indelocator





Patient_id = CA209009_XX (XX: the id in the above table)








    • 2. Truncating PBRM1 alterations in validation cohort (N=28)































Variant_

Tumor_
Tumor_



patient_
Hugo_
Chromo-
Start_
End_
Classi-
Reference_
Seq_
Seq_
Protein_


id
Symbol
some
position
position
fication
Allele
Allele1
Allele2
Change





CA8808
PBRM1
3
52595873
52595873
Frame_
G
G

p.Q1415fs







Shift_











Del









KA4076
NA
NA
NA
NA
NA



NA





KE5236
PBRM1
3
52597356
52597359
Frame_
AGG
AGG

p.LP1310fs







Shift_
T
T









Del









KE6262
PBRM1
3
52643586
52643596
Frame_
ATG
ATG

p.EDSH782fs







Shift_
AGA
AGA









Del
GTC
GTC










CT
CT







MC1838
NA
NA
NA
NA
NA



NA





PD_005
PBRM1
3
52668656
52668656
Nonsense_
G
G
T
p.Y389*







Mutation









PD_007
NA
NA
NA
NA
NA



NA





PD_010
PBRM1
3
52702580
52702580
Nonsense_
A
A
C
p.Y106*







Mutation









PD_011
NA
NA
NA
NA
NA



NA





PD_012
NA
NA
NA
NA
NA



NA





PD_013
PBRM1
3
52597487
52597488
Frame_
CC
CC

p.E1315fs







Shift_











Del









PD_014
PBRM1
3
52610662
52610663
Frame_
AG
AG

p.F1211fs







Shift_











Del









PD_015
PBRM1
3
52637540
52637540
Frame_
AG
AG

p.R941fs







Shift_











Del









PD_018
NA
NA
NA
NA
NA



NA





PD_019
NA
NA
NA
NA
NA



NA





PD_020
NA
NA
NA
NA
NA



NA





PD_021
PBRM1
3
52713723
52713723
Frame_
C
C

p.G2fs







Shift_











Del









PD_022
NA
NA
NA
NA
NA



NA





PD_023
PBRM1
3
52663053
52663053
Splice_
T
T
A








Site









PD_024
NA
NA
NA
NA
NA



NA





PD_025
PBRM1
3
52595829
52595829
Frame_
C
C

p.G1429fs







Shift_











Del









PD_026
NA
NA
NA
NA
NA



NA





RCC.PD1.
NA
NA
NA
NA
NA



NA


DNA.1026














RCC.PD1.
PBRM1
3
52595804
52595804
Frame_
C
C

p.A1438fs


DNA.1101




Shift_











Del









RCC.PD1.
NA
NA
NA
NA
NA



NA


DNA.1137














RCC.PD1.
NA
NA
NA
NA
NA



NA


DNA.944














RCC.PD1.
NA
NA
NA
NA
NA



NA


DNA.949














VA1008
PBRM1
3
52643943
52643943
Frame_
T
T

p.K619fs







Shift_











Del


















patient_
Variant_
i_
t_alt_
t_ref_
clonal_




id
Type
tumor_f
count
count
dm






CA8808
DEL
0.259090909
57
163
not
strelka,








evaluable
indelocator






KA4076
NA
NA
NA
NA
NA
NA






KE5236
DEL
0.09
 8
 85
not
indelocator








evaluable







KE6262
DEL
0.098214286
11
101
1
strelka,









indelocator






MC1838
NA
NA
NA
NA
NA
NA






PD_005
SNP
0.287356
25
 62
1
NA






PD_007
NA
NA
NA
NA
NA
NA






PD_010
SNP
0.15
 6
 34
0







PD_011
NA
NA
NA
NA
NA
NA






PD_012
NA
NA
NA
NA
NA
NA






PD_013
DEL
0.1171875
15
113
1
strelka,









indelocator






PD_014
DEL
0.288888889
26
 64
1
strelka,









indelocator






PD_015
DEL
0.204545455
18
 70
1
strelka,









indelocator






PD_018
NA
NA
NA
NA
NA
NA






PD_019
NA
NA
NA
NA
NA
NA






PD_020
NA
NA
NA
NA
NA
NA






PD_021
DEL
0.36
18
 32
1
strelka,









indelocator






PD_022
NA
NA
NA
NA
NA
NA






PD_023
SNP
0.214286
 9
 33
1
NA






PD_024
NA
NA
NA
NA
NA
NA






PD_025
DEL
0.154411765
21
115
1
strelka,









indelocator






PD_026
NA
NA
NA
NA
NA
NA






RCC.PD1.
NA
NA
NA
NA
NA
NA



DNA.1026












RCC.PD1.
DEL
0.133540373
43
279
1
strelka,



DNA.1101





indelocator






RCC.PD1.
NA
NA
NA
NA
NA
NA



DNA.1137












RCC.PD1.
NA
NA
NA
NA
NA
NA



DNA.944












RCC.PD1.
NA
NA
NA
NA
NA
NA



DNA.949












VA1008
DEL
0.06
15
253
not
indelocator








evaluable









II. Subjects

In one embodiment, the subject for whom predicted likelihood of efficacy of an immune checkpoint therapy is determined, is a mammal (e.g., mouse, rat, primate, non-human mammal, domestic animal, such as a dog, cat, cow, horse, and the like), and is preferably a human.


In another embodiment of the methods of the present invention, the subject has not undergone treatment, such as chemotherapy, radiation therapy, targeted therapy, and/or immune checkpoint therapy. In still another embodiment, the subject has undergone treatment, such as chemotherapy, radiation therapy, targeted therapy, and/or immune checkpoint therapy.


In certain embodiments, the subject has had surgery to remove cancerous or precancerous tissue. In other embodiments, the cancerous tissue has not been removed, e.g., the cancerous tissue may be located in an inoperable region of the body, such as in a tissue that is essential for life, or in a region where a surgical procedure would cause considerable risk of harm to the patient.


The methods of the present invention can be used to determine the responsiveness to anti-immune checkpoint therapies of renal cell carcinomas, particularly because kidney cancers are genomically different from many cancers according to cancer-related mutational load and composition. However, as described in herein, the methods of the present invention can, in certain embodiments, be applied to cancers other than renal cell carcinoma. In one embodiment, the cancers are solid tumors, such as lung cancer, melanoma, and/or renal cell carcinoma. In another embodiment, the cancer is an epithelial cancer such as, but not limited to, brain cancer (e.g., glioblastomas) bladder cancer, breast cancer, cervical cancer, colon cancer, gynecologic cancers, renal cancer, laryngeal cancer, lung cancer, oral cancer, head and neck cancer, ovarian cancer, pancreatic cancer, prostate cancer, or skin cancer. In still other embodiments, the cancer is breast cancer, prostate cancer, lung cancer, or colon cancer. In still other embodiments, the epithelial cancer is non-small-cell lung cancer, nonpapillary renal cell carcinoma, cervical carcinoma, ovarian carcinoma (e.g., serous ovarian carcinoma), or breast carcinoma. The epithelial cancers may be characterized in various other ways including, but not limited to, serous, endometrioid, mucinous, clear cell, brenner, or undifferentiated. In some embodiments, the cancers are mesenchymal tumors, such as sarcoma.


III. Sample Collection, Preparation and Separation

In some embodiments, biomarker amount and/or activity measurement(s) in a sample from a subject is compared to a predetermined control (standard) sample. The sample from the subject is typically from a diseased tissue, such as cancer cells or tissues. The control sample can be from the same subject or from a different subject. The control sample is typically a normal, non-diseased sample. However, in some embodiments, such as for staging of disease or for evaluating the efficacy of treatment, the control sample can be from a diseased tissue. The control sample can be a combination of samples from several different subjects. In some embodiments, the biomarker amount and/or activity measurement(s) from a subject is compared to a pre-determined level. This pre-determined level is typically obtained from normal samples. As described herein, a “pre-determined” biomarker amount and/or activity measurement(s) may be a biomarker amount and/or activity measurement(s) used to, by way of example only, evaluate a subject that may be selected for treatment, evaluate a response to an immune checkpoint therapy, and/or evaluate a response to a combination immune checkpoint therapy. A pre-determined biomarker amount and/or activity measurement(s) may be determined in populations of patients with or without cancer. The pre-determined biomarker amount and/or activity measurement(s) can be a single number, equally applicable to every patient, or the pre-determined biomarker amount and/or activity measurement(s) can vary according to specific subpopulations of patients. Age, weight, height, and other factors of a subject may affect the pre-determined biomarker amount and/or activity measurement(s) of the individual. Furthermore, the pre-determined biomarker amount and/or activity can be determined for each subject individually. In one embodiment, the amounts determined and/or compared in a method described herein are based on absolute measurements.


In another embodiment, the amounts determined and/or compared in a method described herein are based on relative measurements, such as ratios (e.g., biomarker copy numbers, level, and/or activity before a treatment vs. after a treatment, such biomarker measurements relative to a spiked or man-made control, such biomarker measurements relative to the expression of a housekeeping gene, and the like). For example, the relative analysis can be based on the ratio of pre-treatment biomarker measurement as compared to post-treatment biomarker measurement. Pre-treatment biomarker measurement can be made at any time prior to initiation of anti-cancer therapy. Post-treatment biomarker measurement can be made at any time after initiation of anti-cancer therapy. In some embodiments, post-treatment biomarker measurements are made 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 weeks or more after initiation of anti-cancer therapy, and even longer toward indefinitely for continued monitoring. Treatment can comprise anti-cancer therapy, such as a therapeutic regimen comprising an anti-PD1 monoclonal antibody (e.g., nivolumab) alone or in combination with other anti-cancer agents, such as anti-PD-L1/PD-L2 antibodies, anti-VEGF agents (e.g., bevacizumab), agents described in the Examples, Figures, and Tables, or anti-PBRM1 agents.


The pre-determined biomarker amount and/or activity measurement(s) can be any suitable standard. For example, the pre-determined biomarker amount and/or activity measurement(s) can be obtained from the same or a different human for whom a patient selection is being assessed. In one embodiment, the pre-determined biomarker amount and/or activity measurement(s) can be obtained from a previous assessment of the same patient. In such a manner, the progress of the selection of the patient can be monitored over time. In addition, the control can be obtained from an assessment of another human or multiple humans, e.g., selected groups of humans, if the subject is a human. In such a manner, the extent of the selection of the human for whom selection is being assessed can be compared to suitable other humans, e.g., other humans who are in a similar situation to the human of interest, such as those suffering from similar or the same condition(s) and/or of the same ethnic group.


In some embodiments of the present invention the change of biomarker amount and/or activity measurement(s) from the pre-determined level is about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, or 5.0 fold or greater, or any range in between, inclusive. Such cutoff values apply equally when the measurement is based on relative changes, such as based on the ratio of pre-treatment biomarker measurement as compared to post-treatment biomarker measurement.


Biological samples can be collected from a variety of sources from a patient including a body fluid sample, cell sample, or a tissue sample comprising nucleic acids and/or proteins. “Body fluids” refer to fluids that are excreted or secreted from the body as well as fluids that are normally not (e.g., amniotic fluid, aqueous humor, bile, blood and blood plasma, cerebrospinal fluid, cerumen and earwax, cowper's fluid or pre-ejaculatory fluid, chyle, chyme, stool, female ejaculate, interstitial fluid, intracellular fluid, lymph, menses, breast milk, mucus, pleural fluid, pus, saliva, sebum, semen, serum, sweat, synovial fluid, tears, urine, vaginal lubrication, vitreous humor, vomit). In a preferred embodiment, the subject and/or control sample is selected from the group consisting of cells, cell lines, histological slides, paraffin embedded tissues, biopsies, whole blood, nipple aspirate, serum, plasma, buccal scrape, saliva, cerebrospinal fluid, urine, stool, and bone marrow. In one embodiment, the sample is serum, plasma, or urine. In another embodiment, the sample is serum.


The samples can be collected from individuals repeatedly over a longitudinal period of time (e.g., once or more on the order of days, weeks, months, annually, biannually, etc.). Obtaining numerous samples from an individual over a period of time can be used to verify results from earlier detections and/or to identify an alteration in biological pattern as a result of, for example, disease progression, drug treatment, etc. For example, subject samples can be taken and monitored every month, every two months, or combinations of one, two, or three month intervals according to the present invention. In addition, the biomarker amount and/or activity measurements of the subject obtained over time can be conveniently compared with each other, as well as with those of normal controls during the monitoring period, thereby providing the subject's own values, as an internal, or personal, control for long-term monitoring.


Sample preparation and separation can involve any of the procedures, depending on the type of sample collected and/or analysis of biomarker measurement(s). Such procedures include, by way of example only, concentration, dilution, adjustment of pH, removal of high abundance polypeptides (e.g., albumin, gamma globulin, and transferrin, etc.), addition of preservatives and calibrants, addition of protease inhibitors, addition of denaturants, desalting of samples, concentration of sample proteins, extraction and purification of lipids.


The sample preparation can also isolate molecules that are bound in non-covalent complexes to other protein (e.g., carrier proteins). This process may isolate those molecules bound to a specific carrier protein (e.g., albumin), or use a more general process, such as the release of bound molecules from all carrier proteins via protein denaturation, for example using an acid, followed by removal of the carrier proteins.


Removal of undesired proteins (e.g., high abundance, uninformative, or undetectable proteins) from a sample can be achieved using high affinity reagents, high molecular weight filters, ultracentrifugation and/or electrodialysis. High affinity reagents include antibodies or other reagents (e.g., aptamers) that selectively bind to high abundance proteins. Sample preparation could also include ion exchange chromatography, metal ion affinity chromatography, gel filtration, hydrophobic chromatography, chromatofocusing, adsorption chromatography, isoelectric focusing and related techniques. Molecular weight filters include membranes that separate molecules on the basis of size and molecular weight. Such filters may further employ reverse osmosis, nanofiltration, ultrafiltration and microfiltration.


Ultracentrifugation is a method for removing undesired polypeptides from a sample. Ultracentrifugation is the centrifugation of a sample at about 15,000-60,000 rpm while monitoring with an optical system the sedimentation (or lack thereof) of particles. Electrodialysis is a procedure which uses an electromembrane or semipermable membrane in a process in which ions are transported through semi-permeable membranes from one solution to another under the influence of a potential gradient. Since the membranes used in electrodialysis may have the ability to selectively transport ions having positive or negative charge, reject ions of the opposite charge, or to allow species to migrate through a semipermable membrane based on size and charge, it renders electrodialysis useful for concentration, removal, or separation of electrolytes.


Separation and purification in the present invention may include any procedure known in the art, such as capillary electrophoresis (e.g., in capillary or on-chip) or chromatography (e.g., in capillary, column or on a chip). Electrophoresis is a method which can be used to separate ionic molecules under the influence of an electric field. Electrophoresis can be conducted in a gel, capillary, or in a microchannel on a chip. Examples of gels used for electrophoresis include starch, acrylamide, polyethylene oxides, agarose, or combinations thereof. A gel can be modified by its cross-linking, addition of detergents, or denaturants, immobilization of enzymes or antibodies (affinity electrophoresis) or substrates (zymography) and incorporation of a pH gradient. Examples of capillaries used for electrophoresis include capillaries that interface with an electrospray.


Capillary electrophoresis (CE) is preferred for separating complex hydrophilic molecules and highly charged solutes. CE technology can also be implemented on microfluidic chips. Depending on the types of capillary and buffers used, CE can be further segmented into separation techniques such as capillary zone electrophoresis (CZE), capillary isoelectric focusing (CIEF), capillary isotachophoresis (cITP) and capillary electrochromatography (CEC). An embodiment to couple CE techniques to electrospray ionization involves the use of volatile solutions, for example, aqueous mixtures containing a volatile acid and/or base and an organic such as an alcohol or acetonitrile.


Capillary isotachophoresis (cITP) is a technique in which the analytes move through the capillary at a constant speed but are nevertheless separated by their respective mobilities. Capillary zone electrophoresis (CZE), also known as free-solution CE (FSCE), is based on differences in the electrophoretic mobility of the species, determined by the charge on the molecule, and the frictional resistance the molecule encounters during migration which is often directly proportional to the size of the molecule. Capillary isoelectric focusing (CLEF) allows weakly-ionizable amphoteric molecules, to be separated by electrophoresis in a pH gradient. CEC is a hybrid technique between traditional high performance liquid chromatography (HPLC) and CE.


Separation and purification techniques used in the present invention include any chromatography procedures known in the art. Chromatography can be based on the differential adsorption and elution of certain analytes or partitioning of analytes between mobile and stationary phases. Different examples of chromatography include, but not limited to, liquid chromatography (LC), gas chromatography (GC), high performance liquid chromatography (HPLC), etc.


IV. Biomarker Nucleic Acids and Polypeptides

One aspect of the present invention pertains to the use of isolated nucleic acid molecules that correspond to biomarker nucleic acids that encode a biomarker polypeptide or a portion of such a polypeptide. As used herein, the term “nucleic acid molecule” is intended to include DNA molecules (e.g., cDNA or genomic DNA) and RNA molecules (e.g., mRNA) and analogs of the DNA or RNA generated using nucleotide analogs. The nucleic acid molecule can be single-stranded or double-stranded, but preferably is double-stranded DNA.


An “isolated” nucleic acid molecule is one which is separated from other nucleic acid molecules which are present in the natural source of the nucleic acid molecule. Preferably, an “isolated” nucleic acid molecule is free of sequences (preferably protein-encoding sequences) which naturally flank the nucleic acid (i.e., sequences located at the 5′ and 3′ ends of the nucleic acid) in the genomic DNA of the organism from which the nucleic acid is derived. For example, in various embodiments, the isolated nucleic acid molecule can contain less than about 5 kB, 4 kB, 3 kB, 2 kB, 1 kB, 0.5 kB or 0.1 kB of nucleotide sequences which naturally flank the nucleic acid molecule in genomic DNA of the cell from which the nucleic acid is derived. Moreover, an “isolated” nucleic acid molecule, such as a cDNA molecule, can be substantially free of other cellular material or culture medium when produced by recombinant techniques, or substantially free of chemical precursors or other chemicals when chemically synthesized.


A biomarker nucleic acid molecule of the present invention can be isolated using standard molecular biology techniques and the sequence information in the database records described herein. Using all or a portion of such nucleic acid sequences, nucleic acid molecules of the present invention can be isolated using standard hybridization and cloning techniques (e.g., as described in Sambrook et al., ed., Molecular Cloning: A Laboratory Manual, 2nd ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 1989).


A nucleic acid molecule of the present invention can be amplified using cDNA, mRNA, or genomic DNA as a template and appropriate oligonucleotide primers according to standard PCR amplification techniques. The nucleic acid molecules so amplified can be cloned into an appropriate vector and characterized by DNA sequence analysis. Furthermore, oligonucleotides corresponding to all or a portion of a nucleic acid molecule of the present invention can be prepared by standard synthetic techniques, e.g., using an automated DNA synthesizer.


Moreover, a nucleic acid molecule of the present invention can comprise only a portion of a nucleic acid sequence, wherein the full length nucleic acid sequence comprises a marker of the present invention or which encodes a polypeptide corresponding to a marker of the present invention. Such nucleic acid molecules can be used, for example, as a probe or primer. The probe/primer typically is used as one or more substantially purified oligonucleotides. The oligonucleotide typically comprises a region of nucleotide sequence that hybridizes under stringent conditions to at least about 7, preferably about 15, more preferably about 25, 50, 75, 100, 125, 150, 175, 200, 250, 300, 350, or 400 or more consecutive nucleotides of a biomarker nucleic acid sequence. Probes based on the sequence of a biomarker nucleic acid molecule can be used to detect transcripts or genomic sequences corresponding to one or more markers of the present invention. The probe comprises a label group attached thereto, e.g., a radioisotope, a fluorescent compound, an enzyme, or an enzyme co-factor.


A biomarker nucleic acid molecules that differ, due to degeneracy of the genetic code, from the nucleotide sequence of nucleic acid molecules encoding a protein which corresponds to the biomarker, and thus encode the same protein, are also contemplated.


In addition, it will be appreciated by those skilled in the art that DNA sequence polymorphisms that lead to changes in the amino acid sequence can exist within a population (e.g., the human population). Such genetic polymorphisms can exist among individuals within a population due to natural allelic variation. An allele is one of a group of genes which occur alternatively at a given genetic locus. In addition, it will be appreciated that DNA polymorphisms that affect RNA expression levels can also exist that may affect the overall expression level of that gene (e.g., by affecting regulation or degradation).


The term “allele,” which is used interchangeably herein with “allelic variant,” refers to alternative forms of a gene or portions thereof. Alleles occupy the same locus or position on homologous chromosomes. When a subject has two identical alleles of a gene, the subject is said to be homozygous for the gene or allele. When a subject has two different alleles of a gene, the subject is said to be heterozygous for the gene or allele. For example, biomarker alleles can differ from each other in a single nucleotide, or several nucleotides, and can include substitutions, deletions, and insertions of nucleotides. An allele of a gene can also be a form of a gene containing one or more mutations.


The term “allelic variant of a polymorphic region of gene” or “allelic variant”, used interchangeably herein, refers to an alternative form of a gene having one of several possible nucleotide sequences found in that region of the gene in the population. As used herein, allelic variant is meant to encompass functional allelic variants, non-functional allelic variants, SNPs, mutations and polymorphisms.


The term “single nucleotide polymorphism” (SNP) refers to a polymorphic site occupied by a single nucleotide, which is the site of variation between allelic sequences. The site is usually preceded by and followed by highly conserved sequences of the allele (e.g., sequences that vary in less than 1/100 or 1/1000 members of a population). A SNP usually arises due to substitution of one nucleotide for another at the polymorphic site. SNPs can also arise from a deletion of a nucleotide or an insertion of a nucleotide relative to a reference allele. Typically the polymorphic site is occupied by a base other than the reference base. For example, where the reference allele contains the base “T” (thymidine) at the polymorphic site, the altered allele can contain a “C” (cytidine), “G” (guanine), or “A” (adenine) at the polymorphic site. SNP's may occur in protein-coding nucleic acid sequences, in which case they may give rise to a defective or otherwise variant protein, or genetic disease. Such a SNP may alter the coding sequence of the gene and therefore specify another amino acid (a “missense” SNP) or a SNP may introduce a stop codon (a “nonsense” SNP). When a SNP does not alter the amino acid sequence of a protein, the SNP is called “silent.” SNP's may also occur in noncoding regions of the nucleotide sequence. This may result in defective protein expression, e.g., as a result of alternative spicing, or it may have no effect on the function of the protein.


As used herein, the terms “gene” and “recombinant gene” refer to nucleic acid molecules comprising an open reading frame encoding a polypeptide corresponding to a marker of the present invention. Such natural allelic variations can typically result in 1-5% variance in the nucleotide sequence of a given gene. Alternative alleles can be identified by sequencing the gene of interest in a number of different individuals. This can be readily carried out by using hybridization probes to identify the same genetic locus in a variety of individuals. Any and all such nucleotide variations and resulting amino acid polymorphisms or variations that are the result of natural allelic variation and that do not alter the functional activity are intended to be within the scope of the present invention.


In another embodiment, a biomarker nucleic acid molecule is at least 7, 15, 20, 25, 30, 40, 60, 80, 100, 150, 200, 250, 300, 350, 400, 450, 550, 650, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2200, 2400, 2600, 2800, 3000, 3500, 4000, 4500, or more nucleotides in length and hybridizes under stringent conditions to a nucleic acid molecule corresponding to a marker of the present invention or to a nucleic acid molecule encoding a protein corresponding to a marker of the present invention. As used herein, the term “hybridizes under stringent conditions” is intended to describe conditions for hybridization and washing under which nucleotide sequences at least 60% (65%, 70%, 75%, 80%, preferably 85%) identical to each other typically remain hybridized to each other. Such stringent conditions are known to those skilled in the art and can be found in sections 6.3.1-6.3.6 of Current Protocols in Molecular Biology, John Wiley & Sons, N.Y. (1989). A preferred, non-limiting example of stringent hybridization conditions are hybridization in 6× sodium chloride/sodium citrate (SSC) at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 50-65° C.


In addition to naturally-occurring allelic variants of a nucleic acid molecule of the present invention that can exist in the population, the skilled artisan will further appreciate that sequence changes can be introduced by mutation thereby leading to changes in the amino acid sequence of the encoded protein, without altering the biological activity of the protein encoded thereby. For example, one can make nucleotide substitutions leading to amino acid substitutions at “non-essential” amino acid residues. A “non-essential” amino acid residue is a residue that can be altered from the wild-type sequence without altering the biological activity, whereas an “essential” amino acid residue is required for biological activity. For example, amino acid residues that are not conserved or only semi-conserved among homologs of various species may be non-essential for activity and thus would be likely targets for alteration. Alternatively, amino acid residues that are conserved among the homologs of various species (e.g., murine and human) may be essential for activity and thus would not be likely targets for alteration.


Accordingly, another aspect of the present invention pertains to nucleic acid molecules encoding a polypeptide of the present invention that contain changes in amino acid residues that are not essential for activity. Such polypeptides differ in amino acid sequence from the naturally-occurring proteins which correspond to the markers of the present invention, yet retain biological activity. In one embodiment, a biomarker protein has an amino acid sequence that is at least about 40% identical, 50%, 60%, 70%, 75%, 80%, 83%, 85%, 87.5%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or identical to the amino acid sequence of a biomarker protein described herein.


An isolated nucleic acid molecule encoding a variant protein can be created by introducing one or more nucleotide substitutions, additions or deletions into the nucleotide sequence of nucleic acids of the present invention, such that one or more amino acid residue substitutions, additions, or deletions are introduced into the encoded protein. Mutations can be introduced by standard techniques, such as site-directed mutagenesis and PCR-mediated mutagenesis. Preferably, conservative amino acid substitutions are made at one or more predicted non-essential amino acid residues. A “conservative amino acid substitution” is one in which the amino acid residue is replaced with an amino acid residue having a similar side chain. Families of amino acid residues having similar side chains have been defined in the art. These families include amino acids with basic side chains (e.g., lysine, arginine, histidine), acidic side chains (e.g., aspartic acid, glutamic acid), uncharged polar side chains (e.g., glycine, asparagine, glutamine, serine, threonine, tyrosine, cysteine), non-polar side chains (e.g., alanine, valine, leucine, isoleucine, proline, phenylalanine, methionine, tryptophan), beta-branched side chains (e.g., threonine, valine, isoleucine) and aromatic side chains (e.g., tyrosine, phenylalanine, tryptophan, histidine). Alternatively, mutations can be introduced randomly along all or part of the coding sequence, such as by saturation mutagenesis, and the resultant mutants can be screened for biological activity to identify mutants that retain activity. Following mutagenesis, the encoded protein can be expressed recombinantly and the activity of the protein can be determined.


In some embodiments, the present invention further contemplates the use of anti-biomarker antisense nucleic acid molecules, i.e., molecules which are complementary to a sense nucleic acid of the present invention, e.g., complementary to the coding strand of a double-stranded cDNA molecule corresponding to a marker of the present invention or complementary to an mRNA sequence corresponding to a marker of the present invention. Accordingly, an antisense nucleic acid molecule of the present invention can hydrogen bond to (i.e. anneal with) a sense nucleic acid of the present invention. The antisense nucleic acid can be complementary to an entire coding strand, or to only a portion thereof, e.g., all or part of the protein coding region (or open reading frame). An antisense nucleic acid molecule can also be antisense to all or part of a non-coding region of the coding strand of a nucleotide sequence encoding a polypeptide of the present invention. The non-coding regions (“5′ and 3′ untranslated regions”) are the 5′ and 3′ sequences which flank the coding region and are not translated into amino acids.


An antisense oligonucleotide can be, for example, about 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 or more nucleotides in length. An antisense nucleic acid can be constructed using chemical synthesis and enzymatic ligation reactions using procedures known in the art. For example, an antisense nucleic acid (e.g., an antisense oligonucleotide) can be chemically synthesized using naturally occurring nucleotides or variously modified nucleotides designed to increase the biological stability of the molecules or to increase the physical stability of the duplex formed between the antisense and sense nucleic acids, e.g., phosphorothioate derivatives and acridine substituted nucleotides can be used. Examples of modified nucleotides which can be used to generate the antisense nucleic acid include 5-fluorouracil, 5-bromouracil, 5-chlorouracil, 5-iodouracil, hypoxanthine, xanthine, 4-acetylcytosine, 5-(carboxyhydroxylmethyl) uracil, 5-carboxymethylaminomethyl-2-thiouridine, 5-carboxymethylaminomethyluracil, dihydrouracil, beta-D-galactosylqueosine, inosine, N6-isopentenyladenine, 1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-adenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, beta-D-mannosylqueosine, 5′-methoxycarboxymethyluracil, 5-methoxyuracil, 2-methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid (v), wybutoxosine, pseudouracil, queosine, 2-thiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, 5-methyluracil, uracil-5-oxyacetic acid methylester, uracil-5-oxyacetic acid (v), 5-methyl-2-thiouracil, 3-(3-amino-3-N-2-carboxypropyl) uracil, (acp3)w, and 2,6-diaminopurine. Alternatively, the antisense nucleic acid can be produced biologically using an expression vector into which a nucleic acid has been sub-cloned in an antisense orientation (i.e., RNA transcribed from the inserted nucleic acid will be of an antisense orientation to a target nucleic acid of interest, described further in the following subsection).


The antisense nucleic acid molecules of the present invention are typically administered to a subject or generated in situ such that they hybridize with or bind to cellular mRNA and/or genomic DNA encoding a polypeptide corresponding to a selected marker of the present invention to thereby inhibit expression of the marker, e.g., by inhibiting transcription and/or translation. The hybridization can be by conventional nucleotide complementarity to form a stable duplex, or, for example, in the case of an antisense nucleic acid molecule which binds to DNA duplexes, through specific interactions in the major groove of the double helix. Examples of a route of administration of antisense nucleic acid molecules of the present invention includes direct injection at a tissue site or infusion of the antisense nucleic acid into a blood- or bone marrow-associated body fluid. Alternatively, antisense nucleic acid molecules can be modified to target selected cells and then administered systemically. For example, for systemic administration, antisense molecules can be modified such that they specifically bind to receptors or antigens expressed on a selected cell surface, e.g., by linking the antisense nucleic acid molecules to peptides or antibodies which bind to cell surface receptors or antigens. The antisense nucleic acid molecules can also be delivered to cells using the vectors described herein. To achieve sufficient intracellular concentrations of the antisense molecules, vector constructs in which the antisense nucleic acid molecule is placed under the control of a strong pol II or pol III promoter are preferred.


An antisense nucleic acid molecule of the present invention can be an α-anomeric nucleic acid molecule. An α-anomeric nucleic acid molecule forms specific double-stranded hybrids with complementary RNA in which, contrary to the usual α-units, the strands run parallel to each other (Gaultier et al. (1987) Nucleic Acids Res. 15:6625-6641). The antisense nucleic acid molecule can also comprise a 2′-o-methylribonucleotide (Inoue et al. (1987) Nucleic Acids Res. 15:6131-6148) or a chimeric RNA-DNA analogue (Inoue et al. (1987) FEBS Lett. 215:327-330).


The present invention also encompasses ribozymes. Ribozymes are catalytic RNA molecules with ribonuclease activity which are capable of cleaving a single-stranded nucleic acid, such as an mRNA, to which they have a complementary region. Thus, ribozymes (e.g., hammerhead ribozymes as described in Haselhoff and Gerlach (1988) Nature 334:585-591) can be used to catalytically cleave mRNA transcripts to thereby inhibit translation of the protein encoded by the mRNA. A ribozyme having specificity for a nucleic acid molecule encoding a polypeptide corresponding to a marker of the present invention can be designed based upon the nucleotide sequence of a cDNA corresponding to the marker. For example, a derivative of a Tetrahymena L-19 IVS RNA can be constructed in which the nucleotide sequence of the active site is complementary to the nucleotide sequence to be cleaved (see Cech et al. U.S. Pat. No. 4,987,071; and Cech et al. U.S. Pat. No. 5,116,742). Alternatively, an mRNA encoding a polypeptide of the present invention can be used to select a catalytic RNA having a specific ribonuclease activity from a pool of RNA molecules (see, e.g., Bartel and Szostak (1993) Science 261:1411-1418).


The present invention also encompasses nucleic acid molecules which form triple helical structures. For example, expression of a biomarker protein can be inhibited by targeting nucleotide sequences complementary to the regulatory region of the gene encoding the polypeptide (e.g., the promoter and/or enhancer) to form triple helical structures that prevent transcription of the gene in target cells. See generally Helene (1991) Anticancer Drug Des. 6(6):569-84; Helene (1992) Ann. N.Y. Acad. Sci. 660:27-36; and Maher (1992) Bioassays 14(12):807-15.


In various embodiments, the nucleic acid molecules of the present invention can be modified at the base moiety, sugar moiety or phosphate backbone to improve, e.g., the stability, hybridization, or solubility of the molecule. For example, the deoxyribose phosphate backbone of the nucleic acid molecules can be modified to generate peptide nucleic acid molecules (see Hyrup et al. (1996) Bioorganic & Medicinal Chemistry 4(1): 5-23). As used herein, the terms “peptide nucleic acids” or “PNAs” refer to nucleic acid mimics, e.g., DNA mimics, in which the deoxyribose phosphate backbone is replaced by a pseudopeptide backbone and only the four natural nucleobases are retained. The neutral backbone of PNAs has been shown to allow for specific hybridization to DNA and RNA under conditions of low ionic strength. The synthesis of PNA oligomers can be performed using standard solid phase peptide synthesis protocols as described in Hyrup et al. (1996), supra; Perry-O'Keefe et al. (1996) Proc. Natl. Acad. Sci. USA 93:14670-675.


PNAs can be used in therapeutic and diagnostic applications. For example, PNAs can be used as antisense or antigene agents for sequence-specific modulation of gene expression by, e.g., inducing transcription or translation arrest or inhibiting replication. PNAs can also be used, e.g., in the analysis of single base pair mutations in a gene by, e.g., PNA directed PCR clamping; as artificial restriction enzymes when used in combination with other enzymes, e.g., 51 nucleases (Hyrup (1996), supra; or as probes or primers for DNA sequence and hybridization (Hyrup (1996), supra; Perry-O'Keefe et al. (1996) Proc. Natl. Acad. Sci. USA 93:14670-14675).


In another embodiment, PNAs can be modified, e.g., to enhance their stability or cellular uptake, by attaching lipophilic or other helper groups to PNA, by the formation of PNA-DNA chimeras, or by the use of liposomes or other techniques of drug delivery known in the art. For example, PNA-DNA chimeras can be generated which can combine the advantageous properties of PNA and DNA. Such chimeras allow DNA recognition enzymes, e.g., RNASE H and DNA polymerases, to interact with the DNA portion while the PNA portion would provide high binding affinity and specificity. PNA-DNA chimeras can be linked using linkers of appropriate lengths selected in terms of base stacking, number of bonds between the nucleobases, and orientation (Hyrup (1996), supra). The synthesis of PNA-DNA chimeras can be performed as described in Hyrup (1996), supra, and Finn et al. (1996) Nucleic Acids Res. 24(17):3357-3363. For example, a DNA chain can be synthesized on a solid support using standard phosphoramidite coupling chemistry and modified nucleoside analogs. Compounds such as 5′-(4-methoxytrityl)amino-5′-deoxy-thymidine phosphoramidite can be used as a link between the PNA and the 5′ end of DNA (Mag et al. (1989) Nucleic Acids Res. 17:5973-5988). PNA monomers are then coupled in a step-wise manner to produce a chimeric molecule with a 5′ PNA segment and a 3′ DNA segment (Finn et al. (1996) Nucleic Acids Res. 24:3357-3363). Alternatively, chimeric molecules can be synthesized with a 5′ DNA segment and a 3′ PNA segment (Peterser et al. (1975) Bioorganic Med. Chem. Lett. 5:1119-11124).


In other embodiments, the oligonucleotide can include other appended groups such as peptides (e.g., for targeting host cell receptors in vivo), or agents facilitating transport across the cell membrane (see, e.g., Letsinger et al. (1989) Proc. Natl. Acad. Sci. USA 86:6553-6556; Lemaitre et al. (1987) Proc. Natl. Acad. Sci. USA 84:648-652; PCT Publication No. WO 88/09810) or the blood-brain barrier (see, e.g., PCT Publication No. WO 89/10134). In addition, oligonucleotides can be modified with hybridization-triggered cleavage agents (see, e.g., Krol et al. (1988) Bio/Techniques 6:958-976) or intercalating agents (see, e.g., Zon (1988) Pharm. Res. 5:539-549). To this end, the oligonucleotide can be conjugated to another molecule, e.g., a peptide, hybridization triggered cross-linking agent, transport agent, hybridization-triggered cleavage agent, etc.


Another aspect of the present invention pertains to the use of biomarker proteins and biologically active portions thereof. In one embodiment, the native polypeptide corresponding to a marker can be isolated from cells or tissue sources by an appropriate purification scheme using standard protein purification techniques. In another embodiment, polypeptides corresponding to a marker of the present invention are produced by recombinant DNA techniques. Alternative to recombinant expression, a polypeptide corresponding to a marker of the present invention can be synthesized chemically using standard peptide synthesis techniques.


An “isolated” or “purified” protein or biologically active portion thereof is substantially free of cellular material or other contaminating proteins from the cell or tissue source from which the protein is derived, or substantially free of chemical precursors or other chemicals when chemically synthesized. The language “substantially free of cellular material” includes preparations of protein in which the protein is separated from cellular components of the cells from which it is isolated or recombinantly produced. Thus, protein that is substantially free of cellular material includes preparations of protein having less than about 30%, 20%, 10%, or 5% (by dry weight) of heterologous protein (also referred to herein as a “contaminating protein”). When the protein or biologically active portion thereof is recombinantly produced, it is also preferably substantially free of culture medium, i.e., culture medium represents less than about 20%, 10%, or 5% of the volume of the protein preparation. When the protein is produced by chemical synthesis, it is preferably substantially free of chemical precursors or other chemicals, i.e., it is separated from chemical precursors or other chemicals which are involved in the synthesis of the protein. Accordingly such preparations of the protein have less than about 30%, 20%, 10%, 5% (by dry weight) of chemical precursors or compounds other than the polypeptide of interest.


Biologically active portions of a biomarker polypeptide include polypeptides comprising amino acid sequences sufficiently identical to or derived from a biomarker protein amino acid sequence described herein, but which includes fewer amino acids than the full length protein, and exhibit at least one activity of the corresponding full-length protein. Typically, biologically active portions comprise a domain or motif with at least one activity of the corresponding protein. A biologically active portion of a protein of the present invention can be a polypeptide which is, for example, 10, 25, 50, 100 or more amino acids in length. Moreover, other biologically active portions, in which other regions of the protein are deleted, can be prepared by recombinant techniques and evaluated for one or more of the functional activities of the native form of a polypeptide of the present invention.


Preferred polypeptides have an amino acid sequence of a biomarker protein encoded by a nucleic acid molecule described herein. Other useful proteins are substantially identical (e.g., at least about 40%, preferably 50%, 60%, 70%, 75%, 80%, 83%, 85%, 88%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%) to one of these sequences and retain the functional activity of the protein of the corresponding naturally-occurring protein yet differ in amino acid sequence due to natural allelic variation or mutagenesis.


To determine the percent identity of two amino acid sequences or of two nucleic acids, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in the sequence of a first amino acid or nucleic acid sequence for optimal alignment with a second amino or nucleic acid sequence). The amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared. When a position in the first sequence is occupied by the same amino acid residue or nucleotide as the corresponding position in the second sequence, then the molecules are identical at that position. The percent identity between the two sequences is a function of the number of identical positions shared by the sequences (i.e., % identity=# of identical positions/total # of positions (e.g., overlapping positions)×100). In one embodiment the two sequences are the same length.


The determination of percent identity between two sequences can be accomplished using a mathematical algorithm. A preferred, non-limiting example of a mathematical algorithm utilized for the comparison of two sequences is the algorithm of Karlin and Altschul (1990) Proc. Natl. Acad. Sci. USA 87:2264-2268, modified as in Karlin and Altschul (1993) Proc. Natl. Acad. Sci. USA 90:5873-5877. Such an algorithm is incorporated into the NBLAST and XBLAST programs of Altschul, et al. (1990)J Mol. Biol. 215:403-410. BLAST nucleotide searches can be performed with the NBLAST program, score=100, wordlength=12 to obtain nucleotide sequences homologous to a nucleic acid molecules of the present invention. BLAST protein searches can be performed with the XBLAST program, score=50, wordlength=3 to obtain amino acid sequences homologous to a protein molecules of the present invention. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al. (1997) Nucleic Acids Res. 25:3389-3402. Alternatively, PSI-Blast can be used to perform an iterated search which detects distant relationships between molecules. When utilizing BLAST, Gapped BLAST, and PSI-Blast programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used. See http://www.ncbi.nlm.nih.gov. Another preferred, non-limiting example of a mathematical algorithm utilized for the comparison of sequences is the algorithm of Myers and Miller, (1988) Comput Appl Biosci, 4:11-7. Such an algorithm is incorporated into the ALIGN program (version 2.0) which is part of the GCG sequence alignment software package. When utilizing the ALIGN program for comparing amino acid sequences, a PAM120 weight residue table, a gap length penalty of 12, and a gap penalty of 4 can be used. Yet another useful algorithm for identifying regions of local sequence similarity and alignment is the FASTA algorithm as described in Pearson and Lipman (1988) Proc. Natl. Acad. Sci. USA 85:2444-2448. When using the FASTA algorithm for comparing nucleotide or amino acid sequences, a PAM120 weight residue table can, for example, be used with a k-tuple value of 2.


The percent identity between two sequences can be determined using techniques similar to those described above, with or without allowing gaps. In calculating percent identity, only exact matches are counted.


The present invention also provides chimeric or fusion proteins corresponding to a biomarker protein. As used herein, a “chimeric protein” or “fusion protein” comprises all or part (preferably a biologically active part) of a polypeptide corresponding to a marker of the present invention operably linked to a heterologous polypeptide (i.e., a polypeptide other than the polypeptide corresponding to the marker). Within the fusion protein, the term “operably linked” is intended to indicate that the polypeptide of the present invention and the heterologous polypeptide are fused in-frame to each other. The heterologous polypeptide can be fused to the amino-terminus or the carboxyl-terminus of the polypeptide of the present invention.


One useful fusion protein is a GST fusion protein in which a polypeptide corresponding to a marker of the present invention is fused to the carboxyl terminus of GST sequences. Such fusion proteins can facilitate the purification of a recombinant polypeptide of the present invention.


In another embodiment, the fusion protein contains a heterologous signal sequence, immunoglobulin fusion protein, toxin, or other useful protein sequence. Chimeric and fusion proteins of the present invention can be produced by standard recombinant DNA techniques. In another embodiment, the fusion gene can be synthesized by conventional techniques including automated DNA synthesizers. Alternatively, PCR amplification of gene fragments can be carried out using anchor primers which give rise to complementary overhangs between two consecutive gene fragments which can subsequently be annealed and re-amplified to generate a chimeric gene sequence (see, e.g., Ausubel et al., supra). Moreover, many expression vectors are commercially available that already encode a fusion moiety (e.g., a GST polypeptide). A nucleic acid encoding a polypeptide of the present invention can be cloned into such an expression vector such that the fusion moiety is linked in-frame to the polypeptide of the present invention.


A signal sequence can be used to facilitate secretion and isolation of the secreted protein or other proteins of interest. Signal sequences are typically characterized by a core of hydrophobic amino acids which are generally cleaved from the mature protein during secretion in one or more cleavage events. Such signal peptides contain processing sites that allow cleavage of the signal sequence from the mature proteins as they pass through the secretory pathway. Thus, the present invention pertains to the described polypeptides having a signal sequence, as well as to polypeptides from which the signal sequence has been proteolytically cleaved (i.e., the cleavage products). In one embodiment, a nucleic acid sequence encoding a signal sequence can be operably linked in an expression vector to a protein of interest, such as a protein which is ordinarily not secreted or is otherwise difficult to isolate. The signal sequence directs secretion of the protein, such as from a eukaryotic host into which the expression vector is transformed, and the signal sequence is subsequently or concurrently cleaved. The protein can then be readily purified from the extracellular medium by art recognized methods. Alternatively, the signal sequence can be linked to the protein of interest using a sequence which facilitates purification, such as with a GST domain.


The present invention also pertains to variants of the biomarker polypeptides described herein. Such variants have an altered amino acid sequence which can function as either agonists (mimetics) or as antagonists. Variants can be generated by mutagenesis, e.g., discrete point mutation or truncation. An agonist can retain substantially the same, or a subset, of the biological activities of the naturally occurring form of the protein. An antagonist of a protein can inhibit one or more of the activities of the naturally occurring form of the protein by, for example, competitively binding to a downstream or upstream member of a cellular signaling cascade which includes the protein of interest. Thus, specific biological effects can be elicited by treatment with a variant of limited function. Treatment of a subject with a variant having a subset of the biological activities of the naturally occurring form of the protein can have fewer side effects in a subject relative to treatment with the naturally occurring form of the protein.


Variants of a biomarker protein which function as either agonists (mimetics) or as antagonists can be identified by screening combinatorial libraries of mutants, e.g., truncation mutants, of the protein of the present invention for agonist or antagonist activity. In one embodiment, a variegated library of variants is generated by combinatorial mutagenesis at the nucleic acid level and is encoded by a variegated gene library. A variegated library of variants can be produced by, for example, enzymatically ligating a mixture of synthetic oligonucleotides into gene sequences such that a degenerate set of potential protein sequences is expressible as individual polypeptides, or alternatively, as a set of larger fusion proteins (e.g., for phage display). There are a variety of methods which can be used to produce libraries of potential variants of the polypeptides of the present invention from a degenerate oligonucleotide sequence. Methods for synthesizing degenerate oligonucleotides are known in the art (see, e.g., Narang (1983) Tetrahedron 39:3; Itakura et al. (1984) Annu. Rev. Biochem. 53:323; Itakura et al. (1984) Science 198:1056; Ike et al. (1983) Nucleic Acid Res. 11:477).


In addition, libraries of fragments of the coding sequence of a polypeptide corresponding to a marker of the present invention can be used to generate a variegated population of polypeptides for screening and subsequent selection of variants. For example, a library of coding sequence fragments can be generated by treating a double stranded PCR fragment of the coding sequence of interest with a nuclease under conditions wherein nicking occurs only about once per molecule, denaturing the double stranded DNA, renaturing the DNA to form double stranded DNA which can include sense/antisense pairs from different nicked products, removing single stranded portions from reformed duplexes by treatment with S1 nuclease, and ligating the resulting fragment library into an expression vector. By this method, an expression library can be derived which encodes amino terminal and internal fragments of various sizes of the protein of interest.


Several techniques are known in the art for screening gene products of combinatorial libraries made by point mutations or truncation, and for screening cDNA libraries for gene products having a selected property. The most widely used techniques, which are amenable to high throughput analysis, for screening large gene libraries typically include cloning the gene library into replicable expression vectors, transforming appropriate cells with the resulting library of vectors, and expressing the combinatorial genes under conditions in which detection of a desired activity facilitates isolation of the vector encoding the gene whose product was detected. Recursive ensemble mutagenesis (REM), a technique which enhances the frequency of functional mutants in the libraries, can be used in combination with the screening assays to identify variants of a protein of the present invention (Arkin and Yourvan (1992) Proc. Natl. Acad. Sci. USA 89:7811-7815; Delgrave et al. 91993) Protein Engineering 6(3):327-331).


The production and use of biomarker nucleic acid and/or biomarker polypeptide molecules described herein can be facilitated by using standard recombinant techniques. In some embodiments, such techniques use vectors, preferably expression vectors, containing a nucleic acid encoding a biomarker polypeptide or a portion of such a polypeptide. As used herein, the term “vector” refers to a nucleic acid molecule capable of transporting another nucleic acid to which it has been linked. One type of vector is a “plasmid”, which refers to a circular double stranded DNA loop into which additional DNA segments can be ligated. Another type of vector is a viral vector, wherein additional DNA segments can be ligated into the viral genome. Certain vectors are capable of autonomous replication in a host cell into which they are introduced (e.g., bacterial vectors having a bacterial origin of replication and episomal mammalian vectors). Other vectors (e.g., non-episomal mammalian vectors) are integrated into the genome of a host cell upon introduction into the host cell, and thereby are replicated along with the host genome. Moreover, certain vectors, namely expression vectors, are capable of directing the expression of genes to which they are operably linked. In general, expression vectors of utility in recombinant DNA techniques are often in the form of plasmids (vectors). However, the present invention is intended to include such other forms of expression vectors, such as viral vectors (e.g., replication defective retroviruses, adenoviruses and adeno-associated viruses), which serve equivalent functions.


The recombinant expression vectors of the present invention comprise a nucleic acid of the present invention in a form suitable for expression of the nucleic acid in a host cell. This means that the recombinant expression vectors include one or more regulatory sequences, selected on the basis of the host cells to be used for expression, which is operably linked to the nucleic acid sequence to be expressed. Within a recombinant expression vector, “operably linked” is intended to mean that the nucleotide sequence of interest is linked to the regulatory sequence(s) in a manner which allows for expression of the nucleotide sequence (e.g., in an in vitro transcription/translation system or in a host cell when the vector is introduced into the host cell). The term “regulatory sequence” is intended to include promoters, enhancers and other expression control elements (e.g., polyadenylation signals). Such regulatory sequences are described, for example, in Goeddel, Methods in Enzymology: Gene Expression Technology vol. 185, Academic Press, San Diego, Calif. (1991). Regulatory sequences include those which direct constitutive expression of a nucleotide sequence in many types of host cell and those which direct expression of the nucleotide sequence only in certain host cells (e.g., tissue-specific regulatory sequences). It will be appreciated by those skilled in the art that the design of the expression vector can depend on such factors as the choice of the host cell to be transformed, the level of expression of protein desired, and the like. The expression vectors of the present invention can be introduced into host cells to thereby produce proteins or peptides, including fusion proteins or peptides, encoded by nucleic acids as described herein.


The recombinant expression vectors for use in the present invention can be designed for expression of a polypeptide corresponding to a marker of the present invention in prokaryotic (e.g., E. coli) or eukaryotic cells (e.g., insect cells {using baculovirus expression vectors}, yeast cells or mammalian cells). Suitable host cells are discussed further in Goeddel, supra. Alternatively, the recombinant expression vector can be transcribed and translated in vitro, for example using T7 promoter regulatory sequences and T7 polymerase.


Expression of proteins in prokaryotes is most often carried out in E. coli with vectors containing constitutive or inducible promoters directing the expression of either fusion or non-fusion proteins. Fusion vectors add a number of amino acids to a protein encoded therein, usually to the amino terminus of the recombinant protein. Such fusion vectors typically serve three purposes: 1) to increase expression of recombinant protein; 2) to increase the solubility of the recombinant protein; and 3) to aid in the purification of the recombinant protein by acting as a ligand in affinity purification. Often, in fusion expression vectors, a proteolytic cleavage site is introduced at the junction of the fusion moiety and the recombinant protein to enable separation of the recombinant protein from the fusion moiety subsequent to purification of the fusion protein. Such enzymes, and their cognate recognition sequences, include Factor Xa, thrombin and enterokinase. Typical fusion expression vectors include pGEX (Pharmacia Biotech Inc; Smith and Johnson, 1988, Gene 67:31-40), pMAL (New England Biolabs, Beverly, Mass.) and pRIT5 (Pharmacia, Piscataway, N.J.) which fuse glutathione S-transferase (GST), maltose E binding protein, or protein A, respectively, to the target recombinant protein.


Examples of suitable inducible non-fusion E. coli expression vectors include pTrc (Amann et al. (1988) Gene 69:301-315) and pET 11d (Studier et al., p. 60-89, In Gene Expression Technology: Methods in Enzymology vol. 185, Academic Press, San Diego, Calif., 1991). Target biomarker nucleic acid expression from the pTrc vector relies on host RNA polymerase transcription from a hybrid trp-lac fusion promoter. Target biomarker nucleic acid expression from the pET 11d vector relies on transcription from a T7 gn10-lac fusion promoter mediated by a co-expressed viral RNA polymerase (T7 gn1). This viral polymerase is supplied by host strains BL21 (DE3) or HMS174(DE3) from a resident prophage harboring a T7 gn1 gene under the transcriptional control of the lacUV 5 promoter.


One strategy to maximize recombinant protein expression in E. coli is to express the protein in a host bacterium with an impaired capacity to proteolytically cleave the recombinant protein (Gottesman, p. 119-128, In Gene Expression Technology: Methods in Enzymology vol. 185, Academic Press, San Diego, Calif., 1990. Another strategy is to alter the nucleic acid sequence of the nucleic acid to be inserted into an expression vector so that the individual codons for each amino acid are those preferentially utilized in E. coli (Wada et al., (1992) Nucleic Acids Res. 20:2111-2118). Such alteration of nucleic acid sequences of the present invention can be carried out by standard DNA synthesis techniques.


In another embodiment, the expression vector is a yeast expression vector. Examples of vectors for expression in yeast S. cerevisiae include pYepSecl (Baldari et al. (1987) EMBO J. 6:229-234), pMFa (Kurjan and Herskowitz (1982) Cell 30:933-943), pJRY88 (Schultz et al. (1987) Gene 54:113-123), pYES2 (Invitrogen Corporation, San Diego, Calif.), and pPicZ (Invitrogen Corp, San Diego, Calif.).


Alternatively, the expression vector is a baculovirus expression vector. Baculovirus vectors available for expression of proteins in cultured insect cells (e.g., Sf 9 cells) include the pAc series (Smith et al. (1983) Mol. Cell Biol. 3:2156-2165) and the pVL series (Lucklow and Summers (1989) Virology 170:31-39).


In yet another embodiment, a nucleic acid of the present invention is expressed in mammalian cells using a mammalian expression vector. Examples of mammalian expression vectors include pCDM8 (Seed (1987) Nature 329:840) and pMT2PC (Kaufman et al. (1987) EMBO J. 6:187-195). When used in mammalian cells, the expression vector's control functions are often provided by viral regulatory elements. For example, commonly used promoters are derived from polyoma, Adenovirus 2, cytomegalovirus and Simian Virus 40. For other suitable expression systems for both prokaryotic and eukaryotic cells see chapters 16 and 17 of Sambrook et al., supra.


In another embodiment, the recombinant mammalian expression vector is capable of directing expression of the nucleic acid preferentially in a particular cell type (e.g., tissue-specific regulatory elements are used to express the nucleic acid). Tissue-specific regulatory elements are known in the art. Non-limiting examples of suitable tissue-specific promoters include the albumin promoter (liver-specific; Pinkert et al. (1987) Genes Dev. 1:268-277), lymphoid-specific promoters (Calame and Eaton (1988) Adv. Immunol. 43:235-275), in particular promoters of T cell receptors (Winoto and Baltimore (1989) EMBO J. 8:729-733) and immunoglobulins (Banerji et al. (1983) Cell 33:729-740; Queen and Baltimore (1983) Cell 33:741-748), neuron-specific promoters (e.g., the neurofilament promoter; Byrne and Ruddle (1989) Proc. Natl. Acad. Sci. USA 86:5473-5477), pancreas-specific promoters (Edlund et al. (1985) Science 230:912-916), and mammary gland-specific promoters (e.g., milk whey promoter; U.S. Pat. No. 4,873,316 and European Application Publication No. 264,166). Developmentally-regulated promoters are also encompassed, for example the murine hox promoters (Kessel and Gruss (1990) Science 249:374-379) and the α-fetoprotein promoter (Camper and Tilghman (1989) Genes Dev. 3:537-546).


The present invention further provides a recombinant expression vector comprising a DNA molecule cloned into the expression vector in an antisense orientation. That is, the DNA molecule is operably linked to a regulatory sequence in a manner which allows for expression (by transcription of the DNA molecule) of an RNA molecule which is antisense to the mRNA encoding a polypeptide of the present invention. Regulatory sequences operably linked to a nucleic acid cloned in the antisense orientation can be chosen which direct the continuous expression of the antisense RNA molecule in a variety of cell types, for instance viral promoters and/or enhancers, or regulatory sequences can be chosen which direct constitutive, tissue-specific or cell type specific expression of antisense RNA. The antisense expression vector can be in the form of a recombinant plasmid, phagemid, or attenuated virus in which antisense nucleic acids are produced under the control of a high efficiency regulatory region, the activity of which can be determined by the cell type into which the vector is introduced. For a discussion of the regulation of gene expression using antisense genes (see Weintraub et al. (1986) Trends in Genetics, Vol. 1(1)).


Another aspect of the present invention pertains to host cells into which a recombinant expression vector of the present invention has been introduced. The terms “host cell” and “recombinant host cell” are used interchangeably herein. It is understood that such terms refer not only to the particular subject cell but to the progeny or potential progeny of such a cell. Because certain modifications may occur in succeeding generations due to either mutation or environmental influences, such progeny may not, in fact, be identical to the parent cell, but are still included within the scope of the term as used herein.


A host cell can be any prokaryotic (e.g., E. coli) or eukaryotic cell (e.g., insect cells, yeast or mammalian cells).


Vector DNA can be introduced into prokaryotic or eukaryotic cells via conventional transformation or transfection techniques. As used herein, the terms “transformation” and “transfection” are intended to refer to a variety of art-recognized techniques for introducing foreign nucleic acid into a host cell, including calcium phosphate or calcium chloride co-precipitation, DEAE-dextran-mediated transfection, lipofection, or electroporation. Suitable methods for transforming or transfecting host cells can be found in Sambrook, et al. (supra), and other laboratory manuals.


For stable transfection of mammalian cells, it is known that, depending upon the expression vector and transfection technique used, only a small fraction of cells may integrate the foreign DNA into their genome. In order to identify and select these integrants, a gene that encodes a selectable marker (e.g., for resistance to antibiotics) is generally introduced into the host cells along with the gene of interest. Preferred selectable markers include those which confer resistance to drugs, such as G418, hygromycin and methotrexate. Cells stably transfected with the introduced nucleic acid can be identified by drug selection (e.g., cells that have incorporated the selectable marker gene will survive, while the other cells die).


V. Analyzing Biomarker Nucleic Acids and Polypeptides

Biomarker nucleic acids and/or biomarker polypeptides can be analyzed according to the methods described herein and techniques known to the skilled artisan to identify such genetic or expression alterations useful for the present invention including, but not limited to, 1) an alteration in the level of a biomarker transcript or polypeptide, 2) a deletion or addition of one or more nucleotides from a biomarker gene, 4) a substitution of one or more nucleotides of a biomarker gene, 5) aberrant modification of a biomarker gene, such as an expression regulatory region, and the like.


a. Methods for Detection of Copy Number


Methods of evaluating the copy number of a biomarker nucleic acid are well known to those of skill in the art. The presence or absence of chromosomal gain or loss can be evaluated simply by a determination of copy number of the regions or markers identified herein.


In one embodiment, a biological sample is tested for the presence of copy number changes in genomic loci containing the genomic marker. A copy number of at least 3, 4, 5, 6, 7, 8, 9, or 10 is predictive of poorer outcome of anti-immune checkpoint treatment.


Methods of evaluating the copy number of a biomarker locus include, but are not limited to, hybridization-based assays. Hybridization-based assays include, but are not limited to, traditional “direct probe” methods, such as Southern blots, in situ hybridization (e.g., FISH and FISH plus SKY) methods, and “comparative probe” methods, such as comparative genomic hybridization (CGH), e.g., cDNA-based or oligonucleotide-based CGH. The methods can be used in a wide variety of formats including, but not limited to, substrate (e.g. membrane or glass) bound methods or array-based approaches.


In one embodiment, evaluating the biomarker gene copy number in a sample involves a Southern Blot. In a Southern Blot, the genomic DNA (typically fragmented and separated on an electrophoretic gel) is hybridized to a probe specific for the target region. Comparison of the intensity of the hybridization signal from the probe for the target region with control probe signal from analysis of normal genomic DNA (e.g., a non-amplified portion of the same or related cell, tissue, organ, etc.) provides an estimate of the relative copy number of the target nucleic acid. Alternatively, a Northern blot may be utilized for evaluating the copy number of encoding nucleic acid in a sample. In a Northern blot, mRNA is hybridized to a probe specific for the target region. Comparison of the intensity of the hybridization signal from the probe for the target region with control probe signal from analysis of normal RNA (e.g., a non-amplified portion of the same or related cell, tissue, organ, etc.) provides an estimate of the relative copy number of the target nucleic acid. Alternatively, other methods well known in the art to detect RNA can be used, such that higher or lower expression relative to an appropriate control (e.g., a non-amplified portion of the same or related cell tissue, organ, etc.) provides an estimate of the relative copy number of the target nucleic acid.


An alternative means for determining genomic copy number is in situ hybridization (e.g., Angerer (1987) Meth. Enzymol 152: 649). Generally, in situ hybridization comprises the following steps: (1) fixation of tissue or biological structure to be analyzed; (2) prehybridization treatment of the biological structure to increase accessibility of target DNA, and to reduce nonspecific binding; (3) hybridization of the mixture of nucleic acids to the nucleic acid in the biological structure or tissue; (4) post-hybridization washes to remove nucleic acid fragments not bound in the hybridization and (5) detection of the hybridized nucleic acid fragments. The reagent used in each of these steps and the conditions for use vary depending on the particular application. In a typical in situ hybridization assay, cells are fixed to a solid support, typically a glass slide. If a nucleic acid is to be probed, the cells are typically denatured with heat or alkali. The cells are then contacted with a hybridization solution at a moderate temperature to permit annealing of labeled probes specific to the nucleic acid sequence encoding the protein. The targets (e.g., cells) are then typically washed at a predetermined stringency or at an increasing stringency until an appropriate signal to noise ratio is obtained. The probes are typically labeled, e.g., with radioisotopes or fluorescent reporters. In one embodiment, probes are sufficiently long so as to specifically hybridize with the target nucleic acid(s) under stringent conditions. Probes generally range in length from about 200 bases to about 1000 bases. In some applications it is necessary to block the hybridization capacity of repetitive sequences. Thus, in some embodiments, tRNA, human genomic DNA, or Cot-I DNA is used to block non-specific hybridization.


An alternative means for determining genomic copy number is comparative genomic hybridization. In general, genomic DNA is isolated from normal reference cells, as well as from test cells (e.g., tumor cells) and amplified, if necessary. The two nucleic acids are differentially labeled and then hybridized in situ to metaphase chromosomes of a reference cell. The repetitive sequences in both the reference and test DNAs are either removed or their hybridization capacity is reduced by some means, for example by prehybridization with appropriate blocking nucleic acids and/or including such blocking nucleic acid sequences for said repetitive sequences during said hybridization. The bound, labeled DNA sequences are then rendered in a visualizable form, if necessary. Chromosomal regions in the test cells which are at increased or decreased copy number can be identified by detecting regions where the ratio of signal from the two DNAs is altered. For example, those regions that have decreased in copy number in the test cells will show relatively lower signal from the test DNA than the reference compared to other regions of the genome. Regions that have been increased in copy number in the test cells will show relatively higher signal from the test DNA. Where there are chromosomal deletions or multiplications, differences in the ratio of the signals from the two labels will be detected and the ratio will provide a measure of the copy number. In another embodiment of CGH, array CGH (aCGH), the immobilized chromosome element is replaced with a collection of solid support bound target nucleic acids on an array, allowing for a large or complete percentage of the genome to be represented in the collection of solid support bound targets. Target nucleic acids may comprise cDNAs, genomic DNAs, oligonucleotides (e.g., to detect single nucleotide polymorphisms) and the like. Array-based CGH may also be performed with single-color labeling (as opposed to labeling the control and the possible tumor sample with two different dyes and mixing them prior to hybridization, which will yield a ratio due to competitive hybridization of probes on the arrays). In single color CGH, the control is labeled and hybridized to one array and absolute signals are read, and the possible tumor sample is labeled and hybridized to a second array (with identical content) and absolute signals are read. Copy number difference is calculated based on absolute signals from the two arrays. Methods of preparing immobilized chromosomes or arrays and performing comparative genomic hybridization are well known in the art (see, e.g., U.S. Pat. Nos. 6,335,167; 6,197,501; 5,830,645; and 5,665,549 and Albertson (1984) EMBO J. 3: 1227-1234; Pinkel (1988) Proc. Natl. Acad. Sci. USA 85: 9138-9142; EPO Pub. No. 430,402; Methods in Molecular Biology, Vol. 33: In situ Hybridization Protocols, Choo, ed., Humana Press, Totowa, N.J. (1994), etc.) In another embodiment, the hybridization protocol of Pinkel, et al. (1998) Nature Genetics 20: 207-211, or of Kallioniemi (1992) Proc. Natl Acad Sci USA 89:5321-5325 (1992) is used.


In still another embodiment, amplification-based assays can be used to measure copy number. In such amplification-based assays, the nucleic acid sequences act as a template in an amplification reaction (e.g., Polymerase Chain Reaction (PCR). In a quantitative amplification, the amount of amplification product will be proportional to the amount of template in the original sample. Comparison to appropriate controls, e.g. healthy tissue, provides a measure of the copy number.


Methods of “quantitative” amplification are well known to those of skill in the art. For example, quantitative PCR involves simultaneously co-amplifying a known quantity of a control sequence using the same primers. This provides an internal standard that may be used to calibrate the PCR reaction. Detailed protocols for quantitative PCR are provided in Innis, et al. (1990) PCR Protocols, A Guide to Methods and Applications, Academic Press, Inc. N.Y.). Measurement of DNA copy number at microsatellite loci using quantitative PCR analysis is described in Ginzonger, et al. (2000) Cancer Research 60:5405-5409. The known nucleic acid sequence for the genes is sufficient to enable one of skill in the art to routinely select primers to amplify any portion of the gene. Fluorogenic quantitative PCR may also be used in the methods of the present invention. In fluorogenic quantitative PCR, quantitation is based on amount of fluorescence signals, e.g., TaqMan and SYBR green.


Other suitable amplification methods include, but are not limited to, ligase chain reaction (LCR) (see Wu and Wallace (1989) Genomics 4: 560, Landegren, et al. (1988) Science 241:1077, and Barringer et al. (1990) Gene 89: 117), transcription amplification (Kwoh, et al. (1989) Proc. Natl. Acad. Sci. USA 86: 1173), self-sustained sequence replication (Guatelli, et al. (1990) Proc. Nat. Acad. Sci. USA 87: 1874), dot PCR, and linker adapter PCR, etc.


Loss of heterozygosity (LOH) and major copy proportion (MCP) mapping (Wang, Z. C., et al. (2004) Cancer Res 64(1):64-71; Seymour, A. B., et al. (1994) Cancer Res 54, 2761-4; Hahn, S. A., et al. (1995) Cancer Res 55, 4670-5; Kimura, M., et al. (1996) Genes Chromosomes Cancer 17, 88-93; Li et al., (2008) MBC Bioinform. 9, 204-219) may also be used to identify regions of amplification or deletion.


b. Methods for Detection of Biomarker Nucleic Acid Expression


Biomarker expression may be assessed by any of a wide variety of well known methods for detecting expression of a transcribed molecule or protein. Non-limiting examples of such methods include immunological methods for detection of secreted, cell-surface, cytoplasmic, or nuclear proteins, protein purification methods, protein function or activity assays, nucleic acid hybridization methods, nucleic acid reverse transcription methods, and nucleic acid amplification methods.


In preferred embodiments, activity of a particular gene is characterized by a measure of gene transcript (e.g. mRNA), by a measure of the quantity of translated protein, or by a measure of gene product activity. Marker expression can be monitored in a variety of ways, including by detecting mRNA levels, protein levels, or protein activity, any of which can be measured using standard techniques. Detection can involve quantification of the level of gene expression (e.g., genomic DNA, cDNA, mRNA, protein, or enzyme activity), or, alternatively, can be a qualitative assessment of the level of gene expression, in particular in comparison with a control level. The type of level being detected will be clear from the context.


In another embodiment, detecting or determining expression levels of a biomarker and functionally similar homologs thereof, including a fragment or genetic alteration thereof (e.g., in regulatory or promoter regions thereof) comprises detecting or determining RNA levels for the marker of interest. In one embodiment, one or more cells from the subject to be tested are obtained and RNA is isolated from the cells. In a preferred embodiment, a sample of breast tissue cells is obtained from the subject.


In one embodiment, RNA is obtained from a single cell. For example, a cell can be isolated from a tissue sample by laser capture microdissection (LCM). Using this technique, a cell can be isolated from a tissue section, including a stained tissue section, thereby assuring that the desired cell is isolated (see, e.g., Bonner et al. (1997) Science 278:1481; Emmert-Buck et al. (1996) Science 274:998; Fend et al. (1999)Am. J Path. 154:61 and Murakami et al. (2000) Kidney Int. 58:1346). For example, Murakami et al., supra, describe isolation of a cell from a previously immunostained tissue section.


It is also possible to obtain cells from a subject and culture the cells in vitro, such as to obtain a larger population of cells from which RNA can be extracted. Methods for establishing cultures of non-transformed cells, i.e., primary cell cultures, are known in the art.


When isolating RNA from tissue samples or cells from individuals, it may be important to prevent any further changes in gene expression after the tissue or cells has been removed from the subject. Changes in expression levels are known to change rapidly following perturbations, e.g., heat shock or activation with lipopolysaccharide (LPS) or other reagents. In addition, the RNA in the tissue and cells may quickly become degraded. Accordingly, in a preferred embodiment, the tissue or cells obtained from a subject is snap frozen as soon as possible.


RNA can be extracted from the tissue sample by a variety of methods, e.g., the guanidium thiocyanate lysis followed by CsCl centrifugation (Chirgwin et al. (1979) Biochemistry 18:5294-5299). RNA from single cells can be obtained as described in methods for preparing cDNA libraries from single cells, such as those described in Dulac, C. (1998) Curr. Top. Dev. Biol. 36:245 and Jena et al. (1996) J Immunol. Methods 190:199. Care to avoid RNA degradation must be taken, e.g., by inclusion of RNAsin.


The RNA sample can then be enriched in particular species. In one embodiment, poly(A)+RNA is isolated from the RNA sample. In general, such purification takes advantage of the poly-A tails on mRNA. In particular and as noted above, poly-T oligonucleotides may be immobilized within on a solid support to serve as affinity ligands for mRNA. Kits for this purpose are commercially available, e.g., the MessageMaker kit (Life Technologies, Grand Island, N.Y.).


In a preferred embodiment, the RNA population is enriched in marker sequences. Enrichment can be undertaken, e.g., by primer-specific cDNA synthesis, or multiple rounds of linear amplification based on cDNA synthesis and template-directed in vitro transcription (see, e.g., Wang et al. (1989) PNAS 86, 9717; Dulac et al., supra, and Jena et al., supra).


The population of RNA, enriched or not in particular species or sequences, can further be amplified. As defined herein, an “amplification process” is designed to strengthen, increase, or augment a molecule within the RNA. For example, where RNA is mRNA, an amplification process such as RT-PCR can be utilized to amplify the mRNA, such that a signal is detectable or detection is enhanced. Such an amplification process is beneficial particularly when the biological, tissue, or tumor sample is of a small size or volume.


Various amplification and detection methods can be used. For example, it is within the scope of the present invention to reverse transcribe mRNA into cDNA followed by polymerase chain reaction (RT-PCR); or, to use a single enzyme for both steps as described in U.S. Pat. No. 5,322,770, or reverse transcribe mRNA into cDNA followed by symmetric gap ligase chain reaction (RT-AGLCR) as described by R. L. Marshall, et al., PCR Methods and Applications 4: 80-84 (1994). Real time PCR may also be used.


