DETECTION MEANS, COMPOSITIONS AND METHODS FOR MODULATING SYNOVIAL SARCOMA CELLS

Information

  • Patent Application
  • 20220154282
  • Publication Number
    20220154282
  • Date Filed
    March 12, 2020
    4 years ago
  • Date Published
    May 19, 2022
    2 years ago
Abstract
The present invention provides novel compositions and methods based on the discovery of the mechanisms and gene expression programs associated with synovial sarcoma. In particular, core oncogenic programs were expressed by a distinct subpopulation of malignant cells and associated with poor clinical outcome, a cell cycle program distinguished cycling from non-cycling cells, with cycling cells having a tendency to be poorly differentiated and indicative of increased risk of metastatic disease, and a (de)differentiation program that can identify poorly differentiated cells, the absence of which was prognostic of metastasis free survival. Methods of treatment include use of HDAC and CDK4/6 inhibitors to block oncogenic program to selectively target synovial sarcoma cells. Finally, macrophages and T cells can mimic the effect of SS18-SSX inhibition by secreting TNFa and IFNg, which allows for adoptive cell therapy to provide cells with increased expression of TNFa and IFNg.
Description
REFERENCE TO AN ELECTRONIC SEQUENCE LISTING

The contents of the electronic sequence listing (BROD-4110WP_ST25.txt”; Size is 12 Kilobytes and it was created on Mar. 12, 2020) is herein incorporated by reference in its entirety.


TECHNICAL FIELD

The subject matter disclosed herein is generally directed to compositions and methods for modulating synovial sarcoma cells and responses by targeting SS18-SSX oncoprotein/core oncogenic program.


BACKGROUND

Synovial sarcoma (SyS) is a highly aggressive mesenchymal neoplasm that accounts for 10-20% of all soft-tissue sarcomas in young adults (1). It is invariably driven by the SS18-SSX oncoprotein, where the BAF subunit SS18 is fused to the repressive domain of SSX1, SSX2 or, rarely, SSX4. The BAF complex, the mammalian ortholog of SWI/SNF, is a major chromatin regulator involved in gene activation, whereas the SSX genes represent a family of highly immunogenic cancer-testis antigens involved in transcriptional repression. SS18-SSX promotes gene activation by changing the BAF complex configuration and chromatin targeting, while it also mediates gene silencing by forming a complex with ATF2 and TLE1.


Despite the relatively low number of secondary mutations, SyS tumors display different degrees of cellular differentiation and plasticity, and are classified accordingly as monophasic (mesenchymal cells), biphasic (mesenchymal and epithelial cells), or poorly differentiated (undifferentiated cells). The co-existence of distinct cellular phenotypes and morphologies in a single SyS tumor provides a unique opportunity to explore intratumor heterogeneity and cell state transitions. However, since human SyS has been studied primarily in established cellular models and through bulk profiling of tumor tissues, the molecular features of the different SyS subpopulations have so far remained elusive. In particular, because it remains unclear how this malignant cellular diversity comes about, which malignant cell states drive tumor progression, and how to selectively target aggressive synovial sarcoma cells to blunt tumor growth and dissemination, identification of cellular states, genetic drivers and bases for therapeutic strategies for this aggressive malignancy are needed.


Citation or identification of any document in this application is not an admission that such document is available as prior art to the present invention.


SUMMARY

In certain example embodiments, methods of detecting an expression signature in synovial sarcoma (Sys) tumor are provided, comprising detecting in tumor cells obtained from a subject the expression or activity of a malignant cell gene signature comprising one or more genes or polypeptides selected from Table 6. In embodiments, the one or more genes or polypeptides are selected from the epithelial malignant signature of Table 1E, the mesenchymal malignant cell signature of Table 1D, the core oncogenic expression signature of Table 1A.1, and/or the cell cycle malignant signature of Table 1C. In certain example embodiments the core oncogenic signature may comprise the core oncogenic upregulated signature of Table 1A.2 or the core oncogenic downregulated signature of Table 1A.3.


In some embodiments, the methods comprise detecting a cell cycle malignant signature, which is indicative of increased risk of metastatic disease, an increased number of cycling cells and/or the presence of an increase of poorly differentiated cells.


In some embodiments, the methods comprise detecting core oncogenic upregulated malignant signatures, core oncogenic downregulated signature, or a combination thereof are detected, wherein detecting is indicative of increased metastatic Sys disease.


In certain embodiments, the method comprises detecting the epithelial malignant signature, the mesenchymal malignant signature or a combination thereof. In embodiments, the absence of the mesenchymal or epithelial malignant signature is indicative of higher progression free survival.


Methods for diagnosing a subject with Sys are also provided, and comprise detecting one or more signatures from Tables 1A-E. Methods of diagnosing a subject with increased risk of metastatic disease are also provided and can comprise detecting one or more signatures of Table 1A-1E.


In certain embodiments, methods of treating SyS in a subject in need thereof are provided, comprising administering an inhibitor of HDAC, CDK4/6, or a combination thereof to selectively target synovial sarcoma cells. In some embodiments, methods of treating may further comprise administering immune checkpoint inhibitors.


In embodiments, methods of distinguishing Sys from other cancer types and sarcomas are provided and comprise detecting a signature comprising a fusion program signature comprising one or more genes or polypeptides of Table 8.


In embodiments, methods of detecting a subject at high risk for metastatic disease comprising detecting core oncogenic program gene signatures. Methods of monitoring therapy are also provided and can comprise detecting the expression or activity of one or more gene signatures of Tables 1A-1E in tumor samples obtained from the subject for at least two time points. In embodiments, at least one sample is obtained before treatment, on some embodiments, at least one sample is obtained after treatment.


Methods of treatment can comprise in some embodiments targeting one or more genes or polypeptides of one or more signatures of Tables 1A-1E. Methods of treatment can also comprise treating a subject with SyS comprising administration of an isolated or engineered CD8+ T cell characterized by expression of an expansion program as defined in Table 1F, or a CD8+ T cell characterized by increased expression of IFN gamma or macrophage with increased expression of TNF alpha. Isolated or engineered CD8+ T cells characterized by increased expression of IFN gamma and/or macrophages with increased expression of TNF alpha are also provided. Methods of treatment for Synovial Sarcoma can comprise treatment with TNF and IFN-gamma, in some embodiments, the treatment providing a synergistic effect. Methods of treatment comprising administration of a modulator of one or more genes of cell cycle signature as defined in Table 1C, a SS18-SSX signature as defined in Table 8, or a combination thereof are also provided. In embodiments, administration of both modulators provides a synergistic effect.


In certain embodiments, the one or more agents comprise an antibody, small molecule, small molecule degrader, genetic modifying agent, antibody-like protein scaffold, aptamer, protein, or any combination thereof. In certain embodiments, the genetic modifying agent comprises a CRISPR system, RNAi system, a zinc finger nuclease system, a TALE, or a meganuclease. In certain embodiments, the CRISPR system comprises Cas9, Cas12, or Cas14. In certain embodiments, the CRISPR system comprises a dCas fused or otherwise linked to a nucleotide deaminase. In certain embodiments, the nucleotide deaminase is a cytidine deaminase or an adenosine deaminase. In certain embodiments, the dCas is a dCas9, dCas12, dCas13, or dCas14.


Methods of treating Synovial Sarcoma (Sys) in a subject are provided comprising: i) detecting the expression or activity of a malignant cell gene signature is a sample from a subject, the signature comprising one or more biomarkers selected from the group consisting of: a) epithelial malignant signature as defined in Table 1E; b) mesenchymal malignant cell signature as defined in Table 1D; c) cell cycle signature as defined in Table 1C; d) core oncogenic signature as defined in Table 1A.1; e) a fusion signature as defined in Table 8; or f) a combination thereof and ii) administering an effective amount of a modulating agent of the signature. In an aspect, the modulating agent is inhibitor of HDAC, CDK4/6, or a combination thereof, to selectively target synovial sarcoma cells.


These and other aspects, objects, features, and advantages of the example embodiments will become apparent to those having ordinary skill in the art upon consideration of the following detailed description of illustrated example embodiments.





BRIEF DESCRIPTION OF THE DRAWINGS

An understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention may be utilized, and the accompanying drawings of which:



FIG. 1A-1C—Mapping the cellular ecosystem of SyS tumors with single-cell transcriptomics. (1A) Study workflow. (1B) Converging assignments of cell identity. t-SNE plots of single cells (dots), shaded according to (1) tumor sample, (2) inferred cell type, (3) SS18-SSX1/2 fusion detection, (4) CNV detection, and (5) differential similarity to SyS compared to other sarcomas (see Methods). (1C) Inferred large-scale CNVs distinguish malignant (top) from non-malignant (bottom) cells, and are concordant with WES data. The inferred CNVs (amplifications in gray, and deletions in black) are shown along the chromosomes (x axis) for each cell (y axis).



FIG. 2A-2D—Consistent classification of cells based on transcriptomic and genetic features. (2A) Converging assignments of cell identity. tSNE plots of single cells (dots), colored according to (1) tumor sample, (2) inferred cell type, (3) SS18-SSX1/2 and MEOX2-AGMO fusion detection, (4) SSX1/2 gene detection (mRNA level >0), (5) MEOX2 and AGMO gene detection (mRNA level >0), (6-12) overall expression of well-established cell type markers (provided in Table 4). (2B) tSNE plots of single cells (dots), sequenced with a droplet-based approach (Zheng et al. Nat. Commun. 8, 14049 (2017)), colored according to (1) tumor sample, (2) inferred cell type, (3) SSX1/2 gene detection (mRNA level >0). (2C) tSNE plots of malignant cells (dots), sequenced with a droplet-based approach (Zheng et al. Nat. Commun. 8, 14049 (2017)), shaded according to the different malignant programs. (2D) Differential similarity to SyS compared to other sarcomas (Methods) is distinguishing malignant from non-malignant cells.



FIG. 3A-3C—Identifying the unique characteristics of SyS cells. (3A) The SyS program includes genes which are overexpressed by malignant cells compared to all types of non-malignant cells in the cohort; the expression of this program distinguishes between SyS and non-SyS cancer types, including those with hallmark BAF complex genomic aberrations: malignant rhabdoid tumor (MRT), epitheloid sarcoma (EpS), renal medullary carcinoma (RMC), small-cell carcinoma of the ovary, hypercalcemic type (SCCOHT), and SMARCA4-deficient thoracic sarcomas (SA4DTS). (3B) MEOX2 expression is highest in SyS tumors compared to other cancer types. (3C) MEOX2, and the cancer testis antigens CTAG1A, CTAG1B (encoding for NY-ESO-1), and PRAME are included in the SyS program; the expression of these genes across the malignant and non-malignant cells is shown.



FIG. 4A-4F—Intratumor heterogeneity couples between de-differentiation, cell cycle, and the core oncogenic program. (4A) t-SNE plots of malignant cells (dots), shaded by: (1) sample, (2) the epithelial vs. mesenchymal differentiation scores, (3) cycling status, and (4) the expression of the core oncogenic state. In (1), the mesenchymal and epithelial subpopulations of the biphasic tumors (BP), and the poorly differentiated (PD) tumor are marked with dashed circles. The other tumors are monophasic. (4B) Top core oncogenic genes (rows) across the malignant cells (columns), sorted according to the overall expression of the core oncogenic program (bottom bar). Top bar: biphasic tumor and sample. (4C) Left: Differentiation trajectories. A spectrum of malignant cell states along the mesenchymal to epithelial x axis and the stem-like to differentiated y axis; right: The expression of a G2/M phase signature (y axis) vs. the expression of a G1/S phase signature (x axis) across the malignant cells; in both plots the cells are shaded according to the expression of the cell cycle program, uncovering a strong association between cell cycle and poor differentiation (see also FIGS. 12B-12F). (4D) The percentage of cycling and poorly differentiated cells, among malignant cells with a high (above median) and low (below median) overall expression of the core oncogenic program. (4E-4F) In situ detection of core oncogenic, epithelial and mesenchymal programs. (4E) Immunofluorescence (t-CyCIF) and (4F) immunohistochemical stains of differentiation and core oncogenic markers.



FIG. 5A, 5B—The core oncogenic program and de-differentiation are associated with aggressive and metastatic disease. (5A) The expression of the different malignant programs across 34 SyS tumors (McBride et al. Cancer Cell (2018) doi:10.1016/j.ccell.2018.05.002), stratified according to tumor type: biphasic (BP), monophasic (MP), and poorly differentiated (PD). (5B) The programs are predictive of metastatic disease in an independent cohort obtained from 58 SyS patients (Banito et al. Cancer Cell 33:527-541.e8 (2018)). Kaplan-Meier (KM) curves of metastasis free survival, when stratifying the patients by high (top 25%), low (bottom 25%), or intermediate (remainder) expression of the respective program. Number of subjects at risk indicated at the bottom. P: COX regression p-value; Pc: COX regression p-value when controlling for fusion type and patient age group.



FIG. 6A, 6B—The core oncogenic program captures inter-patient variation. The inter-patient variation of the program was evaluated based on an independent RNA-Seq cohort from 64 SyS tumors (McBride et al. Cancer Cell (2018), doi:10.1016/j.ccell.2018.05.002), which were previously classified into two transcriptionally distinct clusters (McBride et al. Cancer Cell (2018), doi:10.1016/j.ccell.2018.05.002), denoted here as MYC-high and MYC-low. (6A) The overall expression of the program is correlated with the second Principle Component (PC2) of the data, and is significantly higher in the MYC-high cluster (P=1.66*10−7, t-test). (6B) The core oncogenic genes (columns) mostly correlated with PC2 are shown across the tumors (columns). Tumors are sorted according to PC2 (bottom bar).



FIG. 7A-7F—The SS18-SSX oncoprotein sustains the core oncogenic program, cell cycle, and dedifferentiation. (7A) Co-embedding (using PCA and canonical correlation analyses (Butler et al. Nat Biotechnol 36:411 (2018)) of ASKA and SYO1 cells (dots), shaded by: (1) condition, the overall expression of the (2) the cell cycle, (3) core oncogenic, and (4) mesenchymal differentiation (Taube et al. PNAS 107:15449-15454 (2010); Gröger et al. PLOS ONE 7:e51136 (2012)) programs. (7B) The overall expression of the cell cycle and core oncogenic programs is repressed in cells with the SSX shRNA (shSSX), while mesenchymal differentiation (Taube et al. PNAS 107:15449-15454 (2010); Gröger et al. PLOS ONE 7:e51136 (2012)) is induced; the shSSX impact on the core oncogenic and mesenchymal programs are observed both in the cycling and non-cycling cells. (7C) The expression of the overlapping fusion and core oncogenic program genes (columns) across the ASKA and SYO1 cells (rows), with a control (shCt) or SSX (shSSX) shRNA. The cells are sorted according to the overall expression of the fusion program (rightmost bar). (7D-7E) The fusion program distinguishes SyS from (7D) other cancer types and (7E) other sarcomas. (7F) The most overrepresented gene sets in the fusion program, when considering the induced (left) and repressed (right) genes, stratified to direct (black) and indirect (grey) target genes.



FIGS. 8A-8C—Cancer-immune interactions. (8A) The fusion KD is inducing multiple immune responses. The topmost differentially expressed pathways in SyS cells with SS18-SSX (shSSX) vs. control (shCt) shRNA. The overall expression of each pathway is shown, when stratifying the cells according to their cycling status. (8B) Inferred level of various immune cell types is associated with the malignant programs in bulk SyS tumors, when controlling for tumor purity. (8C) Short-term (4-6 hours) TNF treatment repressed the core oncogenic and fusion programs, but the effect was not observed after 24 h.



FIGS. 9A-9F—Immune cells and their association with malignant cell states. (9A) TNF and IFNγ are detected primarily in macrophages and T cells, respectively. (9B) TNF and IFNγ synergistically repress the core oncogenic and fusion programs (see also FIG. 8C). (9C) t-SNE plots of immune and stroma cells (dots), colored according to inferred cell type (left) and sample (right). (9D) T cell exhaustion is correlated with T cell cytotoxicity. The cytotoxicity (x axis) and exhaustion (y axis) scores of CD8 T cells, colored according to the T cell expansion program (see Methods). (9E) The effector vs. exhaustion scores of CD8 T cells in SyS and melanoma (top; Methods), and their predicted responsiveness to immune checkpoint blockade (Sade-Feldman et al. Cell 175:998-1013.e20 (2018)) (bottom; Methods). (9F) SyS tumors manifest a cold phenotype. The inferred level of intratumoral immune cells is exceptionally low in SyS tumors compared to (left) other cancer types and (right) other sarcomas.



FIGS. 10A-10D—Exploring the cancer-immune interplay in SyS. (10A) tSNE plots of macrophages, shaded according to inferred cell subtype, and the M1/M2 polarization scores (expression of the M1 minus M2 program), according to previously defined gene signatures (Janky et al. PLOS Comput. Biol. 10, e1003731 (2014)), and new signatures defined here by comparing between the two macrophage clusters (Table 12). (10B) The M1/M2 polarization scores of the M1-like and M2-like macrophages, according to previously defined gene signatures (Janky et al. PLOS Comput. Biol. 10, e1003731 (2014)). (10C) Gene-gene correlations across macrophages in SyS (top) and melanoma (Jerby-Arnon et al. Cell. 175, 984-997.e24 (2018)), when considering genes from M1 and M2 signatures (10C) as previously defined (Martinez et al. J. Immunol. Baltim. Md. 1950. 177, 7303-7311 (2006)), and as defined here (Table 12). (10D) The prognostic value of T cell infiltration levels (Methods) in (left) melanoma, (middle) sarcoma and (right) SyS (Li et al. BMC Bioinformatics. 12, 323 (2011)). Kaplan-Meier (KM) curves stratified by high (top 25%), low (bottom 25%), or intermediate (remainder) T cell infiltration levels. Number of subjects at risk indicated at the bottom. P: COX regression p-value.



FIG. 11—Blocking the core oncogenic program as a therapeutic strategy. Here Applicants show the results of the pharmacological single/combinatorial interventions of cell viability and single-cell transcriptome (in two synovial sarcoma cell lines and mesenchymal stems cells). Applicants' findings demonstrate that the SS18-SSX oncoprotein sustains de-differentiation, proliferation and the core oncogenic program, while immune cells in the tumor microenvironment can repress the core oncogenic and fusion programs through TNF and IFNγ secretion; inhibition of HDAC and CDK4/6 inhibitors mimic these effects.



FIGS. 12A-12F—Associations between poor differentiation, cell cycle and the core oncogenic program. (12A) The expression of the top epithelial and mesenchymal program genes (rows) across the malignant cells (columns), sorted according to their epithelial vs. mesenchymal differentiation scores (topmost bar). Top bar: biphasic tumor, cell cycling status, epithelial vs. non-epithelial cell status, and tumor. (12B) The expression of the G2/M phase signatures (y axis) vs. the expression of the G1/S phase signature (x axis) across the malignant cells, shaded according to their cycling states. (12C) The differentiation scores of cycling and non-cycling malignant cells, shown across all tumors together and when stratifying the cells according to their tumor sample (only tumors with at least 10 cycling cells are shown). (12D-12F) Left: A spectrum of malignant cell states along the mesenchymal to epithelial x axis and the stem-like to differentiated y axis; middle: The expression of a G2/M phase signatures (y axis) vs. the expression of a G1/S phase signature (x axis) across the malignant cells; right: The percentage of cycling and poorly differentiated cells, among malignant cells with a high (above median) and low (below median) overall expression of the core oncogenic program. In (12D) only the malignant cells which were sequenced with a droplet-based approach are shown, in (12E) only malignant cells from treatment naïve tumors and (12F) post-treatment tumors are shown.



FIG. 13A-13F A single-cell map of the cellular ecosystem of synovial sarcoma tumors (13A-D) Consistent assignment of cell identity. t-SNE plots of scRNA-Seq profiles (dots), shaded by either (13A) tumor sample, (13B) inferred cell type, (13C) SS18-SSX1/2 fusion detection, (13D) CNA detection, and (13E) differential similarity to SyS compared to other sarcomas (Methods). Dashed ovals (13A): mesenchymal and epithelial malignant subpopulations of biphasic (BP) tumors or poorly differentiated (PD) tumor. (13F) Inferred large-scale CNAs distinguish malignant (top) from non-malignant (bottom) cells, and are concordant with WES data (bold). The CNAs (gray: amplifications, black: deletions) are shown along the chromosomes (x axis) for each cell (y axis).



FIG. 14A-14D SyS tumors manifest antitumor immunity with limited immune infiltration. FIG. 14A Immune and stroma cells in SyS tumors. t-SNE of immune and stroma cell profiles (dots), shaded by inferred cell type (left) or sample (right). (14B) The CD8 T cell expansion program is associated with particularly high cytotoxicity and lower than expected exhaustion. The cytotoxicity (x axis) and exhaustion (y axis) scores of SyS CD8 T cells, colored by the score of the T cell expansion program (METHODS). (14C) CD8 T cells in SyS (light gray) have higher effector programs than in melanoma (dark gray). Distribution of effector vs. exhaustion scores (x axis, top, METHODS) or an immune checkpoint blockade responsiveness program (x axis, bottom, METHODS) in CD8 T cells from each cancer type. (14D) SyS tumors manifest a particularly cold phenotype. Overall Expression of the immune cell signatures (y axis, METHODS) in SyS tumors (dark gray) and other cancer types (left panel) or other sarcomas (right panel).



FIG. 15A-15C—(15A) Distinct differentiation pattern in biphasic tumors. Single cell profiles dots arranged by the first two diffusion-map components (DCs) for representative examples of a biphasic (SyS12, left) and monophasic (SyS11, right) tumors, and shadred by the Overall Expression of the epithelial vs. mesenchymal programs (bar). (15B) Core oncogenic program genes. Normalized expression (centered TPM values, bar) of the top 100 genes in the core oncogenic program (columns) across the malignant cells (rows), sorted according to the Overall Expression of the program (bar plot, right). Leftmost bars: biphasic tumor and sample ID. (15C) The program is expressed in a higher proportion of cycling and poorly differentiated cells. Fraction of malignant cells (y axis) with a high (above median, black) and low (below median, gray) Overall Expression of the core oncogenic program, in cells stratified by cycling and differentiation status (x axis).



FIG. 16 The core oncogenic program and de-differentiation co-vary within and across tumors and are associated with aggressive and cold tumors. Inferred level of immune cell types is associated with the malignant programs in bulk SyS tumors, when controlling for tumor purity. Partial correlation (bar) between the inferred level of each immune subset (rows) and the core oncogenic and differentiation levels (columns).



FIG. 17 The genetic driver and immune cells form two opposing forces in shaping SyS malignant cell states. Overlap of SS18-SSX and core oncogenic programs. Expression (centered TPM) of genes (rows) shared between the fusion and core oncogenic programs across the Aska and SYO1 cells (columns), with a control (shCt) or SSX (shSSX) shRNA. Cells are ordered by the Overall Expression of the SS18-SSX program (bottom plot) and labeled by type and condition (bar, top).



FIG. 18A-18I The core oncogenic program can be selectively blocked in SyS cells by combined HDAC and CDK4/6 inhibitors. (18A) Gene regulatory model of control of the core oncogenic program by SS18-SSX. Light gray/gray: genes that are induced/repressed in the core oncogenic program. Banded light Gray: genes that are repressed in the core oncogenic program and directly repressed by HDAC1-SS18-SSX. Blunt arrows: repression; pointy arrows: activation. Thick edges represent paths from SS18-SSX to p21. (18B) Model of regulation and intervention in the core oncogenic program. SS18-SSX activates the core oncogenic program in an HDAC-dependent manner and promotes cell cycle through direct activation of CDK6 and CCND2 (CycD) transcription. The core program suppresses p21 and inhibits immunogenic features. HDAC and CDK6 inhibitors target SyS dependencies. (18C-18F) TNF, HDAC and CDK6 inhibitors suppress the core oncogenic program. Overall Expression of the core oncogenic program (18B), SS18-SSX program (18C), an immune resistance program identified in melanoma (18D), and MHC-1 genes (18E) in SyS cells and MSCs (x axis). (18C-18F) *P<0.1, **P<0.01, ***P<1*10−3, ****P<1*10−4, t-test. (18F,18G) Selective toxicity for SyS cell lines. (18G) Viability (y axis) of SyS cell lines and MSCs (x axis) under different drugs (x axis, *P<5*10−2, **P<5*10−3, ***P<5*10−4, ANOVA test). (18H) Selective toxicity to SyS lines vs. MSC (y axis, −log10(P-value), ANOVA) in each treatment (x axis). In (18C-18G) middle line: median; box edges: 25th and 75th percentiles, whiskers: most extreme points that do not exceed IQR*1.5; further outliers are marked individually. (18I) Model of intrinsic and microenvironment determinants of SyS cell states. Left: The SS18-SSX oncoprotein sustains de-differentiation, proliferation and the core oncogenic program. Right: immune cells in the tumor microenvironment can repress the core oncogenic and SS18-SSX programs through TNF and IFNγ secretion. Combined inhibition of HDAC and CDK4/6 mimics these effects selectively in SyS cells.



FIG. 19—The SyS program distinguishes between SyS and non-SyS cancer types. Distribution of the SyS program Overall Expression (y axis) across BAF driven tumors (left, x axis) and in TCGA (right, x axis). Middle line: median; box edges: 25th and 75th percentiles, whiskers: most extreme points that do not exceed ±IQR*1.5; further outliers are marked individually; P-value: Wilcoxon-rank sum test; AUC: Area Under the receiver operating characteristic Curve.



FIG. 20A-20C Characterizing mesenchymal, epithelial and poorly differentiated malignant cells. FIG. 20A Epithelial and mesenchymal program genes. The expression of the top epithelial and mesenchymal program genes (rows) across the malignant cells (columns), with cells sorted according to the difference in epithelial vs. mesenchymal OE scores (bottom plot). Topmost bar: epithelial vs. non-epithelial cell status, and sample. Canonical markers include HLA-B, HLA-C, IFITM2, IRF7, XAF1, and immune-related genes are CDH1, EPCAM, MUC1, SNAI2, TCF4, ZEB1 and ZEB 2). FIG. 20B RNA velocities are visualized on top of the two first principle components (PCs), showing the state and velocity of the malignant cells obtained from patient SyS12 using the droplet-based approach. FIG. 20C t-SNE plots of malignant cells obtained from patient SyS12 before and after treatment, revealing a subpopulation of mesenchymal cells without copy number amplifications in chromosomes 15, 18 and 19 (FIG. 1G).



FIG. 21A-21C The core oncogenic program is detected using different approaches and datasets. FIG. 21A Agreement between the core oncogenic program detected by a PCA and an iNMF approach. Overall Expression (OE) of the core oncogenic program across malignant SyS cells, as identified in the PCA-based approach (x axis) and in the integrative-NMF approach (y axis) (METHODS). FIG. 21B-FIG. 21C Program Overall Expression captures inter-tumor variation and the MYC-high cluster in 64 SyS tumors from an independent RNA-Seq cohort. The tumors were previously classified into two transcriptionally distinct clusters, denoted here as MYC-high and MYC-low. FIG. 21B For each tumor (dots), shown is the Overall Expression (OE) of the core oncogenic program (y axis) vs. the projection on the second Principle Component (PC2) of the data. FIG. 21C Normalized expression (centered log-transformed RPKM) of the core oncogenic program genes (columns) most correlated with PC2 across the tumors (columns). Tumors are sorted by their PC2 projection (bottom bar).



FIG. 22A-22C Characterizing the transcriptional impact of SS18-SSX inhibition and tumor microenvironment cytokines on synovial sarcoma cells. FIG. 22A Biological processes regulated in the SS18-SSX program. Gene sets (rows) most enriched (−log10(P-value), hypergeometric test, x axis) in induced (left) and repressed (right) SS18-SSX program genes, which are either direct (black bars) or indirect (grey bars) targets of SS18-SSX based on ChIP-Seq data (35, 36) and genetic perturbation. Vertical line denotes statistical significance following multiple hypotheses correction. FIG. 22B The SS18-SSX program distinguishes SyS from other cancer types and other sarcomas. Overall Expression of the SS18-SSX program (y axis) in either TCGA samples (n=9,391, top), stratified by cancer types (x axis), or in another independent cohort of sarcoma tumors (n=164, bottom) (48). Middle line: median; box edges: 25th and 75th percentiles, whiskers: most extreme points that do not exceed ±IQR*1.5; further outliers are marked individually. **P<0.01, ***P<1*10−3, ****P<1*10−4, t-test. FIG. 22C Repression of the core oncogenic and SS18-SSX programs by short term TNF treatment is not sustained long term. Distribution of Overall Expression scores (y axis) of the core oncogenic program and the direct and indirect SS18-SSX programs (x axis) in control cells (light gray) and cells treated with TNF for 4-6 hours (right) or more than 24 hours (left).



FIG. 23A-23C. HDAC and CDK4/6 inhibitors synergistically repress the core oncogenic program and induce cell autonomous immune responses. Distribution of the expression (y axis) of core oncogenic genes (FIG. 23A), as well as the Overall Expression of TNF (FIG. 23B) and IFN (FIG. 23C) signaling pathways in SyS cells and MSCs (x axis) under different treatments (legend). Middle line: median; box edges: 25th and 75th percentiles, whiskers: most extreme points that do not exceed ±IQR*1.5; further outliers are marked individually. **P<0.01, ***P<1*10−3, ****P<1*10−4, t-test.





The figures herein are for illustrative purposes only and are not necessarily drawn to scale.


DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS
General Definitions

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Definitions of common terms and techniques in molecular biology may be found in Molecular Cloning: A Laboratory Manual, 2nd edition (1989) (Sambrook, Fritsch, and Maniatis); Molecular Cloning: A Laboratory Manual, 4th edition (2012) (Green and Sambrook); Current Protocols in Molecular Biology (1987) (F. M. Ausubel et al. eds.); the series Methods in Enzymology (Academic Press, Inc.): PCR 2: A Practical Approach (1995) (M. J. MacPherson, B. D. Hames, and G. R. Taylor eds.): Antibodies, A Laboratory Manual (1988) (Harlow and Lane, eds.): Antibodies A Laboratory Manual, 2nd edition 2013 (E. A. Greenfield ed.); Animal Cell Culture (1987) (R. I. Freshney, ed.); Benjamin Lewin, Genes IX, published by Jones and Bartlet, 2008 (ISBN 0763752223); Kendrew et al. (eds.), The Encyclopedia of Molecular Biology, published by Blackwell Science Ltd., 1994 (ISBN 0632021829); Robert A. Meyers (ed.), Molecular Biology and Biotechnology: a Comprehensive Desk Reference, published by VCH Publishers, Inc., 1995 (ISBN 9780471185710); Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992); and Marten H. Hofker and Jan van Deursen, Transgenic Mouse Methods and Protocols, 2nd edition (2011).


As used herein, the singular forms “a”, “an”, and “the” include both singular and plural referents unless the context clearly dictates otherwise.


The term “optional” or “optionally” means that the subsequent described event, circumstance or substituent may or may not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.


The recitation of numerical ranges by endpoints includes all numbers and fractions subsumed within the respective ranges, as well as the recited endpoints.


The terms “about” or “approximately” as used herein when referring to a measurable value such as a parameter, an amount, a temporal duration, and the like, are meant to encompass variations of and from the specified value, such as variations of +/−10% or less, +/−5% or less, +/−1% or less, and +/−0.1% or less of and from the specified value, insofar such variations are appropriate to perform in the disclosed invention. It is to be understood that the value to which the modifier “about” or “approximately” refers is itself also specifically, and preferably, disclosed.


As used herein, a “biological sample” may contain whole cells and/or live cells and/or cell debris. The biological sample may contain (or be derived from) a “bodily fluid”. The present invention encompasses embodiments wherein the bodily fluid is selected from amniotic fluid, aqueous humour, vitreous humour, bile, blood serum, breast milk, cerebrospinal fluid, cerumen (earwax), chyle, chyme, endolymph, perilymph, exudates, feces, female ejaculate, gastric acid, gastric juice, lymph, mucus (including nasal drainage and phlegm), pericardial fluid, peritoneal fluid, pleural fluid, pus, rheum, saliva, sebum (skin oil), semen, sputum, synovial fluid, sweat, tears, urine, vaginal secretion, vomit and mixtures of one or more thereof. Biological samples include cell cultures, bodily fluids, cell cultures from bodily fluids. Bodily fluids may be obtained from a mammal organism, for example by puncture, or other collecting or sampling procedures.


The terms “subject,” “individual,” and “patient” are used interchangeably herein to refer to a vertebrate, preferably a mammal, more preferably a human. Mammals include, but are not limited to, murines, simians, humans, farm animals, sport animals, and pets. Tissues, cells and their progeny of a biological entity obtained in vivo or cultured in vitro are also encompassed.


Various embodiments are described hereinafter. It should be noted that the specific embodiments are not intended as an exhaustive description or as a limitation to the broader aspects discussed herein. One aspect described in conjunction with a particular embodiment is not necessarily limited to that embodiment and can be practiced with any other embodiment(s). Reference throughout this specification to “one embodiment”, “an embodiment,” “an example embodiment,” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” or “an example embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to a person skilled in the art from this disclosure, in one or more embodiments. Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention. For example, in the appended claims, any of the claimed embodiments can be used in any combination.


Reference is made to International Application No. PCT/US2018/024082, published as WO2018175924A1 on Sep. 27, 2018.


All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as though each individual publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.


Overview

Embodiments disclosed herein provide methods and compositions for modulating an innate immune response, in particular an innate lymphoid cell class 2 innate immune response by modulating activity of SS18-SSX oncoprotein. Embodiments disclosed herein also provide for methods of monitoring an innate immune response in response to disease or treatment.


Oncogenic program comprises dedifferentiations, cell cycle and new cellular modality.


Differentiation trajectory includes mesenchymal and epithelial lineage programs, with mesenchymal program overlapping signatures of epithelial to mesenchymal transition (s1 and s4) and comprises markers of ZEB1, ZEB2, PDGFRA and SNAI2).


Applicants disclose herein methods and systems used to comprehensively map and interrogate cell states in Synovial Sarcoma (SyS), along with their regulatory circuits and clinical implications. Applicants demonstrate that the SS18-SSX oncoprotein and the tumor microenvironment coordinately shape cell states in SyS, with the present invention providing modulating, regulating and/or targeting of the programs to result in more effective treatment strategies. In particular, Applicants leverage scRNA-Seq data to map cell states in human SyS tumors to reveal the core oncogenic program associated with aggressive disease. Applicants further identified that TNF and IFNγ repress the program, and counteract the transcriptional alterations induced by the oncoprotein. Advantageously, Applicants discovered that targeting the program with HDAC and CDK4/6 inhibitors repressed the program and was detrimental to SyS cells, while sparing nonmalignant cells. Accordingly, the discovery provides a basis for the development of specific therapeutic strategies of Sys.


The discovery presented herein identifies programs tightly linked to clinical outcomes. The overall expression of the programs in bulk tumors can be used for synovial sarcoma patient stratification. The methods and compositions described herein may be used to shift the balance of cellular responses in Synovial Sarcoma patients in order to treat inflammatory allergic diseases and cancer.


Expression Signatures

In certain example embodiments, the therapeutic, diagnostic, and screening methods disclosed herein target, detect, or otherwise make use of one or more biomarkers of an expression signature. As used herein, the term “biomarker” can refer to a gene, an mRNA, cDNA, an antisense transcript, a miRNA, a polypeptide, a protein, a protein fragment, or any other nucleic acid sequence or polypeptide sequence that indicates either gene expression levels or protein production levels. Accordingly, it should be understood that reference to a “signature” in the context of those embodiments may encompass any biomarker or biomarkers whose expression profile or whose occurrence is associated with a specific cell type, subtype, or cell state of a specific cell type or subtype within a population of cells (e.g., Synovial Sarcoma cells) or a specific biological program. As used herein the term “module” or “biological program” can be used interchangeably with “expression program” and refers to a set of biomarkers that share a role in a biological function (e.g., an activation program, cell differentiation program, proliferation program). Biological programs can include a pattern of biomarker expression that result in a corresponding physiological event or phenotypic trait. Biological programs can include up to several hundred biomarkers that are expressed in a spatially and temporally controlled fashion. Expression of individual biomarkers can be shared between biological programs. Expression of individual biomarkers can be shared among different single cell types; however, expression of a biological program may be cell type specific or temporally specific (e.g., the biological program is expressed in a cell type at a specific time). Expression of a biological program may be regulated by a master switch, such as a nuclear receptor or transcription factor. As used herein, the term “topic” refers to a biological program. Topics are described further herein. The biological program (topic) can be modeled as a distribution over expressed biomarkers.


In certain embodiments, the expression of the signatures disclosed herein (e.g., core oncogenic signature) is dependent on epigenetic modification of the biomarkers or regulatory elements associated with the signatures (e.g., chromatin modifications or chromatin accessibility). Thus, in certain embodiments, use of signature biomarkers includes epigenetic modifications of the biomarkers that may be detected or modulated. As used herein, the terms “signature”, “expression profile”, or “expression program” may be used interchangeably (e.g., expression of genes, expression of gene products or polypeptides). It is to be understood that also when referring to proteins (e.g. differentially expressed proteins), such may fall within the definition of “gene” signature. Levels of expression or activity may be compared between different cells in order to characterize or identify for instance signatures specific for cell (sub)populations. Increased or decreased expression or activity or prevalence of signature biomarkers may be compared between different cells in order to characterize or identify for instance specific cell (sub)populations. The detection of a signature in single cells may be used to identify and quantitate, for instance, specific cell (sub)populations. A signature may include a biomarker whose expression or occurrence is specific to a cell (sub)population, such that expression or occurrence is exclusive to the cell (sub)population. An expression signature as used herein, may thus refer to any set of up- and/or down-regulated biomarkers that are representative of a cell type or subtype. An expression signature as used herein, may also refer to any set of up- and/or down-regulated biomarkers between different cells or cell (sub)populations derived from a gene-expression profile. For example, an expression signature may comprise a list of biomarkers differentially expressed in a distinction of interest.


The signature according to certain embodiments of the present invention may comprise or consist of one or more biomarkers, such as for instance 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of two or more biomarkers, such as for instance 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of three or more biomarkers, such as for instance 3, 4, 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of four or more biomarkers, such as for instance 4, 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of five or more biomarkers, such as for instance 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of six or more biomarkers for instance 6, 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of seven or more biomarkers, such as for instance 7, 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of eight or more biomarkers, such as for instance 8, 9, 10 or more. In certain embodiments, the signature may comprise or consist of nine or more biomarkers, such as for instance 9, 10 or more. In certain embodiments, the signature may comprise or consist of ten or more biomarkers, such as for instance 10, 11, 12, 13, 14, 15, or more. It is to be understood that a signature according to the invention may for instance also include different types of biomarkers combined (e.g. genes and proteins).


In certain embodiments, a signature is characterized as being specific for a particular cell or cell (sub)population if it is upregulated or only present, detected or detectable in that particular cell or cell (sub)population, or alternatively is downregulated or only absent, or undetectable in that particular cell or cell (sub)population. In this context, a signature consists of one or more differentially expressed genes/proteins or differential epigenetic elements when comparing different cells or cell (sub)populations, including comparing different cells or cell (sub)populations (e.g., synovial sarcoma cells), as well as comparing malignant cells or malignant cell (sub)populations with other non-malignant cells or non-malignant cell (sub)populations. It is to be understood that “differentially expressed” biomarkers include biomarkers which are up- or down-regulated as well as biomarkers which are turned on or off. When referring to up- or down-regulation, in certain embodiments, such up- or down-regulation is preferably at least two-fold, such as two-fold, three-fold, four-fold, five-fold, or more, such as for instance at least ten-fold, at least 20-fold, at least 30-fold, at least 40-fold, at least 50-fold, or more. Alternatively, or in addition, differential expression may be determined based on common statistical tests, as is known in the art. Differential expression of biomarkers may also be determined by comparing expression of biomarkers in a population of cells or in a single cell. In certain embodiments, expression of one or more biomarkers is mutually exclusive in cells having a different cell state or subtype (e.g., two genes are not expressed at the same time). In certain embodiments, a specific signature may have one or more biomarkers upregulated or downregulated as compared to other biomarkers in the signature within a single cell (see, e.g., Table 4). Thus a cell type or subtype can be determined by determining the pattern of expression in a single cell.


As discussed herein, differentially expressed biomarkers may be differentially expressed on a single cell level, or may be differentially expressed on a cell population level. Preferably, the differentially expressed biomarkers as discussed herein, such as constituting the expression signatures as discussed herein, when as to the cell population level, refer to biomarkers that are differentially expressed in all or substantially all cells of the population (such as at least 80%, preferably at least 90%, such as at least 95% of the individual cells). This allows one to define a particular subpopulation of cells. As referred to herein, a “subpopulation” of cells preferably refers to a particular subset of cells of a particular cell type (e.g., Synovial Sarcoma) which can be distinguished or are uniquely identifiable and set apart from other cells of this cell type. The cell subpopulation may be phenotypically characterized, and is preferably characterized by the signature as discussed herein. A cell (sub)population as referred to herein may constitute of a (sub)population of cells of a particular cell type characterized by a specific cell state.


When referring to induction, or alternatively suppression of a particular signature, preferable is meant induction or alternatively suppression (or upregulation or downregulation) of at least one biomarker of the signature, such as for instance at least two, at least three, at least four, at least five, at least six, or all biomarkers of the signature.


Example gene signatures and topics are further described below.


Malignant Programs

In certain embodiments, a malignant signature (e.g., signature of differentially expressed genes between malignant cells and non-malignant cells, e.g. epithelial cells, CAFs, CD8 and CD4 T cells, B cells, NK cells, macrophages, or mastocytes; or genes that can be modulated by HDAC and CDK4/6 inhibitors) comprises one or more biomarkers selected from one of Tables 1A-1E. In particular embodiments when core oncogenic program gene signatures of Table 1A is upregulated, or the core oncogenic gene signatures of Table 1B is downregulated, or a combination thereof are detected, the detected signature is indicative of increased metastatic disease.









TABLE 1A.1





Core Oncogenic Program





















AFG3L1P
CD63
EIF4EBP1
LARP1
NDUFA4
PRKDC
SULT1A1


AGPAT2
CD7
ELAC2
LDHB
NDUFA7
PSMA5
SUMF2


AGPAT5
CDK2AP1
ELOVL1
LECT1
NDUFA8
PSMA7
SYNPR


AHCY
CECR5
EML3
LGALS1
NDUFAB1
PSMB7
TBCD


AKR1B1
CHCHD1
ENO1
LINC00115
NDUFB10
PSMD4
TCEB2


AKR1C3
CHCHD2
EPRS
LINC00116
NDUFB11
PSMG3
TELO2


AKT1
CIAPIN1
ERGIC3
LINC00516
NDUFB2
PTPRF
TFAP2A


ALDH1A1
CKAP5
ETAA1
LINC00665
NDUFB3
PTPRS
THY1


ALG3
CLDN4
EXOSC4
LOC100131234
NDUFB4
PUS7
TIGD1


ALX4
CLNS1A
EXOSC7
LOC100272216
NDUFB7
PXDN
TIMM13


ANAPC7
CNPY2
FADD
LOC101101776
NDUFB9
PYCR1
TIMM8B


ANKRD26P1
COA5
FADS2
LOC202781
NDUFS6
RABAC1
TKT


APEH
COL18A1
FAM178A
LOC375295
NDUFS8
RABL6
TMA7


APEX1
COL5A1
FAM19A5
LOC441081
NEDD8
RANBP1
TMC6


APP
COL6A2
FAM213B
LOC654433
NEFL
RBM26
TMEM101


APRT
COL9A3
FAM50B
LOXL1
NHP2
RBM6
TMEM147


ARF5
COX4I1
FARSA
LSM4
NIPSNAP3A
RBX1
TMEM177


ARL6IP4
COX5A
FARSB
LSM7
NKAIN4
REST
TMSB10


ARL6IP5
COX5B
FBN3
LUC7L3
NME1
RGMA
TMTC2


ASB13
COX6A1
FGF19
LY6E
NME2
RGS10
TOMM40


ATF7IP
COX6B1
FGF9
MAB21L1
NNT
RHOBTB3
TOMM6


ATIC
COX6C
FLAD1
MAGEA4
NOMO1
RNASEK
TOMM7


ATP5A1
COX7C
FMO1
MAGEA9
NOMO2
RNPC3
TRAPPC1


ATP5C1
CRIP1
FRG1B
MAGEC2
NPEPL1
RNPEP
TSPAN3


ATP5E
CRLF1
FSD1
MAP1B
NRBP2
ROMO1
TSR3


ATP5G2
CRMP1
G6PC3
MATN3
NREP
RUVBL1
TSTA3


ATP5I
CSAG3
GABPB1-AS1
MBD6
NSMF
RUVBL2
TTYH3


ATP5J
CSE1L
GADD45GIP1
MDH2
NSUN5
SARS2
TUBG1


ATP5J2
CSRP2BP
GAPDH
MDK
NSUN5P1
SELENBP1
TUFM


ATP5O
CST3
GCN1L1
METTL3
NSUN5P2
SEMA3A
TUSC3


ATR
CSTB
GDI2
MFSD3
NT5DC2
SERF2
TWIST2


ATRAID
CSTF3
GEMIN7
MGC21881
NUBP2
SERTAD4
TXN


AUP1
CTAG1A
GGH
MGST1
NUDT5
SETD4
TXNDC17


AURKAIP1
CTAG1B
GLB1L
MGST3
NUTF2
SFN
TXNDC5


BCAP31
CYC1
GLB1L2
MIF
OBSL1
SGK196
TXNDC9


BCL7C
CYHR1
GLI1
MIS18A
OGG1
SH2D4A
UBA52


BMP1
DAD1
GNAS
MKKS
OST4
SH3PXD2B
UBE2T


BOP1
DANCR
GNB2L1
MMP14
OXLD1
SHMT2
UBE3B


BRK1
DBNDD1
GNPTAB
MRPL12
PAFAH1B3
SIGIRR
UCK2


BSG
DCHS1
GOLM1
MRPL15
PARK7
SIM2
UCP2


BTF3
DCP1B
GPR124
MRPL17
PATZ1
SIX1
UPK3B


C11orf48
DCTPP1
GPR126
MRPL28
PAX3
SLC25A23
UQCR10


C14orf2
DCXR
GPRC5B
MRPL35
PAX9
SLC25A6
UQCR11


C16orf88
DGCR6L
GSTO2
MRPL4
PCDHA3
SLC35B4
UQCRB


C17orf76-AS1
DHFR
GUSB
MRPL52
PDCD11
SLC6A15
UQCRC1


C1QBP
DNMT3A
H19
MRPS17
PDCD5
SMARCA4
UQCRQ


C2orf68
DPEP3
HERC2
MRPS21
PDIA4
SMC2
USMG5


C4orf48
DPYSL2
HERC2P7
MRPS26
PEBP1
SMC3
USP5


C7orf73
DYNLRB1
HIGD2A
MRPS34
PET100
SNHG6
VARS


C9orf16
DYNLT1
HINT1
MTG1
PFKL
SNRPD2
VCAN


CAD
EDF1
HMG20B
MTRNR2L1
PFKP
SNRPD3
VKORC1


CALML3
EEF1B2
HN1L
MTRNR2L10
PFN1
SNRPF
VPS28


CAPNS1
EEF1D
HNRNPD
MTRNR2L2
PFN1P2
SOX11
VPS72


CBX6
EEF1G
HOXD11
MTRNR2L6
PGD
SPCS1
VSNL1


CCDC137
EIF2AK1
HOXD9
MTRNR2L8
PGLS
SPDYE8P
WDR12


CCDC140
EIF3C
HSD17B10
MYBBP1A
PHF14
SRI
YWHAB


CCT3
EIF3H
HYAL2
MZT2B
PIGM
SRM
ZNF212


CD320
EIF3K
HYLS1
NACA
PIGQ
SRSF9
ZNF605


IFT81
IMP3
ICT1
NAT14
PIGT
SSNA1
ING4


IRS4
ITM2C
ITPA
NDUFA1
PKD2
SSR4
JMJD8


KDM1A
KIAA0020
KIF1A
NDUFA11
PLP2
SSX2
KRT14


KRT15
KRT8
KRTCAP2
NDUFA13
PMS2P5
SSX2B
LAMA2


POLR1B
POLR2F
PPIA
NDUFA3
POLD2
STAG3L1
PPIB


PPIP5K2
PPP1R16A
PRDX2
PRDX4
PRELID1
STAG3L2
STAG3L3


STAG3L4
STARD4-AS1
SULF2
DDX3Y
IFRD1
NFKBIZ
SRSF3


AKIRIN1
DDX5
FOSL2
IRF1
NR4A1
TNFAIP3
CDKN1A


AMD1
DLX2
GADD45B
JUN
NR4A2
TNFRSF12A
CKS2


ARC
DNAJA1
GEM
JUNB
NR4A3
TOB1
CLK1


ATF3
DNAJA4
GTF2B
JUND
PAFAH1B2
TRIB1
COQ10B


ATF4
DNAJB1
H3F3B
KLF10
PER1
TSPYL1
CSRNP1


BHLHE40
DNAJB9
HBP1
KLF4
PER2
TSPYL2
CYCS


BRD2
DUSP1
HERPUD1
KLF6
PPP1R15A
TUBA1A
DDIT3


BTG1
DUSP2
HES1
KLHL15
RGS16
TUBA1B
DDX3X


BTG2
EGR1
HSP90AA1
LMNA
RHOB
TUBB2A
EIF4A3


C12orf44
EGR2
HSP90AB1
LOC284454
RIPK4
TUBB4B
EIF5


C6orf62
EGR3
HSPA1A
MAFF
RRP12
UBB
ERF


CCNL1
EIF1
HSPA1B
MCL1
SAT1
UBC
ETF1


FOSL1
IER3
HSPA8
MIR22HG
SELK
XBP1
NFATC1


FAM53C
ID2
HSPH1
MLF1
SERTAD1
YWHAG
NFATC2


FOS
ID3
ICAM1
MXD1
SF1
ZBTB21
NFKBIA


FOSB
IER2
ID1
MYADM
SIK1
ZFAND5
SLC25A44


SOCS3
SLC25A25
ZFP36
















TABLE 1A.2





Core Oncogenic Program Upregulated





















AFG3L1P
CD63
EIF4EBP1
LARP1
NDUFA4
PRKDC
SULT1A1


AGPAT2
CD7
ELAC2
LDHB
NDUFA7
PSMA5
SUMF2


AGPAT5
CDK2AP1
ELOVL1
LECT1
NDUFA8
PSMA7
SYNPR


AHCY
CECR5
EML3
LGALS1
NDUFAB1
PSMB7
TBCD


AKR1B1
CHCHD1
ENO1
LINC00115
NDUFB10
PSMD4
TCEB2


AKR1C3
CHCHD2
EPRS
LINC00116
NDUFB11
PSMG3
TELO2


AKT1
CIAPIN1
ERGIC3
LINC00516
NDUFB2
PTPRF
TFAP2A


ALDH1A1
CKAP5
ETAA1
LINC00665
NDUFB3
PTPRS
THY1


ALG3
CLDN4
EXOSC4
LOC100131234
NDUFB4
PUS7
TIGD1


ALX4
CLNS1A
EXOSC7
LOC100272216
NDUFB7
PXDN
TIMM13


ANAPC7
CNPY2
FADD
LOC101101776
NDUFB9
PYCR1
TIMM8B


ANKRD26P1
COA5
FADS2
LOC202781
NDUFS6
RABAC1
TKT


APEH
COL18A1
FAM178A
LOC375295
NDUFS8
RABL6
TMA7


APEX1
COL5A1
FAM19A5
LOC441081
NEDD8
RANBP1
TMC6


APP
COL6A2
FAM213B
LOC654433
NEFL
RBM26
TMEM101


APRT
COL9A3
FAM50B
LOXL1
NHP2
RBM6
TMEM147


ARF5
COX4I1
FARSA
LSM4
NIPSNAP3A
RBX1
TMEM177


ARL6IP4
COX5A
FARSB
LSM7
NKAIN4
REST
TMSB10


ARL6IP5
COX5B
FBN3
LUC7L3
NME1
RGMA
TMTC2


ASB13
COX6A1
FGF19
LY6E
NME2
RGS10
TOMM40


ATF7IP
COX6B1
FGF9
MAB21L1
NNT
RHOBTB3
TOMM6


ATIC
COX6C
FLAD1
MAGEA4
NOMO1
RNASEK
TOMM7


ATP5A1
COX7C
FMO1
MAGEA9
NOMO2
RNPC3
TRAPPC1


ATP5C1
CRIP1
FRG1B
MAGEC2
NPEPL1
RNPEP
TSPAN3


ATP5E
CRLF1
FSD1
MAP1B
NRBP2
ROMO1
TSR3


ATP5G2
CRMP1
G6PC3
MATN3
NREP
RUVBL1
TSTA3


ATP5I
CSAG3
GABPB1-AS1
MBD6
NSMF
RUVBL2
TTYH3


ATP5J
CSE1L
GADD45GIP1
MDH2
NSUN5
SARS2
TUBG1


ATP5J2
CSRP2BP
GAPDH
MDK
NSUN5P1
SELENBP1
TUFM


ATP5O
CST3
GCN1L1
METTL3
NSUN5P2
SEMA3A
TUSC3


ATR
CSTB
GDI2
MFSD3
NT5DC2
SERF2
TWIST2


ATRAID
CSTF3
GEMIN7
MGC21881
NUBP2
SERTAD4
TXN


AUP1
CTAG1A
GGH
MGST1
NUDT5
SETD4
TXNDC17


AURKAIP1
CTAG1B
GLB1L
MGST3
NUTF2
SFN
TXNDC5


BCAP31
CYC1
GLB1L2
MIF
OBSL1
SGK196
TXNDC9


BCL7C
CYHR1
GLI1
MIS18A
OGG1
SH2D4A
UBA52


BMP1
DAD1
GNAS
MKKS
OST4
SH3PXD2B
UBE2T


BOP1
DANCR
GNB2L1
MMP14
OXLD1
SHMT2
UBE3B


BRK1
DBNDD1
GNPTAB
MRPL12
PAFAH1B3
SIGIRR
UCK2


BSG
DCHS1
GOLM1
MRPL15
PARK7
SIM2
UCP2


BTF3
DCP1B
GPR124
MRPL17
PATZ1
SIX1
UPK3B


C11orf48
DCTPP1
GPR126
MRPL28
PAX3
SLC25A23
UQCR10


C14orf2
DCXR
GPRC5B
MRPL35
PAX9
SLC25A6
UQCR11


C16orf88
DGCR6L
GSTO2
MRPL4
PCDHA3
SLC35B4
UQCRB


C17orf76-AS1
DHFR
GUSB
MRPL52
PDCD11
SLC6A15
UQCRC1


C1QBP
DNMT3A
H19
MRPS17
PDCD5
SMARCA4
UQCRQ


C2orf68
DPEP3
HERC2
MRPS21
PDIA4
SMC2
USMG5


C4orf48
DPYSL2
HERC2P7
MRPS26
PEBP1
SMC3
USP5


C7orf73
DYNLRB1
HIGD2A
MRPS34
PET100
SNHG6
VARS


C9orf16
DYNLT1
HINT1
MTG1
PFKL
SNRPD2
VCAN


CAD
EDF1
HMG20B
MTRNR2L1
PFKP
SNRPD3
VKORC1


CALML3
EEF1B2
HN1L
MTRNR2L10
PFN1
SNRPF
VPS28


CAPNS1
EEF1D
HNRNPD
MTRNR2L2
PFN1P2
SOX11
VPS72


CBX6
EEF1G
HOXD11
MTRNR2L6
PGD
SPCS1
VSNL1


CCDC137
EIF2AK1
HOXD9
MTRNR2L8
PGLS
SPDYE8P
WDR12


CCDC140
EIF3C
HSD17B10
MYBBP1A
PHF14
SRI
YWHAB


CCT3
EIF3H
HYAL2
MZT2B
PIGM
SRM
ZNF212


CD320
EIF3K
HYLS1
NACA
PIGQ
SRSF9
ZNF605




ICT1
NAT14
PIGT
SSNA1




IFT81
NDUFA1
PKD2
SSR4




IMP3
NDUFA11
PLP2
SSX2




ING4
NDUFA13
PMS2P5
SSX2B




IRS4
NDUFA3
POLD2
STAG3L1




ITM2C

POLR1B
STAG3L2




ITPA

POLR2F
STAG3L3




JMJD8

PPIA
STAG3L4




KDM1A

PPIB
STARD4-AS1




KIAA0020

PPIP5K2
SULF2




KIF1A

PPP1R16A




KRT14

PRDX2




KRT15

PRDX4




KRT8

PRELID1




KRTCAP2




LAMA2
















TABLE 1A.3





Core Oncogenic Program Downregulated




















AKIRIN1
DDX5
FOSL2
IRF1
NR4A1
TNFAIP3


AMD1
DLX2
GADD45B
JUN
NR4A2
TNFRSF12A


ARC
DNAJA1
GEM
JUNB
NR4A3
TOB1


ATF3
DNAJA4
GTF2B
JUND
PAFAH1B2
TRIB1


ATF4
DNAJB1
H3F3B
KLF10
PER1
TSPYL1


BHLHE40
DNAJB9
HBP1
KLF4
PER2
TSPYL2


BRD2
DUSP1
HERPUD1
KLF6
PPP1R15A
TUBA1A


BTG1
DUSP2
HES1
KLHL15
RGS16
TUBA1B


BTG2
EGR1
HSP90AA1
LMNA
RHOB
TUBB2A


C12orf44
EGR2
HSP90AB1
LOC284454
RIPK4
TUBB4B


C6orf62
EGR3
HSPA1A
MAFF
RRP12
UBB


CCNL1
EIF1
HSPA1B
MCL1
SAT1
UBC


CDKN1A
EIF4A3
HSPA8
MIR22HG
SELK
XBP1


CKS2
EIF5
HSPH1
MLF1
SERTAD1
YWHAG


CLK1
ERF
ICAM1
MXD1
SF1
ZBTB21


COQ10B
ETF1
ID1
MYADM
SIK1
ZFAND5


CSRNP1
FAM53C
ID2
NFATC1
SLC25A25
ZFP36


CYCS
FOS
ID3
NFATC2
SLC25A44


DDIT3
FOSB
IER2
NFKBIA
SOCS3


DDX3X
FOSL1
IER3
NFKBIZ
SRSF3


DDX3Y

IFRD1
















TABLE 1C





Malignant Cell Cycle Program

















ANLN



ARHGAP11A



ATAD5



BIRC5



BRCA2



BUB1B



C21orf58



CASC5



CCNA2



CCNB2



CCNE2



CDC6



CDKN3



CENPE



CENPF



CENPH



CENPK



CENPW



CHAF1B



CLSPN



DHFR



DNA2



DTL



EZH2



FANCA



FANCD2



FANCI



FOXM1



GINS2



HELLS



KIAA0101



KIF11



KIF14



KIF18A



KIF20B



KIF2C



KNSTRN



KNTC1



MAD2L1



MCM2



MCM3



MCM4



MCM5



MKI67



MLF1IP



NCAPD2



NCAPG2



NUSAP1



OAS3



OIP5



ORC6



PRC1



PSMC3IP



PTTG1



RACGAP1



RFC4



RNASEH2A



RRM2



SGOL2



SMC4



SPAG5



SPDL1



STIL



TCF19



TIMELESS



TK1



TOP2A



TPX2



TYMS



UBE2C



UBE2T



UHRF1



WDHD1



ZWINT










In particular embodiments, cell cycle program genes are detected, in particular embodiments, detecting is indicative of increased risk of metastatic disease, with absence i.e. detection of high differentiations is prognostic of metastasis free survival.









TABLE 1D





Mesenchymal Cell Malignant Program

















AASS



ADAM33



AKAP13



ANKRD44



ARMCX3



ATP1B2



BMP5



C14orf37



C14orf39



C16orf45



C1orf151-NBL1



CACNB2



CADM1



CALD1



CCBE1



CCDC88A



CD302



CLIP3



CNRIP1



CNTLN



COL1A2



COL21A1



COL4A1



COL4A2



COL5A1



COL5A2



COL6A3



COL8A1



CPXM1



CRTAP



CXCL12



CYGB



DAB2



DCN



DEGS1



DNAJA4



DNAJC12



DNM3OS



DZIP1



EDNRA



EGFR



EMP1



F2R



FBXO32



FERMT2



FGF10



FHL1



FKBP7



FLJ42709



FLNB



FN1



FOSL2



FRZB



FSTL1



GALNT18



GEM



GFPT2



GFRA1



GPM6B



GPX7



GSTA4



GSTM5



GYPC



HAAO



HCG11



HENMT1



HMGCLL1



HOXC10



HOXC9



HSD17B11



IFFO1



IL17RD



IL1R1



INHBA



INPP4B



ITPRIPL2



KIF26B



LAMA2



LAMB1



LEF1



LEPRE1



LOXL2



LRP1



LUM



MEF2A



MEOX2



MFAP4



MLF1



MMP2



MSN



MSRB3



MXRA5



MYL9



NCAM1



NDNF



NDOR1



NEDD4



NEFH



NID1



NID2



NR4A2



NUDT11



OXER1



PALLD



PDGFRA



PDIA5



PDLIM4



PDZRN3



PLIN2



PLK1S1



PLSCR4



PMP22



PPP1R15B



PROS1



QKI



QPRT



RAB31



RAI14



RASL11B



RBMS3



RCBTB2



RCN3



RGL1



RGS3



RHOJ



RUNX1T1



SEMA6A



SERTAD1



SESN1



SH3PXD2A



SIX1



SLC2A10



SNAI2



SPARC



ST3GAL3



STARD13



TCF12



TCF4



TGFB1I1



TMEM30B



TMEM45A



TNFRSF19



TSC22D3



UBE2E2



UBL3



UNC5B



WIF1



WNT16



ZEB1



ZEB2



ZFHX4



ZNF302

















TABLE 1E





Epithelial Cell Malignant Program

















ABCG1



ABHD11



ABRACL



ACOT7



ACP5



ADAMTSL2



AES



AGPAT2



AGRN



AGTRAP



AHNAK2



AIG1



AKR1C3



ALDH1A3



ALDH3A2



ALDH4A1



ALOX15



ANK3



ANO9



ANXA11



ANXA3



AP1M2



APOE



APP



ARHGAP8



ARID5A



ARRDC1



ASS1



ATHL1



ATP6V0E2



BAIAP2L1



BARX2



BCAM



BSCL2



C14orf1



C19orf21



C19orf33



C1GALT1C1



C1orf210



CAP2



CAPN6



CARD16



CARNS1



CBLC



CCDC153



CCDC24



CCND1



CD151



CD55



CD59



CD7



CD74



CD9



CDCP1



CDH1



CDH3



CDH4



CDK2AP2



CHST9



CKB



CLDN3



CLDN4



CLDN7



CLIC3



CLU



COL12A1



CRB3



CRIP1



CRIP2



CXADR



CXCL1



CYB561



CYBA



CYFIP2



CYHR1



CYP39A1



CYP4X1



CYSTM1



DBNDD2



DCXR



DDR1



DDX58



DHCR7



DMKN



DRD1



DSP



EFCAB4A



EFNA5



ELOVL1



ELOVL7



EMB



ENO2



ENPP5



ENTPD3



EPB41L5



EPCAM



EPHA2



EPS8L2



ERBB2



ERBB3



ESRP1



ESRP2



EZR



F11R



F2RL1



FAAH



FAAH2



FAM111A



FAM167A



FAM213A



FAM221A



FAM65C



FAM84B



FBXO2



FBXO44



FGF19



FGFRL1



FMO2



FXYD3



FXYD5



FZD6



GALNT3



GAS6



GCHFR



GPR56



GPRC5A



GPRC5C



GRB7



GSDMD



HERC6



HIGD2A



HLA-B



HMGA1



HOOK2



HPN



HSPB2



IFITM1



IFITM2



IFITM5



IGFBP6



IGSF9



INADL



INF2



IQGAP1



IRF6



IRF7



ISLR



ITGA3



ITGB4



ITGB8



ITPR2



ITPR3



JUP



KIAA1522



KIAA1598



KIF1A



KLF5



KLK1



KLK10



KLK11



KLK7



KLK8



KRT18



KRT19



KRT7



KRT8



KRTCAP3



LBH



LECT1



LGALS3BP



LIME1



LLGL2



LOC100505761



LOC541471



LOC646329



LPAR2



LPIN3



LRRC16A



LSR



LY6E



LYPD6B



MAGI1



MAL2



MAP7



MBOAT1



MCAM



MDK



MFSD3



MGAT4B



MIF4GD



MLXIPL



MPZL2



MSLN



MSMO1



MSX2



MUC1



MX1



MYH9



MYO6



NCOA7



NDUFA4L2



NDUFS8



NET1



NPNT



NSMF



NT5DC1



NT5E



NUDT14



OAS1



OCIAD2



OCLN



ORMDL2



P4HTM



PARD6B



PARP8



PARP9



PARVG



PCBD1



PDGFB



PDHX



PDLIM1



PDLIM2



PERP



PHYHD1



PIGV



PIM1



PKP3



PKP4



PLEKHB1



PLEKHG1



PLEKHN1



PLLP



PLXDC2



PLXNA2



PLXNB1



PNOC



PNP



PPL



PPP1CA



PPP1R16A



PPP1R1B



PPP1R9A



PRKCG



PRPH



PRR15



PRR15L



PRSS8



PSME1



PSME2



PTGER4



PTGES



PTN



PTPRF



PTRH1



RAB3IP



RALGPS1



RASSF7



RBM47



REC8



REEP2



RGL3



RHBDF2



RHBDL1



RIPK4



ROBO3



RTN3



S100A16



S100A4



S100A6



SAMD12



SCG5



SCNN1A



SCRN2



SEC11C



SECTM1



SELENBP1



SEMA3B



SGPL1



SH3YL1



SHANK2



SHANK2-AS3



SIM2



SLC11A2



SLC12A2



SLC16A5



SLC25A25



SLC25A29



SLC29A1



SLC35F2



SLC50A1



SLC6A9



SLC7A5



SLC7A8



SLFN5



SLPI



SMAD1



SMPDL3B



SORT1



SOX14



SPINT1



SPINT2



ST14



ST3GAL5



STAP2



STRA13



STRA6



STXBP2



SULF1



SULF2



SUMF1



SVIP



SYNGR2



SYTL1



TACSTD2



TAPBPL



TCF7L2



TENM1



TFAP2B



TFAP2C



TLE2



TLE6



TM4SF1



TM7SF2



TMC4



TMCC3



TMEM125



TMEM176B



TNFAIP2



TNFRSF12A



TNFRSF14



TNFRSF21



TNFSF13



TNKS1BP1



TNNI3



TNNT1



TOM1L1



TPD52



TSPO



TUBB2B



TUBB3



UCP2



VAMP8



WDR34



WDR54



WFDC2



XAF1



ZDHHC12



ZMAT1



ZNF165



ZNF423



ZNF664

















TABLE 1F







Expansion Program


T cell expansion











UP
DOWN















ANO6
ABHD3
MAFF



B2M
ALDH6A1
MAP4K4



BCL10
ALG13
MASTL



BHLHE40
AMICA1
MFNG



C6orf25
ARF5
MVD



CADM1
ARHGAP25
NAGPA



CHSY1
ARID3B
NBPF10



CMAHP
ARL5B
NDUFA1



CREB3
ARSD
NDUFB9



CRIP2
ATP5O
NFATC1



CX3CR1
ATXN10
NUDCD1



DDX20
ATXN7L1
OAS2



DHRS7
BANF1
OMA1



DSCR3
C14orf1
ORAOV1



EIF4A2
C9orf72
PECAM1



FAM83D
CBLL1
PECR



FCGR3A
CCR7
PFN1



FCRL6
CD27
PNPLA6



FGFBP2
CD28
PSMD3



GNLY
CD83
PTPRB



GPR114
CD84
REST



GPR56
CDK2
RGPD6



GRIPAP1
CETN2
SEC13



GSR
CLDND1
SLC25A32



GZMB
CLIC5
SNAP47



GZMH
CMBL
SOCS3



HOPX
CORO1A
TAGAP



HSDL1
COTL1
TBC1D7



KAT2B
CRTAM
THAP5



KIR2DS4
DCTN6
TIMMDC1



KLRD1
ECHDC1
TMPPE



KRR1
EDEM1
TOMM22



LTBP4
EIF3G
TOX



LZTR1
EXOSC2
TRAPPC5



MALAT1
FAIM3
TRIT1



METTL13
FCGRT
TRPT1



MGAT4A
GALM
TSPAN3



MMD
GGA2
URGCP



MRPL33
GPR183
USP16



N4BP2L2
GSKIP
VPS33B



NKG7
GUK1
VPS52



NSFP1
GUSBP3
YIPF5



PDCD7
GUSBP9



PFKM
GZMK



PLEKHA5
GZMM



PLEKHG3
HGS



PRF1
HIF1AN



RAD52
HIST1H1E



RASSF1
HNRPLL



RPP38
HPCAL1



RPS10
ISG20L2



SEC23B
JUNB



SERPINB9
KIAA0226



TM4SF19
KIAA1551



TSFM
KREMEN1



TTC21B
LDHB



ZDHHC7
LIMS1



ZNF41
LYST










In one example embodiment, the expression signature consists of overrepresented gene sets when considering induced and repressed genes, with both direct and indirect genes, as provided in FIG. 4. In some embodiments, the up-regulated targets are selected from E2F targets, RNA splicing, RNA processing, RNA binding, Ribonucleoprotein complex, Poly-a RNA binding, mRNA metabolic process, G2M checkpoint, Myc targets, Oxidative phosphorylation, Single-cell cell cycle, Single-cell oncogenic, Embryo development, Neurogenesis, Organ morphogenesis, Pattern specification process, Tissue development, Hedgehog signaling, Wnt beta catenin signaling, Single-cell synovial sarcoma. In one example embodiment, the down-regulated targets are selected from Cell junction, Extracellular matrix, Positive regulation of cell death, Regulation of cell differentiation, Regulation of cell proliferation, Regulation of organismal development, Response to lipid, Tissue development, Apoptosis, Coagulation, Epithelial mesenchymal transition, Hypoxia, Tnfa signaling via NFKb, Single-cell anti-oncogenic, Single-cell mesenchymal, Embryonic morphogenesis, Epithelium development, and Regulation of cell death.


In certain embodiments, Sys induces the malignant gene signature in synovial sarcoma cells and the Sys cells can be selectively targeted and this signature can be modulated by treatment with an inhibitor of HDAC or an inhibitor of CDK4/6.


In one example embodiment the malignant gene signature comprises ALDH1A1 and at least N additional biomarker from Tables 1A-1E, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or 51.


Malignant Epithelial Cell Signature

In one example embodiment, the Malignant Epithelial Program signature consists of one or more of ABCG1, ABHD11, ABRACL, ACOT7, ACP5, ADAMTSL2, AES, AGPAT2, AGRN, AGTRAP, AHNAK2, AIG1, AKR1C3, ALDH1A3, ALDH3A2, ALDH4A1, ALOX15, ANK3, ANO9, ANXA11, ANXA3, AP1M2, APOE, APP, ARHGAP8, ARID5A, ARRDC1, ASS1, ATHL1, ATP6VOE2, BAIAP2L1, BARX2, BCAM, BSCL2, C14orf1, C19orf21, C19orf33, C1GALT1C1, C1orf210, CAP2, CAPN6, CARD16, CARNS1, CBLC, CCDC153, CCDC24, CCND1, CD151, CD55, CD59, CD7, CD74, CD9, CDCP1, CDH1, CDH3, CDH4, CDK2AP2, CHST9, CKB, CLDN3, CLDN4, CLDN7, CLIC3, CLU, COL12A1, CRB3, CRIP1, CRIP2, CXADR, CXCL1, CYB561, CYBA, CYFIP2, CYHR1, CYP39A1, CYP4X1, CYSTM1, DBNDD2, DCXR, DDR1, DDX58, DHCR7, DMKN, DRD1, DSP, EFCAB4A, EFNA5, ELOVL1, ELOVL7, EMB, ENO2, ENPP5, ENTPD3, EPB41L5, EPCAM, EPHA2, EPS8L2, ERBB2, ERBB3, ESRP1, ESRP2, EZR, F11R, F2RL1, FAAH, FAAH2, FAM111A, FAM167A, FAM213A, FAM221A, FAM65C, FAM84B, FBXO2, FBXO44, FGF19, FGFRL1, FMO2, FXYD3, FXYD5, FZD6, GALNT3, GAS6, GCHFR, GPR56, GPRC5A, GPRC5C, GRB7, GSDMD, HERC6, HIGD2A, HLA-B, HMGA1, HOOK2, HPN, HSPB2, IFITM1, IFITM2, IFITM5, IGFBP6, IGSF9, INADL, INF2, IQGAP1, IRF6, IRF7, ISLR, ITGA3, ITGB4, ITGB8, ITPR2, ITPR3, JUP, KIAA1522, KIAA1598, KIF1A, KLF5, KLK1, KLK10, KLK11, KLK7, KLK8, KRT18, KRT19, KRT7, KRT8, KRTCAP3, LBH, LECT1, LGALS3BP, LIME1, LLGL2, LOC100505761, L00541471, LOC646329, LPAR2, LPIN3, LRRC16A, LSR, LY6E, LYPD6B, MAGI1, MAL2, MAP7, MBOAT1, MCAM, MDK, MFSD3, MGAT4B, MIF4GD, MLXIPL, MPZL2, MSLN, MSMO1, MSX2, MUC1, MX1, MYH9, MYO6, NCOA7, NDUFA4L2, NDUFS8, NET1, NPNT, NSMF, NT5DC1, NTSE, NUDT14, OAS1, OCIAD2, OCLN, ORMDL2, P4HTM, PARD6B, PARP8, PARP9, PARVG, PCBD1, PDGFB, PDHX, PDLIM1, PDLIM2, PERP, PHYHD1, PIGV, PIM1, PKP3, PKP4, PLEKHB1, PLEKHG1, PLEKHN1, PLLP, PLXDC2, PLXNA2, PLXNB1, PNOC, PNP, PPL, PPP1CA, PPP1R16A, PPP1R1B, PPP1R9A, PRKCG, PRPH, PRR15, PRR15L, PRSS8, PSME1, PSME2, PTGER4, PTGES, PTN, PTPRF, PTRH1, RAB3IP, RALGPS1, RASSF7, RBM47, REC8, REEP2, RGL3, RHBDF2, RHBDL1, RIPK4, ROBO3, RTN3, S100A16, S100A4, S100A6, SAMD12, SCG5, SCNN1A, SCRN2, SEC11C, SECTM1, SELENBP1, SEMA3B, SGPL1, SH3YL1, SHANK2, SHANK2-AS3, SIM2, SLC11A2, SLC12A2, SLC16A5, SLC25A25, SLC25A29, SLC29A1, SLC35F2, SLC50A1, SLC6A9, SLC7A5, SLC7A8, SLFN5, SLPI, SMAD1, SMPDL3B, SORT1, SOX14, SPINT1, SPINT2, ST14, ST3GAL5, STAP2, STRA13, STRA6, STXBP2, SULF1, SULF2, SUMF1, SVIP, SYNGR2, SYTL1, TACSTD2, TAPBPL, TCF7L2, TENM1, TFAP2B, TFAP2C, TLE2, TLE6, TM4SF1, TM7SF2, TMC4, TMCC3, TMEM125, TMEM176B, TNFAIP2, TNFRSF12A, TNFRSF14, TNFRSF21, TNFSF13, TNKS1BP1, TNNI3, TNNT1, TOM1L1, TPD52, TSPO, TUBB2B, TUBB3, UCP2, VAMP8, WDR34, WDR54, WFDC2, XAF1, ZDHHC12, ZMAT1, ZNF165, ZNF423, and ZNF664.


Malignant Mesenchymal Cell Signature

In one example embodiment, a malignant mesenchymal cell signature comprises one or more genes or polypeptides selected from the group consisting of: ANLN, CLSPN, KNSTRN, RFC4, ARHGAP11A, DHFR, KNTC1, RNASEH2A, ATAD5, DNA2, MAD2L1, RRM2, BIRC5, DTL, MCM2, SGOL2, BRCA2, EZH2, MCM3, SMC4, BUB1B, FANCA, MCM4, SPAG5, C21orf58, FANCD2, MCM5, SPDL1, CASC5, FANCI, MKI67, STIL, CCNA2, FOXM1, MLF1IP, TCF19, CCNB2, GINS2, NCAPD2, TIMELESS, CCNE2, HELLS, NCAPG2, TK1, CDC6, KIAA0101, NUSAP1, TOP2A, CDKN3, KIF11, OAS3, TPX2, CENPE, KIF14, OIP5, TYMS, CENPF, KIF18A, ORC6, UBE2C, CENPH, KIF20B, PRC1, UBE2T, CENPK, KIF2C, PSMC3IP, UHRF1, CENPW, PTTG1, WDHD1, CHAF1B, RACGAP1, ZWINT.


Modulation Using a HDAC Inhibitor, CDK4/6 Inhibitor, or a Combination Thereof.

The following section provides multiple example embodiments for modulating one or more malignant signatures associated with Sys. Methods may include administration to subjects at risk for or having Sys, including metastatic or at risk for having metastatic Sys. Thus, the embodiments may be used to prevent and/or treat Sys or metastatic Sys.


In another aspect, methods of treatment may comprise administering a HDAC inhibitor, a CDK4/6 inhibitor or a combination thereof, to a subject in need thereof. In certain example embodiments, a subject in need thereof may be a subject at risk for or having synovial sarcoma.


HDAC Inhibitor

In certain embodiments, the agent capable of modulating a signature as described herein is an HDAC inhibitor. Examples of HDAC inhibitors include hydroxamic acid derivatives, Short Chain Fatty Acids (SCFAs), cyclic tetrapeptides, benzamide derivatives, or electrophilic ketone derivatives, as defined herein. Specific non-limiting examples of HDAC inhibitors include: A) Hydroxamic acid derivatives selected from m-carboxycinnamic acid bishydroxamide (CBHA), Trichostatin A (TSA), Trichostatin C, Salicylhydroxamic Acid, Azelaic Bishydroxamic Acid (ABHA), Azelaic-1-Hydroxamate-9-Anilide (AAHA), 6-(3-Chlorophenylureido) carpoic Hydroxamic Acid (3C1-UCHA), Oxamflatin, A-161906, Scriptaid, PXD-101, LAQ-824, CHAP, MW2796, and MW2996; B) Cyclic tetrapeptides selected from Trapoxin A, FR901228 (FK 228 or Depsipeptide), FR225497, Apicidin, CHAP, HC-Toxin, WF27082, and Chlamydocin; C) Short Chain Fatty Acids (SCFAs) selected from Sodium Butyrate, Isovalerate, Valerate, 4 Phenylbutyrate (4-PBA), Phenylbutyrate (PB), Propionate, Butyramide, Isobutyramide, Phenylacetate, 3-Bromopropionate, Tributyrin, Valproic Acid and Valproate; D) Benzamide Derivatives selected from C 1-994, MS-27-275 (MS-275) and a 3′-amino derivative of MS-27-275; E) Electrophilic Ketone Derivatives selected from a trifluoromethyl ketone and an α-keto amide such as an N-methyl-α-ketoamide; and F) Miscellaneous HDAC inhibitors including natural products, psammaplins and Depudecin.


Additional examples of HDAC inhibitors include vorinostat, romidepsin, chidamide, panobinostat, belinostat, mocetinostat, abexinostat, entinostat, resminostat, givinostat, quisinostat, CI-994, BML-210, M344, NVP-LAQ824, suberoylanilide hydroxamic acid (SAHA), MS-275, TSA, LAQ-824, trapoxin, depsipeptide, and tacedinaline.


Further examples of HDAC inhibitors include trichostatin A (TSA) ((R,2E,4E)-7-(4-(dimethylamino)phenyl)-N-hydroxy-4,6-dimethyl-7-oxohepta-2,4-dienamide); sulfonamides such as oxamflatin ((E)-N-hydroxy-5-(3-(phenylsulfonamido)phenyl)pent-2-en-4-ynamide). Other hydroxamic-acid-sulfonamide inhibitors of histone deacetylase are described in: Lavoie et al. (2001) Bioorg. Med. Chem. Lett. 11:2847-50; Bouchain et al. (2003) J. Med. Chem. 846:820-830; Bouchain et al. (2003) Curr. Med. Chem. 10:2359-2372; Marson et al. (2004) Bioorg. Med. Chem. Lett. 14:2477-2481; Finn et al. (2005) Helv. Chim. Acta 88:1630-1657; International Patent Publication Nos. WO 2002/030879, WO 2003/082288, WO 2005/0011661, WO 2005/108367, WO 2006123121, WO 2006/017214, WO 2006/017215, and US Patent Publication No. 2005/0234033. Other structural classes of histone deacetylase inhibitors include short chain fatty acids, cyclic peptides, and benzamides. Acharya et al. (2005) Mol. Pharmacol. 68:917-932.


Other examples of HDAC inhibitors include those disclosed in, e.g., Dokmanovic et al. (2007) Mol. Cancer. Res. 5:981; U.S. Pat. Nos. 7,642,275; 7,683,185; 7,732,475; 7,737,184; 7,741,494; 7,772,245; 7,795,304; 7,799,825; 7,803,800; 7,842,727; 7,842,835; U.S. Patent Publication No. 2010/0317739; U.S. Patent Publication No. 2010/0311794; U.S. Patent Publication No. 2010/0310500; U.S. Patent Publication No. 2010/0292320; and U.S. Patent Publication No. 2010/0291003.


CDK4/6 Inhibitor

In certain embodiments, the agent capable of modulating a signature as described herein is a cell cycle inhibitor (see e.g., Dickson and Schwartz, Development of cell-cycle inhibitors for cancer therapy, Curr Oncol. 2009 March; 16(2): 36-43). In one embodiment, the agent capable of modulating a signature as described herein is a CDK4/6 inhibitor, such as LEE011, palbociclib (PD-0332991), and Abemaciclib (LY2835219) (see, e.g., U.S. Pat. No. 9,259,399B2; International Patent Publication No. WO 2016/025650A1; US Patent Publication No. 2014/0031325; US Patent Publication No. 2014/0080838; US Patent Publication No. 2013/0303543; US Patent Publication No. 2007/0027147; US Patent Publication No. 2003/0229026; US Patent Publication No 2004/0048915; US Patent Publication No. 2004/0006074; and US Patent Publication No. 2007/0179118, each of which is incorporated herein by reference in its entirety). Currently there are three CDK4/6 inhibitors that are either approved or in late-stage development: palbociclib (PD-0332991; Pfizer), ribociclib (LEE011; Novartis), and abemaciclib (LY2835219; Lilly) (see e.g., Hamilton and Infante, Targeting CDK4/6 in patients with cancer, Cancer Treatment Reviews, Volume 45, April 2016, Pages 129-138).


Checkpoint Inhibitors

Because immune checkpoint inhibitors target the interactions between different cells in the tumor, their impact depends on multicellular circuits between malignant and non-malignant cells (Tirosh et al., 2016a). In principle, resistance can stem from different compartment of the tumor's ecosystem, for example, the proportion of different cell types (e.g., T cells, macrophages, fibroblasts), the intrinsic state of each cell (e.g., memory or dysfunctional T cell), and the impact of one cell on the proportions and states of other cells in the tumor (e.g., malignant cells inducing T cell dysfunction by expressing PD-L1 or promoting T cell memory formation by presenting neoantigens). These different facets are interconnected through the cellular ecosystem: intrinsic cellular states control the expression of secreted factors and cell surface receptors that in turn affect the presence and state of other cells, and vice versa. In particular, brisk tumor infiltration with T cell has been associated with patient survival and improved immunotherapy responses (Fridman et al., 2012), but the determinants that dictate if a tumor will have high (“hot”) or low (“cold”) levels of T cell infiltration are only partially understood. Among multiple factors, malignant cells may play an important role in determining this phenotype (Spranger et al., 2015). Resolving this relationship with bulk genomics approaches has been challenging; single-cell RNA-seq (scRNA-seq) of tumors (Li et al., 2017; Patel et al., 2014; Tirosh et al., 2016a, 2016b; Venteicher et al., 2017) has the potential to shed light on a wide range of immune evasion mechanisms and immune suppression programs.