Other known amplification methods which can be utilized herein include but are not limited to the so-called “NASBA” or “3SR” technique described in PNAS USA 87: 1874-1878 (1990) and also described in Nature 350 (No. 6313): 91-92 (1991); Q-beta amplification as described in published European Patent Application (EPA) No. 4544610; strand displacement amplification (as described in G. T. Walker et al., Clin. Chem. 42: 9-13 (1996) and European Patent Application No. 684315; target mediated amplification, as described by PCT Publication WO9322461; PCR; ligase chain reaction (LCR) (see, e.g., Wu and Wallace, Genomics 4, 560 (1989), Landegren et al., Science 241, 1077 (1988)); self-sustained sequence replication (SSR) (see, e.g., Guatelli et al., Proc. Nat. Acad. Sci. USA, 87, 1874 (1990)); and transcription amplification (see, e.g., Kwoh et al., Proc. Natl. Acad. Sci. USA 86, 1173 (1989)).


Many techniques are known in the state of the art for determining absolute and relative levels of gene expression, commonly used techniques suitable for use in the present invention include Northern analysis, RNase protection assays (RPA), microarrays and PCR-based techniques, such as quantitative PCR and differential display PCR. For example, Northern blotting involves running a preparation of RNA on a denaturing agarose gel, and transferring it to a suitable support, such as activated cellulose, nitrocellulose or glass or nylon membranes. Radiolabeled cDNA or RNA is then hybridized to the preparation, washed and analyzed by autoradiography.


In situ hybridization visualization may also be employed, wherein a radioactively labeled antisense RNA probe is hybridized with a thin section of a biopsy sample, washed, cleaved with RNase and exposed to a sensitive emulsion for autoradiography. The samples may be stained with hematoxylin to demonstrate the histological composition of the sample, and dark field imaging with a suitable light filter shows the developed emulsion. Non-radioactive labels such as digoxigenin may also be used.


Alternatively, mRNA expression can be detected on a DNA array, chip or a microarray. Labeled nucleic acids of a test sample obtained from a subject may be hybridized to a solid surface comprising biomarker DNA. Positive hybridization signal is obtained with the sample containing biomarker transcripts. Methods of preparing DNA arrays and their use are well known in the art (see, e.g., U.S. Pat. Nos: 6,618,6796; 6,379,897; 6,664,377; 6,451,536; 548,257; U.S. 20030157485 and Schena et al. (1995) Science 20, 467-470; Gerhold et al. (1999) Trends In Biochem. Sci. 24, 168-173; and Lennon et al. (2000) Drug Discovery Today 5, 59-65, which are herein incorporated by reference in their entirety). Serial Analysis of Gene Expression (SAGE) can also be performed (See for example U.S. Patent Application 20030215858).


To monitor mRNA levels, for example, mRNA is extracted from the biological sample to be tested, reverse transcribed, and fluorescently-labeled cDNA probes are generated. The microarrays capable of hybridizing to marker cDNA are then probed with the labeled cDNA probes, the slides scanned and fluorescence intensity measured. This intensity correlates with the hybridization intensity and expression levels.


Types of probes that can be used in the methods described herein include cDNA, riboprobes, synthetic oligonucleotides and genomic probes. The type of probe used will generally be dictated by the particular situation, such as riboprobes for in situ hybridization, and cDNA for Northern blotting, for example. In one embodiment, the probe is directed to nucleotide regions unique to the RNA. The probes may be as short as is required to differentially recognize marker mRNA transcripts, and may be as short as, for example, 15 bases; however, probes of at least 17, 18, 19 or 20 or more bases can be used. In one embodiment, the primers and probes hybridize specifically under stringent conditions to a DNA fragment having the nucleotide sequence corresponding to the marker. As herein used, the term “stringent conditions” means hybridization will occur only if there is at least 95% identity in nucleotide sequences. In another embodiment, hybridization under “stringent conditions” occurs when there is at least 97% identity between the sequences.


The form of labeling of the probes may be any that is appropriate, such as the use of radioisotopes, for example, 32P and 35S. Labeling with radioisotopes may be achieved, whether the probe is synthesized chemically or biologically, by the use of suitably labeled bases.


In one embodiment, the biological sample contains polypeptide molecules from the test subject. Alternatively, the biological sample can contain mRNA molecules from the test subject or genomic DNA molecules from the test subject.


In another embodiment, the methods further involve obtaining a control biological sample from a control subject, contacting the control sample with a compound or agent capable of detecting marker polypeptide, mRNA, genomic DNA, or fragments thereof, such that the presence of the marker polypeptide, mRNA, genomic DNA, or fragments thereof, is detected in the biological sample, and comparing the presence of the marker polypeptide, mRNA, genomic DNA, or fragments thereof, in the control sample with the presence of the marker polypeptide, mRNA, genomic DNA, or fragments thereof in the test sample.


c. Methods for Detection of Biomarker Protein Expression


The activity or level of a biomarker protein can be detected and/or quantified by detecting or quantifying the expressed polypeptide. The polypeptide can be detected and quantified by any of a number of means well known to those of skill in the art. Aberrant levels of polypeptide expression of the polypeptides encoded by a biomarker nucleic acid and functionally similar homologs thereof, including a fragment or genetic alteration thereof (e.g., in regulatory or promoter regions thereof) are associated with the likelihood of response of a cancer to an immune checkpoint therapy. Any method known in the art for detecting polypeptides can be used. Such methods include, but are not limited to, immunodiffusion, immunoelectrophoresis, radioimmunoassay (MA), enzyme-linked immunosorbent assays (ELISAs), immunofluorescent assays, Western blotting, binder-ligand assays, immunohistochemical techniques, agglutination, complement assays, high performance liquid chromatography (HPLC), thin layer chromatography (TLC), hyperdiffusion chromatography, and the like (e.g., Basic and Clinical Immunology, Sites and Terr, eds., Appleton and Lange, Norwalk, Conn. pp 217-262, 1991 which is incorporated by reference). Preferred are binder-ligand immunoassay methods including reacting antibodies with an epitope or epitopes and competitively displacing a labeled polypeptide or derivative thereof.


For example, ELISA and MA procedures may be conducted such that a desired biomarker protein standard is labeled (with a radioisotope such as 125I or 35S, or an assayable enzyme, such as horseradish peroxidase or alkaline phosphatase), and, together with the unlabelled sample, brought into contact with the corresponding antibody, whereon a second antibody is used to bind the first, and radioactivity or the immobilized enzyme assayed (competitive assay). Alternatively, the biomarker protein in the sample is allowed to react with the corresponding immobilized antibody, radioisotope- or enzyme-labeled anti-biomarker proteinantibody is allowed to react with the system, and radioactivity or the enzyme assayed (ELISA-sandwich assay). Other conventional methods may also be employed as suitable.


The above techniques may be conducted essentially as a “one-step” or “two-step” assay. A “one-step” assay involves contacting antigen with immobilized antibody and, without washing, contacting the mixture with labeled antibody. A “two-step” assay involves washing before contacting, the mixture with labeled antibody. Other conventional methods may also be employed as suitable.


In one embodiment, a method for measuring biomarker protein levels comprises the steps of: contacting a biological specimen with an antibody or variant (e.g., fragment) thereof which selectively binds the biomarker protein, and detecting whether said antibody or variant thereof is bound to said sample and thereby measuring the levels of the biomarker protein.


Enzymatic and radiolabeling of biomarker protein and/or the antibodies may be effected by conventional means. Such means will generally include covalent linking of the enzyme to the antigen or the antibody in question, such as by glutaraldehyde, specifically so as not to adversely affect the activity of the enzyme, by which is meant that the enzyme must still be capable of interacting with its substrate, although it is not necessary for all of the enzyme to be active, provided that enough remains active to permit the assay to be effected. Indeed, some techniques for binding enzyme are non-specific (such as using formaldehyde), and will only yield a proportion of active enzyme.


It is usually desirable to immobilize one component of the assay system on a support, thereby allowing other components of the system to be brought into contact with the component and readily removed without laborious and time-consuming labor. It is possible for a second phase to be immobilized away from the first, but one phase is usually sufficient.


It is possible to immobilize the enzyme itself on a support, but if solid-phase enzyme is required, then this is generally best achieved by binding to antibody and affixing the antibody to a support, models and systems for which are well-known in the art. Simple polyethylene may provide a suitable support.


Enzymes employable for labeling are not particularly limited, but may be selected from the members of the oxidase group, for example. These catalyze production of hydrogen peroxide by reaction with their substrates, and glucose oxidase is often used for its good stability, ease of availability and cheapness, as well as the ready availability of its substrate (glucose). Activity of the oxidase may be assayed by measuring the concentration of hydrogen peroxide formed after reaction of the enzyme-labeled antibody with the substrate under controlled conditions well-known in the art.


Other techniques may be used to detect biomarker protein according to a practitioner's preference based upon the present disclosure. One such technique is Western blotting (Towbin et at., Proc. Nat. Acad. Sci. 76:4350 (1979)), wherein a suitably treated sample is run on an SDS-PAGE gel before being transferred to a solid support, such as a nitrocellulose filter. Anti-biomarker protein antibodies (unlabeled) are then brought into contact with the support and assayed by a secondary immunological reagent, such as labeled protein A or anti-immunoglobulin (suitable labels including 125I, horseradish peroxidase and alkaline phosphatase). Chromatographic detection may also be used.


Immunohistochemistry may be used to detect expression of biomarker protein, e.g., in a biopsy sample. A suitable antibody is brought into contact with, for example, a thin layer of cells, washed, and then contacted with a second, labeled antibody. Labeling may be by fluorescent markers, enzymes, such as peroxidase, avidin, or radiolabelling. The assay is scored visually, using microscopy.


Anti-biomarker protein antibodies, such as intrabodies, may also be used for imaging purposes, for example, to detect the presence of biomarker protein in cells and tissues of a subject. Suitable labels include radioisotopes, iodine (125I, 121I), carbon (14C), sulphur (35S), tritium (3H), indium (112In), and technetium (99mTc), fluorescent labels, such as fluorescein and rhodamine, and biotin.


For in vivo imaging purposes, antibodies are not detectable, as such, from outside the body, and so must be labeled, or otherwise modified, to permit detection. Markers for this purpose may be any that do not substantially interfere with the antibody binding, but which allow external detection. Suitable markers may include those that may be detected by X-radiography, NMR or MM. For X-radiographic techniques, suitable markers include any radioisotope that emits detectable radiation but that is not overtly harmful to the subject, such as barium or cesium, for example. Suitable markers for NMR and MM generally include those with a detectable characteristic spin, such as deuterium, which may be incorporated into the antibody by suitable labeling of nutrients for the relevant hybridoma, for example.


The size of the subject, and the imaging system used, will determine the quantity of imaging moiety needed to produce diagnostic images. In the case of a radioisotope moiety, for a human subject, the quantity of radioactivity injected will normally range from about 5 to 20 millicuries of technetium-99. The labeled antibody or antibody fragment will then preferentially accumulate at the location of cells which contain biomarker protein. The labeled antibody or antibody fragment can then be detected using known techniques.


Antibodies that may be used to detect biomarker protein include any antibody, whether natural or synthetic, full length or a fragment thereof, monoclonal or polyclonal, that binds sufficiently strongly and specifically to the biomarker protein to be detected. An antibody may have a Kd of at most about 10−6M, 10−7M, 10−8M, 10−9M, 10−10M, 10−11M, 10−12M. The phrase “specifically binds” refers to binding of, for example, an antibody to an epitope or antigen or antigenic determinant in such a manner that binding can be displaced or competed with a second preparation of identical or similar epitope, antigen or antigenic determinant. An antibody may bind preferentially to the biomarker protein relative to other proteins, such as related proteins.


Antibodies are commercially available or may be prepared according to methods known in the art.


Antibodies and derivatives thereof that may be used encompass polyclonal or monoclonal antibodies, chimeric, human, humanized, primatized (CDR-grafted), veneered or single-chain antibodies as well as functional fragments, i.e., biomarker protein binding fragments, of antibodies. For example, antibody fragments capable of binding to a biomarker protein or portions thereof, including, but not limited to, Fv, Fab, Fab′ and F(ab′) 2 fragments can be used. Such fragments can be produced by enzymatic cleavage or by recombinant techniques. For example, papain or pepsin cleavage can generate Fab or F(ab′) 2 fragments, respectively. Other proteases with the requisite substrate specificity can also be used to generate Fab or F(ab′) 2 fragments. Antibodies can also be produced in a variety of truncated forms using antibody genes in which one or more stop codons have been introduced upstream of the natural stop site. For example, a chimeric gene encoding a F(ab′) 2 heavy chain portion can be designed to include DNA sequences encoding the CH, domain and hinge region of the heavy chain.


Synthetic and engineered antibodies are described in, e.g., Cabilly et al., U.S. Pat. No. 4,816,567 Cabilly et al., European Patent No. 0,125,023 B1; Boss et al., U.S. Pat. No. 4,816,397; Boss et al., European Patent No. 0,120,694 B1; Neuberger, M. S. et al., WO 86/01533; Neuberger, M. S. et al., European Patent No. 0,194,276 B1; Winter, U.S. Pat. No. 5,225,539; Winter, European Patent No. 0,239,400 B1; Queen et al., European Patent No. 0451216 B1; and Padlan, E. A. et al., EP 0519596 A1. See also, Newman, R. et al., BioTechnology, 10: 1455-1460 (1992), regarding primatized antibody, and Ladner et al., U.S. Pat. No. 4,946,778 and Bird, R. E. et al., Science, 242: 423-426 (1988)) regarding single-chain antibodies. Antibodies produced from a library, e.g., phage display library, may also be used.


In some embodiments, agents that specifically bind to a biomarker protein other than antibodies are used, such as peptides. Peptides that specifically bind to a biomarker protein can be identified by any means known in the art. For example, specific peptide binders of a biomarker protein can be screened for using peptide phage display libraries.


d. Methods for Detection of Biomarker Structural Alterations


The following illustrative methods can be used to identify the presence of a structural alteration in a biomarker nucleic acid and/or biomarker polypeptide molecule in order to, for example, identify PBRM1 proteins that having mutations such as described herein.


In certain embodiments, detection of the alteration involves the use of a probe/primer in a polymerase chain reaction (PCR) (see, e.g., U.S. Pat. Nos. 4,683,195 and 4,683,202), such as anchor PCR or RACE PCR, or, alternatively, in a ligation chain reaction (LCR) (see, e.g., Landegran et al. (1988) Science 241:1077-1080; and Nakazawa et al. (1994) Proc. Natl. Acad. Sci. USA 91:360-364), the latter of which can be particularly useful for detecting point mutations in a biomarker nucleic acid such as a biomarker gene (see Abravaya et al. (1995) Nucleic Acids Res. 23:675-682). This method can include the steps of collecting a sample of cells from a subject, isolating nucleic acid (e.g., genomic, mRNA or both) from the cells of the sample, contacting the nucleic acid sample with one or more primers which specifically hybridize to a biomarker gene under conditions such that hybridization and amplification of the biomarker gene (if present) occurs, and detecting the presence or absence of an amplification product, or detecting the size of the amplification product and comparing the length to a control sample. It is anticipated that PCR and/or LCR may be desirable to use as a preliminary amplification step in conjunction with any of the techniques used for detecting mutations described herein.


Alternative amplification methods include: self sustained sequence replication (Guatelli, J. C. et al. (1990) Proc. Natl. Acad. Sci. USA 87:1874-1878), transcriptional amplification system (Kwoh, D. Y. et al. (1989) Proc. Natl. Acad. Sci. USA 86:1173-1177), Q-Beta Replicase (Lizardi, P. M. et al. (1988) Bio-Technology 6:1197), or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of skill in the art. These detection schemes are especially useful for the detection of nucleic acid molecules if such molecules are present in very low numbers.


In an alternative embodiment, mutations in a biomarker nucleic acid from a sample cell can be identified by alterations in restriction enzyme cleavage patterns. For example, sample and control DNA is isolated, amplified (optionally), digested with one or more restriction endonucleases, and fragment length sizes are determined by gel electrophoresis and compared. Differences in fragment length sizes between sample and control DNA indicates mutations in the sample DNA. Moreover, the use of sequence specific ribozymes (see, for example, U.S. Pat. No. 5,498,531) can be used to score for the presence of specific mutations by development or loss of a ribozyme cleavage site.


In other embodiments, genetic mutations in biomarker nucleic acid can be identified by hybridizing a sample and control nucleic acids, e.g., DNA or RNA, to high density arrays containing hundreds or thousands of oligonucleotide probes (Cronin, M. T. et al. (1996) Hum. Mutat. 7:244-255; Kozal, M. J. et al. (1996) Nat. Med. 2:753-759). For example, biomarker genetic mutations can be identified in two dimensional arrays containing light-generated DNA probes as described in Cronin et al. (1996) supra. Briefly, a first hybridization array of probes can be used to scan through long stretches of DNA in a sample and control to identify base changes between the sequences by making linear arrays of sequential, overlapping probes. This step allows the identification of point mutations. This step is followed by a second hybridization array that allows the characterization of specific mutations by using smaller, specialized probe arrays complementary to all variants or mutations detected. Each mutation array is composed of parallel probe sets, one complementary to the wild-type gene and the other complementary to the mutant gene. Such biomarker genetic mutations can be identified in a variety of contexts, including, for example, germline and somatic mutations.


In yet another embodiment, any of a variety of sequencing reactions known in the art can be used to directly sequence a biomarker gene and detect mutations by comparing the sequence of the sample biomarker with the corresponding wild-type (control) sequence. Examples of sequencing reactions include those based on techniques developed by Maxam and Gilbert (1977) Proc. Natl. Acad. Sci. USA 74:560 or Sanger (1977) Proc. Natl. Acad Sci. USA 74:5463. It is also contemplated that any of a variety of automated sequencing procedures can be utilized when performing the diagnostic assays (Naeve (1995) Biotechniques 19:448-53), including sequencing by mass spectrometry (see, e.g., PCT International Publication No. WO 94/16101; Cohen et al. (1996) Adv. Chromatogr. 36:127-162; and Griffin et al. (1993) Appl. Biochem. Biotechnol. 38:147-159).


Other methods for detecting mutations in a biomarker gene include methods in which protection from cleavage agents is used to detect mismatched bases in RNA/RNA or RNA/DNA heteroduplexes (Myers et al. (1985) Science 230:1242). In general, the art technique of “mismatch cleavage” starts by providing heteroduplexes formed by hybridizing (labeled) RNA or DNA containing the wild-type biomarker sequence with potentially mutant RNA or DNA obtained from a tissue sample. The double-stranded duplexes are treated with an agent which cleaves single-stranded regions of the duplex such as which will exist due to base pair mismatches between the control and sample strands. For instance, RNA/DNA duplexes can be treated with RNase and DNA/DNA hybrids treated with SI nuclease to enzymatically digest the mismatched regions. In other embodiments, either DNA/DNA or RNA/DNA duplexes can be treated with hydroxylamine or osmium tetroxide and with piperidine in order to digest mismatched regions. After digestion of the mismatched regions, the resulting material is then separated by size on denaturing polyacrylamide gels to determine the site of mutation. See, for example, Cotton et al. (1988) Proc. Natl. Acad. Sci. USA 85:4397 and Saleeba et al. (1992) Methods Enzymol. 217:286-295. In a preferred embodiment, the control DNA or RNA can be labeled for detection.


In still another embodiment, the mismatch cleavage reaction employs one or more proteins that recognize mismatched base pairs in double-stranded DNA (so called “DNA mismatch repair” enzymes) in defined systems for detecting and mapping point mutations in biomarker cDNAs obtained from samples of cells. For example, the mutY enzyme of E. coli cleaves A at G/A mismatches and the thymidine DNA glycosylase from HeLa cells cleaves T at G/T mismatches (Hsu et al. (1994) Carcinogenesis 15:1657-1662). According to an exemplary embodiment, a probe based on a biomarker sequence, e.g., a wild-type biomarker treated with a DNA mismatch repair enzyme, and the cleavage products, if any, can be detected from electrophoresis protocols or the like (e.g., U.S. Pat. No. 5,459,039.)


In other embodiments, alterations in electrophoretic mobility can be used to identify mutations in biomarker genes. For example, single strand conformation polymorphism (SSCP) may be used to detect differences in electrophoretic mobility between mutant and wild type nucleic acids (Orita et al. (1989) Proc Natl. Acad. Sci USA 86:2766; see also Cotton (1993) Mutat. Res. 285:125-144 and Hayashi (1992) Genet. Anal. Tech. Appl. 9:73-79). Single-stranded DNA fragments of sample and control biomarker nucleic acids will be denatured and allowed to renature. The secondary structure of single-stranded nucleic acids varies according to sequence, the resulting alteration in electrophoretic mobility enables the detection of even a single base change. The DNA fragments may be labeled or detected with labeled probes. The sensitivity of the assay may be enhanced by using RNA (rather than DNA), in which the secondary structure is more sensitive to a change in sequence. In a preferred embodiment, the subject method utilizes heteroduplex analysis to separate double stranded heteroduplex molecules on the basis of changes in electrophoretic mobility (Keen et al. (1991) Trends Genet. 7:5).


In yet another embodiment the movement of mutant or wild-type fragments in polyacrylamide gels containing a gradient of denaturant is assayed using denaturing gradient gel electrophoresis (DGGE) (Myers et al. (1985) Nature 313:495). When DGGE is used as the method of analysis, DNA will be modified to ensure that it does not completely denature, for example by adding a GC clamp of approximately 40 bp of high-melting GC-rich DNA by PCR. In a further embodiment, a temperature gradient is used in place of a denaturing gradient to identify differences in the mobility of control and sample DNA (Rosenbaum and Reissner (1987) Biophys. Chem. 265:12753).


Examples of other techniques for detecting point mutations include, but are not limited to, selective oligonucleotide hybridization, selective amplification, or selective primer extension. For example, oligonucleotide primers may be prepared in which the known mutation is placed centrally and then hybridized to target DNA under conditions which permit hybridization only if a perfect match is found (Saiki et al. (1986) Nature 324:163; Saiki et al. (1989) Proc. Natl. Acad. Sci. USA 86:6230). Such allele specific oligonucleotides are hybridized to PCR amplified target DNA or a number of different mutations when the oligonucleotides are attached to the hybridizing membrane and hybridized with labeled target DNA.


Alternatively, allele specific amplification technology which depends on selective PCR amplification may be used in conjunction with the instant invention. Oligonucleotides used as primers for specific amplification may carry the mutation of interest in the center of the molecule (so that amplification depends on differential hybridization) (Gibbs et al. (1989) Nucleic Acids Res. 17:2437-2448) or at the extreme 3′ end of one primer where, under appropriate conditions, mismatch can prevent, or reduce polymerase extension (Prossner (1993) Tibtech 11:238). In addition it may be desirable to introduce a novel restriction site in the region of the mutation to create cleavage-based detection (Gasparini et al. (1992) Mol. Cell Probes 6:1). It is anticipated that in certain embodiments amplification may also be performed using Taq ligase for amplification (Barany (1991) Proc. Natl. Acad. Sci USA 88:189). In such cases, ligation will occur only if there is a perfect match at the 3′ end of the 5′ sequence making it possible to detect the presence of a known mutation at a specific site by looking for the presence or absence of amplification.


3. Anti-Cancer Therapies

The efficacy of immune checkpoint therapy is predicted according to biomarker amount and/or activity associated with a cancer in a subject according to the methods described herein. In one embodiment, such immune checkpoint therapy or combinations of therapies (e.g., anti-PD-1 antibodies) can be administered once a subject is indicated as being a likely responder to immune checkpoint therapy. In another embodiment, such immune checkpoint therapy can be avoided once a subject is indicated as not being a likely responder to immune checkpoint therapy and an alternative treatment regimen, such as targeted and/or untargeted anti-cancer therapies can be administered. Combination therapies are also contemplated and can comprise, for example, one or more chemotherapeutic agents and radiation, one or more chemotherapeutic agents and immunotherapy, or one or more chemotherapeutic agents, radiation and chemotherapy, each combination of which can be with immune checkpoint therapy.


The term “targeted therapy” refers to administration of agents that selectively interact with a chosen biomolecule to thereby treat cancer. For example, anti-PBRM1 agents, such as therapeutic monoclonal blocking antibodies, which are well-known in the art and described above, can be used to target tumor microenvironments and cells expressing unwanted PBRM1. Similarly, nivolumab (Opdivo®) is a human IgG4 anti-PD-1 monoclonal antibody that blocks PD-1 activity (see, for example, Wang et al. (2014) Cancer Immunol. Res. 2:846-856; Johnson et al. (2015) Ther. Adv. Med. Oncol. 7:97-106; and Sundar et al. (2015) Ther. Adv. Med. Oncol. 7:85-96).


Immunotherapy is one form of targeted therapy that may comprise, for example, the use of cancer vaccines and/or sensitized antigen presenting cells. For example, an oncolytic virus is a virus that is able to infect and lyse cancer cells, while leaving normal cells unharmed, making them potentially useful in cancer therapy. Replication of oncolytic viruses both facilitates tumor cell destruction and also produces dose amplification at the tumor site. They may also act as vectors for anticancer genes, allowing them to be specifically delivered to the tumor site. The immunotherapy can involve passive immunity for short-term protection of a host, achieved by the administration of pre-formed antibody directed against a cancer antigen or disease antigen (e.g., administration of a monoclonal antibody, optionally linked to a chemotherapeutic agent or toxin, to a tumor antigen). Immunotherapy can also focus on using the cytotoxic lymphocyte-recognized epitopes of cancer cell lines. Alternatively, antisense polynucleotides, ribozymes, RNA interference molecules, triple helix polynucleotides and the like, can be used to selectively modulate biomolecules that are linked to the initiation, progression, and/or pathology of a tumor or cancer.


The term “untargeted therapy” referes to administration of agents that do not selectively interact with a chosen biomolecule yet treat cancer. Representative examples of untargeted therapies include, without limitation, chemotherapy, gene therapy, and radiation therapy.


In one embodiment, chemotherapy is used. Chemotherapy includes the administration of a chemotherapeutic agent. Such a chemotherapeutic agent may be, but is not limited to, those selected from among the following groups of compounds: platinum compounds, cytotoxic antibiotics, antimetabolities, anti-mitotic agents, alkylating agents, arsenic compounds, DNA topoisomerase inhibitors, taxanes, nucleoside analogues, plant alkaloids, and toxins; and synthetic derivatives thereof. Exemplary compounds include, but are not limited to, alkylating agents: cisplatin, treosulfan, and trofosfamide; plant alkaloids: vinblastine, paclitaxel, docetaxol; DNA topoisomerase inhibitors: teniposide, crisnatol, and mitomycin; anti-folates: methotrexate, mycophenolic acid, and hydroxyurea; pyrimidine analogs: 5-fluorouracil, doxifluridine, and cytosine arabinoside; purine analogs: mercaptopurine and thioguanine; DNA antimetabolites: 2′-deoxy-5-fluorouridine, aphidicolin glycinate, and pyrazoloimidazole; and antimitotic agents: halichondrin, colchicine, and rhizoxin. Compositions comprising one or more chemotherapeutic agents (e.g., FLAG, CHOP) may also be used. FLAG comprises fludarabine, cytosine arabinoside (Ara-C) and G-CSF. CHOP comprises cyclophosphamide, vincristine, doxorubicin, and prednisone. In another embodiments, PARP (e.g., PARP-1 and/or PARP-2) inhibitors are used and such inhibitors are well known in the art (e.g., Olaparib, ABT-888, BSI-201, BGP-15 (N-Gene Research Laboratories, Inc.); INO-1001 (Inotek Pharmaceuticals Inc.); PJ34 (Soriano et al., 2001; Pacher et al., 2002b); 3-aminobenzamide (Trevigen); 4-amino-1, 8-naphthalimide; (Trevigen); 6(5H)-phenanthridinone (Trevigen); benzamide (U.S. Pat. Re. 36,397); and NU1025 (Bowman et al.). The mechanism of action is generally related to the ability of PARP inhibitors to bind PARP and decrease its activity. PARP catalyzes the conversion of .beta.-nicotinamide adenine dinucleotide (NAD+) into nicotinamide and poly-ADP-ribose (PAR). Both poly (ADP-ribose) and PARP have been linked to regulation of transcription, cell proliferation, genomic stability, and carcinogenesis (Bouchard V. J. et. al. Experimental Hematology, Volume 31, Number 6, June 2003, pp. 446-454(9); Herceg Z.; Wang Z.-Q. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, Volume 477, Number 1, 2 Jun. 2001, pp. 97-110(14)). Poly(ADP-ribose) polymerase 1 (PARP1) is a key molecule in the repair of DNA single-strand breaks (SSBs) (de Murcia J. et al. 1997. Proc Natl Acad Sci USA 94:7303-7307; Schreiber V, Dantzer F, Ame J C, de Murcia G (2006) Nat Rev Mol Cell Biol 7:517-528; Wang Z Q, et al. (1997) Genes Dev 11:2347-2358). Knockout of SSB repair by inhibition of PARP1 function induces DNA double-strand breaks (DSBs) that can trigger synthetic lethality in cancer cells with defective homology-directed DSB repair (Bryant H E, et al. (2005) Nature 434:913-917; Farmer H, et al. (2005) Nature 434:917-921). The foregoing examples of chemotherapeutic agents are illustrative, and are not intended to be limiting.


In another embodiment, radiation therapy is used. The radiation used in radiation therapy can be ionizing radiation. Radiation therapy can also be gamma rays, X-rays, or proton beams. Examples of radiation therapy include, but are not limited to, external-beam radiation therapy, interstitial implantation of radioisotopes (I-125, palladium, iridium), radioisotopes such as strontium-89, thoracic radiation therapy, intraperitoneal P-32 radiation therapy, and/or total abdominal and pelvic radiation therapy. For a general overview of radiation therapy, see Hellman, Chapter 16: Principles of Cancer Management: Radiation Therapy, 6th edition, 2001, DeVita et al., eds., J. B. Lippencott Company, Philadelphia. The radiation therapy can be administered as external beam radiation or teletherapy wherein the radiation is directed from a remote source. The radiation treatment can also be administered as internal therapy or brachytherapy wherein a radioactive source is placed inside the body close to cancer cells or a tumor mass. Also encompassed is the use of photodynamic therapy comprising the administration of photosensitizers, such as hematoporphyrin and its derivatives, Vertoporfin (BPD-MA), phthalocyanine, photosensitizer Pc4, demethoxy-hypocrellin A; and 2BA-2-DMHA.


In another embodiment, hormone therapy is used. Hormonal therapeutic treatments can comprise, for example, hormonal agonists, hormonal antagonists (e.g., flutamide, bicalutamide, tamoxifen, raloxifene, leuprolide acetate (LUPRON), LH-RH antagonists), inhibitors of hormone biosynthesis and processing, and steroids (e.g., dexamethasone, retinoids, deltoids, betamethasone, cortisol, cortisone, prednisone, dehydrotestosterone, glucocorticoids, mineralocorticoids, estrogen, testosterone, progestins), vitamin A derivatives (e.g., all-trans retinoic acid (ATRA)); vitamin D3 analogs; antigestagens (e.g., mifepristone, onapristone), or antiandrogens (e.g., cyproterone acetate).


In another embodiment, hyperthermia, a procedure in which body tissue is exposed to high temperatures (up to 106° F.) is used. Heat may help shrink tumors by damaging cells or depriving them of substances they need to live. Hyperthermia therapy can be local, regional, and whole-body hyperthermia, using external and internal heating devices. Hyperthermia is almost always used with other forms of therapy (e.g., radiation therapy, chemotherapy, and biological therapy) to try to increase their effectiveness. Local hyperthermia refers to heat that is applied to a very small area, such as a tumor. The area may be heated externally with high-frequency waves aimed at a tumor from a device outside the body. To achieve internal heating, one of several types of sterile probes may be used, including thin, heated wires or hollow tubes filled with warm water; implanted microwave antennae; and radiofrequency electrodes. In regional hyperthermia, an organ or a limb is heated. Magnets and devices that produce high energy are placed over the region to be heated. In another approach, called perfusion, some of the patient's blood is removed, heated, and then pumped (perfused) into the region that is to be heated internally. Whole-body heating is used to treat metastatic cancer that has spread throughout the body. It can be accomplished using warm-water blankets, hot wax, inductive coils (like those in electric blankets), or thermal chambers (similar to large incubators). Hyperthermia does not cause any marked increase in radiation side effects or complications. Heat applied directly to the skin, however, can cause discomfort or even significant local pain in about half the patients treated. It can also cause blisters, which generally heal rapidly.


In still another embodiment, photodynamic therapy (also called PDT, photoradiation therapy, phototherapy, or photochemotherapy) is used for the treatment of some types of cancer. It is based on the discovery that certain chemicals known as photosensitizing agents can kill one-celled organisms when the organisms are exposed to a particular type of light. PDT destroys cancer cells through the use of a fixed-frequency laser light in combination with a photosensitizing agent. In PDT, the photosensitizing agent is injected into the bloodstream and absorbed by cells all over the body. The agent remains in cancer cells for a longer time than it does in normal cells. When the treated cancer cells are exposed to laser light, the photosensitizing agent absorbs the light and produces an active form of oxygen that destroys the treated cancer cells. Light exposure must be timed carefully so that it occurs when most of the photosensitizing agent has left healthy cells but is still present in the cancer cells. The laser light used in PDT can be directed through a fiber-optic (a very thin glass strand). The fiber-optic is placed close to the cancer to deliver the proper amount of light. The fiber-optic can be directed through a bronchoscope into the lungs for the treatment of lung cancer or through an endoscope into the esophagus for the treatment of esophageal cancer. An advantage of PDT is that it causes minimal damage to healthy tissue. However, because the laser light currently in use cannot pass through more than about 3 centimeters of tissue (a little more than one and an eighth inch), PDT is mainly used to treat tumors on or just under the skin or on the lining of internal organs. Photodynamic therapy makes the skin and eyes sensitive to light for 6 weeks or more after treatment. Patients are advised to avoid direct sunlight and bright indoor light for at least 6 weeks. If patients must go outdoors, they need to wear protective clothing, including sunglasses. Other temporary side effects of PDT are related to the treatment of specific areas and can include coughing, trouble swallowing, abdominal pain, and painful breathing or shortness of breath. In December 1995, the U.S. Food and Drug Administration (FDA) approved a photosensitizing agent called porfimer sodium, or Photofrin®, to relieve symptoms of esophageal cancer that is causing an obstruction and for esophageal cancer that cannot be satisfactorily treated with lasers alone. In January 1998, the FDA approved porfimer sodium for the treatment of early nonsmall cell lung cancer in patients for whom the usual treatments for lung cancer are not appropriate. The National Cancer Institute and other institutions are supporting clinical trials (research studies) to evaluate the use of photodynamic therapy for several types of cancer, including cancers of the bladder, brain, larynx, and oral cavity.


In yet another embodiment, laser therapy is used to harness high-intensity light to destroy cancer cells. This technique is often used to relieve symptoms of cancer such as bleeding or obstruction, especially when the cancer cannot be cured by other treatments. It may also be used to treat cancer by shrinking or destroying tumors. The term “laser” stands for light amplification by stimulated emission of radiation. Ordinary light, such as that from a light bulb, has many wavelengths and spreads in all directions. Laser light, on the other hand, has a specific wavelength and is focused in a narrow beam. This type of high-intensity light contains a lot of energy. Lasers are very powerful and may be used to cut through steel or to shape diamonds. Lasers also can be used for very precise surgical work, such as repairing a damaged retina in the eye or cutting through tissue (in place of a scalpel). Although there are several different kinds of lasers, only three kinds have gained wide use in medicine: Carbon dioxide (CO2) laser—This type of laser can remove thin layers from the skin's surface without penetrating the deeper layers. This technique is particularly useful in treating tumors that have not spread deep into the skin and certain precancerous conditions. As an alternative to traditional scalpel surgery, the CO2 laser is also able to cut the skin. The laser is used in this way to remove skin cancers. Neodymium:yttrium-aluminum-garnet (Nd:YAG) laser—Light from this laser can penetrate deeper into tissue than light from the other types of lasers, and it can cause blood to clot quickly. It can be carried through optical fibers to less accessible parts of the body. This type of laser is sometimes used to treat throat cancers. Argon laser—This laser can pass through only superficial layers of tissue and is therefore useful in dermatology and in eye surgery. It also is used with light-sensitive dyes to treat tumors in a procedure known as photodynamic therapy (PDT). Lasers have several advantages over standard surgical tools, including: Lasers are more precise than scalpels. Tissue near an incision is protected, since there is little contact with surrounding skin or other tissue. The heat produced by lasers sterilizes the surgery site, thus reducing the risk of infection. Less operating time may be needed because the precision of the laser allows for a smaller incision. Healing time is often shortened; since laser heat seals blood vessels, there is less bleeding, swelling, or scarring. Laser surgery may be less complicated. For example, with fiber optics, laser light can be directed to parts of the body without making a large incision. More procedures may be done on an outpatient basis. Lasers can be used in two ways to treat cancer: by shrinking or destroying a tumor with heat, or by activating a chemical—known as a photosensitizing agent—that destroys cancer cells. In PDT, a photosensitizing agent is retained in cancer cells and can be stimulated by light to cause a reaction that kills cancer cells. CO2 and Nd:YAG lasers are used to shrink or destroy tumors. They may be used with endoscopes, tubes that allow physicians to see into certain areas of the body, such as the bladder. The light from some lasers can be transmitted through a flexible endoscope fitted with fiber optics. This allows physicians to see and work in parts of the body that could not otherwise be reached except by surgery and therefore allows very precise aiming of the laser beam. Lasers also may be used with low-power microscopes, giving the doctor a clear view of the site being treated. Used with other instruments, laser systems can produce a cutting area as small as 200 microns in diameter—less than the width of a very fine thread. Lasers are used to treat many types of cancer. Laser surgery is a standard treatment for certain stages of glottis (vocal cord), cervical, skin, lung, vaginal, vulvar, and penile cancers. In addition to its use to destroy the cancer, laser surgery is also used to help relieve symptoms caused by cancer (palliative care). For example, lasers may be used to shrink or destroy a tumor that is blocking a patient's trachea (windpipe), making it easier to breathe. It is also sometimes used for palliation in colorectal and anal cancer. Laser-induced interstitial thermotherapy (LITT) is one of the most recent developments in laser therapy. LITT uses the same idea as a cancer treatment called hyperthermia; that heat may help shrink tumors by damaging cells or depriving them of substances they need to live. In this treatment, lasers are directed to interstitial areas (areas between organs) in the body. The laser light then raises the temperature of the tumor, which damages or destroys cancer cells.


The duration and/or dose of treatment with anti-immune checkpoint therapies may vary according to the particular anti-immune checkpoint agent or combination thereof. An appropriate treatment time for a particular cancer therapeutic agent will be appreciated by the skilled artisan. The present invention contemplates the continued assessment of optimal treatment schedules for each cancer therapeutic agent, where the phenotype of the cancer of the subject as determined by the methods of the present invention is a factor in determining optimal treatment doses and schedules.


Any means for the introduction of a polynucleotide into mammals, human or non-human, or cells thereof may be adapted to the practice of this invention for the delivery of the various constructs of the present invention into the intended recipient. In one embodiment of the present invention, the DNA constructs are delivered to cells by transfection, i.e., by delivery of “naked” DNA or in a complex with a colloidal dispersion system. A colloidal system includes macromolecule complexes, nanocapsules, microspheres, beads, and lipid-based systems including oil-in-water emulsions, micelles, mixed micelles, and liposomes. The preferred colloidal system of this invention is a lipid-complexed or liposome-formulated DNA. In the former approach, prior to formulation of DNA, e.g., with lipid, a plasmid containing a transgene bearing the desired DNA constructs may first be experimentally optimized for expression (e.g., inclusion of an intron in the 5′ untranslated region and elimination of unnecessary sequences (Felgner, et al., Ann NY Acad Sci 126-139, 1995). Formulation of DNA, e.g. with various lipid or liposome materials, may then be effected using known methods and materials and delivered to the recipient mammal. See, e.g., Canonico et al, Am J Respir Cell Mol Biol 10:24-29, 1994; Tsan et al, Am J Physiol 268; Alton et al., Nat Genet. 5:135-142, 1993 and U.S. Pat. No. 5,679,647 by Carson et al.


The targeting of liposomes can be classified based on anatomical and mechanistic factors. Anatomical classification is based on the level of selectivity, for example, organ-specific, cell-specific, and organelle-specific. Mechanistic targeting can be distinguished based upon whether it is passive or active. Passive targeting utilizes the natural tendency of liposomes to distribute to cells of the reticulo-endothelial system (RES) in organs, which contain sinusoidal capillaries. Active targeting, on the other hand, involves alteration of the liposome by coupling the liposome to a specific ligand such as a monoclonal antibody, sugar, glycolipid, or protein, or by changing the composition or size of the liposome in order to achieve targeting to organs and cell types other than the naturally occurring sites of localization.


The surface of the targeted delivery system may be modified in a variety of ways. In the case of a liposomal targeted delivery system, lipid groups can be incorporated into the lipid bilayer of the liposome in order to maintain the targeting ligand in stable association with the liposomal bilayer. Various linking groups can be used for joining the lipid chains to the targeting ligand. Naked DNA or DNA associated with a delivery vehicle, e.g., liposomes, can be administered to several sites in a subject (see below).


Nucleic acids can be delivered in any desired vector. These include viral or non-viral vectors, including adenovirus vectors, adeno-associated virus vectors, retrovirus vectors, lentivirus vectors, and plasmid vectors. Exemplary types of viruses include HSV (herpes simplex virus), AAV (adeno associated virus), HIV (human immunodeficiency virus), BIV (bovine immunodeficiency virus), and MLV (murine leukemia virus). Nucleic acids can be administered in any desired format that provides sufficiently efficient delivery levels, including in virus particles, in liposomes, in nanoparticles, and complexed to polymers.


The nucleic acids encoding a protein or nucleic acid of interest may be in a plasmid or viral vector, or other vector as is known in the art. Such vectors are well known and any can be selected for a particular application. In one embodiment of the present invention, the gene delivery vehicle comprises a promoter and a demethylase coding sequence. Preferred promoters are tissue-specific promoters and promoters which are activated by cellular proliferation, such as the thymidine kinase and thymidylate synthase promoters. Other preferred promoters include promoters which are activatable by infection with a virus, such as the α- and β-interferon promoters, and promoters which are activatable by a hormone, such as estrogen. Other promoters which can be used include the Moloney virus LTR, the CMV promoter, and the mouse albumin promoter. A promoter may be constitutive or inducible.


In another embodiment, naked polynucleotide molecules are used as gene delivery vehicles, as described in WO 90/11092 and U.S. Pat. No. 5,580,859. Such gene delivery vehicles can be either growth factor DNA or RNA and, in certain embodiments, are linked to killed adenovirus. Curiel et al., Hum. Gene. Ther. 3:147-154, 1992. Other vehicles which can optionally be used include DNA-ligand (Wu et al., J. Biol. Chem. 264:16985-16987, 1989), lipid-DNA combinations (Felgner et al., Proc. Natl. Acad. Sci. USA 84:7413 7417, 1989), liposomes (Wang et al., Proc. Natl. Acad. Sci. 84:7851-7855, 1987) and microprojectiles (Williams et al., Proc. Natl. Acad. Sci. 88:2726-2730, 1991). A gene delivery vehicle can optionally comprise viral sequences such as a viral origin of replication or packaging signal. These viral sequences can be selected from viruses such as astrovirus, coronavirus, orthomyxovirus, papovavirus, paramyxovirus, parvovirus, picornavirus, poxvirus, retrovirus, togavirus or adenovirus. In a preferred embodiment, the growth factor gene delivery vehicle is a recombinant retroviral vector. Recombinant retroviruses and various uses thereof have been described in numerous references including, for example, Mann et al., Cell 33:153, 1983, Cane and Mulligan, Proc. Nat'l. Acad. Sci. USA 81:6349, 1984, Miller et al., Human Gene Therapy 1:5-14, 1990, U.S. Pat. Nos. 4,405,712, 4,861,719, and 4,980,289, and PCT Application Nos. WO 89/02,468, WO 89/05,349, and WO 90/02,806. Numerous retroviral gene delivery vehicles can be utilized in the present invention, including for example those described in EP 0,415,731; WO 90/07936; WO 94/03622; WO 93/25698; WO 93/25234; U.S. Pat. No. 5,219,740; WO 9311230; WO 9310218; Vile and Hart, Cancer Res. 53:3860-3864, 1993; Vile and Hart, Cancer Res. 53:962-967, 1993; Ram et al., Cancer Res. 53:83-88, 1993; Takamiya et al., J. Neurosci. Res. 33:493-503, 1992; Baba et al., J. Neurosurg. 79:729-735, 1993 (U.S. Pat. No. 4,777,127, GB 2,200,651, EP 0,345,242 and WO91/02805).


Other viral vector systems that can be used to deliver a polynucleotide of the present invention have been derived from herpes virus, e.g., Herpes Simplex Virus (U.S. Pat. No. 5,631,236 by Woo et al., issued May 20, 1997 and WO 00/08191 by Neurovex), vaccinia virus (Ridgeway (1988) Ridgeway, “Mammalian expression vectors,” In: Rodriguez R L, Denhardt D T, ed. Vectors: A survey of molecular cloning vectors and their uses. Stoneham: Butterworth; Baichwal and Sugden (1986) “Vectors for gene transfer derived from animal DNA viruses: Transient and stable expression of transferred genes,” In: Kucherlapati R, ed. Gene transfer. New York: Plenum Press; Coupar et al. (1988) Gene, 68:1-10), and several RNA viruses. Preferred viruses include an alphavirus, a poxivirus, an arena virus, a vaccinia virus, a polio virus, and the like. They offer several attractive features for various mammalian cells (Friedmann (1989) Science, 244:1275-1281; Ridgeway, 1988, supra; Baichwal and Sugden, 1986, supra; Coupar et al., 1988; Horwich et al. (1990) J. Virol., 64:642-650).


In other embodiments, target DNA in the genome can be manipulated using well-known methods in the art. For example, the target DNA in the genome can be manipulated by deletion, insertion, and/or mutation are retroviral insertion, artificial chromosome techniques, gene insertion, random insertion with tissue specific promoters, gene targeting, transposable elements and/or any other method for introducing foreign DNA or producing modified DNA/modified nuclear DNA. Other modification techniques include deleting DNA sequences from a genome and/or altering nuclear DNA sequences. Nuclear DNA sequences, for example, may be altered by site-directed mutagenesis.


In other embodiments, recombinant biomarker polypeptides, and fragments thereof, can be administered to subjects. In some embodiments, fusion proteins can be constructed and administered which have enhanced biological properties. In addition, the biomarker polypeptides, and fragment thereof, can be modified according to well-known pharmacological methods in the art (e.g., pegylation, glycosylation, oligomerization, etc.) in order to further enhance desirable biological activities, such as increased bioavailability and decreased proteolytic degradation.


4. Clincal Efficacy

Clinical efficacy can be measured by any method known in the art. For example, the response to a therapy, such as anti-immune checkpoint therapies, relates to any response of the cancer, e.g., a tumor, to the therapy, preferably to a change in tumor mass and/or volume after initiation of neoadjuvant or adjuvant chemotherapy. Tumor response may be assessed in a neoadjuvant or adjuvant situation where the size of a tumor after systemic intervention can be compared to the initial size and dimensions as measured by CT, PET, mammogram, ultrasound or palpation and the cellularity of a tumor can be estimated histologically and compared to the cellularity of a tumor biopsy taken before initiation of treatment. Response may also be assessed by caliper measurement or pathological examination of the tumor after biopsy or surgical resection. Response may be recorded in a quantitative fashion like percentage change in tumor volume or cellularity or using a semi-quantitative scoring system such as residual cancer burden (Symmans et al., J. Clin. Oncol. (2007) 25:4414-4422) or Miller-Payne score (Ogston et al., (2003) Breast (Edinburgh, Scotland) 12:320-327) in a qualitative fashion like “pathological complete response” (pCR), “clinical complete remission” (cCR), “clinical partial remission” (cPR), “clinical stable disease” (cSD), “clinical progressive disease” (cPD) or other qualitative criteria. Assessment of tumor response may be performed early after the onset of neoadjuvant or adjuvant therapy, e.g., after a few hours, days, weeks or preferably after a few months. A typical endpoint for response assessment is upon termination of neoadjuvant chemotherapy or upon surgical removal of residual tumor cells and/or the tumor bed.


In some embodiments, clinical efficacy of the therapeutic treatments described herein may be determined by measuring the clinical benefit rate (CBR). The clinical benefit rate is measured by determining the sum of the percentage of patients who are in complete remission (CR), the number of patients who are in partial remission (PR) and the number of patients having stable disease (SD) at a time point at least 6 months out from the end of therapy. The shorthand for this formula is CBR=CR+PR+SD over 6 months. In some embodiments, the CBR for a particular anti-immune checkpoint therapeutic regimen is at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or more.


Additional criteria for evaluating the response to anti-immune checkpoint therapies are related to “survival,” which includes all of the following: survival until mortality, also known as overall survival (wherein said mortality may be either irrespective of cause or tumor related); “recurrence-free survival” (wherein the term recurrence shall include both localized and distant recurrence); metastasis free survival; disease free survival (wherein the term disease shall include cancer and diseases associated therewith). The length of said survival may be calculated by reference to a defined start point (e.g., time of diagnosis or start of treatment) and end point (e.g., death, recurrence or metastasis). In addition, criteria for efficacy of treatment can be expanded to include response to chemotherapy, probability of survival, probability of metastasis within a given time period, and probability of tumor recurrence.


For example, in order to determine appropriate threshold values, a particular anti-immune checkpoint therapeutic regimen can be administered to a population of subjects and the outcome can be correlated to biomarker measurements that were determined prior to administration of any immune checkpoint therapy. The outcome measurement may be pathologic response to therapy given in the neoadjuvant setting. Alternatively, outcome measures, such as overall survival and disease-free survival can be monitored over a period of time for subjects following immune checkpoint therapy for whom biomarker measurement values are known. In certain embodiments, the same doses of anti-immune checkpoint agents are administered to each subject. In related embodiments, the doses administered are standard doses known in the art for anti-immune checkpoint agents. The period of time for which subjects are monitored can vary. For example, subjects may be monitored for at least 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 25, 30, 35, 40, 45, 50, 55, or 60 months. Biomarker measurement threshold values that correlate to outcome of an immune checkpoint therapy can be determined using methods such as those described in the Examples section.


5. Further Uses and Methods of the Present Invention

The methods described herein can be used in a variety of diagnostic, prognostic, and therapeutic applications. In any method described herein, such as a diagnostic method, prognostic method, therapeutic method, or combination thereof, all steps of the method can be performed by a single actor or, alternatively, by more than one actor. For example, diagnosis can be performed directly by the actor providing therapeutic treatment. Alternatively, a person providing a therapeutic agent can request that a diagnostic assay be performed. The diagnostician and/or the therapeutic interventionist can interpret the diagnostic assay results to determine a therapeutic strategy. Similarly, such alternative processes can apply to other assays, such as prognostic assays. The compositions described herein can also be used in a variety of diagnostic, prognostic, and therapeutic applications regarding biomarkers described herein, such as those listed in Table 1. Moreover, any method of diagnosis, prognosis, prevention, and the like described herein can be be applied to a therapy or test agent of interest, such as immune checkpoint therapies, EGFR therapies, anti-cancer therapies, and the like.


a. Screening Methods


One aspect of the present invention relates to screening assays, including non-cell based assays. In one embodiment, the assays provide a method for identifying whether a cancer is likely to respond to immune checkpoint therapy and/or whether an agent can inhibit the growth of or kill a cancer cell that is unlikely to respond to immune checkpoint therapy.


In one embodiment, the present invention relates to assays for screening test agents which bind to, or modulate the biological activity of, at least one biomarker listed in Table 1. In one embodiment, a method for identifying such an agent entails determining the ability of the agent to modulate, e.g. inhibit, the at least one biomarker listed in Table 1.


In one embodiment, an assay is a cell-free or cell-based assay, comprising contacting at least one biomarker listed in Table 1, with a test agent, and determining the ability of the test agent to modulate (e.g. inhibit) the enzymatic activity of the biomarker, such as by measuring direct binding of substrates or by measuring indirect parameters as described below.


For example, in a direct binding assay, biomarker protein (or their respective target polypeptides or molecules) can be coupled with a radioisotope or enzymatic label such that binding can be determined by detecting the labeled protein or molecule in a complex. For example, the targets can be labeled with 125I, 35S, 14C, or 3H, either directly or indirectly, and the radioisotope detected by direct counting of radioemmission or by scintillation counting. Alternatively, the targets can be enzymatically labeled with, for example, horseradish peroxidase, alkaline phosphatase, or luciferase, and the enzymatic label detected by determination of conversion of an appropriate substrate to product. Determining the interaction between biomarker and substrate can also be accomplished using standard binding or enzymatic analysis assays. In one or more embodiments of the above described assay methods, it may be desirable to immobilize polypeptides or molecules to facilitate separation of complexed from uncomplexed forms of one or both of the proteins or molecules, as well as to accommodate automation of the assay.


Binding of a test agent to a target can be accomplished in any vessel suitable for containing the reactants. Non-limiting examples of such vessels include microtiter plates, test tubes, and micro-centrifuge tubes. Immobilized forms of the antibodies of the present invention can also include antibodies bound to a solid phase like a porous, microporous (with an average pore diameter less than about one micron) or macroporous (with an average pore diameter of more than about 10 microns) material, such as a membrane, cellulose, nitrocellulose, or glass fibers; a bead, such as that made of agarose or polyacrylamide or latex; or a surface of a dish, plate, or well, such as one made of polystyrene.


In an alternative embodiment, determining the ability of the agent to modulate the interaction between the biomarker and a substrate or a biomarker and its natural binding partner can be accomplished by determining the ability of the test agent to modulate the activity of a polypeptide or other product that functions downstream or upstream of its position within the signaling pathway (e.g., feedback loops). Such feedback loops are well-known in the art (see, for example, Chen and Guillemin (2009) Int. J. Tryptophan Res. 2:1-19).


The present invention further pertains to novel agents identified by the above-described screening assays. Accordingly, it is within the scope of this invention to further use an agent identified as described herein in an appropriate animal model. For example, an agent identified as described herein can be used in an animal model to determine the efficacy, toxicity, or side effects of treatment with such an agent. Alternatively, an antibody identified as described herein can be used in an animal model to determine the mechanism of action of such an agent.


b. Predictive Medicine


The present invention also pertains to the field of predictive medicine in which diagnostic assays, prognostic assays, and monitoring clinical trials are used for prognostic (predictive) purposes to thereby treat an individual prophylactically. Accordingly, one aspect of the present invention relates to diagnostic assays for determining the amount and/or activity level of a biomarker listed in Table 1 in the context of a biological sample (e.g., blood, serum, cells, or tissue) to thereby determine whether an individual afflicted with a cancer is likely to respond to immune checkpoint therapy, whether in an original or recurrent cancer. Such assays can be used for prognostic or predictive purpose to thereby prophylactically treat an individual prior to the onset or after recurrence of a disorder characterized by or associated with biomarker polypeptide, nucleic acid expression or activity. The skilled artisan will appreciate that any method can use one or more (e.g., combinations) of biomarkers listed in Table 1.


Another aspect of the present invention pertains to monitoring the influence of agents (e.g., drugs, compounds, and small nucleic acid-based molecules) on the expression or activity of a biomarker listed in Table 1. These and other agents are described in further detail in the following sections.


The skilled artisan will also appreciated that, in certain embodiments, the methods of the present invention implement a computer program and computer system. For example, a computer program can be used to perform the algorithms described herein. A computer system can also store and manipulate data generated by the methods of the present invention which comprises a plurality of biomarker signal changes/profiles which can be used by a computer system in implementing the methods of this invention. In certain embodiments, a computer system receives biomarker expression data; (ii) stores the data; and (iii) compares the data in any number of ways described herein (e.g., analysis relative to appropriate controls) to determine the state of informative biomarkers from cancerous or pre-cancerous tissue. In other embodiments, a computer system (i) compares the determined expression biomarker level to a threshold value; and (ii) outputs an indication of whether said biomarker level is significantly modulated (e.g., above or below) the threshold value, or a phenotype based on said indication.


In certain embodiments, such computer systems are also considered part of the present invention. Numerous types of computer systems can be used to implement the analytic methods of this invention according to knowledge possessed by a skilled artisan in the bioinformatics and/or computer arts. Several software components can be loaded into memory during operation of such a computer system. The software components can comprise both software components that are standard in the art and components that are special to the present invention (e.g., dCHIP software described in Lin et al. (2004) Bioinformatics 20, 1233-1240; radial basis machine learning algorithms (RBM) known in the art).


The methods of the present invention can also be programmed or modeled in mathematical software packages that allow symbolic entry of equations and high-level specification of processing, including specific algorithms to be used, thereby freeing a user of the need to procedurally program individual equations and algorithms. Such packages include, e.g., Matlab from Mathworks (Natick, Mass.), Mathematica from Wolfram Research (Champaign, Ill.) or S-Plus from MathSoft (Seattle, Wash.).


In certain embodiments, the computer comprises a database for storage of biomarker data. Such stored profiles can be accessed and used to perform comparisons of interest at a later point in time. For example, biomarker expression profiles of a sample derived from the non-cancerous tissue of a subject and/or profiles generated from population-based distributions of informative loci of interest in relevant populations of the same species can be stored and later compared to that of a sample derived from the cancerous tissue of the subject or tissue suspected of being cancerous of the subject.


In addition to the exemplary program structures and computer systems described herein, other, alternative program structures and computer systems will be readily apparent to the skilled artisan. Such alternative systems, which do not depart from the above described computer system and programs structures either in spirit or in scope, are therefore intended to be comprehended within the accompanying claims.


c. Diagnostic Assays


The present invention provides, in part, methods, systems, and code for accurately classifying whether a biological sample is associated with a cancer that is likely to respond to immune checkpoint therapy. In some embodiments, the present invention is useful for classifying a sample (e.g., from a subject) as associated with or at risk for responding to or not responding to immune checkpoint therapy using a statistical algorithm and/or empirical data (e.g., the amount or activity of a biomarker listed in Table 1).


An exemplary method for detecting the amount or activity of a biomarker listed in Table 1, and thus useful for classifying whether a sample is likely or unlikely to respond to immune checkpoint therapy involves obtaining a biological sample from a test subject and contacting the biological sample with an agent, such as a protein-binding agent like an antibody or antigen-binding fragment thereof, or a nucleic acid-binding agent like an oligonucleotide, capable of detecting the amount or activity of the biomarker in the biological sample. In some embodiments, at least one antibody or antigen-binding fragment thereof is used, wherein two, three, four, five, six, seven, eight, nine, ten, or more such antibodies or antibody fragments can be used in combination (e.g., in sandwich ELISAs) or in serial. In certain instances, the statistical algorithm is a single learning statistical classifier system. For example, a single learning statistical classifier system can be used to classify a sample as a based upon a prediction or probability value and the presence or level of the biomarker. The use of a single learning statistical classifier system typically classifies the sample as, for example, a likely immune checkpoint therapy responder or progressor sample with a sensitivity, specificity, positive predictive value, negative predictive value, and/or overall accuracy of at least about 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.


Other suitable statistical algorithms are well known to those of skill in the art. For example, learning statistical classifier systems include a machine learning algorithmic technique capable of adapting to complex data sets (e.g., panel of markers of interest) and making decisions based upon such data sets. In some embodiments, a single learning statistical classifier system such as a classification tree (e.g., random forest) is used. In other embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more learning statistical classifier systems are used, preferably in tandem. Examples of learning statistical classifier systems include, but are not limited to, those using inductive learning (e.g., decision/classification trees such as random forests, classification and regression trees (C&RT), boosted trees, etc.), Probably Approximately Correct (PAC) learning, connectionist learning (e.g., neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), network structures, perceptrons such as multi-layer perceptrons, multi-layer feed-forward networks, applications of neural networks, Bayesian learning in belief networks, etc.), reinforcement learning (e.g., passive learning in a known environment such as naive learning, adaptive dynamic learning, and temporal difference learning, passive learning in an unknown environment, active learning in an unknown environment, learning action-value functions, applications of reinforcement learning, etc.), and genetic algorithms and evolutionary programming. Other learning statistical classifier systems include support vector machines (e.g., Kernel methods), multivariate adaptive regression splines (MARS), Levenberg-Marquardt algorithms, Gauss-Newton algorithms, mixtures of Gaussians, gradient descent algorithms, and learning vector quantization (LVQ). In certain embodiments, the method of the present invention further comprises sending the sample classification results to a clinician, e.g., an oncologist.


In another embodiment, the diagnosis of a subject is followed by administering to the individual a therapeutically effective amount of a defined treatment based upon the diagnosis.


In one embodiment, the methods further involve obtaining a control biological sample (e.g., biological sample from a subject who does not have a cancer or whose cancer is susceptible to immune checkpoint therapy), a biological sample from the subject during remission, or a biological sample from the subject during treatment for developing a cancer progressing despite immune checkpoint therapy.


d. Prognostic Assays


The diagnostic methods described herein can furthermore be utilized to identify subjects having or at risk of developing a cancer that is likely or unlikely to be responsive to immune checkpoint therapy. The assays described herein, such as the preceding diagnostic assays or the following assays, can be utilized to identify a subject having or at risk of developing a disorder associated with a misregulation of the amount or activity of at least one biomarker described in Table 1, such as in cancer. Alternatively, the prognostic assays can be utilized to identify a subject having or at risk for developing a disorder associated with a misregulation of the at least one biomarker described in Table 1, such as in cancer. Furthermore, the prognostic assays described herein can be used to determine whether a subject can be administered an agent (e.g., an agonist, antagonist, peptidomimetic, polypeptide, peptide, nucleic acid, small molecule, or other drug candidate) to treat a disease or disorder associated with the aberrant biomarker expression or activity.


e. Treatment Methods


The compositions described herein (including dual binding antibodies and derivatives and conjugates thereof) can be used in a variety of in vitro and in vivo therapeutic applications using the formulations and/or combinations described herein. In one embodiment, anti-immune checkpoint agents can be used to treat cancers determined to be responsive thereto. For example, antibodies that block the interaction between PD-L1, PD-L2, and/or CTLA-4 and their receptors (e.g., PD-L1 binding to PD-1, PD-L2 binding to PD-1, and the like) can be used to treat cancer in subjects identified as likely responding thereto.


6. Pharmaceutical Compositions

In another aspect, the present invention provides pharmaceutically acceptable compositions which comprise a therapeutically-effective amount of an agent that modulates (e.g., decreases) biomarker expression and/or activity, formulated together with one or more pharmaceutically acceptable carriers (additives) and/or diluents. As described in detail below, the pharmaceutical compositions of the present invention may be specially formulated for administration in solid or liquid form, including those adapted for the following: (1) oral administration, for example, drenches (aqueous or non-aqueous solutions or suspensions), tablets, boluses, powders, granules, pastes; (2) parenteral administration, for example, by subcutaneous, intramuscular or intravenous injection as, for example, a sterile solution or suspension; (3) topical application, for example, as a cream, ointment or spray applied to the skin; (4) intravaginally or intrarectally, for example, as a pessary, cream or foam; or (5) aerosol, for example, as an aqueous aerosol, liposomal preparation or solid particles containing the compound.


The phrase “therapeutically-effective amount” as used herein means that amount of an agent that modulates (e.g., inhibits) biomarker expression and/or activity, or expression and/or activity of the complex, or composition comprising an agent that modulates (e.g., inhibits) biomarker expression and/or activity, or expression and/or activity of the complex, which is effective for producing some desired therapeutic effect, e.g., cancer treatment, at a reasonable benefit/risk ratio.


The phrase “pharmaceutically acceptable” is employed herein to refer to those agents, materials, compositions, and/or dosage forms which are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings and animals without excessive toxicity, irritation, allergic response, or other problem or complication, commensurate with a reasonable benefit/risk ratio.


The phrase “pharmaceutically-acceptable carrier” as used herein means a pharmaceutically-acceptable material, composition or vehicle, such as a liquid or solid filler, diluent, excipient, solvent or encapsulating material, involved in carrying or transporting the subject chemical from one organ, or portion of the body, to another organ, or portion of the body. Each carrier must be “acceptable” in the sense of being compatible with the other ingredients of the formulation and not injurious to the subject. Some examples of materials which can serve as pharmaceutically-acceptable carriers include: (1) sugars, such as lactose, glucose and sucrose; (2) starches, such as corn starch and potato starch; (3) cellulose, and its derivatives, such as sodium carboxymethyl cellulose, ethyl cellulose and cellulose acetate; (4) powdered tragacanth; (5) malt; (6) gelatin; (7) talc; (8) excipients, such as cocoa butter and suppository waxes; (9) oils, such as peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, corn oil and soybean oil; (10) glycols, such as propylene glycol; (11) polyols, such as glycerin, sorbitol, mannitol and polyethylene glycol; (12) esters, such as ethyl oleate and ethyl laurate; (13) agar; (14) buffering agents, such as magnesium hydroxide and aluminum hydroxide; (15) alginic acid; (16) pyrogen-free water; (17) isotonic saline; (18) Ringer's solution; (19) ethyl alcohol; (20) phosphate buffer solutions; and (21) other non-toxic compatible substances employed in pharmaceutical formulations.


The term “pharmaceutically-acceptable salts” refers to the relatively non-toxic, inorganic and organic acid addition salts of the agents that modulates (e.g., inhibits) biomarker expression and/or activity, or expression and/or activity of the complex encompassed by the present invention. These salts can be prepared in situ during the final isolation and purification of the respiration uncoupling agents, or by separately reacting a purified respiration uncoupling agent in its free base form with a suitable organic or inorganic acid, and isolating the salt thus formed. Representative salts include the hydrobromide, hydrochloride, sulfate, bisulfate, phosphate, nitrate, acetate, valerate, oleate, palmitate, stearate, laurate, benzoate, lactate, phosphate, tosylate, citrate, maleate, fumarate, succinate, tartrate, napthylate, mesylate, glucoheptonate, lactobionate, and laurylsulphonate salts and the like (See, for example, Berge et al. (1977) “Pharmaceutical Salts”, J. Pharm. Sci. 66:1-19).


In other cases, the agents useful in the methods of the present invention may contain one or more acidic functional groups and, thus, are capable of forming pharmaceutically-acceptable salts with pharmaceutically-acceptable bases. The term “pharmaceutically-acceptable salts” in these instances refers to the relatively non-toxic, inorganic and organic base addition salts of agents that modulates (e.g., inhibits) biomarker expression and/or activity, or expression and/or activity of the complex. These salts can likewise be prepared in situ during the final isolation and purification of the respiration uncoupling agents, or by separately reacting the purified respiration uncoupling agent in its free acid form with a suitable base, such as the hydroxide, carbonate or bicarbonate of a pharmaceutically-acceptable metal cation, with ammonia, or with a pharmaceutically-acceptable organic primary, secondary or tertiary amine. Representative alkali or alkaline earth salts include the lithium, sodium, potassium, calcium, magnesium, and aluminum salts and the like. Representative organic amines useful for the formation of base addition salts include ethylamine, diethylamine, ethylenediamine, ethanolamine, diethanolamine, piperazine and the like (see, for example, Berge et al., supra).


Wetting agents, emulsifiers and lubricants, such as sodium lauryl sulfate and magnesium stearate, as well as coloring agents, release agents, coating agents, sweetening, flavoring and perfuming agents, preservatives and antioxidants can also be present in the compositions.


Examples of pharmaceutically-acceptable antioxidants include: (1) water soluble antioxidants, such as ascorbic acid, cysteine hydrochloride, sodium bisulfate, sodium metabisulfite, sodium sulfite and the like; (2) oil-soluble antioxidants, such as ascorbyl palmitate, butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), lecithin, propyl gallate, alpha-tocopherol, and the like; and (3) metal chelating agents, such as citric acid, ethylenediamine tetraacetic acid (EDTA), sorbitol, tartaric acid, phosphoric acid, and the like.


Formulations useful in the methods of the present invention include those suitable for oral, nasal, topical (including buccal and sublingual), rectal, vaginal, aerosol and/or parenteral administration. The formulations may conveniently be presented in unit dosage form and may be prepared by any methods well known in the art of pharmacy. The amount of active ingredient which can be combined with a carrier material to produce a single dosage form will vary depending upon the host being treated, the particular mode of administration. The amount of active ingredient, which can be combined with a carrier material to produce a single dosage form will generally be that amount of the compound which produces a therapeutic effect. Generally, out of one hundred percent, this amount will range from about 1 percent to about ninety-nine percent of active ingredient, preferably from about 5 percent to about 70 percent, most preferably from about 10 percent to about 30 percent.


Methods of preparing these formulations or compositions include the step of bringing into association an agent that modulates (e.g., inhibits) biomarker expression and/or activity, with the carrier and, optionally, one or more accessory ingredients. In general, the formulations are prepared by uniformly and intimately bringing into association a respiration uncoupling agent with liquid carriers, or finely divided solid carriers, or both, and then, if necessary, shaping the product.


Formulations suitable for oral administration may be in the form of capsules, cachets, pills, tablets, lozenges (using a flavored basis, usually sucrose and acacia or tragacanth), powders, granules, or as a solution or a suspension in an aqueous or non-aqueous liquid, or as an oil-in-water or water-in-oil liquid emulsion, or as an elixir or syrup, or as pastilles (using an inert base, such as gelatin and glycerin, or sucrose and acacia) and/or as mouth washes and the like, each containing a predetermined amount of a respiration uncoupling agent as an active ingredient. A compound may also be administered as a bolus, electuary or paste.


In solid dosage forms for oral administration (capsules, tablets, pills, dragees, powders, granules and the like), the active ingredient is mixed with one or more pharmaceutically-acceptable carriers, such as sodium citrate or dicalcium phosphate, and/or any of the following: (1) fillers or extenders, such as starches, lactose, sucrose, glucose, mannitol, and/or silicic acid; (2) binders, such as, for example, carboxymethylcellulose, alginates, gelatin, polyvinyl pyrrolidone, sucrose and/or acacia; (3) humectants, such as glycerol; (4) disintegrating agents, such as agar-agar, calcium carbonate, potato or tapioca starch, alginic acid, certain silicates, and sodium carbonate; (5) solution retarding agents, such as paraffin; (6) absorption accelerators, such as quaternary ammonium compounds; (7) wetting agents, such as, for example, acetyl alcohol and glycerol monostearate; (8) absorbents, such as kaolin and bentonite clay; (9) lubricants, such a talc, calcium stearate, magnesium stearate, solid polyethylene glycols, sodium lauryl sulfate, and mixtures thereof; and (10) coloring agents. In the case of capsules, tablets and pills, the pharmaceutical compositions may also comprise buffering agents. Solid compositions of a similar type may also be employed as fillers in soft and hard-filled gelatin capsules using such excipients as lactose or milk sugars, as well as high molecular weight polyethylene glycols and the like.


A tablet may be made by compression or molding, optionally with one or more accessory ingredients. Compressed tablets may be prepared using binder (for example, gelatin or hydroxypropylmethyl cellulose), lubricant, inert diluent, preservative, disintegrant (for example, sodium starch glycolate or cross-linked sodium carboxymethyl cellulose), surface-active or dispersing agent. Molded tablets may be made by molding in a suitable machine a mixture of the powdered peptide or peptidomimetic moistened with an inert liquid diluent.