Phased Combination

In certain embodiments, a subject in need thereof is treated with a combination therapy, which may be a phased combination therapy. The phased combination therapy may be a treatment regimen comprising checkpoint inhibition followed by a CDK4/6 inhibitor, an HDAC inhibitor, an/or checkpoint inhibitor combination. Checkpoint inhibitors may be administered at regular intervals, for example, daily, weekly, every two weeks, every month. The combination therapy may be administered when a signature disclosed herein is detected. This may be after two weeks to six months after the initial checkpoint inhibition. The immunotherapy may be adoptive cell transfer therapy, as described herein or may be an inhibitor of any check point protein described herein. The checkpoint blockade therapy may comprise anti-TIM3, anti-CTLA4, anti-PD-L1, anti-PD1, anti-TIGIT, anti-LAG3, or combinations thereof. Specific check point inhibitors include, but are not limited to anti-CTLA4 antibodies (e.g., Ipilimumab), anti-PD-1 antibodies (e.g., Nivolumab, Pembrolizumab), and anti-PD-L1 antibodies (e.g., Atezolizumab). Dosages for the immunotherapy and/or CDK4/6 inhibitors may be determined according to the standard of care for each therapy and may be incorporated into the standard of care (see, e.g., Rivalland et al., Standard of care in immunotherapy trials: Challenges and considerations, Hum Vaccin Immunother. 2017 July; 13(9): 2164-2178; and Pernas et al., CDK4/6 inhibition in breast cancer: current practice and future directions, Ther Adv Med Oncol. 2018). The standard of care is the current treatment that is accepted by medical experts as a proper treatment for a certain type of disease and that is widely used by healthcare professionals. Standard or care is also called best practice, standard medical care, and standard therapy.


Methods of Treatment

Treatment with Adoptive Cell Transfer


In embodiments, methods of treatment of Sys may comprise treatment with adoptive cell therapy via CD8 T cells, CAR T and/or macrophages. In embodiments, macrophages are edited to provide increased IFNgamma, CD8 T cells are edited to provide increased TNF expression, or a combination thereof. In embodiments, methods of treatment include adoptive cell therapy utilizing CD8 and/or CAR T cells edited to have the expansion program phenotype as provided herein. As described further in the examples, IFNg and TNF was strongly associated with the repression of the core oncogenic program in malignant cells. Further, the T cells in SyS tumors have been found to have a cytotoxic potential which might be unleashed by immune checkpoint blockade. Accordingly, the methods of treatment using these adoptive cell therapies have potential to modulate, reduce and/or repress the oncogenic program in malignant cells and/or increase cytotoxicity.


As used herein, “ACT”, “adoptive cell therapy” and “adoptive cell transfer” may be used interchangeably. In certain embodiments, Adoptive cell therapy (ACT) can refer to the transfer of cells to a patient with the goal of transferring the functionality and characteristics into the new host by engraftment of the cells (see, e.g., Mettananda et al., Editing an α-globin enhancer in primary human hematopoietic stem cells as a treatment for β-thalassemia, Nat Commun. 2017 Sep. 4; 8(1):424). As used herein, the term “engraft” or “engraftment” refers to the process of cell incorporation into a tissue of interest in vivo through contact with existing cells of the tissue. Adoptive cell therapy (ACT) can refer to the transfer of cells, most commonly immune-derived cells, back into the same patient or into a new recipient host with the goal of transferring the immunologic functionality and characteristics into the new host. If possible, use of autologous cells helps the recipient by minimizing GVHD issues. The adoptive transfer of autologous tumor infiltrating lymphocytes (TIL) (Zacharakis et al., (2018) Nat Med. 2018 June; 24(6):724-730; Besser et al., (2010) Clin. Cancer Res 16 (9) 2646-55; Dudley et al., (2002) Science 298 (5594): 850-4; and Dudley et al., (2005) Journal of Clinical Oncology 23 (10): 2346-57.) or genetically re-directed peripheral blood mononuclear cells (Johnson et al., (2009) Blood 114 (3): 535-46; and Morgan et al., (2006) Science 314(5796) 126-9) has been used to successfully treat patients with advanced solid tumors, including melanoma, metastatic breast cancer and colorectal carcinoma, as well as patients with CD19-expressing hematologic malignancies (Kalos et al., (2011) Science Translational Medicine 3 (95): 95ra73). In certain embodiments, allogenic cells immune cells are transferred (see, e.g., Ren et al., (2017) Clin Cancer Res 23 (9) 2255-2266). As described further herein, allogenic cells can be edited to reduce alloreactivity and prevent graft-versus-host disease. Thus, use of allogenic cells allows for cells to be obtained from healthy donors and prepared for use in patients as opposed to preparing autologous cells from a patient after diagnosis.


Aspects of the invention involve the adoptive transfer of immune system cells, such as T cells, specific for selected antigens, such as tumor associated antigens or tumor specific neoantigens (see, e.g., Maus et al., 2014, Adoptive Immunotherapy for Cancer or Viruses, Annual Review of Immunology, Vol. 32: 189-225; Rosenberg and Restifo, 2015, Adoptive cell transfer as personalized immunotherapy for human cancer, Science Vol. 348 no. 6230 pp. 62-68; Restifo et al., 2015, Adoptive immunotherapy for cancer: harnessing the T cell response. Nat. Rev. Immunol. 12(4): 269-281; and Jenson and Riddell, 2014, Design and implementation of adoptive therapy with chimeric antigen receptor-modified T cells. Immunol Rev. 257(1): 127-144; and Rajasagi et al., 2014, Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia. Blood. 2014 Jul. 17; 124(3):453-62).


In certain embodiments, an antigen (such as a tumor antigen) to be targeted in adoptive cell therapy (such as particularly CAR or TCR T-cell therapy) of a disease (such as particularly of tumor or cancer) may be selected from a group consisting of: B cell maturation antigen (BCMA) (see, e.g., Friedman et al., Effective Targeting of Multiple BCMA-Expressing Hematological Malignancies by Anti-BCMA CAR T Cells, Hum Gene Ther. 2018 Mar. 8; Berdeja J G, et al. Durable clinical responses in heavily pretreated patients with relapsed/refractory multiple myeloma: updated results from a multicenter study of bb2121 anti-Bcma CAR T cell therapy. Blood. 2017; 130:740; and Mouhieddine and Ghobrial, Immunotherapy in Multiple Myeloma: The Era of CAR T Cell Therapy, Hematologist, May-June 2018, Volume 15, issue 3); PSA (prostate-specific antigen); prostate-specific membrane antigen (PSMA); PSCA (Prostate stem cell antigen); Tyrosine-protein kinase transmembrane receptor ROR1; fibroblast activation protein (FAP); Tumor-associated glycoprotein 72 (TAG72); Carcinoembryonic antigen (CEA); Epithelial cell adhesion molecule (EPCAM); Mesothelin; Human Epidermal growth factor Receptor 2 (ERBB2 (Her2/neu)); Prostate; Prostatic acid phosphatase (PAP); elongation factor 2 mutant (ELF2M); Insulin-like growth factor 1 receptor (IGF-1R); gplOO; BCR-ABL (breakpoint cluster region-Abelson); tyrosinase; New York esophageal squamous cell carcinoma 1 (NY-ESO-1); κ-light chain, LAGE (L antigen); MAGE (melanoma antigen); Melanoma-associated antigen 1 (MAGE-A1); MAGE A3; MAGE A6; legumain; Human papillomavirus (HPV) E6; HPV E7; prostein; survivin; PCTA1 (Galectin 8); Melan-A/MART-1; Ras mutant; TRP-1 (tyrosinase related protein 1, or gp75); Tyrosinase-related Protein 2 (TRP2); TRP-2/INT2 (TRP-2/intron 2); RAGE (renal antigen); receptor for advanced glycation end products 1 (RAGE1); Renal ubiquitous 1, 2 (RU1, RU2); intestinal carboxyl esterase (iCE); Heat shock protein 70-2 (HSP70-2) mutant; thyroid stimulating hormone receptor (TSHR); CD123; CD171; CD19; CD20; CD22; CD26; CD30; CD33; CD44v7/8 (cluster of differentiation 44, exons 7/8); CD53; CD92; CD100; CD148; CD150; CD200; CD261; CD262; CD362; CS-1 (CD2 subset 1, CRACC, SLAMF7, CD319, and 19A24); C-type lectin-like molecule-1 (CLL-1); ganglioside GD3 (aNeu5Ac(2-8)aNeu5Ac(2-3)bDGalp(1-4)bDG1cp(1-1)Cer); Tn antigen (Tn Ag); Fms-Like Tyrosine Kinase 3 (FLT3); CD38; CD138; CD44v6; B7H3 (CD276); KIT (CD117); Interleukin-13 receptor subunit alpha-2 (IL-13Ra2); Interleukin 11 receptor alpha (IL-11Ra); prostate stem cell antigen (PSCA); Protease Serine 21 (PRSS21); vascular endothelial growth factor receptor 2 (VEGFR2); Lewis(Y) antigen; CD24; Platelet-derived growth factor receptor beta (PDGFR-beta); stage-specific embryonic antigen-4 (SSEA-4); Mucin 1, cell surface associated (MUC1); mucin 16 (MUC16); epidermal growth factor receptor (EGFR); epidermal growth factor receptor variant III (EGFRvIII); neural cell adhesion molecule (NCAM); carbonic anhydrase IX (CAIX); Proteasome (Prosome, Macropain) Subunit, Beta Type, 9 (LMP2); ephrin type-A receptor 2 (EphA2); Ephrin B2; Fucosyl GM1; sialyl Lewis adhesion molecule (sLe); ganglioside GM3 (aNeu5Ac(2-3)bDGalp(1-4)bDG1cp(1-1)Cer); TGS5; high molecular weight-melanoma-associated antigen (HMWMAA); o-acetyl-GD2 ganglioside (OAcGD2); Folate receptor alpha; Folate receptor beta; tumor endothelial marker 1 (TEM1/CD248); tumor endothelial marker 7-related (TEM7R); claudin 6 (CLDN6); G protein-coupled receptor class C group 5, member D (GPRC5D); chromosome X open reading frame 61 (CXORF61); CD97; CD179a; anaplastic lymphoma kinase (ALK); Polysialic acid; placenta-specific 1 (PLAC1); hexasaccharide portion of globoH glycoceramide (GloboH); mammary gland differentiation antigen (NY-BR-1); uroplakin 2 (UPK2); Hepatitis A virus cellular receptor 1 (HAVCR1); adrenoceptor beta 3 (ADRB3); pannexin 3 (PANX3); G protein-coupled receptor 20 (GPR20); lymphocyte antigen 6 complex, locus K 9 (LY6K); Olfactory receptor 51E2 (OR51E2); TCR Gamma Alternate Reading Frame Protein (TARP); Wilms tumor protein (WT1); ETS translocation-variant gene 6, located on chromosome 12p (ETV6-AML); sperm protein 17 (SPA17); X Antigen Family, Member 1A (XAGE1); angiopoietin-binding cell surface receptor 2 (Tie 2); CT (cancer/testis (antigen)); melanoma cancer testis antigen-1 (MAD-CT-1); melanoma cancer testis antigen-2 (MAD-CT-2); Fos-related antigen 1; p53; p53 mutant; human Telomerase reverse transcriptase (hTERT); sarcoma translocation breakpoints; melanoma inhibitor of apoptosis (ML-IAP); ERG (transmembrane protease, serine 2 (TMPRSS2) ETS fusion gene); N-Acetyl glucosaminyl-transferase V (NA17); paired box protein Pax-3 (PAX3); Androgen receptor; Cyclin B1; Cyclin D1; v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN); Ras Homolog Family Member C (RhoC); Cytochrome P450 1B1 (CYP1B1); CCCTC-Binding Factor (Zinc Finger Protein)-Like (BORIS); Squamous Cell Carcinoma Antigen Recognized By T Cells-1 or 3 (SART1, SART3); Paired box protein Pax-5 (PAX5); proacrosin binding protein sp32 (OY-TES1); lymphocyte-specific protein tyrosine kinase (LCK); A kinase anchor protein 4 (AKAP-4); synovial sarcoma, X breakpoint-1, -2, -3 or -4 (SSX1, SSX2, SSX3, SSX4); CD79a; CD79b; CD72; Leukocyte-associated immunoglobulin-like receptor 1 (LAIR1); Fc fragment of IgA receptor (FCAR); Leukocyte immunoglobulin-like receptor subfamily A member 2 (LILRA2); CD300 molecule-like family member f (CD300LF); C-type lectin domain family 12 member A (CLEC12A); bone marrow stromal cell antigen 2 (BST2); EGF-like module-containing mucin-like hormone receptor-like 2 (EMR2); lymphocyte antigen 75 (LY75); Glypican-3 (GPC3); Fc receptor-like 5 (FCRL5); mouse double minute 2 homolog (MDM2); livin; alphafetoprotein (AFP); transmembrane activator and CAML Interactor (TACI); B-cell activating factor receptor (BAFF-R); V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS); immunoglobulin lambda-like polypeptide 1 (IGLL1); 707-AP (707 alanine proline); ART-4 (adenocarcinoma antigen recognized by T4 cells); BAGE (B antigen; b-catenin/m, b-catenin/mutated); CAMEL (CTL-recognized antigen on melanoma); CAP1 (carcinoembryonic antigen peptide 1); CASP-8 (caspase-8); CDC27m (cell-division cycle 27 mutated); CDK4/m (cycline-dependent kinase 4 mutated); Cyp-B (cyclophilin B); DAM (differentiation antigen melanoma); EGP-2 (epithelial glycoprotein 2); EGP-40 (epithelial glycoprotein 40); Erbb2, 3, 4 (erythroblastic leukemia viral oncogene homolog-2, -3, 4); FBP (folate binding protein); fAchR (Fetal acetylcholine receptor); G250 (glycoprotein 250); GAGE (G antigen); GnT-V (N-acetylglucosaminyltransferase V); HAGE (helicose antigen); ULA-A (human leukocyte antigen-A); HST2 (human signet ring tumor 2); KIAA0205; KDR (kinase insert domain receptor); LDLR/FUT (low density lipid receptor/GDP L-fucose: b-D-galactosidase 2-a-L fucosyltransferase); L1CAM (L1 cell adhesion molecule); MC1R (melanocortin 1 receptor); Myosin/m (myosin mutated); MUM-1, -2, -3 (melanoma ubiquitous mutated 1, 2, 3); NA88-A (NA cDNA clone of patient M88); KG2D (Natural killer group 2, member D) ligands; oncofetal antigen (h5T4); p190 minor bcr-abl (protein of 190KD bcr-abl); Pml/RARa (promyelocytic leukaemia/retinoic acid receptor a); PRAME (preferentially expressed antigen of melanoma); SAGE (sarcoma antigen); TEL/AML1 (translocation Ets-family leukemia/acute myeloid leukemia 1); TPI/m (triosephosphate isomerase mutated); CD70; and any combination thereof.


In certain embodiments, an antigen to be targeted in adoptive cell therapy (such as particularly CAR or TCR T-cell therapy) of a disease (such as particularly of tumor or cancer) is a tumor-specific antigen (TSA).


In certain embodiments, an antigen to be targeted in adoptive cell therapy (such as particularly CAR or TCR T-cell therapy) of a disease (such as particularly of tumor or cancer) is a neoantigen.


In certain embodiments, an antigen to be targeted in adoptive cell therapy (such as particularly CAR or TCR T-cell therapy) of a disease (such as particularly of tumor or cancer) is a tumor-associated antigen (TAA).


In certain embodiments, an antigen to be targeted in adoptive cell therapy (such as particularly CAR or TCR T-cell therapy) of a disease (such as particularly of tumor or cancer) is a universal tumor antigen. In certain preferred embodiments, the universal tumor antigen is selected from the group consisting of: a human telomerase reverse transcriptase (hTERT), survivin, mouse double minute 2 homolog (MDM2), cytochrome P450 1B 1 (CYP1B), HER2/neu, Wilms' tumor gene 1 (WT1), livin, alphafetoprotein (AFP), carcinoembryonic antigen (CEA), mucin 16 (MUC16), MUC1, prostate-specific membrane antigen (PSMA), p53, cyclin (Dl), and any combinations thereof.


In certain embodiments, an antigen (such as a tumor antigen) to be targeted in adoptive cell therapy (such as particularly CAR or TCR T-cell therapy) of a disease (such as particularly of tumor or cancer) may be selected from a group consisting of: CD19, BCMA, CD70, CLL-1, MAGE A3, MAGE A6, HPV E6, HPV E7, WT1, CD22, CD171, ROR1, MUC16, and SSX2. In certain preferred embodiments, the antigen may be CD19. For example, CD19 may be targeted in hematologic malignancies, such as in lymphomas, more particularly in B-cell lymphomas, such as without limitation in diffuse large B-cell lymphoma, primary mediastinal b-cell lymphoma, transformed follicular lymphoma, marginal zone lymphoma, mantle cell lymphoma, acute lymphoblastic leukemia including adult and pediatric ALL, non-Hodgkin lymphoma, indolent non-Hodgkin lymphoma, or chronic lymphocytic leukemia. For example, BCMA may be targeted in multiple myeloma or plasma cell leukemia (see, e.g., 2018 American Association for Cancer Research (AACR) Annual meeting Poster: Allogeneic Chimeric Antigen Receptor T Cells Targeting B Cell Maturation Antigen). For example, CLL1 may be targeted in acute myeloid leukemia. For example, MAGE A3, MAGE A6, SSX2, and/or KRAS may be targeted in solid tumors. For example, HPV E6 and/or HPV E7 may be targeted in cervical cancer or head and neck cancer. For example, WT1 may be targeted in acute myeloid leukemia (AML), myelodysplastic syndromes (MDS), chronic myeloid leukemia (CML), non-small cell lung cancer, breast, pancreatic, ovarian or colorectal cancers, or mesothelioma. For example, CD22 may be targeted in B cell malignancies, including non-Hodgkin lymphoma, diffuse large B-cell lymphoma, or acute lymphoblastic leukemia. For example, CD171 may be targeted in neuroblastoma, glioblastoma, or lung, pancreatic, or ovarian cancers. For example, ROR1 may be targeted in ROR1+ malignancies, including non-small cell lung cancer, triple negative breast cancer, pancreatic cancer, prostate cancer, ALL, chronic lymphocytic leukemia, or mantle cell lymphoma. For example, MUC16 may be targeted in MUC16ecto+ epithelial ovarian, fallopian tube or primary peritoneal cancer. For example, CD70 may be targeted in both hematologic malignancies as well as in solid cancers such as renal cell carcinoma (RCC), gliomas (e.g., GBM), and head and neck cancers (HNSCC). CD70 is expressed in both hematologic malignancies as well as in solid cancers, while its expression in normal tissues is restricted to a subset of lymphoid cell types (see, e.g., 2018 American Association for Cancer Research (AACR) Annual meeting Poster: Allogeneic CRISPR Engineered Anti-CD70 CAR-T Cells Demonstrate Potent Preclinical Activity Against Both Solid and Hematological Cancer Cells).


Various strategies may for example be employed to genetically modify T cells by altering the specificity of the T cell receptor (TCR) for example by introducing new TCR a and β chains with selected peptide specificity (see U.S. Pat. No. 8,697,854; PCT Patent Publications: WO2003020763, WO2004033685, WO2004044004, WO2005114215, WO2006000830, WO2008038002, WO2008039818, WO2004074322, WO2005113595, WO2006125962, WO2013166321, WO2013039889, WO2014018863, WO2014083173; U.S. Pat. No. 8,088,379).


As an alternative to, or addition to, TCR modifications, chimeric antigen receptors (CARs) may be used in order to generate immunoresponsive cells, such as T cells, specific for selected targets, such as malignant cells, with a wide variety of receptor chimera constructs having been described (see U.S. Pat. Nos. 5,843,728; 5,851,828; 5,912,170; 6,004,811; 6,284,240; 6,392,013; 6,410,014; 6,753,162; 8,211,422; and, PCT Publication WO9215322).


In general, CARs are comprised of an extracellular domain, a transmembrane domain, and an intracellular domain, wherein the extracellular domain comprises an antigen-binding domain that is specific for a predetermined target. While the antigen-binding domain of a CAR is often an antibody or antibody fragment (e.g., a single chain variable fragment, scFv), the binding domain is not particularly limited so long as it results in specific recognition of a target. For example, in some embodiments, the antigen-binding domain may comprise a receptor, such that the CAR is capable of binding to the ligand of the receptor. Alternatively, the antigen-binding domain may comprise a ligand, such that the CAR is capable of binding the endogenous receptor of that ligand.


The antigen-binding domain of a CAR is generally separated from the transmembrane domain by a hinge or spacer. The spacer is also not particularly limited, and it is designed to provide the CAR with flexibility. For example, a spacer domain may comprise a portion of a human Fc domain, including a portion of the CH3 domain, or the hinge region of any immunoglobulin, such as IgA, IgD, IgE, IgG, or IgM, or variants thereof. Furthermore, the hinge region may be modified so as to prevent off-target binding by FcRs or other potential interfering objects. For example, the hinge may comprise an IgG4 Fc domain with or without a S228P, L235E, and/or N297Q mutation (according to Kabat numbering) in order to decrease binding to FcRs. Additional spacers/hinges include, but are not limited to, CD4, CD8, and CD28 hinge regions.


The transmembrane domain of a CAR may be derived either from a natural or from a synthetic source. Where the source is natural, the domain may be derived from any membrane bound or transmembrane protein. Transmembrane regions of particular use in this disclosure may be derived from CD8, CD28, CD3, CD45, CD4, CD5, CDS, CD9, CD 16, CD22, CD33, CD37, CD64, CD80, CD86, CD 134, CD137, CD 154, TCR. Alternatively, the transmembrane domain may be synthetic, in which case it will comprise predominantly hydrophobic residues such as leucine and valine. Preferably a triplet of phenylalanine, tryptophan and valine will be found at each end of a synthetic transmembrane domain. Optionally, a short oligo- or polypeptide linker, preferably between 2 and 10 amino acids in length may form the linkage between the transmembrane domain and the cytoplasmic signaling domain of the CAR. A glycine-serine doublet provides a particularly suitable linker.


Alternative CAR constructs may be characterized as belonging to successive generations. First-generation CARs typically consist of a single-chain variable fragment of an antibody specific for an antigen, for example comprising a VL linked to a VH of a specific antibody, linked by a flexible linker, for example by a CD8α hinge domain and a CD8α transmembrane domain, to the transmembrane and intracellular signaling domains of either CD3ζ or FcRγ (scFv-CD3ζ or scFv-FcRγ; see U.S. Pat. Nos. 7,741,465; 5,912,172; and 5,906,936). Second-generation CARs incorporate the intracellular domains of one or more costimulatory molecules, such as CD28, OX40 (CD134), or 4-1BB (CD137) within the endodomain (for example scFv-CD28/OX40/4-1BB-CD3ζ; see U.S. Pat. Nos. 8,911,993; 8,916,381; 8,975,071; 9,101,584; 9,102,760; and 9,102,761). Third-generation CARs include a combination of costimulatory endodomains, such a CD3ζ-chain, CD97, GDI 1a-CD18, CD2, ICOS, CD27, CD154, CDS, OX40, 4-1BB, CD2, CD7, LIGHT, LFA-1, NKG2C, B7-H3, CD30, CD40, PD-1, or CD28 signaling domains (for example scFv-CD28-4-1BB-CD3ζ or scFv-CD28-OX40-CD3ζ; see U.S. Pat. Nos. 8,906,682; 8,399,645; 5,686,281; PCT Publication No. WO 2014/134165; PCT Publication No. WO 2012/079000). In certain embodiments, the primary signaling domain comprises a functional signaling domain of a protein selected from the group consisting of CD3 zeta, CD3 gamma, CD3 delta, CD3 epsilon, common FcR gamma (FCERIG), FcR beta (Fc Epsilon Rib), CD79a, CD79b, Fc gamma RIM, DAP10, and DAP12. In certain preferred embodiments, the primary signaling domain comprises a functional signaling domain of CD3ζ or FcRγ. In certain embodiments, the one or more costimulatory signaling domains comprise a functional signaling domain of a protein selected, each independently, from the group consisting of: CD27, CD28, 4-1BB (CD137), OX40, CD30, CD40, PD-1, ICOS, lymphocyte function-associated antigen-1 (LFA-1), CD2, CD7, LIGHT, NKG2C, B7-H3, a ligand that specifically binds with CD83, CDS, ICAM-1, GITR, BAFFR, HVEM (LIGHTR), SLAMF7, NKp80 (KLRF1), CD160, CD19, CD4, CD8 alpha, CD8 beta, IL2R beta, IL2R gamma, IL7R alpha, ITGA4, VLA1, CD49a, ITGA4, IA4, CD49D, ITGA6, VLA-6, CD49f, ITGAD, CD11d, ITGAE, CD103, ITGAL, CD11a, LFA-1, ITGAM, CD11b, ITGAX, CD11c, ITGB1, CD29, ITGB2, CD18, ITGB7, TNFR2, TRANCE/RANKL, DNAM1 (CD226), SLAMF4 (CD244, 2B4), CD84, CD96 (Tactile), CEACAM1, CRTAM, Ly9 (CD229), CD160 (BY55), PSGL1, CD100 (SEMA4D), CD69, SLAMF6 (NTB-A, Ly108), SLAM (SLAMF1, CD150, IPO-3), BLAME (SLAMF8), SELPLG (CD162), LTBR, LAT, GADS, SLP-76, PAG/Cbp, NKp44, NKp30, NKp46, and NKG2D. In certain embodiments, the one or more costimulatory signaling domains comprise a functional signaling domain of a protein selected, each independently, from the group consisting of 4-1BB, CD27, and CD28. In certain embodiments, a chimeric antigen receptor may have the design as described in U.S. Pat. No. 7,446,190, comprising an intracellular domain of CD3ζ chain (such as amino acid residues 52-163 of the human CD3 zeta chain, as shown in SEQ ID NO: 14 of U.S. Pat. No. 7,446,190), a signaling region from CD28 and an antigen-binding element (or portion or domain; such as scFv). The CD28 portion, when between the zeta chain portion and the antigen-binding element, may suitably include the transmembrane and signaling domains of CD28 (such as amino acid residues 114-220 of SEQ ID NO: 10, full sequence shown in SEQ ID NO: 6 of U.S. Pat. No. 7,446,190; these can include the following portion of CD28 as set forth in Genbank identifier NM_006139 (sequence version 1, 2 or 3): IEVMYPPPYLDNEKSNGTIIHVKGKHLCPSPLFPGPSKPFWVLVVVGGVLACYSLLVT VAFIIFWVRSKRSRLLHSDYMNMTPRRPGPTRKHYQPYAPPRDFAAYRS)) (SEQ ID NO:1). Alternatively, when the zeta sequence lies between the CD28 sequence and the antigen-binding element, intracellular domain of CD28 can be used alone (such as amino sequence set forth in SEQ ID NO: 9 of U.S. Pat. No. 7,446,190). Hence, certain embodiments employ a CAR comprising (a) a zeta chain portion comprising the intracellular domain of human CD3 ζ chain, (b) a costimulatory signaling region, and (c) an antigen-binding element (or portion or domain), wherein the costimulatory signaling region comprises the amino acid sequence encoded by SEQ ID NO: 6 of U.S. Pat. No. 7,446,190.


Alternatively, costimulation may be orchestrated by expressing CARs in antigen-specific T cells, chosen so as to be activated and expanded following engagement of their native αβTCR, for example by antigen on professional antigen-presenting cells, with attendant costimulation. In addition, additional engineered receptors may be provided on the immunoresponsive cells, for example to improve targeting of a T-cell attack and/or minimize side effects


By means of an example and without limitation, Kochenderfer et al., (2009) J Immunother. 32 (7): 689-702 described anti-CD19 chimeric antigen receptors (CAR). FMC63-28Z CAR contained a single chain variable region moiety (scFv) recognizing CD19 derived from the FMC63 mouse hybridoma (described in Nicholson et al., (1997) Molecular Immunology 34: 1157-1165), a portion of the human CD28 molecule, and the intracellular component of the human TCR-molecule. FMC63-CD828BBZ CAR contained the FMC63 scFv, the hinge and transmembrane regions of the CD8 molecule, the cytoplasmic portions of CD28 and 4-1BB, and the cytoplasmic component of the TCR-ζ molecule. The exact sequence of the CD28 molecule included in the FMC63-28Z CAR corresponded to Genbank identifier NM_006139; the sequence included all amino acids starting with the amino acid sequence IEVMYPPPY (SEQ. I.D. No. 2) and continuing all the way to the carboxy-terminus of the protein. To encode the anti-CD19 scFv component of the vector, the authors designed a DNA sequence which was based on a portion of a previously published CAR (Cooper et al., (2003) Blood 101: 1637-1644). This sequence encoded the following components in frame from the 5′ end to the 3′ end: an XhoI site, the human granulocyte-macrophage colony-stimulating factor (GM-CSF) receptor α-chain signal sequence, the FMC63 light chain variable region (as in Nicholson et al., supra), a linker peptide (as in Cooper et al., supra), the FMC63 heavy chain variable region (as in Nicholson et al., supra), and a NotI site. A plasmid encoding this sequence was digested with XhoI and NotI. To form the MSGV-FMC63-28Z retroviral vector, the XhoI and Nothdigested fragment encoding the FMC63 scFv was ligated into a second XhoI and NotI-digested fragment that encoded the MSGV retroviral backbone (as in Hughes et al., (2005) Human Gene Therapy 16: 457-472) as well as part of the extracellular portion of human CD28, the entire transmembrane and cytoplasmic portion of human CD28, and the cytoplasmic portion of the human TCR-molecule (as in Maher et al., 2002) Nature Biotechnology 20: 70-75). The FMC63-28Z CAR is included in the KTE-C19 (axicabtagene ciloleucel) anti-CD19 CAR-T therapy product in development by Kite Pharma, Inc. for the treatment of inter alia patients with relapsed/refractory aggressive B-cell non-Hodgkin lymphoma (NHL). Accordingly, in certain embodiments, cells intended for adoptive cell therapies, more particularly immunoresponsive cells such as T cells, may express the FMC63-28Z CAR as described by Kochenderfer et al. (supra). Hence, in certain embodiments, cells intended for adoptive cell therapies, more particularly immunoresponsive cells such as T cells, may comprise a CAR comprising an extracellular antigen-binding element (or portion or domain; such as scFv) that specifically binds to an antigen, an intracellular signaling domain comprising an intracellular domain of a CD3 ζ chain, and a costimulatory signaling region comprising a signaling domain of CD28. Preferably, the CD28 amino acid sequence is as set forth in Genbank identifier NM 006139 (sequence version 1, 2 or 3) starting with the amino acid sequence IEVMYPPPY (SEQ ID NO: 2) and continuing all the way to the carboxy-terminus of the protein. The sequence is reproduced herein: IEVMYPPPYLDNEKSNGTIIHVKGKHLCPSPLFPGPSKPFWVLVVVGGVLACYSLLVT VAFIIFWVRSKRSRLLHSDYMNMTPRRPGPTRKHYQPYAPPRDFAAYRS (SEQ ID NO: 1). Preferably, the antigen is CD19, more preferably the antigen-binding element is an anti-CD19 scFv, even more preferably the anti-CD19 scFv as described by Kochenderfer et al. (supra).


Additional anti-CD19 CARs are further described in International Patent Publication No. WO 2015/187528. More particularly, Example 1 and Table 1 of WO 2015/187528, incorporated by reference herein, demonstrate the generation of anti-CD19 CARs based on a fully human anti-CD19 monoclonal antibody (47G4, as described in US Patent Publication No. 2010/0104509) and murine anti-CD19 monoclonal antibody (as described in Nicholson et al. and explained above). Various combinations of a signal sequence (human CD8-alpha or GM-CSF receptor), extracellular and transmembrane regions (human CD8-alpha) and intracellular T-cell signalling domains (CD28-CD3ζ; 4-1BB-CD3ζ; CD27-CD3ζ; CD28-CD27-CD3ζ, 4-1BB-CD27-CD3ζ; CD27-4-1BB-CD3ζ; CD28-CD27-FcεRI gamma chain; or CD28-FcεRI gamma chain) were disclosed. Hence, in certain embodiments, cells intended for adoptive cell therapies, more particularly immunoresponsive cells such as T cells, may comprise a CAR comprising an extracellular antigen-binding element that specifically binds to an antigen, an extracellular and transmembrane region as set forth in Table 1 of WO 2015/187528 and an intracellular T-cell signalling domain as set forth in Table 1 of WO 2015/187528. Preferably, the antigen is CD19, more preferably the antigen-binding element is an anti-CD19 scFv, even more preferably the mouse or human anti-CD19 scFv as described in Example 1 of WO 2015/187528. In certain embodiments, the CAR comprises, consists essentially of or consists of an amino acid sequence of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, or SEQ ID NO: 13 as set forth in Table 1 of WO 2015/187528.


By means of an example and without limitation, chimeric antigen receptor that recognizes the CD70 antigen is described in International Patent Publication No. WO 2012/058460A2 (see also, Park et al., CD70 as a target for chimeric antigen receptor T cells in head and neck squamous cell carcinoma, Oral Oncol. 2018 March; 78:145-150; and Jin et al., CD70, a novel target of CAR T-cell therapy for gliomas, Neuro Oncol. 2018 Jan. 10; 20(1):55-65). CD70 is expressed by diffuse large B-cell and follicular lymphoma and also by the malignant cells of Hodgkins lymphoma, Waldenstrom's macroglobulinemia and multiple myeloma, and by HTLV-1- and EBV-associated malignancies. (Agathanggelou et al. Am. J. Pathol. 1995; 147: 1152-1160; Hunter et al., Blood 2004; 104:4881. 26; Lens et al., J Immunol. 2005; 174:6212-6219; Baba et al., J Virol. 2008; 82:3843-3852.) In addition, CD70 is expressed by non-hematological malignancies such as renal cell carcinoma and glioblastoma. (Junker et al., J Urol. 2005; 173:2150-2153; Chahlavi et al., Cancer Res 2005; 65:5428-5438) Physiologically, CD70 expression is transient and restricted to a subset of highly activated T, B, and dendritic cells.


By means of an example and without limitation, chimeric antigen receptor that recognizes BCMA has been described (see, e.g., US Patent Publication No. 2016/0046724 A1; International Patent Publication Nos. WO 2016/014789 A2, WO 2017/211900 A1, WO 2015/158671 A1, WO2018028647A1, and WO 2013/154760 A1; and US Patent Publication Nos. 2018/0085444 A1 and 2017/0283504 A1).


In certain embodiments, the immune cell may, in addition to a CAR or exogenous TCR as described herein, further comprise a chimeric inhibitory receptor (inhibitory CAR) that specifically binds to a second target antigen and is capable of inducing an inhibitory or immunosuppressive or repressive signal to the cell upon recognition of the second target antigen. In certain embodiments, the chimeric inhibitory receptor comprises an extracellular antigen-binding element (or portion or domain) configured to specifically bind to a target antigen, a transmembrane domain, and an intracellular immunosuppressive or repressive signaling domain. In certain embodiments, the second target antigen is an antigen that is not expressed on the surface of a cancer cell or infected cell or the expression of which is downregulated on a cancer cell or an infected cell. In certain embodiments, the second target antigen is an MHC-class I molecule. In certain embodiments, the intracellular signaling domain comprises a functional signaling portion of an immune checkpoint molecule, such as for example PD-1 or CTLA4. Advantageously, the inclusion of such inhibitory CAR reduces the chance of the engineered immune cells attacking non-target (e.g., non-cancer) tissues.


Alternatively, T-cells expressing CARs may be further modified to reduce or eliminate expression of endogenous TCRs in order to reduce off-target effects. Reduction or elimination of endogenous TCRs can reduce off-target effects and increase the effectiveness of the T cells (U.S. Pat. No. 9,181,527). T cells stably lacking expression of a functional TCR may be produced using a variety of approaches. T cells internalize, sort, and degrade the entire T cell receptor as a complex, with a half-life of about 10 hours in resting T cells and 3 hours in stimulated T cells (von Essen, M. et al. 2004. J. Immunol. 173:384-393). Proper functioning of the TCR complex requires the proper stoichiometric ratio of the proteins that compose the TCR complex. TCR function also requires two functioning TCR zeta proteins with ITAM motifs. The activation of the TCR upon engagement of its WIC-peptide ligand requires the engagement of several TCRs on the same T cell, which all must signal properly. Thus, if a TCR complex is destabilized with proteins that do not associate properly or cannot signal optimally, the T cell will not become activated sufficiently to begin a cellular response.


Accordingly, in some embodiments, TCR expression may eliminated using RNA interference (e.g., shRNA, siRNA, miRNA, etc.), CRISPR, or other methods that target the nucleic acids encoding specific TCRs (e.g., TCR-α and TCR-β) and/or CD3 chains in primary T cells. By blocking expression of one or more of these proteins, the T cell will no longer produce one or more of the key components of the TCR complex, thereby destabilizing the TCR complex and preventing cell surface expression of a functional TCR.


In some instances, CAR may also comprise a switch mechanism for controlling expression and/or activation of the CAR. For example, a CAR may comprise an extracellular, transmembrane, and intracellular domain, in which the extracellular domain comprises a target-specific binding element that comprises a label, binding domain, or tag that is specific for a molecule other than the target antigen that is expressed on or by a target cell. In such embodiments, the specificity of the CAR is provided by a second construct that comprises a target antigen binding domain (e.g., an scFv or a bispecific antibody that is specific for both the target antigen and the label or tag on the CAR) and a domain that is recognized by or binds to the label, binding domain, or tag on the CAR. See, e.g., International Patent Publication Nos. WO 2013/044225, WO 2016/000304, WO 2015/057834, WO 2015/057852, WO 2016/070061, U.S. Pat. No. 9,233,125, and US Patent Publication No. 2016/0129109. In this way, a T-cell that expresses the CAR can be administered to a subject, but the CAR cannot bind its target antigen until the second composition comprising an antigen-specific binding domain is administered.


Alternative switch mechanisms include CARs that require multimerization in order to activate their signaling function (see, e.g., US Patent Publication Nos. 2015/0368342, US 2016/0175359, US 2015/0368360) and/or an exogenous signal, such as a small molecule drug (US 2016/0166613, Yung et al., Science, 2015), in order to elicit a T-cell response. Some CARs may also comprise a “suicide switch” to induce cell death of the CAR T-cells following treatment (Buddee et al., PLoS One, 2013) or to downregulate expression of the CAR following binding to the target antigen (WO 2016/011210).


Alternative techniques may be used to transform target immunoresponsive cells, such as protoplast fusion, lipofection, transfection or electroporation. A wide variety of vectors may be used, such as retroviral vectors, lentiviral vectors, adenoviral vectors, adeno-associated viral vectors, plasmids or transposons, such as a Sleeping Beauty transposon (see U.S. Pat. Nos. 6,489,458; 7,148,203; 7,160,682; 7,985,739; 8,227,432), may be used to introduce CARs, for example using 2nd generation antigen-specific CARs signaling through CD3ζ and either CD28 or CD137. Viral vectors may for example include vectors based on HIV, SV40, EBV, HSV or BPV.


Cells that are targeted for transformation may for example include T cells, Natural Killer (NK) cells, cytotoxic T lymphocytes (CTL), regulatory T cells, human embryonic stem cells, tumor-infiltrating lymphocytes (TIL) or a pluripotent stem cell from which lymphoid cells may be differentiated. T cells expressing a desired CAR may for example be selected through co-culture with γ-irradiated activating and propagating cells (AaPC), which co-express the cancer antigen and co-stimulatory molecules. The engineered CAR T-cells may be expanded, for example by co-culture on AaPC in presence of soluble factors, such as IL-2 and IL-21. This expansion may for example be carried out so as to provide memory CAR+ T cells (which may for example be assayed by non-enzymatic digital array and/or multi-panel flow cytometry). In this way, CAR T cells may be provided that have specific cytotoxic activity against antigen-bearing tumors (optionally in conjunction with production of desired chemokines such as interferon-γ). CART cells of this kind may for example be used in animal models, for example to treat tumor xenografts.


In certain embodiments, ACT includes co-transferring CD4+Th1 cells and CD8+ CTLs to induce a synergistic antitumour response (see, e.g., Li et al., Adoptive cell therapy with CD4+T helper 1 cells and CD8+ cytotoxic T cells enhances complete rejection of an established tumour, leading to generation of endogenous memory responses to non-targeted tumour epitopes. Clin Transl Immunology. 2017 October; 6(10): e160).


In certain embodiments, Th17 cells are transferred to a subject in need thereof. Th17 cells have been reported to directly eradicate melanoma tumors in mice to a greater extent than Th1 cells (Muranski P, et al., Tumor-specific Th17-polarized cells eradicate large established melanoma. Blood. 2008 Jul. 15; 112(2):362-73; and Martin-Orozco N, et al., T helper 17 cells promote cytotoxic T cell activation in tumor immunity. Immunity. 2009 Nov. 20; 31(5):787-98). Those studies involved an adoptive T cell transfer (ACT) therapy approach, which takes advantage of CD4+ T cells that express a TCR recognizing tyrosinase tumor antigen. Exploitation of the TCR leads to rapid expansion of Th17 populations to large numbers ex vivo for reinfusion into the autologous tumor-bearing hosts.


In certain embodiments, ACT may include autologous iPSC-based vaccines, such as irradiated iPSCs in autologous anti-tumor vaccines (see e.g., Kooreman, Nigel G. et al., Autologous iPSC-Based Vaccines Elicit Anti-tumor Responses In Vivo, Cell Stem Cell 22, 1-13, 2018, doi.org/10.1016/j.stem.2018.01.016).


Unlike T-cell receptors (TCRs) that are MHC restricted, CARs can potentially bind any cell surface-expressed antigen and can thus be more universally used to treat patients (see Irving et al., Engineering Chimeric Antigen Receptor T-Cells for Racing in Solid Tumors: Don't Forget the Fuel, Front. Immunol., 3 Apr. 2017, doi.org/10.3389/fimmu.2017.00267). In certain embodiments, in the absence of endogenous T-cell infiltrate (e.g., due to aberrant antigen processing and presentation), which precludes the use of TIL therapy and immune checkpoint blockade, the transfer of CAR T-cells may be used to treat patients (see, e.g., Hinrichs C S, Rosenberg S A. Exploiting the curative potential of adoptive T-cell therapy for cancer. Immunol Rev (2014) 257(1):56-71. doi:10.1111/imr.12132).


Approaches such as the foregoing may be adapted to provide methods of treating and/or increasing survival of a subject having a disease, such as a neoplasia, for example by administering an effective amount of an immunoresponsive cell comprising an antigen recognizing receptor that binds a selected antigen, wherein the binding activates the immunoresponsive cell, thereby treating or preventing the disease (such as a neoplasia, a pathogen infection, an autoimmune disorder, or an allogeneic transplant reaction).


In certain embodiments, the treatment can be administered after lymphodepleting pretreatment in the form of chemotherapy (typically a combination of cyclophosphamide and fludarabine) or radiation therapy. Initial studies in ACT had short lived responses and the transferred cells did not persist in vivo for very long (Houot et al., T-cell-based immunotherapy: adoptive cell transfer and checkpoint inhibition. Cancer Immunol Res (2015) 3(10):1115-22; and Kamta et al., Advancing Cancer Therapy with Present and Emerging Immuno-Oncology Approaches. Front. Oncol. (2017) 7:64). Immune suppressor cells like Tregs and MDSCs may attenuate the activity of transferred cells by outcompeting them for the necessary cytokines. Not being bound by a theory lymphodepleting pretreatment may eliminate the suppressor cells allowing the TILs to persist.


In one embodiment, the treatment can be administrated into patients undergoing an immunosuppressive treatment (e.g., glucocorticoid treatment). The cells or population of cells, may be made resistant to at least one immunosuppressive agent due to the inactivation of a gene encoding a receptor for such immunosuppressive agent. In certain embodiments, the immunosuppressive treatment provides for the selection and expansion of the immunoresponsive T cells within the patient.


In certain embodiments, the treatment can be administered before primary treatment (e.g., surgery or radiation therapy) to shrink a tumor before the primary treatment. In another embodiment, the treatment can be administered after primary treatment to remove any remaining cancer cells.


In certain embodiments, immunometabolic barriers can be targeted therapeutically prior to and/or during ACT to enhance responses to ACT or CAR T-cell therapy and to support endogenous immunity (see, e.g., Irving et al., Engineering Chimeric Antigen Receptor T-Cells for Racing in Solid Tumors: Don't Forget the Fuel, Front. Immunol., 3 Apr. 2017, doi.org/10.3389/fimmu.2017. 00267).


The administration of cells or population of cells, such as immune system cells or cell populations, such as more particularly immunoresponsive cells or cell populations, as disclosed herein may be carried out in any convenient manner, including by aerosol inhalation, injection, ingestion, transfusion, implantation or transplantation. The cells or population of cells may be administered to a patient subcutaneously, intradermally, intratumorally, intranodally, intramedullary, intramuscularly, intrathecally, by intravenous or intralymphatic injection, or intraperitoneally. In some embodiments, the disclosed CARs may be delivered or administered into a cavity formed by the resection of tumor tissue (i.e. intracavity delivery) or directly into a tumor prior to resection (i.e. intratumoral delivery). In one embodiment, the cell compositions of the present invention are preferably administered by intravenous injection.


The administration of the cells or population of cells can consist of the administration of 104-109 cells per kg body weight, preferably 105 to 106 cells/kg body weight including all integer values of cell numbers within those ranges. Dosing in CAR T cell therapies may for example involve administration of from 106 to 109 cells/kg, with or without a course of lymphodepletion, for example with cyclophosphamide. The cells or population of cells can be administrated in one or more doses. In another embodiment, the effective amount (e.g. number) of cells are administrated as a single dose. In another embodiment, the effective amount of cells are administrated as more than one dose over a period time. Timing of administration is within the judgment of managing physician and depends on the clinical condition of the patient. The cells or population of cells may be obtained from any source, such as a blood bank or a donor. While individual needs vary, determination of optimal ranges of effective amounts of a given cell type for a particular disease or conditions are within the skill of one in the art. An effective amount means an amount which provides a therapeutic or prophylactic benefit. The dosage administrated will be dependent upon the age, health and weight of the recipient, kind of concurrent treatment, if any, frequency of treatment and the nature of the effect desired.


In another embodiment, the effective amount of cells or composition comprising those cells are administrated parenterally. The administration can be an intravenous administration. The administration can be done directly by injection within a tumor.


To guard against possible adverse reactions, engineered immunoresponsive cells may be equipped with a transgenic safety switch, in the form of a transgene that renders the cells vulnerable to exposure to a specific signal. For example, the herpes simplex viral thymidine kinase (TK) gene may be used in this way, for example by introduction into allogeneic T lymphocytes used as donor lymphocyte infusions following stem cell transplantation (Greco, et al., Improving the safety of cell therapy with the TK-suicide gene. Front. Pharmacol. 2015; 6: 95). In such cells, administration of a nucleoside prodrug such as ganciclovir or acyclovir causes cell death. Alternative safety switch constructs include inducible caspase 9, for example triggered by administration of a small-molecule dimerizer that brings together two nonfunctional icasp9 molecules to form the active enzyme. A wide variety of alternative approaches to implementing cellular proliferation controls have been described (see U.S. Patent Publication No. 2013/0071414; PCT Patent Publication Nos. WO 2011/146862, WO 2014/011987, WO 2013/040371; Zhou et al. BLOOD, 2014, 123/25:3895-3905; Di Stasi et al., The New England Journal of Medicine 2011; 365:1673-1683; Sadelain M, The New England Journal of Medicine 2011; 365:1735-173; Ramos et al., Stem Cells 28(6):1107-15 (2010)).


In a further refinement of adoptive therapies, genome editing may be used to tailor immunoresponsive cells to alternative implementations, for example providing edited CAR T cells (see Poirot et al., 2015, Multiplex genome edited T-cell manufacturing platform for “off-the-shelf” adoptive T-cell immunotherapies, Cancer Res 75 (18): 3853; Ren et al., 2017, Multiplex genome editing to generate universal CAR T cells resistant to PD1 inhibition, Clin Cancer Res. 2017 May 1; 23(9):2255-2266. doi: 10.1158/1078-0432.CCR-16-1300. Epub 2016 Nov. 4; Qasim et al., 2017, Molecular remission of infant B-ALL after infusion of universal TALEN gene-edited CAR T cells, Sci Transl Med. 2017 Jan. 25; 9(374); Legut, et al., 2018, CRISPR-mediated TCR replacement generates superior anticancer transgenic T cells. Blood, 131(3), 311-322; and Georgiadis et al., Long Terminal Repeat CRISPR-CAR-Coupled “Universal” T Cells Mediate Potent Anti-leukemic Effects, Molecular Therapy, In Press, Corrected Proof, Available online 6 Mar. 2018). Cells may be edited using any CRISPR system and method of use thereof as described herein. CRISPR systems may be delivered to an immune cell by any method described herein. In preferred embodiments, cells are edited ex vivo and transferred to a subject in need thereof. Immunoresponsive cells, CAR T cells or any cells used for adoptive cell transfer may be edited. Editing may be performed for example to insert or knock-in an exogenous gene, such as an exogenous gene encoding a CAR or a TCR, at a preselected locus in a cell (e.g. TRAC locus); to eliminate potential alloreactive T-cell receptors (TCR) or to prevent inappropriate pairing between endogenous and exogenous TCR chains, such as to knock-out or knock-down expression of an endogenous TCR in a cell; to disrupt the target of a chemotherapeutic agent in a cell; to block an immune checkpoint, such as to knock-out or knock-down expression of an immune checkpoint protein or receptor in a cell; to knock-out or knock-down expression of other gene or genes in a cell, the reduced expression or lack of expression of which can enhance the efficacy of adoptive therapies using the cell; to knock-out or knock-down expression of an endogenous gene in a cell, said endogenous gene encoding an antigen targeted by an exogenous CAR or TCR; to knock-out or knock-down expression of one or more WIC constituent proteins in a cell; to activate a T cell; to modulate cells such that the cells are resistant to exhaustion or dysfunction; and/or increase the differentiation and/or proliferation of functionally exhausted or dysfunctional CD8+ T-cells (see PCT Patent Publications: WO2013176915, WO2014059173, WO2014172606, WO2014184744, and WO2014191128).


In certain embodiments, editing may result in inactivation of a gene. By inactivating a gene, it is intended that the gene of interest is not expressed in a functional protein form. In a particular embodiment, the CRISPR system specifically catalyzes cleavage in one targeted gene thereby inactivating said targeted gene. The nucleic acid strand breaks caused are commonly repaired through the distinct mechanisms of homologous recombination or non-homologous end joining (NHEJ). However, NHEJ is an imperfect repair process that often results in changes to the DNA sequence at the site of the cleavage. Repair via non-homologous end joining (NHEJ) often results in small insertions or deletions (Indel) and can be used for the creation of specific gene knockouts. Cells in which a cleavage induced mutagenesis event has occurred can be identified and/or selected by well-known methods in the art. In certain embodiments, homology directed repair (HDR) is used to concurrently inactivate a gene (e.g., TRAC) and insert an endogenous TCR or CAR into the inactivated locus.


Hence, in certain embodiments, editing of cells (such as by CRISPR/Cas), particularly cells intended for adoptive cell therapies, more particularly immunoresponsive cells such as T cells, may be performed to insert or knock-in an exogenous gene, such as an exogenous gene encoding a CAR or a TCR, at a preselected locus in a cell. Conventionally, nucleic acid molecules encoding CARs or TCRs are transfected or transduced to cells using randomly integrating vectors, which, depending on the site of integration, may lead to clonal expansion, oncogenic transformation, variegated transgene expression and/or transcriptional silencing of the transgene. Directing of transgene(s) to a specific locus in a cell can minimize or avoid such risks and advantageously provide for uniform expression of the transgene(s) by the cells. Without limitation, suitable ‘safe harbor’ loci for directed transgene integration include CCR5 or AAVS1. Homology-directed repair (HDR) strategies are known and described elsewhere in this specification allowing to insert transgenes into desired loci (e.g., TRAC locus).


Further suitable loci for insertion of transgenes, in particular CAR or exogenous TCR transgenes, include without limitation loci comprising genes coding for constituents of endogenous T-cell receptor, such as T-cell receptor alpha locus (TRA) or T-cell receptor beta locus (TRB), for example T-cell receptor alpha constant (TRAC) locus, T-cell receptor beta constant 1 (TRBC1) locus or T-cell receptor beta constant 2 (TRBC1) locus. Advantageously, insertion of a transgene into such locus can simultaneously achieve expression of the transgene, potentially controlled by the endogenous promoter, and knock-out expression of the endogenous TCR. This approach has been exemplified in Eyquem et al., (2017) Nature 543: 113-117, wherein the authors used CRISPR/Cas9 gene editing to knock-in a DNA molecule encoding a CD19-specific CAR into the TRAC locus downstream of the endogenous promoter; the CAR-T cells obtained by CRISPR were significantly superior in terms of reduced tonic CAR signaling and exhaustion.


T cell receptors (TCR) are cell surface receptors that participate in the activation of T cells in response to the presentation of antigen. The TCR is generally made from two chains, α and β, which assemble to form a heterodimer and associates with the CD3-transducing subunits to form the T cell receptor complex present on the cell surface. Each α and β chain of the TCR consists of an immunoglobulin-like N-terminal variable (V) and constant (C) region, a hydrophobic transmembrane domain, and a short cytoplasmic region. As for immunoglobulin molecules, the variable region of the α and β chains are generated by V(D)J recombination, creating a large diversity of antigen specificities within the population of T cells. However, in contrast to immunoglobulins that recognize intact antigen, T cells are activated by processed peptide fragments in association with an MHC molecule, introducing an extra dimension to antigen recognition by T cells, known as MHC restriction. Recognition of MHC disparities between the donor and recipient through the T cell receptor leads to T cell proliferation and the potential development of graft versus host disease (GVHD). The inactivation of TCRα or TCRβ can result in the elimination of the TCR from the surface of T cells preventing recognition of alloantigen and thus GVHD. However, TCR disruption generally results in the elimination of the CD3 signaling component and alters the means of further T cell expansion.


Hence, in certain embodiments, editing of cells (such as by CRISPR/Cas), particularly cells intended for adoptive cell therapies, more particularly immunoresponsive cells such as T cells, may be performed to knock-out or knock-down expression of an endogenous TCR in a cell. For example, NHEJ-based or HDR-based gene editing approaches can be employed to disrupt the endogenous TCR alpha and/or beta chain genes. For example, gene editing system or systems, such as CRISPR/Cas system or systems, can be designed to target a sequence found within the TCR beta chain conserved between the beta 1 and beta 2 constant region genes (TRBC1 and TRBC2) and/or to target the constant region of the TCR alpha chain (TRAC) gene.


Allogeneic cells are rapidly rejected by the host immune system. It has been demonstrated that, allogeneic leukocytes present in non-irradiated blood products will persist for no more than 5 to 6 days (Boni, Muranski et al. 2008 Blood 1; 112(12):4746-54). Thus, to prevent rejection of allogeneic cells, the host's immune system usually has to be suppressed to some extent. However, in the case of adoptive cell transfer the use of immunosuppressive drugs also have a detrimental effect on the introduced therapeutic T cells. Therefore, to effectively use an adoptive immunotherapy approach in these conditions, the introduced cells would need to be resistant to the immunosuppressive treatment. Thus, in a particular embodiment, the present invention further comprises a step of modifying T cells to make them resistant to an immunosuppressive agent, preferably by inactivating at least one gene encoding a target for an immunosuppressive agent. An immunosuppressive agent is an agent that suppresses immune function by one of several mechanisms of action. An immunosuppressive agent can be, but is not limited to a calcineurin inhibitor, a target of rapamycin, an interleukin-2 receptor α-chain blocker, an inhibitor of inosine monophosphate dehydrogenase, an inhibitor of dihydrofolic acid reductase, a corticosteroid or an immunosuppressive antimetabolite. The present invention allows conferring immunosuppressive resistance to T cells for immunotherapy by inactivating the target of the immunosuppressive agent in T cells. As non-limiting examples, targets for an immunosuppressive agent can be a receptor for an immunosuppressive agent such as: CD52, glucocorticoid receptor (GR), a FKBP family gene member and a cyclophilin family gene member.


In certain embodiments, editing of cells (such as by CRISPR/Cas), particularly cells intended for adoptive cell therapies, more particularly immunoresponsive cells such as T cells, may be performed to block an immune checkpoint, such as to knock-out or knock-down expression of an immune checkpoint protein or receptor in a cell. Immune checkpoints are inhibitory pathways that slow down or stop immune reactions and prevent excessive tissue damage from uncontrolled activity of immune cells. In certain embodiments, the immune checkpoint targeted is the programmed death-1 (PD-1 or CD279) gene (PDCD1). In other embodiments, the immune checkpoint targeted is cytotoxic T-lymphocyte-associated antigen (CTLA-4). In additional embodiments, the immune checkpoint targeted is another member of the CD28 and CTLA4 Ig superfamily such as BTLA, LAG3, ICOS, PDL1 or KIR. In further additional embodiments, the immune checkpoint targeted is a member of the TNFR superfamily such as CD40, OX40, CD137, GITR, CD27 or TIM-3.


Additional immune checkpoints include Src homology 2 domain-containing protein tyrosine phosphatase 1 (SHP-1) (Watson H A, et al., SHP-1: the next checkpoint target for cancer immunotherapy? Biochem Soc Trans. 2016 Apr. 15; 44(2):356-62). SHP-1 is a widely expressed inhibitory protein tyrosine phosphatase (PTP). In T-cells, it is a negative regulator of antigen-dependent activation and proliferation. It is a cytosolic protein, and therefore not amenable to antibody-mediated therapies, but its role in activation and proliferation makes it an attractive target for genetic manipulation in adoptive transfer strategies, such as chimeric antigen receptor (CAR) T cells. Immune checkpoints may also include T cell immunoreceptor with Ig and ITIM domains (TIGIT/Vstm3/WUCAM/VSIG9) and VISTA (Le Mercier I, et al., (2015) Beyond CTLA-4 and PD-1, the generation Z of negative checkpoint regulators. Front. Immunol. 6:418).


WO2014172606 relates to the use of MT1 and/or MT2 inhibitors to increase proliferation and/or activity of exhausted CD8+ T-cells and to decrease CD8+ T-cell exhaustion (e.g., decrease functionally exhausted or unresponsive CD8+ immune cells). In certain embodiments, metallothioneins are targeted by gene editing in adoptively transferred T cells.


In certain embodiments, targets of gene editing may be at least one targeted locus involved in the expression of an immune checkpoint protein. Such targets may include, but are not limited to CTLA4, PPP2CA, PPP2CB, PTPN6, PTPN22, PDCD1, ICOS (CD278), PDL1, KIR, LAG3, HAVCR2, BTLA, CD160, TIGIT, CD96, CRTAM, LAIR1, SIGLEC7, SIGLEC9, CD244 (2B4), TNFRSF10B, TNFRSF10A, CASP8, CASP10, CASP3, CASP6, CASP7, FADD, FAS, TGFBRII, TGFRBRI, SMAD2, SMAD3, SMAD4, SMAD10, SKI, SKIL, TGIF1, IL10RA, IL10RB, HMOX2, IL6R, IL6ST, EIF2AK4, CSK, PAG1, SIT1, FOXP3, PRDM1, BATF, VISTA, GUCY1A2, GUCY1A3, GUCY1B2, GUCY1B3, MT1, MT2, CD40, OX40, CD137, GITR, CD27, SHP-1, TIM-3, CEACAM-1, CEACAM-3, or CEACAM-5. In preferred embodiments, the gene locus involved in the expression of PD-1 or CTLA-4 genes is targeted. In other preferred embodiments, combinations of genes are targeted, such as but not limited to PD-1 and TIGIT.


By means of an example and without limitation, WO2016196388 concerns an engineered T cell comprising (a) a genetically engineered antigen receptor that specifically binds to an antigen, which receptor may be a CAR; and (b) a disrupted gene encoding a PD-L1, an agent for disruption of a gene encoding a PD-L1, and/or disruption of a gene encoding PD-L1, wherein the disruption of the gene may be mediated by a gene editing nuclease, a zinc finger nuclease (ZFN), CRISPR/Cas9 and/or TALEN. WO2015142675 relates to immune effector cells comprising a CAR in combination with an agent (such as CRISPR, TALEN or ZFN) that increases the efficacy of the immune effector cells in the treatment of cancer, wherein the agent may inhibit an immune inhibitory molecule, such as PD1, PD-L1, CTLA-4, TIM-3, LAG-3, VISTA, BTLA, TIGIT, LAIR1, CD160, 2B4, TGFR beta, CEACAM-1, CEACAM-3, or CEACAM-5. Ren et al., (2017) Clin Cancer Res 23 (9) 2255-2266 performed lentiviral delivery of CAR and electro-transfer of Cas9 mRNA and gRNAs targeting endogenous TCR, β-2 microglobulin (B2M) and PD1 simultaneously, to generate gene-disrupted allogeneic CART cells deficient of TCR, HLA class I molecule and PD1.


In certain embodiments, cells may be engineered to express a CAR, wherein expression and/or function of methylcytosine dioxygenase genes (TET1, TET2 and/or TET3) in the cells has been reduced or eliminated, such as by CRISPR, ZNF or TALEN (for example, as described in WO201704916).


In certain embodiments, editing of cells (such as by CRISPR/Cas), particularly cells intended for adoptive cell therapies, more particularly immunoresponsive cells such as T cells, may be performed to knock-out or knock-down expression of an endogenous gene in a cell, said endogenous gene encoding an antigen targeted by an exogenous CAR or TCR, thereby reducing the likelihood of targeting of the engineered cells. In certain embodiments, the targeted antigen may be one or more antigen selected from the group consisting of CD38, CD138, CS-1, CD33, CD26, CD30, CD53, CD92, CD100, CD148, CD150, CD200, CD261, CD262, CD362, human telomerase reverse transcriptase (hTERT), survivin, mouse double minute 2 homolog (MDM2), cytochrome P450 1B1 (CYP1B), HER2/neu, Wilms' tumor gene 1 (WT1), livin, alphafetoprotein (AFP), carcinoembryonic antigen (CEA), mucin 16 (MUC16), MUC1, prostate-specific membrane antigen (PSMA), p53, cyclin (D1), B cell maturation antigen (BCMA), transmembrane activator and CAML Interactor (TACI), and B-cell activating factor receptor (BAFF-R) (for example, as described in WO2016011210 and WO2017011804).


In certain embodiments, editing of cells (such as by CRISPR/Cas), particularly cells intended for adoptive cell therapies, more particularly immunoresponsive cells such as T cells, may be performed to knock-out or knock-down expression of one or more MHC constituent proteins, such as one or more HLA proteins and/or beta-2 microglobulin (B2M), in a cell, whereby rejection of non-autologous (e.g., allogeneic) cells by the recipient's immune system can be reduced or avoided. In preferred embodiments, one or more HLA class I proteins, such as HLA-A, B and/or C, and/or B2M may be knocked-out or knocked-down. Preferably, B2M may be knocked-out or knocked-down. By means of an example, Ren et al., (2017) Clin Cancer Res 23 (9) 2255-2266 performed lentiviral delivery of CAR and electro-transfer of Cas9 mRNA and gRNAs targeting endogenous TCR, β-2 microglobulin (B2M) and PD1 simultaneously, to generate gene-disrupted allogeneic CAR T cells deficient of TCR, HLA class I molecule and PD1.


In other embodiments, at least two genes are edited. Pairs of genes may include, but are not limited to PD1 and TCRα, PD1 and TCRβ, CTLA-4 and TCRα, CTLA-4 and TCRβ, LAG3 and TCRα, LAG3 and TCRβ, Tim3 and TCRα, Tim3 and TCRβ, BTLA and TCRα, BTLA and TCRβ, BY55 and TCRα, BY55 and TCRβ, TIGIT and TCRα, TIGIT and TCRβ, B7H5 and TCRα, B7H5 and TCRβ, LAIR1 and TCRα, LAIR1 and TCRβ, SIGLEC10 and TCRα, SIGLEC10 and TCRβ, 2B4 and TCRα, 2B4 and TCRβ, B2M and TCRα, B2M and TCRβ.


In certain embodiments, a cell may be multiply edited (multiplex genome editing) as taught herein to (1) knock-out or knock-down expression of an endogenous TCR (for example, TRBC1, TRBC2 and/or TRAC), (2) knock-out or knock-down expression of an immune checkpoint protein or receptor (for example PD1, PD-L1 and/or CTLA4); and (3) knock-out or knock-down expression of one or more MHC constituent proteins (for example, HLA-A, B and/or C, and/or B2M, preferably B2M).


Whether prior to or after genetic modification of the T cells, the T cells can be activated and expanded generally using methods as described, for example, in U.S. Pat. Nos. 6,352,694; 6,534,055; 6,905,680; 5,858,358; 6,887,466; 6,905,681; 7,144,575; 7,232,566; 7,175,843; 5,883,223; 6,905,874; 6,797,514; 6,867,041; and 7,572,631. T cells can be expanded in vitro or in vivo.


Immune cells may be obtained using any method known in the art. In one embodiment, allogenic T cells may be obtained from healthy subjects. In one embodiment T cells that have infiltrated a tumor are isolated. T cells may be removed during surgery. T cells may be isolated after removal of tumor tissue by biopsy. T cells may be isolated by any means known in the art. In one embodiment, T cells are obtained by apheresis. In one embodiment, the method may comprise obtaining a bulk population of T cells from a tumor sample by any suitable method known in the art. For example, a bulk population of T cells can be obtained from a tumor sample by dissociating the tumor sample into a cell suspension from which specific cell populations can be selected. Suitable methods of obtaining a bulk population of T cells may include, but are not limited to, any one or more of mechanically dissociating (e.g., mincing) the tumor, enzymatically dissociating (e.g., digesting) the tumor, and aspiration (e.g., as with a needle).


The bulk population of T cells obtained from a tumor sample may comprise any suitable type of T cell. Preferably, the bulk population of T cells obtained from a tumor sample comprises tumor infiltrating lymphocytes (TILs).


The tumor sample may be obtained from any mammal. Unless stated otherwise, as used herein, the term “mammal” refers to any mammal including, but not limited to, mammals of the order Logomorpha, such as rabbits; the order Carnivora, including Felines (cats) and Canines (dogs); the order Artiodactyla, including Bovines (cows) and Swines (pigs); or of the order Perssodactyla, including Equines (horses). The mammals may be non-human primates, e.g., of the order Primates, Ceboids, or Simoids (monkeys) or of the order Anthropoids (humans and apes). In some embodiments, the mammal may be a mammal of the order Rodentia, such as mice and hamsters. Preferably, the mammal is a non-human primate or a human. An especially preferred mammal is the human.


T cells can be obtained from a number of sources, including peripheral blood mononuclear cells (PBMC), bone marrow, lymph node tissue, spleen tissue, and tumors. In certain embodiments of the present invention, T cells can be obtained from a unit of blood collected from a subject using any number of techniques known to the skilled artisan, such as Ficoll separation. In one preferred embodiment, cells from the circulating blood of an individual are obtained by apheresis or leukapheresis. The apheresis product typically contains lymphocytes, including T cells, monocytes, granulocytes, B cells, other nucleated white blood cells, red blood cells, and platelets. In one embodiment, the cells collected by apheresis may be washed to remove the plasma fraction and to place the cells in an appropriate buffer or media for subsequent processing steps. In one embodiment of the invention, the cells are washed with phosphate buffered saline (PBS). In an alternative embodiment, the wash solution lacks calcium and may lack magnesium or may lack many if not all divalent cations. Initial activation steps in the absence of calcium lead to magnified activation. As those of ordinary skill in the art would readily appreciate a washing step may be accomplished by methods known to those in the art, such as by using a semi-automated “flow-through” centrifuge (for example, the Cobe 2991 cell processor) according to the manufacturer's instructions. After washing, the cells may be resuspended in a variety of biocompatible buffers, such as, for example, Ca-free, Mg-free PBS. Alternatively, the undesirable components of the apheresis sample may be removed and the cells directly resuspended in culture media.


In another embodiment, T cells are isolated from peripheral blood lymphocytes by lysing the red blood cells and depleting the monocytes, for example, by centrifugation through a PERCOLL™ gradient. A specific subpopulation of T cells, such as CD28+, CD4+, CDC, CD45RA+, and CD45RO+ T cells, can be further isolated by positive or negative selection techniques. For example, in one preferred embodiment, T cells are isolated by incubation with anti-CD3/anti-CD28 (i.e., 3X28)-conjugated beads, such as DYNABEADS® M-450 CD3/CD28 T, or XCYTE DYNABEADS™ for a time period sufficient for positive selection of the desired T cells. In one embodiment, the time period is about 30 minutes. In a further embodiment, the time period ranges from 30 minutes to 36 hours or longer and all integer values there between. In a further embodiment, the time period is at least 1, 2, 3, 4, 5, or 6 hours. In yet another preferred embodiment, the time period is 10 to 24 hours. In one preferred embodiment, the incubation time period is 24 hours. For isolation of T cells from patients with leukemia, use of longer incubation times, such as 24 hours, can increase cell yield. Longer incubation times may be used to isolate T cells in any situation where there are few T cells as compared to other cell types, such in isolating tumor infiltrating lymphocytes (TIL) from tumor tissue or from immunocompromised individuals. Further, use of longer incubation times can increase the efficiency of capture of CD8+ T cells.


Enrichment of a T cell population by negative selection can be accomplished with a combination of antibodies directed to surface markers unique to the negatively selected cells. A preferred method is cell sorting and/or selection via negative magnetic immunoadherence or flow cytometry that uses a cocktail of monoclonal antibodies directed to cell surface markers present on the cells negatively selected. For example, to enrich for CD4+ cells by negative selection, a monoclonal antibody cocktail typically includes antibodies to CD14, CD20, CD11b, CD16, HLA-DR, and CD8.


Further, monocyte populations (i.e., CD14+ cells) may be depleted from blood preparations by a variety of methodologies, including anti-CD14 coated beads or columns, or utilization of the phagocytotic activity of these cells to facilitate removal. Accordingly, in one embodiment, the invention uses paramagnetic particles of a size sufficient to be engulfed by phagocytotic monocytes. In certain embodiments, the paramagnetic particles are commercially available beads, for example, those produced by Life Technologies under the trade name Dynabeads™. In one embodiment, other non-specific cells are removed by coating the paramagnetic particles with “irrelevant” proteins (e.g., serum proteins or antibodies). Irrelevant proteins and antibodies include those proteins and antibodies or fragments thereof that do not specifically target the T cells to be isolated. In certain embodiments, the irrelevant beads include beads coated with sheep anti-mouse antibodies, goat anti-mouse antibodies, and human serum albumin.


In brief, such depletion of monocytes is performed by preincubating T cells isolated from whole blood, apheresed peripheral blood, or tumors with one or more varieties of irrelevant or non-antibody coupled paramagnetic particles at any amount that allows for removal of monocytes (approximately a 20:1 bead:cell ratio) for about 30 minutes to 2 hours at 22 to 37 degrees C., followed by magnetic removal of cells which have attached to or engulfed the paramagnetic particles. Such separation can be performed using standard methods available in the art. For example, any magnetic separation methodology may be used including a variety of which are commercially available, (e.g., DYNAL® Magnetic Particle Concentrator (DYNAL MPC®)). Assurance of requisite depletion can be monitored by a variety of methodologies known to those of ordinary skill in the art, including flow cytometric analysis of CD14 positive cells, before and after depletion.


For isolation of a desired population of cells by positive or negative selection, the concentration of cells and surface (e.g., particles such as beads) can be varied. In certain embodiments, it may be desirable to significantly decrease the volume in which beads and cells are mixed together (i.e., increase the concentration of cells), to ensure maximum contact of cells and beads. For example, in one embodiment, a concentration of 2 billion cells/ml is used. In one embodiment, a concentration of 1 billion cells/ml is used. In a further embodiment, greater than 100 million cells/ml is used. In a further embodiment, a concentration of cells of 10, 15, 20, 25, 30, 35, 40, 45, or 50 million cells/ml is used. In yet another embodiment, a concentration of cells from 75, 80, 85, 90, 95, or 100 million cells/ml is used. In further embodiments, concentrations of 125 or 150 million cells/ml can be used. Using high concentrations can result in increased cell yield, cell activation, and cell expansion. Further, use of high cell concentrations allows more efficient capture of cells that may weakly express target antigens of interest, such as CD28-negative T cells, or from samples where there are many tumor cells present (i.e., leukemic blood, tumor tissue, etc). Such populations of cells may have therapeutic value and would be desirable to obtain. For example, using high concentration of cells allows more efficient selection of CD8+ T cells that normally have weaker CD28 expression.


In a related embodiment, it may be desirable to use lower concentrations of cells. By significantly diluting the mixture of T cells and surface (e.g., particles such as beads), interactions between the particles and cells is minimized. This selects for cells that express high amounts of desired antigens to be bound to the particles. For example, CD4+ T cells express higher levels of CD28 and are more efficiently captured than CD8+ T cells in dilute concentrations. In one embodiment, the concentration of cells used is 5×106/ml. In other embodiments, the concentration used can be from about 1×105/ml to 1×106/ml, and any integer value in between.


T cells can also be frozen. Wishing not to be bound by theory, the freeze and subsequent thaw step provides a more uniform product by removing granulocytes and to some extent monocytes in the cell population. After a washing step to remove plasma and platelets, the cells may be suspended in a freezing solution. While many freezing solutions and parameters are known in the art and will be useful in this context, one method involves using PBS containing 20% DMSO and 8% human serum albumin, or other suitable cell freezing media, the cells then are frozen to −80° C. at a rate of 1° per minute and stored in the vapor phase of a liquid nitrogen storage tank. Other methods of controlled freezing may be used as well as uncontrolled freezing immediately at −20° C. or in liquid nitrogen.


T cells for use in the present invention may also be antigen-specific T cells. For example, tumor-specific T cells can be used. In certain embodiments, antigen-specific T cells can be isolated from a patient of interest, such as a patient afflicted with a cancer or an infectious disease. In one embodiment, neoepitopes are determined for a subject and T cells specific to these antigens are isolated. Antigen-specific cells for use in expansion may also be generated in vitro using any number of methods known in the art, for example, as described in U.S. Patent Publication No. US 20040224402 entitled, Generation and Isolation of Antigen-Specific T Cells, or in U.S. Pat. No. 6,040,177. Antigen-specific cells for use in the present invention may also be generated using any number of methods known in the art, for example, as described in Current Protocols in Immunology, or Current Protocols in Cell Biology, both published by John Wiley & Sons, Inc., Boston, Mass.


In a related embodiment, it may be desirable to sort or otherwise positively select (e.g. via magnetic selection) the antigen specific cells prior to or following one or two rounds of expansion. Sorting or positively selecting antigen-specific cells can be carried out using peptide-MHC tetramers (Altman, et al., Science. 1996 Oct. 4; 274(5284):94-6). In another embodiment, the adaptable tetramer technology approach is used (Andersen et al., 2012 Nat Protoc. 7:891-902). Tetramers are limited by the need to utilize predicted binding peptides based on prior hypotheses, and the restriction to specific HLAs. Peptide-MHC tetramers can be generated using techniques known in the art and can be made with any MEW molecule of interest and any antigen of interest as described herein. Specific epitopes to be used in this context can be identified using numerous assays known in the art. For example, the ability of a polypeptide to bind to MEW class I may be evaluated indirectly by monitoring the ability to promote incorporation of 125I labeled β2-microglobulin (β2m) into MEW class I/β2m/peptide heterotrimeric complexes (see Parker et al., J. Immunol. 152:163, 1994).


In one embodiment cells are directly labeled with an epitope-specific reagent for isolation by flow cytometry followed by characterization of phenotype and TCRs. In one embodiment, T cells are isolated by contacting with T cell specific antibodies. Sorting of antigen-specific T cells, or generally any cells of the present invention, can be carried out using any of a variety of commercially available cell sorters, including, but not limited to, MoFlo sorter (DakoCytomation, Fort Collins, Colo.), FACSAria™, FACSArray™, FACSVantage™, BD™ LSR II, and FACSCalibur™ (BD Biosciences, San Jose, Calif.).


In a preferred embodiment, the method comprises selecting cells that also express CD3. The method may comprise specifically selecting the cells in any suitable manner. Preferably, the selecting is carried out using flow cytometry. The flow cytometry may be carried out using any suitable method known in the art. The flow cytometry may employ any suitable antibodies and stains. Preferably, the antibody is chosen such that it specifically recognizes and binds to the particular biomarker being selected. For example, the specific selection of CD3, CD8, TIM-3, LAG-3, 4-1BB, or PD-1 may be carried out using anti-CD3, anti-CD8, anti-TIM-3, anti-LAG-3, anti-4-1BB, or anti-PD-1 antibodies, respectively. The antibody or antibodies may be conjugated to a bead (e.g., a magnetic bead) or to a fluorochrome. Preferably, the flow cytometry is fluorescence-activated cell sorting (FACS). TCRs expressed on T cells can be selected based on reactivity to autologous tumors. Additionally, T cells that are reactive to tumors can be selected for based on markers using the methods described in patent publication Nos. WO2014133567 and WO2014133568, herein incorporated by reference in their entirety. Additionally, activated T cells can be selected for based on surface expression of CD107a.


In one embodiment of the invention, the method further comprises expanding the numbers of T cells in the enriched cell population. Such methods are described in U.S. Pat. No. 8,637,307 and is herein incorporated by reference in its entirety. The numbers of T cells may be increased at least about 3-fold (or 4-, 5-, 6-, 7-, 8-, or 9-fold), more preferably at least about 10-fold (or 20-, 30-, 40-, 50-, 60-, 70-, 80-, or 90-fold), more preferably at least about 100-fold, more preferably at least about 1,000 fold, or most preferably at least about 100,000-fold. The numbers of T cells may be expanded using any suitable method known in the art. Exemplary methods of expanding the numbers of cells are described in patent publication No. WO 2003057171, U.S. Pat. No. 8,034,334, and U.S. Patent Application Publication No. 2012/0244133, each of which is incorporated herein by reference.


In one embodiment, ex vivo T cell expansion can be performed by isolation of T cells and subsequent stimulation or activation followed by further expansion. In one embodiment of the invention, the T cells may be stimulated or activated by a single agent. In another embodiment, T cells are stimulated or activated with two agents, one that induces a primary signal and a second that is a co-stimulatory signal. Ligands useful for stimulating a single signal or stimulating a primary signal and an accessory molecule that stimulates a second signal may be used in soluble form. Ligands may be attached to the surface of a cell, to an Engineered Multivalent Signaling Platform (EMSP), or immobilized on a surface. In a preferred embodiment both primary and secondary agents are co-immobilized on a surface, for example a bead or a cell. In one embodiment, the molecule providing the primary activation signal may be a CD3 ligand, and the co-stimulatory molecule may be a CD28 ligand or 4-1BB ligand.


In certain embodiments, T cells comprising a CAR or an exogenous TCR, may be manufactured as described in WO2015120096, by a method comprising: enriching a population of lymphocytes obtained from a donor subject; stimulating the population of lymphocytes with one or more T-cell stimulating agents to produce a population of activated T cells, wherein the stimulation is performed in a closed system using serum-free culture medium; transducing the population of activated T cells with a viral vector comprising a nucleic acid molecule which encodes the CAR or TCR, using a single cycle transduction to produce a population of transduced T cells, wherein the transduction is performed in a closed system using serum-free culture medium; and expanding the population of transduced T cells for a predetermined time to produce a population of engineered T cells, wherein the expansion is performed in a closed system using serum-free culture medium. In certain embodiments, T cells comprising a CAR or an exogenous TCR, may be manufactured as described in WO2015120096, by a method comprising: obtaining a population of lymphocytes; stimulating the population of lymphocytes with one or more stimulating agents to produce a population of activated T cells, wherein the stimulation is performed in a closed system using serum-free culture medium; transducing the population of activated T cells with a viral vector comprising a nucleic acid molecule which encodes the CAR or TCR, using at least one cycle transduction to produce a population of transduced T cells, wherein the transduction is performed in a closed system using serum-free culture medium; and expanding the population of transduced T cells to produce a population of engineered T cells, wherein the expansion is performed in a closed system using serum-free culture medium. The predetermined time for expanding the population of transduced T cells may be 3 days. The time from enriching the population of lymphocytes to producing the engineered T cells may be 6 days. The closed system may be a closed bag system. Further provided is population of T cells comprising a CAR or an exogenous TCR obtainable or obtained by said method, and a pharmaceutical composition comprising such cells.


In certain embodiments, T cell maturation or differentiation in vitro may be delayed or inhibited by the method as described in WO2017070395, comprising contacting one or more T cells from a subject in need of a T cell therapy with an AKT inhibitor (such as, e.g., one or a combination of two or more AKT inhibitors disclosed in claim 8 of WO2017070395) and at least one of exogenous Interleukin-7 (IL-7) and exogenous Interleukin-15 (IL-15), wherein the resulting T cells exhibit delayed maturation or differentiation, and/or wherein the resulting T cells exhibit improved T cell function (such as, e.g., increased T cell proliferation; increased cytokine production; and/or increased cytolytic activity) relative to a T cell function of a T cell cultured in the absence of an AKT inhibitor.


In certain embodiments, a patient in need of a T cell therapy may be conditioned by a method as described in WO2016191756 comprising administering to the patient a dose of cyclophosphamide between 200 mg/m2/day and 2000 mg/m2/day and a dose of fludarabine between 20 mg/m2/day and 900 mg/m2/day.


Modulation of One or More Biomarkers of a Malignant Expression Signature

In certain embodiments, a method of treating Sys cells comprises administering or more agents capable of modulating expression, activity, or function of one or more biomarkers of the malignant gene signatures defined in Tables 1A-1E.


Modulation of an Expansion Signature

In certain embodiments, a method of selectively treating Sys cells or reducing or repressing metastasis comprises administering one or more agents capable of modulating expression, activity, or function of one or more biomarkers of the malignant signatures in Tables 1A-1E. In another example embodiment, method of selectively targeting synovial sarcoma cells comprises administering one or more agents capable of modulating expression, activity, or function of one or more biomarkers of the malignant signatures defined at any one of Tables 1A-1E.


Modulation of Cell-Type Specific Biological Programs

In another aspect, embodiments disclosed herein provide a method of modulating an malignant signature comprising administering to a population of cells comprising Sys cells, one or more agents capable of modulating expression, activity of one or more signatures as defined in Tables 1A to 1E.


In one example embodiment, the method comprises administering to a population of cells comprising Sys cells one or more agents capable of modulating expression, activity of one or more biological programs characterized by one or more of Tables 1A-1E.


In one example embodiment, the method comprises administering to a population of cells comprising Sys cells one or more agents capable of modulating expression, activity of one or more biological programs characterized by the one or more of the signatures of Tables 1A-1E.


In certain example embodiments, the agent suppresses one of the above biological programs, whereby Sys cells are selectively targeted while sparing non-malignant cells. The one or more agents may comprise agent(s) that modulate the expression, activity or function of one or more genes of or polypeptides in Tables 1A-1E.


In certain example embodiments, the population of cells is in vivo. In certain embodiments, the in vivo population is present in the gut of a subject. In other example embodiments, the population of cell is an in vitro or ex vivo population of cells. In certain other example embodiments, the population of cells is an intestinal organoid.


Modulation and Modulating Agents

As used herein, “modulating” or “to modulate” generally means either reducing or inhibiting the expression or activity of, or alternatively increasing the expression or activity of a target or antigen. In particular, “modulating” or “to modulate” can mean either reducing or inhibiting the activity of, or alternatively increasing a (relevant or intended) biological activity of, a target or antigen as measured using a suitable in vitro, cellular or in vivo assay (which will usually depend on the target involved), by at least 5%, at least 10%, at least 25%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or more, compared to activity of the target in the same assay under the same conditions but without the presence of an agent. An “increase” or “decrease” refers to a statistically significant increase or decrease respectively. For the avoidance of doubt, an increase or decrease will be at least 10% relative to a reference, such as at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 97%, at least 98%, or more, up to and including at least 100% or more, in the case of an increase, for example, at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 6-fold, at least 7-fold, at least 8-fold, at least 9-fold, at least 10-fold, at least 50-fold, at least 100-fold, or more. “Modulating” can also involve effecting a change (which can either be an increase or a decrease) in affinity, avidity, specificity and/or selectivity of a target or antigen. “Modulating” can also mean effecting a change with respect to one or more biological or physiological mechanisms, effects, responses, functions, pathways or activities in which the target or antigen (or in which its substrate(s), ligand(s) or pathway(s) are involved, such as its signaling pathway or metabolic pathway and their associated biological or physiological effects) is involved. Again, as will be clear to the skilled person, such an action as an agonist or an antagonist can be determined in any suitable manner and/or using any suitable assay known or described herein (e.g., in vitro or cellular assay), depending on the target or antigen involved.