Tablets, and other solid dosage forms, such as dragees, capsules, pills and granules, may optionally be scored or prepared with coatings and shells, such as enteric coatings and other coatings well known in the pharmaceutical-formulating art. They may also be formulated so as to provide slow or controlled release of the active ingredient therein using, for example, hydroxypropylmethyl cellulose in varying proportions to provide the desired release profile, other polymer matrices, liposomes and/or microspheres. They may be sterilized by, for example, filtration through a bacteria-retaining filter, or by incorporating sterilizing agents in the form of sterile solid compositions, which can be dissolved in sterile water, or some other sterile injectable medium immediately before use. These compositions may also optionally contain opacifying agents and may be of a composition that they release the active ingredient(s) only, or preferentially, in a certain portion of the gastrointestinal tract, optionally, in a delayed manner. Examples of embedding compositions, which can be used include polymeric substances and waxes. The active ingredient can also be in micro-encapsulated form, if appropriate, with one or more of the above-described excipients.


Liquid dosage forms for oral administration include pharmaceutically acceptable emulsions, microemulsions, solutions, suspensions, syrups and elixirs. In addition to the active ingredient, the liquid dosage forms may contain inert diluents commonly used in the art, such as, for example, water or other solvents, solubilizing agents and emulsifiers, such as ethyl alcohol, isopropyl alcohol, ethyl carbonate, ethyl acetate, benzyl alcohol, benzyl benzoate, propylene glycol, 1,3-butylene glycol, oils (in particular, cottonseed, groundnut, corn, germ, olive, castor and sesame oils), glycerol, tetrahydrofuryl alcohol, polyethylene glycols and fatty acid esters of sorbitan, and mixtures thereof.


Besides inert diluents, the oral compositions can also include adjuvants such as wetting agents, emulsifying and suspending agents, sweetening, flavoring, coloring, perfuming and preservative agents.


Suspensions, in addition to the active agent may contain suspending agents as, for example, ethoxylated isostearyl alcohols, polyoxyethylene sorbitol and sorbitan esters, microcrystalline cellulose, aluminum metahydroxide, bentonite, agar-agar and tragacanth, and mixtures thereof.


Formulations for rectal or vaginal administration may be presented as a suppository, which may be prepared by mixing one or more respiration uncoupling agents with one or more suitable nonirritating excipients or carriers comprising, for example, cocoa butter, polyethylene glycol, a suppository wax or a salicylate, and which is solid at room temperature, but liquid at body temperature and, therefore, will melt in the rectum or vaginal cavity and release the active agent.


Formulations which are suitable for vaginal administration also include pessaries, tampons, creams, gels, pastes, foams or spray formulations containing such carriers as are known in the art to be appropriate.


Dosage forms for the topical or transdermal administration of an agent that modulates (e.g., inhibits) biomarker expression and/or activity include powders, sprays, ointments, pastes, creams, lotions, gels, solutions, patches and inhalants. The active component may be mixed under sterile conditions with a pharmaceutically-acceptable carrier, and with any preservatives, buffers, or propellants which may be required.


The ointments, pastes, creams and gels may contain, in addition to a respiration uncoupling agent, excipients, such as animal and vegetable fats, oils, waxes, paraffins, starch, tragacanth, cellulose derivatives, polyethylene glycols, silicones, bentonites, silicic acid, talc and zinc oxide, or mixtures thereof.


Powders and sprays can contain, in addition to an agent that modulates (e.g., inhibits) biomarker expression and/or activity, excipients such as lactose, talc, silicic acid, aluminum hydroxide, calcium silicates and polyamide powder, or mixtures of these substances. Sprays can additionally contain customary propellants, such as chlorofluorohydrocarbons and volatile unsubstituted hydrocarbons, such as butane and propane.


The agent that modulates (e.g., inhibits) biomarker expression and/or activity, can be alternatively administered by aerosol. This is accomplished by preparing an aqueous aerosol, liposomal preparation or solid particles containing the compound. A nonaqueous (e.g., fluorocarbon propellant) suspension could be used. Sonic nebulizers are preferred because they minimize exposing the agent to shear, which can result in degradation of the compound.


Ordinarily, an aqueous aerosol is made by formulating an aqueous solution or suspension of the agent together with conventional pharmaceutically acceptable carriers and stabilizers. The carriers and stabilizers vary with the requirements of the particular compound, but typically include nonionic surfactants (Tweens, Pluronics, or polyethylene glycol), innocuous proteins like serum albumin, sorbitan esters, oleic acid, lecithin, amino acids such as glycine, buffers, salts, sugars or sugar alcohols. Aerosols generally are prepared from isotonic solutions.


Transdermal patches have the added advantage of providing controlled delivery of a respiration uncoupling agent to the body. Such dosage forms can be made by dissolving or dispersing the agent in the proper medium. Absorption enhancers can also be used to increase the flux of the peptidomimetic across the skin. The rate of such flux can be controlled by either providing a rate controlling membrane or dispersing the peptidomimetic in a polymer matrix or gel.


Ophthalmic formulations, eye ointments, powders, solutions and the like, are also contemplated as being within the scope of this invention.


Pharmaceutical compositions of this invention suitable for parenteral administration comprise one or more respiration uncoupling agents in combination with one or more pharmaceutically-acceptable sterile isotonic aqueous or nonaqueous solutions, dispersions, suspensions or emulsions, or sterile powders which may be reconstituted into sterile injectable solutions or dispersions just prior to use, which may contain antioxidants, buffers, bacteriostats, solutes which render the formulation isotonic with the blood of the intended recipient or suspending or thickening agents.


Examples of suitable aqueous and nonaqueous carriers which may be employed in the pharmaceutical compositions of the present invention include water, ethanol, polyols (such as glycerol, propylene glycol, polyethylene glycol, and the like), and suitable mixtures thereof, vegetable oils, such as olive oil, and injectable organic esters, such as ethyl oleate. Proper fluidity can be maintained, for example, by the use of coating materials, such as lecithin, by the maintenance of the required particle size in the case of dispersions, and by the use of surfactants.


These compositions may also contain adjuvants such as preservatives, wetting agents, emulsifying agents and dispersing agents. Prevention of the action of microorganisms may be ensured by the inclusion of various antibacterial and antifungal agents, for example, paraben, chlorobutanol, phenol sorbic acid, and the like. It may also be desirable to include isotonic agents, such as sugars, sodium chloride, and the like into the compositions. In addition, prolonged absorption of the injectable pharmaceutical form may be brought about by the inclusion of agents which delay absorption such as aluminum monostearate and gelatin.


In some cases, in order to prolong the effect of a drug, it is desirable to slow the absorption of the drug from subcutaneous or intramuscular injection. This may be accomplished by the use of a liquid suspension of crystalline or amorphous material having poor water solubility. The rate of absorption of the drug then depends upon its rate of dissolution, which, in turn, may depend upon crystal size and crystalline form. Alternatively, delayed absorption of a parenterally-administered drug form is accomplished by dissolving or suspending the drug in an oil vehicle.


Injectable depot forms are made by forming microencapsule matrices of an agent that modulates (e.g., inhibits) biomarker expression and/or activity, in biodegradable polymers such as polylactide-polyglycolide. Depending on the ratio of drug to polymer, and the nature of the particular polymer employed, the rate of drug release can be controlled. Examples of other biodegradable polymers include poly(orthoesters) and poly(anhydrides). Depot injectable formulations are also prepared by entrapping the drug in liposomes or microemulsions, which are compatible with body tissue.


When the respiration uncoupling agents of the present invention are administered as pharmaceuticals, to humans and animals, they can be given per se or as a pharmaceutical composition containing, for example, 0.1 to 99.5% (more preferably, 0.5 to 90%) of active ingredient in combination with a pharmaceutically acceptable carrier.


Actual dosage levels of the active ingredients in the pharmaceutical compositions of this invention may be determined by the methods of the present invention so as to obtain an amount of the active ingredient, which is effective to achieve the desired therapeutic response for a particular subject, composition, and mode of administration, without being toxic to the subject.


The nucleic acid molecules of the present invention can be inserted into vectors and used as gene therapy vectors. Gene therapy vectors can be delivered to a subject by, for example, intravenous injection, local administration (see U.S. Pat. No. 5,328,470) or by stereotactic injection (see e.g., Chen et al. (1994) Proc. Natl. Acad. Sci. USA 91:3054 3057). The pharmaceutical preparation of the gene therapy vector can include the gene therapy vector in an acceptable diluent, or can comprise a slow release matrix in which the gene delivery vehicle is imbedded. Alternatively, where the complete gene delivery vector can be produced intact from recombinant cells, e.g., retroviral vectors, the pharmaceutical preparation can include one or more cells which produce the gene delivery system.


The present invention also encompasses kits for detecting and/or modulating biomarkers described herein. A kit of the present invention may also include instructional materials disclosing or describing the use of the kit or an antibody of the disclosed invention in a method of the disclosed invention as provided herein. A kit may also include additional components to facilitate the particular application for which the kit is designed. For example, a kit may additionally contain means of detecting the label (e.g., enzyme substrates for enzymatic labels, filter sets to detect fluorescent labels, appropriate secondary labels such as a sheep anti-mouse-HRP, etc.) and reagents necessary for controls (e.g., control biological samples or standards). A kit may additionally include buffers and other reagents recognized for use in a method of the disclosed invention. Non-limiting examples include agents to reduce non-specific binding, such as a carrier protein or a detergent.


Exemplification

This invention is further illustrated by the following examples, which should not be construed as limiting.


Example 1: Materials and Methods for Example 2

a. Clinical Cohort Consolidation


The training cohort was gathered from patients enrolled in CA209-009 (NCT01358721), a study of nivolumab (BMS-936558) monotherapy in metastatic renal cell carcinoma. The validation cohort was gathered from patients at the Dana-Farber Cancer Institute and Memorial Sloan Kettering Cancer Institute who received immune checkpoint therapy as monotherapy or in combination with other immune checkpoint or targeted therapies and had banked adequate pre-treatment tumor tissue for whole exome characterization. All patients provided consent to an Institutional Review Board protocol that allows research molecular characterization of tumor and germline samples.


b. DNA and RNA Extraction and Sequencing


After fixation and mounting, 5-10 10 μm slices from formalin-fixed, paraffin-embedded (FFPE) tumor blocks were obtained, and tumor-enriched tissue was macrodissected. Paraffin was removed from FFPE sections and cores using CitriSolv™ (Fisher Scientific, Hampton, N.H.), followed by ethanol washes and tissue lysis overnight at 56° C. Samples were then incubated at 90° C. to remove DNA crosslinks, and DNA- and when possible, RNA-extraction was performed using Qiagen AllPrep DNA/RNA Mini Kit (#51306, Qiagen, Hilden, Germany). Germline DNA was obtained from adjacent PBMCs.


Whole exome and whole transcriptome sequencing of tumor and germline samples were performed as previously described (Van Allen et al. (2015) Science 350:207-211; Van Allen et al. (2014) Nat. Med. 20:682-688). All samples in the training cohort were sequenced using the Illumina exome, while a portion of the samples in the validation cohort were sequenced using the Agilent exome (Table 4A). The Illumina exome uses Illumina's in-solution DNA probe based hybrid selection method to target approximately 37.7 Mb of mainly exonic territory, using similar principles as the Broad Institute-Agilent Technologies developed in-solution RNA probe based hybrid selection method (Agilent SureSelect™ All Exon V2) (Fisher et al. (2011) Genome Biol. 12:R1; Gnirke et al. (2009) Nat. Biotechnol. 27:182-189) to generate Illumina exome sequencing libraries.


Pooled libraries were normalized to 2 nM and denatured using 0.2 N NaOH prior to sequencing. Flowcell cluster amplification and sequencing were performed according to the manufacturer's protocols using either the HiSeq 2000 v3 or HiSeq 2500. Each run was a 76 bp paired-end with a dual eight-base index barcode read. Data was analyzed using the Broad Picard Pipeline, which includes de-multiplexing and data aggregation.


Exome sequence data processing was performed using established analytical pipelines at the Broad Institute. A BAM file was produced using the Picard pipeline (at the World Wide Web address of picard.sourceforge.net), which aligns the tumor and normal sequences to the hg19 human genome build using Illumina sequencing reads. The BAM was uploaded into the Firehose pipeline (at the World Wide Web address of broadinstitute.org/cancer/cga/Firehose), which manages input and output files to be executed by GenePattern (Reich et al. (2006) Nat. Genet. 38:500-501). Samples with mean target coverage less than 25× in the tumor and less than 15× in matched normal were excluded.


Quality control modules within Firehose were applied to all sequencing data for comparison of the origin of tumor and normal genotypes and to assess fingerprinting concordance. Cross-contamination of samples was estimated using ContEst (Cibulskis et al. (2011) Bioinformatics 27:2601-2602). Samples with ContEst estimates exceeding 5% were excluded from analysis.


c. Whole Exome and Whole Transcriptome Analyses


MuTect was applied to identify somatic single-nucleotide variants (Cibulskis et al. (2013) Nat. Biotechnol. 31:213-219). Strelka was used to identify somatic insertions and deletions (Saunders et al. (2012) Bioinformatics 28:1811-1817) across the whole exome. Indelocator, which detects small insertions and deletions after local realignment of tumor and normal sequences, was additionally applied to provide further sensitivity to detect indels in PBRM1 (Cancer Genome Atlas Research (2011) Nature 474:609-615). The union of indels called by Strelka and Indelocator was used for final analysis. Artifacts introduced by DNA oxidation during sequencing were computationally removed using a filter-based method (Costello et al. (2013) Nuc. Acids Res. 41:e67). All somatic mutations detected by whole-exome sequencing were analyzed for potential false positive calls by performing a comparison to mutation calls from a panel of 2,500 germline DNA samples (Stachler et al. (2015) Nat. Genet. 47:1047-1055). Mutations found in germline samples were removed from analysis. Annotation of identified variants was done using Oncotator (available at the World Wide Web address of www.broadinstitute.org/cancer/cga/oncotator). All nonsynonymous alterations in PBRM1 were manually reviewed in Integrated Genomics Viewer (IGV_2.3.57) for sequencing quality (Thorvaldsdottir et al. (2013) Brief Bioinform 14:178-192).


Copy ratios were calculated for each captured target by dividing the tumor coverage by the median coverage obtained in a set of reference normal samples. The resulting copy ratios were segmented using the circular binary segmentation algorithm (Olshen et al. (2004) Biostatistics 5:557-572). Allelic copy number alterations were called while taking into account sample-specific overall chromosomal aberrations (focality) (Brastianos et al. (2015) Cancer Discov. 5:1164-1177). Inference of mutational clonality, tumor purity, and tumor ploidy was accomplished with ABSOLUTE (Carter et al. (2012) Nat Biotechnol. 30:413-421). Samples had to have estimated tumor purity greater than 10% to be included in the final analysis. As a final quality control metric to ensure adequate sequencing coverage and tumor purity to detect relevant oncogenic events, all samples had to have at least one nonsynonymous mutation in at least one high confidence or candidate cancer driver gene to be included in the final analysis (Tamborero et al. (2013) Sci. Rep. 3:2650).


The 4-digit HLA type for each sample was inferred using Polysolver (Shukla et al. (2015) Nat. Biotechnol. 33:1152-1158). Neo-epitopes were predicted for each patient by defining all novel amino acid 9mers and 10mers resulting from each single nucleotide polymorphism and indel and determining whether the predicted binding affinity to the patient's germline HLA alleles was <500 nM using NetMHCpan (v2.4) (Hoof et al. (2009) Immunogenetics 61:1-13; Karosiene et al. (2013) Immunogenetics 65:711-724; Nielsen et al. (2007) PLoS One 2:e796).


d. TCGA Analysis


Whole exome mutations annotation files (MAFs) and whole transcriptome gene expression data (RSEM) were downloaded from the Firebrowse KIRC TCGA data release (2016_01_28). Samples with whole transcriptome sequencing in normal tissue only, as well as samples derived from FFPE (N=3), were excluded from analysis.


e. Serum Biomarker Analyses


Serum biomarker analyses were performed as described previously in Choueiri et al. (2016) Clin. Cancer Res. 22:5461-5471.


f. Statistical Analyses


Comparisons of neoantigen and mutational load between response groups and of expression levels of individual genes between PBRM1-truncated and PBRM1-wildtype tumors were done with the non-parametric Wilcoxon rank-sum test. Comparisons of the proportion of patients with truncating alterations in PBRM1 by clinical response group were done with the Pearson's chi-squared test without continuity correction. Kaplan-Meier analyses were done using the R packages survival and survminer. All comparisons were two-sided with an alpha-level of 0.05. All statistical analyses were done in R version 3.2.3.


Given the low mutational burden and high tumor microenvironment immune activity characteristic of renal cell carcinoma, it is believed that specific somatic genetic features other than mutational load drive response to immune checkpoint inhibitors in ccRCC. As part of a prospective clinical trial (Choueiri et al. (2016), supra), Applicants analyzed a clinical cohort of 91 patients with metastatic clear cell renal cell carcinoma (mRCC) treated with anti-PD1 therapy (nivolumab) (FIG. 1A; Arm 1: 0.3 mg/kg (N=22); Arm 2: 2 mg/kg (N=22); Arm 3: 10 mg/kg (N=23), and Arm 4: 10 mg/kg (N=24)). Among 56 patients with attempted whole exome sequenceing of pre-treatment tumors, 34 had high-quality whole exome sequencing (WES) for discovery of genetic predictors of response to immune checkpoint therapy, and then validated the findings in an independent cohort of WES of pre-treatment tumors from 28 patients (FIG. 1B). Applicants also analyzed pre-treatment whole transcriptome sequencing (WTS) from a subset of 42 patients from both the training and validation cohorts to assess the impact of genetic changes associated with treatment response on tumor gene expression and immune infiltration.


Example 2: Loss-of-Function of PBRM1 Correlates with Response to Anti-PD1/PD-L1 Therapy in Renal Cell Carcinoma

Quality-control metrics were applied to both the training and validation cohorts to ensure sensitive mutation detection (Cibulskis et al. (2011), supra) (FIG. 1B and Table 2A). Of the samples included in the final analysis, average exome-wide target coverage was 140-fold for tumor samples (range: 27-210) and 91-fold (range: 48-168) for matched germline samples. Analysis methods used herein include somatic mutation identification (single nucleotide polymorphisms and insertions and deletions) (as in Cibulskis et al. (2013), supra and Saunders et al. (2012) Bioinformatics 28:1811-1817), human lymphocyte antigen (HLA) typing from germline WES (as in Shukla et al. (2015) Nat. Biotechnol. 33:1152-1158), neoantigen prediction (as in Hoof et al. (2009) Immunogenetics 61:1-13), and estimation of mutational clonality and tumor purity and ploidy (Carter et al. (2012) Nat Biotechnol. 30:413-421) using established methods (as in Example 1 and FIG. 1B). In the training cohort, of the 56 out of 91 patients for whom adequate pre-treatment tissue was available for WES, 34 passed quality control and were included in the final analysis (FIG. 1B). For example, sample VA1008 having a chromosome 3p deletion was excluded as having low tumor purity (estimated tumor purity=0.11). Among these 56 pairs matched tumor and normal samples, Sample 2_664 contains germline BAM only, while Sample 4_49 contains tumor BAM only. As quality control for sequenced tissure, 6 of 56 samples were excluded due to poor tumor coverage. They were Samples 4_54 (0.079×), 9_47 (0.30×), 8_100 (7.71×), 11_5 (8.69×), 1_72 (9.63×), and 9_66 (8.72×). Another sample, 9_119 (26.88×), was not excluded. For this sample, with estimated tumor purity of 0.49 and mean target coverage of 27×, a sensitivity of ˜90% detected a heterozygous mutation in CA-209009-9_119 (see Cibulskis et al. (2013), supra). Quality control for copy number was also performed.









TABLE 2A





Sequencing Metrics and Inclusion/Exclusion Criteria for Whole Exome


Sequencing in Training Cohort (N = 56)



















patient_id
tumor_mtc
normal_mtc
bait_set
absolute_inferred_purity





CA209009_1_20
34.147062
92.688228
whole_exome_illumina_coding_v1
0.51


CA209009_1_32
165.03915
80.054054
whole_exome_illumina_coding_v1
0.39


CA209009_1_62
163.21171
81.718582
whole_exome_illumina_coding_v1
0.49


CA209009_11_10
153.907825
93.664757
whole_exome_illumina_coding_v1
0.76


CA209009_11_11
81.495132
108.704189
whole_exome_illumina_coding_v1
0.38


CA209009_11_14
157.03659
99.990083
whole_exome_illumina_coding_v1
0.25


CA209009_11_25
150.766602
87.494869
whole_exome_illumina_coding_v1
0.32


CA209009_11_56
136.739597
86.544731
whole_exome_illumina_coding_v1
0.56


CA209009_11_79
112.238316
91.306045
whole_exome_illumina_coding_v1
0.8


CA209009_11_93
193.622831
94.511787
whole_exome_illumina_coding_v1
0.2


CA209009_12_115
150.50973
89.411498
whole_exome_illumina_coding_v1
0.63


CA209009_13_90
147.435982
79.577243
whole_exome_illumina_coding_v1
0.3


CA209009_13_96
189.081727
96.347659
whole_exome_illumina_coding_v1
0.67


CA209009_2_102
130.03582
97.794738
whole_exome_illumina_coding_v1
0.48


CA209009_2_58
142.586967
82.644492
whole_exome_illumina_coding_v1
0.69


CA209009_2_84
166.18581
88.436816
whole_exome_illumina_coding_v1
0.45


CA209009_3_114
135.707278
77.721511
whole_exome_illumina_coding_v1
0.29


CA209009_3_117
173.22159
68.856331
whole_exome_illumina_coding_v1
0.33


CA209009_3_15
143.012126
79.906338
whole_exome_illumina_coding_v1
0.69


CA209009_4_68
107.126976
88.452741
whole_exome_illumina_coding_v1
0.17


CA209009_5_1
157.143939
89.39856
whole_exome_illumina_coding_v1
0.2


CA209009_5_106
176.007671
81.059438
whole_exome_illumina_coding_v1
0.35


CA209009_5_18
139.328276
75.654059
whole_exome_illumina_coding_v1
0.21


CA209009_5_21
178.624687
105.356301
whole_exome_illumina_coding_v1
0.51


CA209009_5_41
138.664874
93.93237
whole_exome_illumina_coding_v1
0.19


CA209009_5_50
162.205322
85.879444
whole_exome_illumina_coding_v1
0.31


CA209009_5_73
158.127987
100.10628
whole_exome_illumina_coding_v1
0.6


CA209009_6_39
147.571574
114.169462
whole_exome_illumina_coding_v1
0.13


CA209009_8_105
152.057615
91.424807
whole_exome_illumina_coding_v1
0.48


CA209009_9_119
26.875509
90.734659
whole_exome_illumina_coding_v1
0.49


CA209009_9_27
125.149722
97.245404
whole_exome_illumina_coding_v1
0.34


CA209009_9_52
131.064027
90.415506
whole_exome_illumina_coding_v1
0.54


CA209009_9_97
210.012354
98.486524
whole_exome_illumina_coding_v1
0.38


CA209009_2_85
43.586957
168.436641
whole_exome_illumina_coding_v1
0.13


CA209009_5_2
159.912441
69.844188
whole_exome_illumina_coding_v1
0.52


CA209009_5_29
150.205436
89.123637
whole_exome_illumina_coding_v1
NA


CA209009_6_99
34.101887
117.822339
whole_exome_illumina_coding_v1
0.36


CA209009_1_72
9.627872
94.01896
whole_exome_illumina_coding_v1
NA


CA209009_11_5
8.689284
89.713424
whole_exome_illumina_coding_v1
0.36


CA209009_4_54
0.007939
84.883698
whole_exome_illumina_coding_v1
NA


CA209009_8_100
7.711684
105.962605
whole_exome_illumina_coding_v1
0.34


CA209009_9_47
0.298156
95.4427
whole_exome_illumina_coding_v1
NA


CA209009_9_66
8.71954
98.033649
whole_exome_illumina_coding_v1
0.46


CA209009_1_43
105.603458
72.354112
whole_exome_illumina_coding_v1
0.06


CA209009_11_12
162.560923
104.266666
whole_exome_illumina_coding_v1
0.05


CA209009_11_24
166.047506
75.247762
whole_exome_illumina_coding_v1
0.1


CA209009_11_40
154.736269
87.045058
whole_exome_illumina_coding_v1
0.1


CA209009_11_8
154.801856
83.048353
whole_exome_illumina_coding_v1
NA


CA209009_13_103
138.626523
96.365324
whole_exome_illumina_coding_v1
NA


CA209009_3_26
159.566974
100.887491
whole_exome_illumina_coding_v1
0.07


CA209009_4_95
143.956046
90.060356
whole_exome_illumina_coding_v1
0.09


CA209009_5_17
129.343681
81.980679
whole_exome_illumina_coding_v1
0.04


CA209009_5_22
144.076612
97.672268
whole_exome_illumina_coding_v1
0.06


CA209009_5_28
162.443009
89.968028
whole_exome_illumina_coding_v1
0.08


CA209009_5_6
145.806274
83.646769
whole_exome_illumina_coding_v1
0.07


CA209009_9_45
132.158193
79.179771
whole_exome_illumina_coding_v1
0.06















patient_id
absolute_inferred_ploidy
genome_doubling
exclusion_reason







CA209009_1_20
1.78
0
0



CA209009_1_32
2.21
0
0



CA209009_1_62
1.83
0
0



CA209009_11_10
2
0
0



CA209009_11_11
3.56
1
0



CA209009_11_14
3.67
1
0



CA209009_11_25
1.99
0
0



CA209009_11_56
1.89
0
0



CA209009_11_79
1.96
0
0



CA209009_11_93
3
1
0



CA209009_12_115
1.64
0
0



CA209009_13_90
2.67
1
0



CA209009_13_96
1.8
0
0



CA209009_2_102
1.97
0
0



CA209009_2_58
1.81
0
0



CA209009_2_84
1.93
0
0



CA209009_3_114
1.97
0
0



CA209009_3_117
1.87
0
0



CA209009_3_15
1.97
0
0



CA209009_4_68
3.19
1
0



CA209009_5_1
3.6
1
0



CA209009_5_106
1.9
0
0



CA209009_5_18
2.3
0
0



CA209009_5_21
3.39
1
0



CA209009_5_41
4.28
1
0



CA209009_5_50
1.81
0
0



CA209009_5_73
1.83
0
0



CA209009_6_39
1.92
0
0



CA209009_8_105
2.06
0
0



CA209009_9_119
3.08
1
0



CA209009_9_27
1.93
0
0



CA209009_9_52
1.88
0
0



CA209009_9_97
2.2
0
0



CA209009_2_85
4.12
1
0



CA209009_5_2
1.68
0
EarlyDeath



CA209009_5_29
NA
NA
EarlyDeath



CA209009_6_99
2.77
1
EarlyDeath



CA209009_1_72
NA
NA
LowCoverage



CA209009_11_5
1.98
0
LowCoverage



CA209009_4_54
NA
NA
LowCoverage



CA209009_8_100
2.01
0
LowCoverage



CA209009_9_47
NA
NA
LowCoverage



CA209009_9_66
2.16
0
LowCoverage



CA209009_1_43
2.43
0
LowPurity



CA209009_11_12
2.74
0
LowPurity



CA209009_11_24
2.46
0
LowPurity



CA209009_11_40
2.44
0
LowPurity



CA209009_11_8
NA
NA
LowPurity



CA209009_13_103
NA
NA
LowPurity



CA209009_3_26
2.96
0
LowPurity



CA209009_4_95
2.57
0
LowPurity



CA209009_5_17
3.61
1
LowPurity



CA209009_5_22
2.91
0
LowPurity



CA209009_5_28
2.45
0
LowPurity



CA209009_5_6
2.69
0
LowPurity



CA209009_9_45
2.58
0
LowPurity










To stratify clinical cohort between patients who most clearly derived durable clinical benefit from anti-PD1 therapy and those who did not, three response categories were defined based on a composite end point incorporating RECIST criteria (Eisenhauer et al. (2009) Eur. J. Cancer 45:228-247), tumor shrinkage, and progression-free survival (PFS) (FIGS. 2-3 and Table 2B). “Extreme responders” included all patients with complete response (CR) or partial response (PR) by RECIST. Patients with stable disease (SD) as their best response by RECIST were also considered extreme responders if they had objective reduction in tumor size lasting at least 6 months, such as at least 12 months. “Extreme progressors” experienced early tumor growth: progressive disease (PD) by RECIST as best response with progression in less than 3 months. An intermediate group of patients who experienced SD or PR with objective tumor shrinkage lasting less than 6 months (or sometimes less than 12 months as indicated in certain figures) or PD with PFS longer than 3 months were called “intermediate benefit.” One patient (5_50) was classified as an “extreme responder” despite experiencing a short period of early tumor progression (PFS=2.9 months), which likely represented pseudo-progression, as further follow-up showed sustained tumor remission (FIGS. 1B and 2A-2B). Three patients who experienced death on-treatment prior to the first staging scans were excluded from analysis (Table 2A). Not evaluable (NE): No RECIST evaluation made. Mixed response (X): Simultaneous tumor shrinkage and growth.


Detailed clinical information and immunohistochemical staining was available for all 91 patients in the training cohort. Pre-treatment immunohistochemical staining for PD-L1 was positive at ≥1% for 30% of patients and at ≥5% for 16% of patients, which is generally representative of other large cohorts of clear cell RCC (Table 2B) (Motzer et al. (2015), supra). Duration of overall survival did not vary significantly by dose of therapy, patient gender, or PD-L1 immunohistochemical staining in Kaplan-Meier analyses, while objective tumor response by RECIST substantially prolonged duration of overall survival (FIG. 3A-3D). For a summary of results, see FIG. 11.









TABLE 2B





Clinical characteristics of patients receiving anti-PD1 therapy (nivolumab) in


training cohort (N = 91)

























patient_id
wes
sex
age
treatment_group
best_RECIST
max_tumor_change
os_days
os_censor
pfs_days
pfs_censor





CA209009_1_20
1
M
76
 10 mg/kg-N
CR
−100
1065
1
1022
1


CA209009_2_48
0
M
62
  2 mg/kg
SD
−3
983
1
983
1


CA209009_9_45
0
M
60
 10 mg/kg-N
CR
−86
953
1
949
1


CA209009_9_88
0
F
63
 10 mg/kg
PR
−77
878
1
878
1


CA209009_5_106
1
F
61
  2 mg/kg
PR
−61
870
1
821
1


CA209009_2_58
1
F
55
  2 mg/kg
PR
−50
988
1
687
1


CA209009_3_15
1
F
73
 10 mg/kg-N
SD
−10
1013
0
663
0


CA209009_15_81
0
F
45
 10 mg/kg
SD
−28
912
1
548
0


CA209009_11_14
1
M
59
 10 mg/kg-N
PR
−86
1025
1
541
0


CA209009_14_107
0
M
82
 10 mg/kg
SD
2
817
1
529
0


CA209009_9_34
0
F
61
  2 mg/kg
PR
−59
957
1
500
0


CA209009_11_93
1
M
64
 10 mg/kg
PR
−43
684
0
500
0


CA209009_9_47
0
M
69
0.3 mg/kg
SD
−44
1003
1
499
0


CA209009_15_94
0
F
41
 10 mg/kg
SD
−31
912
1
463
0


CA209009_9_119
1
M
72
 10 mg/kg
PR
−52
773
1
414
0


CA209009_13_111
0
M
68
 10 mg/kg-N
SD
−3
834
1
374
0


CA209009_11_13
0
F
64
 10 mg/kg-N
SD
0
1094
1
337
0


CA209009_15_75
0
F
70
  2 mg/kg
SD
−10
821
0
295
1


CA209009_11_57
0
M
48
 10 mg/kg
SD
0
969
1
295
0


CA209009_11_8
0
M
60
0.3 mg/kg
PR
−73
1051
1
292
0


CA209009_13_103
0
M
51
 10 mg/kg
SD
4
862
1
289
0


CA209009_4_54
0
F
65
0.3 mg/kg
SD
8
976
1
254
0


CA209009_9_30
0
F
60
 10 mg/kg
PR
−37
365
0
246
0


CA209009_9_52
1
F
63
 10 mg/kg-N
SD
−5
995
1
246
0


CA209009_14_89
0
M
78
  2 mg/kg
SD
−21
293
0
237
0


CA209009_5_4
0
M
65
  2 mg/kg
SD
5
914
1
220
0


CA209009_1_32
1
M
65
 10 mg/kg-N
SD
−13
240
0
213
1


CA209009_15_76
0
M
42
 10 mg/kg
PR
−44
662
0
209
0


CA209009_3_114
1
F
57
0.3 mg/kg
PR
−51
340
1
208
0


CA209009_5_22
0
M
63
  2 mg/kg
PR
−43
197
0
197
0


CA209009_8_100
0
F
58
  2 mg/kg
SD
5
798
1
184
1


CA209009_11_10
1
F
64
 10 mg/kg-N
SD
0
1058
1
173
1


CA209009_10_112
0
M
54
 10 mg/kg
SD
17
772
0
171
0


CA209009_5_17
0
M
55
 10 mg/kg-N
SD
−4
169
1
169
0


CA209009_9_74
0
M
67
0.3 mg/kg
SD
4
440
0
163
0


CA209009_2_64
0
M
59
 10 mg/kg
SD
2
766
0
157
1


CA209009_11_79
1
F
61
  2 mg/kg
SD
9
873
1
130
0


CA209009_5_23
0
M
66
 10 mg/kg-N
SD
−2
149
1
127
1


CA209009_4_49
0
M
60
 10 mg/kg
SD
3
605
0
123
0


CA209009_11_71
0
M
57
0.3 mg/kg
SD
12
954
1
123
0


CA209009_11_11
1
M
50
 10 mg/kg-N
SD
9
1024
1
122
0


CA209009_2_102
1
M
64
0.3 mg/kg
SD
3
165
1
108
0


CA209009_5_6
0
F
59
 10 mg/kg-N
SD
−19
155
0
99
0


CA209009_2_84
1
F
55
0.3 mg/kg
SD
8
680
0
88
0


CA209009_1_62
1
F
48
 10 mg/kg
SD
7
106
1
87
0


CA209009_12_115
1
M
60
  2 mg/kg
SD
15
366
1
87
0


CA209009_1_118
0
M
82
0.3 mg/kg
PD
29
177
0
86
0


CA209009_5_50
1
F
63
 10 mg/kg-N
SD
−67
982
1
86
0


CA209009_11_5
0
F
63
 10 mg/kg-N
SD
0
492
0
85
0


CA209009_1_86
0
M
71
 10 mg/kg
SD
7
464
0
85
0


CA209009_14_80
0
M
61
0.3 mg/kg
SD
4
147
0
82
0


CA209009_2_42
0
M
42
0.3 mg/kg
NE
.
81
0
81
0


CA209009_14_59
0
M
64
0.3 mg/kg
SD
8
991
1
81
0


CA209009_11_56
1
F
62
  2 mg/kg
SD
17
992
1
81
0


CA209009_11_40
0
M
32
 10 mg/kg-N
PD
8
974
1
80
0


CA209009_14_87
0
F
62
  2 mg/kg
SD
19
350
1
75
0


CA209009_5_73
1
M
77
  2 mg/kg
PD
11
178
0
58
0


CA209009_11_24
0
M
70
 10 mg/kg-N
PD
6
62
0
51
0


CA209009_8_105
1
M
64
 10 mg/kg
PD
17
118
0
45
0


CA209009_15_83
0
M
46
0.3 mg/kg
PD
24
486
0
45
0


CA209009_1_43
0
M
74
 10 mg/kg-N
PD
−8
968
1
44
0


CA209009_15_77
0
M
50
0.3 mg/kg
PD
1
132
0
44
0


CA209009_1_72
0
M
58
 10 mg/kg
PD
6
283
0
43
0


CA209009_5_28
0
M
47
 10 mg/kg-N
PD
30
43
1
43
0


CA209009_5_18
1
M
68
0.3 mg/kg
PD
65
111
1
43
0


CA209009_5_21
1
M
64
 10 mg/kg
SD
−13
349
1
43
1


CA209009_5_41
1
M
66
 10 mg/kg
PD
10
195
1
42
0


CA209009_2_85
1
M
36
  2 mg/kg
SD
−12
712
0
42
1


CA209009_6_99
0
M
50
  2 mg/kg
NE
.
41
0
41
0


CA209009_4_68
1
M
69
0.3 mg/kg
PD
−4
740
0
39
0


CA209009_11_38
0
M
69
 10 mg/kg-N
PD
0
955
1
39
0


CA209009_13_51
0
M
45
 10 mg/kg-N
PD
12
967
1
39
0


CA209009_11_25
1
F
72
 10 mg/kg-N
PD
16
167
0
39
0


CA209009_5_1
1
M
49
 10 mg/kg
PD
24
43
1
38
0


CA209009_3_117
1
M
58
 10 mg/kg-N
SD
−10
64
1
38
1


CA209009_13_96
1
F
75
  2 mg/kg
PD
12
582
1
37
0


CA209009_6_39
1
M
72
0.3 mg/kg
PD
13
306
0
37
0


CA209009_9_97
1
M
54
  2 mg/kg
PD
22
871
1
37
0


CA209009_14_69
0
F
40
 10 mg/kg
PD
53
303
0
37
0


CA209009_5_3
0
F
57
 10 mg/kg
PD
64
43
1
37
0


CA209009_9_66
0
M
47
  2 mg/kg
PD
38
470
0
36
0


CA209009_4_95
0
M
55
  2 mg/kg
PD
47
92
0
36
0


CA209009_13_90
1
M
54
0.3 mg/kg
PD
57
499
0
36
0


CA209009_9_27
1
M
59
0.3 mg/kg
PD
36
968
1
35
0


CA209009_14_98
0
M
65
0.3 mg/kg
PD
−49
398
0
32
0


CA209009_5_2
0
M
48
0.3 mg/kg
NE
.
31
0
31
0


CA209009_3_26
0
M
55
  2 mg/kg
NE
.
33
0
28
0


CA209009_5_63
0
F
71
  2 mg/kg
NE
.
189
1
22
0


CA209009_5_29
0
M
67
0.3 mg/kg
NE
.
937
1
1
1


CA209009_13_36
0
F
69
 10 mg/kg-N
NE
.
741
0
1
1


CA209009_11_12
0
F
69
 10 mg/kg
NE
.
179
0
1
1













patient_id
cell_membrane_pdl1_0percent
cell_membrane_pdl1_1plus_percent
cell_membrane_pdl1_2plus_percent





CA209009_1_20
100
0
0


CA209009_2_48
100
0
0


CA209009_9_45
97
1
1


CA209009_9_88
100
0
0


CA209009_5_106
100
0
0


CA209009_2_58
20
25
25


CA209009_3_15
99
1
0


CA209009_15_81
NA
NA
NA


CA209009_11_14
100
0
0


CA209009_14_107
100
0
0


CA209009_9_34
95
2
2


CA209009_11_93
100
0
0


CA209009_9_47
100
0
0


CA209009_15_94
100
0
0


CA209009_9_119
100
0
0


CA209009_13_111
100
0
0


CA209009_11_13
100
0
0


CA209009_15_75
100
0
0


CA209009_11_57
95
4
1


CA209009_11_8
97
3
0


CA209009_13_103
94
3
2


CA209009_4_54
100
0
0


CA209009_9_30
100
0
0


CA209009_9_52
100
0
0


CA209009_14_89
100
0
0


CA209009_5_4
100
0
0


CA209009_1_32
100
0
0


CA209009_15_76
99
1
0


CA209009_3_114
100
0
0


CA209009_5_22
70
5
10


CA209009_8_100
100
0
0


CA209009_11_10
97
3
0


CA209009_10_112
98
2
0


CA209009_5_17
100
0
0


CA209009_9_74
100
0
0


CA209009_2_64
100
0
0


CA209009_11_79
100
0
0


CA209009_5_23
100
0
0


CA209009_4_49
99
1
0


CA209009_11_71
100
0
0


CA209009_11_11
100
0
0


CA209009_2_102
100
0
0


CA209009_5_6
NA
NA
NA


CA209009_2_84
95
5
0


CA209009_1_62
NA
NA
NA


CA209009_12_115
100
0
0


CA209009_1_118
98
1
1


CA209009_5_50
91
5
3


CA209009_11_5
100
0
0


CA209009_1_86
100
0
0


CA209009_14_80
100
0
0


CA209009_2_42
95
3
2


CA209009_14_59
84
10
5


CA209009_11_56
100
0
0


CA209009_11_40
100
0
0


CA209009_14_87
38
26
30


CA209009_5_73
100
0
0


CA209009_11_24
100
0
0


CA209009_8_105
100
0
0


CA209009_15_83
100
0
0


CA209009_1_43
100
0
0


CA209009_15_77
94
6
0


CA209009_1_72
95
5
0


CA209009_5_28
97
1
2


CA209009_5_18
NA
NA
NA


CA209009_5_21
96
4
0


CA209009_5_41
100
0
0


CA209009_2_85
95
2
2


CA209009_6_99
97
3
0


CA209009_4_68
100
0
0


CA209009_11_38
95
5
0


CA209009_13_51
100
0
0


CA209009_11_25
100
0
0


CA209009_5_1
100
0
0


CA209009_3_117
98
2
0


CA209009_13_96
100
0
0


CA209009_6_39
100
0
0


CA209009_9_97
100
0
0


CA209009_14_69
100
0
0


CA209009_5_3
97
2
1


CA209009_9_66
100
0
0


CA209009_4_95
85
10
4


CA209009_13_90
25
35
20


CA209009_9_27
68
30
2


CA209009_14_98
97
1
1


CA209009_5_2
100
0
0


CA209009_3_26
91
5
3


CA209009_5_63
95
3
1


CA209009_5_29
100
0
0


CA209009_13_36
NA
NA
NA


CA209009_11_12
NA
NA
NA
















patient_id
cell_membrane_pdl1_3plus_percent
pdl1_positive_1percent
pdl1_positive_5percent
response_category







CA209009_1_20
0
0
0
extreme_responder



CA209009_2_48
0
0
0
extreme_responder



CA209009_9_45
1
1
0
extreme_responder



CA209009_9_88
0
0
0
extreme_responder



CA209009_5_106
0
0
0
extreme_responder



CA209009_2_58
30
1
1
extreme_responder



CA209009_3_15
0
0
0
extreme_responder



CA209009_15_81
NA
NA
NA
extreme_responder



CA209009_11_14
0
0
0
extreme_responder



CA209009_14_107
0
0
0
extreme_responder



CA209009_9_34
1
1
0
extreme_responder



CA209009_11_93
0
0
0
extreme_responder



CA209009_9_47
0
0
0
extreme_responder



CA209009_15_94
0
0
0
extreme_responder



CA209009_9_119
0
0
0
extreme_responder



CA209009_13_111
0
0
0
extreme_responder



CA209009_11_13
0
0
0
extreme_responder



CA209009_15_75
0
0
0
stable_disease



CA209009_11_57
0
1
1
stable_disease



CA209009_11_8
0
1
0
extreme_responder



CA209009_13_103
1
1
0
stable_disease



CA209009_4_54
0
0
0
stable_disease



CA209009_9_30
0
0
0
extreme_responder



CA209009_9_52
0
0
0
stable_disease



CA209009_14_89
0
0
0
stable_disease



CA209009_5_4
0
0
0
stable_disease



CA209009_1_32
0
0
0
stable_disease



CA209009_15_76
0
1
0
extreme_responder



CA209009_3_114
0
0
0
extreme_responder



CA209009_5_22
15
1
1
extreme_responder



CA209009_8_100
0
0
0
stable_disease



CA209009_11_10
0
1
0
stable_disease



CA209009_10_112
0
1
0
stable_disease



CA209009_5_17
0
0
0
stable_disease



CA209009_9_74
0
0
0
stable_disease



CA209009_2_64
0
0
0
stable_disease



CA209009_11_79
0
0
0
stable_disease



CA209009_5_23
0
0
0
stable_disease



CA209009_4_49
0
0
0
stable_disease



CA209009_11_71
0
0
0
stable_disease



CA209009_11_11
0
0
0
stable_disease



CA209009_2_102
0
0
0
stable_disease



CA209009_5_6
NA
NA
NA
stable_disease



CA209009_2_84
0
0
0
stable_disease



CA209009_1_62
NA
NA
NA
stable_disease



CA209009_12_115
0
0
0
stable_disease



CA209009_1_118
0
1
0
extreme_progressor



CA209009_5_50
1
1
1
extreme_responder



CA209009_11_5
0
0
0
stable_disease



CA209009_1_86
0
0
0
stable_disease



CA209009_14_80
0
0
0
stable_disease



CA209009_2_42
0
0
1
not_evaluable



CA209009_14_59
1
1
1
stable_disease



CA209009_11_56
0
0
0
stable_disease



CA209009_11_40
0
0
0
extreme_progressor



CA209009_14_87
6
1
1
stable_disease



CA209009_5_73
0
0
0
extreme_progressor



CA209009_11_24
0
0
0
extreme_progressor



CA209009_8_105
0
0
0
extreme_progressor



CA209009_15_83
0
0
0
extreme_progressor



CA209009_1_43
0
0
0
extreme_progressor



CA209009_15_77
0
1
0
extreme_progressor



CA209009_1_72
0
1
1
extreme_progressor



CA209009_5_28
0
1
0
extreme_progressor



CA209009_5_18
NA
NA
NA
extreme_progressor



CA209009_5_21
0
1
0
stable_disease



CA209009_5_41
0
0
0
extreme_progressor



CA209009_2_85
1
1
0
stable_disease



CA209009_6_99
0
1
0
not_evaluable



CA209009_4_68
0
0
0
extreme_progressor



CA209009_11_38
0
1
1
extreme_progressor



CA209009_13_51
0
0
0
extreme_progressor



CA209009_11_25
0
0
0
extreme_progressor



CA209009_5_1
0
0
0
extreme_progressor



CA209009_3_117
0
0
0
stable_disease



CA209009_13_96
0
0
0
extreme_progressor



CA209009_6_39
0
0
0
extreme_progressor



CA209009_9_97
0
0
0
extreme_progressor



CA209009_14_69
0
0
0
extreme_progressor



CA209009_5_3
0
1
0
extreme_progressor



CA209009_9_66
0
0
0
extreme_progressor



CA209009_4_95
1
1
1
extreme_progressor



CA209009_13_90
20
1
1
extreme_progressor



CA209009_9_27
0
1
1
extreme_progressor



CA209009_14_98
1
1
0
extreme_progressor



CA209009_5_2
0
0
0
not_evaluable



CA209009_3_26
1
1
1
not_evaluable



CA209009_5_63
1
1
1
not_evaluable



CA209009_5_29
0
0
0
not_evaluable



CA209009_13_36
NA
NA
NA
not_evaluable



CA209009_11_12
NA
NA
NA
not_evaluable










All patients listed in Table 2B were treated with nivolumab. For sex, M represents male and F represents female.


On whole exome analysis, overall mutational loads were moderate in the 34 patients with high-quality WES in the training cohort (median 116.5, range 70-255), and mutational burden did not predict response to therapy, nor did burden of clonal mutations or the ratio of subclonal to clonal mutations (p>0.05 for all; Wilcoxon rank sum) (FIG. 4A-4B, Table 2C). Thus, the role of mutations in particular genes in mediating response needs to be solved. To identify significantly mutated genes in this cohort, MutSigCV (Lawrence et al. (2013) Nature 499:214-218, available at the website of the Broad Insitute of the World Wide Web address of software.broadinstitute.orgicancer/software/genepattern/modules/docs/MutSigCV) program was implemented to identify genes mutated more frequently than expected by chance, after correcting for patient-specific mutation frequencies and spectra and gene-specific mutation rates, expression levels, and replication times. This analysis identified six significantly mutated genes (Table 2D), consistent with prior studies of ccRCC, including VHL, PBRM1, and SETD2 (Cancer Genome Atlas Research, 2013). Of these 6, mutations in PBRM1 were more common in extreme responders to anti-PD1 therapy than in extreme progressors (p=0.019; Pearson's chi-squared) (FIG. 5). It was also noted that some subjects had deletions in various chromosomes. For example, subjects CA209009_12_115 and KE6262 had arm-level monoallelic deletion of chromosome 15, including B2M; subjects PD_005, PD_007, CA209009_5_1, and CA209009_13_96 had arm-level monoallelic deletion of chromosome 6, including HLA-A, HLA-B, HLA-C, TAP1, TAP2, and TAPBP; subject VA1008 had focal monoallelic deletion of chromosome 6, including HLA-A, HLA-B, HLA-C, TAP1, TAP2, and TAPBP; subject CA209009_8_105 had focal monoallelic deletion of chromosome 6, including HLA-A, HLA-B, and HLA-C; subject CA209009_11_25 had arm-level monoallelic deletion of chromosome 6, including HLA-A, HLA-B, HLA-C, and TAPBP; subject CA209009_11_93 had a large monoallelic deletion of chromosome 6, including HLA-B, HLA-C, and TAPBP; and subject CA209009_5_503 had a large monoallelic deletion of chromosome 6, including HLA-A, HLA-B, TAP1, TAP2, and TAPBP. Furthermore, it was observed that truncating mutations (frameshift indels, nonsense, or splice-site) in PBRM1 occurred significantly more frequently in the extreme responders (p=0.0064; Pearson's chi-squared) after correcting for false discovery among the 6 genes mutated significantly in the training cohort (q=0.039; Benjamini-Hochberg) (FIG. 4C, Table 2E). All truncating PBRM1 alterations were in the context of chromosome 3p deletions (FIG. 4B), resulting in expected complete loss-of-function of PBRM1. Most of these alterations were predicted to be clonal (present in all tumor cells), with the two subclonal alterations found in one patient with stable disease and another with extreme response to anti-PD1 therapy (Table 2E). Patients with truncating mutations in PBRM1 had significantly prolonged progression-free survival compared to those without truncating alterations in PBRM1 (p=0.042) (FIG. 4D), and prolonged overall survival as well (p=0.014) (FIG. 6), with sustained reductions in tumor burden (FIG. 7B). Of note, two of the three extreme progressors with PBRM1 truncating mutations had long OS (>1.5 years), and all three were still alive at the time of censoring (FIG. 7B). Additionally, of the three patients with SD and objective tumor regression but PFS of insufficient duration to be considered an exceptional responder, 2 were PBRM1 mutants, while the third (2_85) had relatively low tumor sequencing coverage over PBRM1 (48-fold) and low tumor purity (estimated 13% tumor cells), making it possible that we were insufficiently powered to detect a PBRM1 mutation in this patient. In a focused search for PBRM1 alterations in the 6 tumors initially excluded from analysis for quality-control reasons (FIG. 1B), two additional truncating mutations were found. One was a poorly-supported splice site alteration (4/35 reads, all in reverse direction) in an extreme progressor (4_95), while the other was a well-supported nonsense alteration (22/417 reads) in an extreme responder (5_6).









TABLE 2C





Summary of Mutational Burden in Training Cohort (N = 34)



















sample
all_mutations
all_nonsynonymous
all_synonymous
clonal_mutations





CA209009_1_20
80
50
30
51


CA209009_1_32
125
89
36
103


CA209009_1_62
110
82
28
68


CA209009_11_10
135
85
50
84


CA209009_11_11
100
63
37
64


CA209009_11_14
128
86
42
107


CA209009_11_25
157
113
44
107


CA209009_11_56
125
102
23
55


CA209009_11_79
78
54
24
47


CA209009_11_93
125
91
34
101


CA209009_12_115
90
64
26
52


CA209009_13_90
140
102
38
96


CA209009_13_96
255
157
98
83


CA209009_2_102
166
115
51
92


CA209009_2_58
82
60
22
39


CA209009_2_84
117
87
30
71


CA209009_2_85
83
52
31
72


CA209009_3_114
83
59
24
65


CA209009_3_117
86
65
21
71


CA209009_3_15
110
75
35
77


CA209009_4_68
132
102
30
108


CA209009_5_1
84
61
23
66


CA209009_5_106
104
76
28
70


CA209009_5_18
81
61
20
51


CA209009_5_21
128
82
46
92


CA209009_5_41
102
73
29
87


CA209009_5_50
171
126
45
122


CA209009_5_73
70
45
25
36


CA209009_6_39
126
86
40
117


CA209009_8_105
152
105
47
118


CA209009_9_119
97
62
35
81


CA209009_9_27
128
98
30
78


CA209009_9_52
125
94
31
94


CA209009_9_97
116
82
34
77















sample
subclonal_mutations
clonality_unknown
all_neoantigens







CA209009_1_20
18
11
32



CA209009_1_32
12
10
134



CA209009_1_62
23
19
172



CA209009_11_10
26
25
93



CA209009_11_11
25
11
110



CA209009_11_14
11
10
114



CA209009_11_25
33
17
139



CA209009_11_56
62
8
100



CA209009_11_79
22
9
170



CA209009_11_93
17
7
177



CA209009_12_115
26
12
148



CA209009_13_90
39
5
180



CA209009_13_96
107
65
153



CA209009_2_102
44
30
149



CA209009_2_58
33
10
64



CA209009_2_84
28
18
106



CA209009_2_85
0
11
57



CA209009_3_114
10
8
82



CA209009_3_117
14
1
80



CA209009_3_15
18
15
116



CA209009_4_68
18
6
126



CA209009_5_1
11
7
99



CA209009_5_106
26
8
83



CA209009_5_18
29
1
233



CA209009_5_21
21
15
100



CA209009_5_41
5
10
127



CA209009_5_50
31
18
242



CA209009_5_73
29
5
59



CA209009_6_39
2
7
65



CA209009_8_105
18
16
246



CA209009_9_119
2
14
70



CA209009_9_27
41
9
189



CA209009_9_52
15
16
143



CA209009_9_97
26
13
144

















TABLE 2D





MutSigCV results in training cohort (N = 34)




























code









rank
gene
longname
len
nnei
nncd
nsil
nmis
nstp
nspl
nind





1
VHL
von
650
489
0
0
8
5
1
9




Hippel-




Lindau




tumor




suppressor


2
PBRM1
polybromo 1
5417
111
0
1
0
4
4
12


3
SETD2
SET
7777
25
0
1
4
8
0
2




domain




containing 2


4
ATXN7L1
ataxin 7-
2723
76
0
0
2
0
0
3




like 1


5
GUCY2C
guanylate
3326
16
0
0
0
1
0
2




cyclase




2C (heat




stable




enterotoxin




receptor)


6
KDM5C
lysine
4879
24
0
0
1
2
1
3




(K)-




specific




demethylase




5C


















rank
nnon
npat
nsite
pCV
pCL
pFN
P
q





1
23
23
22
1.00E−16
8.18E−01
9.67E−01
3.77E−15
3.46E−11


2
20
20
20
1.00E−16
1
7.19E−01
3.77E−15
3.46E−11


3
14
13
14
2.26E−13
1
5.37E−01
6.80E−12
4.16E−08


4
5
5
3
1.06E−07
8.25E−04
1.85E−02
2.54E−09
1.17E−05


5
3
3
2
2.00E−05
3.00E−03
4.78E−01
5.68E−07
2.08E−03


6
7
7
7
4.46E−07
1
3.50E−01
4.10E−06
1.25E−02









Only six identified genes, among 18,345 genes tested, are shown in Table 2D.









TABLE 2E





Truncating PBRM1 alterations in patients training


cohort passing whole exome quality control (N = 34)




























PBRM1_



Variant_

Tumor_
Tumor_


patient_
Hugo_

mean_
Chromo-
Start_
End_
Classi-
Reference_
Seq_
Seq_


id
Symbol

coverage
some
position
position
fication
Allele
Allele1
Allele2





CA209009_
PBRM1
1
119.39
3
52663008
52663008
Nonsense_
C
C
A


9_97






Mutation








CA209009_
PBRM1
1
 97.9
3
52613205
52613205
Frame_
T
T
-


9_52






Shift_












Del








CA209009_
PBRM1
0
248.99
3
52598081
52598101
In_Frame_
TCA
TCA
-


9_27






Del
TCA
TCA











TCT
TCT











ACC
ACC











ACT
ACT











TTA
TTA











GCA
GCA






CA209009_
PBRM1
1
 28.57
3
52682459
52682459
Splice_
C
C
G


9_119






Site








CA209009_
NA
0
146.47
NA
NA
NA
NA





8_105















CA209009_
NA
0
130.38
NA
NA
NA
NA





6_39















CA209009_
NA
0
181.65
NA
NA
NA
NA





5_73















CA209009_
PBRM1
1
135.79
3
52712515
52712515
Splice_
C
C
T


5_50






Site








CA209009_
NA
0
123.22
NA
NA
NA
NA





5_41















CA209009_
PBRM1
1
125.64
3
52613210
52613210
Frame_
T
T
-


5_21






Shift_












Del








CA209009_
PBRM1
1
126.07
3
52678748
52678748
Nonsense_
C
C
A


5_18






Mutation








CA209009_
PBRM1
1
155.18
3
52620610
52620614
Frame_
ATTTT
ATTTT
-


5_106






Shift_












Del








CA209009_
NA
0
138.81
NA
NA
NA
NA





5_1















CA209009_
NA
0
100.73
NA
NA
NA
NA





4_68















CA209009_
PBRM1
1
 94.84
3
52613194
52613194
Nonsense_
C
C
A


3_15






Mutation








CA209009_
PBRM1
1
146.69
3
52643375
52643375
Nonsense_
G
G
A


3_117






Mutation








CA209009_
PBRM1
1
111.22
3
52662964
52662964
Frame_
A
A
-


3_114






Shift_












Del








CA209009_
NA
0
 47.52
NA
NA
NA
NA
NA
NA
NA


2_85















CA209009_
PBRM1
1
130.86
3
52696272
52696272
Frame_
T
T
-


2_84






Shift_












Del








CA209009_
NA
0
 95.31
NA
NA
NA
NA





2_58















CA209009_
PBRM1
1
266.4
3
52663052
52663052
Splice_
C
C
T


2_102






Site








CA209009_
PBRM1
1
164.39
3
52643489
52643489
Frame_
A
A
-


13_96






Shift_












Del








CA209009_
NA
0
124.87
NA
NA
NA
NA





13_90















CA209009_
NA
0
115.51
NA
NA
NA
NA





12_115















CA209009_
PBRM1
1
173.78
3
52651277
52651277
Splice_
C
C
T


11_93






Site








CA209009_
PBRM1
1
 67.19
3
52621487
52621487
Frame_
T
T
-


11_79






Shift_












Del








CA209009_
NA
0
221.56
NA
NA
NA
NA





11_56















CA209009_
NA
0
124.35
NA
NA
NA
NA





11_25















CA209009_
PBRM1
1
131.63
3
52623201
52623201
Frame_
G
G
-


11_14






Shift_












Del








CA209009_
NA
0
 62.73
NA
NA
NA
NA





11_11















CA209009_
PBRM1
1
 89.9
3
52623120
52623120
Frame_
G
G
-


11_10






Shift_












Del








CA209009_
PBRM1
1
131.16
3
52613062
52613068
Splice_
ACA
ACA
-


1_62






Site
CTC
CTC











A
A






CA209009_
NA
0
120.85
NA
NA
NA
NA





1_32















CA209009_
PBRM1
1
 28.98
3
52649455
52649456
Frame_
-
-
T


1_20






Shift_












Ins



















patient_
Protein_
Variant_
i_
t_alt_
t_ref_

Indel_



id
Change
Type
tumor_f
count
count
clonal
Caller






CA209009_
p.E417*
SNP
0.278481
22
 57
1
NA



9_97













CA209009_
p.D1148fs
DEL
0.235955056
21
 68
1
strelka,



9_52






indelocator






CA209009_
p.AKVVDDE1249del
DEL
0.15
14
 77
not
indelocator



9_27





evaluable







CA209009_

SNP
0.666667
10
  5
1
NA



9_119













CA209009_
NA
NA
NA
NA
NA
NA
NA



8_105













CA209009_
NA
NA
NA
NA
NA
NA
NA



6_39













CA209009_
NA
NA
NA
NA
NA
NA
NA



5_73













CA209009_

SNP
0.213592
22
 81
1
NA



5_50













CA209009_
NA
NA
NA
NA
NA
NA
NA



5_41













CA209009_
p.K1146fs
DEL
0.441666667
53
 67
1
strelka,



5_21






indelocator






CA209009_
p.E291*
SNP
0.150943
 8
 45
1
NA



5_18













CA209009_
p.KI1087fs
DEL
0.067137809
19
264
0
strelka,



5_106






indelocator






CA209009_
NA
NA
NA
NA
NA
NA
NA



5_1













CA209009_
NA
NA
NA
NA
NA
NA
NA



4_68













CA209009_
p.E1105*
SNP
0.53
53
 47
1
NA



3_15













CA209009_
p.Q809*
SNP
0.288
36
 89
1
NA



3_117













CA209009_
p.N463fs
DEL
0.108695652
10
 82
1
strelka,



3_114






indelocator






CA209009_
NA
NA
NA
NA
NA
NA
NA



2_85













CA209009_
p.K135fs
DEL
0.171428571
12
 58
1
strelka,



2_84






indelocator






CA209009_
NA
NA
NA
NA
NA
NA
NA



2_58













CA209009_

SNP
0.235849
25
 81
1
NA



2_102













CA209009_
p.S818fs
DEL
0.402654867
91
135
1
strelka,



13_96






indelocator






CA209009_
NA
NA
NA
NA
NA
NA
NA



13_90













CA209009_
NA
NA
NA
NA
NA
NA
NA



12_115













CA209009_

SNP
0.12766
 6
 41
1
NA



11_93













CA209009_
p.N1017fs
DEL
0.464285714
13
 15
1
strelka,



11_79






indelocator






CA209009_
NA
NA
NA
NA
NA
NA
NA



11_56













CA209009_
NA
NA
NA
NA
NA
NA
NA



11_25













CA209009_
p.D965fs
DEL
0.25
15
 45
1
strelka,



11_14






indelocator






CA209009_
NA
NA
NA
NA
NA
NA
NA



11_11













CA209009_
p.I992fs
DEL
0.55
55
 45
1
strelka,



11_10






indelocator






CA209009_

DEL
0.17370892
37
176
0
strelka



1_62













CA209009_
NA
NA
NA
NA
NA
NA
NA



1_32













CA209009_
p.H627fs
INS
0.363636364
8
 14
1
strelka,



1_20






indelocator









A validation cohort of 41 patients (see Table 3) treated with immune checkpoint therapy for alterations in PBRM1 was then examined to confirm the association between PBRM1 mutational status and response to immune checkpoint therapy. After limiting analyses to those treated with immune checkpoint monotherapy and applying the same quality control standards and definitions of clinical response as in the training cohort, PBRM1 status was assessed in 28 patients (FIGS. 7A-7B and Tables 4A-4C). Extreme responders to immune checkpoint therapy were significantly more likely than extreme progressors to harbor truncating alterations in PBRM1 (8/13 vs. 1/7, p=0.043; Pearson's chi-squared) (FIG. 7C-7D). Again, all but one truncating event in PBRM1 occurred in the setting of chromosome 3p deletion, though this was likely a false negative due to low tumor purity (FIG. 8). One patient (VA1008) likely had CN loss over chromose 3p, though low tumor purity made calling this deletion difficult.


In examining germline variants in WES of germline tissue across both the training and validation cohorts (N=91), including samples that failed quality control for tumor WES, 4 nonsynonymous variants (all in extreme responders), but no truncating alterations in PBRM1, were observed (Table 5A). Further analysis covers the frameshift and nonsense variants in genes thought to be associated with hereditary cancer syndromes (Hart et al. (2016) BMJ Open 6:e010332), as well as genes involved in JAK/STAT signaling and immune checkpoints. Almost all alterations were heterozygous and have been previously observed in a database of germline variants from more than 60 thousand ethnically diverse individuals (ExAC) (Lek et al. (2016) Nature 536:285-291) (Table 5B). Two patients (CA8808: extreme responder and RCC.1101: stable disease) had a heterozygous frameshift alteration in PD-L2 (p.L10 fs), which has been observed at frequency of 0.2% in ExAC.


In the somatic space, alterations affecting antigen presentation machinery were rare. In the training cohort, no patients harbored nonsynonymous alterations in TAP1, TAP2, B2M, TAPBP, or any of the HLA Class I alleles. One patient with intermediate benefit (12_115) had a heterozygous deletion of B2M. Six patients (2 extreme responders, 1 intermediate benefit, 2 extreme progressors) had loss of heterozygosity (LOH) in chromosome 6p affecting the HLA and TAP loci. In the validation cohort, one patient had a nonsense mutation in TAP1 (VA1008; extreme responder), one had a missense mutation in B2M (PD_021; extreme progressor), and one had LOH of B2M (KE6262; extreme responder). Three patients had LOH over chromosome 6p (2 extreme responders, 1 intermediate benefit; see also FIG. 12).









TABLE 3







Clinical cohort consolidation











Sequencing

Number of


Cohort name
platform
Drug
patients













DFCI Agilent
Agilent
nivolumab
5


DFCI Kurelt
Illumina
atezolizumab
2




atezolizumab + bevacizumab
3




axitinib + avelumab
2




axitinib + pembrolizumab
4




nivolumab
13




nivolumab + ipilimumab
5


MSKCC
Agilent
nivolumab
6




nivolumab + ipilimumab
1
















TABLE 4A







Sequencing Metrics and Inclusion/Exclusion Criteria for Whole Exome


Sequencing in Validation Cohort (N = 41)














patient_id
tumor_mtc
normal_mtc
bait_set
absolute_inferred_purity
absolute_inferred_ploidy
genome_doubling
exclusion_reason

















RCC-
131.1804
84.152599
whole_exome_illumina_coding_v1
0.55
1.76
0
0


PD_010


RCC-
129.113198
117.952235
whole_exome_illumina_coding_v1
0.38
1.94
0
MixedResponse


PD_030


BL5166_T1
145.877429
122.453144
whole_exome_agilent_1
NA
NA
NA
LowPurity


RCC-
NA
89.149597
whole_exome_illumina_coding_v1
NA
NA
NA
FailedSequencing


PD_029


RCC-
126.512191
83.258112
whole_exome_illumina_coding_v1
0.5
1.91
0
CombinationT


PD_001






KI


RCC-
126.083408
90.8862
whole_exome_illumina_coding_v1
0.46
1.9
0
CombinationT


PD_003






KI


RCC-
135.674171
100.347012
whole_exome_illumina_coding_v1
0.4
3.78
1
CombinationT


PD_004






KI


RCC-
149.115421
72.503658
whole_exome_illumina_coding_v1
0.5
2.37
0
CombinationT


PD_006






KI


RCC-
159.873929
91.682176
whole_exome_illumina_coding_v1
0.39
1.95
0
CombinationT


PD_008






KI


RCC-
180.675064
91.140713
whole_exome_illumina_coding_v1
0.45
2.99
1
CombinationT


PD_027






KI


RCC-
150.285278
98.786695
whole_exome_illumina_coding_v1
0.25
1.79
0
CombinationT


PD_028






KI


RCC-
178.994864
93.923124
whole_exome_illumina_coding_v1
0.55
2.07
0
CombinationT


PD_031






KI


RCC-
106.860416
72.680181
whole_exome_illumina_coding_v1
0.5
1.89
0
CombinationT


PD_002






KI


RCC-
125.608438
102.090575
whole_exome_illumina_coding_v1
0.47
1.82
0
0


PD_005


RCC-
141.661729
101.362659
whole_exome_illumina_coding_v1
0.57
1.82
0
0


PD_007


RCC-
122.598167
87.563055
whole_exome_illumina_coding_v1
0.41
1.99
0
PapillaryRCC


PD_009


RCC-
104.135516
82.878525
whole_exome_illumina_coding_v1
0.32
2.08
0
0


PD_011


RCC-
133.950619
87.549415
whole_exome_illumina_coding_v1
0.39
2.01
0
0


PD_012


RCC-
145.082205
91.170952
whole_exome_illumina_coding_v1
0.22
3.44
1
0


PD_013


RCC-
127.306107
78.539083
whole_exome_illumina_coding_v1
0.25
4.02
1
0


PD_014


RCC-
105.708638
93.290512
whole_exome_illumina_coding_v1
0.36
2.1
0
0


PD_015


RCC-
145.443729
95.372761
whole_exome_illumina_coding_v1
0.61
1.84
0
0


PD_018


RCC-
148.823821
87.774525
whole_exome_illumina_coding_v1
0.42
1.97
0
0


PD_019


RCC-
151.788377
102.972091
whole_exome_illumina_coding_v1
0.18
1.86
0
0


PD_020


RCC-
159.181781
95.98438
whole_exome_illumina_coding_v1
0.58
1.9
0
0


PD_021


RCC-
148.651377
98.276519
whole_exome_illumina_coding_v1
0.53
1.97
0
0


PD_022


RCC-
135.431357
86.807511
whole_exome_illumina_coding_v1
0.19
2.65
1
0


PD_023


RCC-
92.006306
83.700183
whole_exome_illumina_coding_v1
0.27
2.01
0
0


PD_024


RCC-
74.164294
48.102291
whole_exome_illumina_coding_v1
0.35
1.99
0
0


PD_025


RCC-
166.502187
89.436443
whole_exome_illumina_coding_v1
0.76
1.98
0
0


PD_026


CA8808_T1
123.07315
103.475727
whole_exome_agilent_1
0.43
1.96
0
0


KA4076_T1
126.229037
120.209259
whole_exome_agilent_1
0.56
2.03
0
0


KE5236_T1
132.886302
140.196056
whole_exome_agilent_1
0.33
2
0
0


KE6262_T1
99.539361
106.858872
whole_exome_agilent_1
0.11
4.16
1
0


MC1838_T1
149.730846
118.307339
whole_exome_agilent_1
0.41
1.99
0
0


VA1008_T1
142.542157
89.429498
whole_exome_agilent_1
0.14
1.74
0
0


RCC.PD1.DNA.1101.T
92.348009
81.023695
whole_exome_agilent_1.1_refseq_plus_3_boosters
0.3
1.97
0
0


RCC.PD1.DNA.1137.T
71.474257
96.238769
whole_exome_agilent_1.1_refseq_plus_3_boosters
0.31
3.43
1
0


RCC.PD1.DNA.1026.T
136.955167
87.050978
whole_exome_agilent_1.1_refseq_plus_3_boosters
0.26
2.15
0
0


RCC.PD1.DNA.944.T
126.472115
95.64198
whole_exome_agilent_1.1_refseq_plus_3_boosters
0.66
1.89
0
0


RCC.PD1.DNA.949.T
101.276419
96.351667
whole_exome_agilent_1.1_refseq_plus_3_boosters
0.43
3.91
1
0
















TABLE 4B





Clinical Information for Immune-Checkpoint-Treated Patients in Validation


Cohort (N = 41)























patient_id
drug
best_recist
sex
age
max_tumor_change
histology
os_days
os_censor





VA1008
nivolumab +
PR
M
76
−96
clear-
1135
1



ipilimumab




cell


RCC.PD1.DNA.949
nivolumab
PR
F
60
−40
clear-
364
0








cell


RCC.PD1.DNA.944
nivolumab
PD
M
47
37
clear-
134
1








cell


RCC.PD1.DNA.1137
nivolumab
SD
F
61
16
clear-
1584
1








cell


RCC.PD1.DNA.1101
nivolumab
SD
M
67
4
clear-
439
0








cell


RCC.PD1.DNA.1026
nivolumab
CR
M
60
−87
clear-
1442
1








cell


RCC-
axitinib +
PR
M
68
−49
clear-
165
1


PD_031
avelumab




cell


RCC-
nivolumab
X
M
72
−43
clear-
395
0


PD_030





cell


RCC-
nivolumab
PR
M
54
−49
clear-
856
0


PD_029





cell


RCC-
atezolizumab +
PR
M
77
−43
clear-
210
1


PD_028
bevacizumab




cell


RCC-
axitinib +
PR
M
59
−42
clear-
210
1


PD_027
avelumab




cell


RCC-
nivolumab
SD
F
70
20
clear-
377
1


PD_026





cell


RCC-
nivolumab
SD
M
74
−23
clear-
1724
1


PD_025





cell


RCC-
nivolumab
PD
M
52
30
clear-
304
0


PD_024





cell


RCC-
atezolizumab
PR
M
69
−88
clear-
637
1


PD_023





cell


RCC-
nivolumab
PD
F
66
NA
clear-
247
1


PD_022





cell


RCC-
nivolumab
PD
F
63
NA
clear-
185
0


PD_021





cell


RCC-
nivolumab
PD
F
64
NA
clear-
203
1


PD_020





cell


RCC-
nivolumab
SD
M
60
−11
clear-
230
1


PD_019





cell


RCC-
nivolumab
PR
F
69
−82
clear-
1189
0


PD_018





cell


RCC-
nivolumab
PD
M
71
6
clear-
814
0


PD_015





cell


RCC-
nivolumab +
SD
F
68
−5
clear-
433
1


PD_014
ipilimumab




cell


RCC-
nivolumab +
PR
M
66
−32
clear-
399
1


PD_013
ipilimumab




cell


RCC-
atezolizumab
PD
M
67
−50
clear-
581
1


PD_012





cell


RCC-
nivolumab
PD
M
40
−37
clear-
327
0


PD_011





cell


RCC-
nivolumab +
CR
M
51
−51
clear-
454
1


PD_010
ipilimumab




cell


RCC-
nivolumab +
PD
M
56
8
papillary
377
1


PD_009
ipilimumab


RCC-
axitinib +
PR
F
69
−69
clear-
462
1


PD_008
pembrolizumab




cell


RCC-
nivolumab +
PR
M
60
−42
clear-
448
1


PD_007
ipilimumab




cell


RCC-
axitinib +
PR
M
68
−52
clear-
398
1


PD_006
pembrolizumab




cell


RCC-
nivolumab
PD
M
62
NA
clear-
277
1


PD_005





cell


RCC-
axitinib +
SD
M
54
−16
clear-
481
1


PD_004
pembrolizumab




cell


RCC-
atezolizumab +
SD
M
52
−16
clear-
679
1


PD_003
bevacizumab




cell


RCC-
atezolizumab +
SD
M
65
−14
clear-
534
1


PD_002
bevacizumab




cell


RCC-
axitinib +
PR
F
66
−53
clear-
572
1


PD_001
pembrolizumab




cell


MC1838
nivolumab
PD
M
64
93
clear-
622
0








cell


KE6262
nivolumab
PR
M
68
−60
clear-
903
1








cell


KE5236
nivolumab
PD
M
58
70
clear-
997
1








cell


KA4076
nivolumab
PD
F
61
59
clear-
727
0








cell


CA8808
nivolumab
PR
M
62
−55
clear-
560
1








cell


BL5166
nivolumab
SD
M
64
−11
clear-
622
0








cell















patient_id
pfs_days
pfs_censor
prior_immune_checkpoint
response_category
response_toni





VA1008
1135
1
0
extreme_responder
extreme_responder


RCC.PD1.DNA.949
235
0
0
extreme_responder
extreme_responder


RCC.PD1.DNA.944
67
0
0
extreme_progressor
extreme_progressor


RCC.PD1.DNA.1137
119
0
0
stable_disease
stable_disease


RCC.PD1.DNA.1101
171
0
0
stable_disease
stable_disease


RCC.PD1.DNA.1026
357
1
0
extreme_responder
extreme_responder


RCC-
123
0
0
extreme_responder
stable_disease


PD_031


RCC-
93
0
0
not_evaluable
stable_disease


PD_030


RCC-
189
0
0
extreme_responder
extreme_responder


PD_029


RCC-
210
1
0
extreme_responder
extreme_responder


PD_028


RCC-
210
1
0
extreme_responder
extreme_responder


PD_027


RCC-
171
0
0
stable_disease
stable_disease


PD_026


RCC-
333
0
0
stable_disease
extreme_responder


PD_025


RCC-
41
0
0
extreme_progressor
extreme_progressor


PD_024


RCC-
637
1
0
extreme_responder
extreme_responder


PD_023


RCC-
80
0
0
extreme_progressor
extreme_progressor


PD_022


RCC-
68
0
0
extreme_progressor
extreme_progressor


PD_021


RCC-
47
0
0
extreme_progressor
extreme_progressor


PD_020


RCC-
220
0
0
stable_disease
extreme_responder


PD_019


RCC-
672
0
0
extreme_responder
extreme_responder


PD_018


RCC-
105
0
0
stable_disease
stable_disease


PD_015


RCC-
433
1
0
extreme_responder
extreme_responder


PD_014


RCC-
399
1
0
extreme_responder
extreme_responder


PD_013


RCC-
61
0
0
extreme_progressor
extreme_progressor


PD_012


RCC-
205
0
0
stable_disease
extreme_responder


PD_011


RCC-
454
1
0
extreme_responder
extreme_responder


PD_010


RCC-
89
0
0
extreme_progressor
extreme_progressor


PD_009


RCC-
462
1
0
extreme_responder
extreme_responder


PD_008


RCC-
448
1
0
extreme_responder
extreme_responder


PD_007


RCC-
398
1
0
extreme_responder
extreme_responder


PD_006


RCC-
168
0
0
stable_disease
stable_disease


PD_005


RCC-
481
1
0
extreme_responder
extreme_responder


PD_004


RCC-
479
0
0
extreme_responder
extreme_responder


PD_003


RCC-
255
0
1
stable_disease
extreme_responder


PD_002


RCC-
572
1
0
extreme_responder
extreme_responder


PD_001


MC1838
60
0
0
extreme_progressor
extreme_progressor


KE6262
163
0
0
extreme_responder
stable_disease


KE5236
165
0
0
stable_disease
stable_disease


KA4076
107
0
0
stable_disease
stable_disease


CA8808
558
0
0
extreme_responder
extreme_responder


BL5166
156
0
0
stable_disease
stable_disease









For sex, M represents male and F represents female.









TABLE 4C





Truncating PBRM1 alterations in validation cohort





























Variant_

Tumor_
Tumor_



patient_
Hugo_
Chromo-
Start_
End_
Classi-
Reference_
Seq_
Seq_
Protein_


id
Symbol
some
position
position
fication
Allele
Allele1
Allele2
Change





CA8808
PBRM1
3
52595873
52595873
Frame_
G
G
-
p.Q1415fs







Shift_











Del









KA4076
NA
NA
NA
NA
NA



NA





KE5236
PBRM1
3
52597356
52597359
Frame_
AG
AG
-
p.LP1310fs







Shift_
GT
GT









Del









KE6262
PBRM1
3
52643586
52643596
Frame_
ATG
ATG
-
p.EDSH782fs







Shift_
AGA
AGA









Del
GTC
GTC










CT
CT







MC1838
NA
NA
NA
NA
NA



NA





PD_005
PBRM1
3
52668656
52668656
Nonsense_
G
G
T
p.Y389*







Mutation









PD_007
NA
NA
NA
NA
NA



NA





PD_010
PBRM1
3
52702580
52702580
Nonsense_
A
A
C
p.Y106*







Mutation









PD_011
NA
NA
NA
NA
NA



NA





PD_012
NA
NA
NA
NA
NA



NA





PD_013
PBRM1
3
52597487
52597488
Frame_
CC
CC
-
p.E1315fs







Shift_











Del









PD_014
PBRM1
3
52610662
52610663
Frame_
AG
AG
-
p.F1211fs







Shift_











Del









PD_015
PBRM1
3
52637540
52637540
Frame_
AG
AG
-
p.R941fs







Shift_











Del









PD_018
NA
NA
NA
NA
NA



NA





PD_019
NA
NA
NA
NA
NA



NA





PD_020
NA
NA
NA
NA
NA



NA





PD_021
PBRM1
3
52713723
52713723
Frame_
C
C
-
p.G2fs







Shift_











Del









PD_022
NA
NA
NA
NA
NA



NA





PD_023
PBRM1
3
52663053
52663053
Splice_
T
T
A








Site









PD_024
NA
NA
NA
NA
NA



NA





PD_025
PBRM1
3
52595829
52595829
Frame_
C
C
-
p.G1429fs







Shift_











Del









PD_026
NA
NA
NA
NA
NA



NA





RCC.PD1.
NA
NA
NA
NA
NA



NA


DNA.1026














RCC.PD1.
PBRM1
3
52595804
52595804
Frame_
C
C
-
p.A1438fs


DNA.1101




Shift_











Del









RCC.PD1.
NA
NA
NA
NA
NA



NA


DNA.1137














RCC.PD1.
NA
NA
NA
NA
NA



NA


DNA.944














RCC.PD1.
NA
NA
NA
NA
NA



NA


DNA.949














VA1008
PBRM1
3
52643943
52643943
Frame_
T
T
-
p.K619fs







Shift_











Del


















patient_
Variant_
i_
t_alt_
t_ref_
clonal_




id
Type
tumor_f
count
count
dm






CA8808
DEL
0.259090909
57
163
not
strelka,








evaluable
indelocator






KA4076
NA
NA
NA
NA
NA
NA






KE5236
DEL
0.09
 8
 85
not
indelocator








evaluable







KE6262
DEL
0.098214286
11
101
1
strelka,









indelocator






MC1838
NA
NA
NA
NA
NA
NA






PD_005
SNP
0.287356
25
 62
1
NA






PD_007
NA
NA
NA
NA
NA
NA






PD_010
SNP
0.15
 6
 34
0







PD_011
NA
NA
NA
NA
NA
NA






PD_012
NA
NA
NA
NA
NA
NA






PD_013
DEL
0.1171875
15
113
1
strelka,









indelocator






PD_014
DEL
0.288888889
26
 64
1
strelka,









indelocator






PD_015
DEL
0.204545455
18
 70
1
strelka,









indelocator






PD_018
NA
NA
NA
NA
NA
NA






PD_019
NA
NA
NA
NA
NA
NA






PD_020
NA
NA
NA
NA
NA
NA






PD_021
DEL
0.36
18
 32
1
strelka,









indelocator






PD_022
NA
NA
NA
NA
NA
NA






PD_023
SNP
0.214286
 9
 33
1
NA






PD_024
NA
NA
NA
NA
NA
NA






PD_025
DEL
0.154411765
21
115
1
strelka,









indelocator






PD_026
NA
NA
NA
NA
NA
NA






RCC.PD1.
NA
NA
NA
NA
NA
NA



DNA.1026












RCC.PD1.
DEL
0.133540373
43
279
1
strelka,



DNA.1101





indelocator






RCC.PD1.
NA
NA
NA
NA
NA
NA



DNA.1137












RCC.PD1.
NA
NA
NA
NA
NA
NA



DNA.944












RCC.PD1.
NA
NA
NA
NA
NA
NA



DNA.949












VA1008
DEL
0.06
15
253
not
indelocator








evaluable
















TABLE 4D





Truncating PBRM1 alterations in patients receiving immune checkpoint therapy


in combination with angiogenesis inhibitor or tyrosine kinase inhibitor in validation cohort


(N = 9)






















patient_id
Hugo_Symbol
Chromosome
Start_position
End_position
Variant_Classification
Reference_Allele
Tumor_Seq_Allele1





PD_001
NA
NA
NA
NA
NA
NA
NA


PD_003
NA
NA
NA
NA
NA
NA
NA


PD_004
PBRM1
3
52584647
52584647
Frame_Shift_Del
C
C


PD_006
PBRM1
3
52643374
52643374
Missense_Mutation
T
T


PD_008
PBRM1
3
52643907
52643907
Frame_Shift_Del
A
A


PD_027
PBRM1
3
52682460
52682460
Splice_Site
T
T


PD_028
NA
NA
NA
NA
NA
NA
NA


PD_031
PBRM1
3
52712580
52712580
Nonsense_Mutation
G
G


PD_002
NA
NA
NA
NA
NA
NA
NA



















patient_id
Tumor_Seq_Allele2
Protein_Change
i_tumor_f
t_alt_count
t_ref_count
clonal_dm
indel_caller







PD_001
NA
NA
NA
NA
NA
NA
NA



PD_003
NA
NA
NA
NA
NA
NA
NA



PD_004

p.V1476fs
0.55
6
5
1
indelocator



PD_006
G
p.Q809P
0.378788
25
41
1
NA



PD_008

p.N631fs
0.25
13
40
1
strelka,










indelocator



PD_027
G

0.510638
24
23
1
NA



PD_028
NA
NA
NA
NA
NA
NA
NA



PD_031
A
p.R58*
0.323529
22
46
1
NA



PD_002
NA
NA
NA
NA
NA
NA
NA

















TABLE 5A





Germline variants in PBRM1 in training and validation cohorts (N = 91)





















sample
Start_position
End_position
Reference_Allele
Tumor_Seq_Allele1
Tumor_Seq_Allele2
Variant_Classification





CA209009_1_20
52584587
52584587
G
G
A
Missense_Mutation


PD_027
52597433
52597433
C
C
T
Missense_Mutation


PD_029
52643398
52643398
C
C
T
Missense_Mutation


PD_031
52668826
52668826
G
G
A
Missense_Mutation


















sample
cDNA_Change
Codon_Change
Protein_Change
t_alt_count
t_ref_count
ExAC_AF







CA209009_1_20
c.4636C > T
c. (4636-4638)
P.P1546S
6
2
0.0004622





Ccc >





Tcc



PD_027
c.3997G > A
c. (3997-3999)
p.E1333K
41
57
4.12E−05





Gag >





Aag



PD_029
c.2543G > A
c. (2542-2544)
p.R848H
38
54
8.24E−06





cGc >





cAc



PD_031
c.1093C > T
c. (1093-1095)
P.R365C
35
35
0.0002141





Cgc >





Tgc










All samples had germline variations (SNPs) on PBRMI (Chrom. 3).









TABLE 5B





Germline variants in cancer susceptibility genes


in training and validation cohorts (N = 91)






























Tumor_
Tumor_
Variant_




Hugo_
Chromo-
Start_
End_
Reference_
Seq_
Seq_
Classi-
Variant_


Sample
Symbol
some
position
position
Allele
Allele1
Allele2
fication
Type





PD_020
TYK2
19
 10472493
 10472493
G
G
A
Nonsense_
SNP










Mutation






KE62621
FANCF
11
 22646654
 22646654
G
G
A
Nonsense_
SNP










Mutation






CA209009_
BRIP1
17
 59937229
 59937229
C
C
A
Nonsense_
SNP


9_47







Mutation






CA209009_
BUB1B
15
 40502334
 40502334
C
C
T
Nonsense_
SNP


5_50







Mutation






CA209009_
XRCC2
 7
152345927
152345927
G
G
A
Nonsense_
SNP


11_10







Mutation






CA209009_
FANCM
14
 45636336
 45636336
C
C
T
Nonsense_
SNP


5_28







Mutation






CA209009_
FANCM
14
 45667921
 45667921
C
C
T
Nonsense_
SNP


1_43







Mutation






RCC.PD1.
PDCD1LG2
 9
 5522576
 5522576
G
G
-
Frame_
DEL


DNA.1101







Shift_











Del






CA8808
PDCD1LG2
 9
 5522576
 5522576
G
G
-
Frame_
DEL










Shift_











Del






CA209009_
FANCL
 2
 58386928
 58386929
-
-
TA
Frame_
INS


5_22






AT
Shift_











Ins






PD_011
BRCA2
13
 32972626
 32972626
A
A
T
Nonsense_
SNP










Mutation






PD_003
BRCA2
13
 32972626
 32972626
A
A
T
Nonsense_
SNP










Mutation






PD_013
MSR1
 8
 16012594
 16012594
G
G
A
Nonsense_
SNP










Mutation






CA209009_
AR
X
 66766357
 66766374
GG
GG
-
In_
DEL


9_52




CG
CG

Frame_








GC
GC

Del








GG
GG










CG
CG










GC
GC








RCC.PD1.
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


DNA.1101




GT
GT

Shift_








T
T

Del






RCC.PD1.
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


DNA.1082




GT
GT

Shift_








T
T

Del






RCC.PD1.
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


DNA.1026




GT
GT

Shift_








T
T

Del






PD_027
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL







GT
GT

Shift_








T
T

Del






PD_026
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL







GT
GT

Shift_








T
T

Del






PD_009
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL







GT
GT

Shift_








T
T

Del






PD_006
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL







GT
GT

Shift_








T
T

Del






PD_004
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL







GT
GT

Shift_








T
T

Del






PD_002
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL







GT
GT

Shift_








T
T

Del






KA40761
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL







GT
GT

Shift_








T
T

Del






CA209009_
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


9_45




GT
GT

Shift_








T
T

Del






CA209009_
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


9_119




GT
GT

Shift_








T
T

Del






CA209009_
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


5_17




GT
GT

Shift_








T
T

Del






CA209009_
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


5_1




GT
GT

Shift_








T
T

Del






CA209009_
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


3_26




GT
GT

Shift_








T
T

Del






CA209009_
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


3_114




GT
GT

Shift_








T
T

Del






CA209009_
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


11_79




GT
GT

Shift_








T
T

Del






CA209009_
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


11_5




GT
GT

Shift_








T
T

Del






CA209009_
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


11_40




GT
GT

Shift_








T
T

Del






CA209009_
GEN1
 2
 17962994
 17962998
AA
AA
-
Frame_
DEL


11_12




GT
GT

Shift_








T
T

Del






PD_005
IL16
15
 81565493
 81565494
-
-
A
Frame_
INS










Shift_











Ins






CA209009_
AR
X
 66766358
 66766359
GC
GC
-
Frame_
DEL


9_52







Shift_











Del




















cDNA_
Codon_
Protein_
dbSNP_
t_alt_
t_ref_
Ex



Sample
Change
Change
Change
RS
count
count
AC_AF






PD_020
c.1912C > T
c.(1912-1914)
p.R638*

 85
105
8.24E-06





Cga > Tga











KE62621
c.703C > T
c.(703-705)
p.Q235*

101
 75
8.24E-06





Caa > Taa











CA209009_
c.133G > T
c.(133-135)
p.E45*

 70
101
8.24E-06



9_47

Gag > Tag











CA209009_
c.2308C > T
c.(2308-2310)
p.R770*

 54
 74
1.65E-05



5_50

Cga > Tga











CA209009_
c.643C > T
c.(643-645)
p.R215*

 22
 21
4.12E-05



11_10

Cga > Tga











CA209009_
c.1894C > T
c.(1894-1896)
p.R632*

 50
 55
6.59E-05



5_28

Cga > Tga











CA209009_
c.5713C > T
c.(5713-5715)
p.R1905*

 41
 38
0.0008813



1_43

Cga > Tga











RCC.PD1.
c.30delG
c.(28-30)
p.L10fs

 82
 62
0.001985



DNA.1101

ctgfs











CA8808
c.30delG
c.(28-30)
p.L10fs

110
107
0.001985





ctgfs











CA209009_
c.1114_
c.(1114-1116)
p.T372fs

 38
 53
0.002834



5_22
1115insATTA
accfs











PD_011
c.9976A > T
c.(9976-9978)
p.K3326*

 49
 48
0.00701





Aaa > Taa











PD_003
c.9976A > T
c.(9976-9978)
p.K3326*

 61
 42
0.00701





Aaa > Taa











PD_013
c.931C > T
c.(931-933)
p.R311*

 45
 50
0.007348





Cga > Tga











CA209009_




104
233
0.01489



9_52













RCC.PD1.
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 27
 31
0.088



DNA.1101
2519delAAGTT
aagttgfs











RCC.PD1.
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 22
 34
0.088



DNA.1082
2519delAAGTT
aagttgfs











RCC.PD1.
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 59
  0
0.088



DNA.1026
2519delAAGTT
aagttgfs











PD_027
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 32
 32
0.088




2519delAAGTT
aagttgfs











PD_026
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 41
 36
0.088




2519delAAGTT
aagttgfs











PD_009
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 30
 27
0.088




2519delAAGTT
aagttgfs











PD_006
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 22
 36
0.088




2519delAAGTT
aagttgfs











PD_004
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 38
 35
0.088




2519delAAGTT
aagttgfs











PD_002
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 50
  0
0.088




2519delAAGTT
aagttgfs











KA40761
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 57
 53
0.088




2519delAAGTT
aagttgfs











CA209009_
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 47
  0
0.088



9_45
2519delAAGTT
aagttgfs











CA209009_
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 25
 27
0.088



9_119
2519delAAGTT
aagttgfs











CA209009_
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 33
  0
0.088



5_17
2519delAAGTT
aagttgfs











CA209009_
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 24
 20
0.088



5_1
2519delAAGTT
aagttgfs











CA209009_
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 27
 29
0.088



3_26
2519delAAGTT
aagttgfs











CA209009_
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 26
 25
0.088



3_114
2519delAAGTT
aagttgfs











CA209009_
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 21
 20
0.088



11_79
2519delAAGTT
aagttgfs











CA209009_
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 24
 34
0.088



11_5
2519delAAGTT
aagttgfs











CA209009_
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 23
 20
0.088



11_40
2519delAAGTT
aagttgfs











CA209009_
c.2515_
c.(2515-2520)
p.KL839fs
rs149936944
 32
 38
0.088



11_12
2519delAAGTT
aagttgfs











PD_005
c.738_
c.(739-741)
p.K247fs

 37
 43





739insA
aaafs











CA209009_
c.1370_
c.(1369-1371)
p.G473fs

104
233
0.01489



9_52
1371delGC
ggcfs









To further characterize the effect of PBRM1 truncating alterations on the tumor-immune microenvironment, publicly available genomic data from the Cancer Genome Atlas (TCGA) clear-cell RCC (KIRC) cohort with matched whole exome and whole transcriptome sequencing (Cancer Genome Atlas Research, 2013) were analyzed. A study of immune checkpoint expression in patient samples as well as in TCGA KIRC showed that the expression profiles of multiple cytokines were not significantly changed by PBRM1 truncation (FIG. 9).














TABLE 5C







cell_type
size
enrichment_score
normalized_enrichment_score
nominal_pvalue
fdr_qvalue





MACROPHAGES
33
0.70394427
1.9490428
0.0056926
0.006311779


TH1 CELLS
27
0.5918025
1.8246216
0.007067138
0.034245696


TH2 CELLS
26
0.5829762
1.7125602
0.021484375
0.06695327


T CELLS
16
0.7640042
1.6438433
0.06903353
0.08587655


CYTOTOXIC
16
0.65174156
1.4963382
0.120229006
0.15507422


CELLS


TFH CELLS
31
0.43897825
1.4072554
0.08946322
0.2030306


T HELPER
22
0.4563994
1.2234894
0.26215646
0.38399327


CELLS


IDC
31
0.37844718
1.1912937
0.2651515
0.37435225


CD8 T
36
0.34407476
1.1225885
0.33661416
0.4134009


CELLS


NEUTROPHILS
27
0.3371452
0.93078756
0.5449331
0.60742164


TCM CELLS
35
0.28329915
0.8316694
0.65294117
0.68357456


MAST
28
0.20472227
0.60319847
0.9122203
0.8787396


CELLS


B CELLS
28
−0.35
−0.93
0.517
0.989


EOSINOPHILS
30
−0.23
−0.78
0.811
0.686
















cell_type
fwer_pvalue
rank_at_max
leading_edge
enriched_set







MACROPHAGES
0.006
2520
tags = 58%, list = 12%,
wild






signal = 66%
type



TH1 CELLS
0.049
4931
tags = 59%, list = 24%,
wild






signal = 78%
type



TH2 CELLS
0.122
2914
tags = 42%, list = 14%,
wild






signal = 49%
type



T CELLS
0.2
1192
tags = 69%, list = 6%,
wild






signal = 73%
type



CYTOTOXIC
0.385
3495
tags = 63%, list = 17%,
wild



CELLS


signal = 75%
type



TFH CELLS
0.522
2940
tags = 26%, list = 14%,
wild






signal = 30%
type



T HELPER
0.756
3268
tags = 27%, list = 16%,
wild



CELLS


signal = 32%
type



IDC
0.783
4467
tags = 35%, list = 22%,
wild






signal = 45%
type



CD8 T
0.845
1014
tags = 17%, list = 5%,
wild



CELLS


signal = 18%
type



NEUTROPHILS
0.934
3421
tags = 41%, list = 17%,
wild






signal = 49%
type



TCM CELLS
0.964
2289
tags = 17%, list = 11%,
wild






signal = 19%
type



MAST
0.988
6169
tags = 36%, list = 30%,
wild



CELLS


signal = 51%
type



B CELLS
0.928
4315
tags = 43%, list = 21%,
mutant






signal = 54%



EOSINOPHILS
0.971
1013
tags = 13%, list = 5%,
mutant






singal = 14%










This study found that patients with truncating alterations in PBRM1 are more likely to experience extreme response to immune checkpoint monotherapy than patients who are PBRM1-wildtype (FIG. 10). Meanwhile, nonsynonymous mutational burden, neoantigen burden, and PD-L1 staining did not distinguish clinical benefit groups, in contrast to findings in melanoma and non-small cell lung cancer (Rizvi et al. (2015) Science 348:124-128; Snyder et al. (2014) N. Engl. J. Med. 371:2189-2199; Van Allen et al. (2015) Science 350:207-211).


PBRM1 is a component of the BAF (Brg/Brahma-associated factors) or mammalian SWI/SNF complex, which is involved in ATP-dependent chromatin remodeling, and is one of the most commonly mutated genes in ccRCC. Nonsynonymous mutations in PBRM1 are seen in up to 41% of patients with ccRCC (Varela et al. (2011) Nature 469:539-542), with a majority of mutations being truncating alterations. Chromosome 3p deletions over the PBRM1 locus are also highly prevalent in ccRCC (>91% of samples), as are alterations in other components of the SWI/SNF complex, including BAP1 and SETD2, suggesting that epigenetic regulation and oncogenic metabolism are major components of ccRCC (Cancer Genome Atlas Research (2013), supra). The tumor suppressor role of PBRM1 loss in ccRCC is most often associated with metabolism, hypoxia response, and cell adhesion (Chowdhury et al. (2016), supra) but it may have interesting effects on the tumor-immune microenvironment as well.


Additionally, restoration of PBRM1 expression in PBRM1-deficient tumor cell lines leads to increased expression of genes in the interleukin-6-mediated signaling pathway (GO:0070102) (Chowdhury et al. (2016), supra, and was observed lower levels of IL-6 in the baseline serum of patients with PBRM1-truncated tumors in this study. Increased production of IL-6 mediates STAT3 activation, which has been identified as a potential orchestrator of an immunosuppressive cytokine network (Yu et al. (2009) Nat. Rev. Cancer 9:798-809), and promotes tumorigenesis in EGFR-mutant lung carcinomas (Gao et al. (2007) J Clin. Invest. 117:3846-3856). Activation of the Jak2/Stat3 pathway has been further associated with an immunosuppressive tumor microenvironment in Pten-null mice that develop prostatic neoplasia, and blockade of this pathway can restore the anti-tumor immune response (Toso et al. (2014) Cell Rep. 9:75-89). This finding is further supported by decreased macrophage and T cell infiltration in PBRM1-truncated tumors, along with decreased CRP and increased IP-10 immediately before treatment.


Taken together, these results indicate that PBRM1 status may have wide-ranging effects on tumor-immune microenvironment interactions. Clinically, alterations in PBRM1 have previously been linked with prognosis and response to other cancer therapies. A possibility cannot be fully excluded that PBRM1 has prognostic rather than predictive value. One study in 145 patients found that PBRM1-mutant tumors were associated with favorable prognosis, especially relative to BAP1-mutant tumors (Kapur et al. (2013), supra), while another study in 609 patients found no effect of PBRM1 mutations on cancer-specific survival (Hakimi et al. (2013) Clin. Cancer Res. 19:3259-3267). These studies did not distinguish between truncating and non-truncating (missense mutations, in-frame indel) variants or assess chromosome 3p.21 deletions, which could impact the ultimate presence of PBRM1 protein. Immunohistochemical staining for PBRM1 in 657 ccRCC cases found worse cancer-specific survival and progression-free survival in poorly staining samples (Nam et al. (2015) Urol. Oncol. 33:340.e9-e16), and a similar study in 204 ccRCC cases also found that loss of PBRM1 protein expression is associated with poor differentiation, late tumor stage, and shorter duration of patient overall survival (Pawlowski et al. (2013) Int. J. Cancer 132:E11-E17).


Previous studies have also investigated whether pre-treatment molecular characteristics of ccRCC are correlated to response to therapy. In a cohort of 258 patients with RCC, those with PBRM1-mutant cancers were found to have longer PFS with first-line everolimus compared to those who were PBRM1-wildtype, though this finding did not hold after multiple hypothesis testing (Hsieh et al. (2016) Eur Urol. pii: S0302-2838(16)30701-1). No effect of PBRM1 status was seen with first-line sunitinib followed by everolimus in the same trial. Another study in 27 patients treated with vascular endothelial growth factor (VEGF) targeted therapies (sunitinib and pazopanib) found that PBRM1 alterations were significantly enriched in responders (Fay et al. (2016)1 Natl. Compr. Canc. Netw. 14:820-824), while a third study in cohort of 79 patients receiving mTOR inhibitors (everolimus and temsirolimus) found no association between PBRM1 status and response (Kwiatkowski et al. (2016) Clin. Cancer Res. 22:2445-2452). Another study including 117 pre-treatment tumors found no association between somatic mutations in PBRM1 and response to sunitinib (Beuselinck et al. (2015) Clin. Cancer Res. 21:1329-1339). Thus, the observed association between PBRM1 mutations and increased likelihood of clinical benefit from immune checkpoint therapy is a novel finding not readily explained by general decreased tumor aggressiveness or increased responsiveness to therapy in PBRM1-mutant tumors. Additionally, all extreme responders in this study were required to have objective decrease in tumor burden following immune checkpoint therapy, making it unlikely that the prognostic benefit of PBRM1 mutation alone, if real, could explain the results of this study.


This finding of increased responsiveness to immune checkpoint therapy in patients with metastatic ccRCC harboring truncating mutations in PBRM1 in independent training and validation cohorts totaling 61 patients argues for further validation in larger immunotherapy-treated RCC cohorts and for concerted effort towards characterizing the impact of SWI/SNF complex alterations on tumor-immune activity. Integration of whole exome and whole transcriptome sequencing from patient tumors identified potential downstream effects of PBRM1 alterations on immune cell infiltration. These results are believed to have important implications for exploration of PBRM1 and immune mediation, as well as guiding patient selection for immune checkpoint therapy in renal cell carcinoma, where up to 40% of patients have PBRM1-mutant disease. This finding may is also believed tobe more generally relevant in cancer immunotherapy, as more than 20% of human cancers contain a mutation in at least one subunit of the SWI/SNF or BAF complexes (Kadoch et al. (2013) Nat. Genet. 45:592-601; Shain and Pollack (2013) PLoS One 8:e55119). These results can be further applied to untreated RCC cohorts (Sato et al. (2013), supra).


Example 3: Further Confirmation of Data and Results Shown in Examples 1-2

The following provide further confirmation of the data and results provided above in Examples 1-2 by inter alia further demonstrating the data and results in additional cohorts. Generally, the following materials and methods were used to determine the further confirmation:


a. Clinical Cohort Consolidation


The discovery cohort was gathered from patients enrolled in p-009 (NCT01358721), a study of nivolumab (BMS-936558) monotherapy in metastatic renal cell carcinoma (Choueiri et al. (2016) Clin. Cancer Res. 22:5461-5471). Progression-free survival and overall survival were measured from Cycle 1 Day 1 (time zero) of nivolumab administration. The validation cohort was gathered from patients at the Dana-Farber Cancer Institute, Memorial Sloan Kettering Cancer Institute, and Johns Hopkins University who received anti-PD-(L)1 therapy as monotherapy or in combination with other immune checkpoint therapies and had banked adequate pre-treatment tumor tissue for molecular characterization. In addition, patients with ccRCC also treated with anti-PD-(L)1 based therapy from the Mayo Clinic with targeted panel sequencing that included the PBRM1 gene region were included in the validation cohort. All patients were consented on an Institutional Review Board protocol that allows research molecular characterization of tumor and germline samples. Each IRB at the respective institution from the validation cohort obtained approval for 1) collection and analysis of samples, and 2) sending samples to the Dana-Farber Center for genomic analysis.


b. DNA and RNA Extraction and Sequencing


All samples from the discovery cohort and those from the Dana-Farber Cancer Institute and Memorial Sloan Kettering Cancer Institute were processed for DNA (and if possible, RNA) extraction and whole exome sequencing through standard workflows (Van Allen et al. (2014) Nat. Med. 20:682-688). After fixation and mounting, 5-10 10 μm slices from either Qiagen RNAlater (discovery cohort) or formalin-fixed, paraffin-embedded (FFPE, validation cohort) tumor blocks were obtained, and tumor-enriched tissue was macrodissected. Paraffin was removed from FFPE sections and cores using CitriSolv™ (Fisher Scientific), followed by ethanol washes and tissue lysis overnight at 56° C. Samples were then incubated at 90° C. to remove DNA crosslinks, and DNA- and when possible, RNA-extraction was performed using Qiagen AllPrep DNA/RNA Mini Kit (#51306). Germline DNA was obtained from adjacent PBMCs. Whole exome and whole transcriptome sequencing of tumor and germline samples were performed as previously described in Van Allen et al. (2015) Science 350:207-211 and Van Allen et al. (2014) Nat. Med. 20:682-688. All samples in the discovery cohort were sequenced using the Illumina exome, while a portion of the samples in the validation cohort were sequenced using the Agilent exome (Table 6E). The Illumina exome uses Illumina's in-solution DNA probe based hybrid selection method to target approximately 37.7 Mb of mainly exonic territory, using similar principles as the Broad Institute-Agilent Technologies developed in-solution RNA probe based hybrid selection method (Agilent SureSelect All Exon V2) (Gnirke et al. (2009) Nat. Biotechnol. 27:182-189; Fisher et al. (2011) Genome Biol. 12:R1) to generate Illumina exome sequencing libraries. Pooled libraries were normalized to 2 nM and denatured using 0.2 N NaOH prior to sequencing. Flowcell cluster amplification and sequencing were performed according to the manufacturer's protocols using either the HiSeq 2000 v3 or HiSeq 2500. Each run was a 76 bp paired-end with a dual eight-base index barcode read. Data were analyzed using the Broad Picard Pipeline, which includes de-multiplexing and data aggregation. Exome sequence data processing was performed using established analytical pipelines at the Broad Institute. A BAM file was produced using the Picard pipeline (available on the World Wide Web at picard.sourceforge.net/), which aligns the tumor and normal sequences to the hg19 human genome build using Illumina sequencing reads. The BAM was uploaded into the Firehose pipeline (available on the World Wide Web at broadinstitute.org/cancer/cga/Firehose), which manages input and output files to be executed by GenePattern (Reich et al. (2006) Nat. Genet. 38:500-501). Samples with mean target coverage less than 25× in the tumor and less than 15× in matched normal were excluded. Quality control modules within Firehose were applied to all sequencing data for comparison of the origin of tumor and normal genotypes and to assess fingerprinting concordance. Cross-contamination of samples was estimated using ContEst (Cibulskis et al. (2011) Bioinform. 27:2601-2602). Samples with ContEst estimates exceeding 5% were excluded from analysis. Clinical characteristics from samples that were excluded due to poor quality did not differ significantly from those that were included in the final analysis.


c. Whole Exome and Whole Transcriptome Analyses


MuTect was applied to identify somatic single-nucleotide variants (Cibulskis et al. (2013) Nat. Biotechnol. 31:213-219). Strelka was used to identify somatic insertions and deletions (Saunders et al. (2012) Bioinform. 28:1811-1817) across the whole exome. Indelocator, which detects small insertions and deletions after local realignment of tumor and normal sequences, was additionally applied to provide further sensitivity to detect indels in PBRM1 (Cancer Genome Atlas Research (2011) Nature 474:609-615). The union of indels called by Strelka and Indelocator was used for final analysis. Artifacts introduced by DNA oxidation during sequencing were computationally removed using a filter-based method (Costello et al. (2013) Nuc. Acids Res. 41:e67). All somatic mutations detected by whole-exome sequencing were analyzed for potential false positive calls by performing a comparison to mutation calls from a panel of 2,500 germline DNA samples (Stachler et al. (2015) Nat. Genet. 47:1047-1055). Mutations found in germline samples were removed from analysis. Annotation of identified variants was done using Oncotator (available on the World Wide Web at broadinstitute.org/cancer/cga/oncotator). All nonsynonymous alterations in PBRM1 were manually reviewed in Integrated Genomics Viewer (IGV_2.3.57) for sequencing quality (Thorvaldsdottir et al. (2013) Brief Bioinform. 14:178-192). PBRM1 LOF events were defined as truncating mutations: nonsense mutations, frameshift insertions and deletions, and splice-site mutations. In-frame insertions and deletions, missense mutations, and other alterations presumed not to be truncating were considered separately. Copy ratios were calculated for each captured target by dividing the tumor coverage by the median coverage obtained in a set of reference normal samples. The resulting copy ratios were segmented using the circular binary segmentation algorithm (Olshen et al. (2004) Biostatistics 5:557-572). Allelic copy number alterations were called while taking into account sample-specific overall chromosomal aberrations (focality) (Brastianos et al. (2015) Cancer Discov. 5:1164-1177). Inference of mutational clonality, tumor purity, and tumor ploidy was accomplished with ABSOLUTE (Carter et al. (2012) Nat. Biotechnol. 30:413-421). Mutations were considered clonal if the expected cancer cell fraction (CCF) of the mutation as estimated by ABSOLUTE was 1, or if the probability of the mutation being clonal was greater than that of the mutation being subclonal. For the discovery cohort, samples were required to have estimated tumor purity greater than 10% to be included in the final analysis. For the validation cohort, samples included in the analysis were required to have either (a) estimated tumor purity greater than 10%, or (b) estimated tumor purity below 10% but sufficient sequencing coverage over the PBRM1 region that there would still be adequate power to detect a clonal PBRM1 alteration if it were to exist. As a final quality control metric to ensure adequate sequencing coverage and tumor purity to detect relevant oncogenic events, all samples had to have at least one nonsynonymous mutation in at least one high confidence or candidate cancer driver gene to be included in the final analysis (Tamborero et al. (2013) Sci. Rep. 3:2650). Mutation calls for patients from patients from Johns Hopkins University included in the validation cohort were processed through in-house standard analytic pipelines and supplied by Mark Ball, MD (Anagnostou et al. (2017) Cancer Disc. 7:264-276).


d. Targeted Sequencing Analyses


Fourteen samples with targeted panel genetic sequencing were used in the validation cohort. Panel sequencing data was acquired using standard pipelines from commercial molecular profiling laboratories: FoundationOne® (Foundation Medicine, Palo Alto, Calif.) and Canis Molecular Intelligence (Canis Life Sciences, Phoenix, Ariz.) (Table 6E). A subset of these samples had PBRM1 immunohistochemical staining (IHC, Table 6G. All samples with canonical LOF mutations (frameshift insertions, frameshift deletions, splice site mutations) and available PBRM1 IHC had negative staining, indicating true PBRM1 LOF. One patient (MCA6) with missense mutation N258S, also had negative IHC staining, and was labeled a PBRM1-LOF mutant accordingly.


e. Cell Line Analysis


Whole transcriptome sequencing from PBAF-deficient and PBAF-proficient A704 cell lines was produced as previously described in Gao et al. (2017) Proc. Natl. Acad. Sci. USA 114:1027-1032 and is available on Gene Expression Omnibus (GEO) under Accession PRJNA371283. Differential gene expression analysis was conducted using the Bioconductor software package Empirical Analysis of Digital Gene Expression Data in R (edgeR). This package is optimized for differential expression analysis of RNA-seq data with biological replication. Raw read count data from RNA-seq analysis of two PBRM1-null cell lines, two BRG1-null cell lines, and two PBRM1- and BRG1-wild type cell lines were analyzed for differential expression between PBRM1-null (A704) and wildtype (A704BAF180 wt), and BRG1-null (A704BAF180 wt, BRG1−/−) and wild type cell lines (A704BAF180 wt). In order to assess PBAF complex functionality as a whole, the top 100 positively differentially expressed genes by quasi-likelihood F test in mutants vs. wild type from both PBRM1 and BRG1 analyses were intersected to get a final list of 48 genes significantly up-regulated in PBAF null cell lines. The same analysis was performed for the top 100 negatively differentially expressed genes, and the resulting list was 43 genes significantly up-regulated in PBAF wild type cell lines. GSEA (available on the World Wide Web at software.broadinstitute.org/gsea/index.jsp) was performed to test whether any biologically-relevant gene sets were differentially expressed between PBAF-null vs. wildtype and BRG1 null vs. wildtype cell lines. In accordance with previously proposed methods in Liberzon et al. (2015) Cell Sys. 1:417-425, the Hallmark gene sets (N=50) were used for an initial GSEA run, and subsequent GSEA analyses were conducted using the Founders gene sets for any Hallmark gene set significantly enriched in both PBRM1 and BRG1 null cell lines (N=5). A false discovery rate (FDR) q-value of 0.25 was used as a significance threshold for all analyses. This process resulted in a list of gene sets significantly enriched in PBAF-null vs. wildtype cell lines. GSEA analyses were repeated for RNA-Seq from untreated patient tumors from the TCGA. Gene Ontology (GO, available on the World Wide Web at geneontology.org/) term analysis was performed to identify pathways or functional associations of the core enriched genes in A704BAF180−/− versus A704BAF180 wt from the Kegg Cytokine-Cytokine Receptor Interaction gene set. Core enriched genes for A704BAF180−/− (N=53) were defined as those with a GSEA enrichment score greater than the prior gene, starting from the top of the GSEA ranked gene list (i.e., all genes until the peak of the GSEA enrichment plot). Core enriched genes for A704BAF180 wt (N=18) were those whose enrichment score was less than the prior gene, starting from the bottom of the GSEA ranked list (i.e., all genes after the trough of the GSEA enrichment plot).


f. Transcriptome Analysis


Whole transcriptome sequencing was derived from three sources: patient samples from the discovery and validation cohorts, the TCGA clear cell renal cell carcinoma (KIRC) cohort, the TCGA cutaneous melanoma (SKCM) cohort, and an independent previously published cohort of untreated clear cell renal cell carcinoma tumors (Sato) (Sato et al. (2013) Nat. Genet. 45:860-867). For the patient samples, whole transcriptome sequencing from FFPE tissues were aligned using STAR (Dobin et al. (2012) Bioinform. 29:15-21) and then quantified with RSEM (Li et al. (2011) BMC Bioinform. 12:323) to yield gene-level expression in transcripts per million (TPM). Because patient samples came from two independent cohorts, ComBat (Li et al. (2011) BMC Bioinform. 12:323) was applied prior to analyzing patient-derived RNA sequencing. Principal components analysis (PCA) was completed before and after implementing ComBat to ensure that batch effects were eliminated (Johnson et al. (2007) Biostat. 8:118-127). The final patient cohort for RNA-seq analysis included N=18 PBRM1-LOF samples and N=14 PBRM1-intact samples. For the TCGA cohort, whole exome mutation annotation files (MAFs) and whole transcriptome gene expression data were downloaded from the Firebrowse KIRC TCGA data release (2016_01_28). KIRC tumors were divided into those with truncating mutations in PBRM1 (nonsense, splice-site, frameshift) (N=102), those with intact PBRM1 function (no mutation or silent mutation) (N=288), and those with other mutations in PBRM1 (missense or inframe indel) (N=25). RNA-seq from germline samples was excluded. For the Sato cohort, whole exome mutation annotation files and gene expression data from the final analysis in the published paper were used (Sato et al. (2013) Nat. Genet. 45:860-867). The MAFs were downloaded from the online supplemental materials from the published paper and gene expression data were kindly supplied by personal communication with the authors.


g. Statistical Analyses


All comparisons of continuous variables between groups (clinical benefit vs. no clinical benefit or PBRM1-LOF vs. PBRM1-intact) were done with the non-parametric Wilcoxon rank-sum test (wilcox.test( ) R function, two-sided, from stats package) or Student's t test (t. test( ) R function, two-sided, from stats package), depending on whether distributions were expected to be approximately normal. Comparisons of the proportion of patients with truncating alterations in PBRM1 by clinical response group were done with Fisher's exact tests when comparing CB and NCB (fisher.test( ) R function, two-sided, from stats package) and Fisher-Freeman-Halton Exact tests when comparing CB, IB, and NCB fisher.test( ) R function with 2×3 contingency table, two-sided, from stats package). Kaplan-Meier analyses were done using the R packages survival and survminer. Significance testing for differences in progression-free survival or overall survival were calculated using the log-rank test. All comparisons were two-sided with an alpha level of 0.05. MutSig2CV was used to identify genes of interest among all those mutated in the discovery cohort. Subsequently, the Benjamini-Hochberg method for controlling false discovery rate (FDR) was applied to control for multiple hypothesis testing among the seven genes of interest with a threshold of q<0.1. All statistical analyses and figures were generated in R version 3.3.2.


Immune checkpoint inhibitors, such as nivolumab, extend the survival of a subset of patients with metastatic ccRCC (Motzer et al. (2015) N. Engl. J. Med. 373:1803-1813). Whether specific genomic features of ccRCC are associated with clinical benefit is unclear. In contrast to other human tumor types that respond to immunotherapy, such as non-small cell lung cancer (NSCLC), melanoma, and microsatellite-unstable colorectal adenocarcinoma, ccRCC harbors a low burden of somatic mutations (Snyder et al. (2014) N. Engl. J. Med. 371:2189-2199; Rizvi et al. (2015) Science 348:124-128; Le et al. (2015) N. Engl. J. Med. 372:2509-2520; Van Allen et al. (2015) Science 350:207-211). Melanoma and NSCLC typically harbor 10 to 400 mutations per megabase (Mb) and these genetic variants can generate tumor-specific antigens (neoantigens) that stimulate a strong anti-tumor immune response (Motzer et al. (2015) N. Engl. J. Med. 373:1803-1813; Snyder et al. (2014)N. Engl. J. Med. 371:2189-2199; Rizvi et al. (2015) Science 348:124-128; Le et al. (2015) N. Engl. J. Med. 372:2509-2520). In contrast, ccRCC harbors an average of only 1.1 mutations/Mb (Cancer Genome Atlas Research (2013) Nature 499:43-49; de Velasco et al. (2016) Cancer Immunol. Res. 4:820-822), yet it ranks highly among tumor types in terms of immune cytolytic activity (Rooney et al. (2015) Cell 160:48-618), immune infiltration score, and T cell infiltration score in the tumor microenvironment (Senbabaoglu et al. (2016) Genome Biol. 17:231).


It was hypothesized that distinct molecular mechanisms underlie the immunologically active tumor microenvironment and responsiveness to immune checkpoint therapy in patients with ccRCC. As part of a prospective clinical trial (Choueiri et al. (2016) Clin. Cancer Res. 22:5461-5471), pre-treatment tumors from 35 patients with metastatic ccRCC on a clinical trial of anti-programmed cell death-1 receptor (anti-PD-1) therapy (nivolumab) were analyzed. Whole exome sequencing (WES) from paired tumor/normal tissue was performed to identify genetic correlates of clinical benefit. To validate the findings, an independent cohort of 63 patients with metastatic ccRCC treated with therapies blocking PD-1 (e.g., nivolumab) or its ligand, PD-L1 (e.g., atezolizumab), were analyzed (FIG. 13A and Table 6A).


Baseline clinical and demographic features in the discovery cohort have been previously described, and the subset of patients with complete pre-treatment molecular profiling did not differ substantially in clinical or demographic features from patients whose data did not pass technical quality control (FIGS. 14A-14B) or from the larger published cohort (Choueiri et al. (2016) Clin. Cancer Res. 22:5461-5471). Given previous evidence suggesting that refined clinical stratifications are necessary to assess clinical benefit from immune checkpoint blockade (Wolchok et al. (2009) Clin. Cancer Res. 15:7412-7420), a composite response endpoint incorporating RECIST (Response Evaluation Criteria In Solid Tumors) (Eisenhauer et al. (2009) Eur. J. Cancer 45:228-247), radiographic tumor shrinkage, and progression-free survival (PFS), was defined (FIG. 13B and Table 6B). Clinical benefit (CB) included patients with complete response (CR) or partial response (PR) by RECIST 1.1 (i.e., tumor shrinkage >30% from baseline) (Eisenhauer et al. (2009) Eur. J. Cancer 45:228-247) or stable disease (SD) if they had any objective reduction in tumor burden lasting at least 6 months. This modification to include some patients with SD is intended to differentiate those patients with naturally indolent disease (i.e., slow tumor growth not surpassing 20% of baseline tumor size) from those with tumor response to immune checkpoint inhibitors (Gofrit et al. (2015) Springer Plus 4:580). No clinical benefit (NCB) patients experienced progressive disease (PD) by RECIST 1.1 and were discontinued from immunotherapy within three months. All other patients were termed “intermediate benefit” (IB). One patient in the discovery cohort was classified as CB despite PFS<6 months because there was continued tumor shrinkage (˜67% of baseline tumor size) after an initial period of minor tumor progression, and the patient had overall survival exceeding 32 months (FIGS. 15A-15B). Consistent with prior observations (Motzer et al. (2015) N. Engl. J. Med. 373:1803-1813), the dose of nivolumab, patient gender, and baseline PD-L1 immunohistochemical staining from metastatic biopsies did not predict patient overall survival (OS) following initiation of anti-PD-1 therapy (p>0.05 for all; log-rank test) (FIG. 16).


Mean exome-wide target coverage in the discovery cohort was 128-fold for tumor sequencing and 91-fold for matched germline sequencing (Tables 6A and 6E). Overall, nonsynonymous mutation burden was moderate in the discovery cohort (median 82 per exome, range 45-157). The tumors of patients with CB and those with NCB showed similar mutation burdens and intratumoral heterogeneity (FIGS. 13C-13D and Table 6C). Mutations and copy number alterations affecting antigen presentation machinery and HLA class I alleles were uncommon and were present in tumors of both CB and NCB patients (FIGS. 17A-17B).


The analyses were next focused on the mutations most likely to be functionally important. MutSig2CV (Lawrence et al. (2013) Nature 499:214-218) was applied to identify genes recurrently mutated in the discovery cohort. Of these genes, the search was limited to highly deleterious variants, meaning known hotspot or putative truncating (frameshift insertion or deletion, nonsense mutation, or splice-site) mutations. Of the seven recurrently mutated genes (FIG. 18A) (Cancer Genome Atlas Research (2013) Nature 499:43-49), PBRM1 was the only gene in which truncating, or loss-of-function (LOF), mutations were enriched in tumors from patients in the CB vs. NCB group (9/11 vs. 3/13; Fisher's exact p=0.012, q=0.086, odds ratio for CB=12.93, 95% C.I. 1.54-190.8) (FIG. 18B and Table 6D). In this cohort, all truncating PBRM1 alterations co-occurred with deletion of the non-mutated allele on chromosome 3p (FIG. 18A), resulting in complete LOF of PBRM1, and most of the mutations were predicted to be clonal (present in all tumor cells) (Table 6D). Prior large-scale sequencing studies have shown that PBRM1 LOF alterations occur in up to 41% of ccRCC tumors (Varela et al. (2011) Nature 469:539-542) and are commonly clonal events present in all or nearly all tumor cells (Gerlinger et al. (2014) Nat. Genet. 46:225-233). Patients whose tumors showed biallelic PBRM1 loss had significantly prolonged OS and PFS compared to patients without PBRM1 LOF (log-rank p=0.0074 and p=0.029, respectively) (FIGS. 18C and 19), and they experienced sustained reductions in tumor burden (FIG. 18D).


To evaluate the reproducibility of this finding, matched pre-treatment tumor and germline genomic data were examined from an additional 63 patients treated with anti-PD-(L)1 therapy, either alone or in combination with anti-CTLA-4 therapy. Of these 63 patients, PBRM1 mutation status was derived from WES in 49 patients and panel sequencing in 14 patients (FIGS. 20A-20B and Tables 6E-6F). Tumors from CB patients were more likely to harbor truncating alterations in PBRM1 (17/27 vs. 4/19, Fisher's exact p=0.0071, odds ratio for CB=6.10, 95% C.I. 1.42-32.64) (FIGS. 20C-20D and Table 6G). Although copy number alterations in all samples in the validation cohort could not be assessed, it is believed that the PBRM1 LOF mutations represented biallelic loss, as chromosome 3p deletions are nearly ubiquitous in ccRCC (Cancer Genome Atlas Research (2013) Nature 499:43-49). Notably, one of the four NCB patients whose tumor showed a PBRM1 LOF mutation also had an alteration in B2M, which codes for a protein important in antigen presentation. This provides a potential explanation for the patient's lack of clinical benefit from immune checkpoint blockade therapy despite having a truncating PBRM1 mutation.


While primary analyses excluded patients with intermediate benefit (TB) due to the unclear effect of immune checkpoint blockade therapy on patient outcomes in this group, the observed trend between PBRM1 mutation status and clinical benefit persisted with the inclusion of these patients as an intermediate phenotype. In both the discovery and validation cohorts, patients in the IB group had intermediate rates of PBRM1 LOF (82%, 64%, 23% for CB, IB, NCB in the discovery cohort and 63%, 41%, 21% for CB, IB, NCB in the validation cohort; Fisher-Freeman-Halton Exact p=0.017 and 0.017). Additionally, while no difference in clinical benefit was observed between treatment-naive and previously-treated patients in the discovery cohort (FIGS. 15A-15B), the progression-free survival benefit conferred by PBRM1 LOF was more prominent in tumors from previously-treated patients compared to those from patients receiving anti-PD-1 therapy as their first cancer therapy (p=0.009) (FIG. 21 and Table 6).


The PBRM1 gene codes for BAF180, a subunit of the PBAF subtype of the SWI/SNF chromatin remodeling complex. The PBAF complex suppresses the hypoxia transcriptional signature in VHL−/− ccRCC (Nargund et al. (2017) Cell Reports 18:2893-2906; Gao et al. (2017) Proc. Natl. Acad. Sci. USA 114:1027-1032), but its effects on tumor-immune interactions have not been thoroughly studied. To explore the potential impact of this complex on the immunophenotype of ccRCC, previously reported whole transcriptome sequencing (RNA-seq) data from A704 ccRCC cell lines with perturbations in the PBAF complex (Gao et al. (2017) Proc. Natl. Acad. Sci. USA 114:1027-1032/9) were analyzed. Loss of BAF180 or the related PBAF subunit BRG1, encoded by the gene SMARCA4, prevent formation of the intact PBAF complex (Gao et al. (2017) Proc. Natl. Acad. Sci. USA 114:1027-1032). Gene expression analyses of BAF180-null (A704BAF180−/−) cell lines vs. PBAF-wildtype (A704BAF180 wt) cell lines were performed and gene expression analyses of BRG1-null (A704BAF180 wt, BRG1−/−) cell lines vs. PBAF-wildtype (A704BAF180 wt) cell lines were also performed (FIG. 22A). Differential gene expression analysis showed substantial overlaps (˜50%) between the top 100 genes differentially expressed in A704BAF180−/− vs. A704BAF180 wt and A704BAF180 wt, BRG1−/− vs. A704BAF180 wt (Table 61). This reflects the fact that BAF180 is essential to the PBAF but not the BAF complex, while BRG1 is a required subunit of both. Thus, the BAF180-null and BRG1-null cell lines have some shared characteristics but are also biologically and phenotypically distinct. Gene set enrichment analysis (GSEA) on 50 “hallmark” gene sets representing major biological processes (Subramanian et al. (2005) Proc. Natl. Acad. Sci. USA 102:15545-15550) revealed five gene sets whose expression was significantly enriched in cell lines that were PBAF-deficient. These included genes linked to IL6/JAK-STAT3 signaling, TNF-α signaling via NF-κB, and IL2/STATS signaling (FIG. 22A and Tables 6J-6K). As expected, the hallmark hypoxia gene set was upregulated in A704BAF180−/− vs. A704BAF180 wt cell lines (family-wise error rate—FWER q=0.071) (Table 6J) (Gao et al. (2017) Proc. Natl. Acad. Sci. USA 114:1027-1032). Across the more refined “founder” gene sets describing these five significantly enriched hallmark gene sets, the most strongly enriched gene set in PBAF-deficient cell lines was the KEGG cytokine-cytokine receptor interaction gene set (FWER q=0.0020 for A704BAF180−/− vs. A704BAF180 wt and q=0.023 for A704BAF180 wt, BRG1−/− vs. A704BAF180 wt) (FIG. 22A and Tables 6L-6U). This gene set includes both immune-stimulatory (e.g., IL12, CCL21) and immune-inhibitory (e.g., IL10) genes, but Gene Ontology term analysis showed that the genes most strongly enriched in PBAF-deficient cell lines were immune-stimulatory (Table 6V). Previously reported GSEA analysis of untreated ccRCC from The Cancer Genome Atlas (TCGA) and a murine model of PBRM1 loss also show amplified transcriptional outputs of HIF1 and STAT3, involved in hypoxia response and JAKSTAT signaling respectively, in PBRM1-mutant vs. PBRMJ-wildtype states (Nargund et al. (2017) Cell Reports 18:2893-2906). GSEA analysis of RNA-seq from pre-treatment tumors in the discovery and validation cohorts of this study (n=18 PBRM1-LOF vs. n=14 PBRM1-intact) confirmed increased expression of the hypoxia and IL6/JAK-STAT3 gene sets in the PBRM1-LOF tumors (FIG. 22B and Tables 6W-6X). Given JAK-STAT3 pathway gene involvement in the interferon gamma (IFN-g-) signaling pathway and IFN-g-dependent cancer immunostimulation (Sharma et al. (2017) Cell 168:707-723), differential expression of these genes may impact PBRM1-LOF patients' response to anti-PD-(L)1 therapy. In addition to assessing tumor-intrinsic gene expression with GSEA, the quality of the tumor-immune microenvironment in PBRM1-LOF vs. PBRM1-intact ccRCC was further characterized in three independent cohorts: TCGA (Cancer Genome Atlas Research (2013) Nature 499:43-49), an independent cohort of untreated ccRCC tumors (Sato) (Sato et al. (2013) Nat. Genet. 45:860-867), and patient tumors. In all three cohorts, tumors harboring LOF mutations in PBRM1 showed lower expression of immune inhibitory ligands (e.g., CD276 and BTLA) (Ramsay (2013) Br. J. Haematol. 116:313-325) than those without PBRM1 mutations. This finding was unexpected as high PD-L1 staining is associated with increased responsiveness to anti-PD-1 and anti-PD-L1 agents in other cancer types (Rosenberg et al. (2016) Lancet 387:1909-1920; Topalian et al. (2012) N. Engl. J. Med. 366:2443-2454) and despite the fact that these differences were relatively small and in the context of differing degrees of tumor-stromal admixture (FIGS. 23A-23C) (Senbabaoglu et al. (2016) Genome Biol. 17:231). LOF mutations in VHL, the most commonly mutated gene in the TCGA ccRCC cohort, were also examined. VHL mutation status did not correlate with immune related gene expression (FIG. 24), indicating that observed differences in immune gene expression in the context of PBRM1 LOF is believed to be specific to the PBRM1 gene.


Based on the foregoing, it has been shown that patients with metastatic ccRCC harboring truncating mutations in PBRM1 experienced increased clinical benefit from immune checkpoint therapy. It is believed that this is due to distinct immune-related gene expression profiles in PBRM1-mutant or PBAF-deficient tumor cells compared to their PBAF-intact counterparts, as shown by RNA-seq analyses described herein. In vivo studies of mice harboring tumor clones with inactivation of PBRM1—or the related essential PBAF complex components ARID2 or BRD7—show that cells with PBAF loss are more sensitive to T-cell-mediated cytotoxicity compared to their PBAF-intact counterparts (Pan et al. (2018) Science, in press), which helps to explain the conflicting results regarding PBRM1 mutation status as a prognostic variable in ccRCC (in the absence of immunotherapy) in prior studies (Beuselinck et al. (2015) Clin. Cancer Res. 21:1329-1339; Fay et al. (2016)1 Natl. Compr. Canc. Netw. 14:820-824; Hakimi et al. (2013) Clin. Cancer Res. 19:3259-3267; Hsieh et al. (2017) Eur. Urol. 71:405-414; Kapur et al. (2013) Lancet Oncol. 14:159-167; Kwiatkowski et al. (2016) Clin. Cancer Res. 22:2445-2452; Nam et al. (2015) Urol. Oncol. 33:340.e349-316; Pawlowski et al. (2013) Int. J. Cancer 132:E11-E17; Uhlen et al. (2017) Science 357:pii eaan2507). PBRM1 also previously has been linked to longer PFS with VEGF-targeted therapies (Carlo et al. (2017) Kidney Cancer 1:49-56). Additional in vivo studies can be used to further confirm the results described herein. Given the high prevalence of PBRM1 LOF in ccRCC and of SWI/SNF alterations across all cancer types (more than 20%) (Kadoch et al. (2013) Nat. Genet. 45:592-601), these results have important implications as a molecular tool for considering immunotherapy-responsiveness in ccRCC and across cancer types.