Modulating can, for example, also involve allosteric modulation of the target and/or reducing or inhibiting the binding of the target to one of its substrates or ligands and/or competing with a natural ligand, substrate for binding to the target. Modulating can also involve activating the target or the mechanism or pathway in which it is involved. Modulating can for example also involve effecting a change in respect of the folding or confirmation of the target, or in respect of the ability of the target to fold, to change its conformation (for example, upon binding of a ligand), to associate with other (sub)units, or to disassociate. Modulating can for example also involve effecting a change in the ability of the target to signal, phosphorylate, dephosphorylate, and the like.


As used herein, an “agent” can refer to a protein-binding agent that permits modulation of activity of proteins or disrupts interactions of proteins and other biomolecules, such as but not limited to disrupting protein-protein interaction, ligand-receptor interaction, or protein-nucleic acid interaction. Agents can also refer to DNA targeting or RNA targeting agents. Agents can also refer to a protein. Agents may include a fragment, derivative and analog of an active agent. The terms “fragment,” “derivative” and “analog” when referring to polypeptides as used herein refers to polypeptides which either retain substantially the same biological function or activity as such polypeptides. An analog includes a proprotein which can be activated by cleavage of the proprotein portion to produce an active mature polypeptide. Such agents include, but are not limited to, antibodies (“antibodies” includes antigen-binding portions of antibodies such as epitope- or antigen-binding peptides, paratopes, functional CDRs; recombinant antibodies; chimeric antibodies; humanized antibodies; nanobodies; tribodies; midibodies; or antigen-binding derivatives, analogs, variants, portions, or fragments thereof), protein-binding agents, nucleic acid molecules, small molecules, recombinant protein, peptides, aptamers, avimers and protein-binding derivatives, portions or fragments thereof. An “agent” as used herein, may also refer to an agent that inhibits expression of a gene, such as but not limited to a DNA targeting agent (e.g., CRISPR system, TALE, Zinc finger protein) or RNA targeting agent (e.g., inhibitory nucleic acid molecules such as RNAi, miRNA, ribozyme).


In certain embodiments, the agent modulates Sys malignant signature. In certain embodiments, the agent is an inhibitor of HDAC and/or CDK4/6.


The composition of the invention can also advantageously be formulated in order to release inhibitor of HDAC and/or CDK4/6 in the subject in a timely controlled fashion. In a particular embodiment, the composition of the invention is formulated for controlled release of inhibitor of HDAC and/or CDK4/6.


In some embodiments, the modulating agent modulated one or more biomarkers of a) epithelial malignant signature as defined in Table 1E; b) mesenchymal malignant cell signature as defined in Table 1D; c) cell cycle signature as defined in Table 1C; d) core oncogenic signature as defined in Table 1A.1; e) a fusion signature as defined in Table 8; or f) a combination thereof. In certain embodiments, an effective amount of the modulating agent is administered.


In certain embodiments, the agent is capable of inhibitor of HDAC and/or CDK4/6. In certain embodiments, HDAC and/or CDK4/6 expression is inhibited, e.g., by a DNA targeting agent (e.g., CRISPR system, TALE, Zinc finger protein) or a RNA targeting agent (e.g., inhibitory nucleic acid molecules). In certain embodiments, the antagonist is an antibody or fragment thereof. In certain embodiments, the antibody is specific for HDAC and/or CDK4/6.


The agents of the present invention may be modified, such that they acquire advantageous properties for therapeutic use (e.g., stability and specificity), but maintain their biological activity.


It is well known that the properties of certain proteins can be modulated by attachment of polyethylene glycol (PEG) polymers, which increases the hydrodynamic volume of the protein and thereby slows its clearance by kidney filtration. (See, e.g., Clark et al., J. Biol. Chem. 271: 21969-21977 (1996)). Therefore, it is envisioned that certain agents can be PEGylated (e.g., on peptide residues) to provide enhanced therapeutic benefits such as, for example, increased efficacy by extending half-life in vivo. In certain embodiments, PEGylation of the agents may be used to extend the serum half-life of the agents and allow for particular agents to be capable of crossing the blood-brain barrier. Thus, in one embodiment, PEGylating inhibitor of HDAC and/or CDK4/6 improve the pharmacokinetics and pharmacodynamics of the inhibitors.


In regards to peptide PEGylation methods, reference is made to Lu et al., Int. J. Pept. Protein Res. 43: 127-38 (1994); Lu et al., Pept. Res. 6: 140-6 (1993); Felix et al., Int. J. Pept. Protein Res. 46: 253-64 (1995); Gaertner et al., Bioconjug. Chem. 7: 38-44 (1996); Tsutsumi et al., Thromb. Haemost. 77: 168-73 (1997); Francis et al., hit. J. Hematol. 68: 1-18 (1998); Roberts et al., J. Pharm. Sci. 87: 1440-45 (1998); and Tan et al., Protein Expr. Purif. 12: 45-52 (1998). Polyethylene glycol or PEG is meant to encompass any of the forms of PEG that have been used to derivatize other proteins, including, but not limited to, mono-(C1-10) alkoxy or aryloxy-polyethylene glycol. Suitable PEG moieties include, for example, 40 kDa methoxy poly(ethylene glycol) propionaldehyde (Dow, Midland, Mich.); 60 kDa methoxy poly(ethylene glycol) propionaldehyde (Dow, Midland, Mich.); 40 kDa methoxy poly(ethylene glycol) maleimido-propionamide (Dow, Midland, Mich.); 31 kDa alpha-methyl-w-(3-oxopropoxy), polyoxyethylene (NOF Corporation, Tokyo); mPEG2-NHS-40k (Nektar); mPEG2-MAL-40k (Nektar), SUNBRIGHT GL2-400MA ((PEG)240 kDa) (NOF Corporation, Tokyo), SUNBRIGHT ME-200MA (PEG20 kDa) (NOF Corporation, Tokyo). The PEG groups are generally attached to the peptide via acylation or alkylation through a reactive group on the PEG moiety (for example, a maleimide, an aldehyde, amino, thiol, or ester group) to a reactive group on the peptide (for example, an aldehyde, amino, thiol, a maleimide, or ester group).


The PEG molecule(s) may be covalently attached to any Lys, Cys, or K(CO(CH2)2SH) residues at any position in a peptide. In certain embodiments, the peptides described herein can be PEGylated directly to any amino acid at the N-terminus by way of the N-terminal amino group. A “linker arm” may be added to a peptide to facilitate PEGylation. PEGylation at the thiol side-chain of cysteine has been widely reported (see, e.g., Caliceti & Veronese, Adv. Drug Deliv. Rev. 55: 1261-77 (2003)). If there is no cysteine residue in the peptide, a cysteine residue can be introduced through substitution or by adding a cysteine to the N-terminal amino acid. PEGylaeion can be effected through the side chains of a cysteine residue added to the N-terminal amino acid.


In exemplary embodiments, the PEG molecule(s) may be covalently attached to an amide group in the C-terminus of a peptide. In preferred embodiments, there is at least one PEG molecule covalently attached to the peptide. In certain embodiments, the PEG molecule used in modifying an agent of the present invention is branched while in other embodiments, the PEG molecule may be linear. In particular aspects, the PEG molecule is between 1 kDa and 100 kDa in molecular weight. In further aspects, the PEG molecule is selected from 10, 20, 30, 40, 50, 60, and 80 kDa. In further still aspects, it is selected from 20, 40, or 60 kDa. Where there are two PEG molecules covalently attached to the agent of the present invention, each is 1 to 40 kDa and in particular aspects, they have molecular weights of 20 and 20 kDa, 10 and 30 kDa, 30 and 30 kDa, 20 and 40 kDa, or 40 and 40 kDa. In particular aspects, the agent (e.g., neuromedin U receptor agonists or antagonists) contain mPEG-cysteine. The mPEG in mPEG-cysteine can have various molecular weights. The range of the molecular weight is preferably 5 kDa to 200 kDa, more preferably 5 kDa to 100 kDa, and further preferably 20 kDa to 60 kDA. The mPEG can be linear or branched.


In particular embodiments, the agents (include a protecting group covalently joined to the N-terminal amino group. In exemplary embodiments, a protecting group covalently joined to the N-terminal amino group of the agent reduces the reactivity of the amino terminus under in vivo conditions. Amino protecting groups include —C1-10 alkyl, —C1-10 substituted alkyl, —C2-10 alkenyl, —C2-10 substituted alkenyl, aryl, —C1-6 alkyl aryl, —C(O)—(CH2)1-6-COOH, —C(O)—C1-6 alkyl, —C(O)-aryl, —C(O)—O—C1-6 alkyl, or C(O)—O-aryl. In particular embodiments, the amino terminus protecting group is selected from the group consisting of acetyl, propyl, succinyl, benzyl, benzyloxycarbonyl, and t-butyloxycarbonyl. In other embodiments, deamination of the N-terminal amino acid is another modification that may be used for reducing the reactivity of the amino terminus under in vivo conditions.


Chemically modified compositions of the agents wherein the agent is linked to a polymer are also included within the scope of the present invention. The polymer selected is usually modified to have a single reactive group, such as an active ester for acylation or an aldehyde for alkylation, so that the degree of polymerization may be controlled. Included within the scope of polymers is a mixture of polymers. Preferably, for therapeutic use of the end-product preparation, the polymer will be pharmaceutically acceptable. The polymer or mixture thereof may include but is not limited to polyethylene glycol (PEG), monomethoxy-polyethylene glycol, dextran, cellulose, or other carbohydrate based polymers, poly-(N-vinyl pyrrolidone) polyethylene glycol, propylene glycol homopolymers, a polypropylene oxide/ethylene oxide co-polymer, polyoxyethylated polyols (for example, glycerol), and polyvinyl alcohol.


In other embodiments, the agents are modified by PEGylation, cholesterylation, or palmitoylation. The modification can be to any amino acid residue. In preferred embodiments, the modification is to the N-terminal amino acid of the agent, either directly to the N-terminal amino acid or by way coupling to the thiol group of a cysteine residue added to the N-terminus or a linker added to the N-terminus such as trimesoyl tris(3,5-dibromosalicylate (Ttds). In certain embodiments, the N-terminus of the agent comprises a cysteine residue to which a protecting group is coupled to the N-terminal amino group of the cysteine residue and the cysteine thiolate group is derivatized with N-ethylmaleimide, PEG group, cholesterol group, or palmitoyl group. In other embodiments, an acetylated cysteine residue is added to the N-terminus of the agents, and the thiol group of the cysteine is derivatized with N-ethylmaleimide, PEG group, cholesterol group, or palmitoyl group. In certain embodiments, the agent of the present invention is a conjugate. In certain embodiments, the agent of the present invention is a polypeptide consisting of an amino acid sequence which is bound with a methoxypolyethylene glycol(s) via a linker.


Substitutions of amino acids may be used to modify an agent of the present invention. The phrase “substitution of amino acids” as used herein encompasses substitution of amino acids that are the result of both conservative and non-conservative substitutions. Conservative substitutions are the replacement of an amino acid residue by another similar residue in a polypeptide. Typical but not limiting conservative substitutions are the replacements, for one another, among the aliphatic amino acids Ala, Val, Leu and Ile; interchange of Ser and Thr containing hydroxy residues, interchange of the acidic residues Asp and Glu, interchange between the amide-containing residues Asn and Gln, interchange of the basic residues Lys and Arg, interchange of the aromatic residues Phe and Tyr, and interchange of the small-sized amino acids Ala, Ser, Thr, Met, and Gly. Non-conservative substitutions are the replacement, in a polypeptide, of an amino acid residue by another residue which is not biologically similar. For example, the replacement of an amino acid residue with another residue that has a substantially different charge, a substantially different hydrophobicity, or a substantially different spatial configuration.


In certain embodiments, the present invention provides for one or more therapeutic agents. In certain embodiments, the one or more agents comprises a small molecule inhibitor, small molecule degrader (e.g., PROTAC), genetic modifying agent, antibody, antibody fragment, antibody-like protein scaffold, aptamer, protein, or any combination thereof.


The terms “therapeutic agent”, “therapeutic capable agent” or “treatment agent” are used interchangeably and refer to a molecule or compound that confers some beneficial effect upon administration to a subject. The beneficial effect includes enablement of diagnostic determinations; amelioration of a disease, symptom, disorder, or pathological condition; reducing or preventing the onset of a disease, symptom, disorder or condition; and generally counteracting a disease, symptom, disorder or pathological condition.


As used herein, “treatment” or “treating,” or “palliating” or “ameliorating” are used interchangeably. These terms refer to an approach for obtaining beneficial or desired results including but not limited to a therapeutic benefit and/or a prophylactic benefit. By therapeutic benefit is meant any therapeutically relevant improvement in or effect on one or more diseases, conditions, or symptoms under treatment. For prophylactic benefit, the compositions may be administered to a subject at risk of developing a particular disease, condition, or symptom, or to a subject reporting one or more of the physiological symptoms of a disease, even though the disease, condition, or symptom may not have yet been manifested. As used herein “treating” includes ameliorating, curing, preventing it from becoming worse, slowing the rate of progression, or preventing the disorder from re-occurring (i.e., to prevent a relapse). In certain embodiments, the present invention provides for one or more therapeutic agents against combinations of targets identified. Targeting the identified combinations may provide for enhanced or otherwise previously unknown activity in the treatment of disease.


In certain embodiments, the one or more agents is a small molecule. The term “small molecule” refers to compounds, preferably organic compounds, with a size comparable to those organic molecules generally used in pharmaceuticals. The term excludes biological macromolecules (e.g., proteins, peptides, nucleic acids, etc.). Preferred small organic molecules range in size up to about 5000 Da, e.g., up to about 4000, preferably up to 3000 Da, more preferably up to 2000 Da, even more preferably up to about 1000 Da, e.g., up to about 900, 800, 700, 600 or up to about 500 Da. In certain embodiments, the small molecule may act as an antagonist or agonist (e.g., blocking a binding site or activating a receptor by binding to a ligand binding site).


One type of small molecule applicable to the present invention is a degrader molecule. Proteolysis Targeting Chimera (PROTAC) technology is a rapidly emerging alternative therapeutic strategy with the potential to address many of the challenges currently faced in modern drug development programs. PROTAC technology employs small molecules that recruit target proteins for ubiquitination and removal by the proteasome (see, e.g., Zhou et al., Discovery of a Small-Molecule Degrader of Bromodomain and Extra-Terminal (BET) Proteins with Picomolar Cellular Potencies and Capable of Achieving Tumor Regression. J. Med. Chem. 2018, 61, 462-481; Bondeson and Crews, Targeted Protein Degradation by Small Molecules, Annu Rev Pharmacol Toxicol. 2017 Jan. 6; 57: 107-123; and Lai et al., Modular PROTAC Design for the Degradation of Oncogenic BCR-ABL Angew Chem Int Ed Engl. 2016 Jan. 11; 55(2): 807-810).


In certain embodiments, combinations of targets are modulated (e.g., ALDH1A1 and one or more targets related to a gene signature gene). In certain embodiments, an agent against one of the targets in a combination may already be known or used clinically. In certain embodiments, targeting the combination may require less of the agent as compared to the current standard of care and provide for less toxicity and improved treatment.


Immune Checkpoint

Immune checkpoints are regulators of the immune system. These pathways are crucial for self-tolerance, which prevents the immune system from attacking cells indiscriminately. Modulating immune checkpoint activity may reduce a Sys phenotype or signature. In certain embodiments, a combination treatment may include inhibitors of HDAC and/or CDK4/6 and a checkpoint agonist. Immune checkpoint agonists may activate checkpoint signaling, for example, by binding to the checkpoint protein. The agonists may include a ligand (e.g., PD-L1). PD-1 agonist antibodies that mimic PD-1 ligand (PD-L1) have been described (see, e.g., US Patent Publication No. 2017/0088618A1; International Patent Publication No. WO 2018/053405 A1). Such agonist antibodies against any receptor described herein are applicable to the present invention.


Antibodies

The term “antibody” is used interchangeably with the term “immunoglobulin” herein, and includes intact antibodies, fragments of antibodies, e.g., Fab, F(ab′)2 fragments, and intact antibodies and fragments that have been mutated either in their constant and/or variable region (e.g., mutations to produce chimeric, partially humanized, or fully humanized antibodies, as well as to produce antibodies with a desired trait, e.g., enhanced binding and/or reduced FcR binding). The term “fragment” refers to a part or portion of an antibody or antibody chain comprising fewer amino acid residues than an intact or complete antibody or antibody chain. Fragments can be obtained via chemical or enzymatic treatment of an intact or complete antibody or antibody chain. Fragments can also be obtained by recombinant means. Exemplary fragments include Fab, Fab′, F(ab′)2, Fabc, Fd, dAb, VHH and scFv and/or Fv fragments.


As used herein, a preparation of antibody protein having less than about 50% of non-antibody protein (also referred to herein as a “contaminating protein”), or of chemical precursors, is considered to be “substantially free.” 40%, 30%, 20%, 10% and more preferably 5% (by dry weight), of non-antibody protein, or of chemical precursors is considered to be substantially free. When the antibody 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 30%, preferably less than about 20%, more preferably less than about 10%, and most preferably less than about 5% of the volume or mass of the protein preparation.


The term “antigen-binding fragment” refers to a polypeptide fragment of an immunoglobulin or antibody that binds antigen or competes with intact antibody (i.e., with the intact antibody from which they were derived) for antigen binding (i.e., specific binding). As such these antibodies or fragments thereof are included in the scope of the invention, provided that the antibody or fragment binds specifically to a target molecule.


It is intended that the term “antibody” encompass any Ig class or any Ig subclass (e.g. the IgG1, IgG2, IgG3, and IgG4 subclassess of IgG) obtained from any source (e.g., humans and non-human primates, and in rodents, lagomorphs, caprines, bovines, equines, ovines, etc.).


The term “Ig class” or “immunoglobulin class”, as used herein, refers to the five classes of immunoglobulin that have been identified in humans and higher mammals, IgG, IgM, IgA, IgD, and IgE. The term “Ig subclass” refers to the two subclasses of IgM (H and L), three subclasses of IgA (IgA1, IgA2, and secretory IgA), and four subclasses of IgG (IgG1, IgG2, IgG3, and IgG4) that have been identified in humans and higher mammals. The antibodies can exist in monomeric or polymeric form; for example, lgM antibodies exist in pentameric form, and IgA antibodies exist in monomeric, dimeric or multimeric form.


The term “IgG subclass” refers to the four subclasses of immunoglobulin class IgG—IgG1, IgG2, IgG3, and IgG4 that have been identified in humans and higher mammals by the heavy chains of the immunoglobulins, V1-γ4, respectively. The term “single-chain immunoglobulin” or “single-chain antibody” (used interchangeably herein) refers to a protein having a two-polypeptide chain structure consisting of a heavy and a light chain, said chains being stabilized, for example, by interchain peptide linkers, which has the ability to specifically bind antigen. The term “domain” refers to a globular region of a heavy or light chain polypeptide comprising peptide loops (e.g., comprising 3 to 4 peptide loops) stabilized, for example, by β pleated sheet and/or intrachain disulfide bond. Domains are further referred to herein as “constant” or “variable”, based on the relative lack of sequence variation within the domains of various class members in the case of a “constant” domain, or the significant variation within the domains of various class members in the case of a “variable” domain. Antibody or polypeptide “domains” are often referred to interchangeably in the art as antibody or polypeptide “regions”. The “constant” domains of an antibody light chain are referred to interchangeably as “light chain constant regions”, “light chain constant domains”, “CL” regions or “CL” domains. The “constant” domains of an antibody heavy chain are referred to interchangeably as “heavy chain constant regions”, “heavy chain constant domains”, “CH” regions or “CH” domains). The “variable” domains of an antibody light chain are referred to interchangeably as “light chain variable regions”, “light chain variable domains”, “VL” regions or “VL” domains). The “variable” domains of an antibody heavy chain are referred to interchangeably as “heavy chain constant regions”, “heavy chain constant domains”, “VH” regions or “VH” domains).


The term “region” can also refer to a part or portion of an antibody chain or antibody chain domain (e.g., a part or portion of a heavy or light chain or a part or portion of a constant or variable domain, as defined herein), as well as more discrete parts or portions of said chains or domains. For example, light and heavy chains or light and heavy chain variable domains include “complementarity determining regions” or “CDRs” interspersed among “framework regions” or “FRs”, as defined herein.


The term “conformation” refers to the tertiary structure of a protein or polypeptide (e.g., an antibody, antibody chain, domain or region thereof). For example, the phrase “light (or heavy) chain conformation” refers to the tertiary structure of a light (or heavy) chain variable region, and the phrase “antibody conformation” or “antibody fragment conformation” refers to the tertiary structure of an antibody or fragment thereof.


The term “antibody-like protein scaffolds” or “engineered protein scaffolds” broadly encompasses proteinaceous non-immunoglobulin specific-binding agents, typically obtained by combinatorial engineering (such as site-directed random mutagenesis in combination with phage display or other molecular selection techniques). Usually, such scaffolds are derived from robust and small soluble monomeric proteins (such as Kunitz inhibitors or lipocalins) or from a stably folded extra-membrane domain of a cell surface receptor (such as protein A, fibronectin or the ankyrin repeat).


Such scaffolds have been extensively reviewed in Binz et al. (Engineering novel binding proteins from nonimmunoglobulin domains. Nat Biotechnol 2005, 23:1257-1268), Gebauer and Skerra (Engineered protein scaffolds as next-generation antibody therapeutics. Curr Opin Chem Biol. 2009, 13:245-55), Gill and Damle (Biopharmaceutical drug discovery using novel protein scaffolds. Curr Opin Biotechnol 2006, 17:653-658), Skerra (Engineered protein scaffolds for molecular recognition. J Mol Recognit 2000, 13:167-187), and Skerra (Alternative non-antibody scaffolds for molecular recognition. Curr Opin Biotechnol 2007, 18:295-304), and include without limitation affibodies, based on the Z-domain of staphylococcal protein A, a three-helix bundle of 58 residues providing an interface on two of its alpha-helices (Nygren, Alternative binding proteins: Affibody binding proteins developed from a small three-helix bundle scaffold. FEBS J 2008, 275:2668-2676); engineered Kunitz domains based on a small (ca. 58 residues) and robust, disulphide-crosslinked serine protease inhibitor, typically of human origin (e.g. LACI-D1), which can be engineered for different protease specificities (Nixon and Wood, Engineered protein inhibitors of proteases. Curr Opin Drug Discov Dev 2006, 9:261-268); monobodies or adnectins based on the 10th extracellular domain of human fibronectin III (10Fn3), which adopts an Ig-like beta-sandwich fold (94 residues) with 2-3 exposed loops, but lacks the central disulphide bridge (Koide and Koide, Monobodies: antibody mimics based on the scaffold of the fibronectin type III domain. Methods Mol Biol 2007, 352:95-109); anticalins derived from the lipocalins, a diverse family of eight-stranded beta-barrel proteins (ca. 180 residues) that naturally form binding sites for small ligands by means of four structurally variable loops at the open end, which are abundant in humans, insects, and many other organisms (Skerra, Alternative binding proteins: Anticalins—harnessing the structural plasticity of the lipocalin ligand pocket to engineer novel binding activities. FEBS J 2008, 275:2677-2683); DARPins, designed ankyrin repeat domains (166 residues), which provide a rigid interface arising from typically three repeated beta-turns (Stumpp et al., DARPins: a new generation of protein therapeutics. Drug Discov Today 2008, 13:695-701); avimers (multimerized LDLR-A module) (Silverman et al., Multivalent avimer proteins evolved by exon shuffling of a family of human receptor domains. Nat Biotechnol 2005, 23:1556-1561); and cysteine-rich knottin peptides (Kolmar, Alternative binding proteins: biological activity and therapeutic potential of cystine-knot miniproteins. FEBS J 2008, 275:2684-2690).


“Specific binding” of an antibody means that the antibody exhibits appreciable affinity for a particular antigen or epitope and, generally, does not exhibit significant cross reactivity. “Appreciable” binding includes binding with an affinity of at least 25 μM. Antibodies with affinities greater than 1×107 M−1 (or a dissociation coefficient of 1 μM or less or a dissociation coefficient of 1 nm or less) typically bind with correspondingly greater specificity. Values intermediate of those set forth herein are also intended to be within the scope of the present invention and antibodies of the invention bind with a range of affinities, for example, 100 nM or less, 75 nM or less, 50 nM or less, 25 nM or less, for example 10 nM or less, 5 nM or less, 1 nM or less, or in embodiments 500 pM or less, 100 pM or less, 50 pM or less or 25 pM or less. An antibody that “does not exhibit significant crossreactivity” is one that will not appreciably bind to an entity other than its target (e.g., a different epitope or a different molecule). For example, an antibody that specifically binds to a target molecule will appreciably bind the target molecule but will not significantly react with non-target molecules or peptides. An antibody specific for a particular epitope will, for example, not significantly crossreact with remote epitopes on the same protein or peptide. Specific binding can be determined according to any art-recognized means for determining such binding. Preferably, specific binding is determined according to Scatchard analysis and/or competitive binding assays.


As used herein, the term “affinity” refers to the strength of the binding of a single antigen-combining site with an antigenic determinant. Affinity depends on the closeness of stereochemical fit between antibody combining sites and antigen determinants, on the size of the area of contact between them, on the distribution of charged and hydrophobic groups, etc. Antibody affinity can be measured by equilibrium dialysis or by the kinetic BIACORE™ method. The dissociation constant, Kd, and the association constant, Ka, are quantitative measures of affinity.


As used herein, the term “monoclonal antibody” refers to an antibody derived from a clonal population of antibody-producing cells (e.g., B lymphocytes or B cells) which is homogeneous in structure and antigen specificity. The term “polyclonal antibody” refers to a plurality of antibodies originating from different clonal populations of antibody-producing cells which are heterogeneous in their structure and epitope specificity but which recognize a common antigen. Monoclonal and polyclonal antibodies may exist within bodily fluids, as crude preparations, or may be purified, as described herein.


The term “binding portion” of an antibody (or “antibody portion”) includes one or more complete domains, e.g., a pair of complete domains, as well as fragments of an antibody that retain the ability to specifically bind to a target molecule. It has been shown that the binding function of an antibody can be performed by fragments of a full-length antibody. Binding fragments are produced by recombinant DNA techniques, or by enzymatic or chemical cleavage of intact immunoglobulins. Binding fragments include Fab, Fab′, F(ab′)2, Fabc, Fd, dAb, Fv, single chains, single-chain antibodies, e.g., scFv, and single domain antibodies.


“Humanized” forms of non-human (e.g., murine) antibodies are chimeric antibodies that contain minimal sequence derived from non-human immunoglobulin. For the most part, humanized antibodies are human immunoglobulins (recipient antibody) in which residues from a hypervariable region of the recipient are replaced by residues from a hypervariable region of a non-human species (donor antibody) such as mouse, rat, rabbit or nonhuman primate having the desired specificity, affinity, and capacity. In some instances, FR residues of the human immunoglobulin are replaced by corresponding non-human residues. Furthermore, humanized antibodies may comprise residues that are not found in the recipient antibody or in the donor antibody. These modifications are made to further refine antibody performance. In general, the humanized antibody will comprise substantially all of at least one, and typically two, variable domains, in which all or substantially all of the hypervariable regions correspond to those of a non-human immunoglobulin and all or substantially all of the FR regions are those of a human immunoglobulin sequence. The humanized antibody optionally also will comprise at least a portion of an immunoglobulin constant region (Fc), typically that of a human immunoglobulin.


Examples of portions of antibodies or epitope-binding proteins encompassed by the present definition include: (i) the Fab fragment, having VL, CL, VH and CH1 domains; (ii) the Fab′ fragment, which is a Fab fragment having one or more cysteine residues at the C-terminus of the CH1 domain; (iii) the Fd fragment having VH and CH1 domains; (iv) the Fd′ fragment having VH and CH1 domains and one or more cysteine residues at the C-terminus of the CHI domain; (v) the Fv fragment having the VL and VH domains of a single arm of an antibody; (vi) the dAb fragment (Ward et al., 341 Nature 544 (1989)) which consists of a VH domain or a VL domain that binds antigen; (vii) isolated CDR regions or isolated CDR regions presented in a functional framework; (viii) F(ab′)2 fragments which are bivalent fragments including two Fab′ fragments linked by a disulphide bridge at the hinge region; (ix) single chain antibody molecules (e.g., single chain Fv; scFv) (Bird et al., 242 Science 423 (1988); and Huston et al., 85 PNAS 5879 (1988)); (x) “diabodies” with two antigen binding sites, comprising a heavy chain variable domain (VH) connected to a light chain variable domain (VL) in the same polypeptide chain (see, e.g., EP 404,097; WO 93/11161; Hollinger et al., 90 PNAS 6444 (1993)); (xi) “linear antibodies” comprising a pair of tandem Fd segments (VH-Ch1-VH-Ch1) which, together with complementary light chain polypeptides, form a pair of antigen binding regions (Zapata et al., Protein Eng. 8(10):1057-62 (1995); and U.S. Pat. No. 5,641,870).


As used herein, a “blocking” antibody or an antibody “antagonist” is one which inhibits or reduces biological activity of the antigen(s) it binds. For example, an antagonist antibody may bind an antigen or antigen receptor and inhibit the ability to suppress a response. In certain embodiments, the blocking antibodies or antagonist antibodies or portions thereof described herein completely inhibit the biological activity of the antigen(s).


Antibodies may act as agonists or antagonists of the recognized polypeptides. For example, the present invention includes antibodies which disrupt receptor/ligand interactions either partially or fully. The invention features both receptor-specific antibodies and ligand-specific antibodies. The invention also features receptor-specific antibodies which do not prevent ligand binding but prevent receptor activation. Receptor activation (i.e., signaling) may be determined by techniques described herein or otherwise known in the art. For example, receptor activation can be determined by detecting the phosphorylation (e.g., tyrosine or serine/threonine) of the receptor or of one of its down-stream substrates by immunoprecipitation followed by western blot analysis. In specific embodiments, antibodies are provided that inhibit ligand activity or receptor activity by at least 95%, at least 90%, at least 85%, at least 80%, at least 75%, at least 70%, at least 60%, or at least 50% of the activity in absence of the antibody.


The invention also features receptor-specific antibodies which both prevent ligand binding and receptor activation as well as antibodies that recognize the receptor-ligand complex. Likewise, encompassed by the invention are neutralizing antibodies which bind the ligand and prevent binding of the ligand to the receptor, as well as antibodies which bind the ligand, thereby preventing receptor activation, but do not prevent the ligand from binding the receptor. Further included in the invention are antibodies which activate the receptor. These antibodies may act as receptor agonists, i.e., potentiate or activate either all or a subset of the biological activities of the ligand-mediated receptor activation, for example, by inducing dimerization of the receptor. The antibodies may be specified as agonists, antagonists or inverse agonists for biological activities comprising the specific biological activities of the peptides disclosed herein. The antibody agonists and antagonists can be made using methods known in the art. See, e.g., PCT publication WO 96/40281; U.S. Pat. No. 5,811,097; Deng et al., Blood 92(6):1981-1988 (1998); Chen et al., Cancer Res. 58(16):3668-3678 (1998); Harrop et al., J. Immunol. 161(4):1786-1794 (1998); Zhu et al., Cancer Res. 58(15):3209-3214 (1998); Yoon et al., J. Immunol. 160(7):3170-3179 (1998); Prat et al., J. Cell. Sci. III (Pt2):237-247 (1998); Pitard et al., J. Immunol. Methods 205(2):177-190 (1997); Liautard et al., Cytokine 9(4):233-241 (1997); Carlson et al., J. Biol. Chem. 272(17):11295-11301 (1997); Taryman et al., Neuron 14(4):755-762 (1995); Muller et al., Structure 6(9):1153-1167 (1998); Bartunek et al., Cytokine 8(1):14-20 (1996).


The antibodies as defined for the present invention include derivatives that are modified, i.e., by the covalent attachment of any type of molecule to the antibody such that covalent attachment does not prevent the antibody from generating an anti-idiotypic response. For example, but not by way of limitation, the antibody derivatives include antibodies that have been modified, e.g., by glycosylation, acetylation, pegylation, phosphylation, amidation, derivatization by known protecting/blocking groups, proteolytic cleavage, linkage to a cellular ligand or other protein, etc. Any of numerous chemical modifications may be carried out by known techniques, including, but not limited to specific chemical cleavage, acetylation, formylation, metabolic synthesis of tunicamycin, etc. Additionally, the derivative may contain one or more non-classical amino acids.


Simple binding assays can be used to screen for or detect agents that bind to a target protein, or disrupt the interaction between proteins (e.g., a receptor and a ligand). Because certain targets of the present invention are transmembrane proteins, assays that use the soluble forms of these proteins rather than full-length protein can be used, in some embodiments. Soluble forms include, for example, those lacking the transmembrane domain and/or those comprising the IgV domain or fragments thereof which retain their ability to bind their cognate binding partners. Further, agents that inhibit or enhance protein interactions for use in the compositions and methods described herein, can include recombinant peptido-mimetics.


Detection methods useful in screening assays include antibody-based methods, detection of a reporter moiety, detection of cytokines as described herein, and detection of a gene signature as described herein.


Another variation of assays to determine binding of a receptor protein to a ligand protein is through the use of affinity biosensor methods. Such methods may be based on the piezoelectric effect, electrochemistry, or optical methods, such as ellipsometry, optical wave guidance, and surface plasmon resonance (SPR).


The disclosure also encompasses nucleic acid molecules, in particular those that inhibit HDAC and/or CDK4/6. Exemplary nucleic acid molecules include aptamers, siRNA, artificial microRNA, interfering RNA or RNAi, dsRNA, ribozymes, antisense oligonucleotides, and DNA expression cassettes encoding said nucleic acid molecules. Preferably, the nucleic acid molecule is an antisense oligonucleotide. Antisense oligonucleotides (ASO) generally inhibit their target by binding target mRNA and sterically blocking expression by obstructing the ribosome. ASOs can also inhibit their target by binding target mRNA thus forming a DNA-RNA hybrid that can be a substance for RNase H. Preferred ASOs include Locked Nucleic Acid (LNA), Peptide Nucleic Acid (PNA), and morpholinos Preferably, the nucleic acid molecule is an RNAi molecule, i.e., RNA interference molecule. Preferred RNAi molecules include siRNA, shRNA, and artificial miRNA. The design and production of siRNA molecules is well known to one of skill in the art (e.g., Hajeri P B, Singh S K. Drug Discov Today. 2009 14(17-18):851-8). The nucleic acid molecule inhibitors may be chemically synthesized and provided directly to cells of interest. The nucleic acid compound may be provided to a cell as part of a gene delivery vehicle. Such a vehicle is preferably a liposome or a viral gene delivery vehicle.


Genetic Modifying Agents

In certain embodiments, the one or more modulating agents may be a genetic modifying agent. The genetic modifying agent may comprise a CRISPR system, a zinc finger nuclease system, a TALEN, a meganuclease or RNAi system.


CRISPR-Cas Modification

In some embodiments, a polynucleotide of the present invention described elsewhere herein can be modified using a CRISPR-Cas and/or Cas-based system.


In general, a CRISPR-Cas or CRISPR system as used herein and in other documents, such as WO 2014/093622 (PCT/US2013/074667), refers collectively to transcripts and other elements involved in the expression of or directing the activity of CRISPR-associated (“Cas”) genes, including sequences encoding a Cas gene, a tracr (trans-activating CRISPR) sequence (e.g., tracrRNA or an active partial tracrRNA), a tracr-mate sequence (encompassing a “direct repeat” and a tracrRNA-processed partial direct repeat in the context of an endogenous CRISPR system), a guide sequence (also referred to as a “spacer” in the context of an endogenous CRISPR system), or “RNA(s)” as that term is herein used (e.g., RNA(s) to guide Cas, such as Cas9, e.g., CRISPR RNA and transactivating (tracr) RNA or a single guide RNA (sgRNA) (chimeric RNA)) or other sequences and transcripts from a CRISPR locus. In general, a CRISPR system is characterized by elements that promote the formation of a CRISPR complex at the site of a target sequence (also referred to as a protospacer in the context of an endogenous CRISPR system). See, e.g, Shmakov et al. (2015) “Discovery and Functional Characterization of Diverse Class 2 CRISPR-Cas Systems”, Molecular Cell, DOI: dx.doi.org/10. 1016/j.molcel.2015.10.008.


CRISPR-Cas systems can generally fall into two classes based on their architectures of their effector molecules, which are each further subdivided by type and subtype. The two class are Class 1 and Class 2. Class 1 CRISPR-Cas systems have effector modules composed of multiple Cas proteins, some of which form crRNA-binding complexes, while Class 2 CRISPR-Cas systems include a single, multi-domain crRNA-binding protein.


In some embodiments, the CRISPR-Cas system that can be used to modify a polynucleotide of the present invention described herein can be a Class 1 CRISPR-Cas system. In some embodiments, the CRISPR-Cas system that can be used to modify a polynucleotide of the present invention described herein can be a Class 2 CRISPR-Cas system.


Class 1 CRISPR-Cas Systems

In some embodiments, the CRISPR-Cas system that can be used to modify a polynucleotide of the present invention described herein can be a Class 1 CRISPR-Cas system. Class 1 CRISPR-Cas systems are divided into types I, II, and IV. Makarova et al. 2020. Nat. Rev. 18: 67-83., particularly as described in FIG. 1. Type I CRISPR-Cas systems are divided into 9 subtypes (I-A, I-B, I-C, I-D, I-E, I-F1, I-F2, I-F3, and IG). Makarova et al., 2020. Class 1, Type I CRISPR-Cas systems can contain a Cas3 protein that can have helicase activity. Type III CRISPR-Cas systems are divided into 6 subtypes (III-A, III-B, III-E, and III-F). Type III CRISPR-Cas systems can contain a Cas10 that can include an RNA recognition motif called Palm and a cyclase domain that can cleave polynucleotides. Makarova et al., 2020. Type IV CRISPR-Cas systems are divided into 3 subtypes. (IV-A, IV-B, and IV-C). Makarova et al., 2020. Class 1 systems also include CRISPR-Cas variants, including Type I-A, I-B, I-E, I-F and I-U variants, which can include variants carried by transposons and plasmids, including versions of subtype I-F encoded by a large family of Tn7-like transposon and smaller groups of Tn7-like transposons that encode similarly degraded subtype I-B systems. Peters et al., PNAS 114 (35) (2017); DOI: 10.1073/pnas.1709035114; see also, Makarova et al. 2018. The CRISPR Journal, v. 1, n5, FIG. 5.


The Class 1 systems typically use a multi-protein effector complex, which can, in some embodiments, include ancillary proteins, such as one or more proteins in a complex referred to as a CRISPR-associated complex for antiviral defense (Cascade), one or more adaptation proteins (e.g., Cas1, Cas2, RNA nuclease), and/or one or more accessory proteins (e.g., Cas 4, DNA nuclease), CRISPR associated Rossman fold (CARF) domain containing proteins, and/or RNA transcriptase.


The backbone of the Class 1 CRISPR-Cas system effector complexes can be formed by RNA recognition motif domain-containing protein(s) of the repeat-associated mysterious proteins (RAMPs) family subunits (e.g., Cas 5, Cas6, and/or Cas7). RAMP proteins are characterized by having one or more RNA recognition motif domains. In some embodiments, multiple copies of RAMPS can be present. In some embodiments, the Class I CRISPR-Cas system can include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more Cas5, Cas6, and/or Cas 7 proteins. In some embodiments, the Cas6 protein is an RNAse, which can be responsible for pre-crRNA processing. When present in a Class 1 CRISPR-Cas system, Cas6 can be optionally physically associated with the effector complex.


Class 1 CRISPR-Cas system effector complexes can, in some embodiments, also include a large subunit. The large subunit can be composed of or include a Cas8 and/or Cas10 protein. See, e.g., FIGS. 1 and 2. Koonin E V, Makarova K S. 2019. Phil. Trans. R. Soc. B 374: 20180087, DOI: 10.1098/rstb.2018.0087 and Makarova et al. 2020.


Class 1 CRISPR-Cas system effector complexes can, in some embodiments, include a small subunit (for example, Cas11). See, e.g., FIGS. 1 and 2. Koonin E V, Makarova K S. 2019 Origins and Evolution of CRISPR-Cas systems. Phil. Trans. R. Soc. B 374: 20180087, DOI: 10.1098/rstb.2018.0087.


In some embodiments, the Class 1 CRISPR-Cas system can be a Type I CRISPR-Cas system. In some embodiments, the Type I CRISPR-Cas system can be a subtype I-A CRISPR-Cas system. In some embodiments, the Type I CRISPR-Cas system can be a subtype I-B CRISPR-Cas system. In some embodiments, the Type I CRISPR-Cas system can be a subtype I-C CRISPR-Cas system. In some embodiments, the Type I CRISPR-Cas system can be a subtype I-D CRISPR-Cas system. In some embodiments, the Type I CRISPR-Cas system can be a subtype I-E CRISPR-Cas system. In some embodiments, the Type I CRISPR-Cas system can be a subtype I-F1 CRISPR-Cas system. In some embodiments, the Type I CRISPR-Cas system can be a subtype I-F2 CRISPR-Cas system. In some embodiments, the Type I CRISPR-Cas system can be a subtype I-F3 CRISPR-Cas system. In some embodiments, the Type I CRISPR-Cas system can be a subtype I-G CRISPR-Cas system. In some embodiments, the Type I CRISPR-Cas system can be a CRISPR Cas variant, such as a Type I-A, I-B, I-E, I-F and I-U variants, which can include variants carried by transposons and plasmids, including versions of subtype I-F encoded by a large family of Tn7-like transposon and smaller groups of Tn7-like transposons that encode similarly degraded subtype I-B systems as previously described.


In some embodiments, the Class 1 CRISPR-Cas system can be a Type III CRISPR-Cas system. In some embodiments, the Type III CRISPR-Cas system can be a subtype III-A CRISPR-Cas system. In some embodiments, the Type III CRISPR-Cas system can be a subtype III-B CRISPR-Cas system. In some embodiments, the Type III CRISPR-Cas system can be a subtype III-C CRISPR-Cas system. In some embodiments, the Type III CRISPR-Cas system can be a subtype III-D CRISPR-Cas system. In some embodiments, the Type III CRISPR-Cas system can be a subtype III-E CRISPR-Cas system. In some embodiments, the Type III CRISPR-Cas system can be a subtype III-F CRISPR-Cas system.