TABLE 6A





Whole exome sequencing metrics and inclusions/exclusions for patients in the


discovery cohort



















patient_id
tumor_mtc
normal_mtc
bait_set
absolute_inferred_purity





RCC_20
34.147062
92.688228
whole_exome_illumina_coding_v1
0.51


RCC_32
165.03915
80.054054
whole_exome_illumina_coding_v1
0.39


RCC_62
163.21171
81.718582
whole_exome_illumina_coding_v1
0.49


RCC_10
153.907825
93.664757
whole_exome_illumina_coding_v1
0.76


RCC_11
81.495132
108.704189
whole_exome_illumina_coding_v1
0.38


RCC_14
157.03659
99.990083
whole_exome_illumina_coding_v1
0.25


RCC_25
150.766602
87.494869
whole_exome_illumina_coding_v1
0.32


RCC_56
136.739597
86.544731
whole_exome_illumina_coding_v1
0.56


RCC_79
112.238316
91.306045
whole_exome_illumina_coding_v1
0.8


RCC_93
193.622831
94.511787
whole_exome_illumina_coding_v1
0.2


RCC_115
150.50973
89.411498
whole_exome_illumina_coding_v1
0.63


RCC_90
147.435982
79.577243
whole_exome_illumina_coding_v1
0.3


RCC_96
189.081727
96.347659
whole_exome_illumina_coding_v1
0.67


RCC_102
130.03582
97.794738
whole_exome_illumina_coding_v1
0.48


RCC_58
142.586967
82.644492
whole_exome_illumina_coding_v1
0.69


RCC_84
166.18581
88.436816
whole_exome_illumina_coding_v1
0.45


RCC_85
43.586957
168.436641
whole_exome_illumina_coding_v1
0.17


RCC_114
135.707278
77.721511
whole_exome_illumina_coding_v1
0.29


RCC_117
173.22159
68.856331
whole_exome_illumina_coding_v1
0.33


RCC_15
143.012126
79.906338
whole_exome_illumina_coding_v1
0.69


RCC_68
107.126976
88.452741
whole_exome_illumina_coding_v1
0.3


RCC_1
157.143939
89.39856
whole_exome_illumina_coding_v1
0.2


RCC_106
176.007671
81.059438
whole_exome_illumina_coding_v1
0.35


RCC_18
139.328276
75.654059
whole_exome_illumina_coding_v1
0.21


RCC_21
178.624687
105.356301
whole_exome_illumina_coding_v1
0.51


RCC_41
138.664874
93.93237
whole_exome_illumina_coding_v1
0.19


RCC_50
162.205322
85.879444
whole_exome_illumina_coding_v1
0.31


RCC_73
158.127987
100.10628
whole_exome_illumina_coding_v1
0.6


RCC_39
147.571574
114.169462
whole_exome_illumina_coding_v1
0.13


RCC_99
34.101887
117.822339
whole_exome_illumina_coding_v1
0.36


RCC_105
152.057615
91.424807
whole_exome_illumina_coding_v1
0.48


RCC_119
26.875509
90.734659
whole_exome_illumina_coding_v1
0.49


RCC_27
125.149722
97.245404
whole_exome_illumina_coding_v1
0.34


RCC_52
131.064027
90.415506
whole_exome_illumina_coding_v1
0.54


RCC_97
210.012354
98.486524
whole_exome_illumina_coding_v1
0.38


RCC_2
159.912441
69.844188
whole_exome_illumina_coding_v1
0.52


RCC_72
9.627872
94.01896
whole_exome_illumina_coding_v1
NA


RCC_5
8.689284
89.713424
whole_exome_illumina_coding_v1
0.36


RCC_54
0.007939
84.883698
whole_exome_illumina_coding_v1
NA


RCC_100
7.711684
105.962605
whole_exome_illumina_coding_v1
0.34


RCC_47
0.298156
95.4427
whole_exome_illumina_coding_v1
NA


RCC_66
8.71954
98.033649
whole_exome_illumina_coding_v1
0.46


RCC_43
105.603458
72.354112
whole_exome_illumina_coding_v1
0.06


RCC_12
162.560923
104.266666
whole_exome_illumina_coding_v1
0.05


RCC_24
166.047506
75.247762
whole_exome_illumina_coding_v1
0.1


RCC_40
154.736269
87.045058
whole_exome_illumina_coding_v1
0.1


RCC_8
154.801856
83.048353
whole_exome_illumina_coding_v1
NA


RCC_103
138.626523
96.365324
whole_exome_illumina_coding_v1


RCC_26
159.566974
100.887491
whole_exome_illumina_coding_v1
0.07


RCC_95
143.956046
90.060356
whole_exome_illumina_coding_v1
0.09


RCC_17
129.343681
81.980679
whole_exome_illumina_coding_v1
0.04


RCC_22
144.076612
97.672268
whole_exome_illumina_coding_v1
0.06


RCC_28
162.443009
89.968028
whole_exome_illumina_coding_v1
0.08


RCC_29
150.205436
89.123637
whole_exome_illumina_coding_v1
NA


RCC_6
145.806274
83.646769
whole_exome_illumina_coding_v1
0.07


RCC_45
132.158193
79.179771
whole_exome_illumina_coding_v1
0.06















patient_id
absolute_inferred_ploidy
genome_doubling
exclusion_reason







RCC_20
1.78
0
0



RCC_32
2.21
0
0



RCC_62
1.83
0
0



RCC_10
2
0
0



RCC_11
3.56
1
0



RCC_14
3.67
1
0



RCC_25
1.99
0
0



RCC_56
1.89
0
0



RCC_79
1.96
0
0



RCC_93
3
1
0



RCC_115
1.64
0
0



RCC_90
2.67
1
0



RCC_96
1.8
0
0



RCC_102
1.97
0
0



RCC_58
1.81
0
0



RCC_84
1.93
0
0



RCC_85
4.08
1
0



RCC_114
1.97
0
0



RCC_117
1.87
0
0



RCC_15
1.97
0
0



RCC_68
1.86
0
0



RCC_1
3.6
1
0



RCC_106
1.9
0
0



RCC_18
2.3
0
0



RCC_21
3.39
1
0



RCC_41
4.28
1
0



RCC_50
1.81
0
0



RCC_73
1.83
0
0



RCC_39
1.92
0
0



RCC_99
2.77
1
0



RCC_105
2.06
0
0



RCC_119
3.08
1
0



RCC_27
1.93
0
0



RCC_52
1.88
0
0



RCC_97
2.2
0
0



RCC_2
1.68
0
DeathUnrelated






Cancer



RCC_72
NA
NA
LowCoverage



RCC_5
1.98
0
LowCoverage



RCC_54
NA
NA
LowCoverage



RCC_100
2.01
0
LowCoverage



RCC_47
NA
NA
LowCoverage



RCC_66
2.16
0
LowCoverage



RCC_43
2.43
0
LowPurity



RCC_12
2.74
0
LowPurity



RCC_24
2.46
0
LowPurity



RCC_40
2.44
0
LowPurity



RCC_8
NA
NA
LowPurity



RCC_103


LowPurity



RCC_26
2.96
0
LowPurity



RCC_95
2.57
0
LowPurity



RCC_17
3.61
1
LowPurity



RCC_22
2.91
0
LowPurity



RCC_28
2.45
0
LowPurity



RCC_29
NA
NA
LowPurity



RCC_6
2.69
0
LowPurity



RCC_45
2.58
0
LowPurity

















TABLE 6B







Clinical characteristics of patients receiving anti-PD1 therapy (nivolumab) in discovery cohort (N = 35) (All patients at wes of 1, nivolumab as drug)












cell
cell
cell


























cell
membrane
membrane
membrane






max




membrane
pdl1
pdl1
pdl1
pdl1
pdl1
























patient


treatment
first
best
tumor
os
os
pfs
pfs
pdl1
1plus
2plus
3plus
positive
positive
response


id
sex
age
group
line
RECIST
change
days
censor
days
censor
0percent
percent
percent
percent
1percent
5percent
category




























RCC_97
MALE
54
2
mg/kg
0
PD
22
871
1
37
0
100
0
0
0
0
0
no




















clinical




















benefit


RCC_52
FEMALE
63
10
mg/kg-N
1
SD
−5
995
1
246
0
100
0
0
0
0
0
clinical




















benefit


RCC_27
MALE
59
0.3
mg/kg
0
PD
36
968
1
35
0
68
30
2
0
1
1
no




















clinical




















benefit


RCC_119
MALE
72
10
mg/kg
0
PR
−52
773
1
414
0
100
0
0
0
0
0
clinical




















benefit


RCC_105
MALE
64
10
mg/kg
0
PD
17
118
0
45
0
100
0
0
0
0
0
no




















clinical




















benefit


RCC_39
MALE
72
0.3
mg/kg
0
PD
13
306
0
37
0
100
0
0
0
0
0
no




















clinical




















benefit


RCC_73
MALE
77
2
mg/kg
0
PD
11
178
0
58
0
100
0
0
0
0
0
no




















clinical




















benefit


RCC_50
FEMALE
63
10
mg/kg-N
1
SD
−67
982
1
86
0
91
5
3
1
1
1
clinical




















benefit


RCC_41
MALE
66
10
mg/kg
0
PD
10
195
1
42
0
100
0
0
0
0
0
no




















clinical




















benefit


RCC_18
MALE
68
0.3
mg/kg
0
PD
65
111
1
43
0
NA
NA
NA
NA
NA
NA
no




















clinical




















benefit


RCC_1
MALE
49
10
mg/kg
0
PD
24
43
1
38
0
100
0
0
0
0
0
no




















clinical




















benefit


RCC_68
MALE
69
0.3
mg/kg
0
PD
−4
740
0
39
0
100
0
0
0
0
0
no




















clinical




















benefit


RCC_15
FEMALE
73
10
mg/kg-N
1
SD
−10
1013
0
663
0
99
1
0
0
0
0
clinical




















benefit


RCC_114
FEMALE
57
0.3
mg/kg
0
PR
−51
340
1
208
0
100
0
0
0
0
0
clinical




















benefit


RCC_84
FEMALE
55
0.3
mg/kg
0
SD
8
680
0
88
0
95
5
0
0
0
0
intermediate




















benefit


RCC_102
MALE
64
0.3
mg/kg
0
SD
3
165
1
108
0
100
0
0
0
0
0
intermediate




















benefit


RCC_96
FEMALE
75
2
mg/kg
0
PD
12
582
1
37
0
100
0
0
0
0
0
no




















clinical




















benefit


RCC_90
MALE
54
0.3
mg/kg
0
PD
57
499
0
36
0
25
35
20
20
1
1
no




















clinical




















benefit


RCC_115
MALE
60
2
mg/kg
0
SD
15
366
1
87
0
100
0
0
0
0
0
intermediate




















benefit


RCC_93
MALE
64
10
mg/kg
0
PR
−43
684
0
500
0
100
0
0
0
0
0
clinical




















benefit


RCC_79
FEMALE
61
2
mg/kg
0
SD
9
873
1
130
0
100
0
0
0
0
0
intermediate




















benefit


RCC_56
FEMALE
62
2
mg/kg
0
SD
17
992
1
81
0
100
0
0
0
0
0
intermediate




















benefit


RCC_25
FEMALE
72
10
mg/kg-N
1
PD
16
167
0
39
0
100
0
0
0
0
0
no




















clinical




















benefit


RCC_14
MALE
59
10
mg/kg-N
1
PR
−86
1025
1
541
0
100
0
0
0
0
0
clinical




















benefit


RCC_11
MALE
50
10
mg/kg-N
1
SD
9
1024
1
122
0
100
0
0
0
0
0
intermediate




















benefit


RCC_62
FEMALE
48
10
mg/kg
0
SD
7
106
1
87
0
NA
NA
NA
NA
NA
NA
intermediate




















benefit


RCC_21
MALE
64
10
mg/kg
0
SD
−13
349
1
43
1
96
4
0
0
1
0
intermediate




















benefit


RCC_106
FEMALE
61
2
mg/kg
0
PR
−61
870
1
821
1
100
0
0
0
0
0
clinical




















benefit


RCC_117
MALE
58
10
mg/kg-N
1
SD
−10
64
1
38
1
98
2
0
0
0
0
intermediate




















benefit


RCC_85
MALE
36
2
mg/kg
0
SD
−12
712
0
42
1
95
2
2
1
1
0
intermediate




















benefit


RCC_58
FEMALE
55
2
mg/kg
0
PR
−50
988
1
687
1
20
25
25
30
1
1
clinical




















benefit


RCC_10
FEMALE
64
10
mg/kg-N
1
SD
0
1058
1
173
1
97
3
0
0
1
0
intermediate




















benefit


RCC_32
MALE
65
10
mg/kg-N
1
SD
−13
240
0
213
1
100
0
0
0
0
0
clinical




















benefit


RCC_20
MALE
76
10
mg/kg-N
1
CR
−100
1065
1
1022
1
100
0
0
0
0
0
clinical




















benefit


RCC_99
MALE
50
2
mg/kg
0
PD
NA
41
0
41
0
97
3
0
0
1
0
no




















clinical




















benefit
















TABLE 6C







Summary of mutational burden in the discovery cohort (N = 35)

















patient
all
all
all
frameshift
muts
muts
muts
nonsyns
nonsyns
nonsyns


id
muts
nonsyns
syns
indels
clonal
subclonal
uneval
clonal
subclonal
uneval




















RCC_20
80
50
30
4
51
18
11
33
13
4


RCC_32
125
89
36
9
103
12
10
77
10
2


RCC_62
110
82
28
1
68
23
19
57
17
8


RCC_10
135
85
50
7
84
26
25
65
17
3


RCC_11
100
63
37
4
64
25
11
44
18
1


RCC_14
128
86
42
5
107
11
10
75
9
2


RCC_25
157
113
44
2
107
33
17
79
27
7


RCC_56
125
102
23
8
55
62
8
43
54
5


RCC_79
78
54
24
6
47
22
9
35
16
3


RCC_93
125
91
34
4
101
17
7
78
12
1


RCC_115
90
64
26
4
52
26
12
39
22
3


RCC_90
140
102
38
5
96
39
5
70
30
2


RCC_96
255
157
98
10
83
107
65
61
83
13


RCC_102
166
115
51
8
92
44
30
73
38
4


RCC_58
82
60
22
2
39
33
10
32
25
3


RCC_84
117
87
30
9
71
28
18
59
22
6


RCC_85
83
52
31
3
72
0
11
50
0
2


RCC_114
83
59
24
2
65
10
8
48
8
3


RCC_117
86
65
21
4
71
14
1
53
11
1


RCC_15
110
75
35
5
77
18
15
58
10
7


RCC_68
132
102
30
4
43
83
6
32
68
2


RCC_1
84
61
23
4
66
11
7
50
9
2


RCC_106
104
76
28
5
70
26
8
50
21
5


RCC_18
81
61
20
2
51
29
1
40
21
0


RCC_21
128
82
46
6
92
21
15
68
13
1


RCC_41
102
73
29
5
87
5
10
65
4
4


RCC_50
171
126
45
10
122
31
18
92
20
14


RCC_73
70
45
25
0
36
29
5
22
23
0


RCC_39
126
86
40
0
117
2
7
81
2
3


RCC_99
155
102
53
6
64
57
34
49
48
5


RCC_105
152
105
47
5
118
18
16
87
15
3


RCC_119
97
62
35
4
81
2
14
60
2
0


RCC_27
128
98
30
8
78
41
9
63
32
3


RCC_52
125
94
31
8
94
15
16
77
13
4


RCC_97
116
82
34
5
77
26
13
62
20
0
















TABLE 6D





Truncating PBRM1 alterations in patients discovery


cohort passing whole exome quality control (N = 35)


























PBRM1_



Variant_

Tumor_
Tumor_


patient_
Hugo_
mean_
Chromo-
Start_
End_
Classi-
Reference_
Seq_
Seq_


id
Symbol
coverage
some
position
position
fication
Allele
Allele1
Allele2





RCC_20
PBRM1
 28.98
3
52649455
52649456
Frame_
-
-
T








Shift_











Ins








RCC_32
NA
120.85
NA
NA
NA
NA








RCC_62
PBRM1
131.16
3
52613062
52613068
Splice_
ACACT
ACACT
-








Site
CA
CA






RCC_10
PBRM1
 89.9
3
52623120
52623120
Frame_
G
G
-








Shift_











Del








RCC_11
NA
 62.73
NA
NA
NA
NA








RCC_14
PBRM1
131.63
3
52623201
52623201
Frame_
G
G
-








Shift_











Del








RCC_25
NA
124.35
NA
NA
NA
NA








RCC_56
NA
221.56
NA
NA
NA
NA








RCC_79
PBRM1
 67.19
3
52621487
52621487
Frame_
T
T
-








Shift_











Del








RCC_93
PBRM1
173.78
3
52651277
52651277
Splice_
C
C
T








Site








RCC_115
NA
115.51
NA
NA
NA
NA








RCC_90
NA
124.87
NA
NA
NA
NA








RCC_96
PBRM1
164.39
3
52643489
52643489
Frame_
A
A
-








Shift_











Del








RCC_102
PBRM1
266.4
3
52663052
52663052
Splice_
C
C
T








Site








RCC_58
NA
 95.31
NA
NA
NA
NA








RCC_84
PBRM1
130.86
3
52696272
52696272
Frame_
T
T
-








Shift_











Del








RCC_85
NA
 47.52
NA
NA
NA
NA
NA
NA
NA





RCC_114
PBRM1
111.22
3
52662964
52662964
Frame_
A
A
-








Shift_











Del








RCC_117
PBRM1
146.69
3
52643375
52643375
Nonsense_
G
G
A








Mutation








RCC_15
PBRM1
 94.84
3
52613194
52613194
Nonsense_
C
C
A








Mutation








RCC_68
NA
100.73
NA
NA
NA
NA








RCC_1
NA
138.81
NA
NA
NA
NA








RCC_106
PBRM1
155.18
3
52620610
52620614
Frame_
ATTTT
ATTTT
-








Shift_











Del








RCC_18
PBRM1
126.07
3
52678748
52678748
Nonsense_
C
C
A








Mutation








RCC_21
PBRM1
125.64
3
52613210
52613210
Frame_
T
T
-








Shift_











Del








RCC_41
NA
123.22
NA
NA
NA
NA








RCC_50
PBRM1
135.79
3
52712515
52712515
Splice_
C
C
T








Site








RCC_73
NA
181.65
NA
NA
NA
NA








RCC_39
NA
130.38
NA
NA
NA
NA








RCC_99
NA
 28.13
NA
NA
NA
NA








RCC_105
NA
146.47
NA
NA
NA
NA








RCC_119
PBRM1
 28.57
3
52682459
52682459
Splice_
C
C
G








Site








RCC_27
PBRM1
248.99
3
52598081
52598101
In_
TCATC
TCATC
-








Frame_
ATCTA
ATCTA









Del
CCACT
CCACT










TTAGC
TTAGC










A
A






RCC_52
PBRM1
 97.9
3
52613205
52613205
Frame_
T
T
-








Shift_











Del








RCC_97
PBRM1
119.39
3
52663008
52663008
Nonsense_
C
C
A








Mutation



















patient_
Protein_
Variant_
i_
t_alt_
t_ref_

Indel_



id
Change
Type
tumor_f
count
count
clonal
Caller






RCC_20
p.H627fs
INS
0.363636364
 8
 14
1
strelka,










indelocator






RCC_32
NA
NA
NA
NA
NA
NA
NA






RCC_62
DEL

0.17370892
37
176
0
strelka






RCC_10
p.I992fs
DEL
0.55
55
 45
1
strelka,










indelocator






RCC_11
NA
NA
NA
NA
NA
NA
NA






RCC_14
p.D965fs
DEL
0.25
15
 45
1
strelka,










indelocator






RCC_25
NA
NA
NA
NA
NA
NA
NA






RCC_56
NA
NA
NA
NA
NA
NA
NA






RCC_79
p.N1017fs
DEL
0.464285714
13
 15
1
strelka,










indelocator






RCC_93

SNP
0.12766
 6
 41
1
NA






RCC_115
NA
NA
NA
NA
NA
NA
NA






RCC_90
NA
NA
NA
NA
NA
NA
NA






RCC_96
p.S818fs
DEL
0.402654867
91
135
1
strelka,










indelocator






RCC_102

SNP
0.235849
25
 81
1
NA






RCC_58
NA
NA
NA
NA
NA
NA
NA






RCC_84
p.K135fs
DEL
0.171428571
12
 58
1
strelka,










indelocator






RCC_85
NA
NA
NA
NA
NA
NA
NA






RCC_114
p.N463fs
DEL
0.108695652
10
 82
1
strelka,










indelocator






RCC_117
p.Q809*
SNP
0.288
36
 89
1
NA






RCC_15
p.E1105*
SNP
0.53
53
 47
1
NA






RCC_68
NA
NA
NA
NA
NA
NA
NA






RCC_1
NA
NA
NA
NA
NA
NA
NA






RCC_106
p.KI1087fs
DEL
0.067137809
19
264
0
strelka,










indelocator






RCC_18
p.E291*
SNP
0.150943
 8
 45
1
NA






RCC_21
p.K1146fs
DEL
0.441666667
53
 67
1
strelka,










indelocator






RCC_41
NA
NA
NA
NA
NA
NA
NA






RCC_50

SNP
0.213592
22
 81
1
NA






RCC_73
NA
NA
NA
NA
NA
NA
NA






RCC_39
NA
NA
NA
NA
NA
NA
NA






RCC_99
NA
NA
NA
NA
NA
NA
NA






RCC_105
NA
NA
NA
NA
NA
NA
NA






RCC_119

SNP
0.666667
10
  5
1
NA






RCC_27
p.
DEL
0.15
14
 77
1
indelocator




AKVVDDE1249del












RCC_52
p.D1148fs
DEL
0.235955056
21
 68
1
strelka,










indelocator






RCC_97
p.E417*
SNP
0.278481
22
 57
1
NA
















TABLE 6E







Sequencing Metrics and Inclusion/Exclusion Criteria for Whole Exome Sequencing in Validation Cohort (N = 67)




















PBRM1

absolute
absolute





sequencing


mean

inferred
inferred
genome
exclusion


patient_id
type
tumor_mtc
normal_mtc
cov
bait_set_or_panel_type
purity
ploidy
doubling
reason



















CA8808_T1
WES
123.07315
103.475727
NA
whole_exome_agilent_1
0.48
1.95
0
0


KA4076_T1
WES
126.229037
120.209259
NA
whole_exome_agilent_1
0.6 
2
0
0


KE5236_T1
WES
132.886302
140.196056
NA
whole_exome_agilent_1
0.32
1.99
0
0


KE6262_T1
WES
99.539361
106.858872
NA
whole_exome_agilent_1
0.13
2.27
1
0


MC1838_T1
WES
149.730846
118.307339
NA
whole_exome_agilent_1
0.38
1.95
0
0


RCC-PD_005
WES
125.608438
102.090575
NA
whole_exome_illumina_coding_v1
0.47
1.82
0
0


RCC-PD_007
WES
141.661729
101.362659
NA
whole_exome_illumina_coding_v1
0.57
1.82
0
0


RCC-PD_010
WES
131.1804
84.152599
NA
whole_exome_illumina_coding_v1
0.55
1.76
0
0


RCC-PD_011
WES
104.135516
82.878525
NA
whole_exome_illumina_coding_v1
0.32
2.08
0
0


RCC-PD_012
WES
150.48938
87.549415
NA
whole_exome_illumina_coding_v1
0.39
2.01
0
0


RCC-PD_013
WES
145.082205
91.170952
NA
whole_exome_illumina_coding_v1
0.22
3.44
1
0


RCC-PD_014
WES
127.306107
78.539083
NA
whole_exome_illumina_coding_v1
0.25
4.02
1
0


RCC-PD_015
WES
105.708638
93.290512
NA
whole_exome_illumina_coding_v1
0.36
2.1
0
0


RCC-PD_018
WES
145.443729
95.372761
NA
whole_exome_illumina_coding_v1
0.61
1.84
0
0


RCC-PD_019
WES
148.823821
87.774525
NA
whole_exome_illumina_coding_v1
0.42
1.97
0
0


RCC-PD_020
WES
151.788377
102.972091
NA
whole_exome_illumina_coding_v1
0.18
1.86
0
0


RCC-PD_021
WES
159.181781
95.98438
NA
whole_exome_illumina_coding_v1
0.58
1.9
0
0


RCC-PD_022
WES
148.651377
98.276519
NA
whole_exome_illumina_coding_v1
0.53
1.97
0
0


RCC-PD_023
WES
135.431357
86.807511
NA
whole_exome_illumina_coding_v1
0.19
2.65
1
0


RCC-PD_024
WES
92.006306
83.700183
NA
whole_exome_illumina_coding_v1
0.27
2.01
0
0


RCC-PD_025
WES
74.164294
48.102291
NA
whole_exome_illumina_coding_v1
0.35
1.99
0
0


RCC-PD_026
WES
166.502187
89.436443
NA
whole_exome_illumina_coding_v1
0.76
1.98
0
0


RCC.PD1.DNA.1026
WES
136.955167
87.050978
NA
whole_exome_agilent_1.1_refseq
0.26
2.15
0
0







plus_3_boosters


RCC.PD1.DNA.1101
WES
92.348009
81.023695
NA
whole_exome_agilent_1.1_refseq
0.3 
1.97
0
0







plus_3_boosters


RCC.PD1.DNA.1137
WES
71.474257
96.238769
NA
whole_exome_agilent_1.1_refseq
0.31
3.43
1
0







plus_3_boosters


RCC.PD1.DNA.944
WES
126.472115
95.64198
NA
whole_exome_agilent_1.1_refseq
0.66
1.89
0
0







plus_3_boosters


RCC.PD1.DNA.949
WES
101.276419
96.351667
NA
whole_exome_agilent_1.1_refseq
0.43
3.91
1
0







plus_3_boosters


VA1008_T1
WES
142.542157
89.429498
NA
whole_exome_agilent_1
0.11
1.99
0
0


PGDX2818T_Ex-
WES
55.34115691
79.77112838
NA
NA
NA
NA
NA
0


RCC032PT1


PGDX2817T_Ex-
WES
103.1856033
69.5272945
NA
NA
NA
NA
NA
0


RCC031PT1


PGDX2816T_Ex-
WES
97.77199375
84.39103979
NA
NA
NA
NA
NA
0


RCC030PT1


PGDX2815T_Ex-
WES
66.32040544
54.48433181
NA
NA
NA
NA
NA
0


RCC029PT1


PGDX2814T_Ex-
WES
118.1997689
73.44450094
NA
NA
NA
NA
NA
0


RCC028PT1


PGDX2813T_Ex-
WES
33.35986673
65.51616024
NA
NA
NA
NA
NA
0


RCC027PT1


PGDX2811T_Ex-
WES
151.2973078
52.69614078
NA
NA
NA
NA
NA
0


RCC025PT1


RCC-PD_029
WES
NA
89.149597
NA
whole_exome_illumina_coding_v1
NA
NA
NA
FailedSequencing


BL5166_T1
WES
145.877429
122.453144
197.8 
whole_exome_agilent_1
0.07
1.91
0
0


RCC-PD_030
WES
129.113198
117.952235
NA
whole_exome_illumina_coding_v1
0.38
1.94
0
MixedResponse


RCC-PD_009
WES
122.598167
87.563055
NA
whole_exome_illumina_coding_v1
0.41
1.99
0
PapillaryRCC


RENAL-15349
WES
191.128712
164.148594
NA
whole_exome_illumina_coding_v1
0.1 
4.23
1
0


CCPM_0600855


RENAL-15349
WES
126.035686
86.198512
144.04
whole_exome_illumina_coding_v1
0.07
2
0
0


CCPM_0600862


RCC-IM_001
WES
179.289403
86.748087
NA
whole_exome_illumina_coding_v1
0.41
1.51
0
0


RCC-IM_002
WES
70.427352
82.150509
NA
whole_exome_illumina_coding_v1
0.45
1.65
0
0


RCC-IM_003
WES
160.061412
172.704074
NA
whole_exome_illumina_coding_v1
NA
NA
NA
NonClearCellRCC


RCC_281066
WES
80.371008
89.496933
NA
whole_exome_illumina_coding_v1
0.44
1.89
0
0


RCC_371982
WES
93.846727
91.06197
NA
whole_exome_illumina_coding_v1
0.48
2.09
0
0


RCC_390392
WES
78.227866
116.306601
NA
whole_exome_illumina_coding_v1
0.46
1.92
0
0


RCC_470874
WES
179.545671
92.161197
NA
whole_exome_illumina_coding_v1
0.45
1.77
0
0


RCC_472770
WES
97.99001
81.592339
NA
whole_exome_illumina_coding_v1
0.39
1.86
0
0


RCC_504642
WES
182.31248
116.982034
NA
whole_exome_illumina_coding_v1
0.47
2.05
0
0


RCC_509214
WES
129.146039
114.189746
NA
whole_exome_illumina_coding_v1
0.57
1.89
0
0


RCC_51974086
WES
159.511692
69.797316
NA
whole_exome_illumina_coding_v1
0.78
1.91
0
0


RCC_554652
WES
223.859717
93.016058
NA
whole_exome_illumina_coding_v1
0.39
2.92
1
0


MCA1
targeted_panel
NA
NA
NA
Caris Molecular
NA
NA
NA
0







Intelligence + 600







gene NGS-2015


MCA2
targeted_panel
NA
NA
NA
FoundationOne- (315 genes,
NA
NA
NA
0







28 introns) 2014


MCA3
targeted_panel
NA
NA
NA
Caris Molecular
NA
NA
NA
0







Intelligence + 600







gene NGS-2015


MCA4
targeted_panel
NA
NA
NA
Caris Molecular
NA
NA
NA
0







Intelligence + 600







gene NGS-2016


MCA5
targeted_panel
NA
NA
NA
FoundationOne- (236
NA
NA
NA
0







genes, 47 introns) 2014


MCA6
targeted_panel
NA
NA
NA
Caris Molecular
NA
NA
NA
0







Intelligence + 600







gene NGS-2016


MCA7
targeted_panel
NA
NA
NA
FoundationOne- (236
NA
NA
NA
0







genes, 47 introns) 2014


MCA8
targeted_panel
NA
NA
NA
Caris Molecular
NA
NA
NA
0







Intelligence + 600







gene NGS-2016


MCA9
targeted_panel
NA
NA
NA
Caris Molecular
NA
NA
NA
0







Intelligence + 600







gene NGS-2017


MCA10
targeted_panel
NA
NA
NA
Caris Molecular
NA
NA
NA
0







Intelligence + 600







gene NGS-2016


MCA11
targeted_panel
NA
NA
NA
FoundationOne- (315
NA
NA
NA
0







genes, 28 introns) 2016


MCA12
targeted_panel
NA
NA
NA
Caris Molecular
NA
NA
NA
0







Intelligence + 600







gene NGS-2015


MCA13
targeted_panel
NA
NA
NA
FoundationOne- (236
NA
NA
NA
0







genes, 47 introns) 2014


MCA14
targeted_panel
NA
NA
NA
Caris Molecular
NA
NA
NA
0







Intelligence + 600







gene NGS-2016
















TABLE 6F







Clinical information for immune checkpoint-treated patients in validation cohort (N = 63)



























max











best


first
tumor

os
os
pfs
pfs
response


patient_id
drug
wes
recist
sex
age
line
change
histology
days
censor
days
censor
category























CA8808
nivolumab
1
PR
MALE
62
0
−55
clear-cell
560
1
558
0
clinical















benefit


KA4076
nivolumab
1
PD
FEMALE
61
0
59
clear-cell
727
0
107
0
intermediate















benefit


KE5236
nivolumab
1
PD
MALE
58
0
70
clear-cell
997
1
165
0
intermediate















benefit


KE6262
nivolumab
1
PR
MALE
68
0
−60
clear-cell
903
1
163
0
clinical















benefit


MC1838
nivolumab
1
PD
MALE
64
0
93
clear-cell
622
0
60
0
no clinical















benefit


RCC-PD_005
nivolumab
1
PD
MALE
62
0
NA
clear-cell
277
1
168
0
intermediate















benefit


RCC-PD_007
nivolumab +
1
PR
MALE
60
1
−42
clear-cell
448
1
448
1
clinical



ipilimumab











benefit


RCC-PD_010
nivolumab +
1
CR
MALE
51
1
−100
clear-cell
454
1
454
1
clinical



ipilimumab











benefit


RCC-PD_011
nivolumab
1
PD
MALE
40
1
−37
clear-cell
327
0
205
0
intermediate















benefit


RCC-PD_012
atezolizumab
1
PD
MALE
67
1
22.7
clear-cell
581
1
61
0
no clinical















benefit


RCC-PD_013
nivolumab +
1
PR
MALE
66
1
−32
clear-cell
399
1
399
1
clinical



ipilimumab











benefit


RCC-PD_014
nivolumab +
1
SD
FEMALE
68
1
−5
clear-cell
433
1
433
1
clinical



ipilimumab











benefit


RCC-PD_015
nivolumab
1
PD
MALE
71
0
6
clear-cell
814
0
105
0
intermediate















benefit


RCC-PD_018
nivolumab
1
PR
FEMALE
69
0
−82
clear-cell
1189
0
672
0
clinical















benefit


RCC-PD_019
nivolumab
1
SD
MALE
60
0
−11
clear-cell
230
1
220
0
clinical















benefit


RCC-PD_020
nivolumab
1
PD
FEMALE
64
0
NA
clear-cell
203
1
47
0
no clinical















benefit


RCC-PD_021
nivolumab
1
PD
FEMALE
63
0
NA
clear-cell
185
0
68
0
no clinical















benefit


RCC-PD_022
nivolumab
1
PD
FEMALE
66
0
NA
clear-cell
247
1
80
0
no clinical















benefit


RCC-PD_023
atezolizumab
1
PR
MALE
69
1
−88
clear-cell
637
1
637
1
clinical















benefit


RCC-PD_024
nivolumab
1
PD
MALE
52
0
30
clear-cell
304
0
41
0
no clinical















benefit


RCC-PD_025
nivolumab
1
SD
MALE
74
0
−23
clear-cell
1724
1
333
0
clinical















benefit


RCC-PD_026
nivolumab
1
SD
FEMALE
70
0
20
clear-cell
377
1
171
0
intermediate















benefit


RCC.PD1.DNA.1026
nivolumab
1
CR
MALE
60
0
−100
clear-cell
1442
1
949
1
clinical















benefit


RCC.PD1.DNA.1101
nivolumab
1
SD
MALE
67
0
4
clear-cell
440
0
163
0
intermediate















benefit


RCC.PD1.DNA.1137
nivolumab
1
SD
FEMALE
61
0
16
clear-cell
1584
1
119
0
intermediate















benefit


RCC.PD1.DNA.944
nivolumab
1
PD
MALE
47
0
37
clear-cell
470
0
36
0
no clinical















benefit


RCC.PD1.DNA.949
nivolumab
1
PR
FEMALE
60
0
−37
clear-cell
364
0
246
0
clinical















benefit


VA1008
nivolumab +
1
PR
MALE
76
0
−96
clear-cell
1135
1
1135
1
clinical



ipilimumab











benefit


PGDX2818T_Ex-
nivolumab
1
CR
MALE
51
0
−100
clear-cell
2533
1
2533
1
clinical


RCC032PT1












benefit


PGDX2817T_Ex-
nivolumab
1
PD
MALE
43
0
42
clear-cell
892
0
112
0
intermediate


RCC031PT1












benefit


PGDX2816T_Ex-
nivolumab
1
PR
MALE
58
0
−71
clear-cell
1755
1
1124
0
clinical


RCC030PT1












benefit


PGDX2815T_Ex-
nivolumab
1
PD
FEMALE
35
0
52
clear-cell
501
0
59
0
no clinical


RCC029PT1












benefit


PGDX2814T_Ex-
nivolumab
1
PD
FEMALE
67
0
33
clear-cell
148
0
51
0
no clinical


RCC028PT1












benefit


PGDX2813T_Ex-
nivolumab
1
CR
MALE
68
0
−100
clear-cell
2208
0
2012
0
clinical


RCC027PT1












benefit


PGDX2811T_Ex-
nivolumab
1
PR
MALE
73
0
−90
clear-cell
2810
1
2810
1
clinical


RCC025PT1












benefit


BL5166_T1
nivolumab
1
SD
MALE
63
0
−11
clear-cell
622
0
156
0
intermediate















benefit


RENAL-15349
nivolumab
1
PR
MALE
67
0
−37
clear-cell
499
1
499
1
clinical


CCPM_0600855












benefit


RENAL-15349
nivolumab
1
PD
MALE
73
0
NA
clear-cell
293
1
66
0
no clinical


CCPM_0600862












benefit


RCC-IM_001
nivolumab
1
PR
NA
40
0
−39
clear-cell
379
0
280
0
clinical















benefit


RCC-IM_002
nivolumab
1
SD
NA
61
0
−16
clear-cell
NA
NA
735
0
clinical















benefit


RCC_281066
nivolumab
1
SD
M
60
0
NA
clear-cell
460
1
460
1
intermediate















benefit


RCC_371982
nivolumab
1
PD
M
70
0
NA
clear-cell
448
0
71
0
no clinical















benefit


RCC_390392
nivolumab
1
SD
M
77
0
2
clear-cell
174
1
55
1
intermediate















benefit


RCC_470874
nivolumab
1
PD
M
59
0
NA
clear-cell
247
0
42
0
no clinical















benefit


RCC_472770
nivolumab
1
PD
M
52
0
NA
clear-cell
558
0
84
0
no clinical















benefit


RCC_504642
nivolumab
1
PD
F
55
0
NA
clear-cell
102
0
41
0
no clinical















benefit


RCC_509214
nivolumab
1
PR
M
44
0
NA
clear-cell
370
0
204
0
clinical















benefit


RCC_51974086
nivolumab
1
SD
F
81
0
3.5
clear-cell
456
0
220
0
intermediate















benefit


RCC_554652
nivolumab
1
PR
F
76
0
NA
clear-cell
484
1
336
1
clinical















benefit


MCA1
atezolizumab
0
PD
NA
NA
0
NA
clear-cell
NA
NA
85
0
no clinical















benefit


MCA2
atezolizumab
0
PD
NA
NA
0
NA
clear-cell
NA
NA
83
0
no clinical















benefit


MCA3
atezolizumab
0
SD
NA
NA
0
−15
clear-cell
NA
NA
337
0
clinical















benefit


MCA4
nivolumab
0
PD
NA
NA
0
NA
clear-cell
NA
NA
145
0
intermediate















benefit


MCA5
nivolumab
0
PD
NA
NA
0
NA
clear-cell
NA
NA
203
0
intermediate















benefit


MCA6
nivolumab
0
CR
NA
NA
0
NA
clear-cell
NA
NA
196
0
clinical















benefit


MCA7
nivolumab
0
PR
NA
NA
0
−50
clear-cell
NA
NA
601
0
clinical















benefit


MCA8
nivolumab
0
PD
NA
NA
0
NA
clear-cell
NA
NA
107
0
no clinical















benefit


MCA9
nivolumab
0
PD
NA
NA
0
NA
clear-cell
NA
NA
31
0
no clinical















benefit


MCA10
nivolumab
0
SD
NA
NA
0
NA
clear-cell
NA
NA
312
0
intermediate















benefit


MCA11
nivolumab
0
SD
NA
NA
0
NA
clear-cell
NA
NA
NA
0
intermediate















benefit


MCA12
nivolumab
0
PR
NA
NA
0
−53
clear-cell
NA
NA
127
0
clinical















benefit


MCA13
nivolumab
0
PR
NA
NA
0
−43
clear-cell
NA
NA
NA
0
clinical















benefit


MCA14
nivolumab
0
PD
NA
NA
0
NA
clear-cell
NA
NA
65
0
no clinical















benefit
















TABLE 6G





Truncating PBRM1 alterations in validation cohort (N = 63)





























Variant_

Tumor_
Tumor_



patient_
Hugo_
Chromo-
Start_
End_
Classi-
Reference_
Seq_
Seq_
Protein_


id
Symbol
some
position
position
fication 
Allele
Allele1
Allele2
Change





CA8808
PBRM1
3
52595873
52595873
Frame_
G
G
-
p.Q1415fs







Shift_











Del









KA4076
NA
NA
NA
NA
NA
NA
NA
NA
NA





KE5236
PBRM1
3
52597356
52597359
Frame_
AG
AG
-
p.LP1310fs







Shift_
GT
GT









Del









KE6262
PBRM1
3
52643586
52643596
Frame_
AT
AT
-
p.EDSH782fs







Shift_
GA
GA









Del
GA
GA










GT
GT










CC
CC










T
T







MC1838
NA
NA
NA
NA
NA
NA
NA
NA
NA





PD_005
PBRM1
3
52668656
52668656
Nonsense_
G
G
T
p.Y389*







Mutation









PD_007
NA
NA
NA
NA
NA
NA
NA
NA
NA





PD_010
PBRM1
3
52702580
52702580
Nonsense_
A
A
C
p.Y106*







Mutation









PD_011
NA
NA
NA
NA
NA
NA
NA
NA
NA





PD_012
PBRM1
3
52620643
52620643
Missense_
C
C
G
p.R1030P







Mutation









PD_013
PBRM1
3
52597487
52597488
Frame_
CC
CC
-
p.E1315fs







Shift_











Del









PD_014
PBRM1
3
52610662
52610663
Frame_
AG
AG
-
p.F1211fs







Shift_











Del









PD_015
PBRM1
3
52637540
52637540
Frame_
AG
AG
-
p.R941fs







Shift_











Del









PD_018
NA
NA
NA
NA
NA
NA
NA
NA
NA





PD_019
NA
NA
NA
NA
NA
NA
NA
NA
NA





PD_020
NA
NA
NA
NA
NA
NA
NA
NA
NA





PD_021
PBRM1
3
52713723
52713723
Frame_
C
C
-
p.G2fs







Shift_











Del









PD_022
NA
NA
NA
NA
NA
NA
NA
NA
NA





PD_023
PBRM1
3
52663053
52663053
Splice_
T
T
A
NA







Site









PD_024
NA
NA
NA
NA
NA
NA
NA
NA
NA





PD_025
PBRM1
3
52595829
52595829
Frame_
C
C
-
p.G1429fs







Shift_











Del









PD_026
NA
NA
NA
NA
NA
NA
NA
NA
NA





RCC.PD1.
NA
NA
NA
NA
NA
NA
NA
NA
NA


DNA.1026














RCC.PD1.
PBRM1
3
52595804
52595804
Frame_
C
C
-
p.A1438fs


DNA.1101




Shift_











Del









RCC.PD1.
NA
NA
NA
NA
NA
NA
NA
NA
NA


DNA.1137














RCC.PD1.
NA
NA
NA
NA
NA
NA
NA
NA
NA


DNA.944














RCC.PD1.
NA
NA
NA
NA
NA
NA
NA
NA
NA


DNA.949














VA1008
PBRM1
3
52643943
52643943
Frame_
T
T
-
p.K619fs







Shift_











Del









PGDX2818T_
PBRM1
3
52652306
52652306
Nonsense_
G
G
T
p.331*


Ex-




Mutation






RCC032PT1














PGDX2817T_
NA
NA
NA
NA
NA
NA
NA
NA
NA


Ex-











RCC031PT1














PGDX2816T_
PBRM1
3
5259824
5259824
Frame_
T
T
-
NA


Ex-




Shift_






RCC030PT1




Del









PGDX2815T_
NA
NA
NA
NA
NA
NA
NA
NA
NA


Ex-











RCC029PT1














PGDX2814T_
PBRM1
3
52618979
52618979
Nonsense_
T
T
A
p.K621*


Ex-




Mutation






RCC028PT1














PGDX2813T_
NA
NA
NA
NA
NA
NA
NA
NA
NA


Ex-











RCC027PT1














PGDX2811T_
PBRM1
3
52657432
52657432
Frame_
T
T
-
NA


Ex-




Shift_






RCC025PT1




Del









BL5166_T1
NA
NA
NA
NA
NA
NA
NA
NA
NA





RENAL-
PBRM1
3
52637555
52637555
Nonsense_
G
G
A
p.R889*


15349_CCPM_




Mutation






0600855














RENAL-
NA
NA
NA
NA
NA
NA
NA
NA
NA


15349_CCPM_











0600862














RCC-IM_001
NA
NA
NA
NA
NA
NA
NA
NA
NA





RCC-IM_002
NA
NA
NA
NA
NA
NA
NA
NA
NA





RCC_281066
NA
NA
NA
NA
NA
NA
NA
NA
NA





RCC_371982
PBRM1
3
52682428
52682428
Frame_
C
C
-
p.A249fs







Shift_











Del









RCC_390392
PBRM1
3
52712515
52712515
Splice_
C
C
-
NA







Site









RCC_470874
NA
NA
NA
NA
NA
NA
NA
NA
NA





RCC_472770
NA
NA
NA
NA
NA
NA
NA
NA
NA





RCC_504642
NA
NA
NA
NA
NA
NA
NA
NA
NA





RCC_509214
PBRM1
3
52662909
52662909
Splice_
C
C
A
NA







Site









RCC_
NA
NA
NA
NA
NA
NA
NA
NA
NA


51974086














RCC_554652
NA
NA
NA
NA
NA
NA
NA
NA
NA





MCA1
NA
NA
NA
NA
NA
NA
NA
NA
NA





MCA2
PBRM1
NA
NA
NA
Splice_
NA
NA
NA
NA







Site









MCA3
PBRM1
NA
NA
NA
Frame_
NA
NA
NA
NA







Shift









MCA4
PBRM1
NA
NA
NA
Frame_
NA
NA
NA
p.N609fs







Shift









MCA5
PBRM1
NA
NA
NA
Frame_
NA
NA
NA
p.Y608fs*34







Shift









MCA6
PBRM1
NA
NA
NA
Missense_
NA
NA
NA
N258S







Mutation_











LOF









MCA7
PBRM1
NA
NA
NA
Nonsense_
NA
NA
NA
p.E1124*







Mutation









MCA8
NA
NA
NA
NA
NA
NA
NA
NA
NA





MCA9
PBRM1
NA
NA
NA
In_
NA
NA
NA
p.W141_







Frame_



L145DelInsC







DelIns









MCA10
NA
NA
NA
NA
NA
NA
NA
NA
NA





MCA11
NA
NA
NA
NA
NA
NA
NA
NA
NA





MCA12
PBRM1
NA
NA
NA
Frame_
NA
NA
NA
p.K154fs







Shift









MCA13
PBRM1
NA
NA
NA
Missense_
NA
NA
NA
p.S681R







Mutation









MCA14
NA
NA
NA
NA
NA
NA
NA
NA
NA



















patient_
Variant_
i_
t_alt_
t_ref_
clonal_
indel_
PBRM1_



id
Type
tumor_f
count
count
dm
caller
IHC






CA8808
DEL
0.259090909
57
163
1
strelka,
NA









indelocator







KA4076
NA
NA
NA
NA
NA
NA
NA






KE5236
DEL
0.09
 8
 85
0
indelocator
NA






KE6262
DEL
0.098214286
11
101
1
strelka,
NA









indelocator







MC1838
NA
NA
NA
NA
NA
NA
NA






PD_005
SNP
0.287356
25
 62
1
NA
NA






PD_007
NA
NA
NA
NA
NA
NA
NA






PD_010
SNP
0.15
 6
 34
0
NA
NA






PD_011
NA
NA
NA
NA
NA
NA
NA






PD_012
SNP
0.358491
38
 68
1
NA
NA






PD_013
DEL
0.1171875
15
113
1
strelka,
NA









indelocator







PD_014
DEL
0.288888889
26
 64
1
strelka,
NA









indelocator







PD_015
DEL
0.204545455
18
 70
1
strelka,
NA









indelocator







PD_018
NA
NA
NA
NA
NA
NA
NA






PD_019
NA
NA
NA
NA
NA
NA
NA






PD_020
NA
NA
NA
NA
NA
NA
NA






PD_021
DEL
0.36
18
 32
1
strelka,
NA









indelocator







PD_022
NA
NA
NA
NA
NA
NA
NA






PD_023
SNP
0.214286
 9
 33
1
NA
NA






PD_024
NA
NA
NA
NA
NA
NA
NA






PD_025
DEL
0.154411765
21
115
1
strelka,
NA









indelocator







PD_026
NA
NA
NA
NA
NA
NA
NA






RCC.PD1.
NA
NA
NA
NA
NA
NA
NA



DNA.1026













RCC.PD1.
DEL
0.133540373
43
279
1
strelka,
NA



DNA.1101





indelocator







RCC.PD1.
NA
NA
NA
NA
NA
NA
NA



DNA.1137













RCC.PD1.
NA
NA
NA
NA
NA
NA
NA



DNA.944













RCC.PD1.
NA
NA
NA
NA
NA
NA
NA



DNA.949













VA1008
DEL
0.06
15
253
1
indelocator
NA






PGDX2818T_
SNP
0.483871
30
 32
NA
NA
NA



Ex-RCC032PT1













PGDX2817T_
NA
NA
NA
NA
NA
NA
NA



Ex-RCC031PT1













PGDX2816T_
DEL
0.315789
18
 39
NA
NA
NA



Ex-RCC030PT1













PGDX2815T_
NA
NA
NA
NA
NA
NA
NA



Ex-RCC029PT1













PGDX2814T_
SNP
0.38
19
 31
NA
NA
NA



Ex-RCC028PT1













PGDX2813T_
NA
NA
NA
NA
NA
NA
NA



Ex-RCC027PT1













PGDX2811T_
DEL
0.327103
35
 72
NA
NA
NA



Ex-RCC025PT1













BL5166_T1
NA
NA
NA
NA
NA
NA
NA






RENAL-15349_
SNP
0.026217
 7
260
0
NA
NA



CCPM_0600855













RENAL-15349_
NA
NA
NA
NA
NA
NA
NA



CCPM_0600862













RCC-IM_001
NA
NA
NA
NA
NA
NA
NA






RCC-IM_002
NA
NA
NA
NA
NA
NA
NA






RCC_281066
NA
NA
NA
NA
NA
NA
NA






RCC_371982
DEL
0.1875
12
 52
1
strelka,
NA









indelocator







RCC_390392
DEL
0.318181818
21
 45
1
strelka,
NA









indelocator







RCC_470874
NA
NA
NA
NA
NA
NA
NA






RCC_472770
NA
NA
NA
NA
NA
NA
NA






RCC_504642
NA
NA
NA
NA
NA
NA
NA






RCC_509214
SNP
0.352941
60
110
1
NA
NA






RCC_51974086
NA
NA
NA
NA
NA
NA
NA






RCC_554652
NA
NA
NA
NA
NA
NA
NA






MCA1
NA
NA
NA
NA
NA
NA
positive






MCA2
SNP
NA
NA
NA
NA
NA
NA






MCA3
FS
0.33
NA
NA
NA
NA
negative






MCA4
FS
0.09
NA
NA
NA
NA
negative






MCAS
FS
NA
NA
NA
NA
NA
negative






MCA6
SNP
0.32
NA
NA
NA
NA
negative






MCA7
SNP
NA
NA
NA
NA
NA
NA






MCA8
NA
NA
NA
NA
NA
NA
positive






MCA9
DELINS
0.21
NA
NA
NA
NA
NA






MCA10
NA
NA
NA
NA
NA
NA
positive






MCA11
NA
NA
NA
NA
NA
NA
NA






MCA12
FS
0.25
NA
NA
NA
NA
negative






MCA13
SNP
NA
NA
NA
NA
NA
NA






MCA14
NA
NA
NA
NA
NA
NA
positive
















TABLE 6H







SWI/SNF genes












Hugo_Symbol
Other Names
BAF
PBAF







ACTL6A
BAF53A
1
1



ACTL6B
BAF53B
1
1



ARID2
BAF200
0
1



BCL7A

1
1



BCL7B

1
1



BCL7C

1
1



BCL11A

1
1



BCL11B

1
1



BRD7

0
1



BRD9

1
0



DPF1
BAF45B
1
0



DPF2
BAF45D
1
0



DPF3
BAF45C
1
0



PBRM1
BAF180
0
1



PHF10
BAF45A
0
1



SMARCA2
BRM
1
0



SMARCA4
BRG
1
1



SMARCB1
BAF47, SNF1, INI1
1
1



SMARCC1
BAF155
1
1



SMARCC2
BAF170
1
1



SMARCE1
BAF57
1
1



SS18

1
0



SS18L1
CREST
1
0



SMARCD1
BAF60A
1
1



SMARCD2
BAF60B
1
1



SMARCD3
BAF60C
1
1



ARID1A
BAF250A
1
0



ARID1B
BAF250B
1
0

















TABLE 6I







Intersection of top 100 positively differentially expressed


genes in PBRM1null and BRG1null, and top 100 negatively


differentially expressed genes in PBRM1 null and BRG1


null, both with respect to wild type using EdgeR










Higher_in_mutant
Higher_in_wildtype







SEMA5B
PRRT1



LOX
ARHGDIB



IL8
PADI3



PHGDH
MYPN



IGFBP3
C19orf21



SCARA3
ITGB4



COL1A1
SUCNR1



JAG1
ERAP2



NTM
ACE2



SFRP4
PADI1



SDC1
SERPINE1



TFPI2
KIAA1486



NMB
B3GNT3



SLC17A3
F2R



CXCL1
PKP3



RASSF2
CHSY3



HMGCS1
ACSL5



SC4MOL
DOCK2



ANGPTL4
CD74



UPB1
TAGLN



PTPRD
FGF5



MACROD2
ADD2



PEG10
TUBA4A



SULF2
HKDC1



KMO
RP11-428C6.1



C1QL4
SPNS2



P2RY6
UNC13D



NPR3
CAPG



SCD
KRTCAP3



TTYH3
SH3KBP1



MAPK12
CLTB



MAPK11
MARCH4



CD70
ABCA13



PDZD2
KRT8



RDH10
WWC1



ITM2B
MT2A



OLR1
MYEOV



NPTXR
ANKRD1



FAM84B
QSOX1



RASSF6
SLC1A1



LGI4
CGN



TNFSF1O
VCAN



FGF9
SEMA6A



NXN



CRYAB



ADAMTS7



PKDCC



MYO10

















TABLE 6J







GSEA for BAF180-null vs. BAF180-wildtype A704 cell lines on Hallmark gene sets






















RANK









FWER
AT
LEADING


NAME
SIZE
ES
NES
NOM p-val
FDR q-val
p-val
MAX
EDGE


















HALLMARK_CHOLESTEROL
73
0.5499064
2.078206
0
0.0015
0.001
1908
tags = 30%,


HOMEOSTASIS







list = 9%,










signal = 33%


HALLMARK_EPITHELIAL
196
0.40123636
1.751494
0
0.01094041
0.016
2701
tags = 27%,


MESENCHYMAL_TRANSITION







list = 12%,










signal = 30%


HALLMARK_HYPOXIA
196
0.36750925
1.6125246
0
0.021424314
0.071
3029
tags = 22%,










list = 14%,










signal = 26%


HALLMARK_IL6_JAK_STAT3
75
0.43879578
1.6340721
0.004149378
0.022036757
0.059
3503
tags = 33%,


SIGNALING







list = 16%,










signal = 39%


HALLMARK_MTORC1_SIGNALING
197
0.37888893
1.6442895
0
0.026279828
0.053
3178
tags = 21%,










list = 14%,










signal = 25%


HALLMARK_E2F_TARGETS
199
0.35565567
1.5548034
0.002409639
0.026504425
0.12
6911
tags = 45%,










list = 31%,










signal = 64%


HALLMARK_MYOGENESIS
185
0.36297417
1.5741123
0
0.026698643
0.103
2192
tags = 20%,










list = 10%,










signal = 22%


HALLMARK_HEDGEHOG_SIGNALING
34
0.4507803
1.4415807
0.052863438
0.050014596
0.298
3180
tags = 32%,










list = 14%,










signal = 38%


HALLMARK_ANGIOGENESIS
33
0.46237797
1.4533101
0.03539823
0.05064617
0.278
809
tags = 15%,










list = 4%,










signal = 16%


HALLMARK_COAGULATION
116
0.35500702
1.4640448
0.01843318
0.051064506
0.252
3903
tags = 28%,










list = 18%,










signal = 33%


HALLMARK_TNFA_SIGNALING
194
0.31302983
1.3606336
0.017456358
0.095439464
0.524
2743
tags = 23%,


VIA_NFKB







list = 12%,










signal = 26%


HALLMARK_IL2_STAT5_SIGNALING
186
0.30282557
1.3082199
0.03163017
0.13568047
0.704
4131
tags = 27%,










list = 19%,










signal = 33%


HALLMARK_APICAL_SURFACE
42
0.3483055
1.189489
0.20134228
0.24039724
0.947
2814
tags = 21%,










list = 13%,










signal = 24%


HALLMARK_PANCREAS_BETA_CELLS
27
0.39650375
1.1929086
0.18644068
0.25035873
0.945
2494
tags = 26%,










list = 11%,










signal = 29%


HALLMARK_KRAS_SIGNALING_DN
160
0.2832103
1.201148
0.091566265
0.2521114
0.935
1663
tags = 14%,










list = 7%,










signal = 15%


HALLMARK_GLYCOLYSIS
199
0.2657333
1.1636928
0.11809045
0.25848737
0.976
2702
tags = 19%,










list = 12%,










signal = 21%


HALLMARK_KRAS_SIGNALING_UP
174
0.27017388
1.1702893
0.120987654
0.25996405
0.969
1960
tags = 16%,










list = 9%,










signal = 17%


HALLMARK_INFLAMMATORY
176
0.27770376
1.2042952
0.096618354
0.2642809
0.932
1973
tags = 16%,


RESPONSE







list = 9%,










signal = 17%


HALLMARK_NOTCH_SIGNALING
32
0.3793043
1.2072315
0.19341564
0.27845338
0.93
1028
tags = 19%,










list = 5%,










signal = 20%


HALLMARK_FATTY_ACID
149
0.26660782
1.1278455
0.19148937
0.31431836
0.989
4502
tags = 28%,


METABOLISM







list = 20%,










signal = 34%


HALLMARK_MYC_TARGETS_V2
58
0.299071
1.0908226
0.27828056
0.379539
0.998
4813
tags = 24%,










list = 22%,










signal = 31%


HALLMARK_WNT_BETA_CATENIN
39
0.3251601
1.0594196
0.36285096
0.4396408
0.999
917
tags = 13%,


SIGNALING







list = 4%,










signal = 13%


HALLMARK_COMPLEMENT
175
0.24138407
1.0466689
0.3208431
0.4541273
0.999
3153
tags = 17%,










list = 14%,










signal = 20%


HALLMARK_G2M_CHECKPOINT
198
0.22897714
1.0028436
0.44444445
0.5551898
1
6567
tags = 35%,










list = 30%,










signal = 49%


HALLMARK_XENOBIOTIC
183
0.21210375
0.92167723
0.6800948
0.77817
1
2503
tags = 14%,


METABOLISM







list = 11%,










signal = 15%


HALLMARK_UV_RESPONSE_DN
143
0.21846533
0.90516925
0.7117347
0.795592
1
3533
tags = 19%,










list = 16%,










signal = 22%


HALLMARK_DNA_REPAIR
147
0.2048023
0.86652255
0.8066826
0.8692663
1
4368
tags = 20%,










list = 20%,










signal = 24%


HALLMARK_ADIPOGENESIS
194
0.1918401
0.8391842
0.8989899
0.8999136
1
3378
tags = 14%,










list = 15%,










signal = 17%


HALLMARK_REACTIVE_OXIGEN
45
0.23113286
0.79606485
0.78132117
0.9033937
1
2952
tags = 18%,


SPECIES_PATHWAY







list = 13%,










signal = 20%


HALLMARK_MYC_TARGETS_V1
200
0.184295
0.8095168
0.96217495
0.91855717
1
6577
tags = 26%,










list = 30%,










signal = 37%
















TABLE 6K







GSEA for BAF180-wildtype, BRG1-null vs. BAF180-wildtype, BRG1-wildtype A704 cell lines on Hallmark gene sets






















RANK









FWER
AT
LEADING


NAME
SIZE
ES
NES
NOM p-val
FDR q-val
p-val
MAX
EDGE


















HALLMARK_INFLAMMATORY_RESPONSE
171
0.37973073
1.4245273
0.007470651
0.12658831
0.577
4252
tags = 33%,










list = 19%,










signal = 41%


HALLMARK_TNFA_SIGNALING_VIA
192
0.37756145
1.4295613
0.003161222
0.1436323
0.553
4171
tags = 34%,


NFKB







list = 19%,










signal = 41%


HALLMARK_E2F_TARGETS
199
0.364751
1.389069
0.009483667
0.15321952
0.702
7987
tags = 43%,










list = 36%,










signal = 66%


HALLMARK_CHOLESTEROL_HOMEOSTASIS
73
0.41791317
1.4319164
0.026995305
0.17345017
0.546
4456
tags = 33%,










list = 20%,










signal = 41%


HALLMARK_IL6_JAK_STAT3_SIGNALING
73
0.43450925
1.4762418
0.012672811
0.2109737
0.38
5300
tags = 45%,










list = 24%,










signal = 59%


HALLMARK_KRAS_SIGNALING_DN
157
0.36467624
1.346272
0.030139936
0.21358259
0.872
5009
tags = 33%,










list = 23%,










signal = 42%


HALLMARK_IL2_STAT5_SIGNALING
186
0.37655368
1.4375755
0.006430868
0.21618104
0.521
4601
tags = 30%,










list = 21%,










signal = 37%


HALLMARK_APICAL_SURFACE
42
0.42272592
1.3133029
0.09125
0.25905415
0.932
5175
tags = 33%,










list = 23%,










signal = 43%


HALLMARK_MYOGENESIS
187
0.34258145
1.2901036
0.063101605
0.26447582
0.964
3826
tags = 25%,










list = 17%,










signal = 30%


HALLMARK_INTERFERON_GAMMA
179
0.34417737
1.2982914
0.05042017
0.2690372
0.955
4307
tags = 28%,


RESPONSE







list = 19%,










signal = 34%


HALLMARK_HEDGEHOG_SIGNALING
34
0.5023723
1.5104364
0.029224904
0.2772426
0.27
5025
tags = 53%,










list = 23%,










signal = 68%


HALLMARK_NOTCH_SIGNALING
31
0.42534694
1.2741792
0.16558862
0.27971512
0.975
2449
tags = 19%,










list = 11%,










signal = 22%


HALLMARK_COMPLEMENT
175
0.32690415
1.2264905
0.10319149
0.35470042
0.994
1842
tags = 14%,










list = 8%,










signal = 15%


HALLMARK_ANGIOGENESIS
32
0.41938755
1.2168443
0.21501273
0.35581103
0.996
2204
tags = 25%,










list = 10%,










signal = 28%


HALLMARK_HYPOXIA
194
0.31979325
1.2073612
0.10867294
0.35997763
0.997
4118
tags = 24%,










list = 19%,










signal = 29%


HALLMARK_EPITHELIAL
196
0.32430506
1.2336999
0.08252948
0.36051062
0.994
2380
tags = 18%,


MESENCHYMAL_TRANSITION







list = 11%,










signal = 20%


HALLMARK_KRAS_SIGNALING_UP
171
0.3172053
1.1798669
0.14618644
0.39506933
0.998
5256
tags = 36%,










list = 24%,










signal = 47%


HALLMARK_ALLOGRAFT_REJECTION
151
0.31554624
1.1654135
0.17849462
0.41372243
1
4307
tags = 27%,










list = 19%,










signal = 33%


HALLMARK_UV_RESPONSE_DN
143
0.32053903
1.1812268
0.17083786
0.4144526
0.998
5057
tags = 35%,










list = 23%,










signal = 45%


HALLMARK_WNT_BETA_CATENIN
39
0.36205238
1.1145409
0.32233503
0.52548987
1
5256
tags = 36%,


SIGNALING







list = 24%,










signal = 47%


HALLMARK_COAGULATION
114
0.30622408
1.1181058
0.2610132
0.53892165
1
6237
tags = 38%,










list = 28%,










signal = 52%


HALLMARK_ESTROGEN_RESPONSE
192
0.28934133
1.0933391
0.2798742
0.5427693
1
5456
tags = 34%,


EARLY







list = 25%,










signal = 44%


HALLMARK_INTERFERON_ALPHA
94
0.30972162
1.0939436
0.31038508
0.5659965
1
1566
tags = 14%,


RESPONSE







list = 7%,










signal = 15%


HALLMARK_BILE_ACID_METABOLISM
105
0.3016164
1.0760891
0.33482143
0.57484317
1
2284
tags = 15%,










list = 10%,










signal = 17%


HALLMARK_G2M_CHECKPOINT
198
0.26063028
0.9995506
0.49058577
0.7349968
1
7925
tags = 36%,










list = 36%,










signal = 55%


HALLMARK_APOPTOSIS
156
0.25963703
0.9737319
0.53347504
0.7582101
1
6508
tags = 35%,










list = 29%,










signal = 49%


HALLMARK_MTORC1_SIGNALING
197
0.2644619
1.001003
0.49367088
0.7585766
1
5007
tags = 22%,










list = 23%,










signal = 28%


HALLMARK_UV_RESPONSE_UP
152
0.26901275
1.0059657
0.46732026
0.7721154
1
5325
tags = 29%,










list = 24%,










signal = 38%


HALLMARK_PEROXISOME
99
0.27292284
0.97732824
0.52553916
0.7735135
1
2300
tags = 15%,










list = 10%,










signal = 17%


HALLMARK_FATTY_ACID_METABOLISM
151
0.25714728
0.95584106
0.57403433
0.7860941
1
5981
tags = 28%,










list = 27%,










signal = 38%


HALLMARK_ANDROGEN_RESPONSE
95
0.26842615
0.9450292
0.57126826
0.78813064
1
5621
tags = 31%,










list = 25%,










signal = 41%


HALLMARK_HEME_METABOLISM
187
0.24242312
0.91873807
0.6635121
0.8300867
1
5715
tags = 26%,










list = 26%,










signal = 34%


HALLMARK_XENOBIOTIC_METABOLISM
183
0.23199143
0.87932944
0.7373949
0.89637834
1
5202
tags = 24%,










list = 23%,










signal = 31%


HALLMARK_P53_PATHWAY
193
0.22611341
0.8573577
0.7903564
0.9159724
1
4727
tags = 23%,










list = 21%,










signal = 29%


HALLMARK_UNFOLDED_PROTEIN
112
0.14780428
0.53494364
1
0.9974952
1
4453
tags = 14%,


RESPONSE







list = 20%,










signal = 18%


HALLMARK_PI3K_AKT_MTOR
98
0.2217106
0.7829281
0.8594104
1
1
7204
tags = 30%,


SIGNALING







list = 32%,










signal = 44%


HALLMARK_ADIPOGENESIS
193
0.20610817
0.7734336
0.91043204
1
1
5963
tags = 23%,










list = 27%,










signal = 31%


HALLMARK_DNA_REPAIR
147
0.19458589
0.7146128
0.9478827
1
1
9455
tags = 36%,










list = 43%,










signal = 62%


HALLMARK_PANCREAS_BETA_CELLS
28
0.24026519
0.6924615
0.8860927
1
1
3531
tags = 21%,










list = 16%,










signal = 25%


HALLMARK_MITOTIC_SPINDLE
197
0.17785007
0.67739034
0.97993666
1
1
6069
tags = 27%,










list = 27%,










signal = 37%


HALLMARK_PROTEIN_SECRETION
95
0.16087638
0.5635202
0.9966254
1
1
7983
tags = 32%,










list = 36%,










signal = 49%
















TABLE 6L







GSEA for BAF180-null vs. BAF180-wildtype A704 cell lines on Cholesterol Homeostasis Founder gene sets






















RANK









FWER
AT
LEADING


NAME
SIZE
ES
NES
NOM p-val
FDR q-val
p-val
MAX
EDGE


















SCHMIDT_POR_TARGETS_IN
24
0.8159034
2.411856
0
0
0
1862
tags = 63%,


LIMB_BUD_UPz







list = 8%,










signal = 68%


REACTOME_CHOLESTEROL
21
0.81049263
2.3614385
0
0
0
2246
tags = 67%,


BIOSYNTHESIS







list = 10%,










signal = 74%


HORTON_SREBF_TARGETS
25
0.76518434
2.307628
0
0
0
1908
tags = 52%,










list = 9%,










signal = 57%


KEGG_STEROID_BIOSYNTHESIS
16
0.8001351
2.1263828
0
0
0
2462
tags = 69%,










list = 11%,










signal = 77%


PODAR_RESPONSE_TO
17
0.76344514
2.0490096
0
2.47E−04
0.001
1302
tags = 53%,


ADAPHOSTIN_DN







list = 6%,










signal = 56%


WENG_POR_TARGETS
18
0.6869103
1.9208144
0.004329004
0.001405882
0.004
1763
tags = 39%,


GLOBAL_UP







list = 8%,










signal = 42%


WENG_POR_TARGETS
37
0.53671414
1.7553303
0.002164502
0.010265792
0.04
1763
tags = 30%,


LIVER_UP







list = 8%,










signal = 32%


LE_EGR2_TARGETS_DN
101
0.4053505
1.6263677
0.004694836
0.02575889
0.109
1862
tags = 18%,










list = 8%,










signal = 19%


JI_RESPONSE_TO_FSH_UP
70
0.43719202
1.6248909
0.004385965
0.02356887
0.111
2601
tags = 33%,










list = 12%,










signal = 37%


HOXA9_DN.V1_DN
184
0.37380037
1.6219271
0
0.022287892
0.116
2709
tags = 23%,










list = 12%,










signal = 26%


BURTON_ADIPOGENESIS_10
28
0.5129609
1.5734106
0.027777778
0.027410874
0.158
2601
tags = 36%,










list = 12%,










signal = 40%


CSR_LATE_UP.V1_DN
156
0.3417356
1.4487041
0.007211539
0.062628604
0.332
3441
tags = 29%,










list = 15%,










signal = 35%


GERY_CEBP_TARGETS
113
0.35047704
1.4065694
0.027210884
0.0758233
0.418
943
tags = 12%,










list = 4%,










signal = 13%


COULOUARN_TEMPORAL
127
0.31372902
1.2847894
0.051764704
0.14626734
0.679
2090
tags = 16%,


TGFB1_SIGNATURE_DN







list = 9%,










signal = l7%


MTOR_UP.V1_UP
152
0.29891714
1.2796576
0.04822335
0.14189139
0.69
3119
tags = 24%,










list = 14%,










signal = 27%


ZHANG_GATA6_TARGETS_DN
62
0.323232
1.1692238
0.21462265
0.25430223
0.893
2796
tags = 24%,










list = 13%,










signal = 28%


UEDA_PERIFERAL_CLOCK
164
0.26870546
1.14285
0.18717949
0.27674678
0.924
2961
tags = 17%,










list = 13%,










signal = 20%


CHANG_CORE_SERUM
198
0.25709897
1.1284494
0.15012106
0.28189048
0.941
2863
tags = 18%,


RESPONSE_DN







list = 13%,










signal = 20%


GUO_TARGETS_OF
91
0.28360868
1.0960777
0.27539504
0.31335095
0.964
1862
tags = 18%,


IRS1_AND_IRS2







list = 8%,










signal = 19%


AK.T_UP.V1_UP
155
0.24117097
1.0295677
0.3721519
0.4158599
0.989
3180
tags = 22%,










list = 14%,










signal = 25%


WENG_POR_DOSAGE
19
0.31340367
0.8814183
0.62068963
0.70459986
0.999
537
tags = 11%,










list = 2%,










signal = 11%
















TABLE 6M







GSEA for BAF180-wildtype, BRG1-null vs. BAF180-wildtype, BRG1-wildtype A704 cell lines on Cholesterol homeostasis founder gene sets






















RANK









FWER
AT
LEADING


NAME
SIZE
ES
NES
NOM p-val
FDR q-val
p-val
MAX
EDGE


















SCHMIDT_POR_TARGETS_IN
24
0.6280739
1.7496891
0.002766252
0.021879772
0.019
6248
tags = 75%,


LIMB_BUD_UP







list = 28%,










signal = 104%


HOXA9_DN.V1_DN
183
0.4092302
1.537419
0.001053741
0.036110204
0.221
6421
tags = 42%,










list = 29%,










signal = 59%


KEGG_STEROID_BIOSYNTHESIS
16
0.61110497
1.5432961
0.02328767
0.038056426
0.206
7054
tags = 75%,










list = 32%,










signal = 110%


REACTOME_CHOLESTEROL
21
0.6144767
1.6537449
0.018469658
0.03882323
0.066
6248
tags = 71%,


BIOSYNTHESIS







list = 28%,










signal = 99%


HORTON_SREBF_TARGETS
25
0.56918824
1.5941461
0.011952192
0.03994539
0.132
6248
tags = 68%,










list = 28%,










signal = 95%


WENG_POR_TARGETS
36
0.51314527
1.5510265
0.01660281
0.040972658
0.194
3212
tags = 36%,


LIVER_UP







list = 14%,










signal = 42%


MTOR_UP.V1_UP
151
0.41329026
1.5625536
0.003229279
0.043739304
0.175
4345
tags = 34%,










list = 20%,










signal = 42%


BURTON_ADIPOGENESIS_10
28
0.5168679
1.5018733
0.024547804
0.0463574
0.311
5741
tags = 57%,










list = 26%,










signal = 77%


PODAR_RESPONSE_TO
17
0.6210147
1.6014676
0.010899182
0.04923035
0.122
2284
tags = 35%,


ADAPHOSTIN_DN







list = 10%,










signal = 39%


COULOUARN_TEMPORAL
127
0.39380088
1.4252076
0.020697167
0.06673416
0.509
4514
tags = 33%,


TGFB1_SIGNATURE_DN







list = 20%,










signal = 41%


JI_RESPONSE_TO_FSH_UP
70
0.42071614
1.4315375
0.024618993
0.06925182
0.493
3117
tags = 31%,










list = 14%,










signal = 36%


CSR_LATE_UP.V1_DN
156
0.37567928
1.4021397
0.013903744
0.07460156
0.577
5293
tags = 39%,










list = 24%,










signal = 51%


CHANG_CORE_SERUM
198
0.37701818
1.431742
0.005208334
0.076068304
0.492
6114
tags = 44%,


RESPONSE_DN







list = 28%,










signal = 60%


WENG_POR_TARGETS
18
0.4988817
1.3003886
0.1488178
0.16067472
0.871
8455
tags = 67%,


GLOBAL_UP







list = 38%,










signal = 108%


GERY_CEBP_TARGETS
112
0.340268
1.2337925
0.12637363
0.23521984
0.971
2102
tags = 14%,










list = 9%,










signal = 16%


ACEVEDO_FGFR1_TARGETS
260
0.29719558
1.1568955
0.16099072
0.35511822
0.996
4730
tags = 28%,


IN_PROSTATE_CANCER







list = 21%,


MODEL_UP







signal = 35%


ZHANG_GATA6_TARGETS_DN
61
0.33623016
1.1039963
0.31348723
0.4427688
1
2678
tags = 21%,










list = 12%,










signal = 24%


UEDA_PERIFERAL_CLOCK
163
0.28692022
1.0742894
0.32627118
0.48380232
1
7055
tags = 33%,










list = 32%,










signal = 48%


LE_EGR2_TARGETS_DN
100
0.28486618
1.0112084
0.48049054
0.5490551
1
6104
tags = 37%,










list = 27%,










signal = 51%


AKT_UP_MTOR_DN.V1_UP
165
0.26690233
0.98991686
0.51160336
0.56489664
1
2910
tags = 16%,










list = 13%,










signal = 19%


AKT_UP.V1_UP
156
0.27506077
1.0140308
0.4493063
0.5691042
1
4288
tags = 25%,










list = 19%,










signal = 31%


GUO_TARGETS_OF
89
0.2898395
1.0152373
0.4472477
0.5953482
1
5192
tags = 30%,


IRS1_AND_IRS2







list = 23%,










signal = 39%


GOTZMANN_EPITHELIAL
190
0.24749233
0.92717135
0.636936
0.66669047
1
3588
tags = 16%,


TO_MESENCHYMAL







list = 16%,


TRANSITION_DN







signal = 19%


UEDA_CENTRAL_CLOCK
81
0.22968177
0.80096096
0.8062284
0.85129535
1
5742
tags = 26%,










list = 26%,










signal = 35%


WENG_POR_DOSAGE
19
0.2510292
0.6606327
0.9015048
0.9546578
1
2705
tags = 16%,










list = 12%,










signal = 18%
















TABLE 6N





GSEA for BAF180-null vs. BAF180-wildtype A704 cell lines on IL6_JAK_STAT Founder gene sets





















GS<br> follow
GS





NAME
link to MSigDB
DETAILS
SIZE
ES
NES





KEGG_CYTOKINE
KEGG_CYTOKINE
Details . . .
181
0.4415354
1.9151524


CYTOKINE_RECEPTOR
CYTOKINE_RECEPTOR


INTERACTION
INTERACTION


TENEDINI
TENEDINI
Details . . .
61
0.395845
1.4293368


MEGAKARYOCYTE
MEGAKARYOCYTE


MARKERS
MARKERS


BIOCARTA
BIOCARTA
Details . . .
16
0.5389521
1.4307966


IL10_PATHWAY
IL10_PATHWAY


KEGG_JAK_STAT
KEGG_JAK_STAT
Details . . .
109
0.27087787
1.0860411


SIGNALING_PATHWAY
SIGNALING_PATHWAY





















RANK







FWER
AT
LEADING



NAME
NOM p-val
FDR q-val
p-val
MAX
EDGE







KEGG_CYTOKINE
0
0.002002002
0.003
2577
tags = 29%,



CYTOKINE_RECEPTOR




list = 12%,



INTERACTION




signal = 33%



TENEDINI
0.03794643
0.07527799
0.263
3503
tags = 26%,



MEGAKARYOCYTE




list = 16%,



MARKERS




signal = 31%



BIOCARTA
0.08045977
0.11208283
0.261
4579
tags = 38%,



IL10_PATHWAY




list = 21%,








signal = 47%



KEGG_JAK_STAT
0.28293738
0.32948533
0.861
2364
tags = 18%,



SIGNALING_PATHWAY




list = 11%,








signal = 20%

















TABLE 6O







GSEA for BAF180-wildtype, BRG1-null vs. BAF180-wildtype, BRG1-wildtype A704 cell lines on IL6_JAK_STAT founder gene sets






















RANK









FWER
AT
LEADING


NAME
SIZE
ES
NES
NOM p-val
FDR q-val
p-val
MAX
EDGE


















KEGG_CYTOKINE_CYTOKINE
174
0.45381105
1.7164165
0
0.023537878
0.023
3351
tags = 30%,


RECEPTOR_INTERACTION







list = 15%,










signal = 36%


BIOCARTA_IL10_PATHWAY
16
0.61396885
1.5512801
0.03076923
0.05661688
0.149
5694
tags = 63%,










list = 26%,










signal = 84%


TENEDINI_MEGAKARYOCYTE_MARKERS
58
0.47523633
1.5752333
0.009779952
0.067951284
0.12
5049
tags = 41%,










list = 23%,










signal = 53%


KEGG_JAK_STAT_SIGNALING
109
0.40122634
1.449788
0.023230089
0.10020399
0.319
6854
tags = 50%,


PATHWAY







list = 31%,










signal = 73%


MODULE_73
17
0.4175205
1.0875453
0.38537273
0.7025281
0.986
2810
tags = 41%,










list = 13%,










signal = 47%


CYTOKINE_BINDING
37
0.26826125
0.82358396
0.74140126
0.8336488
1
3257
tags = 30%,










list = 15%,










signal = 35%


MODULE_265
23
0.3571073
0.9895663
0.5
0.83561146
1
2825
tags = 39%,










list = 13%,










signal = 45%


HEMATOPOIETIN_INTERFERON
25
0.3321352
0.9321065
0.55599475
0.838992
1
4703
tags = 48%,


CLASSD200_DOMAIN_CYTOKINE







list = 21%,


RECEPTOR_ACTIVITY







signal = 61%


GROWTH_FACTOR_BINDING
24
0.3090261
0.86560124
0.67493474
0.86384636
1
3257
tags = 29%,










list = 15%,










signal = 34%


INTERLEUKIN_BINDING
17
0.2834838
0.7304276
0.8388889
0.8643178
1
4459
tags = 41%,










list = 20%,










signal = 51%
















TABLE 6P







GSEA for BAF180-null vs. BAF180-wildtype A704 cell lines on E2F Founder gene sets






















RANK









FWER
AT
LEADING


NAME
SIZE
ES
NES
NOM p-val
FDR q-val
p-val
MAX
EDGE


















GNF2_RFC4
60
0.5323848
1.9698843
0
0.04133063
0.021
6900
tags = 58%,










list = 31%,










signal = 84%


ZHOU_CELL_CYCLE_GENES_IN_IR
85
0.4946525
1.9064773
0
0.069671065
0.069
4682
tags = 42%,


RESPONSE_6HR







list = 21%,










signal = 53%


GRAHAM_NORMAL_QUIESCENT_VS
82
0.48101547
1.8290602
0
0.11817522
0.168
5081
tags = 41%,


NORMAL_DIVIDING_DN







list = 23%,










signal = 54%


MODULE_125
44
0.48651716
1.6430085
0.002150538
0.12799942
0.671
6383
tags = 55%,










list = 29%,










signal = 76%


WHITEFORD_PEDIATRIC_CANCER_MARKERS
115
0.40530172
1.6555282
0.002304148
0.1288557
0.629
6978
tags = 49%,










list = 31%,










signal = 71%


MARKEY_RB1_CHRONIC_LOF_UP
108
0.40898755
1.6469048
0
0.13123506
0.66
3518
tags = 31%,










list = 16%,










signal = 36%


PUJANA_BREAST_CANCER_WITH_BRCA1
56
0.46575648
1.6565605
0.004395605
0.13707851
0.625
7606
tags = 64%,


MUTATED_UP







list = 34%,










signal = 97%


KAMMINGA_EZH2_TARGETS
41
0.52069986
1.7285632
0.002358491
0.13727812
0.394
5684
tags = 39%,










list = 26%,










signal = 52%


MISSIAGLIA_REGULATED_BY_METHYLATION_DN
117
0.41643286
1.7019293
0
0.1373712
0.476
4926
tags = 38%,










list = 22%,










signal = 48%


LY_AGING_PREMATURE_DN
29
0.549658
1.7113805
0.010799136
0.14511141
0.455
3282
tags = 28%,










list = 15%,










signal = 32%


KOBAYASHI_EGFR_SIGNALING_24HR_DN
249
0.3704692
1.6659153
0
0.14565974
0.592
5139
tags = 35%,










list = 23%,










signal = 44%


GNF2_SMC4L1
84
0.42501023
1.6584858
0
0.1458408
0.619
6814
tags = 48%,










list = 31%,










signal = 68%


EXONUCLEASE_ACTIVITY
19
0.5912168
1.6705523
0.019354839
0.15194333
0.576
1943
tags = 32%,










list = 9%,










signal = 35%


MODULE_158
43
0.51076496
1.7321154
0
0.15483478
0.384
6515
tags = 58%,










list = 29%,










signal = 82%


STEIN_ESRRA_TARGETS_RESPONSIVE
39
0.48558497
1.6146711
0.013793103
0.15576415
0.751
4815
tags = 44%,


TO_ESTROGEN_DN







list = 22%,










signal = 56%


ZHOU_CELL_CYCLE_GENES_IN_IR
124
0.3967225
1.6749351
0
0.15974982
0.562
4658
tags = 31%,


RESPONSE_24HR







list = 21%,










signal = 39%


RB_DN.V1_UP
133
0.38019583
1.5704007
0.002380953
0.16487299
0.853
3288
tags = 23%,










list = 15%,










signal = 27%


DUTERTRE_ESTRADIOL_RESPONSE_24HR_UP
318
0.37789592
1.7427595
0
0.16575663
0.354
6577
tags = 48%,










list = 30%,










signal = 67%


REACTOME_DNA_STRAND_ELONGATION
30
0.5053188
1.579908
0.030303031
0.16585435
0.833
3979
tags = 40%,










list = 18%,










signal = 49%


GNF2_PCNA
67
0.43130568
1.5916202
0.006772009
0.16721149
0.803
7863
tags = 58%,










list = 35%,










signal = 90%


YU_BAP1_TARGETS
28
0.5155804
1.5830177
0.022222223
0.16959678
0.823
5137
tags = 46%,










list = 23%,










signal = 60%


ROSTY_CERVICAL_CANCER_PROLIFERATION
139
0.38421938
1.5708452
0.002347418
0.17128387
0.852
5728
tags = 37%,


CLUSTER







list = 26%,










signal = 50%


GNF2_FEN1
56
0.4400318
1.5927137
0.017391304
0.17471416
0.802
6577
tags = 48%,










list = 30%,










signal = 68%


RIZ_ERYTHROID_DIFFERENTIATION
75
0.3998866
1.5336596
0.015037594
0.20419818
0.92
3607
tags = 27%,










list = 16%,










signal = 32%


GNF2_MCM4
53
0.4871741
1.7432508
0.004464286
0.20625171
0.353
7686
tags = 60%,










list = 35%,










signal = 92%


LINDGREN_BLADDER_CANCER_CLUSTER_3_UP
317
0.32804054
1.5342647
0
0.21121016
0.92
4914
tags = 29%,










list = 22%,










signal = 37%


REACTOME_ACTIVATION_OF_ATR_IN
35
0.46436146
1.5067408
0.033259425
0.2163146
0.964
7686
tags = 71%,


RESPONSE_TO_REPLICATION_STRESS







list = 35%,










signal = 109%


REACTOME_G0_AND_EARLY_G1
21
0.51683676
1.5070508
0.046255507
0.22344367
0.964
4974
tags = 48%,










list = 22%,










signal = 61%


RIZ_ERYTHROID_DIFFERENTIATION_CCNE1
39
0.458755
1.5162858
0.015184382
0.22479783
0.952
3485
tags = 28%,










list = 16%,










signal = 33%


MATZUK_MEIOTIC_AND_DNA_REPAIR
36
0.45465472
1.4960755
0.029612755
0.22674586
0.972
3835
tags = 31%,










list = 17%,










signal = 37%


REACTOME_RESOLUTION_OF_AP_SITES
17
0.56890005
1.5084324
0.046511628
0.22864738
0.962
4761
tags = 53%,


VIA_THE_MULTIPLE_NUCLEOTIDE







list = 21%,


PATCH_REPLACEMENT_PATHWAY







signal = 67%


GNF2_TTK
39
0.44800937
1.4839368
0.033936653
0.23896985
0.979
7559
tags = 59%,










list = 34%,










signal = 89%


FRASOR_RESPONSE_TO_SERM_OR
50
0.39338014
1.3936495
0.06004619
0.2458462
1
6900
tags = 46%,


FULVESTRANT_DN







list = 31%,










signal = 67%


ZHANG_TLX_TARGETS_60HR_DN
270
0.30793357
1.3944957
0.012048192
0.24878854
1
6615
tags = 41%,










list = 30%,










signal = 57%


GNF2_RRM1
87
0.36759192
1.3954328
0.04245283
0.2512664
1
6814
tags = 43%,










list = 31%,










signal = 61%


PUJANA_BRCA_CENTERED_NETWORK
117
0.3418764
1.3995072
0.031100478
0.25297934
0.999
6911
tags = 45%,










list = 31%,










signal = 65%


MORI_LARGE_PRE_BII_LYMPHOCYTE_UP
84
0.36455083
1.407738
0.029978586
0.25346023
0.999
7393
tags = 50%,










list = 33%,










signal = 75%


BLUM_RESPONSE_TO_SALIRASIB_DN
332
0.31202888
1.4640918
0
0.2538471
0.987
3979
tags = 23%,










list = 18%,










signal = 28%


REN_BOUND_BY_E2F
60
0.38417438
1.3956681
0.05373832
0.25539997
1
5744
tags = 42%,










list = 26%,










signal = 56%


MORI_PRE_BI_LYMPHOCYTE_UP
76
0.36963466
1.3998244
0.037914693
0.25731882
0.999
6377
tags = 42%,










list = 29%,










signal = 59%


KAUFFMANN_MELANOMA_RELAPSE_UP
60
0.39023167
1.4082451
0.058315333
0.2576113
0.999
6608
tags = 48%,










list = 30%,










signal = 69%


MODULE_54
251
0.32418767
1.465012
0.004938272
0.25948274
0.987
4682
tags = 29%,










list = 21%,










signal = 36%


PUJANA_XPRSS_INT_NETWORK
165
0.32696632
1.3835881
0.021028038
0.2596924
1
6911
tags = 44%,










list = 31%,










signal = 63%


REGULATION_OF_DNA_REPLICATION
19
0.52564275
1.4568967
0.047191013
0.25986636
0.989
5881
tags = 58%,










list = 26%,










signal = 79%


BIOCARTA_G1_PATHWAY
27
0.46366057
1.409431
0.08553971
0.2604318
0.998
2997
tags = 26%,










list = 13%,










signal = 30%


MATZUK_SPERMATOCYTE
68
0.39636797
1.467985
0.023640662
0.26103795
0.984
3691
tags = 24%,










list = 17%,










signal = 28%


SHEPARD_BMYB_MORPHOLINO_DN
180
0.3260228
1.3998913
0.01686747
0.2620695
0.999
3851
tags = 28%,










list = 17%,










signal = 34%


BIOCARTA_MCM_PATHWAY
18
0.523641
1.4506925
0.06263982
0.26282194
0.991
7686
tags = 72%,










list = 35%,










signal = 110%


ISHIDA_E2F_TARGETS
51
0.40538192
1.4102687
0.046908315
0.2641048
0.998
6960
tags = 59%,










list = 31%,










signal = 85%


VANTVEER_BREAST_CANCER_METASTASIS_UP
55
0.391082
1.4123961
0.05
0.26544183
0.996
2904
tags = 29%,










list = 13%,










signal = 33%


ZHANG_TLX_TARGETS_DN
88
0.36839908
1.4143125
0.023255814
0.26721817
0.996
5921
tags = 43%,










list = 27%,










signal = 59%


KEGG_BASE_EXCISION_REPAIR
34
0.4475151
1.4440593
0.041484717
0.26807487
0.993
3979
tags = 35%,










list = 18%,










signal = 43%


KANG_DOXORUBICIN_RESISTANCE_UP
54
0.38853663
1.3756194
0.047727272
0.2681853
1
6814
tags = 46%,










list = 31%,










signal = 67%


GNF2_RFC3
41
0.41752118
1.4149994
0.05689278
0.27123934
0.996
6900
tags = 51%,










list = 31%,










signal = 74%


REACTOME_G2_M_CHECKPOINTS
41
0.4167203
1.4163488
0.06345733
0.27500415
0.996
6732
tags = 56%,










list = 30%,










signal = 80%


SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_UP
149
0.3214344
1.3571204
0.02078522
0.27659324
1
7618
tags = 47%,










list = 34%,










signal = 71%


ODONNELL_TARGETS_OF_MYC_AND_TFRC_DN
44
0.41889706
1.418553
0.04494382
0.27691594
0.996
5684
tags = 48%,










list = 26%,










signal = 64%


MARKEY_RB1_ACUTE_LOF_UP
228
0.303452
1.35813
0.014319809
0.27882314
1
5168
tags = 31%,










list = 23%,










signal = 40%


JOHANSSON_GLIOMAGENESIS_BY_PDGFB_UP
55
0.38219306
1.350983
0.07488987
0.2789827
1
2725
tags = 18%,










list = 12%,










signal = 21%


GNF2_BUB1B
49
0.39280915
1.3487504
0.09512761
0.2793027
1
6911
tags = 43%,










list = 31%,










signal = 62%


SIMBULAN_PARP1_TARGETS_DN
17
0.5216608
1.420628
0.074235804
0.27934334
0.996
4341
tags = 47%,










list = 20%,










signal = 58%


GRAHAM_CML_QUIESCENT_VS_NORMAL
77
0.38616616
1.4342515
0.027586207
0.279565
0.994
4926
tags = 27%,


QUIESCENT_UP







list = 22%,










signal = 35%


LI_WILMS_TUMOR
26
0.46200952
1.423792
0.06772009
0.28035206
0.995
3332
tags = 27%,










list = 15%,










signal = 32%


RIBONUCLEASE_ACTIVITY
21
0.47635773
1.3620135
0.10927835
0.28049374
1
1098
tags = 14%,










list = 5%,










signal = 15%


REACTOME_BASE_EXCISION_REPAIR
19
0.48855054
1.3638277
0.12688172
0.2818866
1
4761
tags = 47%,










list = 21%,










signal = 60%


STEIN_ESR1_TARGETS
80
0.35570252
1.358726
0.065022424
0.28189817
1
4066
tags = 30%,










list = 18%,










signal = 37%


GNF2_CCNA2
67
0.36114088
1.3516709
0.05676856
0.28191626
1
7686
tags = 55%,










list = 35%,










signal = 84%


ZHAN_MULTIPLE_MYELOMA_PR_UP
45
0.40132207
1.3656914
0.08056872
0.28249145
1
6377
tags = 49%,










list = 29%,










signal = 68%


SONG_TARGETS_OF_IE86_CMV_PROTEIN
60
0.39431137
1.4279
0.040865384
0.28549019
0.995
6608
tags = 52%,










list = 30%,










signal = 73%


BURTON_ADIPOGENESIS_PEAK_AT_16HR
39
0.40892777
1.3431141
0.08017817
0.28585017
1
3108
tags = 23%,










list = 14%,










signal = 27%


GNF2_SMC2L1
32
0.4475485
1.4239812
0.05263158
0.2868688
0.995
6911
tags = 50%,










list = 31%,










signal = 72%


MODULE_403
45
0.39438623
1.3375821
0.08017817
0.2914272
1
4748
tags = 36%,










list = 21%,










signal = 45%


PYEON_HPV_POSITIVE_TUMORS_UP
88
0.34147477
1.334982
0.047058824
0.29256615
1
3297
tags = 27%,










list = 15%,










signal = 32%


WILCOX_RESPONSE_TO_PROGESTERONE_UP
139
0.31779614
1.3307937
0.034653466
0.29698354
1
2958
tags = 27%,










list = 13%,










signal = 31%


MANALO_HYPOXIA_DN
284
0.29136848
1.3262402
0.020887729
0.3012804
1
6851
tags = 35%,










list = 31%,










signal = 50%


BENPORATH_PROLIFERATION
144
0.31483632
1.319144
0.030379746
0.30310413
1
4658
tags = 27%,










list = 21%,










signal = 34%


SGCGSSAAA_V$E2F1DP2_01
162
0.30976716
1.3192339
0.023419203
0.30682164
1
6280
tags = 39%,










list = 28%,










signal = 54%


NEGATIVE_REGULATION_OF_CELL_CYCLE
75
0.35150585
1.3213702
0.0548926
0.30749267
1
3835
tags = 24%,










list = 17%,










signal = 29%


BOYAULT_LIVER_CANCER_SUBCLASS_G123_UP
44
0.3800448
1.2972541
0.10294118
0.31085995
1
1804
tags = 16%,










list = 8%,










signal = 17%


RB_P130_DN.V1_UP
121
0.3168952
1.2936934
0.061032865
0.3142914
1
2543
tags = 16%,










list = 11%,










signal = 18%


CHANG_CYCLING_GENES
143
0.31158745
1.2972972
0.06904762
0.31442836
1
6070
tags = 41%,










list = 27%,










signal = 56%


BAKER_HEMATOPOIESIS_STAT3_TARGETS
16
0.49883923
1.3045712
0.14346895
0.31492957
1
5606
tags = 56%,










list = 25%,










signal = 75%


LY_AGING_OLD_DN
54
0.37244374
1.3087479
0.084415585
0.3151152
1
3282
tags = 22%,










list = 15%,










signal = 26%


DNA_METABOLIC_PROCESS
243
0.28880495
1.3062418
0.018735362
0.315527
1
4292
tags = 25%,










list = 19%,










signal = 31%


OLSSON_E2F3_TARGETS_DN
44
0.39295375
1.310525
0.108843535
0.31565085
1
2185
tags = 16%,










list = 10%,










signal = 18%


DNA_REPLICATION
98
0.32644168
1.2999647
0.067567565
0.31694692
1
6608
tags = 45%,










list = 30%,










signal = 64%


MODULE_485
49
0.3743328
1.297536
0.108597286
0.31762144
1
5816
tags = 39%,










list = 26%,










signal = 52%


GNF2_CKS1B
37
0.39218175
1.301194
0.11304348
0.31887347
1
6911
tags = 51%,










list = 31%,










signal = 74%


GARGALOVIC_RESPONSE_TO_OXIDIZED
51
0.36616385
1.2830538
0.12471132
0.33100462
1
4223
tags = 27%,


PHOSPHOLIPIDS_TURQUOISE_DN







list = 19%,










signal = 34%


CROONQUIST_IL6_DEPRIVATION_DN
97
0.31885555
1.276713
0.071078435
0.33237627
1
6762
tags = 43%,










list = 30%,










signal = 62%


V$E2F1_Q6_01
229
0.2868161
1.2773455
0.037037037
0.33474997
1
4825
tags = 28%,










list = 22%,










signal = 35%


AFFAR_YY1_TARGETS_DN
210
0.29184106
1.2774206
0.045
0.3383408
1
3979
tags = 25%,










list = 18%,










signal = 30%


MODULE_397
111
0.31945032
1.2702408
0.056872036
0.34133938
1
3317
tags = 22%,










list = 15%,










signal = 25%


NUCLEASE_ACTIVITY
51
0.35982916
1.263786
0.1388889
0.35147074
1
3059
tags = 20%,










list = 14%,










signal = 23%


REACTOME_ACTIVATION_OF_THE
30
0.40104312
1.2593498
0.17391305
0.3576477
1
6577
tags = 57%,


PRE_REPLICATTVE_COMPLEX







list = 30%,










signal = 80%


GNF2_RRM2
40
0.37629777
1.253236
0.12993039
0.36292648
1
7909
tags = 58%,










list = 36%,










signal = 89%


KORKOLA_TERATOMA
37
0.3854039
1.2543806
0.1388889
0.3644212
1
872
tags = 11%,










list = 4%,










signal = 11%


VANTVEER_BREAST_CANCER_POOR_PROGNOSIS
51
0.35950112
1.2507282
0.12895928
0.36502436
1
5452
tags = 43%,










list = 25%,










signal = 57%


CROONQUIST_NRAS_SIGNALING_DN
72
0.33805963
1.2426128
0.12249443
0.36864632
1
7393
tags = 54%,










list = 33%,










signal = 81%


RB_P107_DN.V1_UP
134
0.29814488
1.2440923
0.0875
0.36879972
1
3979
tags = 28%,










list = 18%,










signal = 34%


REACTOME_EXTENSION_OF_TELOMERES
27
0.4090032
1.2443675
0.17699115
0.37191126
1
3979
tags = 33%,










list = 18%,










signal = 41%


GROSS_HYPOXIA_VIA_ELK3_ONLY_DN
44
0.36082354
1.2449167
0.15034169
0.3743939
1
3865
tags = 27%,










list = 17%,










signal = 33%


ZHENG_GLIOBLASTOMA_PLASTICITY_UP
236
0.27462164
1.2288359
0.050938338
0.37714297
1
4748
tags = 28%,










list = 21%,










signal = 35%


VERNELL_RETINOBLASTOMA_PATHWAY_UP
70
0.32801443
1.2373369
0.12230216
0.3772047
1
6799
tags = 44%,










list = 31%,










signal = 64%


WONG_EMBRYONIC_STEM_CELL_CORE
327
0.2648832
1.2355665
0.038356163
0.377507
1
6597
tags = 33%,










list = 30%,










signal = 47%


CELL_CYCLE_CHECKPOINT_GO_0000075
46
0.3617034
1.2292565
0.14096916
0.37993726
1
3946
tags = 35%,










list = 18%,










signal = 42%


BHATI_G2M_ARREST_BY
107
0.30608615
1.2319682
0.10538641
0.38154486
1
3735
tags = 23%,


2METHOXYESTRADIOL_UP







list = 17%,










signal = 28%


AMUNDSON_GENOTOXIC_SIGNATURE
100
0.30824658
1.230115
0.1091314
0.38194412
1
2516
tags = 16%,










list = 11%,










signal = 18%


RUIZ_TNC_TARGETS_DN
139
0.29295608
1.2226777
0.07259953
0.383599
1
5197
tags = 35%,










list = 23%,










signal = 45%


PETROVA_PROX1_TARGETS_UP
28
0.4008765
1.2238188
0.19130434
0.38432854
1
1909
tags = 25%,










list = 9%,










signal = 27%


MORI_IMMATURE_B_LYMPHOCYTE_DN
88
0.31508708
1.2159486
0.13013698
0.38895854
1
4682
tags = 28%,










list = 21%,










signal = 36%


PID_RB_1PATHWAY
61
0.33674502
1.2167755
0.1477516
0.39085585
1
2997
tags = 20%,










list = 13%,










signal = 23%


KAUFFMANN_DNA_REPLICATION_GENES
136
0.29238856
1.2172049
0.109947644
0.39348933
1
4257
tags = 21%,










list = 19%,










signal = 26%


VECCHI_GASTRIC_CANCER_EARLY_UP
403
0.25194356
1.2026228
0.036745407
0.41713816
1
2649
tags = 13%,










list = 12%,










signal = 15%


BIOCARTA_P53_PATHWAY
16
0.4452219
1.1898873
0.25738397
0.41901195
1
9112
tags = 69%,










list = 41%,










signal = 116%


NEGATIVE_REGULATION_OF_DNA
17
0.452444
1.195021
0.23178808
0.42093435
1
2026
tags = 24%,


METABOLIC_PROCESS







list = 9%,










signal = 26%


V$E2F_01
63
0.32025853
1.1914719
0.17687075
0.42199737
1
5589
tags = 33%,










list = 25%,










signal = 44%


OXFORD_RALA_OR_RALB_TARGETS_UP
48
0.35289615
1.1960196
0.19527897
0.4221113
1
6184
tags = 44%,










list = 28%,










signal = 60%


MODULE_325
51
0.33527386
1.1928161
0.19376393
0.4223233
1
4349
tags = 27%,










list = 20%,










signal = 34%


YAO_TEMPORAL_RESPONSE_TO
30
0.37951565
1.1899841
0.19502075
0.4223635
1
1896
tags = 13%,


PROGESTERONE_CLUSTER_15







list = 9%,










signal = 15%


BENPORATH_ES_CORE_NINE_CORRELATED
94
0.30356047
1.1962134
0.13990825
0.4252052
1
4666
tags = 29%,










list = 21%,










signal = 36%


V$E2F1_Q4
235
0.27007312
1.1974943
0.06388206
0.42586854
1
3607
tags = 18%,










list = 16%,










signal = 22%


MODULE_252
235
0.26360464
1.183151
0.08232445
0.43119183
1
6802
tags = 39%,










list = 31%,










signal = 56%


GNF2_ESPL1
35
0.35511458
1.1791232
0.21149425
0.43350247
1
6911
tags = 51%,










list = 31%,










signal = 75%


MODULE_57
54
0.3385203
1.1802068
0.19222462
0.4345493
1
4926
tags = 26%,










list = 22%,










signal = 33%


CELL_CYCLE_ARREST_GO_0007050
53
0.32590607
1.1636739
0.21198156
0.44674337
1
3835
tags = 19%,










list = 17%,










signal = 23%


WINNEPENNINCKX_MELANOMA_METASTASIS_UP
160
0.27584052
1.1643463
0.12787724
0.4485678
1
7430
tags = 44%,










list = 33%,










signal = 65%


GCNP_SHH_UP_LATE.V1_UP
173
0.26963314
1.165314
0.14563107
0.44976816
1
4748
tags = 28%,










list = 21%,










signal = 35%


V$E2F1_Q4_01
219
0.26317212
1.170919
0.109725684
0.44992062
1
5320
tags = 30%,










list = 24%,










signal = 39%


WHITFIELD_CELL_CYCLE_G1_S
134
0.28168482
1.167068
0.16627635
0.45256835
1
3471
tags = 22%,










list = 16%,










signal = 25%


REGULATION_OF_MITOTIC_CELL_CYCLE
23
0.39925188
1.1654276
0.23284823
0.45296666
1
4815
tags = 39%,










list = 22%,










signal = 50%


WANG_RESPONSE_TO_GSK3_INHIBITOR
345
0.25190452
1.1674849
0.10106383
0.45485333
1
5102
tags = 30%,


SB216763_DN







list = 23%,










signal = 38%


V$E2F_Q3
212
0.2621424
1.1582417
0.1421801
0.45713925
1
5144
tags = 28%,










list = 23%,










signal = 36%


V$E2F_Q6_01
227
0.2575921
1.1522388
0.12200957
0.46274084
1
3595
tags = 21%,










list = 16%,










signal = 24%


SARRIO_EPITHELIAL_MESENCHYMAL
169
0.26500237
1.1530817
0.15384616
0.4639936
1
4703
tags = 31%,


TRANSITION_UP







list = 21%,










signal = 39%


DNA_POLYMERASE_ACTIVITY
17
0.4226581
1.1534702
0.27668846
0.4665431
1
722
tags = 12%,










list = 3%,










signal = 12%


GRAHAM_CML_DIVIDING_VS_NORMAL
164
0.2677485
1.1492176
0.18734793
0.46672526
1
7422
tags = 45%,


QUIESCENT_UP







list = 33%,










signal = 67%


PID_BARD1_PATHWAY
29
0.37072238
1.1467375
0.26406926
0.469454
1
3835
tags = 24%,










list = 17%,










signal = 29%


CHIANG_LIVER_CANCER_SUBCLASS
168
0.26683438
1.1446294
0.16945107
0.47134057
1
5315
tags = 28%,


PROLIFERATION_UP







list = 24%,










signal = 36%


PID_FOXM1_PATHWAY
39
0.3442356
1.1374557
0.25225225
0.47667563
1
4586
tags = 28%,










list = 21%,










signal = 35%


LI_WILMS_TUMOR_VS_FETAL_KIDNEY_1_DN
161
0.26908138
1.1377878
0.18451025
0.4792994
1
3282
tags = 14%,










list = 15%,










signal = 16%


WHITFIELD_CELL_CYCLE_G2
173
0.2631934
1.1348187
0.15801887
0.48018038
1
7199
tags = 38%,










list = 32%,










signal = 55%


GARCIA_TARGETS_OF_FLI1_AND_DAX1_DN
164
0.26511642
1.1380086
0.18266979
0.48210818
1
6630
tags = 38%,










list = 30%,










signal = 54%


REGULATION_OF_DNA_METABOLIC_PROCESS
43
0.34278795
1.1321517
0.25348836
0.48324963
1
4815
tags = 37%,










list = 22%,










signal = 47%


GNF2_CENPF
61
0.31386176
1.1273521
0.24222222
0.4852111
1
6515
tags = 39%,










list = 29%,










signal = 56%


HORIUCHI_WTAP_TARGETS_DN
301
0.24579187
1.1299635
0.14095744
0.48537546
1
5345
tags = 27%,










list = 24%,










signal = 35%


KEGG_CELL_CYCLE
121
0.2787656
1.1380361
0.18075117
0.48547247
1
3986
tags = 21%,










list = 18%,










signal = 25%


V$E2F_Q3_01
225
0.25561398
1.1277008
0.18251929
0.4875135
1
5320
tags = 30%,










list = 24%,










signal = 39%


LINDGREN_BLADDER_CANCER_CLUSTER_1_DN
363
0.23868431
1.1248868
0.13672923
0.48838213
1
4821
tags = 26%,










list = 22%,










signal = 33%


MODULE_198
297
0.24295494
1.1195234
0.1462766
0.49213037
1
6960
tags = 38%,










list = 31%,










signal = 55%


V$E2F_Q6
226
0.25357646
1.1207881
0.1678487
0.4924103
1
3544
tags = 21%,










list = 16%,










signal = 24%


GNF2_CKS2
50
0.3202122
1.1210793
0.2689655
0.49494117
1
6799
tags = 40%,










list = 31%,










signal = 58%


SMID_BREAST_CANCER_LUMINAL_A_DN
17
0.40507963
1.114525
0.30620986
0.5023531
1
6814
tags = 47%,










list = 31%,










signal = 68%


SHEDDEN_LUNG_CANCER_POOR_SURVIVAL_A6
441
0.2315493
1.1123492
0.12224939
0.5043695
1
4682
tags = 22%,










list = 21%,










signal = 27%


GNF2_CENPE
40
0.33155906
1.11093
0.27876106
0.50479126
1
6911
tags = 43%,










list = 31%,










signal = 62%


PEART_HDAC_PROLIFERATION_CLUSTER_DN
72
0.29432467
1.1030699
0.25783134
0.522134
1
872
tags = 7%,










list = 4%,










signal = 7%


WHITFIELD_CELL_CYCLE_S
153
0.25930515
1.1011199
0.19806764
0.52399784
1
3661
tags = 22%,










list = 16%,










signal = 26%


G1_S_TRANSITION_OF_MITOTIC_CELL_CYCLE
27
0.35630274
1.0979434
0.3205945
0.5293809
1
6767
tags = 37%,










list = 30%,










signal = 53%


V$E2F1_Q3
230
0.2446122
1.0888127
0.22250639
0.55160815
1
4895
tags = 27%,










list = 22%,










signal = 35%


V$E2F1DP1RB_01
219
0.24441566
1.084171
0.24449877
0.5609876
1
4815
tags = 26%,










list = 22%,










signal = 32%


DAMAGED_DNA_BINDING
21
0.37748018
1.0742105
0.36645964
0.5652575
1
3592
tags = 29%,










list = 16%,










signal = 34%


WANG_CISPLATIN_RESPONSE_AND_XPC_UP
184
0.24496014
1.0703033
0.284689
0.5663396
1
3979
tags = 23%,










list = 18%,










signal = 28%


V$E2F4DP2_01
226
0.24129184
1.0745329
0.27930173
0.56783473
1
3544
tags = 20%,










list = 16%,










signal = 24%


REGULATION_OF_CELL_CYCLE
176
0.24882938
1.0718728
0.27951807
0.5686671
1
2958
tags = 16%,










list = 13%,










signal = 18%


V$E2F1DP2_01
226
0.24129184
1.0705135
0.27380952
0.5690597
1
3544
tags = 20%,










list = 16%,










signal = 24%


WHITFIELD_CELL_CYCLE_LITERATURE
44
0.32180226
1.0750089
0.34419551
0.56976515
1
4815
tags = 30%,










list = 22%,










signal = 38%


DNA_DEPENDENT_DNA_REPLICATION
54
0.30361858
1.0761642
0.3170163
0.5699367
1
6608
tags = 48%,










list = 30%,










signal = 68%


BURTON_ADIPOGENESIS_3
101
0.26952776
1.0771737
0.29439253
0.57049567
1
6960
tags = 48%,










list = 31%,










signal = 69%


FUJII_YBX1_TARGETS_DN
199
0.2463158
1.0787462
0.2647059
0.57270294
1
5923
tags = 35%,










list = 27%,










signal = 48%


V$E2F1DP1_01
226
0.24129184
1.077429
0.26477543
0.573177
1
3544
tags = 20%,










list = 16%,










signal = 24%


MODULE_451
32
0.3326359
1.0617337
0.36886993
0.58774364
1
2031
tags = 19%,










list = 9%,










signal = 21%


REACTOME_G1_PHASE
34
0.32495195
1.0558343
0.3773585
0.60097677
1
3518
tags = 29%,










list = 16%,










signal = 35%


KEGG_DNA_REPLICATION
36
0.31393066
1.046971
0.33936653
0.6200095
1
3979
tags = 28%,










list = 18%,










signal = 34%


MEIOTIC_CELL_CYCLE
28
0.34077063
1.0479791
0.3842795
0.6204566
1
3568
tags = 29%,










list = 16%,










signal = 34%


V$E2F_03
234
0.23456398
1.042715
0.344473
0.6288928
1
6280
tags = 35%,










list = 28%,










signal = 48%


CELL_CYCLE_GO_0007049
299
0.22635782
1.0385077
0.3508772
0.63457865
1
3835
tags = 18%,










list = 17%,










signal = 21%


GROSS_HYPOXIA_VIA_ELK3_AND_HIF1A_DN
100
0.26218835
1.0370255
0.37214613
0.63548464
1
3112
tags = 19%,










list = 14%,










signal = 22%


PUJANA_BRCA2_PCC_NETWORK
405
0.21916327
1.0386813
0.32258064
0.6376872
1
6591
tags = 35%,










list = 30%,










signal = 48%


GNF2_BUB1
26
0.33931977
1.029488
0.4051724
0.65430546
1
7321
tags = 50%,










list = 33%,










signal = 74%


MEIOSIS_I
16
0.38233972
1.0197504
0.4232456
0.67703986
1
3123
tags = 25%,










list = 14%,










signal = 29%


FOURNIER_ACINAR_DEVELOPMENT_LATE_2
273
0.22042942
1.0125483
0.40149626
0.6774985
1
7446
tags = 39%,










list = 34%,










signal = 58%


V$E2F_Q4
228
0.23197255
1.0203344
0.39847717
0.6787881
1
3544
tags = 20%,










list = 16%,










signal = 23%


V$E2F_02
226
0.2312068
1.0166516
0.4108527
0.679432
1
3544
tags = 20%,










list = 16%,










signal = 23%


GNF2_HMMR
47
0.29403916
1.0129237
0.42388758
0.6797991
1
7559
tags = 47%,










list = 34%,










signal = 71%


KEGG_MISMATCH_REPAIR
23
0.3423624
1.0173788
0.43572986
0.6807465
1
3979
tags = 35%,










list = 18%,










signal = 42%


V$E2F4DP1_01
228
0.22700226
1.0131376
0.41191068
0.6828414
1
3544
tags = 20%,










list = 16%,










signal = 23%


REACTOME_PROCESSIVE_SYNTHESIS
15
0.3860097
1.0136039
0.44124168
0.6850931
1
3979
tags = 40%,


ON_THE_LAGGING_STRAND







list = 18%,










signal = 49%


MODULE_197
167
0.23311728
1.0055324
0.432243
0.6876353
1
5144
tags = 26%,










list = 23%,










signal = 34%


CONCANNON_APOPTOSIS_BY_EPOXOMICIN_DN
155
0.2371657
1.0074743
0.4538835
0.6891798
1
3596
tags = 22%,










list = 16%,










signal = 26%


REACTOME_G1_S_SPECIFIC_TRANSCRIPTION
16
0.35699904
1.0061347
0.44469026
0.689669
1
4815
tags = 38%,










list = 22%,










signal = 48%


WU_APOPTOSIS_BY_CDKN1A_VIA_TP53
52
0.2869195
0.99947405
0.45333335
0.6954689
1
6762
tags = 48%,










list = 30%,










signal = 69%


BASE_EXCISION_REPAIR
16
0.37499917
0.9998841
0.4362416
0.6977798
1
3694
tags = 38%,










list = 17%,










signal = 45%


REACTOME_LAGGING_STRAND_SYNTHESIS
19
0.3588135
1.0008832
0.46420825
0.6983574
1
3979
tags = 37%,










list = 18%,










signal = 45%


POSITIVE_REGULATION_OF_CELL_CYCLE
15
0.3664347
0.9937743
0.45436105
0.7091833
1
5074
tags = 40%,










list = 23%,










signal = 52%


MUELLER_PLURINET
287
0.21411699
0.9904966
0.4526316
0.7120756
1
6383
tags = 32%,










list = 29%,










signal = 45%


YU_MYC_TARGETS_UP
42
0.29533926
0.9892236
0.46389496
0.7123799
1
5602
tags = 31%,










list = 25%,










signal = 41%


RAY_TUMORIGENESIS_BY_ERBB2_CDC25A_UP
96
0.25138888
0.99086094
0.45933014
0.7145532
1
2402
tags = 19%,










list = 11%,










signal = 21%


LEE_EARLY_T_LYMPHOCYTE_UP
97
0.25011945
0.9865701
0.4725537
0.71696126
1
5139
tags = 32%,










list = 23%,










signal = 41%


KAUFFMANN_DNA_REPAIR_GENES
219
0.222108
0.9846902
0.5225653
0.71836126
1
3979
tags = 20%,










list = 18%,










signal = 24%


SHEPARD_CRUSH_AND_BURN_MUTANT_DN
164
0.22918515
0.9804757
0.5090909
0.72380483
1
4748
tags = 27%,










list = 21%,










signal = 34%


V$E2F_Q4_01
227
0.22024323
0.9815795
0.50117093
0.72419137
1
5320
tags = 30%,










list = 24%,










signal = 38%


REACTOME_FANCONI_ANEMIA_PATHWAY
21
0.33812094
0.97732526
0.49082568
0.7300886
1
8209
tags = 52%,










list = 37%,










signal = 83%


HOFFMANN_LARGE_TO_SMALL_PRE
155
0.2293058
0.966305
0.523918
0.7489484
1
6690
tags = 39%,


BII_LYMPHOCYTE_UP







list = 30%,










signal = 55%


PID_FANCONI_PATHWAY
47
0.28501362
0.96698177
0.5075269
0.750541
1
6732
tags = 47%,










list = 30%,










signal = 67%


PIONTEK_PKD1_TARGETS_DN
16
0.36119446
0.9631549
0.506383
0.7512874
1
3510
tags = 38%,










list = 16%,










signal = 45%


CHROMATIN
33
0.30065694
0.9636298
0.5053996
0.75342274
1
2919
tags = 18%,










list = 13%,










signal = 21%


FERREIRA_EWINGS_SARCOMA
159
0.22596973
0.9670589
0.5452323
0.75396377
1
6650
tags = 36%,


UNSTABLE_VS_STABLE_UP







list = 30%,










signal = 51%


GROSS_HYPOXIA_VIA_ELK3_UP
204
0.21958606
0.96824384
0.558753
0.7543387
1
3787
tags = 19%,










list = 17%,










signal = 23%


SCIBETTA_KDM5B_TARGETS_DN
77
0.25283283
0.9583655
0.55133927
0.76184374
1
5602
tags = 32%,










list = 25%,










signal = 43%


EGUCHI_CELL_CYCLE_RB1_TARGETS
23
0.32270378
0.9485936
0.52764976
0.77358365
1
7686
tags = 57%,










list = 35%,










signal = 86%


WHITFIELD_CELL_CYCLE_G2_M
211
0.2133046
0.94932085
0.6278481
0.7749125
1
3989
tags = 20%,










list = 18%,










signal = 24%


RPS14_DN.V1_DN
178
0.21893507
0.95011306
0.5891648
0.77629346
1
4815
tags = 28%,










list = 22%,










signal = 35%


MOLENAAR_TARGETS_OF_CCND1_AND_CDK4_DN
57
0.2673039
0.9522256
0.5271493
0.77740884
1
7382
tags = 49%,










list = 33%,










signal = 73%


V$E2F1_Q6
225
0.21288314
0.95038515
0.59653467
0.7790583
1
3544
tags = 19%,










list = 16%,










signal = 23%


BOYAULT_LIVER_CANCER_SUBCLASS_G3_UP
187
0.21531379
0.93996984
0.6296296
0.7951581
1
6309
tags = 31%,










list = 28%,










signal = 43%


RHODES_UNDIFFERENTIATED_CANCER
68
0.24986419
0.9363587
0.5892473
0.8019492
1
7422
tags = 43%,










list = 33%,










signal = 64%


VANTVEER_BREAST_CANCER_METASTASIS_DN
116
0.22871712
0.9335559
0.6086956
0.80667114
1
4748
tags = 25%,










list = 21%,










signal = 32%


GNF2_CDC2
61
0.25424793
0.9315171
0.5619048
0.8087767
1
7559
tags = 44%,










list = 34%,










signal = 67%


ODONNELL_TFRC_TARGETS_DN
117
0.22895187
0.9288347
0.62954545
0.80948067
1
1958
tags = 12%,










list = 9%,










signal = 13%


GNF2_CCNB2
56
0.26242125
0.9296985
0.60393876
0.8103493
1
7559
tags = 45%,










list = 34%,










signal = 67%


MODULE_123
225
0.2072971
0.9263362
0.68550366
0.81329805
1
3622
tags = 19%,










list = 16%,










signal = 23%


MITSIADES_RESPONSE_TO_APLIDIN_DN
244
0.20661007
0.9159852
0.7218045
0.8253738
1
3967
tags = 18%,










list = 18%,










signal = 21%


BIOCARTA_CELLCYCLE_PATHWAY
22
0.3144141
0.91380036
0.58474576
0.82761246
1
3518
tags = 36%,










list = 16%,










signal = 43%


PETROVA_ENDOTHELIUM_LYMPHATIC
125
0.22451954
0.9163942
0.6810551
0.8278902
1
4748
tags = 30%,


VS_BLOOD_UP







list = 21%,










signal = 37%


BIOCARTA_G2_PATHWAY
24
0.30806628
0.9189605
0.55581397
0.8284411
1
4926
tags = 25%,










list = 22%,










signal = 32%


CHANG_CORE_SERUM_RESPONSE_UP
205
0.20796135
0.9118108
0.7139423
0.82964015
1
3331
tags = 16%,










list = 15%,










signal = 19%


WEST_ADRENOCORTICAL_TUMOR_UP
288
0.19953583
0.90975195
0.7622739
0.8313348
1
3011
tags = 11%,










list = 14%,










signal = 13%


KONG_E2F3_TARGETS
93
0.2333433
0.91645014
0.62918663
0.8315079
1
7618
tags = 53%,










list = 34%,










signal = 80%


CONDENSED_NUCLEAR_CHROMOSOME
18
0.3236068
0.90837693
0.5973742
0.83160794
1
541
tags = 11%,










list = 2%,










signal = 11%


REACTOME_E2F_MEDIATED_REGULATION
31
0.28685817
0.91914606
0.5822222
0.8316486
1
3225
tags = 23%,


OF_DNA_REPLICATION







list = 15%,










signal = 26%


NAKAMURA_CANCER_MICROENVIRONMENT_DN
45
0.26146442
0.90145844
0.625
0.8355533
1
4815
tags = 24%,










list = 22%,










signal = 31%


DNA_INTEGRITY_CHECKPOINT
22
0.31207764
0.9021031
0.6038136
0.8374959
1
2295
tags = 23%,










list = 10%,










signal = 25%


LE_EGR2_TARGETS_UP
107
0.22567828
0.9034158
0.6838565
0.83800644
1
7145
tags = 39%,










list = 32%,










signal = 58%


GOLDRATH_ANTIGEN_RESPONSE
315
0.19614215
0.9041977
0.7881356
0.8396098
1
2823
tags = 14%,










list = 13%,










signal = 16%


CELL_CYCLE_PROCESS
181
0.20733057
0.89436126
0.740099
0.8508811
1
3835
tags = 18%,










list = 17%,










signal = 21%


GEORGES_CELL_CYCLE_MIR192_TARGETS
61
0.24397157
0.88450396
0.6785714
0.8553726
1
5137
tags = 34%,










list = 23%,










signal = 45%


MODULE_337
59
0.24638712
0.8907535
0.67748916
0.8568585
1
4553
tags = 25%,










list = 20%,










signal = 32%


CHROMOSOME
119
0.21626177
0.8845944
0.72616136
0.8587043
1
3952
tags = 19%,










list = 18%,










signal = 23%


CSR_LATE_UP.V1_UP
162
0.20884226
0.88793343
0.7597254
0.8604634
1
6013
tags = 35%,










list = 27%,










signal = 47%


DNA_REPAIR
121
0.21765089
0.88643444
0.7619048
0.86091167
1
4292
tags = 21%,










list = 19%,










signal = 26%


NUCLEAR_CHROMOSOME
52
0.24982874
0.8849984
0.6564417
0.8612027
1
3753
tags = 23%,










list = 17%,










signal = 28%


NADERI_BREAST_CANCER_PROGNOSIS_UP
45
0.251694
0.87826246
0.6903226
0.867421
1
3941
tags = 24%,










list = 18%,










signal = 30%


WEST_ADRENOCORTICAL_TUMOR_MARKERS_UP
20
0.31013876
0.8726998
0.6442953
0.8773783
1
4586
tags = 35%,










list = 21%,










signal = 44%


DNA_RECOMBINATION
41
0.26143017
0.8645332
0.6969697
0.88253117
1
3979
tags = 24%,










list = 18%,










signal = 30%


MODULE_98
383
0.18384965
0.8645767
0.9104859
0.8859621
1
6383
tags = 30%,










list = 29%,










signal = 41%


RESPONSE_TO_DNA_DAMAGE_STIMULUS
155
0.20516442
0.86677235
0.7888349
0.88827926
1
4292
tags = 21%,










list = 19%,










signal = 25%


PID_AURORA_B_PATHWAY
38
0.25923198
0.86506176
0.6898148
0.8884687
1
7282
tags = 39%,










list = 33%,










signal = 59%


INTERPHASE
67
0.23223965
0.856466
0.754023
0.89718485
1
6186
tags = 28%,










list = 28%,










signal = 39%


SASAKI_ADULT_T_CELL_LEUKEMIA
168
0.19772334
0.85330105
0.84155846
0.9005819
1
3112
tags = 17%,










list = 14%,










signal = 19%


ENDONUCLEASE_ACTIVITY
24
0.2890893
0.8475868
0.6753247
0.90952533
1
3694
tags = 21%,










list = 17%,










signal = 25%


SHEPARD_BMYB_TARGETS
67
0.22698352
0.83501667
0.7537155
0.931361
1
6512
tags = 40%,










list = 29%,










signal = 57%


XU_HGF_SIGNALING_NOT_VIA_AKT1_48HR_DN
20
0.29689857
0.8304175
0.6914153
0.93660086
1
2398
tags = 20%,










list = 11%,










signal = 22%


BIOCARTA_ATM_PATHWAY
20
0.28158852
0.81876826
0.73150104
0.95485157
1
7576
tags = 50%,










list = 34%,










signal = 76%


RESPONSE_TO_ENDOGENOUS_STIMULUS
188
0.1866301
0.81233823
0.9328537
0.9625193
1
4417
tags = 21%,










list = 20%,










signal = 26%


WANG_METASTASIS_OF_BREAST
21
0.2829569
0.80946195
0.7505721
0.96375966
1
4066
tags = 24%,


CANCER_ESR1_UP







list = 18%,










signal = 29%


KTGGYRSGAA_UNKNOWN
73
0.21137054
0.7913279
0.87061405
0.98078364
1
3979
tags = 23%,










list = 18%,










signal = 28%


INTERPHASE_OF_MITOTIC_CELL_CYCLE
61
0.21559632
0.78868103
0.8537736
0.980797
1
6186
tags = 28%,










list = 28%,










signal = 39%


GNF2_CDC20
55
0.2195407
0.7934546
0.80227274
0.9814875
1
7559
tags = 42%,










list = 34%,










signal = 63%


GNF2_MKI67
27
0.25541335
0.77952003
0.7891566
0.9831143
1
7863
tags = 52%,










list = 35%,










signal = 80%


DELPUECH_FOXO3_TARGETS_DN
39
0.23067386
0.7819254
0.81038374
0.98327434
1
713
tags = 8%,










list = 3%,










signal = 8%


DOUBLE_STRANDED_DNA_BINDING
32
0.24721268
0.7843362
0.78571427
0.983627
1
8006
tags = 56%,










list = 36%,










signal = 88%


ZHANG_TLX_TARGETS_36HR_DN
183
0.18505661
0.79438126
0.9626168
0.9840848
1
5791
tags = 28%,










list = 26%,










signal = 37%


AMUNDSON_GAMMA_RADIATION_RESPONSE
39
0.23116218
0.7757849
0.8237885
0.9845052
1
4586
tags = 23%,










list = 21%,










signal = 29%


CELL_CYCLE_PHASE
159
0.18245688
0.77151066
0.96889955
0.9863564
1
3835
tags = 17%,










list = 17%,










signal = 20%


GNF2_H2AFX
31
0.245504
0.7668124
0.82905984
0.9885595
1
7863
tags = 42%,










list = 35%,










signal = 65%


BIOCARTA_ATRBRCA_PATHWAY
21
0.2679855
0.75611866
0.79956424
0.9900323
1
7576
tags = 52%,










list = 34%,










signal = 79%


E2F1_UP.V1_UP
181
0.17771947
0.76245344
0.9832134
0.99025416
1
3366
tags = 15%,










list = 15%,










signal = 18%


CHROMOSOMAL_PART
94
0.19201228
0.75130504
0.9491525
0.99154246
1
3952
tags = 19%,










list = 18%,










signal = 23%


NUCLEOTIDYLTRANSFERASE_ACTIVITY
46
0.22064428
0.7574412
0.8724832
0.9923186
1
2397
tags = 11%,










list = 11%,










signal = 12%


PID_ATM_PATHWAY
34
0.23613212
0.74652845
0.85209715
0.99284047
1
3835
tags = 21%,










list = 17%,










signal = 25%


MMS_MOUSE_LYMPH_HIGH_4HRS_UP
34
0.19298783
0.6210885
0.97863245
0.9972357
1
4821
tags = 26%,










list = 22%,










signal = 34%


REACTOME_HOMOLOGOUS
16
0.21169989
0.5575222
0.95424837
0.9975556
1
3979
tags = 31%,


RECOMBINATION_REPAIR_OF







list = 18%,


REPLICATION_IN_DEPENDENT







signal = 38%


DOUBLE_STRAND_BREAKS


PID_ATR_PATHWAY
38
0.1762004
0.57545614
0.9859719
0.99792004
1
7393
tags = 42%,










list = 33%,










signal = 63%


XU_HGF_TARGETS_INDUCED_BY_AKT1_48HR_DN
23
0.20875
0.6040176
0.96196866
0.9987243
1
6431
tags = 26%,










list = 29%,










signal = 37%


PUJANA_BREAST_CANCER_LIT_INT_NETWORK
100
0.14568026
0.5861298
1
0.999418
1
4926
tags = 20%,










list = 22%,










signal = 26%


PID_AURORA_A_PATHWAY
31
0.13039789
0.40519395
1
0.9996472
1
7863
tags = 35%,










list = 35%,










signal = 55%


REACTOME_DOUBLE_STRAND_BREAK_REPAIR
22
0.21720654
0.63386077
0.9259259
0.9999909
1
3979
tags = 27%,










list = 18%,










signal = 33%


BOYAULT_LIVER_CANCER_SUBCLASS_G23_UP
52
0.21013078
0.7321246
0.90531176
1
1
5137
tags = 35%,










list = 23%,










signal = 45%


WAKASUGI_HAVE_ZNF143_BINDING_SITES
57
0.2007666
0.72421956
0.9321267
1
1
2548
tags = 11%,










list = 11%,










signal = 12%


STRUCTURE_SPECIFIC_DNA_BINDING
55
0.20189369
0.7193361
0.9126214
1
1
4340
tags = 24%,










list = 20%,










signal = 29%


RNA_CATABOLIC_PROCESS
21
0.24913102
0.7153406
0.8574514
1
1
1091
tags = 10%,










list = 5%,










signal = 10%


JUBAN_TARGETS_OF_SPI1_AND_FLI1_DN
85
0.185801
0.70965517
0.95194507
1
1
3630
tags = 18%,










list = 16%,










signal = 21%


M_PHASE
104
0.17733969
0.6991719
0.9678161
1
1
3835
tags = 18%,










list = 17%,










signal = 22%


SLEBOS_HEAD_AND_NECK_CANCER_WITH_HPV_UP
79
0.18106677
0.6978613
0.9678161
1
1
6611
tags = 37%,










list = 30%,










signal = 52%


LE_NEURONAL_DIFFERENTIATION_DN
19
0.24310753
0.6830966
0.87350833
1
1
2707
tags = 16%,










list = 12%,










signal = 18%


MODULE_244
183
0.15612298
0.6769737
1
1
1
4989
tags = 18%,










list = 22%,










signal = 23%


KEGG_HOMOLOGOUS_RECOMBINATION
26
0.21819423
0.6692233
0.92050207
1
1
3835
tags = 27%,










list = 17%,










signal = 33%


DEOXYRIBONUCLEASE_ACTIVITY
22
0.2264191
0.6632232
0.9311111
1
1
3059
tags = 18%,










list = 14%,










signal = 21%


MITOTIC_CELL_CYCLE
149
0.15570225
0.6591574
0.9977477
1
1
4815
tags = 19%,










list = 22%,










signal = 24%


CONDENSED_CHROMOSOME
33
0.20675065
0.6579867
0.9380734
1
1
541
tags = 6%,










list = 2%,










signal = 6%


LY_AGING_MIDDLE_DN
16
0.24691017
0.64970356
0.88235295
1
1
5139
tags = 31%,










list = 23%,










signal = 41%


CHROMATIN_BINDING
30
0.20629004
0.64783484
0.9472477
1
1
6704
tags = 40%,










list = 30%,










signal = 57%


FINETTI_BREAST_CANCER_KINOME_RED
16
0.23619422
0.64430344
0.8930818
1
1
8991
tags = 63%,










list = 40%,










signal = 105%


NEMETH_INFLAMMATORY_RESPONSE_LPS_DN
30
0.20279045
0.6362227
0.96444446
1
1
1267
tags = 7%,










list = 6%,










signal = 7%


MODULE_372
23
0.21186408
0.62316877
0.95353985
1
1
2603
tags = 17%,










list = 12%,










signal = 20%


LI_WILMS_TUMOR_ANAPLASTIC_UP
18
0.1786921
0.4808827
0.98940676
1
1
4586
tags = 22%,










list = 21%,










signal = 28%
















TABLE 6Q







GSEA for BAF180-wildtype, BRG1-null vs. BAF180-wildtype, BRG1-wildtype


A704 cell lines on E2F founder gene sets





















FWER
RANK







NOM p-
FDR q-
p-
AT
LEADING


NAME
SIZE
ES
NES
val
val
val
MAX
EDGE


















PYEON_HPV_POSITIVE_TUMORS_UP
86
0.5272385
1.839632
0
0.06262126
0.055
5713
tags = 51%,










list = 26%,










signal = 69%


RIZ_ERYTHROID_DIFFERENTIATION_CCNE1
38
0.5936944
1.7935095
0
0.06979053
0.12
4971
tags = 42%,










list = 22%,










signal = 54%


BIOCARTA_P53_PATHWAY
16
0.68817526
1.7357148
0.001416431
0.12532707
0.298
4769
tags = 63%,










list = 21%,










signal = 80%


MODULE_372
23
0.57266676
1.5990293
0.013477089
0.15318382
0.826
5778
tags = 57%,










list = 26%,










signal = 76%


PYEON_CANCER_HEAD_AND_NECK_VS_CERVICAL_UP
180
0.4247083
1.6037453
0
0.15759799
0.812
5440
tags = 43%,










list = 24%,










signal = 57%


MEIOSIS_I
16
0.63435125
1.6273228
0.027894003
0.16242792
0.727
6862
tags = 75%,










list = 31%,










signal = 108%


WHITFIELD_CELL_CYCLE_S
154
0.44637805
1.6483345
0.001090513
0.16337588
0.637
7229
tags = 55%,










list = 33%,










signal = 80%


BIOCARTA_G1_PATHWAY
28
0.57023114
1.660712
0.005215124
0.16788994
0.586
5440
tags = 57%,










list = 24%,










signal = 76%


MATZUK_MEIOTIC_AND_DNA_REPAIR
34
0.5383647
1.6045825
0.010282776
0.17092769
0.808
5334
tags = 50%,










list = 24%,










signal = 66%


MEIOTIC_CELL_CYCLE
30
0.5437648
1.6319793
0.015625
0.1718576
0.707
6862
tags = 60%,










list = 31%,










signal = 87%


BIOCARTA_ATRBRCA_PATHWAY
21
0.58391106
1.6125102
0.009459459
0.17277452
0.779
6550
tags = 67%,










list = 29%,










signal = 94%


RIZ_ERYTHROID_DIFFERENTIATION
75
0.45804963
1.5684189
0.005675369
0.18671939
0.923
7037
tags = 41%,










list = 32%,










signal = 60%


VERNELL_RETINOBLASTOMA_PATHWAY_UP
70
0.4634217
1.5710841
0.009501188
0.19235954
0.917
7709
tags = 54%,










list = 35%,










signal = 83%


PID_BARD1_PATHWAY
29
0.5659379
1.6823814
0.002635046
0.19266273
0.498
7414
tags = 66%,










list = 33%,










signal = 98%


PETROVA_PROX1_TARGETS_UP
28
0.57863456
1.6649306
0.00511509
0.19333066
0.573
797
tags = 21%,










list = 4%,










signal = 22%


WHITFIELD_CELL_CYCLE_G1_S
134
0.42525694
1.5520701
0.001089325
0.20529872
0.946
4372
tags = 32%,










list = 20%,










signal = 40%


MATZUK_SPERMATOCYTE
66
0.44045216
1.4767342
0.01891253
0.34925577
0.996
5798
tags = 36%,










list = 26%,










signal = 49%


DNA_DEPENDENT_DNA_REPLICATION
54
0.4520749
1.4790777
0.024539877
0.35907367
0.996
6939
tags = 54%,










list = 31%,










signal = 78%


REGULATION_OF_DNA_REPLICATION
19
0.5626945
1.4895319
0.043235704
0.36154857
0.995
6147
tags = 63%,










list = 28%,










signal = 87%


ZHANG_TLX_TARGETS_36HR_DN
183
0.39179507
1.4808265
0.004223865
0.37310576
0.996
8089
tags = 52%,










list = 36%,










signal = 81%


KAUFFMANN_DNA_REPLICATION_GENES
137
0.40041688
1.4609982
0.013100437
0.3859268
0.998
4039
tags = 23%,










list = 18%,










signal = 27%


ZHANG_TLX_TARGETS_DN
88
0.4166838
1.4548726
0.018348623
0.389864
0.998
7790
tags = 53%,










list = 35%,










signal = 82%


DNA_REPLICATION
98
0.40929762
1.4456508
0.019406393
0.4057281
0.999
6351
tags = 42%,










list = 29%,










signal = 58%


PUJANA_XPRSS_INT_NETWORK
164
0.3874114
1.4399135
0.010695187
0.40881744
1
7811
tags = 48%,










list = 35%,










signal = 73%


V$E2F_Q6
226
0.3599759
1.3910922
0.010277492
0.41001374
1
4531
tags = 30%,










list = 20%,










signal = 37%


GROSS_HYPOXIA_VIA_ELK3_ONLY_DN
44
0.40443248
1.2704886
0.14123581
0.41584083
1
4537
tags = 30%,










list = 20%,










signal = 37%


YU_BAP1_TARGETS
28
0.4463093
1.2716041
0.15045395
0.4173932
1
4471
tags = 36%,










list = 20%,










signal = 45%


REACTOME_G0_AND_EARLY_G1
22
0.5040033
1.3914684
0.08412483
0.42002696
1
7061
tags = 55%,










list = 32%,










signal = 80%


XU_HGF_TARGETS_INDUCED_BY_AKT1_48HR_DN
23
0.45905653
1.2723931
0.16021362
0.4201529
1
4818
tags = 48%,










list = 22%,










signal = 61%


DUTERTRE_ESTRADIOL_RESPONSE_24HR_UP
319
0.34870973
1.3779316
0.00509165
0.42113948
1
6500
tags = 39%,










list = 29%,










signal = 54%


DNA_METABOLIC_PROCESS
243
0.36547664
1.3939599
0.008247423
0.42341417
1
6939
tags = 41%,










list = 31%,










signal = 59%


V$E2F1DP2_01
227
0.3330221
1.2727325
0.050209206
0.4239942
1
4539
tags = 27%,










list = 20%,










signal = 33%


V$E2F_Q4
228
0.3573901
1.3796762
0.010341262
0.42525893
1
4531
tags = 29%,










list = 20%,










signal = 37%


V$E2F4DP2_01
227
0.3330221
1.2736936
0.048654243
0.42588946
1
4539
tags = 27%,










list = 20%,










signal = 33%


V$E2F4DP1_01
229
0.33116725
1.2788012
0.04033092
0.42612317
1
5002
tags = 29%,










list = 23%,










signal = 37%


GRAHAM_NORMAL_QUIESCENT_VS_NORMAL_DIVIDING_DN
82
0.3643046
1.2614889
0.12041284
0.42706442
1
10124
tags = 59%,










list = 46%,










signal = 107%


RB_P130_DN.V1_UP
119
0.35087037
1.2746123
0.09638554
0.42837283
1
6445
tags = 35%,










list = 29%,










signal = 49%


KEGG_CELL_CYCLE
121
0.34851927
1.2761647
0.09010989
0.42903692
1
7118
tags = 41%,










list = 32%,










signal = 60%


PUJANA_BRCA_CENTERED_NETWORK
117
0.38084027
1.3818555
0.02753304
0.42950952
1
8801
tags = 56%,










list = 40%,










signal = 92%


PID_RB_1PATHWAY
60
0.41720495
1.3957446
0.056354918
0.4299672
1
5440
tags = 43%,










list = 24%,










signal = 57%


GNF2_RFC4
60
0.378941
1.2620343
0.13739546
0.43020865
1
10232
tags = 53%,










list = 46%,










signal = 99%


V$E2F1_Q4_01
220
0.32797867
1.2637687
0.046632126
0.4302939
1
5052
tags = 30%,










list = 23%,










signal = 38%


KAUFFMANN_DNA_REPAIR_GENES
219
0.3374719
1.2791452
0.0516333
0.43037856
1
8304
tags = 45%,










list = 37%,










signal = 71%


MODULE_485
49
0.39738664
1.2800944
0.13349815
0.43325686
1
4222
tags = 24%,










list = 19%,










signal = 30%


V$E2F_Q3
212
0.36750948
1.4023234
0.00729927
0.43391433
1
7061
tags = 43%,










list = 32%,










signal = 62%


BIOCARTA_ATM_PATHWAY
20
0.532553
1.4095083
0.05
0.43623218
1
6351
tags = 60%,










list = 29%,










signal = 84%


BHATI_G2M_ARREST_BY_2METHOXYES_TRADIOL_UP
107
0.38311595
1.3700684
0.03508772
0.43860245
1
6356
tags = 42%,










list = 29%,










signal = 59%


VANTVEER_BREAST_CANCER_METASTASIS_UP
56
0.38941473
1.2801098
0.13189448
0.43881142
1
4211
tags = 29%,










list = 19%,










signal = 35%


VANTVEER_BREAST_CANCER_POOR_PROGNOSIS
51
0.42154846
1.3641762
0.063275434
0.4390399
1
3547
tags = 27%,










list = 16%,










signal = 33%


DNA_INTEGRITY_CHECKPOINT
23
0.46380734
1.2850869
0.14717478
0.44053853
1
6550
tags = 57%,










list = 29%,










signal = 80%


STEIN_ESRRA_TARGETS_RESPONSIVE_TO_ESTROGEN_DN
40
0.44711083
1.3608043
0.08734177
0.4405746
1
6467
tags = 52%,










list = 29%,










signal = 74%


ZHANG_TLX_TARGETS_60HR_DN
270
0.32390845
1.2554305
0.055158325
0.44097477
1
7716
tags = 41%,










list = 35%,










signal = 63%


KAUFFMANN_MELANOMA_RELAPSE_UP
60
0.38860944
1.2830427
0.10676157
0.44121704
1
8499
tags = 50%,










list = 38%,










signal = 81%


KORKOLA_TERATOMA
37
0.45559233
1.3961036
0.068268016
0.4418932
1
4644
tags = 35%,










list = 21%,










signal = 44%


ENDONUCLEASE_ACTIVITY
24
0.43833843
1.2333641
0.20439845
0.4423011
1
6760
tags = 46%,










list = 30%,










signal = 66%


MODULE_123
219
0.33024868
1.2533337
0.056074765
0.4423153
1
4298
tags = 25%,










list = 19%,










signal = 31%


REGULATION_OF_CELL_CYCLE
176
0.34136325
1.2807763
0.057142857
0.44240785
1
6270
tags = 35%,










list = 28%,










signal = 49%


BIOCARTA_CELLCYCLE_PATHWAY
23
0.45238993
1.2348099
0.20188425
0.4426122
1
5440
tags = 48%,










list = 24%,










signal = 63%


BIDUS_METASTASIS_UP
210
0.32272854
1.2317253
0.08727655
0.44313508
1
7607
tags = 37%,










list = 34%,










signal = 56%


BIOCARTA_G2_PATHWAY
24
0.43736917
1.23886
0.19839142
0.44345433
1
7169
tags = 63%,










list = 32%,










signal = 92%


WAKASUGI_HAVE_ZNF143_BINDING_SITES
57
0.409766
1.3569037
0.06455542
0.44383404
1
9053
tags = 54%,










list = 41%,










signal = 92%


BAKER_HEMATOPOIESIS_STAT3_TARGETS
16
0.5255945
1.3536161
0.12208505
0.44457713
1
4769
tags = 31%,










list = 21%,










signal = 40%


REN_BOUND_BY_E2F
60
0.371754
1.2354999
0.1558753
0.44478825
1
9054
tags = 52%,










list = 41%,










signal = 87%


PUJANA_BREAST_CANCER_WITH_BRCA1_MUTATED_UP
55
0.42610663
1.4033492
0.046116505
0.44482577
1
8637
tags = 58%,










list = 39%,










signal = 95%


NEGATIVE_REGULATION_OF_CELL_CYCLE
74
0.406012
1.411202
0.034319527
0.44537887
1
6053
tags = 41%,










list = 27%,










signal = 56%


SHEPARD_BMYB_MORPHOLINO_DN
181
0.33807123
1.285355
0.05844846
0.44549948
1
4903
tags = 29%,










list = 22%,










signal = 37%


V$E2F_02
227
0.32596663
1.2411405
0.072916664
0.44575807
1
4539
tags = 26%,










list = 20%,










signal = 33%


PEART_HDAC_PROLIFERATION_CLUSTER_DN
71
0.36209452
1.2365865
0.14153132
0.44584346
1
4802
tags = 28%,










list = 22%,










signal = 36%


VANTVEER_BREAST_CANCER_METASTASIS_DN
116
0.34457853
1.2395557
0.120746434
0.4458923
1
4607
tags = 28%,










list = 21%,










signal = 36%


REACTOME_DNA_STRAND_ELONGATION
30
0.46618944
1.3650514
0.09174312
0.44635457
1
2108
tags = 20%,










list = 9%,










signal = 22%


V$E2F_Q3_01
226
0.32628265
1.2423823
0.07017544
0.44670755
1
5052
tags = 30%,










list = 23%,










signal = 38%


CELL_CYCLE_CHECKPOINT_GO_0000075
47
0.44452986
1.419703
0.04101327
0.44720095
1
6550
tags = 47%,










list = 29%,










signal = 66%


CELL_CYCLE_GO_0007049
300
0.33291504
1.3010465
0.022357723
0.4476244
1
6550
tags = 35%,










list = 29%,










signal = 49%


REACTOME_HOMOLOGOUS_RECOMBINATION_REPAIR_OF_REPLICATION_INDEPENDENT—-
16
0.48624235
1.2428929
0.18620689
0.4497434
1
6871
tags = 56%,


DOUBLE_STRAND_BREAKS







list = 31%,










signal = 81%


MODULE_403
46
0.41504624
1.3022362
0.11691542
0.4509891
1
7716
tags = 54%,










list = 35%,










signal = 83%


ODONNELL_TARGETS_OF_MYC_AND_TFRC_DN
44
0.414673
1.2855059
0.11757576
0.4510636
1
8499
tags = 59%,










list = 38%,










signal = 96%


WHITFIELD_CELL_CYCLE_G2
173
0.32773593
1.2274308
0.08870116
0.451402
1
4668
tags = 24%,










list = 21%,










signal = 30%


DNA_DAMAGE_CHECKPOINT
19
0.47475117
1.2434118
0.20430107
0.4525769
1
6550
tags = 58%,










list = 29%,










signal = 82%


CHIANG_LIVER_CANCER_SUBCLASS_PROLIFERATION_UP
168
0.33162174
1.244898
0.082714744
0.45288795
1
4651
tags = 24%,










list = 21%,










signal = 30%


MARKEY_RB1_CHRONIC_LOF_UP
107
0.3516871
1.2457684
0.123903506
0.455226
1
4537
tags = 30%,










list = 20%,










signal = 37%


REACTOME_G2_M_CHECKPOINTS
41
0.4280654
1.3096297
0.12531969
0.45602632
1
9054
tags = 66%,










list = 41%,










signal = 111%


PID_FANCONI_PATHWAY
47
0.44685096
1.4223179
0.050183598
0.45605284
1
8291
tags = 64%,










list = 37%,










signal = 102%


GNF2_SMC4L1
84
0.3596912
1.2468225
0.11907514
0.45696002
1
8761
tags = 46%,










list = 39%,










signal = 76%


V$E2F1_Q3
231
0.336029
1.2855971
0.041322313
0.4570545
1
6428
tags = 35%,










list = 29%,










signal = 49%


V$E2F1DP1RB_01
220
0.3411136
1.3024278
0.026943006
0.45745027
1
5052
tags = 30%,










list = 23%,










signal = 38%


RESPONSE_TO_DNA_DAMAGE_STIMULUS
156
0.35050243
1.3042861
0.046587214
0.45874286
1
8304
tags = 50%,










list = 37%,










signal = 79%


V$E2F1_Q6_01
230
0.37063718
1.4119506
0.010405827
0.45911348
1
7781
tags = 46%,










list = 35%,










signal = 70%


BLUM_RESPONSE_TO_SALIRASIB_DN
333
0.3331813
1.3109615
0.013224822
0.45926988
1
4415
tags = 25%,










list = 20%,










signal = 30%


CHROMATIN
33
0.42863253
1.2906651
0.15817694
0.46022883
1
4588
tags = 39%,










list = 21%,










signal = 50%


V$E2F1_Q4
232
0.337286
1.2925217
0.034020618
0.46110448
1
4570
tags = 25%,










list = 21%,










signal = 32%


V$E2F1DP1_01
227
0.3330221
1.2862234
0.03516029
0.46150312
1
4539
tags = 27%,










list = 20%,










signal = 33%


DNA_POLYMERASE_ACTIVITY
17
0.51129895
1.3051121
0.15912208
0.46312702
1
3049
tags = 29%,










list = 14%,










signal = 34%


REACTOME_ACTIVATION_OF_THE_PRE_REPLICATIVE_COMPLEX
30
0.42164528
1.2219079
0.19325551
0.46342462
1
9054
tags = 60%,










list = 41%,










signal = 101%


NEMETH_INFLAMMATORY_RESPONSE_LPS_DN
30
0.44607013
1.3114657
0.112125166
0.46563882
1
6411
tags = 43%,










list = 29%,










signal = 61%


KAMMINGA_EZH2_TARGETS
41
0.4117876
1.2868892
0.1476846
0.46586663
1
9741
tags = 51%,










list = 44%,










signal = 91%


BOYAULT_LIVER_CANCER_SUBCLASS_G23_UP
52
0.37493795
1.2195616
0.17090909
0.4663689
1
9521
tags = 60%,










list = 43%,










signal = 104%


BOYAULT_LIVER_CANCER_SUBCLASS_G123_UP
44
0.40885055
1.2927094
0.13449565
0.46733078
1
3534
tags = 25%,










list = 16%,










signal = 30%


NEGATIVE_REGULATION_OF_DNA_METABOLIC_PROCESS
17
0.50903845
1.3207972
0.13458756
0.46742555
1
6295
tags = 53%,










list = 28%,










signal = 74%


MODULE_125
44
0.4174832
1.3118248
0.10869565
0.47248983
1
8449
tags = 52%,










list = 38%,










signal = 84%


REACTOME_FANCONI_ANEMIA_PATHWAY
21
0.4878057
1.3140798
0.12264151
0.47280735
1
9441
tags = 71%,










list = 42%,










signal = 124%


ZHOU_CELL_CYCLE_GENES_IN_IR_RESPONSE_6HR
85
0.35011268
1.2140554
0.16743119
0.47437987
1
9054
tags = 53%,










list = 41%,










signal = 89%


V$E2F_Q4_01
227
0.34803805
1.342341
0.017598344
0.47562048
1
5128
tags = 31%,










list = 23%,










signal = 40%


CELL_CYCLE_PROCESS
184
0.32014745
1.214998
0.10492505
0.47582355
1
6500
tags = 35%,










list = 29%,










signal = 49%


V$E2F_Q6_01
226
0.34184265
1.3208154
0.018518519
0.47608158
1
6747
tags = 39%,










list = 30%,










signal = 55%


LINDGREN_BLADDER_CANCER_CLUSTER_3_UP
317
0.33547068
1.315464
0.024464833
0.47640255
1
6342
tags = 32%,










list = 29%,










signal = 44%


FUJII_YBX1_TARGETS_DN
198
0.32220778
1.2118708
0.09375
0.47682393
1
6261
tags = 35%,










list = 28%,










signal = 48%


BIOCARTA_MCM_PATHWAY
18
0.4536684
1.2096198
0.2091768
0.4797364
1
9054
tags = 61%,










list = 41%,










signal = 103%


REACTOME_ACTIVATION_OF_ATR_IN_RESPONSE_TO_REPLICATION_STRESS
35
0.44588655
1.3214197
0.09756097
0.4829918
1
9054
tags = 66%,










list = 41%,










signal = 111%


MODULE_158
43
0.42425537
1.3374326
0.0882353
0.4830363
1
7607
tags = 47%,










list = 34%,










signal = 71%


WILCOX_RESPONSE_TO_PROGESTERONE_UP
139
0.32814986
1.2047398
0.14254859
0.48662063
1
6069
tags = 38%,










list = 27%,










signal = 52%


CHIARETTI_T_ALL_RELAPSE_PROGNOSIS
18
0.50308824
1.3280432
0.13597734
0.48701903
1
7153
tags = 56%,










list = 32%,










signal = 82%


SCIBETTA_KDM5B_TARGETS_DN
77
0.3519979
1.2055135
0.19257541
0.48827815
1
6568
tags = 38%,










list = 30%,










signal = 53%


DNA_REPAIR
121
0.36780778
1.3223782
0.047513813
0.48875025
1
8304
tags = 50%,










list = 37%,










signal = 79%


RB_DN.V1_UP
133
0.36299983
1.3249965
0.04928806
0.4890961
1
4765
tags = 32%,










list = 21%,










signal = 40%


REACTOME_G1_PHASE
34
0.44088387
1.3327861
0.103492886
0.4891206
1
4802
tags = 38%,










list = 22%,










signal = 49%


SGCGSSAAA_V$E2F1DP2_01
163
0.35208312
1.3299948
0.03711559
0.48966816
1
5002
tags = 29%,










list = 23%,










signal = 37%


PETROVA_ENDOTHELIUM_LYMPHATIC_VS_BLOOD_UP
124
0.3330752
1.2003479
0.1480663
0.49648187
1
4601
tags = 27%,










list = 21%,










signal = 34%


PUJANA_BRCA2_PCC_NETWORK
404
0.3018975
1.1938198
0.06458123
0.49655923
1
8801
tags = 46%,










list = 40%,










signal = 75%


OLSSON_E2F3_TARGETS_DN
44
0.37923804
1.1987423
0.1992528
0.49757302
1
2312
tags = 20%,










list = 10%,










signal = 23%


REACTOME_RESOLUTION_OF_AP_SITES_VIA_THE_MULTIPLE_NUCLEOTIDE_PATCH—-
17
0.46676862
1.1972795
0.24386922
0.49794504
1
5930
tags = 47%,