In some embodiments, the Class 1 CRISPR-Cas system can be a Type IV CRISPR-Cas-system. In some embodiments, the Type IV CRISPR-Cas system can be a subtype IV-A CRISPR-Cas system. In some embodiments, the Type IV CRISPR-Cas system can be a subtype IV-B CRISPR-Cas system. In some embodiments, the Type IV CRISPR-Cas system can be a subtype IV-C CRISPR-Cas system.


The effector complex of a Class 1 CRISPR-Cas system can, in some embodiments, include a Cas3 protein that is optionally fused to a Cas2 protein, a Cas4, a Cas5, a Cash, a Cas7, a Cas8, a Cas10, a Cas11, or a combination thereof. In some embodiments, the effector complex of a Class 1 CRISPR-Cas system can have multiple copies, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14, of any one or more Cas proteins.


Class 2 CRISPR-Cas Systems

The compositions, systems, and methods described in greater detail elsewhere herein can be designed and adapted for use with Class 2 CRISPR-Cas systems. Thus, in some embodiments, the CRISPR-Cas system is a Class 2 CRISPR-Cas system. Class 2 systems are distinguished from Class 1 systems in that they have a single, large, multi-domain effector protein. In certain example embodiments, the Class 2 system can be a Type II, Type V, or Type VI system, which are described in Makarova et al. “Evolutionary classification of CRISPR-Cas systems: a burst of class 2 and derived variants” Nature Reviews Microbiology, 18:67-81 (February 2020), incorporated herein by reference. Each type of Class 2 system is further divided into subtypes. See Markova et al. 2020, particularly at Figure. 2. Class 2, Type II systems can be divided into 4 subtypes: II-A, II-B, II-C1, and II-C2. Class 2, Type V systems can be divided into 17 subtypes: V-A, V-B1, V-B2, V-C, V-D, V-E, V-F1, V-F1(V-U3), V-F2, V-F3, V-G, V-H, V-I, V-K (V-U5), V-U1, V-U2, and V-U4. Class 2, Type IV systems can be divided into 5 subtypes: VI-A, VI-B1, VI-B2, VI-C, and VI-D.


The distinguishing feature of these types is that their effector complexes consist of a single, large, multi-domain protein. Type V systems differ from Type II effectors (e.g., Cas9), which contain two nuclear domains that are each responsible for the cleavage of one strand of the target DNA, with the HNH nuclease inserted inside the Ruv-C like nuclease domain sequence. The Type V systems (e.g., Cas12) only contain a RuvC-like nuclease domain that cleaves both strands. Type VI (Cas13) are unrelated to the effectors of Type II and V systems and contain two HEPN domains and target RNA. Cas13 proteins also display collateral activity that is triggered by target recognition. Some Type V systems have also been found to possess this collateral activity with two single-stranded DNA in in vitro contexts.


In some embodiments, the Class 2 system is a Type II system. In some embodiments, the Type II CRISPR-Cas system is a II-A CRISPR-Cas system. In some embodiments, the Type II CRISPR-Cas system is a II-B CRISPR-Cas system. In some embodiments, the Type II CRISPR-Cas system is a II-C1 CRISPR-Cas system. In some embodiments, the Type II CRISPR-Cas system is a II-C2 CRISPR-Cas system. In some embodiments, the Type II system is a Cas9 system. In some embodiments, the Type II system includes a Cas9.


In some embodiments, the Class 2 system is a Type V system. In some embodiments, the Type V CRISPR-Cas system is a V-A CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-B1 CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-B2 CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-C CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-D CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-E CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-F1 CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-F1 (V-U3) CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-F2 CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-F3 CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-G CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-H CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-I CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-K (V-U5) CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-U1 CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-U2 CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system is a V-U4 CRISPR-Cas system. In some embodiments, the Type V CRISPR-Cas system includes a Cas12a (Cpf1), Cas12b (C2c1), Cas12c (C2c3), CasX, and/or Cas14.


In some embodiments the Class 2 system is a Type VI system. In some embodiments, the Type VI CRISPR-Cas system is a VI-A CRISPR-Cas system. In some embodiments, the Type VI CRISPR-Cas system is a VI-B1 CRISPR-Cas system. In some embodiments, the Type VI CRISPR-Cas system is a VI-B2 CRISPR-Cas system. In some embodiments, the Type VI CRISPR-Cas system is a VI-C CRISPR-Cas system. In some embodiments, the Type VI CRISPR-Cas system is a VI-D CRISPR-Cas system. In some embodiments, the Type VI CRISPR-Cas system includes a Cas13a (C2c2), Cas13b (Group 29/30), Cas13c, and/or Cas13d.


Specialized Cas-Based Systems

In some embodiments, the system is a Cas-based system that is capable of performing a specialized function or activity. For example, the Cas protein may be fused, operably coupled to, or otherwise associated with one or more functionals domains. In certain example embodiments, the Cas protein may be a catalytically dead Cas protein (“dCas”) and/or have nickase activity. A nickase is a Cas protein that cuts only one strand of a double stranded target. In such embodiments, the dCas or nickase provide a sequence specific targeting functionality that delivers the functional domain to or proximate a target sequence. Example functional domains that may be fused to, operably coupled to, or otherwise associated with a Cas protein can be or include, but are not limited to a nuclear localization signal (NLS) domain, a nuclear export signal (NES) domain, a translational activation domain, a transcriptional activation domain (e.g. VP64, p65, MyoD1, HSF1, RTA, and SETT/9), a translation initiation domain, a transcriptional repression domain (e.g., a KRAB domain, NuE domain, NcoR domain, and a SID domain such as a SID4X domain), a nuclease domain (e.g., FokI), a histone modification domain (e.g., a histone acetyltransferase), a light inducible/controllable domain, a chemically inducible/controllable domain, a transposase domain, a homologous recombination machinery domain, a recombinase domain, an integrase domain, and combinations thereof. Methods for generating catalytically dead Cas9 or a nickase Cas9 (WO 2014/204725, Ran et al. Cell. 2013 Sep. 12; 154(6):1380-1389), Cas12 (Liu et al. Nature Communications, 8, 2095 (2017), and Cas13 (WO 2019/005884, WO2019/060746) are known in the art and incorporated herein by reference.


In some embodiments, the functional domains can have one or more of the following activities: methylase activity, demethylase activity, translation activation activity, translation initiation activity, translation repression activity, transcription activation activity, transcription repression activity, transcription release factor activity, histone modification activity, nuclease activity, single-strand RNA cleavage activity, double-strand RNA cleavage activity, single-strand DNA cleavage activity, double-strand DNA cleavage activity, molecular switch activity, chemical inducibility, light inducibility, and nucleic acid binding activity. In some embodiments, the one or more functional domains may comprise epitope tags or reporters. Non-limiting examples of epitope tags include histidine (His) tags, V5 tags, FLAG tags, influenza hemagglutinin (HA) tags, Myc tags, VSV-G tags, and thioredoxin (Trx) tags. Examples of reporters include, but are not limited to, glutathione-S-transferase (GST), horseradish peroxidase (HRP), chloramphenicol acetyltransferase (CAT) beta-galactosidase, beta-glucuronidase, luciferase, green fluorescent protein (GFP), HcRed, DsRed, cyan fluorescent protein (CFP), yellow fluorescent protein (YFP), and auto-fluorescent proteins including blue fluorescent protein (BFP).


The one or more functional domain(s) may be positioned at, near, and/or in proximity to a terminus of the effector protein (e.g., a Cas protein). In embodiments having two or more functional domains, each of the two can be positioned at or near or in proximity to a terminus of the effector protein (e.g., a Cas protein). In some embodiments, such as those where the functional domain is operably coupled to the effector protein, the one or more functional domains can be tethered or linked via a suitable linker (including, but not limited to, GlySer linkers) to the effector protein (e.g., a Cas protein). When there is more than one functional domain, the functional domains can be same or different. In some embodiments, all the functional domains are the same. In some embodiments, all of the functional domains are different from each other. In some embodiments, at least two of the functional domains are different from each other. In some embodiments, at least two of the functional domains are the same as each other.


Other Suitable Functional Domains can be Found, for Example, in International Application Publication No. WO 2019/018423.


Split CRISPR-Cas Systems

In some embodiments, the CRISPR-Cas system is a split CRISPR-Cas system. See e.g., Zetche et al., 2015. Nat. Biotechnol. 33(2): 139-142 and WO 2019/018423, the compositions and techniques of which can be used in and/or adapted for use with the present invention. Split CRISPR-Cas proteins are set forth herein and in documents incorporated herein by reference in further detail herein. In certain embodiments, each part of a split CRISPR protein are attached to a member of a specific binding pair, and when bound with each other, the members of the specific binding pair maintain the parts of the CRISPR protein in proximity. In certain embodiments, each part of a split CRISPR protein is associated with an inducible binding pair. An inducible binding pair is one which is capable of being switched “on” or “off” by a protein or small molecule that binds to both members of the inducible binding pair. In some embodiments, CRISPR proteins may preferably split between domains, leaving domains intact. In particular embodiments, said Cas split domains (e.g., RuvC and HNH domains in the case of Cas9) can be simultaneously or sequentially introduced into the cell such that said split Cas domain(s) process the target nucleic acid sequence in the algae cell. The reduced size of the split Cas compared to the wild type Cas allows other methods of delivery of the systems to the cells, such as the use of cell penetrating peptides as described herein.


DNA and RNA Base Editing

In some embodiments, a polynucleotide of the present invention described elsewhere herein can be modified using a base editing system. In some embodiments, a Cas protein is connected or fused to a nucleotide deaminase. Thus, in some embodiments the Cas-based system can be a base editing system. As used herein “base editing” refers generally to the process of polynucleotide modification via a CRISPR-Cas-based or Cas-based system that does not include excising nucleotides to make the modification. Base editing can convert base pairs at precise locations without generating excess undesired editing byproducts that can be made using traditional CRISPR-Cas systems.


In certain example embodiments, the nucleotide deaminase may be a DNA base editor used in combination with a DNA binding Cas protein such as, but not limited to, Class 2 Type II and Type V systems. Two classes of DNA base editors are generally known: cytosine base editors (CBEs) and adenine base editors (ABEs). CBEs convert a C⋅G base pair into a T⋅A base pair (Komor et al. 2016. Nature. 533:420-424; Nishida et al. 2016. Science. 353; and Li et al. Nat. Biotech. 36:324-327) and ABEs convert an A⋅T base pair to a G⋅C base pair. Collectively, CBEs and ABEs can mediate all four possible transition mutations (C to T, A to G, T to C, and G to A). Rees and Liu. 2018. Nat. Rev. Genet. 19(12): 770-788, particularly at FIGS. 1b, 2a-2c, 3a-3f, and Table 1. In some embodiments, the base editing system includes a CBE and/or an ABE. In some embodiments, a polynucleotide of the present invention described elsewhere herein can be modified using a base editing system. Rees and Liu. 2018. Nat. Rev. Gent. 19(12):770-788. Base editors also generally do not need a DNA donor template and/or rely on homology-directed repair. Komor et al. 2016. Nature. 533:420-424; Nishida et al. 2016. Science. 353; and Gaudeli et al. 2017. Nature. 551:464-471. Upon binding to a target locus in the DNA, base pairing between the guide RNA of the system and the target DNA strand leads to displacement of a small segment of ssDNA in an “R-loop”. Nishimasu et al. Cell. 156:935-949. DNA bases within the ssDNA bubble are modified by the enzyme component, such as a deaminase. In some systems, the catalytically disabled Cas protein can be a variant or modified Cas can have nickase functionality and can generate a nick in the non-edited DNA strand to induce cells to repair the non-edited strand using the edited strand as a template. Komor et al. 2016. Nature. 533:420-424; Nishida et al. 2016. Science. 353; and Gaudeli et al. 2017. Nature. 551:464-471.


Other Example Type V base editing systems are described in WO 2018/213708, WO 2018/213726, PCT/US2018/067207, PCT/US2018/067225, and PCT/US2018/067307 which are incorporated by referenced herein.


In certain example embodiments, the base editing system may be a RNA base editing system. As with DNA base editors, a nucleotide deaminase capable of converting nucleotide bases may be fused to a Cas protein. However, in these embodiments, the Cas protein will need to be capable of binding RNA. Example RNA binding Cas proteins include, but are not limited to, RNA-binding Cas9s such as Francisella novicida Cas9 (“FnCas9”), and Class 2 Type VI Cas systems. The nucleotide deaminase may be a cytidine deaminase or an adenosine deaminase, or an adenosine deaminase engineered to have cytidine deaminase activity. In certain example embodiments, the RNA based editor may be used to delete or introduce a post-translation modification site in the expressed mRNA. In contrast to DNA base editors, whose edits are permanent in the modified cell, RNA base editors can provide edits where finer temporal control may be needed, for example in modulating a particular immune response. Example Type VI RNA-base editing systems are described in Cox et al. 2017. Science 358: 1019-1027, WO 2019/005884, WO 2019/005886, WO 2019/071048, PCT/US20018/05179, PCT/US2018/067207, which are incorporated herein by reference. An example FnCas9 system that may be adapted for RNA base editing purposes is described in WO 2016/106236, which is incorporated herein by reference.


An example method for delivery of base-editing systems, including use of a split-intein approach to divide CBE and ABE into reconstituble halves, is described in Levy et al. Nature Biomedical Engineering doi.org/10.1038/s41441-019-0505-5 (2019), which is incorporated herein by reference.


Prime Editors

In some embodiments, a polynucleotide of the present invention described elsewhere herein can be modified using a prime editing system See e.g. Anzalone et al. 2019. Nature. 576: 149-157. Like base editing systems, prime editing systems can be capable of targeted modification of a polynucleotide without generating double stranded breaks and does not require donor templates. Further prime editing systems can be capable of all 12 possible combination swaps. Prime editing can operate via a “search-and-replace” methodology and can mediate targeted insertions, deletions, all 12 possible base-to-base conversion, and combinations thereof. Generally, a prime editing system, as exemplified by PE1, PE2, and PE3 (Id.), can include a reverse transcriptase fused or otherwise coupled or associated with an RNA-programmable nickase, and a prime-editing extended guide RNA (pegRNA) to facility direct copying of genetic information from the extension on the pegRNA into the target polynucleotide. Embodiments that can be used with the present invention include these and variants thereof. Prime editing can have the advantage of lower off-target activity than traditional CRIPSR-Cas systems along with few byproducts and greater or similar efficiency as compared to traditional CRISPR-Cas systems.


In some embodiments, the prime editing guide molecule can specify both the target polynucleotide information (e.g. sequence) and contain a new polynucleotide cargo that replaces target polynucleotides. To initiate transfer from the guide molecule to the target polynucleotide, the PE system can nick the target polynucleotide at a target side to expose a 3′hydroxyl group, which can prime reverse transcription of an edit-encoding extension region of the guide molecule (e.g. a prime editing guide molecule or peg guide molecule) directly into the target site in the target polynucleotide. See e.g. Anzalone et al. 2019. Nature. 576: 149-157, particularly at FIGS. 1b, 1c, related discussion, and Supplementary discussion.


In some embodiments, a prime editing system can be composed of a Cas polypeptide having nickase activity, a reverse transcriptase, and a guide molecule. The Cas polypeptide can lack nuclease activity. The guide molecule can include a target binding sequence as well as a primer binding sequence and a template containing the edited polynucleotide sequence. The guide molecule, Cas polypeptide, and/or reverse transcriptase can be coupled together or otherwise associate with each other to form an effector complex and edit a target sequence. In some embodiments, the Cas polypeptide is a Class 2, Type V Cas polypeptide. In some embodiments, the Cas polypeptide is a Cas9 polypeptide (e.g. is a Cas9 nickase). In some embodiments, the Cas polypeptide is fused to the reverse transcriptase. In some embodiments, the Cas polypeptide is linked to the reverse transcriptase.


In some embodiments, the prime editing system can be a PE1 system or variant thereof, a PE2 system or variant thereof, or a PE3 (e.g. PE3, PE3b) system. See e.g., Anzalone et al. 2019. Nature. 576: 149-157, particularly at pgs. 2-3, FIGS. 2a, 3a-3f, 4a-4b, Extended data FIGS. 3a-3b, 4,


The peg guide molecule can be about 10 to about 200 or more nucleotides in length, such as 10 to/or 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, or 200 or more nucleotides in length. Optimization of the peg guide molecule can be accomplished as described in Anzalone et al. 2019. Nature. 576: 149-157, particularly at pg. 3, FIG. 2a-2b, and Extended Data FIGS. 5a-c.


CRISPR Associated Transposase (CAST) Systems

In some embodiments, a polynucleotide of the present invention described elsewhere herein can be modified using a CRISPR Associated Transposase (“CAST”) system. CAST system can include a Cas protein that is catalytically inactive, or engineered to be catalytically active, and further comprises a transposase (or subunits thereof) that catalyze RNA-guided DNA transposition. Such systems are able to insert DNA sequences at a target site in a DNA molecule without relying on host cell repair machinery. CAST systems can be Class1 or Class 2 CAST systems. An example Class 1 system is described in Klompe et al. Nature, doi:10.1038/s41586-019-1323, which is in incorporated herein by reference. An example Class 2 system is described in Strecker et al. Science. 10/1126/science. aax9181 (2019), and PCT/US2019/066835 which are incorporated herein by reference.


Guide Molecules

The CRISPR-Cas or Cas-Based system described herein can, in some embodiments, include one or more guide molecules. The terms guide molecule, guide sequence and guide polynucleotide, refer to polynucleotides capable of guiding Cas to a target genomic locus and are used interchangeably as in foregoing cited documents such as WO 2014/093622 (PCT/US2013/074667). In general, a guide sequence is any polynucleotide sequence having sufficient complementarity with a target polynucleotide sequence to hybridize with the target sequence and direct sequence-specific binding of a CRISPR complex to the target sequence. The guide molecule can be a polynucleotide.


The ability of a guide sequence (within a nucleic acid-targeting guide RNA) to direct sequence-specific binding of a nucleic acid-targeting complex to a target nucleic acid sequence may be assessed by any suitable assay. For example, the components of a nucleic acid-targeting CRISPR system sufficient to form a nucleic acid-targeting complex, including the guide sequence to be tested, may be provided to a host cell having the corresponding target nucleic acid sequence, such as by transfection with vectors encoding the components of the nucleic acid-targeting complex, followed by an assessment of preferential targeting (e.g., cleavage) within the target nucleic acid sequence, such as by Surveyor assay (Qui et al. 2004. BioTechniques. 36(4)702-707). Similarly, cleavage of a target nucleic acid sequence may be evaluated in a test tube by providing the target nucleic acid sequence, components of a nucleic acid-targeting complex, including the guide sequence to be tested and a control guide sequence different from the test guide sequence, and comparing binding or rate of cleavage at the target sequence between the test and control guide sequence reactions. Other assays are possible and will occur to those skilled in the art.


In some embodiments, the guide molecule is an RNA. The guide molecule(s) (also referred to interchangeably herein as guide polynucleotide and guide sequence) that are included in the CRISPR-Cas or Cas based system can be any polynucleotide sequence having sufficient complementarity with a target nucleic acid sequence to hybridize with the target nucleic acid sequence and direct sequence-specific binding of a nucleic acid-targeting complex to the target nucleic acid sequence. In some embodiments, the degree of complementarity, when optimally aligned using a suitable alignment algorithm, can be about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or more. Optimal alignment may be determined with the use of any suitable algorithm for aligning sequences, non-limiting examples of which include the Smith-Waterman algorithm, the Needleman-Wunsch algorithm, algorithms based on the Burrows-Wheeler Transform (e.g., the Burrows Wheeler Aligner), ClustalW, Clustal X, BLAT, Novoalign (Novocraft Technologies; available at www.novocraft.com), ELAND (Illumina, San Diego, Calif.), SOAP (available at soap.genomics.org.cn), and Maq (available at maq.sourceforge.net).


A guide sequence, and hence a nucleic acid-targeting guide may be selected to target any target nucleic acid sequence. The target sequence may be DNA. The target sequence may be any RNA sequence. In some embodiments, the target sequence may be a sequence within an RNA molecule selected from the group consisting of messenger RNA (mRNA), pre-mRNA, ribosomal RNA (rRNA), transfer RNA (tRNA), micro-RNA (miRNA), small interfering RNA (siRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), double stranded RNA (dsRNA), non-coding RNA (ncRNA), long non-coding RNA (lncRNA), and small cytoplasmatic RNA (scRNA). In some preferred embodiments, the target sequence may be a sequence within an RNA molecule selected from the group consisting of mRNA, pre-mRNA, and rRNA. In some preferred embodiments, the target sequence may be a sequence within an RNA molecule selected from the group consisting of ncRNA, and lncRNA. In some more preferred embodiments, the target sequence may be a sequence within an mRNA molecule or a pre-mRNA molecule.


In some embodiments, a nucleic acid-targeting guide is selected to reduce the degree secondary structure within the nucleic acid-targeting guide. In some embodiments, about or less than about 75%, 50%, 40%, 30%, 25%, 20%, 15%, 10%, 5%, 1%, or fewer of the nucleotides of the nucleic acid-targeting guide participate in self-complementary base pairing when optimally folded. Optimal folding may be determined by any suitable polynucleotide folding algorithm. Some programs are based on calculating the minimal Gibbs free energy. An example of one such algorithm is mFold, as described by Zuker and Stiegler (Nucleic Acids Res. 9 (1981), 133-148). Another example folding algorithm is the online webserver RNAfold, developed at Institute for Theoretical Chemistry at the University of Vienna, using the centroid structure prediction algorithm (see e.g., A. R. Gruber et al., 2008, Cell 106(1): 23-24; and PA Carr and GM Church, 2009, Nature Biotechnology 27(12): 1151-62).


In certain embodiments, a guide RNA or crRNA may comprise, consist essentially of, or consist of a direct repeat (DR) sequence and a guide sequence or spacer sequence. In certain embodiments, the guide RNA or crRNA may comprise, consist essentially of, or consist of a direct repeat sequence fused or linked to a guide sequence or spacer sequence. In certain embodiments, the direct repeat sequence may be located upstream (i.e., 5′) from the guide sequence or spacer sequence. In other embodiments, the direct repeat sequence may be located downstream (i.e., 3′) from the guide sequence or spacer sequence.


In certain embodiments, the crRNA comprises a stem loop, preferably a single stem loop. In certain embodiments, the direct repeat sequence forms a stem loop, preferably a single stem loop.


In certain embodiments, the spacer length of the guide RNA is from 15 to 35 nt. In certain embodiments, the spacer length of the guide RNA is at least 15 nucleotides. In certain embodiments, the spacer length is from 15 to 17 nt, e.g., 15, 16, or 17 nt, from 17 to 20 nt, e.g., 17, 18, 19, or 20 nt, from 20 to 24 nt, e.g., 20, 21, 22, 23, or 24 nt, from 23 to 25 nt, e.g., 23, 24, or 25 nt, from 24 to 27 nt, e.g., 24, 25, 26, or 27 nt, from 27 to 30 nt, e.g., 27, 28, 29, or 30 nt, from 30 to 35 nt, e.g., 30, 31, 32, 33, 34, or 35 nt, or 35 nt or longer.


The “tracrRNA” sequence or analogous terms includes any polynucleotide sequence that has sufficient complementarity with a crRNA sequence to hybridize. In some embodiments, the degree of complementarity between the tracrRNA sequence and crRNA sequence along the length of the shorter of the two when optimally aligned is about or more than about 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 97.5%, 99%, or higher. In some embodiments, the tracr sequence is about or more than about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, or more nucleotides in length. In some embodiments, the tracr sequence and crRNA sequence are contained within a single transcript, such that hybridization between the two produces a transcript having a secondary structure, such as a hairpin.


In general, degree of complementarity is with reference to the optimal alignment of the sca sequence and tracr sequence, along the length of the shorter of the two sequences. Optimal alignment may be determined by any suitable alignment algorithm, and may further account for secondary structures, such as self-complementarity within either the sca sequence or tracr sequence. In some embodiments, the degree of complementarity between the tracr sequence and sca sequence along the length of the shorter of the two when optimally aligned is about or more than about 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 97.5%, 99%, or higher.


In some embodiments, the degree of complementarity between a guide sequence and its corresponding target sequence can be about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or 100%; a guide or RNA or sgRNA can be about or more than about 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 75, or more nucleotides in length; or guide or RNA or sgRNA can be less than about 75, 50, 45, 40, 35, 30, 25, 20, 15, 12, or fewer nucleotides in length; and tracr RNA can be 30 or 50 nucleotides in length. In some embodiments, the degree of complementarity between a guide sequence and its corresponding target sequence is greater than 94.5% or 95% or 95.5% or 96% or 96.5% or 97% or 97.5% or 98% or 98.5% or 99% or 99.5% or 99.9%, or 100%. Off target is less than 100% or 99.9% or 99.5% or 99% or 99% or 98.5% or 98% or 97.5% or 97% or 96.5% or 96% or 95.5% or 95% or 94.5% or 94% or 93% or 92% or 91% or 90% or 89% or 88% or 87% or 86% or 85% or 84% or 83% or 82% or 81% or 80% complementarity between the sequence and the guide, with it advantageous that off target is 100% or 99.9% or 99.5% or 99% or 99% or 98.5% or 98% or 97.5% or 97% or 96.5% or 96% or 95.5% or 95% or 94.5% complementarity between the sequence and the guide.


In some embodiments according to the invention, the guide RNA (capable of guiding Cas to a target locus) may comprise (1) a guide sequence capable of hybridizing to a genomic target locus in the eukaryotic cell; (2) a tracr sequence; and (3) a tracr mate sequence. All (1) to (3) may reside in a single RNA, i.e., an sgRNA (arranged in a 5′ to 3′ orientation), or the tracr RNA may be a different RNA than the RNA containing the guide and tracr sequence. The tracr hybridizes to the tracr mate sequence and directs the CRISPR/Cas complex to the target sequence. Where the tracr RNA is on a different RNA than the RNA containing the guide and tracr sequence, the length of each RNA may be optimized to be shortened from their respective native lengths, and each may be independently chemically modified to protect from degradation by cellular RNase or otherwise increase stability.


Many Modifications to Guide Sequences are Known in the Art and are Further Contemplated within the Context of this Invention. Various Modifications May be Used to Increase the Specificity of Binding to the Target Sequence and/or Increase the Activity of the Cas Protein and/or Reduce Off-Target Effects. Example Guide Sequence Modifications are Described in PCT US2019/045582, Specifically Paragraphs [0178]-[0333]. Which is Incorporated Herein by Reference.


Target Sequences, PAMs, and PFSs
Target Sequences

In the context of formation of a CRISPR complex, “target sequence” refers to a sequence to which a guide sequence is designed to have complementarity, where hybridization between a target sequence and a guide sequence promotes the formation of a CRISPR complex. A target sequence may comprise RNA polynucleotides. The term “target RNA” refers to an RNA polynucleotide being or comprising the target sequence. In other words, the target polynucleotide can be a polynucleotide or a part of a polynucleotide to which a part of the guide sequence is designed to have complementarity with and to which the effector function mediated by the complex comprising the CRISPR effector protein and a guide molecule is to be directed. In some embodiments, a target sequence is located in the nucleus or cytoplasm of a cell.


The guide sequence can specifically bind a target sequence in a target polynucleotide. The target polynucleotide may be DNA. The target polynucleotide may be RNA. The target polynucleotide can have one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc. or more) target sequences. The target polynucleotide can be on a vector. The target polynucleotide can be genomic DNA. The target polynucleotide can be episomal. Other forms of the target polynucleotide are described elsewhere herein.


The target sequence may be DNA. The target sequence may be any RNA sequence. In some embodiments, the target sequence may be a sequence within an RNA molecule selected from the group consisting of messenger RNA (mRNA), pre-mRNA, ribosomal RNA (rRNA), transfer RNA (tRNA), micro-RNA (miRNA), small interfering RNA (siRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), double stranded RNA (dsRNA), non-coding RNA (ncRNA), long non-coding RNA (lncRNA), and small cytoplasmatic RNA (scRNA). In some preferred embodiments, the target sequence (also referred to herein as a target polynucleotide) may be a sequence within an RNA molecule selected from the group consisting of mRNA, pre-mRNA, and rRNA. In some preferred embodiments, the target sequence may be a sequence within an RNA molecule selected from the group consisting of ncRNA, and lncRNA. In some more preferred embodiments, the target sequence may be a sequence within an mRNA molecule or a pre-mRNA molecule.


PAM and PFS Elements

PAM elements are sequences that can be recognized and bound by Cas proteins. Cas proteins/effector complexes can then unwind the dsDNA at a position adjacent to the PAM element. It will be appreciated that Cas proteins and systems that include them that target RNA do not require PAM sequences (Marraffini et al. 2010. Nature. 463:568-571). Instead, many rely on PFSs, which are discussed elsewhere herein. In certain embodiments, the target sequence should be associated with a PAM (protospacer adjacent motif) or PFS (protospacer flanking sequence or site), that is, a short sequence recognized by the CRISPR complex. Depending on the nature of the CRISPR-Cas protein, the target sequence should be selected, such that its complementary sequence in the DNA duplex (also referred to herein as the non-target sequence) is upstream or downstream of the PAM. In the embodiments, the complementary sequence of the target sequence is downstream or 3′ of the PAM or upstream or 5′ of the PAM. The precise sequence and length requirements for the PAM differ depending on the Cas protein used, but PAMs are typically 2-5 base pair sequences adjacent the protospacer (that is, the target sequence). Examples of the natural PAM sequences for different Cas proteins are provided herein below and the skilled person will be able to identify further PAM sequences for use with a given Cas protein.


The ability to recognize different PAM sequences depends on the Cas polypeptide(s) included in the system. See e.g., Gleditzsch et al. 2019. RNA Biology. 16(4):504-517. Table 3 below shows several Cas polypeptides and the PAM sequence they recognize.









TABLE 3







Example PAM Sequences










Cas Protein
PAM Sequence







SpCas9
NGG/NRG



SaCas9
NGRRT or NGRRN



NmeCas9
NNNNGATT



CjCas9
NNNNRYAC



StCas9
NNAGAAW



Cas12a (Cpf1) (including LbCpf1 and
TTTV



AsCpf1)



Cas12b (C2c1)
TTT, TTA, and TTC



Cas12c (C2c3)
TA



Cas12d (CasY)
TA



Cas12e (CasX)
5′-TTCN-3′










In a preferred embodiment, the CRISPR effector protein may recognize a 3′ PAM. In certain embodiments, the CRISPR effector protein may recognize a 3′ PAM which is 5′H, wherein H is A, C or U.


Further, engineering of the PAM Interacting (PI) domain on the Cas protein may allow programing of PAM specificity, improve target site recognition fidelity, and increase the versatility of the CRISPR-Cas protein, for example as described for Cas9 in Kleinstiver B P et al. Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature. 2015 Jul. 23; 523(7561):481-5. doi: 10.1038/nature14592. As further detailed herein, the skilled person will understand that Cas13 proteins may be modified analogously. Gao et al, “Engineered Cpf1 Enzymes with Altered PAM Specificities,” bioRxiv 091611; doi: http://dx.doi.org/10.1101/091611 (Dec. 4, 2016). Doench et al. created a pool of sgRNAs, tiling across all possible target sites of a panel of six endogenous mouse and three endogenous human genes and quantitatively assessed their ability to produce null alleles of their target gene by antibody staining and flow cytometry. The authors showed that optimization of the PAM improved activity and also provided an on-line tool for designing sgRNAs.


PAM sequences can be identified in a polynucleotide using an appropriate design tool, which are commercially available as well as online. Such freely available tools include, but are not limited to, CRISPRFinder and CRISPRTarget. Mojica et al. 2009. Microbiol. 155(Pt. 3):733-740; Atschul et al. 1990. J. Mol. Biol. 215:403-410; Biswass et al. 2013 RNA Biol. 10:817-827; and Grissa et al. 2007. Nucleic Acid Res. 35:W52-57. Experimental approaches to PAM identification can include, but are not limited to, plasmid depletion assays (Jiang et al. 2013. Nat. Biotechnol. 31:233-239; Esvelt et al. 2013. Nat. Methods. 10:1116-1121; Kleinstiver et al. 2015. Nature. 523:481-485), screened by a high-throughput in vivo model called PAM-SCNAR (Pattanayak et al. 2013. Nat. Biotechnol. 31:839-843 and Leenay et al. 2016. Mol. Cell. 16:253), and negative screening (Zetsche et al. 2015. Cell. 163:759-771).


As previously mentioned, CRISPR-Cas systems that target RNA do not typically rely on PAM sequences. Instead such systems typically recognize protospacer flanking sites (PFSs) instead of PAMs Thus, Type VI CRISPR-Cas systems typically recognize protospacer flanking sites (PFSs) instead of PAMs. PFSs represents an analogue to PAMs for RNA targets. Type VI CRISPR-Cas systems employ a Cas13. Some Cas13 proteins analyzed to date, such as Cas13a (C2c2) identified from Leptotrichia shahii (LShCAs13a) have a specific discrimination against G at the 3′ end of the target RNA. The presence of a C at the corresponding crRNA repeat site can indicate that nucleotide pairing at this position is rejected. However, some Cas13 proteins (e.g., LwaCas13a and PspCas13b) do not seem to have a PFS preference. See e.g., Gleditzsch et al. 2019. RNA Biology. 16(4):504-517.


Some Type VI proteins, such as subtype B, have 5′-recognition of D (G, T, A) and a 3′-motif requirement of NAN or NNA. One example is the Cas13b protein identified in Bergeyella zoohelcum (BzCas13b). See e.g., Gleditzsch et al. 2019. RNA Biology. 16(4):504-517.


Overall Type VI CRISPR-Cas systems appear to have less restrictive rules for substrate (e.g., target sequence) recognition than those that target DNA (e.g., Type V and type II).


Zinc Finger Nucleases

In some embodiments, the MARC polynucleotide is modified using a Zinc Finger nuclease or system thereof. One type of programmable DNA-binding domain is provided by artificial zinc-finger (ZF) technology, which involves arrays of ZF modules to target new DNA-binding sites in the genome. Each finger module in a ZF array targets three DNA bases. A customized array of individual zinc finger domains is assembled into a ZF protein (ZFP).


ZFPs can comprise a functional domain. The first synthetic zinc finger nucleases (ZFNs) were developed by fusing a ZF protein to the catalytic domain of the Type IIS restriction enzyme FokI. (Kim, Y. G. et al., 1994, Chimeric restriction endonuclease, Proc. Natl. Acad. Sci. U.S.A. 91, 883-887; Kim, Y. G. et al., 1996, Hybrid restriction enzymes: zinc finger fusions to Fok I cleavage domain. Proc. Natl. Acad. Sci. U.S.A. 93, 1156-1160). Increased cleavage specificity can be attained with decreased off target activity by use of paired ZFN heterodimers, each targeting different nucleotide sequences separated by a short spacer. (Doyon, Y. et al., 2011, Enhancing zinc-finger-nuclease activity with improved obligate heterodimeric architectures. Nat. Methods 8, 74-79). ZFPs can also be designed as transcription activators and repressors and have been used to target many genes in a wide variety of organisms. Exemplary methods of genome editing using ZFNs can be found for example in U.S. Pat. Nos. 6,534,261, 6,607,882, 6,746,838, 6,794,136, 6,824,978, 6,866,997, 6,933,113, 6,979,539, 7,013,219, 7,030,215, 7,220,719, 7,241,573, 7,241,574, 7,585,849, 7,595,376, 6,903,185, and 6,479,626, all of which are specifically incorporated by reference.


Sequences Related to Nucleus Targeting and Transportation

In some embodiments, one or more components (e.g., the Cas protein and/or deaminase) in the composition for engineering cells may comprise one or more sequences related to nucleus targeting and transportation. Such sequence may facilitate the one or more components in the composition for targeting a sequence within a cell. In order to improve targeting of the CRISPR-Cas protein and/or the nucleotide deaminase protein or catalytic domain thereof used in the methods of the present disclosure to the nucleus, it may be advantageous to provide one or both of these components with one or more nuclear localization sequences (NLSs).


In some embodiments, the NLSs used in the context of the present disclosure are heterologous to the proteins. Non-limiting examples of NLSs include an NLS sequence derived from: the NLS of the SV40 virus large T-antigen, having the amino acid sequence PKKKRKV (SEQ ID No. 7) or PKKKRKVEAS (SEQ ID No. 8); the NLS from nucleoplasmin (e.g., the nucleoplasmin bipartite NLS with the sequence KRPAATKKAGQAKKKK (SEQ ID No. 9)); the c-myc NLS having the amino acid sequence PAAKRVKLD (SEQ ID No. 10) or RQRRNELKRSP (SEQ ID No. 11); the hRNPA1 M9 NLS having the sequence NQSSNFGPMKGGNFGGRSSGPYGGGGQYFAKPRNQGGY (SEQ ID No. 12); the sequence RMRIZFKNKGKDTAELRRRRVEVSVELRKAKKDEQILKRRNV (SEQ ID No. 13) of the IBB domain from importin-alpha; the sequences VSRKRPRP (SEQ ID No. 14) and PPKKARED (SEQ ID No. 15) of the myoma T protein; the sequence PQPKKKPL (SEQ ID No. 16) of human p53; the sequence SALIKKKKKMAP (SEQ ID No. 17) of mouse c-abl IV; the sequences DRLRR (SEQ ID No. 18) and PKQKKRK (SEQ ID No. 19) of the influenza virus NS1; the sequence RKLKKKIKKL (SEQ ID No. 20) of the Hepatitis virus delta antigen; the sequence REKKKFLKRR (SEQ ID No. 21) of the mouse Mx1 protein; the sequence KRKGDEVDGVDEVAKKKSKK (SEQ ID No. 22) of the human poly(ADP-ribose) polymerase; and the sequence RKCLQAGMNLEARKTKK (SEQ ID No. 23) of the steroid hormone receptors (human) glucocorticoid. In general, the one or more NLSs are of sufficient strength to drive accumulation of the DNA-targeting Cas protein in a detectable amount in the nucleus of a eukaryotic cell. In general, strength of nuclear localization activity may derive from the number of NLSs in the CRISPR-Cas protein, the particular NLS(s) used, or a combination of these factors. Detection of accumulation in the nucleus may be performed by any suitable technique. For example, a detectable marker may be fused to the nucleic acid-targeting protein, such that location within a cell may be visualized, such as in combination with a means for detecting the location of the nucleus (e.g., a stain specific for the nucleus such as DAPI). Cell nuclei may also be isolated from cells, the contents of which may then be analyzed by any suitable process for detecting protein, such as immunohistochemistry, Western blot, or enzyme activity assay. Accumulation in the nucleus may also be determined indirectly, such as by an assay for the effect of nucleic acid-targeting complex formation (e.g., assay for deaminase activity) at the target sequence, or assay for altered gene expression activity affected by DNA-targeting complex formation and/or DNA-targeting), as compared to a control not exposed to the CRISPR-Cas protein and deaminase protein, or exposed to a CRISPR-Cas and/or deaminase protein lacking the one or more NLSs.


The CRISPR-Cas and/or nucleotide deaminase proteins may be provided with 1 or more, such as with, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more heterologous NLSs. In some embodiments, the proteins comprises about or more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more NLSs at or near the amino-terminus, about or more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more NLSs at or near the carboxy-terminus, or a combination of these (e.g., zero or at least one or more NLS at the amino-terminus and zero or at one or more NLS at the carboxy terminus). When more than one NLS is present, each may be selected independently of the others, such that a single NLS may be present in more than one copy and/or in combination with one or more other NLSs present in one or more copies. In some embodiments, an NLS is considered near the N- or C-terminus when the nearest amino acid of the NLS is within about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 40, 50, or more amino acids along the polypeptide chain from the N- or C-terminus. In preferred embodiments of the CRISPR-Cas proteins, an NLS attached to the C-terminal of the protein.


In certain embodiments, the CRISPR-Cas protein and the deaminase protein are delivered to the cell or expressed within the cell as separate proteins. In these embodiments, each of the CRISPR-Cas and deaminase protein can be provided with one or more NLSs as described herein. In certain embodiments, the CRISPR-Cas and deaminase proteins are delivered to the cell or expressed with the cell as a fusion protein. In these embodiments one or both of the CRISPR-Cas and deaminase protein is provided with one or more NLSs. Where the nucleotide deaminase is fused to an adaptor protein (such as MS2) as described above, the one or more NLS can be provided on the adaptor protein, provided that this does not interfere with aptamer binding. In particular embodiments, the one or more NLS sequences may also function as linker sequences between the nucleotide deaminase and the CRISPR-Cas protein.


In certain embodiments, guides of the disclosure comprise specific binding sites (e.g. aptamers) for adapter proteins, which may be linked to or fused to an nucleotide deaminase or catalytic domain thereof. When such a guide forms a CRISPR complex (e.g., CRISPR-Cas protein binding to guide and target) the adapter proteins bind and, the nucleotide deaminase or catalytic domain thereof associated with the adapter protein is positioned in a spatial orientation which is advantageous for the attributed function to be effective.


The skilled person will understand that modifications to the guide which allow for binding of the adapter+nucleotide deaminase, but not proper positioning of the adapter+nucleotide deaminase (e.g. due to steric hindrance within the three dimensional structure of the CRISPR complex) are modifications which are not intended. The one or more modified guide may be modified at the tetra loop, the stem loop 1, stem loop 2, or stem loop 3, as described herein, preferably at either the tetra loop or stem loop 2, and in some cases at both the tetra loop and stem loop 2.


In some embodiments, a component (e.g., the dead Cas protein, the nucleotide deaminase protein or catalytic domain thereof, or a combination thereof) in the systems may comprise one or more nuclear export signals (NES), one or more nuclear localization signals (NLS), or any combinations thereof. In some cases, the NES may be an HIV Rev NES. In certain cases, the NES may be MAPK NES. When the component is a protein, the NES or NLS may be at the C terminus of component. Alternatively or additionally, the NES or NLS may be at the N terminus of component. In some examples, the Cas protein and optionally said nucleotide deaminase protein or catalytic domain thereof comprise one or more heterologous nuclear export signal(s) (NES(s)) or nuclear localization signal(s) (NLS(s)), preferably an HIV Rev NES or MAPK NES, preferably C-terminal.