REPLACEMENT_PATHWAY







list = 27%,










signal = 64%


EXONUCLEASE_ACTIVITY
19
0.45039612
1.1954068
0.25414366
0.49976397
1
7039
tags = 58%,










list = 32%,










signal = 85%


DNA_DAMAGE_RESPONSESIGNAL_TRANSDUCTION
33
0.40284628
1.1940353
0.22809279
0.49990293
1
6550
tags = 58%,










list = 29%,










signal = 82%


FERREIRA_EWINGS_SARCOMA_UNSTABLE_VS_STABLE_UP
160
0.32071027
1.1905507
0.14209402
0.50172865
1
8499
tags = 45%,










list = 38%,










signal = 72%


NUCLEASE_ACTIVITY
52
0.36660424
1.1719922
0.22738387
0.5480522
1
7039
tags = 42%,










list = 32%,










signal = 62%


MODULE_57
55
0.36214647
1.1732196
0.24759616
0.5482668
1
7061
tags = 44%,










list = 32%,










signal = 64%


YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_15
32
0.39920834
1.1738428
0.23169108
0.5506029
1
3636
tags = 22%,










list = 16%,










signal = 26%


V$E2F1_Q6
226
0.30330324
1.165586
0.16891192
0.5642348
1
8286
tags = 45%,










list = 37%,










signal = 71%


KEGG_HOMOLOGOUS_RECOMBINATION
26
0.40115663
1.1583151
0.27236843
0.5742607
1
6871
tags = 46%,










list = 31%,










signal = 67%


BASE_EXCISION_REPAIR
16
0.4500021
1.161106
0.28125
0.57437444
1
7716
tags = 56%,










list = 35%,










signal = 86%


GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_TURQUOISE_DN
51
0.3541276
1.1587772
0.25531915
0.5773484
1
4500
tags = 24%,










list = 20%,










signal = 29%


REACTOME_BASE_EXCISION_REPAIR
19
0.43393558
1.1473954
0.2892562
0.579132
1
5930
tags = 42%,










list = 27%,










signal = 57%


MMS_MOUSE_LYMPH_HIGH_4HRS_UP
33
0.38256466
1.148576
0.2784314
0.5796485
1
4393
tags = 33%,










list = 20%,










signal = 41%


PID_ATM_PATHWAY
34
0.38246185
1.1525896
0.28025478
0.57977974
1
6351
tags = 41%,










list = 29%,










signal = 58%


GNF2_MCM4
53
0.3524173
1.1454428
0.25826192
0.5810529
1
10124
tags = 51%,










list = 46%,










signal = 93%


MODULE_451
31
0.39108822
1.153259
0.28047183
0.5817704
1
2108
tags = 16%,










list = 9%,










signal = 18%


GRAHAM_CML_QUIESCENT_VS_NORMAL_QUIESCENT_UP
78
0.33614486
1.1502516
0.22916667
0.5830748
1
4740
tags = 21%,










list = 21%,










signal = 26%


CELL_CYCLE_PHASE
162
0.30566037
1.1487713
0.20895523
0.5833879
1
6351
tags = 33%,










list = 29%,










signal = 46%


V$E2F_03
234
0.29410437
1.1418293
0.19378239
0.5845467
1
5052
tags = 28%,










list = 23%,










signal = 36%


CELL_CYCLE_ARREST_GO_0007050
52
0.3528182
1.1535076
0.23947051
0.5854035
1
6038
tags = 35%,










list = 27%,










signal = 47%


POSITIVE_REGULATION_OF_CELL_CYCLE
15
0.45966756
1.1423001
0.30624092
0.5871997
1
4769
tags = 40%,










list = 21%,










signal = 51%


MODULE_337
59
0.3437068
1.139586
0.25894988
0.5875483
1
3988
tags = 27%,










list = 18%,










signal = 33%


LINDGREN_BLADDER_CANCER_CLUSTER_1_DN
359
0.2904739
1.1380037
0.15237135
0.5886735
1
5957
tags = 30%,










list = 27%,










signal = 41%


V$E2F_01
65
0.33490327
1.1119276
0.2972973
0.5961439
1
6939
tags = 38%,










list = 31%,










signal = 56%


GEORGES_CELL_CYCLE_MIR192_TARGETS
61
0.33884498
1.1122179
0.3084223
0.5989164
1
8144
tags = 52%,










list = 37%,










signal = 83%


GCNP_SHH_UP_LATE.V1_UP
171
0.30077168
1.1332275
0.23961662
0.5998562
1
6242
tags = 33%,










list = 28%,










signal = 45%


PID_FOXM1_PATHWAY
39
0.36850864
1.1252751
0.30729166
0.6009434
1
5440
tags = 36%,










list = 24%,










signal = 47%


M_PHASE
107
0.31203735
1.1140449
0.28863636
0.601072
1
6500
tags = 31%,










list = 29%,










signal = 43%


KTGGYRSGAA_UNKNOWN
73
0.32634726
1.1122824
0.295612
0.60253054
1
7550
tags = 45%,










list = 34%,










signal = 68%


LY_AGING_PREMATURE_DN
29
0.3882155
1.1272681
0.32266325
0.60279167
1
2915
tags = 17%,










list = 13%,










signal = 20%


CHANG_CYCLING_GENES
143
0.30817467
1.1310683
0.2454252
0.6029239
1
4768
tags = 24%,










list = 21%,










signal = 31%


RIBONUCLEASE_ACTIVITY
22
0.40811253
1.1258738
0.31636864
0.6031186
1
6343
tags = 32%,










list = 29%,










signal = 44%


REGULATION_OF_DNA_METABOLIC_PROCESS
43
0.35744908
1.1145996
0.30614805
0.60318565
1
6705
tags = 44%,










list = 30%,










signal = 63%


MORI_PRE_BI_LYMPHOCYTE_UP
76
0.3274124
1.1296207
0.2862069
0.60356325
1
4570
tags = 22%,










list = 21%,










signal = 28%


DNA_RECOMBINATION
41
0.36278692
1.1154684
0.296343
0.6041192
1
7474
tags = 56%,










list = 34%,










signal = 84%


WANG_CISPLATIN_RESPONSE_AND_XPC_UP
184
0.29796305
1.1273884
0.23301986
0.60650617
1
5313
tags = 29%,










list = 24%,










signal = 38%


GNF2_PCNA
67
0.32937458
1.115769
0.30154946
0.60693085
1
9683
tags = 49%,










list = 44%,










signal = 87%


STEIN_ESR1_TARGETS
81
0.31779978
1.106642
0.31294116
0.6078658
1
6467
tags = 40%,










list = 29%,










signal = 56%


REACTOME_DOUBLE_STRAND_BREAK_REPAIR
22
0.40390694
1.1041609
0.33781964
0.6080242
1
8192
tags = 59%,










list = 37%,










signal = 94%


RB_P107_DN.V1_UP
133
0.30179462
1.1029329
0.28990227
0.6082117
1
4879
tags = 29%,










list = 22%,










signal = 37%


FINETTI_BREAST_CANCER_KINOME_RED
16
0.4440954
1.1211063
0.3478261
0.60990953
1
7061
tags = 50%,










list = 32%,










signal = 73%


MITSIADES_RESPONSE_TO_APLIDIN_DN
243
0.29167166
1.118732
0.2371134
0.6099311
1
7790
tags = 37%,










list = 35%,










signal = 56%


ROSTY_CERVICAL_CANCER_PROLIFERATION_CLUSTER
139
0.30078912
1.1160735
0.25414366
0.60994726
1
6224
tags = 29%,










list = 28%,










signal = 41%


JOHANSSON_GLIOMAGENESIS_BY_PDGFB_UP
55
0.34037852
1.1043452
0.31604344
0.6111753
1
7070
tags = 42%,










list = 32%,










signal = 61%


RESPONSE_TO_ENDOGENOUS_STIMULUS
190
0.29485098
1.1168075
0.22770199
0.61168456
1
6871
tags = 38%,










list = 31%,










signal = 54%


GNF2_FEN1
56
0.34303924
1.1190727
0.30898204
0.61273277
1
7790
tags = 36%,










list = 35%,










signal = 55%


MITOTIC_CELL_CYCLE_CHECKPOINT
21
0.4117884
1.0959709
0.3785235
0.6262292
1
6500
tags = 43%,










list = 29%,










signal = 61%


LE_EGR2_TARGETS_UP
106
0.3048985
1.0916537
0.31520534
0.63583624
1
4769
tags = 25%,










list = 21%,










signal = 31%


MODULE_303
28
0.38074943
1.0893929
0.3493976
0.63876075
1
7061
tags = 36%,










list = 32%,










signal = 52%


V$E2F1_Q3_01
235
0.28381938
1.0852671
0.31519508
0.6404517
1
5550
tags = 29%,










list = 25%,










signal = 39%


ZHAN_MULTIPLE_MYELOMA_PR_UP
45
0.34538898
1.0859619
0.35732323
0.6420379
1
10124
tags = 58%,










list = 46%,










signal = 106%


AMUNDSON_GENOTOXIC_SIGNATURE
100
0.30098325
1.0834075
0.33333334
0.64254403
1
3583
tags = 19%,










list = 16%,










signal = 23%


ZHOU_CELL_CYCLE_GENES_IN_IR_RESPONSE_24HR
124
0.3019528
1.086621
0.3264418
0.64368314
1
6378
tags = 32%,










list = 29%,










signal = 45%


PUJANA_BREAST_CANCER_LIT_INT_NETWORK
100
0.3024451
1.0792933
0.33707866
0.65143365
1
7632
tags = 43%,










list = 34%,










signal = 65%


WHITFIELD_CELL_CYCLE_M_G1
140
0.29741225
1.0778337
0.3391494
0.65224826
1
7925
tags = 41%,










list = 36%,










signal = 63%


WANG_RESPONSE_TO_GSK3_INHIBITOR_SB216763_DN
345
0.26970476
1.0671172
0.31319234
0.6817713
1
4624
tags = 23%,










list = 21%,










signal = 29%


MODULE_325
51
0.33049893
1.0603688
0.37578028
0.68339276
1
3076
tags = 22%,










list = 14%,










signal = 25%


GARCIA_TARGETS_OF_FLI1_AND_DAX1_DN
165
0.28245273
1.0614592
0.3601695
0.68396115
1
9176
tags = 45%,










list = 41%,










signal = 77%


GRAHAM_CML_DIVIDING_VS_NORMAL_QUIESCENT_UP
165
0.2841179
1.0636351
0.34946236
0.6850291
1
6508
tags = 32%,










list = 29%,










signal = 44%


WINNEPENNINCKX_MELANOMA_METASTASIS_UP
160
0.2851033
1.061746
0.35927504
0.68684745
1
10124
tags = 52%,










list = 46%,










signal = 95%


BOYAULT_LIVER_CANCER_SUBCLASS_G3_UP
187
0.2815569
1.0636468
0.35859126
0.6889028
1
8780
tags = 44%,










list = 40%,










signal = 72%


INTERPHASE
67
0.30789024
1.0435838
0.4054697
0.68922824
1
8527
tags = 52%,










list = 38%,










signal = 85%


REGULATION_OF_MITOTIC_CELL_CYCLE
23
0.37835997
1.0497313
0.42348284
0.6894748
1
7427
tags = 48%,










list = 33%,










signal = 72%


INTERPHASE_OF_MITOTIC_CELL_CYCLE
61
0.3159809
1.0483093
0.39717978
0.6900084
1
8527
tags = 51%,










list = 38%,










signal = 82%


SONG_TARGETS_OF_IE86_CMV_PROTEIN
60
0.3179287
1.0515535
0.39787486
0.69109964
1
7716
tags = 45%,










list = 35%,










signal = 69%


WHITEFORD_PEDIATRIC_CANCER_MARKERS
115
0.2941285
1.0538615
0.39053905
0.69164646
1
8291
tags = 38%,










list = 37%,










signal = 61%


ZHENG_GLIOBLASTOMA_PLASTICITY_UP
234
0.27269864
1.0499946
0.37460977
0.69240123
1
5680
tags = 29%,










list = 26%,










signal = 39%


RAY_TUMORIGENESIS_BY_ERBB2_CDC25A_UP
96
0.29605302
1.0436827
0.39338654
0.69257224
1
4346
tags = 26%,










list = 20%,










signal = 32%


ODONNELL_TFRC_TARGETS_DN
122
0.29402384
1.0546696
0.39606127
0.6930552
1
10025
tags = 56%,










list = 45%,










signal = 101%


KOBAYASHI_EGFR_SIGNALING_24HR_DN
250
0.27339545
1.0520165
0.3783784
0.69338197
1
7790
tags = 41%,










list = 35%,










signal = 62%


LE_NEURONAL_DIFFERENTIATION_DN
19
0.39102486
1.0556614
0.40577716
0.69378126
1
1053
tags = 11%,










list = 5%,










signal = 11%


GCNP_SHH_UP_EARLY.V1_UP
160
0.281336
1.0440953
0.3853606
0.6950538
1
8446
tags = 46%,










list = 38%,










signal = 73%


MODULE_198
297
0.27156198
1.0451778
0.37206933
0.69549567
1
8291
tags = 39%,










list = 37%,










signal = 62%


NUNODA_RESPONSE_TO_DASATINIB_IMATINIB_UP
29
0.35125908
1.0276042
0.448
0.71113324
1
5281
tags = 31%,










list = 24%,










signal = 41%


GNF2_SMC2L1
32
0.34641853
1.025546
0.44287547
0.7134221
1
10025
tags = 59%,










list = 45%,










signal = 108%


BURTON_ADIPOGENESIS_PEAK_AT_16HR
39
0.33349323
1.0278322
0.44237918
0.71399
1
2676
tags = 15%,










list = 12%,










signal = 17%


MODULE_124
95
0.29049054
1.0280728
0.4409722
0.7169952
1
4601
tags = 21%,










list = 21%,










signal = 26%


SLEBOS_HEAD_AND_NECK_CANCER_WITH_HPV_UP
78
0.30232805
1.0287576
0.427907
0.7185713
1
9319
tags = 54%,










list = 42%,










signal = 92%


SHEDDEN_LUNG_CANCER_POOR_SURVIVAL_A6
442
0.2601959
1.0292497
0.40722167
0.7208165
1
5463
tags = 25%,










list = 25%,










signal = 32%


BENPORATH_PROLIFERATION
144
0.2783527
1.0215985
0.44017562
0.7211337
1
10199
tags = 49%,










list = 46%,










signal = 91%


GNF2_TTK
39
0.34152663
1.0301598
0.43167305
0.7218084
1
9683
tags = 51%,










list = 44%,










signal = 91%


CROONQUIST_IL6_DEPRIVATION_DN
97
0.2925624
1.0313257
0.41359448
0.72205865
1
8801
tags = 41%,










list = 40%,










signal = 68%


MODULE_197
167
0.27391145
1.0152683
0.46244636
0.7287274
1
5462
tags = 26%,










list = 25%,










signal = 35%


MODULE_252
234
0.2656011
1.0163232
0.44282743
0.7291807
1
7928
tags = 36%,










list = 36%,










signal = 55%


DAMAGED_DNA_BINDING
21
0.37659937
1.0164671
0.44993323
0.7324266
1
8304
tags = 48%,










list = 37%,










signal = 76%


DEOXYRIBONUCLEASE_ACTIVITY
22
0.36742947
1.0122482
0.45019406
0.7337531
1
6760
tags = 45%,










list = 30%,










signal = 65%


PID_ATR_PATHWAY
38
0.32669598
1.0086408
0.45660377
0.73724896
1
7607
tags = 45%,










list = 34%,










signal = 68%


KEGG_BASE_EXCISION_REPAIR
34
0.33574635
1.0093353
0.44513714
0.7387695
1
9037
tags = 47%,










list = 41%,










signal = 79%


MITOTIC_CELL_CYCLE
150
0.27175382
1.0066271
0.46824542
0.73938453
1
6270
tags = 29%,










list = 28%,










signal = 41%


HORIUCHI_WTAP_TARGETS_DN
301
0.2576669
1.0031742
0.4760936
0.7423474
1
9073
tags = 42%,










list = 41%,










signal = 70%


VECCHI_GASTRIC_CANCER_EARLY_UP
405
0.2534836
1.0040272
0.48944724
0.74324733
1
5182
tags = 25%,










list = 23%,










signal = 32%


AFFAR_YY1_TARGETS_DN
212
0.2632148
0.99917674
0.5031447
0.75014687
1
4600
tags = 23%,










list = 21%,










signal = 28%


MOLENAAR_TARGETS_OF_CCND1_AND_CDK4_DN
57
0.3013021
0.9901799
0.48459715
0.7720615
1
7628
tags = 39%,










list = 34%,










signal = 59%


CROONQUIST_NRAS_SIGNALING_DN
72
0.28571492
0.9843489
0.48963133
0.78501475
1
7061
tags = 33%,










list = 32%,










signal = 49%


MARKEY_RB1_ACUTE_LOF_UP
228
0.25501198
0.9808197
0.5400624
0.7914312
1
4802
tags = 24%,










list = 22%,










signal = 30%


PID_AURORA_A_PATHWAY
31
0.3326504
0.97149783
0.5072084
0.80590636
1
5748
tags = 32%,










list = 26%,










signal = 43%


SHEPARD_CRUSH_AND_BURN_MUTANT_DN
163
0.25931618
0.97226787
0.54291844
0.8076891
1
5778
tags = 28%,










list = 26%,










signal = 37%


MUELLER_PLURINET
285
0.2508906
0.97336334
0.5498458
0.8083684
1
7488
tags = 32%,










list = 34%,










signal = 48%


DOUBLE_STRANDED_DNA_BINDING
32
0.32993537
0.9667149
0.5226064
0.815262
1
7595
tags = 41%,










list = 34%,










signal = 62%


BURTON_ADIPOGENESIS_3
101
0.26917318
0.96023273
0.55937845
0.82939523
1
4802
tags = 22%,










list = 22%,










signal = 28%


MODULE_244
183
0.25261924
0.95404893
0.5822785
0.8420635
1
7686
tags = 32%,










list = 35%,










signal = 48%


NUCLEOTIDYLTRANSFERASE_ACTIVITY
46
0.29995102
0.9524774
0.5477941
0.8424268
1
5100
tags = 22%,










list = 23%,










signal = 28%


ISHIDA_E2F_TARGETS
51
0.29740694
0.9489488
0.55487806
0.84420574
1
9937
tags = 53%,










list = 45%,










signal = 96%


MODULE_98
383
0.24173515
0.9493505
0.653144
0.8468522
1
8192
tags = 37%,










list = 37%,










signal = 57%


STRUCTURE_SPECIFIC_DNA_BINDING
55
0.2926584
0.9435076
0.55741626
0.8508466
1
7595
tags = 36%,










list = 34%,










signal = 55%


BENPORATH_ES_CORE_NINE_CORRELATED
95
0.26689902
0.94446295
0.57652473
0.85213023
1
9303
tags = 49%,










list = 42%,










signal = 85%


RPS14_DN.V1_DN
177
0.24837753
0.9374446
0.6248694
0.8631052
1
4613
tags = 24%,










list = 21%,










signal = 30%


MANALO_HYPOXIA_DN
283
0.24170218
0.9352551
0.663926
0.8649086
1
4570
tags = 19%,










list = 21%,










signal = 24%


GAVIN_FOXP3_TARGETS_CLUSTER_P6
87
0.26028508
0.9114183
0.61290324
0.8867134
1
7134
tags = 33%,










list = 32%,










signal = 49%


GROSS_HYPOXIA_VIA_ELK3_AND_HIF1A_DN
100
0.25491416
0.9097471
0.6453423
0.88704175
1
4370
tags = 24%,










list = 20%,










signal = 30%


LI_WILMS_TUMOR_ANAPLASTIC_UP
19
0.34389392
0.91258436
0.59001315
0.8875584
1
10520
tags = 63%,










list = 47%,










signal = 120%


SHEPARD_BMYB_TARGETS
68
0.26642838
0.9071732
0.6092486
0.8893206
1
1460
tags = 10%,










list = 7%,










signal = 11%


G1_S_TRANSITION_OF_MITOTIC_CELL_CYCLE
27
0.32290238
0.9162803
0.5882353
0.88970286
1
8527
tags = 56%,










list = 38%,










signal = 90%


PID_AURORA_B_PATHWAY
38
0.29612312
0.91309714
0.6307885
0.8900131
1
4370
tags = 21%,










list = 20%,










signal = 26%


GOLDRATH_ANTIGEN_RESPONSE
318
0.23252904
0.91377914
0.7244898
0.8921047
1
4811
tags = 22%,










list = 22%,










signal = 27%


LI_WILMS_TUMOR_VS_FETAL_KIDNEY_1_DN
160
0.24605219
0.91683835
0.65356004
0.89213026
1
7790
tags = 34%,










list = 35%,










signal = 52%


REACTOME_EXTENSION_OF_TELOMERES
27
0.31878284
0.9169291
0.6108949
0.89582115
1
4765
tags = 22%,










list = 21%,










signal = 28%


REACTOME_E2F_MEDIATED_REGULATION_OF_DNA_REPLICATION
32
0.3055041
0.92101496
0.59180975
0.89749575
1
4802
tags = 28%,










list = 22%,










signal = 36%


MISSIAGLIA_REGULATED_BY_METHYLATION_DN
117
0.25388703
0.91750246
0.6426193
0.8984007

8599
tags = 40%,










list = 39%,










signal = 65%


PID_E2F_PATHWAY
72
0.26511717
0.8924725
0.6639248
0.9008714
1
7061
tags = 36%,










list = 32%,










signal = 53%


GNF2_RRM1
87
0.26205435
0.917829
0.6188341
0.9013951
1
10124
tags = 47%,










list = 46%,










signal = 86%


DORMOY_ELAVL1_TARGETS
16
0.35215455
0.89995354
0.6144244
0.9022171
1
4377
tags = 31%,










list = 20%,










signal = 39%


CHROMOSOME
119
0.24726327
0.8942441
0.6868132
0.9040682
1
7750
tags = 37%,










list = 35%,










signal = 56%


E2F1_UP.V1_UP
182
0.23610032
0.8925108
0.72210526
0.9044738
1
8039
tags = 34%,










list = 36%,










signal = 52%


REACTOME_PROCESSIVE_SYNTHESIS_ON_THE_LAGGING_STRAND
15
0.35453758
0.89730215
0.62622035
0.9046432
1
4765
tags = 27%,










list = 21%,










signal = 34%


RNA_CATABOLIC_PROCESS
21
0.32824758
0.8955597
0.6258503
0.9048708
1
6343
tags = 38%,










list = 29%,










signal = 53%


KEGG_MISMATCH_REPAIR
23
0.31686857
0.8839419
0.63289475
0.90528065
1
2063
tags = 13%,










list = 9%,










signal = 14%


SASAKI_ADULT_T_CELL_LEUKEMIA
168
0.23828736
0.8881789
0.7296137
0.90717053
1
6939
tags = 32%,










list = 31%,










signal = 46%


GNF2_BUB1B
49
0.2752565
0.88618875
0.68345326
0.90762746
1
7790
tags = 33%,










list = 35%,










signal = 50%


NUCLEAR_CHROMOSOME
52
0.27598214
0.88433754
0.65824306
0.9079744
1
7686
tags = 40%,










list = 35%,










signal = 62%


YU_MYC_TARGETS_UP
42
0.28265965
0.88031185
0.6594663
0.90950704
1
7442
tags = 38%,










list = 33%,










signal = 57%


NAKAMURA_CANCER_MICROENVIRONMENT_DN
45
0.27968585
0.87747896
0.66625917
0.91193676
1
4904
tags = 18%,










list = 22%,










signal = 23%


MITOSIS
80
0.2530464
0.87246853
0.7086705
0.9189224
1
3765
tags = 16%,










list = 17%,










signal = 19%


PAL_PRMT5_TARGETS_UP
200
0.22849624
0.8638039
0.7713987
0.9189507
1
7104
tags = 31%,










list = 32%,










signal = 45%


LY_AGING_OLD_DN
55
0.26493242
0.8615848
0.71306473
0.92015535
1
6195
tags = 25%,










list = 28%,










signal = 35%


DNA_REPLICATION_INITIATION
16
0.33962247
0.8697947
0.6421499
0.92063504
1
9220
tags = 69%,










list = 42%,










signal = 117%


LI_WILMS_TUMOR_VS_FETAL_KIDNEY_2_UP
29
0.29539564
0.8639146
0.67785233
0.9223553
1
4765
tags = 24%,










list = 21%,










signal = 31%


MODULE_54
250
0.22454439
0.8654779
0.79170984
0.9227561
1
4765
tags = 20%,










list = 21%,










signal = 25%


KANG_DOXORUBICIN_RESISTANCE_UP
54
0.26724526
0.86711955
0.68907565
0.9228687
1
8499
tags = 37%,










list = 38%,










signal = 60%


M_PHASE_OF_MITOTIC_CELL_CYCLE
83
0.24474296
0.8540637
0.72445464
0.925178
1
3765
tags = 16%,










list = 17%,










signal = 19%


FARMER_BREAST_CANCER_CLUSTER_2
33
0.28644726
0.8502835
0.6847682
0.92544687
1
6747
tags = 30%,










list = 30%,










signal = 43%


CONCANNON_APOPTOSIS_BY_EPOXOMICIN_DN
155
0.2304101
0.85201883
0.780065
0.9256471
1
3636
tags = 19%,










list = 16%,










signal = 22%


HOFFMANN_LARGE_TO_SMALL_PRE_BII_LYMPHOCYTE_UP
155
0.23037744
0.856891
0.7775378
0.92635566
1
4570
tags = 21%,










list = 21%,










signal = 27%


RUIZ_TNC_TARGETS_DN
139
0.23044297
0.85473335
0.7619565
0.9272852
1
4570
tags = 23%,










list = 21%,










signal = 29%


HONRADO_BREAST_CANCER_BRCA1_VS_BRCA2
16
0.33600307
0.8473566
0.68105847
0.9275191
1
6069
tags = 50%,










list = 27%,










signal = 69%


LEE_EARLY_T_LYMPHOCYTE_UP
95
0.23925753
0.8433739
0.7483146
0.9317367
1
8958
tags = 46%,










list = 40%,










signal = 77%


GNF2_RFC3
41
0.26695165
0.82920223
0.7265823
0.9473219
1
11198
tags = 56%,










list = 50%,










signal = 113%


REGULATION_OF_MITOSIS
40
0.27735117
0.832746
0.73173803
0.94810116
1
2697
tags = 15%,










list = 12%,










signal = 17%


SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_UP
149
0.22273426
0.8294785
0.80931747
0.9504284
1
6500
tags = 25%,










list = 29%,










signal = 35%


SARRIO_EPITHELIAL_MESENCHYMAL_TRANSITION_UP
168
0.21678355
0.8153494
0.8496802
0.96451414
1
4111
tags = 22%,










list = 19%,










signal = 27%


REACTOME_LAGGING_STRAND_SYNTHESIS
19
0.306489
0.8167572
0.7380952
0.9656359
1
8291
tags = 42%,










list = 37%,










signal = 67%


GNF2_CCNA2
67
0.23950595
0.8076859
0.77870816
0.9740733
1
10124
tags = 46%,










list = 46%,










signal = 85%


INDUCTION_OF_APOPTOSIS_BY_INTRACELLULAR_SIGNALS
22
0.29047993
0.7988762
0.7735602
0.98105544
1
7884
tags = 55%,










list = 35%,










signal = 84%


WONG_EMBRYONIC_STEM_CELL_CORE
327
0.2027311
0.79904824
0.9016227
0.98434985
1
6302
tags = 23%,










list = 28%,










signal = 32%


WANG_METASTASIS_OF_BREAST_CANCER_ESR1_UP
21
0.2875412
0.77814585
0.7785515
0.99479026
1
9675
tags = 57%,










list = 44%,










signal = 101%


CONDENSED_NUCLEAR_CHROMOSOME
18
0.30309433
0.78282785
0.76183844
0.99502367
1
667
tags = 11%,










list = 3%,










signal = 11%


DOUBLE_STRAND_BREAK_REPAIR
23
0.28554407
0.7842453
0.7798913
0.9965019
1
6862
tags = 39%,










list = 31%,










signal = 57%


SIMBULAN_PARP1_TARGETS_DN
17
0.30563325
0.7864138
0.7654321
0.9967682
1
6892
tags = 41%,










list = 31%,










signal = 60%


WHITFIELD_CELL_CYCLE_G2_M
210
0.20307907
0.77888125
0.9089969
0.99722195
1
9195
tags = 43%,










list = 41%,










signal = 72%


GNF2_BUB1
26
0.15265957
0.43421733
0.9986559
0.9989812
1
4570
tags = 15%,










list = 21%,










signal = 19%


RRCCGTTA_UNKNOWN
83
0.22011705
0.77013916
0.8719101
1
1
5913
tags = 28%,










list = 27%,










signal = 38%


CHROMOSOMAL_PART
94
0.21652947
0.7682454
0.8681818
1
1
7750
tags = 35%,










list = 35%,










signal = 54%


CHANG_CORE_SERUM_RESPONSE_UP
205
0.20020889
0.76363
0.9241307
1
1
6196
tags = 26%,










list = 28%,










signal = 36%


HU_GENOTOXIC_DAMAGE_4HR
35
0.24896917
0.7556239
0.8151042
1
1
4168
tags = 14%,










list = 19%,










signal = 18%


SINGLE_STRANDED_DNA_BINDING
34
0.25254515
0.7534236
0.8260309
1
1
7294
tags = 29%,










list = 33%,










signal = 44%


GROSS_HYPOXIA_VIA_ELK3_UP
204
0.19297273
0.7402942
0.9466527
1
1
5253
tags = 20%,










list = 24%,










signal = 25%


CHROMATIN_BINDING
30
0.2501886
0.7395669
0.8548813
1
1
10357
tags = 57%,










list = 47%,










signal = 106%


KONG_E2F3_TARGETS
93
0.20983557
0.7376095
0.89395666
1
1
6500
tags = 31%,










list = 29%,










signal = 44%


GNF2_HMMR
47
0.22674319
0.72354347
0.86419755
1
1
9937
tags = 43%,










list = 45%,










signal = 77%


WU_APOPTOSIS_BY_CDKN1A_VIA_TP53
52
0.22379729
0.7229609
0.87529975
1
1
9176
tags = 48%,










list = 41%,










signal = 82%


WEST_ADRENOCORTICAL_TUMOR_UP
288
0.18519591
0.721704
0.977459
1
1
6500
tags = 24%,










list = 29%,










signal = 33%


NAKAYAMA_SOFT_TISSUE_TUMORS_PCA2_UP
83
0.2060155
0.71584517
0.9195923
1
1
2647
tags = 11%,










list = 12%,










signal = 12%


FOURNIER_ACINAR_DEVELOPMENT_LATE_DN
21
0.25898314
0.70711416
0.86376023
1
1
7925
tags = 43%,










list = 36%,










signal = 67%


GNF2_RRM2
40
0.2229693
0.6947614
0.9031056
1
1
9937
tags = 43%,










list = 45%,










signal = 77%


REACTOME_G1_S_SPECIFIC_TRANSCRIPTION
17
0.2678545
0.6924501
0.88551724
1
1
10494
tags = 65%,










list = 47%,










signal = 123%


KEGG_DNA_REPLICATION
36
0.22526753
0.69209665
0.9036458
1
1
7607
tags = 31%,










list = 34%,










signal = 46%


LI_WILMS_TUMOR
26
0.2432416
0.68723917
0.8931909
1
1
2434
tags = 15%,










list = 11%,










signal = 17%


FOURNIER_ACINAR_DEVELOPMENT_LATE_2
273
0.17572291
0.68083227
0.9938713
1
1
4990
tags = 19%,










list = 22%,










signal = 24%


CHROMOSOMEPERICENTRIC_REGION
31
0.23368138
0.67999655
0.89072424
1
1
9646
tags = 45%,










list = 43%,










signal = 80%


MODULE_308
69
0.19920248
0.67150915
0.9576471
1
1
6261
tags = 29%,










list = 28%,










signal = 40%


MORI_IMMATURE_B_LYMPHOCYTE_DN
88
0.19325547
0.6693833
0.95779604
1
1
7686
tags = 34%,










list = 35%,










signal = 52%


PID_PLK1_PATHWAY
44
0.21271893
0.6673868
0.9159456
1
1
7738
tags = 39%,










list = 35%,










signal = 59%


MODULE_397
111
0.18508032
0.6619494
0.9746696
1
1
9235
tags = 48%,










list = 42%,










signal = 81%


JUBAN_TARGETS_OF_SPI1_AND_FLIl_DN
86
0.19185586
0.66191936
0.962069
1
1
4020
tags = 17%,










list = 18%,










signal = 21%


DELPUECH_FOXO3_TARGETS_DN
39
0.20977807
0.6522092
0.9369483
1
1
2062
tags = 10%,










list = 9%,










signal = 11%


GNF2_CKS2
50
0.1999377
0.6472531
0.94795537
1
1
9683
tags = 40%,










list = 44%,










signal = 71%


GNF2_CENPF
61
0.19403538
0.641711
0.962963
1
1
9937
tags = 38%,










list = 45%,










signal = 68%


WHITFIELD_CELL_CYCLE_LITERATURE
44
0.20166118
0.64135575
0.93658537
1
1
10124
tags = 52%,










list = 46%,










signal = 96%


JEON_SMAD6_TARGETS_DN
18
0.2458375
0.6391381
0.9187675
1
1
2304
tags = 17%,










list = 10%,










signal = 19%


NUCLEAR_CHROMOSOME_PART
33
0.20444147
0.6214067
0.95696205
1
1
7294
tags = 36%,










list = 33%,










signal = 54%


XU_HGF_SIGNALING_NOT_VIA_AKT1_48HR_DN
20
0.2287609
0.6189885
0.9233871
1
1
4372
tags = 20%,










list = 20%,










signal = 25%


MODULE_320
20
0.22742188
0.61654776
0.9306667
1
1
10526
tags = 65%,










list = 47%,










signal = 123%


GNF2_CDC2
61
0.17950688
0.5932809
0.9847775
1
1
9937
tags = 39%,










list = 45%,










signal = 71%


WEST_ADRENOCORTICAL_TUMOR_MARKERS_UP
20
0.21488012
0.5768052
0.96594006
1
1
7442
tags = 40%,










list = 33%,










signal = 60%


REPLICATION_FORK
18
0.21701467
0.57130104
0.9441417
1
1
8291
tags = 44%,










list = 37%,










signal = 71%


EGUCHI_CELL_CYCLE_RB1_TARGETS
23
0.20478497
0.56402063
0.9628647
1
1
7686
tags = 30%,










list = 35%,










signal = 46%


MORI_LARGE_PRE_BII_LYMPHOCYTE_UP
84
0.16080098
0.55553854
1
1
1
9054
tags = 38%,










list = 41%,










signal = 64%


GNF2_ESPL1
35
0.18580903
0.553832
0.9898089
1
1
11198
tags = 51%,










list = 50%,










signal = 104%


GREENBAUM_E2A_TARGETS_UP
33
0.18239568
0.5511335
0.98278147
1
1
6069
tags = 24%,










list = 27%,










signal = 33%


CONDENSED_CHROMOSOME
33
0.18464331
0.5481924
0.9789082
1
1
667
tags = 6%,










list = 3%,










signal = 6%


GNF2_CENPE
40
0.17867468
0.5457817
0.981203
1
1
10025
tags = 40%,










list = 45%,










signal = 73%


SMID_BREAST_CANCER_LUMINAL_A_DN
16
0.21171118
0.54503566
0.9726402
1
1
6195
tags = 19%,










list = 28%,










signal = 26%


GNF2_CKS1B
37
0.17536254
0.53901255
0.98983485
1
1
8422
tags = 30%,










list = 38%,










signal = 48%


PENG_GLUCOSE_DEPRIVATION_DN
160
0.13850647
0.5201427
1
1
1
8176
tags = 29%,










list = 37%,










signal = 46%


GNF2_MKI67
27
0.18082324
0.51792157
0.9797023
1
1
9937
tags = 44%,










list = 45%,










signal = 80%


CHROMOSOME_SEGREGATION
32
0.17246047
0.51544017
0.98840207
1
1
9683
tags = 44%,










list = 44%,










signal = 77%


FRASOR_RESPONSE_TO_SERM_OR_FULVESTRANT_DN
50
0.15912758
0.51243126
0.99511003
1
1
8003
tags = 26%,










list = 36%,










signal = 41%


GNF2_H2AFX
31
0.16294482
0.4806161
0.9974716
1
1
3247
tags = 10%,










list = 15%,










signal = 11%


GNF2_CCNB2
56
0.13789071
0.45175722
0.9988053
1
1
7776
tags = 25%,










list = 35%,










signal = 38%
















TABLE 6R







GSEA for BAF180-null vs. BAF180-wildtype A704cell lines on TNFA Founder gene sets



















NOM



LEADING


NAME
SIZE
ES
NES
p-val
FDR q-val
FWER p-val
RANK AT MAX
EDGE


















HINATA_NFKB_IMMU_INF
17
0.7121733
1.9735836
0
0.01811983
0.012
1356
tags = 47%,










list = 6%,










signal = 50%


PHONG_TNF_TARGETS_UP
62
0.46539056
1.7218692
0
0.14616543
0.179
2611
tags = 27%,










list = 12%,










signal = 31%


SCHOEN_NFKB_SIGNALING
33
0.4909595
1.5692564
0.019955654
0.16486683
0.536
2415
tags = 36%,










list = 11%,










signal = 41%


AMIT_SERUM_RESPONSE_60_MCF10A
56
0.4424991
1.579713
0.004608295
0.17320979
0.494
2639
tags = 27%,










list = 12%,










signal = 30%


MAHAJAN_RESPONSE_TO_IL1A_UP
72
0.40984586
1.5468862
0.012875536
0.17401138
0.599
2709
tags = 28%,










list = 12%,










signal = 32%


LINDSTEDT_DENDRITIC_CELL_MATURATION_A
58
0.41769278
1.5015503
0.015873017
0.18031417
0.732
1356
tags = 21%,










list = 6%,










signal = 22%


MEL18_DN.V1_UP
135
0.34027582
1.4254444
0.012106538
0.1834423
0.885
4045
tags = 35%,










list = 18%,










signal = 42%


ALTEMEIER_RESPONSE_TO_LPS_WITH_MECHANICAL_VENTILATION
107
0.38396505
1.5130521
0.004878049
0.184769
0.706
2743
tags = 25%,










list = 12%,










signal = 29%


FERRARI_RESPONSE_TO_FENRETINIDE_UP
20
0.57468355
1.617784
0.026373627
0.1919037
0.406
1793
tags = 30%,










list = 8%,










signal = 33%


BROWNE_HCMV_INFECTION_2HR_UP
37
0.42980638
1.4351648
0.047493402
0.19261208
0.874
2561
tags = 19%,










list = 12%,










signal = 21%


BMI1_DN_MEL18_DN.V1_UP
139
0.34355652
1.4264567
0.007317073
0.19325998
0.884
2687
tags = 24%,










list = 12%,










signal = 28%


HINATA_NFKB_TARGETS_KERATINOCYTE_UP
85
0.36469486
1.4419237
0.026258206
0.19637743
0.863
1759
tags = 19%,










list = 8%,










signal = 20%


AMIT_EGF_RESPONSE_60_MCF10A
38
0.46031594
1.5187185
0.026431719
0.1965755
0.69
1793
tags = 21%,










list = 8%,










signal = 23%


ZUCCHI_METASTASIS_DN
41
0.49101514
1.6454661
0.002237137
0.19958329
0.33
1960
tags = 20%,










list = 9%,










signal = 21%


GRAHAM_CML_QUIESCENT_VS_CML_DIVIDING_UP
21
0.49748752
1.4050604
0.09896907
0.2010188
0.915
1356
tags = 29%,










list = 6%,










signal = 30%


KRIEG_HYPOXIA_VIA_KDM3A
51
0.4483681
1.5830749
0.017167382
0.2041567
0.488
2542
tags = 27%,










list = 11%,










signal = 31%


SEKI_INFLAMMATORY_RESPONSE_LPS_UP
73
0.3867515
1.4443095
0.0247191
0.20557162
0.859
1356
tags = 21%,










list = 6%,










signal = 22%


TIAN_TNF_SIGNALING_VIA_NFKB
28
0.46444523
1.4525323
0.056947608
0.20914835
0.847
1356
tags = 25%,










list = 6%,










signal = 27%


MATTIOLI_MGUS_VS_MULTIPLE_MYELOMA
16
0.53781915
1.4550939
0.06081081
0.2228664
0.84
3175
tags = 25%,










list = 14%,










signal = 29%


DAZARD_UV_RESPONSE_CLUSTER_G28
19
0.48236227
1.3739526
0.120430104
0.2244189
0.948
1356
tags = 21%,










list = 6%,










signal = 22%


BURTON_ADIPOGENESIS_1
33
0.4289922
1.3806711
0.060538117
0.22504185
0.944
2940
tags = 33%,










list = 13%,










signal = 38%


UZONYI_RESPONSE_TO_LEUKOTRIENE_AND_THROMBIN
36
0.4034385
1.3405854
0.09071274
0.2534478
0.974
1852
tags = 17%,










list = 8%,










signal = 18%


MODULE_178
15
0.5160862
1.3325039
0.14516129
0.25675952
0.981
2015
tags = 33%,










list = 9%,










signal = 37%


HINATA_NFKB_TARGETS_FIBROBLAST_UP
80
0.33661574
1.2855136
0.0900474
0.26094657
0.996
1759
tags = 15%,










list = 8%,










signal = 16%


MCDOWELL_ACUTE_LUNG_INJURY_UP
39
0.41243193
1.3430283
0.08163265
0.26111743
0.974
1447
tags = 18%,










list = 7%,










signal = 19%


BILD_HRAS_ONCOGENIC_SIGNATURE
240
0.2894971
1.2900707
0.03
0.2636602
0.994
2639
tags = 17%,










list = 12%,










signal = 19%


ALK_DN.V1_UP
113
0.31615773
1.2932228
0.039911307
0.2682712
0.994
2807
tags = 24%,










list = 13%,










signal = 27%


KOBAYASHI_EGFR_SIGNALING_6HR_DN
17
0.46688193
1.2732317
0.1633987
0.27098557
0.996
4237
tags = 35%,










list = 19%,










signal = 44%


ZHOU_INFLAMMATORY_RESPONSE_FIMA_UP
442
0.27361786
1.2944902
0.014285714
0.27551138
0.994
2640
tags = 15%,










list = 12%,










signal = 17%


MODULE_362
19
0.4612412
1.3005892
0.13983051
0.27594692
0.993
2015
tags = 32%,










list = 9%,










signal = 35%


WIEDERSCHAIN_TARGETS_OF_BMI1_AND_PCGF2
56
0.3694282
1.310024
0.090322584
0.28232476
0.99
2687
tags = 23%,










list = 12%,










signal = 26%


GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_BLACK_UP
33
0.40448025
1.3025057
0.11088296
0.28378433
0.992
2730
tags = 21%,










list = 12%,










signal = 24%


RASHI_NFKB1_TARGETS
18
0.45575842
1.2493724
0.19027483
0.29257196
1
1356
tags = 17%,










list = 6%,










signal = 18%


PLASARI_TGFB1_TARGETS_10HR_UP
188
0.2913739
1.2534598
0.05822785
0.29428974
1
2815
tags = 21%,










list = 13%,










signal = 24%


P53_DN.V2_UP
117
0.2990527
1.2159227
0.11374407
0.29627326
1
2730
tags = 22%,










list = 12%,










signal = 25%


BROCKE_APOPTOSIS_REVERSED_BY_IL6
137
0.29682255
1.24017
0.08395062
0.29763865
1
2709
tags = 19%,










list = 12%,










signal = 21%


BURTON_ADIPOGENESIS_PEAK_AT_2HR
50
0.34952435
1.2159503
0.17050691
0.30387002
1
2511
tags = 22%,










list = 11%,










signal = 25%


SESTO_RESPONSE_TO_UV_C3
20
0.43132424
1.2187178
0.2138229
0.30712342
1
543
tags = 15%,










list = 2%,










signal = 15%


SUZUKI_RESPONSE_TO_TSA_AND_DECITABINE_1A
19
0.41903538
1.1937535
0.22345133
0.31074792
1
3262
tags = 32%,










list = 15%,










signal = 37%


THEILGAARD_NEUTROPHIL_AT_SKIN_WOUND_UP
73
0.31508383
1.1962698
0.15311004
0.31408814
1
1407
tags = 11%,










list = 6%,










signal = 12%


DAZARD_UV_RESPONSE_CLUSTER_G2
29
0.39094698
1.2193848
0.1912088
0.3144602
1
1856
tags = 21%,










list = 8%,










signal = 23%


PHONG_TNF_RESPONSE_NOT_VIA_P38
330
0.259432
1.1995231
0.05851064
0.3162518
1
3366
tags = 21%,










list = 15%,










signal = 24%


HAHTOLA_MYCOSIS_FUNGOIDES_CD4_UP
58
0.33794093
1.2210118
0.16916488
0.32014048
1
2059
tags = 19%,










list = 9%,










signal = 21%


BMI1_DN.V1_UP
139
0.28091383
1.1807067
0.13711584
0.32538497
1
1705
tags = 17%,










list = 8%,










signal = 19%


ZWANG_CLASS_3_TRANSIENTLY_INDUCED_BY_EGF
206
0.26507318
1.1533595
0.13625866
0.34272358
1
2516
tags = 16%,










list = 11%,










signal = 18%


GRAHAM_CML_QUIESCENT_VS_NORMAL_DIVIDING_UP
50
0.32428753
1.1659062
0.21428572
0.34277838
1
4211
tags = 32%,










list = 19%,










signal = 39%


WANG_TNF_TARGETS
20
0.41113865
1.1544497
0.2805139
0.34758896
1
1896
tags = 20%,










list = 9%,










signal = 22%


GALINDO_IMMUNE_RESPONSE_TO_ENTEROTOXIN
79
0.30575588
1.1547593
0.20238096
0.35437652
1
2059
tags = 16%,










list = 9%,










signal = 18%


ZHOU_INFLAMMATORY_RESPONSE_LIVE_UP
407
0.24007683
1.136981
0.11653116
0.35855886
1
2636
tags = 15%,










list = 12%,










signal = 17%


KIM_WT1_TARGETS_UP
208
0.25509515
1.1377084
0.16707617
0.36462373
1
2919
tags = 17%,










list = 13%,










signal = 19%


MODULE_516
16
0.4157351
1.127287
0.31428573
0.36917233
1
2015
tags = 25%,










list = 9%,










signal = 27%


ZHOU_INFLAMMATORY_RESPONSE_LPS_UP
342
0.23887469
1.1214055
0.13611111
0.37299615
1
3023
tags = 19%,










list = 14%,










signal = 22%


AMIT_EGF_RESPONSE_40_HELA
40
0.33659357
1.1054982
0.28854626
0.3822015
1
1597
tags = 15%,










list = 7%,










signal = 16%


BERENJENO_TRANSFORMED_BY_RHOA_FOREVER_DN
28
0.35863847
1.1066042
0.30997878
0.38779154
1
871
tags = 11%,










list = 4%,










signal = 11%


ABE_VEGFA_TARGETS_30MIN
24
0.36268952
1.1101285
0.30232558
0.3884633
1
2516
tags = 21%,










list = 11%,










signal = 23%


WINZEN_DEGRADED_VIA_KHSRP
97
0.27290994
1.087263
0.2886836
0.4096403
1
1356
tags = 13%,










list = 6%,










signal = 14%


AMIT_EGF_RESPONSE_120_HELA
69
0.2867648
1.0679713
0.3255814
0.41886824
1
1356
tags = 12%,










list = 6%,










signal = 12%


RELA_DN.V1_UP
131
0.25483254
1.0682064
0.28078818
0.42561394
1
2292
tags = 13%,










list = 10%,










signal = 14%


FOSTER_TOLERANT_MACROPHAGE_DN
390
0.22484367
1.0693825
0.22762148
0.43101433
1
3518
tags = 17%,










list = 16%,










signal = 20%


KIM_WT1_TARGETS_12HR_UP
155
0.2512566
1.0699376
0.2912844
0.43737483
1
1861
tags = 14%,










list = 8%,










signal = 15%


DORN_ADENOVIRUS_INFECTION_12HR_DN
33
0.3322664
1.0522577
0.37938598
0.4438031
1
2299
tags = 15%,










list = 10%,










signal = 17%


AMIT_SERUM_RESPONSE_40_MCF10A
30
0.33023232
1.0180423
0.42152467
0.4886379
1
1356
tags = 13%,










list = 6%,










signal = 14%


DIRMEIER_LMP1_RESPONSE_EARLY
62
0.28488106
1.0283298
0.38863635
0.48976937
1
973
tags = 10%,










list = 4%,










signal = 10%


TSAI_RESPONSE_TO_IONIZING_RADIATION
142
0.24693018
1.0185373
0.40714285
0.4951767
1
3391
tags = 20%,










list = 15%,










signal = 24%


SARTIPY_BLUNTED_BY_INSULIN_RESISTANCE_UP
19
0.3673268
1.021166
0.43064183
0.49723047
1
5197
tags = 53%,










list = 23%,










signal = 69%


AMIT_DELAYED_EARLY_GENES
18
0.36878896
1.007622
0.4365256
0.5051059
1
4689
tags = 44%,










list = 21%,










signal = 56%


MODULE_444
17
0.36739218
0.99056363
0.47111112
0.5356595
1
2015
tags = 24%,










list = 9%,










signal = 26%


KIM_WT1_TARGETS_8HR_UP
160
0.22465761
0.95971507
0.5491991
0.59616786
1
1977
tags = 13%,










list = 9%,










signal = 14%


OSWALD_HEMATOPOIETIC_STEM_CELL_IN_COLLAGEN_GEL_UP
217
0.20762564
0.9188322
0.69873416
0.6804433
1
2593
tags = 12%,










list = 12%,










signal = 14%


ZHOU_TNF_SIGNALING_4HR
54
0.25867853
0.90186983
0.6475584
0.7084501
1
2950
tags = 13%,










list = 13%,










signal = 15%


YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_5
26
0.29847574
0.8895064
0.62826085
0.7239441
1
6098
tags = 50%,










list = 27%,










signal = 69%


CASORELLI_ACUTE_PROMYELOCYTIC_LEUKEMIA_UP
160
0.20001574
0.85706544
0.82422805
0.7685506
1
2726
tags = 15%,










list = 12%,










signal = 17%


NEMETH_INFLAMMATORY_RESPONSE_LPS_UP
83
0.22265787
0.8598357
0.7648352
0.7736369
1
2701
tags = 17%,










list = 12%,










signal = 19%


DORN_ADENOVIRUS_INFECTION_48HR_DN
39
0.24452195
0.8070011
0.79223746
0.8429193
1
2299
tags = 13%,










list = 10%,










signal = 14%


GESERICK_TERT_TARGETS_DN
20
0.25546053
0.73272026
0.829932
0.9210089
1
2726
tags = 15%,










list = 12%,










signal = 17%


ZHOU_TNF_SIGNALING_30MIN
52
0.2028513
0.7123619
0.9402299
0.9262648
1
2321
tags = 10%,










list = 10%,










signal = 11%
















TABLE 6S





GSEA for BAF180-wildtype, BRG1-null vs. BAF180-wildtype, BRG1-wildtype A704 cell lines on TNFA founder gene sets



















NAME
SIZE
ES
NES
NOM p-val





HINATA_NFKB_IMMU_INF
16
0.7956981
2.0275013
0


GRAHAM_CML_QUIESCENT_VS_CML_DIVIDING_UP
19
0.7392491
1.9318271
0


TIAN_TNF_SIGNALING_VIA_NFKB
28
0.60884565
1.7448359
0.001347709


LINDSTEDT_DENDRITIC_CELL_MATURATION_A
60
0.532906
1.751974
0


MAHAJAN_RESPONSE_TO_IL1A_UP
71
0.5248131
1.7701857
0


ALTEMEIER_RESPONSE_TO_LPS_WITH_MECHANICAL_VENTILATION
107
0.48787904
1.7344346
0


ZHANG_RESPONSE_TO_IKK_INHIBITOR_AND_TNF_UP
210
0.4503261
1.7148049
0


SEKI_INFLAMMATORY_RESPONSE_LPS_UP
73
0.48960188
1.6954869
0


SCHOEN_NFKB_SIGNALING
33
0.59241396
1.7797453
0.001270648


AMIT_EGF_RESPONSE_40_HELA
41
0.5300786
1.6392726
0.003880983


PHONG_TNF_TARGETS_UP
61
0.49051544
1.6150428
0.001175088


RASHI_NFKB1_TARGETS
18
0.6302222
1.619761
0.005449591


FERRARI_RESPONSE_TO_FENRETINIDE_UP
20
0.60883987
1.6258345
0.009370817


ZHOU_INFLAMMATORY_RESPONSE_LPS_UP
342
0.41023487
1.6067731
0


GRAHAM_CML_QUIESCENT_VS_NORMAL_DIVIDING_UP
50
0.5050564
1.5985647
0.01183432


ZHOU_INFLAMMATORY_RESPONSE_FIMA_UP
441
0.39674795
1.570265
0


DAZARD_UV_RESPONSE_CLUSTER_G28
18
0.5986828
1.5612339
0.014986376


ZWANG_CLASS_3_TRANSIENTLY_INDUCED_BY_EGF
208
0.4030676
1.5428655
0.002109705


ZHOU_INFLAMMATORY_RESPONSE_LIVE_UP
407
0.3903081
1.5362784
0


MODULE_178
15
0.5890012
1.4863334
0.04403409


HINATA_NFKB_TARGETS_KERATINOCYTE_UP
83
0.4311268
1.4915149
0.011534025


SUZUKI_RESPONSE_TO_TSA_AND_DECITABINE_1A
21
0.5281676
1.4615421
0.057534248


GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_TURQUOISE_UP
76
0.42781955
1.4670728
0.019813519


HINATA_NFKB_TARGETS_FIBROBLAST_UP
80
0.42352152
1.462203
0.022016222


BROWNE_HCMV_INFECTION_2HR_UP
37
0.46076813
1.453608
0.043533932


DAZARD_UV_RESPONSE_CLUSTER_G2
29
0.49761274
1.4343255
0.057544757


BILD_HRAS_ONCOGENIC_SIGNATURE
243
0.36789003
1.4173068
0.007216495


AMIT_EGF_RESPONSE_60_MCF10A
39
0.46236536
1.4214058
0.061868686


BURTON_ADIPOGENESIS_1
33
0.45634285
1.3591001
0.11485643


PHONG_TNF_RESPONSE_NOT_VIA_P38
331
0.34545162
1.3622378
0.005081301


MODULE_362
19
0.51584405
1.368848
0.11307902


BERENJENO_TRANSFORMED_BY_RHOA_FOREVER_DN
30
0.46400875
1.3645186
0.10263158


THEILGAARD_NEUTROPHIL_AT_SKIN_WOUND_UP
74
0.40106997
1.3705429
0.051008303


WINZEN_DEGRADED_VIA_KHSRP
97
0.38334823
1.3715631
0.043829296


ZUCCHI_METASTASIS_DN
40
0.44579694
1.3737904
0.07151665


TSAI_RESPONSE_TO_IONIZING_RADIATION
141
0.36060056
1.3297915
0.04972973


PHONG_TNF_RESPONSE_VIA_P38_PARTIAL
156
0.35237262
1.3252659
0.036324788


YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_5
26
0.46070793
1.312177
0.13009198


RASHI_RESPONSE_TO_IONIZING_RADIATION_2
120
0.3642182
1.3125203
0.057585824


BROCKE_APOPTOSIS_REVERSED_BY_IL6
137
0.35121885
1.3012718
0.050438598


FOSTER_TOLERANT_MACROPHAGE_DN
387
0.32867518
1.2934465
0.01510574


MATTIOLI_MGUS_VS_MULTIPLE_MYELOMA
16
0.50519097
1.2941579
0.1520548


WANG_TNF_TARGETS
21
0.46284714
1.278999
0.16331995


GHANDHI_DIRECT_IRRADIATION_UP
95
0.36340415
1.2851771
0.107102595


GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_BLACK_UP
33
0.4297364
1.2800468
0.14431673


OSWALD_HEMATOPOIETIC_STEM_CELL_IN_COLLAGEN_GEL_UP
218
0.32804552
1.2585387
0.045501553


NEWMAN_ERCC6_TARGETS_UP
25
0.44994745
1.2648051
0.17866324


AMIT_SERUM_RESPONSE_40_MCF10A
31
0.42799193
1.2603892
0.1658031


DORN_ADENOVIRUS_INFECTION_12HR_DN
33
0.42553425
1.2495928
0.17994858


MODULE_516
16
0.49408945
1.2413995
0.20144928


AMIT_SERUM_RESPONSE_60_MCF10A
56
0.38155204
1.2370442
0.16791044


CASORELLI_ACUTE_PROMYELOCYTIC_LEUKEMIA_UP
162
0.33160824
1.2416271
0.092750534


VILIMAS_NOTCH1_TARGETS_UP
41
0.3928831
1.2243944
0.19524406


GHANDHI_BYSTANDER_IRRADIATION_UP
72
0.3568436
1.2254226
0.14437869


KRIEG_HYPOXIA_VIA_KDM3A
51
0.38546434
1.2154907
0.19682151


DIRMEIER_LMP1_RESPONSE_EARLY
62
0.36244237
1.2077259
0.17562725


UZONYI_RESPONSE_TO_LEUKOTRIENE_AND_THROMBIN
36
0.39616778
1.1982558
0.21474774


BASSO_CD40_SIGNALING_UP
91
0.3448728
1.1989366
0.16912599


KIM_WT1_TARGETS_UP
208
0.31086197
1.1902792
0.12473795


DAUER_STAT3_TARGETS_UP
45
0.37520424
1.1873477
0.23241206


NEMETH_INFLAMMATORY_RESPONSE_LPS_UP
84
0.33773565
1.1683817
0.21658987


AMIT_EGF_RESPONSE_60_HELA
45
0.3688694
1.1710303
0.24368687


MCDOWELL_ACUTE_LUNG_INJURY_UP
39
0.37832105
1.1626438
0.24808185


GALINDO_IMMUNE_RESPONSE_TO_ENTEROTOXIN
80
0.3368297
1.15432
0.24473068


P53_DN.V2_UP
116
0.32193154
1.1497167
0.23281597


SESTO_RESPONSE_TO_UV_C3
20
0.42613018
1.1459695
0.31564626


HAHTOLA_MYCOSIS_FUNGOIDES_CD4_UP
59
0.33914083
1.1251909
0.2789598


LINDSTEDT_DENDRITIC_CELL_MATURATION_B
49
0.34704745
1.1105912
0.3131936


MODULE_444
17
0.4365697
1.1043755
0.33240998


TGFB_UP.V1_UP
169
0.2944326
1.0965124
0.2796158


PLASARI_TGFB1_TARGETS_10HR_UP
185
0.2863604
1.084853
0.29202586


AMIT_EGF_RESPONSE_120_HELA
68
0.3227459
1.0780765
0.35196194


JECHLINGER_EPITHELIAL_TO_MESENCHYMAL_TRANSITION_DN
64
0.3119976
1.0370693
0.41183433


ALK_DN.V1_UP
112
0.2847367
1.0313901
0.4108527


KIM_WT1_TARGETS_12HR_UP
155
0.27995437
1.0381294
0.39804772


RELA_DN.V1_UP
131
0.2814069
1.0236616
0.41202185


WATTEL_AUTONOMOUS_THYROID_ADENOMA_DN
47
0.31701338
1.0098777
0.46683046


DORN_ADENOVIRUS_INFECTION_24HR_DN
43
0.3237261
1.0126069
0.4390244


SCIAN_INVERSED_TARGETS_OF_TP53_AND_TP73_DN
29
0.35221815
1.0133808
0.46535948


AMIT_DELAYED_EARLY_GENES
18
0.37205786
0.98065007
0.5014045


GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_MAGENTA_UP
27
0.33355108
0.96410793
0.53754944


SARTIPY_BLUNTED_BY_INSULIN_RESISTANCE_UP
19
0.3614058
0.95636606
0.55617195


DORN_ADENOVIRUS_INFECTION_48HR_DN
39
0.30416617
0.94512963
0.56375


NAGASHIMA_NRG1_SIGNALING_UP
170
0.24747676
0.9379042
0.62955034


ADDYA_ERYTHROID_DIFFERENTIATON_BY_HEMIN
67
0.27544057
0.92320794
0.6049238


PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_DN
129
0.24788302
0.9115519
0.65832424


DORN_ADENOVIRUS_INFECTION_12HR_UP
28
0.29113752
0.8355935
0.7047619


ZHOU_TNF_SIGNALING_30MIN
51
0.24958822
0.80074
0.7856273


ZHOU_TNF_SIGNALING_4HR
54
0.24637176
0.7917333
0.78297365


ABE_VEGFA_TARGETS_30MIN
25
0.24672422
0.7019547
0.8776316


ABE_VEGFA_TARGETS
16
0.24924366
0.636744
0.9274756


RASHI_RESPONSE_TO_IONIZING_RADIATION_1
41
0.18592338
0.5865687
0.9736842



















RANK






FWER
AT
LEADING



NAME
FDR q-val
p-val
MAX
EDGE







HINATA_NFKB_IMMU_INF
9.48E−04
0.001
396
tags = 50%,







list = 2%,







signal = 51%



GRAHAM_CML_QUIESCENT_VS_CML_DIVIDING_UP
0.003449188
0.007
1742
tags = 47%,







list = 8%,







signal = 51%



TIAN_TNF_SIGNALING_VIA_NFKB
0.01672752
0.1
2312
tags = 43%,







list = 10%,







signal = 48%



LINDSTEDT_DENDRITIC_CELL_MATURATION_A
0.017720906
0.089
3084
tags = 35%,







list = 14%,







signal = 41%



MAHAJAN_RESPONSE_TO_IL1A_UP
0.017990522
0.074
4196
tags = 38%,







list = 19%,







signal = 47%



ALTEMEIER_RESPONSE_TO_LPS_WITH_MECHANICAL_VENTILATION
0.018068742
0.123
4346
tags = 42%,







list = 20%,







signal = 52%



ZHANG_RESPONSE_TO_IKK_INHIBITOR_AND_TNF_UP
0.018946424
0.148
4335
tags = 34%,







list = 20%,







signal = 42%



SEKI_INFLAMMATORY_RESPONSE_LPS_UP
0.020372774
0.177
1933
tags = 29%,







list = 9%,







signal = 31%



SCHOEN_NFKB_SIGNALING
0.020783762
0.064
2376
tags = 36%,







list = 11%,







signal = 41%



AMIT_EGF_RESPONSE_40_HELA
0.036635086
0.324
4095
tags = 41%,







list = 18%,







signal = 51%



PHONG_TNF_TARGETS_UP
0.03720449
0.401
4095
tags = 39%,







list = 18%,







signal = 48%



RASHI_NFKB1_TARGETS
0.037906855
0.383
4623
tags = 67%,







list = 21%,







signal = 84%



FERRARI_RESPONSE_TO_FENRETINIDE_UP
0.03825266
0.363
1332
tags = 25%,







list = 6%,







signal = 27%



ZHOU_INFLAMMATORY_RESPONSE_LPS_UP
0.039013453
0.442
3916
tags = 30%,







list = 18%,







signal = 35%



GRAHAM_CML_QUIESCENT_VS_NORMAL_DIVIDING_UP
0.039890602
0.474
4220
tags = 38%,







list = 19%,







signal = 47%



ZHOU_INFLAMMATORY_RESPONSE_FIMA_UP
0.050020583
0.571
4761
tags = 33%,







list = 21%,







signal = 42%



DAZARD_UV_RESPONSE_CLUSTER_G28
0.052049164
0.604
244
tags = 22%,







list = 1%,







signal = 22%



ZWANG_CLASS_3_TRANSIENTLY_INDUCED_BY_EGF
0.061013937
0.685
4404
tags = 32%,







list = 20%,







signal = 39%



ZHOU_INFLAMMATORY_RESPONSE_LIVE_UP
0.061709113
0.711
5270
tags = 34%,







list = 24%,







signal = 44%



MODULE_178
0.09094207
0.875
2349
tags = 33%,







list = 11%,







signal = 37%



HINATA_NFKB_TARGETS_KERATINOCYTE_UP
0.09156335
0.859
3619
tags = 31%,







list = 16%,







signal = 37%



SUZUKI_RESPONSE_TO_TSA_AND_DECITABINE_1A
0.10243626
0.932
5765
tags = 48%,







list = 26%,







signal = 64%



GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_TURQUOISE_UP
0.105884045
0.917
6481
tags = 54%,







list = 29%,







signal = 76%



HINATA_NFKB_TARGETS_FIBROBLAST_UP
0.10618031
0.932
3497
tags = 25%,







list = 16%,







signal = 30%



BROWNE_HCMV_INFECTION_2HR_UP
0.10621111
0.943
2312
tags = 24%,







list = 10%,







signal = 27%



DAZARD_UV_RESPONSE_CLUSTER_G2
0.12164423
0.968
430
tags = 17%,







list = 2%,







signal = 18%



BILD_HRAS_ONCOGENIC_SIGNATURE
0.13088778
0.98
3413
tags = 22%,







list = 15%,







signal = 26%



AMIT_EGF_RESPONSE_60_MCF10A
0.13170351
0.978
2243
tags = 26%,







list = 10%,







signal = 28%



BURTON_ADIPOGENESIS_1
0.1669525
0.999
6703
tags = 55%,







list = 30%,







signal = 78%



PHONG_TNF_RESPONSE_NOT_VIA_P38
0.16772734
0.999
3368
tags = 21%,







list = 15%,







signal = 25%



MODULE_362
0.16896467
0.998
2349
tags = 32%,







list = 11%,







signal = 35%



BERENJENO_TRANSFORMED_BY_RHOA_FOREVER_DN
0.1695761
0.999
3424
tags = 30%,







list = 15%,







signal = 35%



THEILGAARD_NEUTROPHIL_AT_SKIN_WOUND_UP
0.17244785
0.998
5202
tags = 32%,







list = 23%,







signal = 42%



WINZEN_DEGRADED_VIA_KHSRP
0.17665227
0.998
3916
tags = 32%,







list = 18%,







signal = 39%



ZUCCHI_METASTASIS_DN
0.17920427
0.998
3432
tags = 30%,







list = 15%,







signal = 35%



TSAI_RESPONSE_TO_IONIZING_RADIATION
0.20238431
1
5213
tags = 34%,







list = 23%,







signal = 44%



PHONG_TNF_RESPONSE_VIA_P38_PARTIAL
0.20380807
1
5078
tags = 33%,







list = 23%,







signal = 43%



YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_5
0.21185845
1
5068
tags = 38%,







list = 23%,







signal = 50%



RASHI_RESPONSE_TO_IONIZING_RADIATION_2
0.21702695
1
5049
tags = 33%,







list = 23%,







signal = 43%



BROCKE_APOPTOSIS_REVERSED_BY_IL6
0.22373448
1
5272
tags = 34%,







list = 24%,







signal = 45%



FOSTER_TOLERANT_MACROPHAGE_DN
0.22482397
1
6068
tags = 33%,







list = 27%,







signal = 45%



MATTIOLI_MGUS_VS_MULTIPLE_MYELOMA
0.22912467
1
2035
tags = 19%,







list = 9%,







signal = 21%



WANG_TNF_TARGETS
0.2309146
1
2928
tags = 29%,







list = 13%,







signal = 33%



GHANDHI_DIRECT_IRRADIATION_UP
0.23219457
1
3382
tags = 25%,







list = 15%,







signal = 30%



GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_BLACK_UP
0.234369
1
4877
tags = 39%,







list = 22%,







signal = 50%



OSWALD_HEMATOPOIETIC_STEM_CELL_IN_COLLAGEN_GEL_UP
0.24825019
1
4491
tags = 28%,







list = 20%,







signal = 35%



NEWMAN_ERCC6_TARGETS_UP
0.2489162
1
1827
tags = 24%,







list = 8%,







signal = 26%



AMIT_SERUM_RESPONSE_40_MCF10A
0.25068888
1
4118
tags = 32%,







list = 19%,







signal = 40%



DORN_ADENOVIRUS_INFECTION_12HR_DN
0.25761074
1
4877
tags = 30%,







list = 22%,







signal = 39%



MODULE_516
0.26132855
1
5680
tags = 56%,







list = 26%,







signal = 76%



AMIT_SERUM_RESPONSE_60_MCF10A
0.2633078
1
2151
tags = 21%,







list = 10%,







signal = 24%



CASORELLI_ACUTE_PROMYELOCYTIC_LEUKEMIA_UP
0.2660269
1
5835
tags = 35%,







list = 26%,







signal = 47%



VILIMAS_NOTCH1_TARGETS_UP
0.27467248
1
4323
tags = 37%,







list = 19%,







signal = 45%



GHANDHI_BYSTANDER_IRRADIATION_UP
0.27798596
1
2083
tags = 21%,







list = 9%,







signal = 23%



KRIEG_HYPOXIA_VIA_KDM3A
0.2853903
1
1078
tags = 14%,







list = 5%,







signal = 14%



DIRMEIER_LMP1_RESPONSE_EARLY
0.29365915
1
4323
tags = 32%,







list = 19%,







signal = 40%



UZONYI_RESPONSE_TO_LEUKOTRIENE_AND_THROMBIN
0.2995725
1
4213
tags = 33%,







list = 19%,







signal = 41%



BASSO_CD40_SIGNALING_UP
0.30355766
1
4491
tags = 35%,







list = 20%,







signal = 44%



KIM_WT1_TARGETS_UP
0.30792785
1
5077
tags = 33%,







list = 23%,







signal = 42%



DAUER_STAT3_TARGETS_UP
0.30873922
1
4095
tags = 33%,







list = 18%,







signal = 41%



NEMETH_INFLAMMATORY_RESPONSE_LPS_UP
0.3318771
1
4718
tags = 32%,







list = 21%,







signal = 41%



AMIT_EGF_RESPONSE_60_HELA
0.3323425
1
6174
tags = 49%,







list = 28%,







signal = 68%



MCDOWELL_ACUTE_LUNG_INJURY_UP
0.337064
1
4061
tags = 28%,







list = 18%,







signal = 34%



GALINDO_IMMUNE_RESPONSE_TO_ENTEROTOXIN
0.34732458
1
4491
tags = 31%,







list = 20%,







signal = 39%



P53_DN.V2_UP
0.3502771
1
2996
tags = 19%,







list = 13%,







signal = 22%



SESTO_RESPONSE_TO_UV_C3
0.35206577
1
920
tags = 15%,







list = 4%,







signal = 16%



HAHTOLA_MYCOSIS_FUNGOIDES_CD4_UP
0.38515478
1
1420
tags = 14%,







list = 6%,







signal = 14%



LINDSTEDT_DENDRITIC_CELL_MATURATION_B
0.40690055
1
4323
tags = 33%,







list = 19%,







signal = 40%



MODULE_444
0.41287807
1
2349
tags = 29%,







list = 11%,







signal = 33%



TGFB_UP.V1_UP
0.42247924
1
4831
tags = 31%,







list = 22%,







signal = 39%



PLASARI_TGFB1_TARGETS_10HR_UP
0.43959522
1
2841
tags = 18%,







list = 13%,







signal = 20%



AMIT_EGF_RESPONSE_120_HELA
0.44629344
1
4168
tags = 31%,







list = 19%,







signal = 38%



JECHLINGER_EPITHELIAL_TO_MESENCHYMAL_TRANSITION_DN
0.51697856
1
4095
tags = 33%,







list = 18%,







signal = 40%



ALK_DN.V1_UP
0.5219166
1
3021
tags = 18%,







list = 14%,







signal = 21%



KIM_WT1_TARGETS_12HR_UP
0.5219282
1
5311
tags = 30%,







list = 24%,







signal = 40%



RELA_DN.V1_UP
0.5316298
1
5093
tags = 30%,







list = 23%,







signal = 38%



WATTEL_AUTONOMOUS_THYROID_ADENOMA_DN
0.5386091
1
5245
tags = 43%,







list = 24%,







signal = 56%



DORN_ADENOVIRUS_INFECTION_24HR_DN
0.5396305
1
4877
tags = 33%,







list = 22%,







signal = 42%



SCIAN_INVERSED_TARGETS_OF_TP53_AND_TP73_DN
0.5451588
1
4323
tags = 24%,







list = 19%,







signal = 30%



AMIT_DELAYED_EARLY_GENES
0.58976126
1
4095
tags = 39%,







list = 18%,







signal = 48%



GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_MAGENTA_UP
0.6154671
1
5718
tags = 33%,







list = 26%,







signal = 45%



SARTIPY_BLUNTED_BY_INSULIN_RESISTANCE_UP
0.62259376
1
4266
tags = 37%,







list = 19%,







signal = 46%



DORN_ADENOVIRUS_INFECTION_48HR_DN
0.63736963
1
5203
tags = 31%,







list = 23%,







signal = 40%



NAGASHIMA_NRG1_SIGNALING_UP
0.6432155
1
4150
tags = 25%,







list = 19%,







signal = 31%



ADDYA_ERYTHROID_DIFFERENTIATON_BY_HEMIN
0.6627822
1
4729
tags = 21%,







list = 21%,







signal = 26%



PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_DN
0.67606705
1
7172
tags = 36%,







list = 32%,







signal = 53%



DORN_ADENOVIRUS_INFECTION_12HR_UP
0.7986275
1
5221
tags = 36%,







list = 24%,







signal = 47%



ZHOU_TNF_SIGNALING_30MIN
0.84232277
1
3128
tags = 14%,







list = 14%,







signal = 16%



ZHOU_TNF_SIGNALING_4HR
0.84545606
1
3277
tags = 13%,







list = 15%,







signal = 15%



ABE_VEGFA_TARGETS_30MIN
0.9370464
1
9755
tags = 56%,







list = 44%,







signal = 100%



ABE_VEGFA_TARGETS
0.97078294
1
8862
tags = 44%,







list = 40%,







signal = 73%



RASHI_RESPONSE_TO_IONIZING_RADIATION_1
0.9792933
1
9321
tags = 46%,







list = 42%,







signal = 80%

















TABLE 6T





GSEA for BAF180-null vs. BAF180-wildtype A704 cell lines on IL2 Founder gene sets



