Templates

In some embodiments, the composition for engineering cells comprise a template, e.g., a recombination template. A template may be a component of another vector as described herein, contained in a separate vector, or provided as a separate polynucleotide. In some embodiments, a recombination template is designed to serve as a template in homologous recombination, such as within or near a target sequence nicked or cleaved by a nucleic acid-targeting effector protein as a part of a nucleic acid-targeting complex.


In an embodiment, the template nucleic acid alters the sequence of the target position. In an embodiment, the template nucleic acid results in the incorporation of a modified, or non-naturally occurring base into the target nucleic acid.


The template sequence may undergo a breakage mediated or catalyzed recombination with the target sequence. In an embodiment, the template nucleic acid may include sequence that corresponds to a site on the target sequence that is cleaved by a Cas protein mediated cleavage event. In an embodiment, the template nucleic acid may include sequence that corresponds to both, a first site on the target sequence that is cleaved in a first Cas protein mediated event, and a second site on the target sequence that is cleaved in a second Cas protein mediated event.


In certain embodiments, the template nucleic acid can include sequence which results in an alteration in the coding sequence of a translated sequence, e.g., one which results in the substitution of one amino acid for another in a protein product, e.g., transforming a mutant allele into a wild type allele, transforming a wild type allele into a mutant allele, and/or introducing a stop codon, insertion of an amino acid residue, deletion of an amino acid residue, or a nonsense mutation. In certain embodiments, the template nucleic acid can include sequence which results in an alteration in a non-coding sequence, e.g., an alteration in an exon or in a 5′ or 3′ non-translated or non-transcribed region. Such alterations include an alteration in a control element, e.g., a promoter, enhancer, and an alteration in a cis-acting or trans-acting control element.


A template nucleic acid having homology with a target position in a target gene may be used to alter the structure of a target sequence. The template sequence may be used to alter an unwanted structure, e.g., an unwanted or mutant nucleotide. The template nucleic acid may include sequence which, when integrated, results in: decreasing the activity of a positive control element; increasing the activity of a positive control element; decreasing the activity of a negative control element; increasing the activity of a negative control element; decreasing the expression of a gene; increasing the expression of a gene; increasing resistance to a disorder or disease; increasing resistance to viral entry; correcting a mutation or altering an unwanted amino acid residue conferring, increasing, abolishing or decreasing a biological property of a gene product, e.g., increasing the enzymatic activity of an enzyme, or increasing the ability of a gene product to interact with another molecule.


The template nucleic acid may include sequence which results in: a change in sequence of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12 or more nucleotides of the target sequence.


A template polynucleotide may be of any suitable length, such as about or more than about 10, 15, 20, 25, 50, 75, 100, 150, 200, 500, 1000, or more nucleotides in length. In an embodiment, the template nucleic acid may be 20+/−10, 30+/−10, 40+/−10, 50+/−10, 60+/−10, 70+/−10, 80+/−10, 90+/−10, 100+/−10, 1 10+/−10, 120+/−10, 130+/−10, 140+/−10, 150+/−10, 160+/−10, 170+/−10, 1 80+/−10, 190+/−10, 200+/−10, 210+/−10, of 220+/−10 nucleotides in length. In an embodiment, the template nucleic acid may be 30+/−20, 40+/−20, 50+/−20, 60+/−20, 70+/−20, 80+/−20, 90+/−20, 100+/−20, 1 10+/−20, 120+/−20, 130+/−20, 140+/−20, I 50+/−20, 160+/−20, 170+/−20, 180+/−20, 190+/−20, 200+/−20, 210+/−20, of 220+/−20 nucleotides in length. In an embodiment, the template nucleic acid is 10 to 1,000, 20 to 900, 30 to 800, 40 to 700, 50 to 600, 50 to 500, 50 to 400, 50 to 300, 50 to 200, or 50 to 100 nucleotides in length.


In some embodiments, the template polynucleotide is complementary to a portion of a polynucleotide comprising the target sequence. When optimally aligned, a template polynucleotide might overlap with one or more nucleotides of a target sequences (e.g. about or more than about 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100 or more nucleotides). In some embodiments, when a template sequence and a polynucleotide comprising a target sequence are optimally aligned, the nearest nucleotide of the template polynucleotide is within about 1, 5, 10, 15, 20, 25, 50, 75, 100, 200, 300, 400, 500, 1000, 5000, 10000, or more nucleotides from the target sequence.


The exogenous polynucleotide template comprises a sequence to be integrated (e.g., a mutated gene). The sequence for integration may be a sequence endogenous or exogenous to the cell. Examples of a sequence to be integrated include polynucleotides encoding a protein or a non-coding RNA (e.g., a microRNA). Thus, the sequence for integration may be operably linked to an appropriate control sequence or sequences. Alternatively, the sequence to be integrated may provide a regulatory function.


An upstream or downstream sequence may comprise from about 20 bp to about 2500 bp, for example, about 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, or 2500 bp. In some methods, the exemplary upstream or downstream sequence have about 200 bp to about 2000 bp, about 600 bp to about 1000 bp, or more particularly about 700 bp to about 1000.


An upstream or downstream sequence may comprise from about 20 bp to about 2500 bp, for example, about 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, or 2500 bp. In some methods, the exemplary upstream or downstream sequence have about 200 bp to about 2000 bp, about 600 bp to about 1000 bp, or more particularly about 700 bp to about 1000


In certain embodiments, one or both homology arms may be shortened to avoid including certain sequence repeat elements. For example, a 5′ homology arm may be shortened to avoid a sequence repeat element. In other embodiments, a 3′ homology arm may be shortened to avoid a sequence repeat element. In some embodiments, both the 5′ and the 3′ homology arms may be shortened to avoid including certain sequence repeat elements.


In some methods, the exogenous polynucleotide template may further comprise a marker. Such a marker may make it easy to screen for targeted integrations. Examples of suitable markers include restriction sites, fluorescent proteins, or selectable markers. The exogenous polynucleotide template of the disclosure can be constructed using recombinant techniques (see, for example, Sambrook et al., 2001 and Ausubel et al., 1996).


In certain embodiments, a template nucleic acid for correcting a mutation may designed for use as a single-stranded oligonucleotide. When using a single-stranded oligonucleotide, 5′ and 3′ homology arms may range up to about 200 base pairs (bp) in length, e.g., at least 25, 50, 75, 100, 125, 150, 175, or 200 bp in length.


Suzuki et al. describe in vivo genome editing via CRISPR/Cas9 mediated homology-independent targeted integration (2016, Nature 540:144-149).


TALE Nucleases

In some embodiments, a TALE nuclease or TALE nuclease system can be used to modify a MARC polynucleotide. In some embodiments, the methods provided herein use isolated, non-naturally occurring, recombinant or engineered DNA binding proteins that comprise TALE monomers or TALE monomers or half monomers as a part of their organizational structure that enable the targeting of nucleic acid sequences with improved efficiency and expanded specificity.


Naturally occurring TALEs or “wild type TALEs” are nucleic acid binding proteins secreted by numerous species of proteobacteria. TALE polypeptides contain a nucleic acid binding domain composed of tandem repeats of highly conserved monomer polypeptides that are predominantly 33, 34 or 35 amino acids in length and that differ from each other mainly in amino acid positions 12 and 13. In advantageous embodiments the nucleic acid is DNA. As used herein, the term “polypeptide monomers”, “TALE monomers” or “monomers” will be used to refer to the highly conserved repetitive polypeptide sequences within the TALE nucleic acid binding domain and the term “repeat variable di-residues” or “RVD” will be used to refer to the highly variable amino acids at positions 12 and 13 of the polypeptide monomers. As provided throughout the disclosure, the amino acid residues of the RVD are depicted using the IUPAC single letter code for amino acids. A general representation of a TALE monomer which is comprised within the DNA binding domain is X1-11-(X12X13)-X14-33 or 34 or 35, where the subscript indicates the amino acid position and X represents any amino acid. X12X13 indicate the RVDs. In some polypeptide monomers, the variable amino acid at position 13 is missing or absent and in such monomers, the RVD consists of a single amino acid. In such cases the RVD may be alternatively represented as X*, where X represents X12 and (*) indicates that X13 is absent. The DNA binding domain comprises several repeats of TALE monomers and this may be represented as (X1-11-(X12X13)-X14-33 or 34 or 35)z, where in an advantageous embodiment, z is at least 5 to 40. In a further advantageous embodiment, z is at least 10 to 26.


The TALE monomers can have a nucleotide binding affinity that is determined by the identity of the amino acids in its RVD. For example, polypeptide monomers with an RVD of NI can preferentially bind to adenine (A), monomers with an RVD of NG can preferentially bind to thymine (T), monomers with an RVD of HD can preferentially bind to cytosine (C) and monomers with an RVD of NN can preferentially bind to both adenine (A) and guanine (G). In some embodiments, monomers with an RVD of IG can preferentially bind to T. Thus, the number and order of the polypeptide monomer repeats in the nucleic acid binding domain of a TALE determines its nucleic acid target specificity. In some embodiments, monomers with an RVD of NS can recognize all four base pairs and can bind to A, T, G or C. The structure and function of TALEs is further described in, for example, Moscou et al., Science 326:1501 (2009); Boch et al., Science 326:1509-1512 (2009); and Zhang et al., Nature Biotechnology 29:149-153 (2011).


The polypeptides used in methods of the invention can be isolated, non-naturally occurring, recombinant or engineered nucleic acid-binding proteins that have nucleic acid or DNA binding regions containing polypeptide monomer repeats that are designed to target specific nucleic acid sequences.


As described herein, polypeptide monomers having an RVD of HN or NH preferentially bind to guanine and thereby allow the generation of TALE polypeptides with high binding specificity for guanine containing target nucleic acid sequences. In some embodiments, polypeptide monomers having RVDs RN, NN, NK, SN, NH, KN, HN, NQ, RG, KH, RH and SS can preferentially bind to guanine. In some embodiments, polypeptide monomers having RVDs RN, NK, NQ, HH, KH, RH, SS and SN can preferentially bind to guanine and can thus allow the generation of TALE polypeptides with high binding specificity for guanine containing target nucleic acid sequences. In some embodiments, polypeptide monomers having RVDs HH, KH, NH, NK, NQ, RH, RN and SS can preferentially bind to guanine and thereby allow the generation of TALE polypeptides with high binding specificity for guanine containing target nucleic acid sequences. In some embodiments, the RVDs that have high binding specificity for guanine are RN, NH RH and KH. Furthermore, polypeptide monomers having an RVD of NV can preferentially bind to adenine and guanine. In some embodiments, monomers having RVDs of H*, HA, KA, N*, NA, NC, NS, RA, and S* bind to adenine, guanine, cytosine and thymine with comparable affinity.


The predetermined N-terminal to C-terminal order of the one or more polypeptide monomers of the nucleic acid or DNA binding domain determines the corresponding predetermined target nucleic acid sequence to which the polypeptides of the invention will bind. As used herein the monomers and at least one or more half monomers are “specifically ordered to target” the genomic locus or gene of interest. In plant genomes, the natural TALE-binding sites always begin with a thymine (T), which may be specified by a cryptic signal within the non-repetitive N-terminus of the TALE polypeptide; in some cases, this region may be referred to as repeat 0. In animal genomes, TALE binding sites do not necessarily have to begin with a thymine (T) and polypeptides of the invention may target DNA sequences that begin with T, A, G or C. The tandem repeat of TALE monomers always ends with a half-length repeat or a stretch of sequence that may share identity with only the first 20 amino acids of a repetitive full-length TALE monomer and this half repeat may be referred to as a half-monomer. Therefore, it follows that the length of the nucleic acid or DNA being targeted is equal to the number of full monomers plus two.


As described in Zhang et al., Nature Biotechnology 29:149-153 (2011), TALE polypeptide binding efficiency may be increased by including amino acid sequences from the “capping regions” that are directly N-terminal or C-terminal of the DNA binding region of naturally occurring TALEs into the engineered TALEs at positions N-terminal or C-terminal of the engineered TALE DNA binding region. Thus, in certain embodiments, the TALE polypeptides described herein further comprise an N-terminal capping region and/or a C-terminal capping region.


An exemplary amino acid sequence of a N-terminal capping region is:









(SEQ ID NO: 3)


M D P I R S R T P S P A R E L L S G P Q P D G V Q





P T A D R G V S P P A G G P L D G L P A R R T M S





R T R L P S P P A P S P A F S A D S F S D L L R Q





F D P S L F N T S L F D S L P P F G A H H T E A A





T G E W D E V Q S G L R A A D A P P P T M R V A V





T A A R P P R A K P A P R R R A A Q P S D A S P A





A Q V D L R T L G Y S Q Q Q Q E K I K P K V R S T





V A Q H H E A L V G H G F T H A H I V A L S Q H P





A A L G T V A V K Y Q D M I A A L P E A T H E A I





V G V G K Q W S G A R A L E A L L T V A G E L R G





P P L Q L D T G Q L L K I A K R G G V T A V E A V





H A W R N A L T G A P L N







An exemplary amino acid sequence of a C-terminal capping region is:









(SEQ ID NO: 4)


R P A L E S I V A Q L S R P D P A L A A L T N D H





L V A L A C L G G R P A L D A V K K G L P H A P A





L I K R T N R R I P E R T S H R V A D H A Q V V R





V L G F F Q C H S H P A Q A F D D A M T Q F G M S





R H G L L Q L F R R V G V T E L E A R S G T L P P





A S Q R W D R I L Q A S G M K R A K P S P T S T Q





T P D Q A S L H A F A D S L E R D L D A P S P M H





E G D Q T R A S






As used herein the predetermined “N-terminus” to “C terminus” orientation of the N-terminal capping region, the DNA binding domain comprising the repeat TALE monomers and the C-terminal capping region provide structural basis for the organization of different domains in the d-TALEs or polypeptides of the invention.


The entire N-terminal and/or C-terminal capping regions are not necessary to enhance the binding activity of the DNA binding region. Therefore, in certain embodiments, fragments of the N-terminal and/or C-terminal capping regions are included in the TALE polypeptides described herein.


In certain embodiments, the TALE polypeptides described herein contain a N-terminal capping region fragment that included at least 10, 20, 30, 40, 50, 54, 60, 70, 80, 87, 90, 94, 100, 102, 110, 117, 120, 130, 140, 147, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260 or 270 amino acids of an N-terminal capping region. In certain embodiments, the N-terminal capping region fragment amino acids are of the C-terminus (the DNA-binding region proximal end) of an N-terminal capping region. As described in Zhang et al., Nature Biotechnology 29:149-153 (2011), N-terminal capping region fragments that include the C-terminal 240 amino acids enhance binding activity equal to the full length capping region, while fragments that include the C-terminal 147 amino acids retain greater than 80% of the efficacy of the full length capping region, and fragments that include the C-terminal 117 amino acids retain greater than 50% of the activity of the full-length capping region.


In some embodiments, the TALE polypeptides described herein contain a C-terminal capping region fragment that included at least 6, 10, 20, 30, 37, 40, 50, 60, 68, 70, 80, 90, 100, 110, 120, 127, 130, 140, 150, 155, 160, 170, 180 amino acids of a C-terminal capping region. In certain embodiments, the C-terminal capping region fragment amino acids are of the N-terminus (the DNA-binding region proximal end) of a C-terminal capping region. As described in Zhang et al., Nature Biotechnology 29:149-153 (2011), C-terminal capping region fragments that include the C-terminal 68 amino acids enhance binding activity equal to the full-length capping region, while fragments that include the C-terminal 20 amino acids retain greater than 50% of the efficacy of the full-length capping region.


In certain embodiments, the capping regions of the TALE polypeptides described herein do not need to have identical sequences to the capping region sequences provided herein. Thus, in some embodiments, the capping region of the TALE polypeptides described herein have sequences that are at least 50%, 60%, 70%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical or share identity to the capping region amino acid sequences provided herein. Sequence identity is related to sequence homology. Homology comparisons may be conducted by eye, or more usually, with the aid of readily available sequence comparison programs. These commercially available computer programs may calculate percent (%) homology between two or more sequences and may also calculate the sequence identity shared by two or more amino acid or nucleic acid sequences. In some preferred embodiments, the capping region of the TALE polypeptides described herein have sequences that are at least 95% identical or share identity to the capping region amino acid sequences provided herein.


Sequence homologies can be generated by any of a number of computer programs known in the art, which include but are not limited to BLAST or FASTA. Suitable computer programs for carrying out alignments like the GCG Wisconsin Bestfit package may also be used. Once the software has produced an optimal alignment, it is possible to calculate % homology, preferably % sequence identity. The software typically does this as part of the sequence comparison and generates a numerical result.


In some embodiments described herein, the TALE polypeptides of the invention include a nucleic acid binding domain linked to the one or more effector domains. The terms “effector domain” or “regulatory and functional domain” refer to a polypeptide sequence that has an activity other than binding to the nucleic acid sequence recognized by the nucleic acid binding domain. By combining a nucleic acid binding domain with one or more effector domains, the polypeptides of the invention may be used to target the one or more functions or activities mediated by the effector domain to a particular target DNA sequence to which the nucleic acid binding domain specifically binds.


In some embodiments of the TALE polypeptides described herein, the activity mediated by the effector domain is a biological activity. For example, in some embodiments the effector domain is a transcriptional inhibitor (i.e., a repressor domain), such as an mSin interaction domain (SID). SID4X domain or a Kruppel-associated box (KRAB) or fragments of the KRAB domain. In some embodiments the effector domain is an enhancer of transcription (i.e. an activation domain), such as the VP16, VP64 or p65 activation domain. In some embodiments, the nucleic acid binding is linked, for example, with an effector domain that includes but is not limited to a transposase, integrase, recombinase, resolvase, invertase, protease, DNA methyltransferase, DNA demethylase, histone acetylase, histone deacetylase, nuclease, transcriptional repressor, transcriptional activator, transcription factor recruiting, protein nuclear-localization signal or cellular uptake signal.


In some embodiments, the effector domain is a protein domain which exhibits activities which include but are not limited to transposase activity, integrase activity, recombinase activity, resolvase activity, invertase activity, protease activity, DNA methyltransferase activity, DNA demethylase activity, histone acetylase activity, histone deacetylase activity, nuclease activity, nuclear-localization signaling activity, transcriptional repressor activity, transcriptional activator activity, transcription factor recruiting activity, or cellular uptake signaling activity. Other preferred embodiments of the invention may include any combination of the activities described herein.


Meganucleases

In some embodiments, a meganuclease or system thereof can be used to modify a MARC polynucleotide. Meganucleases, which are endodeoxyribonucleases characterized by a large recognition site (double-stranded DNA sequences of 12 to 40 base pairs). Exemplary methods for using meganucleases can be found in U.S. Pat. Nos. 8,163,514, 8,133,697, 8,021,867, 8,119,361, 8,119,381, 8,124,369, and 8,129,134, which are specifically incorporated by reference.


Guide Molecules

The methods described herein may be used to screen inhibition of CRISPR systems employing different types of guide molecules. As used herein, the term “guide sequence” and “guide molecule” in the context of a CRISPR-Cas system, comprises any polynucleotide sequence having sufficient complementarity with a target nucleic acid sequence to hybridize with the target nucleic acid sequence and direct sequence-specific binding of a nucleic acid-targeting complex to the target nucleic acid sequence. The guide sequences made using the methods disclosed herein may be a full-length guide sequence, a truncated guide sequence, a full-length sgRNA sequence, a truncated sgRNA sequence, or an E+F sgRNA sequence. In some embodiments, the degree of complementarity of the guide sequence to a given target sequence, when optimally aligned using a suitable alignment algorithm, is about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or more. In certain example embodiments, the guide molecule comprises a guide sequence that may be designed to have at least one mismatch with the target sequence, such that a RNA duplex formed between the guide sequence and the target sequence. Accordingly, the degree of complementarity is preferably less than 99%. For instance, where the guide sequence consists of 24 nucleotides, the degree of complementarity is more particularly about 96% or less. In particular embodiments, the guide sequence is designed to have a stretch of two or more adjacent mismatching nucleotides, such that the degree of complementarity over the entire guide sequence is further reduced. For instance, where the guide sequence consists of 24 nucleotides, the degree of complementarity is more particularly about 96% or less, more particularly, about 92% or less, more particularly about 88% or less, more particularly about 84% or less, more particularly about 80% or less, more particularly about 76% or less, more particularly about 72% or less, depending on whether the stretch of two or more mismatching nucleotides encompasses 2, 3, 4, 5, 6 or 7 nucleotides, etc. In some embodiments, aside from the stretch of one or more mismatching nucleotides, the degree of complementarity, when optimally aligned using a suitable alignment algorithm, is about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or more. Optimal alignment may be determined with the use of any suitable algorithm for aligning sequences, non-limiting example of which include the Smith-Waterman algorithm, the Needleman-Wunsch algorithm, algorithms based on the Burrows-Wheeler Transform (e.g., the Burrows Wheeler Aligner), ClustalW, Clustal X, BLAT, Novoalign (Novocraft Technologies; available at www.novocraft.com), ELAND (Illumina, San Diego, Calif.), SOAP (available at soap.genomics.org.cn), and Maq (available at maq.sourceforge.net). The ability of a guide sequence (within a nucleic acid-targeting guide RNA) to direct sequence-specific binding of a nucleic acid-targeting complex to a target nucleic acid sequence may be assessed by any suitable assay. For example, the components of a nucleic acid-targeting CRISPR system sufficient to form a nucleic acid-targeting complex, including the guide sequence to be tested, may be provided to a host cell having the corresponding target nucleic acid sequence, such as by transfection with vectors encoding the components of the nucleic acid-targeting complex, followed by an assessment of preferential targeting (e.g., cleavage) within the target nucleic acid sequence, such as by Surveyor assay as described herein. Similarly, cleavage of a target nucleic acid sequence (or a sequence in the vicinity thereof) may be evaluated in a test tube by providing the target nucleic acid sequence, components of a nucleic acid-targeting complex, including the guide sequence to be tested and a control guide sequence different from the test guide sequence, and comparing binding or rate of cleavage at or in the vicinity of the target sequence between the test and control guide sequence reactions. Other assays are possible, and will occur to those skilled in the art. A guide sequence, and hence a nucleic acid-targeting guide RNA may be selected to target any target nucleic acid sequence.


In certain embodiments, the guide sequence or spacer length of the guide molecules is from 15 to 50 nt. In certain embodiments, the spacer length of the guide RNA is at least 15 nucleotides. In certain embodiments, the spacer length is from 15 to 17 nt, e.g., 15, 16, or 17 nt, from 17 to 20 nt, e.g., 17, 18, 19, or 20 nt, from 20 to 24 nt, e.g., 20, 21, 22, 23, or 24 nt, from 23 to 25 nt, e.g., 23, 24, or 25 nt, from 24 to 27 nt, e.g., 24, 25, 26, or 27 nt, from 27 to 30 nt, e.g., 27, 28, 29, or 30 nt, from 30 to 35 nt, e.g., 30, 31, 32, 33, 34, or 35 nt, or 35 nt or longer. In certain example embodiment, the guide sequence is 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 40, 41, 42, 43, 44, 45, 46, 47 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 nt.


In some embodiments, the guide sequence is an RNA sequence of between 10 to 50 nt in length, but more particularly of about 20-30 nt advantageously about 20 nt, 23 to 25 nt or 24 nt. The guide sequence is selected so as to ensure that it hybridizes to the target sequence. This is described in greater detail below. Selection can encompass further steps which increase efficacy and specificity.


In some embodiments, the guide sequence has a canonical length (e.g., about 15-30 nt) is used to hybridize with the target RNA or DNA. In some embodiments, a guide molecule is longer than the canonical length (e.g., >30 nt) is used to hybridize with the target RNA or DNA, such that a region of the guide sequence hybridizes with a region of the RNA or DNA strand outside of the Cas-guide target complex. This can be of interest where additional modifications, such deamination of nucleotides is of interest. In alternative embodiments, it is of interest to maintain the limitation of the canonical guide sequence length.


In some embodiments, the sequence of the guide molecule (direct repeat and/or spacer) is selected to reduce the degree secondary structure within the guide molecule. In some embodiments, about or less than about 75%, 50%, 40%, 30%, 25%, 20%, 15%, 10%, 5%, 1%, or fewer of the nucleotides of the nucleic acid-targeting guide RNA participate in self-complementary base pairing when optimally folded. Optimal folding may be determined by any suitable polynucleotide folding algorithm. Some programs are based on calculating the minimal Gibbs free energy. An example of one such algorithm is mFold, as described by Zuker and Stiegler (Nucleic Acids Res. 9 (1981), 133-148). Another example folding algorithm is the online webserver RNAfold, developed at Institute for Theoretical Chemistry at the University of Vienna, using the centroid structure prediction algorithm (see e.g., A. R. Gruber et al., 2008, Cell 106(1): 23-24; and PA Carr and GM Church, 2009, Nature Biotechnology 27(12): 1151-62).


In some embodiments, it is of interest to reduce the susceptibility of the guide molecule to RNA cleavage, such as to cleavage by Cas13. Accordingly, in particular embodiments, the guide molecule is adjusted to avoid cleavage by Cas13 or other RNA-cleaving enzymes.


In certain embodiments, the guide molecule comprises non-naturally occurring nucleic acids and/or non-naturally occurring nucleotides and/or nucleotide analogs, and/or chemical modifications. Preferably, these non-naturally occurring nucleic acids and non-naturally occurring nucleotides are located outside the guide sequence. Non-naturally occurring nucleic acids can include, for example, mixtures of naturally and non-naturally occurring nucleotides. Non-naturally occurring nucleotides and/or nucleotide analogs may be modified at the ribose, phosphate, and/or base moiety. In an embodiment of the invention, a guide nucleic acid comprises ribonucleotides and non-ribonucleotides. In one such embodiment, a guide comprises one or more ribonucleotides and one or more deoxyribonucleotides. In an embodiment of the invention, the guide comprises one or more non-naturally occurring nucleotide or nucleotide analog such as a nucleotide with phosphorothioate linkage, a locked nucleic acid (LNA) nucleotides comprising a methylene bridge between the 2′ and 4′ carbons of the ribose ring, or bridged nucleic acids (BNA). Other examples of modified nucleotides include 2′-O-methyl analogs, 2′-deoxy analogs, or 2′-fluoro analogs. Further examples of modified bases include, but are not limited to, 2-aminopurine, 5-bromo-uridine, pseudouridine, inosine, 7-methylguanosine. Examples of guide RNA chemical modifications include, without limitation, incorporation of 2′-O-methyl (M), 2′-O-methyl 3′ phosphorothioate (MS), S-constrained ethyl(cEt), or 2′-O-methyl 3′ thioPACE (MSP) at one or more terminal nucleotides. Such chemically modified guides can comprise increased stability and increased activity as compared to unmodified guides, though on-target vs. off-target specificity is not predictable. (See, Hendel, 2015, Nat Biotechnol. 33(9):985-9, doi: 10.1038/nbt.3290, published online 29 Jun. 2015 Ragdarm et al., 0215, PNAS, E7110-E7111; Allerson et al., J. Med. Chem. 2005, 48:901-904; Bramsen et al., Front. Genet., 2012, 3:154; Deng et al., PNAS, 2015, 112:11870-11875; Sharma et al., MedChemComm., 2014, 5:1454-1471; Hendel et al., Nat. Biotechnol. (2015) 33(9): 985-989; Li et al., Nature Biomedical Engineering, 2017, 1, 0066 DOI:10.1038/s41551-017-0066). In some embodiments, the 5′ and/or 3′ end of a guide RNA is modified by a variety of functional moieties including fluorescent dyes, polyethylene glycol, cholesterol, proteins, or detection tags. (See Kelly et al., 2016, J. Biotech. 233:74-83). In certain embodiments, a guide comprises ribonucleotides in a region that binds to a target RNA and one or more deoxyribonucletides and/or nucleotide analogs in a region that binds to Cas13. In an embodiment of the invention, deoxyribonucleotides and/or nucleotide analogs are incorporated in engineered guide structures, such as, without limitation, stem-loop regions, and the seed region. For Cas13 guide, in certain embodiments, the modification is not in the 5′-handle of the stem-loop regions. Chemical modification in the 5′-handle of the stem-loop region of a guide may abolish its function (see Li, et al., Nature Biomedical Engineering, 2017, 1:0066). In certain embodiments, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, or 75 nucleotides of a guide is chemically modified. In some embodiments, 3-5 nucleotides at either the 3′ or the 5′ end of a guide is chemically modified. In some embodiments, only minor modifications are introduced in the seed region, such as 2′-F modifications. In some embodiments, 2′-F modification is introduced at the 3′ end of a guide. In certain embodiments, three to five nucleotides at the 5′ and/or the 3′ end of the guide are chemically modified with 2′-O-methyl (M), 2′-O-methyl 3′ phosphorothioate (MS), S-constrained ethyl(cEt), or 2′-O-methyl 3′ thioPACE (MSP). Such modification can enhance genome editing efficiency (see Hendel et al., Nat. Biotechnol. (2015) 33(9): 985-989). In certain embodiments, all of the phosphodiester bonds of a guide are substituted with phosphorothioates (PS) for enhancing levels of gene disruption. In certain embodiments, more than five nucleotides at the 5′ and/or the 3′ end of the guide are chemically modified with 2′-O-Me, 2′-F or S-constrained ethyl(cEt). Such chemically modified guide can mediate enhanced levels of gene disruption (see Ragdarm et al., 0215, PNAS, E7110-E7111). In an embodiment of the invention, a guide is modified to comprise a chemical moiety at its 3′ and/or 5′ end. Such moieties include, but are not limited to amine, azide, alkyne, thio, dibenzocyclooctyne (DBCO), or Rhodamine. In certain embodiment, the chemical moiety is conjugated to the guide by a linker, such as an alkyl chain. In certain embodiments, the chemical moiety of the modified guide can be used to attach the guide to another molecule, such as DNA, RNA, protein, or nanoparticles. Such chemically modified guide can be used to identify or enrich cells generically edited by a CRISPR system (see Lee et al., eLife, 2017, 6:e25312, DOI:10.7554).


In some embodiments, the modification to the guide is a chemical modification, an insertion, a deletion or a split. In some embodiments, the chemical modification includes, but is not limited to, incorporation of 2′-O-methyl (M) analogs, 2′-deoxy analogs, 2-thiouridine analogs, N6-methyladenosine analogs, 2′-fluoro analogs, 2-aminopurine, 5-bromo-uridine, pseudouridine (Ψ), N1-methylpseudouridine (me1Ψ), 5-methoxyuridine(5moU), inosine, 7-methylguanosine, 2′-O-methyl 3′phosphorothioate (MS), S-constrained ethyl(cEt), phosphorothioate (PS), or 2′-O-methyl 3′thioPACE (MSP). In some embodiments, the guide comprises one or more of phosphorothioate modifications. In certain embodiments, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or 25 nucleotides of the guide are chemically modified. In certain embodiments, one or more nucleotides in the seed region are chemically modified. In certain embodiments, one or more nucleotides in the 3′-terminus are chemically modified. In certain embodiments, none of the nucleotides in the 5′-handle is chemically modified. In some embodiments, the chemical modification in the seed region is a minor modification, such as incorporation of a 2′-fluoro analog. In a specific embodiment, one nucleotide of the seed region is replaced with a 2′-fluoro analog. In some embodiments, 5 to 10 nucleotides in the 3′-terminus are chemically modified. Such chemical modifications at the 3′-terminus of the Cas13 CrRNA may improve Cas13 activity. In a specific embodiment, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 nucleotides in the 3′-terminus are replaced with 2′-fluoro analogues. In a specific embodiment, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 nucleotides in the 3′-terminus are replaced with 2′-O-methyl (M) analogs.


In some embodiments, the loop of the 5′-handle of the guide is modified. In some embodiments, the loop of the 5′-handle of the guide is modified to have a deletion, an insertion, a split, or chemical modifications. In certain embodiments, the modified loop comprises 3, 4, or 5 nucleotides. In certain embodiments, the loop comprises the sequence of UCUU, UUUU, UAUU, or UGUU.


In some embodiments, the guide molecule forms a stemloop with a separate non-covalently linked sequence, which can be DNA or RNA. In particular embodiments, the sequences forming the guide are first synthesized using the standard phosphoramidite synthetic protocol (Herdewijn, P., ed., Methods in Molecular Biology Col 288, Oligonucleotide Synthesis: Methods and Applications, Humana Press, New Jersey (2012)). In some embodiments, these sequences can be functionalized to contain an appropriate functional group for ligation using the standard protocol known in the art (Hermanson, G. T., Bioconjugate Techniques, Academic Press (2013)). Examples of functional groups include, but are not limited to, hydroxyl, amine, carboxylic acid, carboxylic acid halide, carboxylic acid active ester, aldehyde, carbonyl, chlorocarbonyl, imidazolylcarbonyl, hydrozide, semicarbazide, thio semicarbazide, thiol, maleimide, haloalkyl, sufonyl, ally, propargyl, diene, alkyne, and azide. Once this sequence is functionalized, a covalent chemical bond or linkage can be formed between this sequence and the direct repeat sequence. Examples of chemical bonds include, but are not limited to, those based on carbamates, ethers, esters, amides, imines, amidines, aminotrizines, hydrozone, disulfides, thioethers, thioesters, phosphorothioates, phosphorodithioates, sulfonamides, sulfonates, fulfones, sulfoxides, ureas, thioureas, hydrazide, oxime, triazole, photolabile linkages, C—C bond forming groups such as Diels-Alder cyclo-addition pairs or ring-closing metathesis pairs, and Michael reaction pairs.


In some embodiments, these stem-loop forming sequences can be chemically synthesized. In some embodiments, the chemical synthesis uses automated, solid-phase oligonucleotide synthesis machines with 2′-acetoxyethyl orthoester (2′-ACE) (Scaringe et al., J. Am. Chem. Soc. (1998) 120: 11820-11821; Scaringe, Methods Enzymol. (2000) 317: 3-18) or 2′-thionocarbamate (2′-TC) chemistry (Dellinger et al., J. Am. Chem. Soc. (2011) 133: 11540-11546; Hendel et al., Nat. Biotechnol. (2015) 33:985-989).


In certain embodiments, the guide molecule comprises (1) a guide sequence capable of hybridizing to a target locus and (2) a tracr mate or direct repeat sequence whereby the direct repeat sequence is located upstream (i.e., 5′) from the guide sequence. In a particular embodiment the seed sequence (i.e. the sequence essential critical for recognition and/or hybridization to the sequence at the target locus) of th guide sequence is approximately within the first 10 nucleotides of the guide sequence.


In a particular embodiment the guide molecule comprises a guide sequence linked to a direct repeat sequence, wherein the direct repeat sequence comprises one or more stem loops or optimized secondary structures. In particular embodiments, the direct repeat has a minimum length of 16 nts and a single stem loop. In further embodiments the direct repeat has a length longer than 16 nts, preferably more than 17 nts, and has more than one stem loops or optimized secondary structures. In particular embodiments the guide molecule comprises or consists of the guide sequence linked to all or part of the natural direct repeat sequence. A typical Type V or Type VI CRISPR-cas guide molecule comprises (in 3′ to 5′ direction or in 5′ to 3′ direction): a guide sequence a first complimentary stretch (the “repeat”), a loop (which is typically 4 or 5 nucleotides long), a second complimentary stretch (the “anti-repeat” being complimentary to the repeat), and a poly A (often poly U in RNA) tail (terminator). In certain embodiments, the direct repeat sequence retains its natural architecture and forms a single stem loop. In particular embodiments, certain aspects of the guide architecture can be modified, for example by addition, subtraction, or substitution of features, whereas certain other aspects of guide architecture are maintained. Preferred locations for engineered guide molecule modifications, including but not limited to insertions, deletions, and substitutions include guide termini and regions of the guide molecule that are exposed when complexed with the CRISPR-Cas protein and/or target, for example the stemloop of the direct repeat sequence.


In particular embodiments, the stem comprises at least about 4 bp comprising complementary X and Y sequences, although stems of more, e.g., 5, 6, 7, 8, 9, 10, 11 or 12 or fewer, e.g., 3, 2, base pairs are also contemplated. Thus, for example X2-10 and Y2-10 (wherein X and Y represent any complementary set of nucleotides) may be contemplated. In one aspect, the stem made of the X and Y nucleotides, together with the loop will form a complete hairpin in the overall secondary structure; and, this may be advantageous and the amount of base pairs can be any amount that forms a complete hairpin. In one aspect, any complementary X:Y basepairing sequence (e.g., as to length) is tolerated, so long as the secondary structure of the entire guide molecule is preserved. In one aspect, the loop that connects the stem made of X:Y basepairs can be any sequence of the same length (e.g., 4 or 5 nucleotides) or longer that does not interrupt the overall secondary structure of the guide molecule. In one aspect, the stemloop can further comprise, e.g. an MS2 aptamer. In one aspect, the stem comprises about 5-7 bp comprising complementary X and Y sequences, although stems of more or fewer basepairs are also contemplated. In one aspect, non-Watson Crick basepairing is contemplated, where such pairing otherwise generally preserves the architecture of the stemloop at that position.


In particular embodiments the natural hairpin or stemloop structure of the guide molecule is extended or replaced by an extended stemloop. It has been demonstrated that extension of the stem can enhance the assembly of the guide molecule with the CRISPR-Cas protein (Chen et al. Cell. (2013); 155(7): 1479-1491). In particular embodiments the stem of the stemloop is extended by at least 1, 2, 3, 4, 5 or more complementary basepairs (i.e. corresponding to the addition of 2, 4, 6, 8, 10 or more nucleotides in the guide molecule). In particular embodiments these are located at the end of the stem, adjacent to the loop of the stemloop.


In particular embodiments, the susceptibility of the guide molecule to RNAses or to decreased expression can be reduced by slight modifications of the sequence of the guide molecule which do not affect its function. For instance, in particular embodiments, premature termination of transcription, such as premature transcription of U6 Pol-III, can be removed by modifying a putative Pol-III terminator (4 consecutive U's) in the guide molecules sequence. Where such sequence modification is required in the stemloop of the guide molecule, it is preferably ensured by a basepair flip.


In a particular embodiment, the direct repeat may be modified to comprise one or more protein-binding RNA aptamers. In a particular embodiment, one or more aptamers may be included such as part of optimized secondary structure. Such aptamers may be capable of binding a bacteriophage coat protein as detailed further herein.


In some embodiments, the guide molecule forms a duplex with a target RNA comprising at least one target cytosine residue to be edited. Upon hybridization of the guide RNA molecule to the target RNA, the cytidine deaminase binds to the single strand RNA in the duplex made accessible by the mismatch in the guide sequence and catalyzes deamination of one or more target cytosine residues comprised within the stretch of mismatching nucleotides.


A guide sequence, and hence a nucleic acid-targeting guide RNA may be selected to target any target nucleic acid sequence. The target sequence may be mRNA.


In certain embodiments, the target sequence should be associated with a PAM (protospacer adjacent motif) or PFS (protospacer flanking sequence or site); that is, a short sequence recognized by the CRISPR complex. Depending on the nature of the CRISPR-Cas protein, the target sequence should be selected such that its complementary sequence in the DNA duplex (also referred to herein as the non-target sequence) is upstream or downstream of the PAM. In the embodiments of the present invention where the CRISPR-Cas protein is a Cas13 protein, the complementary sequence of the target sequence is downstream or 3′ of the PAM or upstream or 5′ of the PAM. The precise sequence and length requirements for the PAM differ depending on the Cas13 protein used, but PAMs are typically 2-5 base pair sequences adjacent the protospacer (that is, the target sequence). Examples of the natural PAM sequences for different Cas13 orthologues are provided herein below and the skilled person will be able to identify further PAM sequences for use with a given Cas13 protein.


Further, engineering of the PAM Interacting (PI) domain may allow programing of PAM specificity, improve target site recognition fidelity, and increase the versatility of the CRISPR-Cas protein, for example as described for Cas9 in Kleinstiver B P et al. Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature. 2015 Jul. 23; 523(7561):481-5. doi: 10.1038/nature14592. As further detailed herein, the skilled person will understand that Cas13 proteins may be modified analogously.


In particular embodiment, the guide is an escorted guide. By “escorted” is meant that the CRISPR-Cas system or complex or guide is delivered to a selected time or place within a cell, so that activity of the CRISPR-Cas system or complex or guide is spatially or temporally controlled. For example, the activity and destination of the 3 CRISPR-Cas system or complex or guide may be controlled by an escort RNA aptamer sequence that has binding affinity for an aptamer ligand, such as a cell surface protein or other localized cellular component. Alternatively, the escort aptamer may for example be responsive to an aptamer effector on or in the cell, such as a transient effector, such as an external energy source that is applied to the cell at a particular time.


The escorted CRISPR-Cas systems or complexes have a guide molecule with a functional structure designed to improve guide molecule structure, architecture, stability, genetic expression, or any combination thereof. Such a structure can include an aptamer.


Aptamers are biomolecules that can be designed or selected to bind tightly to other ligands, for example using a technique called systematic evolution of ligands by exponential enrichment (SELEX; Tuerk C, Gold L: “Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase.” Science 1990, 249:505-510). Nucleic acid aptamers can for example be selected from pools of random-sequence oligonucleotides, with high binding affinities and specificities for a wide range of biomedically relevant targets, suggesting a wide range of therapeutic utilities for aptamers (Keefe, Anthony D., Supriya Pai, and Andrew Ellington. “Aptamers as therapeutics.” Nature Reviews Drug Discovery 9.7 (2010): 537-550). These characteristics also suggest a wide range of uses for aptamers as drug delivery vehicles (Levy-Nissenbaum, Etgar, et al. “Nanotechnology and aptamers: applications in drug delivery.” Trends in biotechnology 26.8 (2008): 442-449; and, Hicke B J, Stephens A W. “Escort aptamers: a delivery service for diagnosis and therapy.” J Clin Invest 2000, 106:923-928.). Aptamers may also be constructed that function as molecular switches, responding to a que by changing properties, such as RNA aptamers that bind fluorophores to mimic the activity of green fluorescent protein (Paige, Jeremy S., Karen Y. Wu, and Samie R. Jaffrey. “RNA mimics of green fluorescent protein.” Science 333.6042 (2011): 642-646). It has also been suggested that aptamers may be used as components of targeted siRNA therapeutic delivery systems, for example targeting cell surface proteins (Zhou, Jiehua, and John J. Rossi. “Aptamer-targeted cell-specific RNA interference.” Silence 1.1 (2010): 4).