NAME
SIZE
ES
NES
NOM p-val





WIERENGA_STAT5A_TARGETS_UP
198
0.37538955
1.6359013
0


WIERENGA_STAT5A_TARGETS_GROUP1
124
0.38390127
1.5493696
0.005


MARZEC_IL2_SIGNALING_UP
105
0.34808874
1.39865
0.039906103


WIERENGA_STAT5A_TARGETS_GROUP2
52
0.40397617
1.404526
0.06026786


IL21_UP.V1_UP
153
0.2727604
1.1800731
0.13333334


IL2_UP.V1_UP
167
0.26439285
1.1303729
0.19518073


IL15_UP.V1_UP
167
0.2663421
1.1419721
0.17298578


ZHENG_FOXP3_TARGETS_UP
23
0.40840292
1.1883539
0.23413567


BOSCO_ALLERGEN_INDUCED_TH2_ASSOCIATED_MODULE
128
0.24078815
0.99573
0.45454547

















RANK





FWER
AT
LEADING


NAME
FDR q-val
p-val
MAX
EDGE





WIERENGA_STAT5A_TARGETS_UP
0.025693554
0.018
2815
tags = 25%,






list = 13%,






signal = 29%


WIERENGA_STAT5A_TARGETS_GROUP1
0.026251249
0.034
2283
tags = 22%,






list = 10%,






signal = 24%


MARZEC_IL2_SIGNALING_UP
0.067707956
0.159
3079
tags = 23%,






list = 14%,






signal = 26%


WIERENGA_STAT5A_TARGETS_GROUP2
0.08636623
0.152
3343
tags = 35%,






list = 15%,






signal = 41%


IL21_UP.V1_UP
0.23306713
0.594
2622
tags = 19%,






list = 12%,






signal = 21%


IL2_UP.V1_UP
0.23517144
0.699
2883
tags = 21%,






list = 13%,






signal = 24%


IL15_UP.V1_UP
0.25491062
0.678
2955
tags = 20%,






list = 13%,






signal = 23%


ZHENG_FOXP3_TARGETS_UP
0.2645548
0.573
4871
tags = 43%,






list = 22%,






signal = 56%


BOSCO_ALLERGEN_INDUCED_TH2_ASSOCIATED_MODULE
0.4475559
0.927
2516
tags = 15%,






list = 11%,






signal = 17%
















TABLE 6U





GSEA for BAF180-wildtype, BRG1-null vs. BAF180-wildtype,


BRG1-wildtype A704 cell lines on IL2 founder gene sets



















NAME
SIZE
ES
NES
NOM p-val





WIERENGA_STAT5A_TARGETS_GROUP1
126
0.49578398
1.8142022
0


WIERENGA_STAT5A_TARGETS_UP
200
0.4889695
1.8709142
0


ZHENG_FOXP3_TARGETS_UP
23
0.60290974
1.654486
0.006868132


WIERENGA_STAT5A_TARGETS_GROUP2
52
0.4778286
1.5371454
0.023866348


MARZEC_IL2_SIGNALING_UP
105
0.3958873
1.4140925
0.030905077


LU_IL4_SIGNALING
85
0.3367652
1.17004
0.19861431


IL2_UP.V1_UP
162
0.31537378
1.1796162
0.16173361


GAVIN_FOXP3_TARGETS_CLUSTER_P4
93
0.31758162
1.1196293
0.28128588


BOSCO_ALLERGEN_INDUCED_TH2_ASSOCIATED_MODULE
128
0.291105
1.0583609
0.3576087


GAVIN_FOXP3_TARGETS_CLUSTER_P7
83
0.31101844
1.0731124
0.35606936


IL15_UP.V1_UP
164
0.27082396
1.0106155
0.46276596


IL21_UP.V1_UP
151
0.2229732
0.8263667
0.81702125


GAVIN_FOXP3_TARGETS_CLUSTER_T4
89
0.24680245
0.85941374
0.73505276

















RANK





FWER
AT
LEADING


NAME
FDR q-val
p-val
MAX
EDGE





WIERENGA_STAT5A_TARGETS_GROUP1
5.42E−04
0.001
5171
tags = 43%,






list = 23%,






signal = 56%


WIERENGA_STAT5A_TARGETS_UP
0.001083333
0.001
4799
tags = 41%,






list = 22%,






signal = 51%


ZHENG_FOXP3_TARGETS_UP
0.0082798
0.021
1681
tags = 30%,






list = 8%,






signal = 33%


WIERENGA_STAT5A_TARGETS_GROUP2
0.025304792
0.088
4799
tags = 42%,






list = 22%,






signal = 54%


MARZEC_IL2_SIGNALING_UP
0.06855733
0.263
5049
tags = 30%,






list = 23%,






signal = 39%


LU_IL4_SIGNALING
0.3729165
0.926
4254
tags = 27%,






list = 19%,






signal = 33%


IL2_UP.V1_UP
0.4066424
0.908
4694
tags = 30%,






list = 21%,






signal = 38%


GAVIN_FOXP3_TARGETS_CLUSTER_P4
0.4438688
0.971
2660
tags = 16%,






list = 12%,






signal = 18%


BOSCO_ALLERGEN_INDUCED_TH2_ASSOCIATED_MODULE
0.48443615
0.995
7046
tags = 38%,






list = 32%,






signal = 55%


GAVIN_FOXP3_TARGETS_CLUSTER_P7
0.5027296
0.993
4197
tags = 28%,






list = 19%,






signal = 34%


IL15_UP.V1_UP
0.5466651
1
4694
tags = 27%,






list = 21%,






signal = 35%


IL21_UP.V1_UP
0.7926685
1
4702
tags = 23%,






list = 21%,






signal = 29%


GAVIN_FOXP3_TARGETS_CLUSTER_T4
0.8019424
1
6570
tags = 36%,






list = 30%,






signal = 51%
















TABLE 6V





Enriched GO terms for KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION


genes in BAF180-mutant GSEA enriched vs. BAF180-mutant GSEA depleted

























Enriched Gene










Results




GO biological

Homo sapiens -




upload_1


Enriched

process
REFLIST
upload_1
upload_1
upload_1
(fold
upload_1


genes
Depleted genes
complete
(20972)
(56)
(expected)
(over/under)
Enrichment)
(P-value)





IL8
CXCR6


CXCL1
IL18
cell surface receptor signaling
2211
50
5.9
+
8.47
1.94E−38




pathway (GO:0007166)


PDGFRB
MET
cytokine-mediated signaling
466
34
1.24
+
27.32
6.70E−38




pathway (GO:0019221)


TNFRSF13C
IL22RA1
cellular response to cytokine
632
36
1.69
+
21.33
6.27E−37




stimulus (GO:0071345)


IL11
HGF
response to cytokine
727
36
1.94
+
18.54
8.86E−35




(GO:0034097)


CCR6
FLT4
positive regulation of response
2028
45
5.42
+
8.31
9.05E−32




to stimulus (GO:0048584)


CCL28
KDR
cellular response to organic
1915
44
5.11
+
8.6
2.74E−31




substance (GO:0071310)


TNFSF10
ACVRL1
cellular response to chemical
2347
45
6.27
+
7.18
5.40E−29




stimulus (GO:0070887)


CX3CL1
IL20RA
response to organic substance
2557
45
6.83
+
6.59
2.26E−27




(GO:0010033)


IL6
MPL
positive regulation of response
266
23
0.71
+
32.38
4.11E−25




to external stimulus




(GO:0032103)


CSF1
LTB
signal transduction
4867
52
13
+
4
1.10E−24




(GO:0007165)


CTF1
IL6ST
chemokine-mediated signaling
73
17
0.19
+
87.21
1.14E−24




pathway (GO:0070098)


CD70
TNFRSF11B
regulation of leukocyte
156
20
0.42
+
48.01
1.34E−24




migration (GO:0002685)


TNFSF14
FIGF
single organism signaling
5262
53
14.05
+
3.77
1.45E−24




(GO:0044700)


CCL5
IL2RB
signaling (GO:0023052)
5266
53
14.06
+
3.77
1.51E−24


CCL19
IL1B
cell communication
5351
53
14.29
+
3.71
3.48E−24




(GO:0007154)


CCL2
EDA
positive regulation of
109
18
0.29
+
61.84
1.11E−23




leukocyte migration




(GO:0002687)


CCL20
IL7R
response to chemical
3875
48
10.35
+
4.64
1.48E−23




(GO:0042221)


TNFRSF10D

regulation of response to
3696
47
9.87
+
4.76
4.17E−23




stimulus (GO:0048583)


TNFRSF1B

cellular response to stimulus
6168
54
16.47
+
3.28
1.27E−22




(GO:0051716)


CX3CR1

positive regulation of protein
895
30
2.39
+
12.55
1.47E−22




phosphorylation




(GO:0001934)


TNFSF9

regulation of response to
712
28
1.9
+
14.73
1.80E−22




external stimulus




(GO:0032101)


CSF2

regulation of cell proliferation
1538
35
4.11
+
8.52
4.52E−22




(GO:0042127)


TNFRSF11A

positive regulation of
937
30
2.5
+
11.99
5.51E−22




phosphorylation




(GO:0042327)


IL21R

positive regulation of
1056
31
2.82
+
10.99
7.59E−22




phosphorus metabolic process




(GO:0010562)


IL17RB

positive regulation of
1056
31
2.82
+
10.99
7.59E−22




phosphate metabolic process




(GO:0045937)


CXCL10

chemotaxis (GO:0006935)
508
25
1.36
+
18.43
8.89E−22


OSMR

taxis (GO:0042330)
509
25
1.36
+
18.39
9.32E−22


INHBE

immune system process
2465
40
6.58
+
6.08
3.13E−21




(GO:0002376)


GDF5

regulation of leukocyte
99
16
0.26
+
60.53
1.74E−20




chemotaxis (GO:0002688)


VEGFB

positive regulation of signal
1433
33
3.83
+
8.62
1.87E−20




transduction (GO:0009967)


CXCL3

immune response
1591
34
4.25
+
8
2.72E−20




(GO:0006955)


CXCL2

cell chemotaxis (GO:0060326)
176
18
0.47
+
38.3
5.48E−20


CCR10

positive regulation of
81
15
0.22
+
69.35
7.30E−20




leukocyte chemotaxis




(GO:0002690)


IL24

regulation of chemotaxis
185
18
0.49
+
36.44
1.32E−19




(GO:0050920)


TNFRSF19

positive regulation of
911
28
2.43
+
11.51
1.37E−19




intracellular signal




transduction (GO:1902533)


CXCL12

positive regulation of protein
1150
30
3.07
+
9.77
1.96E−19




modification process




(GO:0031401)


OSM

positive regulation of cell
1552
33
4.14
+
7.96
2.27E−19




communication (GO:0010647)


EGF

positive regulation of signaling
1560
33
4.17
+
7.92
2.67E−19




(GO:0023056)


VEGFC

positive regulation of
120
16
0.32
+
49.93
3.64E−19




chemotaxis (GO:0050921)


TGFB3

regulation of protein
1302
31
3.48
+
8.92
3.71E−19




phosphorylation




(GO:0001932)


TNFSF13

response to stimulus
7800
55
20.83
+
2.64
6.92E−19




(GO:0050896)


KITLG

positive regulation of
5270
49
14.07
+
3.48
1.07E−18




biological process




(GO:0048518)


LIFR

positive regulation of cellular
4716
47
12.59
+
3.73
2.31E−18




process (GO:0048522)


LIF

positive regulation of protein
1527
32
4.08
+
7.85
2.40E−18




metabolic process




(GO:0051247)


IL20

regulation of locomotion
804
26
2.15
+
12.11
2.64E−18




(GO:0040012)


IL13

regulation of phosphorylation
1398
31
3.73
+
8.3
2.99E−18




(GO:0042325)


BMP7

inflammatory response
462
22
1.23
+
17.83
3.00E−18




(GO:0006954)


IL1R1

regulation of intracellular
1725
33
4.61
+
7.16
6.09E−18




signal transduction




(GO:1902531)


AMHR2

positive regulation of cellular
1433
31
3.83
+
8.1
6.17E−18




protein metabolic process




(GO:0032270)


VEGFA

regulation of cell motility
741
25
1.98
+
12.63
7.94E−18




(GO:2000145)


CXCR4

positive regulation of cell
847
26
2.26
+
11.5
9.61E−18




proliferation (GO:0008284)


INHBB

regulation of cell migration
691
24
1.85
+
13.01
3.43E−17




(GO:0030334)




regulation of cellular
806
25
2.15
+
11.62
5.90E−17




component movement




(GO:0051270)




positive regulation of
1425
30
3.81
+
7.88
8.60E−17




multicellular organismal




process (GO:0051240)




response to external stimulus
1766
32
4.72
+
6.79
1.89E−16




(GO:0009605)




regulation of phosphate
1621
31
4.33
+
7.16
2.23E−16




metabolic process




(GO:0019220)




regulation of phosphorus
1634
31
4.36
+
7.1
2.81E−16




metabolic process




(GO:0051174)




regulation of MAPK cascade
679
23
1.81
+
12.69
5.00E−16




(GO:0043408)




regulation of protein
1675
31
4.47
+
6.93
5.76E−16




modification process




(GO:0031399)




regulation of signal
2752
37
7.35
+
5.04
6.05E−16




transduction (GO:0009966)




regulation of cell
3051
38
8.15
+
4.66
1.71E−15




communication (GO:0010646)




locomotion (GO:0040011)
1188
27
3.17
+
8.51
2.58E−15




regulation of signaling
3102
38
8.28
+
4.59
3.06E−15




(GO:0023051)




positive regulation of immune
954
25
2.55
+
9.81
3.21E−15




system process (GO:0002684)




positive regulation of peptidyl-
167
15
0.45
+
33.64
3.22E−15




tyrosine phosphorylation




(GO:0050731)




positive regulation of cell
400
19
1.07
+
17.79
3.77E−15




migration (GO:0030335)




positive regulation of cell
414
19
1.11
+
17.19
7.08E−15




motility (GO:2000147)




cellular response to tumor
226
16
0.6
+
26.51
7.52E−15




necrosis factor (GO:0071356)




positive regulation of cellular
425
19
1.13
+
16.74
1.14E−14




component movement




(GO:0051272)




defense response
1147
26
3.06
+
8.49
1.65E−14




(GO:0006952)




positive regulation of
442
19
1.18
+
16.1
2.34E−14




locomotion (GO:0040017)




response to tumor necrosis
247
16
0.66
+
24.26
3.00E−14




factor (GO:0034612)




regulation of localization
2485
34
6.64
+
5.12
3.83E−14




(GO:0032879)




regulation of cellular process
10399
56
27.77
+
2.02
7.14E−14




(GO:0050794)




regulation of protein metabolic
2542
34
6.79
+
5.01
7.76E−14




process (GO:0051246)




regulation of cellular protein
2363
33
6.31
+
5.23
9.32E−14




metabolic process




(GO:0032268)




leukocyte chemotaxis
124
13
0.33
+
39.26
1.32E−13




(GO:0030595)




positive regulation of MAPK
487
19
1.3
+
14.61
1.37E−13




cascade (GO:0043410)




regulation of immune system
1392
27
3.72
+
7.26
1.39E−13




process (GO:0002682)




regulation of peptidyl-tyrosine
221
15
0.59
+
25.42
1.95E−13




phosphorylation




(GO:0050730)




response to lipopolysaccharide
294
16
0.79
+
20.38
4.47E−13




(GO:0032496)




positive regulation of
1179
25
3.15
+
7.94
4.57E−13




developmental process




(GO:0051094)




response to molecule of
308
16
0.82
+
19.45
9.18E−13




bacterial origin (GO:0002237)




regulation of biological
10941
56
29.21
+
1.92
1.23E−12




process (GO:0050789)




positive regulation of
2841
34
7.59
+
4.48
2.40E−12




macromolecule metabolic




process (GO:0010604)




positive regulation of cellular
2845
34
7.6
+
4.48
2.51E−12




metabolic process




(GO:0031325)




positive regulation of tyrosine
60
10
0.16
+
62.42
9.52E−12




phosphorylation of STAT




protein (GO:0042531)




response to stress
3214
35
8.58
+
4.08
1.16E−11




(GO:0006950)




positive regulation of
3033
34
8.1
+
4.2
1.77E−11




metabolic process




(GO:0009893)




cell migration (GO:0016477)
862
21
2.3
+
9.12
2.12E−11




biological regulation
11582
56
30.93
+
1.81
2.98E−11




(GO:0065007)




regulation of tyrosine
68
10
0.18
+
55.07
3.28E−11




phosphorylation of STAT




protein (GO:0042509)




positive regulation of STAT
75
10
0.2
+
49.93
8.61E−11




cascade (GO:1904894)




positive regulation of JAK-
75
10
0.2
+
49.93
8.61E−11




STAT cascade (GO:0046427)




response to lipid
817
20
2.18
+
9.17
1.07E−10




(GO:0033993)




localization of cell
950
21
2.54
+
8.28
1.41E−10




(GO:0051674)




cell motility (GO:0048870)
950
21
2.54
+
8.28
1.41E−10




regulation of multicellular
2656
31
7.09
+
4.37
2.64E−10




organismal process




(GO:0051239)




regulation of apoptotic process
1410
24
3.77
+
6.37
3.09E−10




(GO:0042981)




response to other organism
753
19
2.01
+
9.45
3.41E−10




(GO:0051707)




response to external biotic
754
19
2.01
+
9.44
3.50E−10




stimulus (GO:0043207)




regulation of programmed cell
1421
24
3.79
+
6.33
3.66E−10




death (GO:0043067)




response to biotic stimulus
786
19
2.1
+
9.05
7.28E−10




(GO:0009607)




myeloid leukocyte migration
99
10
0.26
+
37.83
1.32E−09




(GO:0097529)




negative regulation of
818
19
2.18
+
8.7
1.47E−09




apoptotic process




(GO:0043066)




regulation of cell death
1522
24
4.06
+
5.91
1.62E−09




(GO:0010941)




response to bacterium
507
16
1.35
+
11.82
1.86E−09




(GO:0009617)




negative regulation of
829
19
2.21
+
8.58
1.86E−09




programmed cell death




(GO:0043069)




response to oxygen-containing
1388
23
3.71
+
6.21
2.26E−09




compound (GO:1901700)




leukocyte migration
351
14
0.94
+
14.94
3.33E−09




(GO:0050900)




movement of cell or
1442
23
3.85
+
5.97
4.99E−09




subcellular component




(GO:0006928)




negative regulation of cell
902
19
2.41
+
7.89
8.14E−09




death (GO:0060548)




regulation of inflammatory
306
13
0.82
+
15.91
1.17E−08




response (GO:0050727)




positive regulation of cell
387
14
1.03
+
13.55
1.22E−08




adhesion (GO:0045785)




regulation of developmental
2263
27
6.04
+
4.47
1.97E−08




process (GO:0050793)




positive regulation of cell
844
18
2.25
+
7.99
3.06E−08




differentiation (GO:0045597)




cellular response to
138
10
0.37
+
27.14
3.37E−08




lipopolysaccharide




(GO:0071222)




cellular response to molecule
144
10
0.38
+
26.01
5.10E−08




of bacterial origin




(GO:0071219)




regulation of STAT cascade
147
10
0.39
+
25.48
6.23E−08




(GO:1904892)




regulation of JAK-STAT
147
10
0.39
+
25.48
6.23E−08




cascade (GO:0046425)




positive regulation of tyrosine
37
7
0.1
+
70.85
9.39E−08




phosphorylation of Stat3




protein (GO:0042517)




regulation of lymphocyte
38
7
0.1
+
68.99
1.13E−07




migration (GO:2000401)




regulation of granulocyte
39
7
0.1
+
67.22
1.35E−07




chemotaxis (GO:0071622)




single-organism process
12622
55
33.7
+
1.63
1.40E−07




(GO:0044699)




monocyte chemotaxis
40
7
0.11
+
65.54
1.61E−07




(GO:0002548)




response to interleukin-1
111
9
0.3
+
30.36
1.62E−07




(GO:0070555)




regulation of multicellular
1714
23
4.58
+
5.03
1.71E−07




organismal development




(GO:2000026)




cellular response to biotic
164
10
0.44
+
22.84
1.80E−07




stimulus (GO:0071216)




positive regulation of
115
9
0.31
+
29.31
2.21E−07




inflammatory response




(GO:0050729)




mononuclear cell migration
42
7
0.11
+
62.42
2.26E−07




(GO:0071674)




regulation of tyrosine
44
7
0.12
+
59.58
3.11E−07




phosphorylation of Stat3




protein (GO:0042516)




tumor necrosis factor-mediated
121
9
0.32
+
27.86
3.45E−07




signaling pathway




(GO:0033209)




positive regulation of ERK1
178
10
0.48
+
21.04
3.97E−07




and ERK2 cascade




(GO:0070374)




regulation of ERK1 and ERK2
248
11
0.66
+
16.61
4.73E−07




cascade (GO:0070372)




cellular response to
85
8
0.23
+
35.25
7.14E−07




interleukin-1 (GO:0071347)




positive regulation of
25
6
0.07
+
89.88
7.26E−07




neutrophil migration




(GO:1902624)




regulation of T cell migration
25
6
0.07
+
89.88
7.26E−07




(GO:2000404)




regulation of cell adhesion
643
15
1.72
+
8.74
8.04E−07




(GO:0030155)




enzyme linked receptor protein
706
15
1.89
+
7.96
2.90E−06




signaling pathway




(GO:0007167)




regulation of neutrophil
32
6
0.09
+
70.22
3.15E−06




migration (GO:1902622)




induction of positive
15
5
0.04
+
>100
5.69E−06




chemotaxis (GO:0050930)




regulation of response to stress
1328
19
3.55
+
5.36
6.00E−06




(GO:0080134)




granulocyte chemotaxis
69
7
0.18
+
37.99
6.90E−06




(GO:0071621)




multicellular organism
4733
34
12.64
+
2.69
8.06E−06




development (GO:0007275)




G-protein coupled receptor
1192
18
3.18
+
5.66
8.20E−06




signaling pathway




(GO:0007186)




anatomical structure
5059
35
13.51
+
2.59
9.96E−06




development (GO:0048856)




positive regulation of transport
915
16
2.44
+
6.55
1.11E−05




(GO:0051050)




granulocyte migration
75
7
0.2
+
34.95
1.22E−05




(GO:0097530)




regulation of secretion
681
14
1.82
+
7.7
1.90E−05




(GO:0051046)




regulation of anatomical
960
16
2.56
+
6.24
2.19E−05




structure morphogenesis




(GO:0022603)




cellular response to growth
459
12
1.23
+
9.79
2.26E−05




factor stimulus (GO:0071363)




positive regulation of T cell
20
5
0.05
+
93.62
2.37E−05




migration (GO:2000406)




positive regulation of
138
8
0.37
+
21.71
3.09E−05




leukocyte proliferation




(GO:0070665)




system development
4138
31
11.05
+
2.81
3.27E−05




(GO:0048731)




positive regulation of
22
5
0.06
+
85.11
3.81E−05




neutrophil chemotaxis




(GO:0090023)




single-organism
5316
35
14.19
+
2.47
4.07E−05




developmental process




(GO:0044767)




response to growth factor
486
12
1.3
+
9.25
4.26E−05




(GO:0070848)




cell activation (GO:0001775)
1017
16
2.72
+
5.89
4.96E−05




leukocyte activation
871
15
2.33
+
6.45
4.96E−05




(GO:0045321)




animal organ development
2958
26
7.9
+
3.29
5.31E−05




(GO:0048513)




lymphocyte migration
52
6
0.14
+
43.21
5.57E−05




(GO:0072676)




developmental process
5402
35
14.42
+
2.43
6.39E−05




(GO:0032502)




negative regulation of cellular
4252
31
11.35
+
2.73
6.46E−05




process (GO:0048523)




regulation of cell
1537
19
4.1
+
4.63
6.64E−05




differentiation (GO:0045595)




positive regulation of
25
5
0.07
+
74.9
7.17E−05




granulocyte chemotaxis




(GO:0071624)




positive regulation of
25
5
0.07
+
74.9
7.17E−05




lymphocyte migration




(GO:2000403)




positive regulation of positive
25
5
0.07
+
74.9
7.17E−05




chemotaxis (GO:0050927)




regulation of positive
26
5
0.07
+
72.02
8.71E−05




chemotaxis (GO:0050926)




negative regulation of
4593
32
12.26
+
2.61
9.30E−05




biological process




(GO:0048519)




regulation of neutrophil
27
5
0.07
+
69.35
1.05E−04




chemotaxis (GO:0090022)




regulation of primary
5810
36
15.51
+
2.32
1.05E−04




metabolic process




(GO:0080090)




positive regulation of cell
320
10
0.85
+
11.7
1.05E−04




activation (GO:0050867)




regulation of macromolecule
5812
36
15.52
+
2.32
1.06E−04




metabolic process




(GO:0060255)




regulation of cellular
5844
36
15.6
+
2.31
1.24E−04




metabolic process




(GO:0031323)




regulation of transport
1792
20
4.79
+
4.18
1.32E−04




(GO:0051049)




regulation of vasculature
243
9
0.65
+
13.87
1.43E−04




development (GO:1901342)




neutrophil chemotaxis
62
6
0.17
+
36.24
1.57E−04




(GO:0030593)




positive regulation of nervous
454
11
1.21
+
9.07
2.43E−04




system development




(GO:0051962)




neutrophil migration
67
6
0.18
+
33.54
2.47E−04




(GO:1990266)




regulation of defense response
702
13
1.87
+
6.94
2.65E−04




(GO:0031347)




cellular response to lipid
458
11
1.22
+
8.99
2.66E−04




(GO:0071396)




single-organism cellular
9804
46
26.18
+
1.76
4.22E−04




process (GO:0044763)




negative regulation of cell
1193
16
3.19
+
5.02
4.59E−04




communication (GO:0010648)




regulation of protein kinase
737
13
1.97
+
6.61
4.67E−04




activity (GO:0045859)




negative regulation of
1197
16
3.2
+
5.01
4.81E−04




signaling (GO:0023057)




positive chemotaxis
37
5
0.1
+
50.61
4.97E−04




(GO:0050918)




single-multicellular organism
5509
34
14.71
+
2.31
5.03E−04




process (GO:0044707)




positive regulation of
131
7
0.35
+
20.01
5.40E−04




leukocyte differentiation




(GO:1902107)




positive regulation of
286
9
0.76
+
11.78
5.68E−04




lymphocyte activation




(GO:0051251)




regulation of metabolic
6167
36
16.47
+
2.19
5.71E−04




process (GO:0019222)




positive regulation of
133
7
0.36
+
19.71
5.98E−04




mononuclear cell proliferation




(GO:0032946)




regulation of cell-cell adhesion
390
10
1.04
+
9.6
6.61E−04




(GO:0022407)




regulation of leukocyte
206
8
0.55
+
14.54
6.64E−04




proliferation (GO:0070663)




regulation of secretion by cell
627
12
1.67
+
7.17
6.85E−04




(GO:1903530)




positive regulation of
392
10
1.05
+
9.55
6.93E−04




neurogenesis (GO:0050769)




response to abiotic stimulus
1073
15
2.87
+
5.24
7.73E−04




(GO:0009628)




cellular process (GO:0009987)
14854
55
39.66
+
1.39
8.07E−04




positive regulation of MAP
218
8
0.58
+
13.74
1.02E−03




kinase activity (GO:0043406)




regulation of angiogenesis
219
8
0.58
+
13.68
1.05E−03




(GO:0045765)




positive regulation of
311
9
0.83
+
10.84
1.15E−03




leukocyte activation




(GO:0002696)




regulation of kinase activity
798
13
2.13
+
6.1
1.16E−03




(GO:0043549)




T cell migration
17
4
0.05
+
88.12
1.26E−03




(GO:0072678)




regulation of leukocyte cell-
317
9
0.85
+
10.63
1.35E−03




cell adhesion (GO:1903037)




oncostatin-M-mediated
4
3
0.01
+
>100
1.57E−03




signaling pathway




(GO:0038165)




leukemia inhibitory factor
4
3
0.01
+
>100
1.57E−03




signaling pathway




(GO:0048861)




dendritic cell chemotaxis
18
4
0.05
+
83.22
1.58E−03




(GO:0002407)




positive regulation of pathway-
48
5
0.13
+
39.01
1.79E−03




restricted SMAD protein




phosphorylation




(GO:0010862)




regulation of MAP kinase
329
9
0.88
+
10.24
1.84E−03




activity (GO:0043405)




regulation of lymphocyte
19
4
0.05
+
78.84
1.95E−03




chemotaxis (GO:1901623)




regulation of calcium ion
99
6
0.26
+
22.7
2.41E−03




import (GO:0090279)




positive regulation of
164
7
0.44
+
15.98
2.43E−03




hemopoiesis (GO:1903708)




regulation of biological quality
3560
26
9.51
+
2.74
2.50E−03




(GO:0065008)




regulation of cytokine
575
11
1.54
+
7.16
2.57E−03




production (GO:0001817)




positive regulation of calcium
52
5
0.14
+
36.01
2.64E−03




ion import (GO:0090280)




positive regulation of cell
454
10
1.21
+
8.25
2.66E−03




development (GO:0010720)




positive regulation of cell-cell
248
8
0.66
+
12.08
2.69E−03




adhesion (GO:0022409)




positive regulation of calcium
101
6
0.27
+
22.25
2.70E−03




ion transport (GO:0051928)




positive regulation of secretion
353
9
0.94
+
9.55
3.30E−03




(GO:0051047)




regulation of catalytic activity
2399
21
6.41
+
3.28
3.32E−03




(GO:0050790)




negative regulation of response
1385
16
3.7
+
4.33
3.48E−03




to stimulus (GO:0048585)




positive regulation of protein
472
10
1.26
+
7.93
3.78E−03




kinase activity (GO:0045860)




dendritic cell migration
23
4
0.06
+
65.13
4.16E−03




(GO:0036336)




positive regulation of
1605
17
4.29
+
3.97
4.64E−03




macromolecule biosynthetic




process (GO:0010557)




positive regulation of cytokine
370
9
0.99
+
9.11
4.87E−03




production (GO:0001819)




positive regulation of mast cell
6
3
0.02
+
>100
5.27E−03




chemotaxis (GO:0060754)




intracellular signal
1628
17
4.35
+
3.91
5.66E−03




transduction (GO:0035556)




SMAD protein signal
61
5
0.16
+
30.7
5.77E−03




transduction (GO:0060395)




regulation of pathway-
61
5
0.16
+
30.7
5.77E−03




restricted SMAD protein




phosphorylation




(GO:0060393)




developmental process
625
11
1.67
+
6.59
5.82E−03




involved in reproduction




(GO:0003006)




regulation of cell activation
506
10
1.35
+
7.4
7.07E−03




(GO:0050865)




positive regulation of kinase
509
10
1.36
+
7.36
7.46E−03




activity (GO:0033674)




positive regulation of acute
27
4
0.07
+
55.48
7.85E−03




inflammatory response




(GO:0002675)




positive regulation of defense
393
9
1.05
+
8.58
7.99E−03




response (GO:0031349)




regulation of mast cell
7
3
0.02
+
>100
8.35E−03




chemotaxis (GO:0060753)




regulation of mononuclear cell
199
7
0.53
+
13.17
8.77E−03




proliferation (GO:0032944)




angiogenesis (GO:0001525)
292
8
0.78
+
10.26
9.07E−03




negative regulation of
67
5
0.18
+
27.95
9.11E−03




hormone secretion




(GO:0046888)




positive regulation of
125
6
0.33
+
17.98
9.25E−03




angiogenesis (GO:0045766)




regulation of calcium ion
201
7
0.54
+
13.04
9.37E−03




transport (GO:0051924)




regulation of protein
970
13
2.59
+
5.02
1.05E−02




localization (GO:0032880)




regulation of transferase
974
13
2.6
+
5
1.10E−02




activity (GO:0051338)




positive regulation of protein
300
8
0.8
+
9.99
1.11E−02




serine/threonine kinase activity




(GO:0071902)




positive regulation of protein
129
6
0.34
+
17.42
1.11E−02




localization to nucleus




(GO:1900182)




reproductive structure
411
9
1.1
+
8.2
1.15E−02




development (GO:0048608)




leukocyte differentiation
302
8
0.81
+
9.92
1.16E−02




(GO:0002521)




regulation of lymphocyte
412
9
1.1
+
8.18
1.18E−02




activation (GO:0051249)




positive regulation of T cell
209
7
0.56
+
12.54
1.21E−02




activation (GO:0050870)




positive regulation of
132
6
0.35
+
17.02
1.26E−02




lymphocyte proliferation




(GO:0050671)




reproductive system
416
9
1.11
+
8.1
1.27E−02




development (GO:0061458)




cell-cell signaling
1158
14
3.09
+
4.53
1.28E−02




(GO:0007267)




signal transduction by protein
421
9
1.12
+
8.01
1.40E−02




phosphorylation




(GO:0023014)




cell proliferation
687
11
1.83
+
6
1.45E−02




(GO:0008283)




positive regulation of
218
7
0.58
+
12.03
1.60E−02




leukocyte cell-cell adhesion




(GO:1903039)




positive regulation of
1762
17
4.7
+
3.61
1.69E−02




biosynthetic process




(GO:0009891)




regulation of protein
220
7
0.59
+
11.92
1.70E−02




localization to nucleus




(GO:1900180)




response to endogenous
1383
15
3.69
+
4.06
1.92E−02




stimulus (GO:0009719)




positive regulation of
142
6
0.38
+
15.82
1.92E−02




vasculature development




(GO:1904018)




regulation of molecular
2910
22
7.77
+
2.83
1.92E−02




function (GO:0065009)




positive regulation of secretion
324
8
0.87
+
9.25
1.95E−02




by cell (GO:1903532)




positive regulation of catalytic
1590
16
4.25
+
3.77
2.14E−02




activity (GO:0043085)




regulation of mononuclear cell
35
4
0.09
+
42.8
2.18E−02




migration (GO:0071675)




positive regulation of ion
229
7
0.61
+
11.45
2.20E−02




transport (GO:0043270)




positive regulation of cell
81
5
0.22
+
23.12
2.29E−02




division (GO:0051781)




regulation of leukocyte
240
7
0.64
+
10.92
2.99E−02




differentiation (GO:1902105)




positive regulation of stem cell
38
4
0.1
+
39.42
3.01E−02




proliferation (GO:2000648)




regulation of protein transport
742
11
1.98
+
5.55
3.04E−02




(GO:0051223)




apoptotic process
900
12
2.4
+
4.99
3.05E−02




(GO:0006915)




regulation of T cell chemotaxis
11
3
0.03
+
>100
3.21E−02




(GO:0010819)




lymphocyte chemotaxis
39
4
0.1
+
38.41
3.34E−02




(GO:0048247)




response to oxygen levels
351
8
0.94
+
8.54
3.51E−02




(GO:0070482)




regulation of leukocyte
472
9
1.26
+
7.14
3.54E−02




activation (GO:0002694)




regulation of protein
474
9
1.27
+
7.11
3.66E−02




serine/threonine kinase activity




(GO:0071900)




positive regulation of peptidyl-
90
5
0.24
+
20.81
3.80E−02




serine phosphorylation




(GO:0033138)




positive regulation of
1875
17
5.01
+
3.4
3.92E−02




molecular function




(GO:0044093)




vasculature development
480
9
1.28
+
7.02
4.05E−02




(GO:0001944)




negative regulation of signal
1099
13
2.93
+
4.43
4.13E−02




transduction (GO:0009968)




positive regulation of epithelial
164
6
0.44
+
13.7
4.36E−02




cell proliferation




(GO:0050679)




cardiovascular system
490
9
1.31
+
6.88
4.77E−02




development (GO:0072358)




multicellular organismal
6584
34
17.58
+
1.93
4.79E−02




process (GO:0032501)




positive regulation of cell-
43
4
0.11
+
34.84
4.89E−02




matrix adhesion (GO:0001954)




negative regulation of
43
4
0.11
+
34.84
4.89E−02




leukocyte apoptotic process




(GO:2000107)




regulation of nervous system
782
11
2.09
+
5.27
4.99E−02




development (GO:0051960)


















Homo sapiens -




upload_1



Depleted Gene Results
REFLIST
upload_1
upload_1
upload_1
(fold
upload_1


GO biological process complete
(20972)
(18)
(expected)
(over/under)
Enrichment)
(P-value)





cell surface receptor signaling pathway
2211
18
1.9
+
9.49
2.12E−14


(GO:0007166)


cytokine-mediated signaling pathway
466
12
0.4
+
30
1.95E−12


(GO:0019221)


cellular response to organic substance
1915
16
1.64
+
9.73
2.45E−11


(GO:0071310)


cellular response to cytokine stimulus
632
12
0.54
+
22.12
7.21E−11


(GO:0071345)


response to cytokine (GO:0034097)
727
12
0.62
+
19.23
3.77E−10


response to chemical (GO:0042221)
3875
18
3.33
+
5.41
5.17E−10


cellular response to chemical stimulus
2347
16
2.01
+
7.94
6.08E−10


(GO:0070887)


response to organic substance
2557
16
2.19
+
7.29
2.35E−09


(GO:0010033)


signal transduction (GO:0007165)
4867
18
4.18
+
4.31
3.13E−08


single organism signaling (GO:0044700)
5262
18
4.52
+
3.99
1.27E−07


signaling (GO:0023052)
5266
18
4.52
+
3.98
1.29E−07


cell communication (GO:0007154)
5351
18
4.59
+
3.92
1.72E−07


positive regulation of multicellular
1425
12
1.22
+
9.81
1.00E−06


organismal process (GO:0051240)


cellular response to stimulus
6168
18
5.29
+
3.4
2.22E−06


(GO:0051716)


regulation of multicellular organismal
2656
14
2.28
+
6.14
4.15E−06


process (GO:0051239)


positive regulation of response to stimulus
2028
12
1.74
+
6.89
5.82E−05


(GO:0048584)


positive regulation of cell proliferation
847
9
0.73
+
12.38
8.18E−05


(GO:0008284)


response to stimulus (GO:0050896)
7800
18
6.69
+
2.69
1.52E−04


positive regulation of intracellular signal
911
9
0.78
+
11.51
1.54E−04


transduction (GO:1902533)


signal transduction by protein
421
7
0.36
+
19.37
2.82E−04


phosphorylation (GO:0023014)


positive regulation of signal transduction
1433
10
1.23
+
8.13
4.77E−04


(GO:0009967)


positive regulation of angiogenesis
125
5
0.11
+
46.6
4.96E−04


(GO:0045766)


positive regulation of response to external
266
6
0.23
+
26.28
5.56E−04


stimulus (GO:0032103)


positive regulation of cellular process
4716
15
4.05
+
3.71
6.26E−04


(GO:0048522)


positive regulation of vasculature
142
5
0.12
+
41.03
9.29E−04


development (GO:1904018)


regulation of cell proliferation
1538
10
1.32
+
7.58
9.31E−04


(GO:0042127)


positive regulation of cell communication
1552
10
1.33
+
7.51
1.01E−03


(GO:0010647)


positive regulation of signaling
1560
10
1.34
+
7.47
1.06E−03


(GO:0023056)


positive regulation of developmental
1179
9
1.01
+
8.89
1.40E−03


process (GO:0051094)


regulation of multicellular organismal
1714
10
1.47
+
6.8
2.58E−03


development (GO:2000026)


positive regulation of macromolecule
2841
12
2.44
+
4.92
2.61E−03


metabolic process (GO:0010604)


regulation of tissue remodeling
68
4
0.06
+
68.54
2.68E−03


(GO:0034103)


positive regulation of protein
895
8
0.77
+
10.41
2.69E−03


phosphorylation (GO:0001934)


regulation of intracellular signal
1725
10
1.48
+
6.75
2.74E−03


transduction (GO:1902531)


positive regulation of endothelial cell
69
4
0.06
+
67.54
2.84E−03


proliferation (GO:0001938)


regulation of developmental process
2263
11
1.94
+
5.66
2.91E−03


(GO:0050793)


positive regulation of biological process
5270
15
4.52
+
3.32
3.01E−03


(GO:0048518)


positive regulation of phosphorylation
937
8
0.8
+
9.95
3.80E−03


(GO:0042327)


positive regulation of cytokine production
370
6
0.32
+
18.89
3.83E−03


(GO:0001819)


regulation of response to stimulus
3696
13
3.17
+
4.1
4.56E−03


(GO:0048583)


regulation of anatomical structure
960
8
0.82
+
9.71
4.57E−03


morphogenesis (GO:0022603)


protein phosphorylation (GO:0006468)
972
8
0.83
+
9.59
5.02E−03


positive regulation of metabolic process
3033
12
2.6
+
4.61
5.41E−03


(GO:0009893)


regulation of programmed cell death
1421
9
1.22
+
7.38
6.83E−03


(GO:0043067)


regulation of angiogenesis (GO:0045765)
219
5
0.19
+
26.6
7.79E−03


enzyme linked receptor protein signaling
706
7
0.61
+
11.55
9.21E−03


pathway (GO:0007167)


positive regulation of phosphorus
1056
8
0.91
+
8.83
9.40E−03


metabolic process (GO:0010562)


positive regulation of phosphate metabolic
1056
8
0.91
+
8.83
9.40E−03


process (GO:0045937)


regulation of response to external stimulus
712
7
0.61
+
11.45
9.75E−03


(GO:0032101)


regulation of cell death (GO:0010941)
1522
9
1.31
+
6.89
1.22E−02


positive regulation of protein metabolic
1527
9
1.31
+
6.87
1.25E−02


process (GO:0051247)


regulation of endothelial cell proliferation
101
4
0.09
+
46.14
1.28E−02


(GO:0001936)


regulation of vasculature development
243
5
0.21
+
23.97
1.29E−02


(GO:1901342)


positive regulation of vascular endothelial
27
3
0.02
+
>100
1.41E−02


growth factor production (GO:0010575)


positive regulation of protein modification
1150
8
0.99
+
8.11
1.78E−02


process (GO:0031401)


regulation of signal transduction
2752
11
2.36
+
4.66
2.12E−02


(GO:0009966)


regulation of vascular endothelial growth
31
3
0.03
+
>100
2.13E−02


factor production (GO:0010574)


regulation of endothelial cell migration
115
4
0.1
+
40.53
2.13E−02


(GO:0010594)


negative regulation of programmed cell
829
7
0.71
+
9.84
2.68E−02


death (GO:0043069)


regulation of cellular process
10399
18
8.93
+
2.02
2.69E−02


(GO:0050794)


positive regulation of lymphocyte
286
5
0.25
+
20.37
2.86E−02


activation (GO:0051251)


positive regulation of cell differentiation
844
7
0.72
+
9.66
3.01E−02


(GO:0045597)


angiogenesis (GO:0001525)
292
5
0.25
+
19.95
3.16E−02


positive chemotaxis (GO:0050918)
37
3
0.03
+
94.47
3.60E−02


positive regulation of lymphocyte
132
4
0.11
+
35.31
3.67E−02


proliferation (GO:0050671)


positive regulation of mononuclear cell
133
4
0.11
+
35.04
3.78E−02


proliferation (GO:0032946)


phosphorylation (GO:0016310)
1290
8
1.11
+
7.23
4.20E−02


positive regulation of leukocyte activation
311
5
0.27
+
18.73
4.29E−02


(GO:0002696)


positive regulation of leukocyte
138
4
0.12
+
33.77
4.37E−02


proliferation (GO:0070665)


regulation of protein phosphorylation
1302
8
1.12
+
7.16
4.50E−02


(GO:0001932)


negative regulation of cell death
902
7
0.77
+
9.04
4.67E−02


(GO:0060548)


regulation of cytokine production
575
6
0.49
+
12.16
4.87E−02


(GO:0001817)


positive regulation of cell activation
320
5
0.27
+
18.2
4.92E−02


(GO:0050867)





Enriched genes = GSEA core enrichment (i.e. top ranked genes until running enrichment score hits peak)


Depleted genes = GSEA most negatively ranked genes (i.e. bottom ranked genes until running enrichment score hits trough)













TABLE 6W





GSEA results for gene sets enriched in pre-treatment patient tumors with truncating mutations in PBRM1



















NAME
SIZE
ES
NES
NOM p-val





HALLMARK_COAGULATION
135
0.4634537
2.5861373
0


HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION
198
0.3778765
2.2784488
0


HALLMARK_TNFA_SIGNALING_VIA_NFKB
199
0.36042312
2.161199
0


HALLMARK_HYPOXIA
198
0.36123425
2.1341348
0


HALLMARK_INTERFERON_ALPHA_RESPONSE
93
0.38837415
2.0058413
0


HALLMARK_BILE_ACID_METABOLISM
112
0.367774
1.9837768
0


HALLMARK_XENOBIOTIC_METABOLISM
199
0.30432338
1.8260752
0


HALLMARK_ANGIOGENESIS
36
0.43100646
1.7682568
0.005780347


HALLMARK_INTERFERON_GAMMA_RESPONSE
191
0.2959238
1.7434925
0


HALLMARK_INFLAMMATORY_RESPONSE
199
0.28598273
1.6895322
0


HALLMARK_GLYCOLYSIS
199
0.27468395
1.6470891
0


HALLMARK_IL6_JAK_STAT3_SIGNALING
87
0.3154689
1.6223565
0


HALLMARK_NOTCH_SIGNALING
32
0.37784022
1.5690641
0.026548672


HALLMARK_P53_PATHWAY
200
0.26244697
1.5487176
0


HALLMARK_APOPTOSIS
160
0.26717153
1.5470705
0.004237288


HALLMARK_IL2_STAT5_SIGNALING
199
0.2597181
1.5294203
0.005555556


HALLMARK_APICAL_JUNCTION
198
0.25475252
1.5265775
0


HALLMARK_MYOGENESIS
200
0.25268936
1.5108361
0


HALLMARK_UV_RESPONSE_DN
144
0.25802347
1.4569446
0


HALLMARK_ESTROGEN_RESPONSE_EARLY
200
0.23530972
1.4246706
0


HALLMARK_WNT_BETA_CATENIN_SIGNALING
42
0.32717755
1.4034909
0.05732484


HALLMARK_HEDGEHOG_SIGNALING
35
0.32948953
1.3634391
0.0882353


HALLMARK_ADIPOGENESIS
196
0.22999962
1.3584664
0.005376344


HALLMARK_CHOLESTEROL_HOMEOSTASIS
73
0.26637354
1.3271515
0.068100356


HALLMARK_REACTIVE_OXIGEN_SPECIES_PATHWAY
47
0.29103118
1.3039039
0.08430233


HALLMARK_APICAL_SURFACE
44
0.29906154
1.3030225
0.10119048


HALLMARK_TGF_BETA_SIGNALING
54
0.25017482
1.1652176
0.221875


HALLMARK_HEME_METABOLISM
196
0.19430974
1.1569836
0.12953368


HALLMARK_PANCREAS_BETA_CELLS
38
0.26110435
1.1266103
0.2633229


HALLMARK_FATTY_ACID_METABOLISM
158
0.18055953
1.0471649
0.3488372


HALLMARK_COMPLEMENT
196
0.16831398
1.003772
0.46195653


HALLMARK_UV_RESPONSE_UP
154
0.17562571
0.9861503
0.5320197


HALLMARK_KRAS_SIGNALING_DN
193
0.1584056
0.9371324
0.6514286


HALLMARK_ANDROGEN_RESPONSE
99
0.1555059
0.82283705
0.875502

















RANK





FWER
AT
LEADING


NAME
FDR q-val
p-val
MAX
EDGE





HALLMARK_COAGULATION
0
0
8963
tags = 52%,






list = 25%,






signal = 69%


HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION
0
0
8606
tags = 40%,






list = 24%,






signal = 52%


HALLMARK_TNFA_SIGNALING_VIA_NFKB
0
0
9110
tags = 40%,






list = 25%,






signal = 54%


HALLMARK_HYPOXIA
0
0
8749
tags = 43%,






list = 24%,






signal = 56%


HALLMARK_INTERFERON_ALPHA_RESPONSE
0
0
9091
tags = 39%,






list = 25%,






signal = 52%


HALLMARK_BILE_ACID_METABOLISM
0
0
8471
tags = 45%,






list = 24%,






signal = 58%


HALLMARK_XENOBIOTIC_METABOLISM
0.003440355
0.007
8951
tags = 42%,






list = 25%,






signal = 56%


HALLMARK_ANGIOGENESIS
0.005257009
0.014
8454
tags = 42%,






list = 24%,






signal = 54%


HALLMARK_INTERFERON_GAMMA_RESPONSE
0.004942737
0.015
9079
tags = 33%,






list = 25%,






signal = 44%


HALLMARK_INFLAMMATORY_RESPONSE
0.00758099
0.027
2626
tags = 16%,






list = 7%,






signal = 17%


HALLMARK_GLYCOLYSIS
0.009826244
0.038
8344
tags = 36%,






list = 23%,






signal = 46%


HALLMARK_IL6_JAK_STAT3_SIGNALING
0.0129132
0.054
9009
tags = 39%,






list = 25%,






signal = 52%


HALLMARK_NOTCH_SIGNALING
0.018096626
0.079
7666
tags = 34%,






list = 21%,






signal = 44%


HALLMARK_P53_PATHWAY
0.01899984
0.09
8833
tags = 32%,






list = 25%,






signal = 42%


HALLMARK_APOPTOSIS
0.017733185
0.09
9110
tags = 34%,






list = 25%,






signal = 45%


HALLMARK_IL2_STAT5_SIGNALING
0.018474342
0.1
8984
tags = 31%,






list = 25%,






signal = 41%


HALLMARK_APICAL_JUNCTION
0.017752696
0.102
7994
tags = 28%,






list = 22%,






signal = 36%


HALLMARK_MYOGENESIS
0.018697744
0.11
8694
tags = 33%,






list = 24%,






signal = 43%


HALLMARK_UV_RESPONSE_DN
0.026672224
0.166
8559
tags = 35%,






list = 24%,






signal = 45%


HALLMARK_ESTROGEN_RESPONSE_EARLY
0.032439258
0.206
7210
tags = 28%,






list = 20%,






signal = 34%


HALLMARK_WNT_BETA_CATENIN_SIGNALING
0.038341142
0.249
2943
tags = 19%,






list = 8%,






signal = 21%


HALLMARK_HEDGEHOG_SIGNALING
0.048595615
0.306
8388
tags = 43%,






list = 23%,






signal = 56%


HALLMARK_ADIPOGENESIS
0.048597757
0.316
3003
tags = 16%,






list = 8%,






signal = 18%


HALLMARK_CHOLESTEROL_HOMEOSTASIS
0.059607573
0.383
11878
tags = 49%,






list = 33%,






signal = 74%


HALLMARK_REACTIVE_OXIGEN_SPECIES_PATHWAY
0.06951901
0.444
6860
tags = 28%,






list = 19%,






signal = 34%


HALLMARK_APICAL_SURFACE
0.06738169
0.446
2182
tags = 18%,






list = 6%,






signal = 19%


HALLMARK_TGF_BETA_SIGNALING
0.1953661
0.862
7986
tags = 33%,






list = 22%,






signal = 43%


HALLMARK_HEME_METABOLISM
0.20019072
0.876
7877
tags = 27%,






list = 22%,






signal = 34%


HALLMARK_PANCREAS_BETA_CELLS
0.23912823
0.926
2627
tags = 16%,






list = 7%,






signal = 17%


HALLMARK_FATTY_ACID_METABOLISM
0.3891211
0.988
8803
tags = 35%,






list = 25%,






signal = 46%


HALLMARK_COMPLEMENT
0.48644838
0.995
8674
tags = 28%,






list = 24%,






signal = 37%


HALLMARK_UV_RESPONSE_UP
0.51797897
0.997
7933
tags = 28%,






list = 22%,






signal = 36%


HALLMARK_KRAS_SIGNALING_DN
0.6393933
1
3260
tags = 13%,






list = 9%,






signal = 15%


HALLMARK_ANDROGEN_RESPONSE
0.8739106
1
8780
tags = 27%,






list = 24%,






signal = 36%
















TABLE 6X





GSEA results for gene sets enriched in pre-treatment patient tumors wildtype at PBRM1



















NAME
SIZE
ES
NES
NOM p-val





HALLMARK_E2F_TARGETS
199
−0.5751047
−2.9363213
0


HALLMARK_G2M_CHECKPOINT
199
−0.5301613
−2.7069154
0


HALLMARK_MYC_TARGETS_V1
199
−0.45365316
−2.341999
0


HALLMARK_OXIDATIVE_PHOSPHORYLATION
199
−0.40938506
−2.0984013
0


HALLMARK_MYC_TARGETS_V2
58
−0.46636462
−1.9861294
0


HALLMARK_SPERMATOGENESIS
128
−0.38931966
−1.8766787
0


HALLMARK_MITOTIC_SPINDLE
199
−0.29650635
−1.5328054
0.003663004


HALLMARK_DNA_REPAIR
143
−0.29704157
−1.4623376
0.01142132


HALLMARK_UNFOLDED_PROTEIN_RESPONSE
112
−0.26182294
−1.2473699
0.11416781


HALLMARK_PROTEIN_SECRETION
96
−0.25048777
−1.146313
0.24350205


HALLMARK_MTORC1_SIGNALING
199
−0.20546418
−1.0482782
0.35511714


HALLMARK_PI3K_AKT_MTOR_SIGNALING
105
−0.17890021
−0.8406621
0.7732096


HALLMARK_ESTROGEN_RESPONSE_LATE
199
−0.15607864
−0.79631424
0.880579


HALLMARK_PEROXISOME
103
−0.16189572
−0.75051486
0.9175978


HALLMARK_KRAS_SIGNALING_UP
198
−0.13005705
−0.66694486
0.996319


HALLMARK_ALLOGRAFT_REJECTION
190
−0.12697595
−0.64140904
0.99506783

















RANK





FWER
AT
LEADING


NAME
FDR q-val
p-val
MAX
EDGE





HALLMARK_E2F_TARGETS
0
0
11706
tags = 68%,






list = 33%,






signal = 100%


HALLMARK_G2M_CHECKPOINT
0
0
8556
tags = 51%,






list = 24%,






signal = 66%


HALLMARK_MYC_TARGETS_V1
0
0
10841
tags = 54%,






list = 30%,






signal = 77%


HALLMARK_OXIDATIVE_PHOSPHORYLATION
0
0
9234
tags = 44%,






list = 26%,






signal = 59%


HALLMARK_MYC_TARGETS_V2
1.10E−04
0.001
5817
tags = 34%,






list = 16%,






signal = 41%


HALLMARK_SPERMATOGENESIS
3.85E−04
0.005
5701
tags = 33%,






list = 16%,






signal = 39%


HALLMARK_MITOTIC_SPINDLE
0.016991025
0.247
5950
tags = 25%,






list = 17%,






signal = 29%


HALLMARK_DNA_REPAIR
0.032129228
0.457
11381
tags = 41%,






list = 32%,






signal = 59%


HALLMARK_UNFOLDED_PROTEIN_RESPONSE
0.18768412
0.985
12007
tags = 40%,






list = 33%,






signal = 60%


HALLMARK_PROTEIN_SECRETION
0.33950403
1
10093
tags = 30%,






list = 28%,






signal = 42%


HALLMARK_MTORC1_SIGNALING
0.53724766
1
12410
tags = 38%,






list = 35%,






signal = 57%


HALLMARK_PI3K_AKT_MTOR_SIGNALING
1
1
10551
tags = 30%,






list = 29%,






signal = 42%


HALLMARK_ESTROGEN_RESPONSE_LATE
1
1
3036
tags = 12%,






list = 8%,






signal = 13%


HALLMARK_PEROXISOME
1
1
4726
tags = 15%,






list = 13%,






signal = 17%


HALLMARK_KRAS_SIGNALING_UP
1
1
3127
tags = 9%,






list = 9%,






signal = 10%


HALLMARK_ALLOGRAFT_REJECTION
0.9873257
1
10584
tags = 28%,






list = 29%,






signal = 39%









INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned herein are hereby incorporated by reference in their entirety as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference. In case of conflict, the present application, including any definitions herein, will control.


Also incorporated by reference in their entirety are any polynucleotide and polypeptide sequences which reference an accession number correlating to an entry in a public database, such as those maintained by The Institute for Genomic Research (TIGR) on the world wide web and/or the National Center for Biotechnology Information (NCBI) on the world wide web.


EQUIVALENTS

Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the present invention described herein. Such equivalents are intended to be encompassed by the following claims.

Claims
  • 1. A method of identifying the likelihood of a cancer in a subject to be responsive to an immune checkpoint therapy, the method comprising: a) obtaining or providing a subject sample from a patient having cancer;b) measuring the amount or activity of at least one biomarker listed in Table 1 in the subject sample; andc) comparing said amount or activity of the at least one biomarker listed in Table 1 in a control sample,wherein the absence of or a significantly decreased amount or activity of the at least one biomarker listed in Table 1 in the subject sample and/or the presence of or a significantly increased amount or activity of the at least one biomarker listed in Table 1 having a loss of function mutation in the subject sample, relative to the control sample identifies the cancer as being more likely to be responsive to the immune checkpoint therapy; and wherein the presence of or a significantly increased amount or activity of the at least one biomarker listed in Table 1 in the subject sample and/or the absence of or a decreased amount or activity of the at least one biomarker listed in Table 1 having a loss of function mutation in the subject sample, relative to the control sample identifies the cancer as being less likely to be responsive to the immune checkpoint therapy.
  • 2. A method of identifying the likelihood of a cancer in a subject to be responsive to immune checkpoint therapy, the method comprising: a) obtaining or providing a subject sample from a patient having cancer, wherein the sample comprises nucleic acid molecules from the subject;b) determining the copy number of at least one biomarker listed in Table 1 in the subject sample; andc) comparing said copy number to that of a control sample,wherein a decreased copy number of the at least one biomarker listed in Table 1 in the in the subject sample and/or an increased copy number of the at least one biomarker listed in Table 1 having a loss of function mutation in the subject sample, relative to the control sample identifies the cancer as being more likely to be responsive to the immune checkpoint therapy; and wherein a wild type or increased copy number of the biomarker in the subject sample and/or or a decreased copy number of the at least one biomarker listed in Table 1 having a loss of function mutation in the sample relative to the control sample identifies the cancer as being less likely to be responsive to the immune checkpoint therapy.
  • 3. The method of claim 1 or 2, further comprising recommending, prescribing, or administering the immune checkpoint therapy if the cancer is determined likely to be responsive to the immune checkpoint therapy or administering an anti-cancer therapy other than the immune checkpoint therapy if the cancer is determined be less likely to be responsive to the immune checkpoint therapy.
  • 4. The method of claim 3, wherein the anti-cancer therapy is selected from the group consisting of targeted therapy, chemotherapy, radiation therapy, and/or hormonal therapy.
  • 5. The method of any one of claims 1-4, wherein the control sample is determined from a cancerous or non-cancerous sample from either the patient or a member of the same species to which the patient belongs.
  • 6. The method of claim 5, wherein the control sample is a cancerous or non-cancerous sample from the patient obtained from an earlier point in time than the patient sample, optionally wherein the control sample is obtained before the patient has received immune checkpoint therapy and the patient sample is obtained after the patient has received immune checkpoint therapy.
  • 7. The method of any one of claims 1-6, wherein the control sample comprises cells or does not comprise cells.
  • 8. The method of any one of claims 1-7, wherein the control sample comprises cancer cells known to be responsive or non-responsive to the immune checkpoint therapy.
  • 9. A method of assessing the efficacy of an agent for treating a cancer in a subject that is unlikely to be responsive to an immune checkpoint therapy, comprising: a) detecting in a first subject sample and maintained in the presence of the agent the amount or activity of at least one biomarker listed in Table 1;b) detecting the amount or activity of the at least one biomarker listed in Table 1 in a second subject sample and maintained in the absence of the test compound; andc) comparing the amount or activity of the at least one biomarker listed in Table 1 from steps a) and b), wherein the presence of or a significantly increased amount or activity of the at least one biomarker listed in Table 1 in the first subject sample and/or the absence of or a decreased amount or activity of the at least one biomarker listed in Table 1 having a loss of function mutation in the first subject sample, relative to at least one subsequent subject sample, indicates that the agent treats the cancer in the subject.
  • 10. A method of assessing the efficacy of an agent for treating a cancer in a subject or prognosing progression of a cancer in a subject, comprising: a) detecting in a subject sample at a first point in time the amount or activity of at least one biomarker listed in Table 1;b) repeating step a) during at least one subsequent point in time after administration of the agent; andc) comparing the expression and/or activity detected in steps a) and b), wherein the presence of or a significantly increased amount or activity of the at least one biomarker listed in Table 1 in the first subject sample and/or the absence of or a decreased amount or activity of the at least one biomarker listed in Table 1 having a loss of function mutation in the first subject sample, relative to at least one subsequent subject sample, indicates that the cancer is unlikely to progress or that the agent treats the cancer in the subject.
  • 11. The method of claim 10, wherein between the first point in time and the subsequent point in time, the subject has undergone treatment, completed treatment, and/or is in remission for the cancer.
  • 12. The method of claim 10, wherein the first and/or at least one subsequent sample is selected from the group consisting of ex vivo and in vivo samples.
  • 13. The method of claim 10, wherein the first and/or at least one subsequent sample is obtained from an animal model of the cancer.
  • 14. The method of claim 10, wherein the first and/or at least one subsequent sample is a portion of a single sample or pooled samples obtained from the subject.
  • 15. A cell-based assay for screening for agents that have a cytotoxic or cytostatic effect on a cancer cell that is unresponsive to an immune checkpoint therapy comprising, contacting the cancer cell with a test agent, and determining the ability of the test agent to decrease the amount or activity of at least one biomarker listed in Table 1 in the subject sample and/or increase the amount or activity of the at least one biomarker listed in Table 1 having a loss of function mutation.
  • 16. The cell-based assay of claim 15, wherein the step of contacting occurs in vivo, ex vivo, or in vitro.
  • 17. The method or assay of any one of claims 1-16, wherein the subject sample and/or the control sample has not been contacted with a renal cell cancer treatment or inhibitor of an immune checkpoint.
  • 18. The method or assay of any one of claims 1-17, wherein the subject has not been administered a renal cell cancer treatment or inhibitor of an immune checkpoint.
  • 19. The method or assay of any one of claims 1-18, further comprising recommending, prescribing, or administering at least one additional anti-cancer therapeutic agent, optionally wherein the at least one additional anti-cancer therapeutic agent is nivolumab and/or an anti-PBRM-1 therapeutic agent.
  • 20. The method or assay of any one of claims 1-19, wherein the subject sample is selected from the group consisting of serum, whole blood, plasma, urine, cells, cell lines, and biopsies.
  • 21. The method or assay of any one of claims 1-20, wherein the amount of the at least one biomarker listed in Table 1 is detected using a reagent which specifically binds with the protein.
  • 22. The method or assay of claim 21, wherein the reagent is selected from the group consisting of an antibody, an antibody derivative, and an antibody fragment.
  • 23. The method or assay of any one of claims 1-20, wherein the at least one biomarker listed in Table 1 is assessed by detecting the presence in the sample of a transcribed polynucleotide or portion thereof.
  • 24. The method or assay of claim 23, wherein the transcribed polynucleotide is an mRNA or a cDNA.
  • 25. The method or assay of claim 23, wherein the step of detecting further comprises amplifying the transcribed polynucleotide.
  • 26. The method or assay of claim 23, wherein the transcribed polynucleotide is detected by identifying a nucleic acid that anneals with the biomarker nucleic acid, or a portion thereof, under stringent hybridization conditions.
  • 27. The method or assay of any one of claims 1-26, wherein the at least one biomarker listed in Table 1 is human PBRM-1, or a fragment thereof.
  • 28. The method or assay of any one of claims 1-27, wherein the immune checkpoint therapy comprises at least one antibody selected from the group consisting of anti-PD-1 antibodies, anti-CTLA-4 antibodies, anti-PD-L1 antibodies, anti-PD-L2 antibodies, and combinations thereof.
  • 29. The method or assay of claim 28, wherein the immune checkpoint therapy comprises nivolumab.
  • 30. The method or assay of any one of claims 1-29, wherein the likelihood of the cancer in the subject to be responsive to immune checkpoint therapy is the likelihood of at least one criteria selected from the group consisting of cellular proliferation, tumor burden, m-stage, metastasis, progressive disease, clinical benefit rate, survival until mortality, pathological complete response, semi-quantitative measures of pathologic response, clinical complete remission, clinical partial remission, clinical stable disease, recurrence-free survival, metastasis free survival, disease free survival, circulating tumor cell decrease, circulating marker response, and RECIST criteria.
  • 31. The method or assay of any one of claims 1-30, wherein the cancer is a solid tumor.
  • 32. The method or assay of any one of claims 1-31, wherein the cancer is a renal cell cancer.
  • 33. The method or assay of any one of claim 32, wherein the renal cell cancer is a clear cell renal cell cancer (ccRcc).
  • 34. The method or assay of any one of claim 33, wherein the clear cell renal cell cancer is a metastatic clear cell renal cell carcinoma (mRCC).
  • 35. The method or assay of any one of claims 1-34, wherein the subject is a mammal.
  • 36. The method or assay of claim 35, wherein the mammal is an animal model of cancer.
  • 37. The method or assay of claim 35, wherein the mammal is a human.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 62/445,094, filed on 11 Jan. 2017; the entire contents of said application are incorporated herein in their entirety by this reference.

PCT Information
Filing Document Filing Date Country Kind
PCT/US18/12209 1/3/2018 WO 00
Provisional Applications (1)
Number Date Country
62445094 Jan 2017 US