Accordingly, in particular embodiments, the guide molecule is modified, e.g., by one or more aptamer(s) designed to improve guide molecule delivery, including delivery across the cellular membrane, to intracellular compartments, or into the nucleus. Such a structure can include, either in addition to the one or more aptamer(s) or without such one or more aptamer(s), moiety(ies) so as to render the guide molecule deliverable, inducible or responsive to a selected effector. The invention accordingly comprehends an guide molecule that responds to normal or pathological physiological conditions, including without limitation pH, hypoxia, O2 concentration, temperature, protein concentration, enzymatic concentration, lipid structure, light exposure, mechanical disruption (e.g. ultrasound waves), magnetic fields, electric fields, or electromagnetic radiation. Inducible systems and energy application can be as described for example, in International Patent Publication WO2019232542 at [0275]-[0302], incorporated herein by reference.


In particular embodiments, the guide molecule is modified by a secondary structure to increase the specificity of the CRISPR-Cas system and the secondary structure can protect against exonuclease activity and allow for 5′ additions to the guide sequence also referred to herein as a protected guide molecule.


In one aspect, the invention provides for hybridizing a “protector RNA” to a sequence of the guide molecule, wherein the “protector RNA” is an RNA strand complementary to the 3′ end of the guide molecule to thereby generate a partially double-stranded guide RNA. In an embodiment of the invention, protecting mismatched bases (i.e. the bases of the guide molecule which do not form part of the guide sequence) with a perfectly complementary protector sequence decreases the likelihood of target RNA binding to the mismatched basepairs at the 3′ end. In particular embodiments of the invention, additional sequences comprising an extended length may also be present within the guide molecule such that the guide comprises a protector sequence within the guide molecule. This “protector sequence” ensures that the guide molecule comprises a “protected sequence” in addition to an “exposed sequence” (comprising the part of the guide sequence hybridizing to the target sequence). In particular embodiments, the guide molecule is modified by the presence of the protector guide to comprise a secondary structure such as a hairpin. Advantageously there are three or four to thirty or more, e.g., about 10 or more, contiguous base pairs having complementarity to the protected sequence, the guide sequence or both. It is advantageous that the protected portion does not impede thermodynamics of the CRISPR-Cas system interacting with its target. By providing such an extension including a partially double stranded guide molecule, the guide molecule is considered protected and results in improved specific binding of the CRISPR-Cas complex, while maintaining specific activity.


In particular embodiments, use is made of a truncated guide (tru-guide), i.e., a guide molecule which comprises a guide sequence which is truncated in length with respect to the canonical guide sequence length. As described by Nowak et al. (Nucleic Acids Res (2016) 44 (20): 9555-9564), such guides may allow catalytically active CRISPR-Cas enzyme to bind its target without cleaving the target RNA. In particular embodiments, a truncated guide is used which allows the binding of the target but retains only nickase activity of the CRISPR-Cas enzyme.


In addition to the above CRISPR-Cas systems, the CRISPR-Cas may be a base editor version, thereof i.e. a catalytically dead Cas linked or fused to a nucleotide deaminase domain. The Cas may be a RNA-binding (e.g. Type VI) on DNA-binding Cas (Type II or V). In certain embodiments, the compositions, systems, and methods may be designed for use with Class 2 systems. In certain example embodiments, the Class 2 systems may be Type II, Type V, and Type VI systems as described in Makarova et al. “Evolutionary classification of CRISPR-Cas systems: a burst of class 2 and derived variants” Nature Reviews Microbiology, 18:67-81 (February 2020), incorporated herein by reference. The distinguishing feature of these types is that their effector complexes consist of a single, large, multi-domain protein. Type V systems differ from Type II effectors (e.g. Cas9) contain two nuclear domains that are each responsible for the cleavage of one strand of the target DNA, with the HNH nuclease inserted inside the Ruv-C like nuclease domain sequence. The Type V systems (e.g. Cas12) only contain a RuvC-like nuclease domain that cleaves both strands. Type VI (Cas13) are unrelated to the effectors of type II and V systems, contain two HEPN domains and target RNA. Cas13 proteins also display collateral activity that is triggered by target recognition. Some Type V systems have also been found to possess this collateral activity two single-stranded DNA in in vitro contexts.


certain example embodiments, the Type V CRISPR-Cas is Cas12a, Cas12b, or Cas12c.


The present invention also contemplates use of the CRISPR-Cas system and the base editor described herein, for treatment in a variety of diseases and disorders. In some embodiments, the invention described herein relates to a method for therapy in which cells are edited ex vivo by CRISPR or the base editor to modulate at least one gene, with subsequent administration of the edited cells to a patient in need thereof. In some embodiments, the editing involves knocking in, knocking out or knocking down expression of at least one target gene in a cell. In particular embodiments, the editing inserts an exogenous, gene, minigene or sequence, which may comprise one or more exons and introns or natural or synthetic introns into the locus of a target gene, a hot-spot locus, a safe harbor locus of the gene genomic locations where new genes or genetic elements can be introduced without disrupting the expression or regulation of adjacent genes, or correction by insertions or deletions one or more mutations in DNA sequences that encode regulatory elements of a target gene. In some embodiment, the editing comprise introducing one or more point mutations in a nucleic acid (e.g., a genomic DNA) in a target cell.


The present disclosure also provides for a base editing system. In general, such a system may comprise a deaminase (e.g., an adenosine deaminase or cytidine deaminase) fused with a Cas protein. The Cas protein may be a dead Cas protein or a Cas nickase protein. In certain examples, the system comprises a mutated form of an adenosine deaminase fused with a dead CRISPR-Cas or CRISPR-Cas nickase. The mutated form of the adenosine deaminase may have both adenosine deaminase and cytidine deaminase activities.


In one aspect, the present disclosure provides an engineered adenosine deaminase. The engineered adenosine deaminase may comprise one or more mutations herein. In some embodiments, the engineered adenosine deaminase has cytidine deaminase activity. In certain examples, the engineered adenosine deaminase has both cytidine deaminase activity and adenosine deaminase. In some cases, the modifications by base editors herein may be used for targeting post-translational signaling or catalysis.


In one aspect, the invention provides a method of modifying or editing a target transcript in a eukaryotic cell. In some embodiments, the method comprises allowing a CRISPR-Cas effector module complex to bind to the target polynucleotide to effect RNA base editing, wherein the CRISPR-Cas effector module complex comprises a Cas effector module complexed with a guide sequence hybridized to a target sequence within said target polynucleotide, wherein said guide sequence is linked to a direct repeat sequence. In some embodiments, the Cas effector module comprises a catalytically inactive CRISPR-Cas protein. In some embodiments, the guide sequence is designed to introduce one or more mismatches to the RNA/RNA duplex formed between the target sequence and the guide sequence. In particular embodiments, the mismatch is an A-C mismatch. In some embodiments, the Cas effector may associate with one or more functional domains (e.g. via fusion protein or suitable linkers). In some embodiments, the effector domain comprises one or more cytindine or adenosine deaminases that mediate endogenous editing of via hydrolytic deamination. In particular embodiments, the effector domain comprises the adenosine deaminase acting on RNA (ADAR) family of enzymes. In particular embodiments, the adenosine deaminase protein or catalytic domain thereof is capable of deaminating adenosine or cytidine in RNA or is an RNA specific adenosine deaminase and/or is a bacterial, human, cephalopod, or Drosophila adenosine deaminase protein or catalytic domain thereof, preferably TadA, more preferably ADAR, optionally huADAR, optionally (hu)ADAR1 or (hu)ADAR2, preferably huADAR2 or catalytic domain thereof. See, e.g. Levy et al., doi:10.1038/s41551-019-0501-5, Rees et al, doi: 10.1038/s41467-019-09983-4; Komor et al, Nature 533(7603), 420-424, Gaudellim et al, Nature 551 (7681), 464-471, Lee, et al., Nature Commun. 9:4804 1-5(2018), Song et al., Biomed End. 36, 536-539 (2018), Lee et al., Sci. Rep. 9, 1662 (2019), Thuronyi, et al., Nat. Biotechnol. 37, 1070-1079 (2019), Anzalone, et al., nature 576 149-157 (2019), and Richter et al., Nat Biotechnol in press (2020), all incorporated herein by reference. Reference is also made to International Patent Publication Nos. WO 2019/005884, WO 2019/005886, WO 2020/028555, WO 2019/060746, WO 2019/071048, WO 2019/084063, and Abudayyeh et al., Science 365:6451, 382-386, doi: 10.1126/science.aax7063, incorporated herein by reference.


RNAi

In certain embodiments, the genetic modifying agent is RNAi (e.g., shRNA). As used herein, “gene silencing” or “gene silenced” in reference to an activity of an RNAi molecule, for example a siRNA or miRNA refers to a decrease in the mRNA level in a cell for a target gene by at least about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 99%, about 100% of the mRNA level found in the cell without the presence of the miRNA or RNA interference molecule. In one preferred embodiment, the mRNA levels are decreased by at least about 70%, about 80%, about 90%, about 95%, about 99%, about 100%.


As used herein, the term “RNAi” refers to any type of interfering RNA, including but not limited to, siRNAi, shRNAi, endogenous microRNA and artificial microRNA. For instance, it includes sequences previously identified as siRNA, regardless of the mechanism of down-stream processing of the RNA (i.e. although siRNAs are believed to have a specific method of in vivo processing resulting in the cleavage of mRNA, such sequences can be incorporated into the vectors in the context of the flanking sequences described herein). The term “RNAi” can include both gene silencing RNAi molecules, and also RNAi effector molecules which activate the expression of a gene.


As used herein, a “siRNA” refers to a nucleic acid that forms a double stranded RNA, which double stranded RNA has the ability to reduce or inhibit expression of a gene or target gene when the siRNA is present or expressed in the same cell as the target gene. The double stranded RNA siRNA can be formed by the complementary strands. In one embodiment, a siRNA refers to a nucleic acid that can form a double stranded siRNA. The sequence of the siRNA can correspond to the full-length target gene, or a subsequence thereof. Typically, the siRNA is at least about 15-50 nucleotides in length (e.g., each complementary sequence of the double stranded siRNA is about 15-50 nucleotides in length, and the double stranded siRNA is about 15-50 base pairs in length, preferably about 19-30 base nucleotides, preferably about 20-25 nucleotides in length, e.g., 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides in length).


As used herein “shRNA” or “small hairpin RNA” (also called stem loop) is a type of siRNA. In one embodiment, these shRNAs are composed of a short, e.g. about 19 to about 25 nucleotide, antisense strand, followed by a nucleotide loop of about 5 to about 9 nucleotides, and the analogous sense strand. Alternatively, the sense strand can precede the nucleotide loop structure and the antisense strand can follow.


The terms “microRNA” or “miRNA” are used interchangeably herein are endogenous RNAs, some of which are known to regulate the expression of protein-coding genes at the posttranscriptional level. Endogenous microRNAs are small RNAs naturally present in the genome that are capable of modulating the productive utilization of mRNA. The term artificial microRNA includes any type of RNA sequence, other than endogenous microRNA, which is capable of modulating the productive utilization of mRNA. MicroRNA sequences have been described in publications such as Lim, et al., Genes & Development, 17, p. 991-1008 (2003), Lim et al Science 299, 1540 (2003), Lee and Ambros Science, 294, 862 (2001), Lau et al., Science 294, 858-861 (2001), Lagos-Quintana et al, Current Biology, 12, 735-739 (2002), Lagos Quintana et al, Science 294, 853-857 (2001), and Lagos-Quintana et al, RNA, 9, 175-179 (2003), which are incorporated by reference. Multiple microRNAs can also be incorporated into a precursor molecule. Furthermore, miRNA-like stem-loops can be expressed in cells as a vehicle to deliver artificial miRNAs and short interfering RNAs (siRNAs) for the purpose of modulating the expression of endogenous genes through the miRNA and or RNAi pathways.


As used herein, “double stranded RNA” or “dsRNA” refers to RNA molecules that are comprised of two strands. Double-stranded molecules include those comprised of a single RNA molecule that doubles back on itself to form a two-stranded structure. For example, the stem loop structure of the progenitor molecules from which the single-stranded miRNA is derived, called the pre-miRNA (Bartel et al. 2004. Cell 1 16:281-297), comprises a dsRNA molecule.


It will be understood by the skilled person that treating as referred to herein encompasses enhancing treatment, or improving treatment efficacy. Treatment may include inhibition of an inflammatory response, enhancing an immune response, tumor regression as well as inhibition of tumor growth, metastasis or tumor cell proliferation, or inhibition or reduction of otherwise deleterious effects associated with the tumor.


Efficaciousness of treatment is determined in association with any known method for diagnosing or treating the particular disease. The invention comprehends a treatment method comprising any one of the methods or uses herein discussed.


The phrase “therapeutically effective amount” as used herein refers to a sufficient amount of a drug, agent, or compound to provide a desired therapeutic effect.


As used herein “patient” refers to any human being receiving or who may receive medical treatment and is used interchangeably herein with the term “subject”.


Therapy or treatment according to the invention may be performed alone or in conjunction with another therapy, and may be provided at home, the doctor's office, a clinic, a hospital's outpatient department, or a hospital. Treatment generally begins at a hospital so that the doctor can observe the therapy's effects closely and make any adjustments that are needed. The duration of the therapy depends on the age and condition of the patient, the stage of the cancer, and how the patient responds to the treatment. Additionally, a person having a greater risk of developing an inflammatory response (e.g., a person who is genetically predisposed or predisposed to allergies or a person having a disease characterized by episodes of inflammation) may receive prophylactic treatment to inhibit or delay symptoms of the disease.


Administration

It will be appreciated that administration of therapeutic entities in accordance with the invention will be administered with suitable carriers, excipients, and other agents that are incorporated into formulations to provide improved transfer, delivery, tolerance, and the like. A multitude of appropriate formulations can be found in the formulary known to all pharmaceutical chemists: Remington's Pharmaceutical Sciences (15th ed, Mack Publishing Company, Easton, Pa. (1975)), particularly Chapter 87 by Blaug, Seymour, therein. These formulations include, for example, powders, pastes, ointments, jellies, waxes, oils, lipids, lipid (cationic or anionic) containing vesicles (such as Lipofectin™), DNA conjugates, anhydrous ab sorption pastes, oil-in-water and water-in-oil emulsions, emulsions carbowax (polyethylene glycols of various molecular weights), semi-solid gels, and semi-solid mixtures containing carbowax. Any of the foregoing mixtures may be appropriate in treatments and therapies in accordance with the present invention, provided that the active ingredient in the formulation is not inactivated by the formulation and the formulation is physiologically compatible and tolerable with the route of administration. See also Baldrick P. “Pharmaceutical excipient development: the need for preclinical guidance.” Regul. Toxicol Pharmacol. 32(2):210-8 (2000), Wang W. “Lyophilization and development of solid protein pharmaceuticals.” Int. J. Pharm. 203(1-2):1-60 (2000), Charman W N “Lipids, lipophilic drugs, and oral drug delivery-some emerging concepts.” J Pharm Sci. 89(8):967-78 (2000), Powell et al. “Compendium of excipients for parenteral formulations” PDA J Pharm Sci Technol. 52:238-311 (1998) and the citations therein for additional information related to formulations, excipients and carriers well known to pharmaceutical chemists.


The medicaments of the invention are prepared in a manner known to those skilled in the art, for example, by means of conventional dissolving, lyophilizing, mixing, granulating or confectioning processes. Methods well known in the art for making formulations are found, for example, in Remington: The Science and Practice of Pharmacy, 20th ed., ed. A. R. Gennaro, 2000, Lippincott Williams & Wilkins, Philadelphia, and Encyclopedia of Pharmaceutical Technology, eds. J. Swarbrick and J. C. Boylan, 1988-1999, Marcel Dekker, New York.


Administration of medicaments of the invention may be by any suitable means that results in a compound concentration that is effective for treating or inhibiting (e.g., by delaying) the development of a disease. The compound is admixed with a suitable carrier substance, e.g., a pharmaceutically acceptable excipient that preserves the therapeutic properties of the compound with which it is administered. One exemplary pharmaceutically acceptable excipient is physiological saline. The suitable carrier substance is generally present in an amount of 1-95% by weight of the total weight of the medicament. The medicament may be provided in a dosage form that is suitable for administration. Thus, the medicament may be in form of, e.g., tablets, capsules, pills, powders, granulates, suspensions, emulsions, solutions, gels including hydrogels, pastes, ointments, creams, plasters, drenches, delivery devices, injectables, implants, sprays, or aerosols.


The agents disclosed herein may be used in a pharmaceutical composition when combined with a pharmaceutically acceptable carrier. Such compositions comprise a therapeutically-effective amount of the agent and a pharmaceutically acceptable carrier. Such a composition may also further comprise (in addition to an agent and a carrier) diluents, fillers, salts, buffers, stabilizers, solubilizers, and other materials well known in the art. Compositions comprising the agent can be administered in the form of salts provided the salts are pharmaceutically acceptable. Salts may be prepared using standard procedures known to those skilled in the art of synthetic organic chemistry.


The term “pharmaceutically acceptable salts” refers to salts prepared from pharmaceutically acceptable non-toxic bases or acids including inorganic or organic bases and inorganic or organic acids. Salts derived from inorganic bases include aluminum, ammonium, calcium, copper, ferric, ferrous, lithium, magnesium, manganic salts, manganous, potassium, sodium, zinc, and the like. Particularly preferred are the ammonium, calcium, magnesium, potassium, and sodium salts. Salts derived from pharmaceutically acceptable organic non-toxic bases include salts of primary, secondary, and tertiary amines, substituted amines including naturally occurring substituted amines, cyclic amines, and basic ion exchange resins, such as arginine, betaine, caffeine, choline, N,N′-dibenzylethylenediamine, diethylamine, 2-diethylaminoethanol, 2-dimethylaminoethanol, ethanolamine, ethylenediamine, N-ethyl-morpholine, N-ethylpiperidine, glucamine, glucosamine, histidine, hydrabamine, isopropylamine, lysine, methylglucamine, morpholine, piperazine, piperidine, polyamine resins, procaine, purines, theobromine, triethylamine, trimethylamine, tripropylamine, tromethamine, and the like. The term “pharmaceutically acceptable salt” further includes all acceptable salts such as acetate, lactobionate, benzenesulfonate, laurate, benzoate, malate, bicarbonate, maleate, bisulfate, mandelate, bitartrate, mesylate, borate, methylbromide, bromide, methylnitrate, calcium edetate, methylsulfate, camsylate, mucate, carbonate, napsylate, chloride, nitrate, clavulanate, N-methylglucamine, citrate, ammonium salt, dihydrochloride, oleate, edetate, oxalate, edisylate, pamoate (embonate), estolate, palmitate, esylate, pantothenate, fumarate, phosphate/diphosphate, gluceptate, polygalacturonate, gluconate, salicylate, glutamate, stearate, glycollylarsanilate, sulfate, hexylresorcinate, subacetate, hydrabamine, succinate, hydrobromide, tannate, hydrochloride, tartrate, hydroxynaphthoate, teoclate, iodide, tosylate, isothionate, triethiodide, lactate, panoate, valerate, and the like which can be used as a dosage form for modifying the solubility or hydrolysis characteristics or can be used in sustained release or pro-drug formulations. It will be understood that, as used herein, references to specific agents (e.g., neuromedin U receptor agonists or antagonists), also include the pharmaceutically acceptable salts thereof.


Methods of administrating the pharmacological compositions, including agonists, antagonists, antibodies or fragments thereof, to an individual include, but are not limited to, intradermal, intrathecal, intramuscular, intraperitoneal, intravenous, subcutaneous, intranasal, epidural, by inhalation, and oral routes. The compositions can be administered by any convenient route, for example by infusion or bolus injection, by absorption through epithelial or mucocutaneous linings (for example, oral mucosa, rectal and intestinal mucosa, and the like), ocular, and the like and can be administered together with other biologically-active agents. Administration can be systemic or local. In addition, it may be advantageous to administer the composition into the central nervous system by any suitable route, including intraventricular and intrathecal injection. Pulmonary administration may also be employed by use of an inhaler or nebulizer, and formulation with an aerosolizing agent. It may also be desirable to administer the agent locally to the area in need of treatment; this may be achieved by, for example, and not by way of limitation, local infusion during surgery, topical application, by injection, by means of a catheter, by means of a suppository, or by means of an implant.


Various delivery systems are known and can be used to administer the pharmacological compositions including, but not limited to, encapsulation in liposomes, microparticles, microcapsules; minicells; polymers; capsules; tablets; and the like. In one embodiment, the agent may be delivered in a vesicle, in particular a liposome. In a liposome, the agent is combined, in addition to other pharmaceutically acceptable carriers, with amphipathic agents such as lipids which exist in aggregated form as micelles, insoluble monolayers, liquid crystals, or lamellar layers in aqueous solution. Suitable lipids for liposomal formulation include, without limitation, monoglycerides, diglycerides, sulfatides, lysolecithin, phospholipids, saponin, bile acids, and the like. Preparation of such liposomal formulations is within the level of skill in the art, as disclosed, for example, in U.S. Pat. Nos. 4,837,028 and 4,737,323. In yet another embodiment, the pharmacological compositions can be delivered in a controlled release system including, but not limited to: a delivery pump (See, for example, Saudek, et al., New Engl. J. Med. 321: 574 (1989) and a semi-permeable polymeric material (See, for example, Howard, et al., J. Neurosurg. 71: 105 (1989)). Additionally, the controlled release system can be placed in proximity of the therapeutic target (e.g., a tumor), thus requiring only a fraction of the systemic dose. See, for example, Goodson, In: Medical Applications of Controlled Release, 1984. (CRC Press, Boca Raton, Fla.).


The amount of the agents which will be effective in the treatment of a particular disorder or condition will depend on the nature of the disorder or condition, and may be determined by standard clinical techniques by those of skill within the art. In addition, in vitro assays may optionally be employed to help identify optimal dosage ranges. The precise dose to be employed in the formulation will also depend on the route of administration, and the overall seriousness of the disease or disorder, and should be decided according to the judgment of the practitioner and each patient's circumstances. Ultimately, the attending physician will decide the amount of the agent with which to treat each individual patient. In certain embodiments, the attending physician will administer low doses of the agent and observe the patient's response. Larger doses of the agent may be administered until the optimal therapeutic effect is obtained for the patient, and at that point the dosage is not increased further. In general, the daily dose range lie within the range of from about 0.001 mg to about 100 mg per kg body weight of a mammal, preferably 0.01 mg to about 50 mg per kg, and most preferably 0.1 to 10 mg per kg, in single or divided doses. On the other hand, it may be necessary to use dosages outside these limits in some cases. In certain embodiments, suitable dosage ranges for intravenous administration of the agent are generally about 5-500 micrograms (μg) of active compound per kilogram (Kg) body weight. Suitable dosage ranges for intranasal administration are generally about 0.01 pg/kg body weight to 1 mg/kg body weight. In certain embodiments, a composition containing an agent of the present invention is subcutaneously injected in adult patients with dose ranges of approximately 5 to 5000 μg/human and preferably approximately 5 to 500 μg/human as a single dose. It is desirable to administer this dosage 1 to 3 times daily. Effective doses may be extrapolated from dose-response curves derived from in vitro or animal model test systems. Suppositories generally contain active ingredient in the range of 0.5% to 10% by weight; oral formulations preferably contain 10% to 95% active ingredient. Ultimately the attending physician will decide on the appropriate duration of therapy using compositions of the present invention. Dosage will also vary according to the age, weight and response of the individual patient.


Methods for administering antibodies for therapeutic use is well known to one skilled in the art. In certain embodiments, small particle aerosols of antibodies or fragments thereof may be administered (see e.g., Piazza et al., J. Infect. Dis., Vol. 166, pp. 1422-1424, 1992; and Brown, Aerosol Science and Technology, Vol. 24, pp. 45-56, 1996). In certain embodiments, antibodies are administered in metered-dose propellant driven aerosols. In preferred embodiments, antibodies are used as agonists to depress inflammatory diseases or allergen-induced asthmatic responses. In certain embodiments, antibodies may be administered in liposomes, i.e., immunoliposomes (see, e.g., Maruyama et al., Biochim. Biophys. Acta, Vol. 1234, pp. 74-80, 1995). In certain embodiments, immunoconjugates, immunoliposomes or immunomicrospheres containing an agent of the present invention is administered by inhalation.


In certain embodiments, antibodies may be topically administered to mucosa, such as the oropharynx, nasal cavity, respiratory tract, gastrointestinal tract, eye such as the conjunctival mucosa, vagina, urogenital mucosa, or for dermal application. In certain embodiments, antibodies are administered to the nasal, bronchial or pulmonary mucosa. In order to obtain optimal delivery of the antibodies to the pulmonary cavity in particular, it may be advantageous to add a surfactant such as a phosphoglyceride, e.g. phosphatidylcholine, and/or a hydrophilic or hydrophobic complex of a positively or negatively charged excipient and a charged antibody of the opposite charge.


Other excipients suitable for pharmaceutical compositions intended for delivery of antibodies to the respiratory tract mucosa may be a) carbohydrates, e.g., monosaccharides such as fructose, galactose, glucose. D-mannose, sorbiose, and the like; disaccharides, such as lactose, trehalose, cellobiose, and the like; cyclodextrins, such as 2-hydroxypropyl-β-cyclodextrin; and polysaccharides, such as raffinose, maltodextrins, dextrans, and the like; b) amino acids, such as glycine, arginine, aspartic acid, glutamic acid, cysteine, lysine and the like; c) organic salts prepared from organic acids and bases, such as sodium citrate, sodium ascorbate, magnesium gluconate, sodium gluconate, tromethamine hydrochloride, and the like: d) peptides and proteins, such as aspartame, human serum albumin, gelatin, and the like; e) alditols, such mannitol, xylitol, and the like, and f) polycationic polymers, such as chitosan or a chitosan salt or derivative.


For dermal application, the antibodies of the present invention may suitably be formulated with one or more of the following excipients: solvents, buffering agents, preservatives, humectants, chelating agents, antioxidants, stabilizers, emulsifying agents, suspending agents, gel-forming agents, ointment bases, penetration enhancers, and skin protective agents.


Examples of solvents are e.g. water, alcohols, vegetable or marine oils (e.g. edible oils like almond oil, castor oil, cacao butter, coconut oil, corn oil, cottonseed oil, linseed oil, olive oil, palm oil, peanut oil, poppy seed oil, rapeseed oil, sesame oil, soybean oil, sunflower oil, and tea seed oil), mineral oils, fatty oils, liquid paraffin, polyethylene glycols, propylene glycols, glycerol, liquid polyalkylsiloxanes, and mixtures thereof.


Examples of buffering agents are e.g. citric acid, acetic acid, tartaric acid, lactic acid, hydrogenphosphoric acid, diethyl amine etc. Suitable examples of preservatives for use in compositions are parabenes, such as methyl, ethyl, propyl p-hydroxybenzoate, butylparaben, isobutylparaben, isopropylparaben, potassium sorbate, sorbic acid, benzoic acid, methyl benzoate, phenoxyethanol, bronopol, bronidox, MDM hydantoin, iodopropynyl butylcarbamate, EDTA, benzalconium chloride, and benzylalcohol, or mixtures of preservatives.


Examples of humectants are glycerin, propylene glycol, sorbitol, lactic acid, urea, and mixtures thereof.


Examples of antioxidants are butylated hydroxy anisole (BHA), ascorbic acid and derivatives thereof, tocopherol and derivatives thereof, cysteine, and mixtures thereof.


Examples of emulsifying agents are naturally occurring gums, e.g. gum acacia or gum tragacanth; naturally occurring phosphatides, e.g. soybean lecithin, sorbitan monooleate derivatives: wool fats; wool alcohols; sorbitan esters; monoglycerides; fatty alcohols; fatty acid esters (e.g. triglycerides of fatty acids); and mixtures thereof.


Examples of suspending agents are e.g. celluloses and cellulose derivatives such as, e.g., carboxymethyl cellulose, hydroxyethylcellulose, hydroxypropylcellulose, hydroxypropylmethylcellulose, carraghenan, acacia gum, arabic gum, tragacanth, and mixtures thereof.


Examples of gel bases, viscosity-increasing agents or components which are able to take up exudate from a wound are: liquid paraffin, polyethylene, fatty oils, colloidal silica or aluminum, zinc soaps, glycerol, propylene glycol, tragacanth, carboxyvinyl polymers, magnesium-aluminum silicates, Carbopol®, hydrophilic polymers such as, e.g. starch or cellulose derivatives such as, e.g., carboxymethylcellulose, hydroxyethylcellulose and other cellulose derivatives, water-swellable hydrocolloids, carragenans, hyaluronates (e.g. hyaluronate gel optionally containing sodium chloride), and alginates including propylene glycol alginate.


Examples of ointment bases are e.g. beeswax, paraffin, cetanol, cetyl palmitate, vegetable oils, sorbitan esters of fatty acids (Span), polyethylene glycols, and condensation products between sorbitan esters of fatty acids and ethylene oxide, e.g. polyoxyethylene sorbitan monooleate (Tween).


Examples of hydrophobic or water-emulsifying ointment bases are paraffins, vegetable oils, animal fats, synthetic glycerides, waxes, lanolin, and liquid polyalkylsiloxanes. Examples of hydrophilic ointment bases are solid macrogols (polyethylene glycols). Other examples of ointment bases are triethanolamine soaps, sulphated fatty alcohol and polysorbates.


Examples of other excipients are polymers such as carmelose, sodium carmelose, hydroxypropylmethylcellulose, hydroxyethylcellulose, hydroxypropylcellulose, pectin, xanthan gum, locust bean gum, acacia gum, gelatin, carbomer, emulsifiers like vitamin E, glyceryl stearates, cetanyl glucoside, collagen, carrageenan, hyaluronates and alginates and chitosans.


The dose of antibody required in humans to be effective in the treatment or prevention of allergic inflammation differs with the type and severity of the allergic condition to be treated, the type of allergen, the age and condition of the patient, etc. Typical doses of antibody to be administered are in the range of 1 μg to 1 g, preferably 1-1000 more preferably 2-500, even more preferably 5-50, most preferably 10-20 μg per unit dosage form. In certain embodiments, infusion of antibodies of the present invention may range from 10-500 mg/m2.


There are a variety of techniques available for introducing nucleic acids into viable cells. The techniques vary depending upon whether the nucleic acid is transferred into cultured cells in vitro, or in vivo in the cells of the intended host. Techniques suitable for the transfer of nucleic acid into mammalian cells in vitro include the use of liposomes, electroporation, microinjection, cell fusion, DEAE-dextran, the calcium phosphate precipitation method, etc. The currently preferred in vivo gene transfer techniques include transfection with viral (typically retroviral) vectors and viral coat protein-liposome mediated transfection.


In another aspect, provided is a pharmaceutical pack or kit, comprising one or more containers filled with one or more of the ingredients of the pharmaceutical compositions and HDAC and/or CDK4/6 inhibitors.


Diagnostic and Screening Methods

The signature as defined herein (being it a gene signature, protein signature or other genetic or epigenetic signature) can be used to indicate the presence of a cell type, a subtype of the cell type, the state of the microenvironment of a population of cells, a particular cell type population or subpopulation, and/or the overall status of the entire cell (sub)population. Furthermore, the signature may be indicative of cells within a population of cells in vivo. The signature may also be used to suggest for instance particular therapies, or to follow up treatment, or to suggest ways to modulate immune systems. The signatures of the present invention may be discovered by analysis of expression profiles of single-cells within a population of cells from isolated samples (e.g. Sys tumor samples), thus allowing the discovery of novel cell subtypes or cell states that were previously invisible or unrecognized. The presence of subtypes or cell states may be determined by subtype specific or cell state specific signatures. The presence of these specific cell (sub)types or cell states may be determined by applying the signature genes to bulk sequencing data in a sample. In certain embodiments, the signatures of the present invention may be microenvironment specific, such as their expression in a particular spatio-temporal context. In certain embodiments, signatures as discussed herein are specific to a particular pathological context. In certain embodiments, a combination of cell subtypes having a particular signature may indicate an outcome. In certain embodiments, the signatures can be used to deconvolute the network of cells present in a particular pathological condition. In certain embodiments, the presence of specific cells and cell subtypes are indicative of a particular response to treatment, such as including increased or decreased susceptibility to treatment. The signature may indicate the presence of one particular cell type. In one embodiment, the novel signatures are used to detect multiple cell states or hierarchies that occur in subpopulations of cells that are linked to particular pathological condition (e.g. inflammation), or linked to a particular outcome or progression of the disease, or linked to a particular response to treatment of the disease.


The invention provides biomarkers (e.g., phenotype specific or cell type) for the identification, diagnosis, prognosis and manipulation of cell properties, for use in a variety of diagnostic and/or therapeutic indications. Biomarkers in the context of the present invention encompasses, without limitation nucleic acids, proteins, reaction products, and metabolites, together with their polymorphisms, mutations, variants, modifications, subunits, fragments, and other analytes or sample-derived measures. In certain embodiments, biomarkers include the signature genes or signature gene products, and/or cells as described herein.


Biomarkers are useful in methods of diagnosing, prognosing and/or staging an immune response in a subject by detecting a first level of expression, activity and/or function of one or more biomarker and comparing the detected level to a control of level wherein a difference in the detected level and the control level indicates that the presence of an immune response in the subject.


The terms “diagnosis” and “monitoring” are commonplace and well-understood in medical practice. By means of further explanation and without limitation the term “diagnosis” generally refers to the process or act of recognising, deciding on or concluding on a disease or condition in a subject on the basis of symptoms and signs and/or from results of various diagnostic procedures (such as, for example, from knowing the presence, absence and/or quantity of one or more biomarkers characteristic of the diagnosed disease or condition).


The terms “prognosing” or “prognosis” generally refer to an anticipation on the progression of a disease or condition and the prospect (e.g., the probability, duration, and/or extent) of recovery. A good prognosis of the diseases or conditions taught herein may generally encompass anticipation of a satisfactory partial or complete recovery from the diseases or conditions, preferably within an acceptable time period. A good prognosis of such may more commonly encompass anticipation of not further worsening or aggravating of such, preferably within a given time period. A poor prognosis of the diseases or conditions as taught herein may generally encompass anticipation of a substandard recovery and/or unsatisfactorily slow recovery, or to substantially no recovery or even further worsening of such.


The biomarkers of the present invention are useful in methods of identifying patient populations at risk or suffering from an immune response based on a detected level of expression, activity and/or function of one or more biomarkers. These biomarkers are also useful in monitoring subjects undergoing treatments and therapies for suitable or aberrant response(s) to determine efficaciousness of the treatment or therapy and for selecting or modifying therapies and treatments that would be efficacious in treating, delaying the progression of or otherwise ameliorating a symptom. The biomarkers provided herein are useful for selecting a group of patients at a specific state of a disease with accuracy that facilitates selection of treatments.


The term “monitoring” generally refers to the follow-up of a disease or a condition in a subject for any changes which may occur over time.


The terms also encompass prediction of a disease. The terms “predicting” or “prediction” generally refer to an advance declaration, indication or foretelling of a disease or condition in a subject not (yet) having said disease or condition. For example, a prediction of a disease or condition in a subject may indicate a probability, chance or risk that the subject will develop said disease or condition, for example within a certain time period or by a certain age. Said probability, chance or risk may be indicated inter alia as an absolute value, range or statistics, or may be indicated relative to a suitable control subject or subject population (such as, e.g., relative to a general, normal or healthy subject or subject population). Hence, the probability, chance or risk that a subject will develop a disease or condition may be advantageously indicated as increased or decreased, or as fold-increased or fold-decreased relative to a suitable control subject or subject population. As used herein, the term “prediction” of the conditions or diseases as taught herein in a subject may also particularly mean that the subject has a ‘positive’ prediction of such, i.e., that the subject is at risk of having such (e.g., the risk is significantly increased vis-à-vis a control subject or subject population). The term “prediction of no” diseases or conditions as taught herein as described herein in a subject may particularly mean that the subject has a ‘negative’ prediction of such, i.e., that the subject's risk of having such is not significantly increased vis-à-vis a control subject or subject population.


Suitably, an altered quantity or phenotype of the immune cells in the subject compared to a control subject having normal immune status or not having a disease comprising an immune component indicates that the subject has an impaired immune status or has a disease comprising an immune component or would benefit from an immune therapy.


Hence, the methods may rely on comparing the quantity of immune cell populations, biomarkers, or gene or gene product signatures measured in samples from patients with reference values, wherein said reference values represent known predictions, diagnoses and/or prognoses of diseases or conditions as taught herein.


For example, distinct reference values may represent the prediction of a risk (e.g., an abnormally elevated risk) of having a given disease or condition as taught herein vs. the prediction of no or normal risk of having said disease or condition. In another example, distinct reference values may represent predictions of differing degrees of risk of having such disease or condition.


In a further example, distinct reference values can represent the diagnosis of a given disease or condition as taught herein vs. the diagnosis of no such disease or condition (such as, e.g., the diagnosis of healthy, or recovered from said disease or condition, etc.). In another example, distinct reference values may represent the diagnosis of such disease or condition of varying severity.


In yet another example, distinct reference values may represent a good prognosis for a given disease or condition as taught herein vs. a poor prognosis for said disease or condition. In a further example, distinct reference values may represent varyingly favourable or unfavourable prognoses for such disease or condition.


Such comparison may generally include any means to determine the presence or absence of at least one difference and optionally of the size of such difference between values being compared. A comparison may include a visual inspection, an arithmetical or statistical comparison of measurements. Such statistical comparisons include, but are not limited to, applying a rule.


Reference values may be established according to known procedures previously employed for other cell populations, biomarkers and gene or gene product signatures. For example, a reference value may be established in an individual or a population of individuals characterised by a particular diagnosis, prediction and/or prognosis of said disease or condition (i.e., for whom said diagnosis, prediction and/or prognosis of the disease or condition holds true). Such population may comprise without limitation 2 or more, 10 or more, 100 or more, or even several hundred or more individuals.


A “deviation” of a first value from a second value may generally encompass any direction (e.g., increase: first value >second value; or decrease: first value <second value) and any extent of alteration.


For example, a deviation may encompass a decrease in a first value by, without limitation, at least about 10% (about 0.9-fold or less), or by at least about 20% (about 0.8-fold or less), or by at least about 30% (about 0.7-fold or less), or by at least about 40% (about 0.6-fold or less), or by at least about 50% (about 0.5-fold or less), or by at least about 60% (about 0.4-fold or less), or by at least about 70% (about 0.3-fold or less), or by at least about 80% (about 0.2-fold or less), or by at least about 90% (about 0.1-fold or less), relative to a second value with which a comparison is being made.


For example, a deviation may encompass an increase of a first value by, without limitation, at least about 10% (about 1.1-fold or more), or by at least about 20% (about 1.2-fold or more), or by at least about 30% (about 1.3-fold or more), or by at least about 40% (about 1.4-fold or more), or by at least about 50% (about 1.5-fold or more), or by at least about 60% (about 1.6-fold or more), or by at least about 70% (about 1.7-fold or more), or by at least about 80% (about 1.8-fold or more), or by at least about 90% (about 1.9-fold or more), or by at least about 100% (about 2-fold or more), or by at least about 150% (about 2.5-fold or more), or by at least about 200% (about 3-fold or more), or by at least about 500% (about 6-fold or more), or by at least about 700% (about 8-fold or more), or like, relative to a second value with which a comparison is being made.


Preferably, a deviation may refer to a statistically significant observed alteration. For example, a deviation may refer to an observed alteration which falls outside of error margins of reference values in a given population (as expressed, for example, by standard deviation or standard error, or by a predetermined multiple thereof, e.g., ±1×SD or ±2×SD or ±3×SD, or ±1×SE or ±2×SE or ±3×SE). Deviation may also refer to a value falling outside of a reference range defined by values in a given population (for example, outside of a range which comprises ≥40%, ≥50%, ≥60%, ≥70%, ≥75% or ≥80% or ≥85% or ≥90% or ≥95% or even ≥100% of values in said population).


In a further embodiment, a deviation may be concluded if an observed alteration is beyond a given threshold or cut-off. Such threshold or cut-off may be selected as generally known in the art to provide for a chosen sensitivity and/or specificity of the prediction methods, e.g., sensitivity and/or specificity of at least 50%, or at least 60%, or at least 70%, or at least 80%, or at least 85%, or at least 90%, or at least 95%.


For example, receiver-operating characteristic (ROC) curve analysis can be used to select an optimal cut-off value of the quantity of a given immune cell population, biomarker or gene or gene product signatures, for clinical use of the present diagnostic tests, based on acceptable sensitivity and specificity, or related performance measures which are well-known per se, such as positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), negative likelihood ratio (LR−), Youden index, or similar.


In one embodiment, the signature genes, biomarkers, and/or cells may be detected or isolated by immunofluorescence, immunohistochemistry (IHC), fluorescence activated cell sorting (FACS), mass spectrometry (MS), mass cytometry (CyTOF), RNA-seq, single cell RNA-seq (described further herein), quantitative RT-PCR, single cell qPCR, FISH, RNA-FISH, MERFISH (multiplex (in situ) RNA FISH) and/or by in situ hybridization. Other methods including absorbance assays and colorimetric assays are known in the art and may be used herein. detection may comprise primers and/or probes or fluorescently bar-coded oligonucleotide probes for hybridization to RNA (see e.g., Geiss G K, et al., Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol. 2008 March; 26(3):317-25).


In certain embodiments, diseases related to Sys as described further herein are diagnosed, prognosed, or monitored. For example, a tissue sample may be obtained and analyzed for specific cell markers (IHC) or specific transcripts (e.g., RNA-FISH). Tissue samples for diagnosis, prognosis or detecting may be obtained by endoscopy. In one embodiment, a sample may be obtained by endoscopy and analyzed by FACS. As used herein, “endoscopy” refers to a procedure that uses an endoscope to examine the interior of a hollow organ or cavity of the body. The endoscope may include a camera and a light source. The endoscope may include tools for dissection or for obtaining a biological sample. A cutting tool can be attached to the end of the endoscope, and the apparatus can then be used to perform surgery. Applications of endoscopy that can be used with the present invention include, but are not limited to examination of the oesophagus, stomach and duodenum (esophagogastroduodenoscopy); small intestine (enteroscopy); large intestine/colon (colonoscopy, sigmoidoscopy); bile duct; rectum (rectoscopy) and anus (anoscopy), both also referred to as (proctoscopy); respiratory tract; nose (rhinoscopy); lower respiratory tract (bronchoscopy); ear (otoscope); urinary tract (cystoscopy); female reproductive system (gynoscopy); cervix (colposcopy); uterus (hysteroscopy); fallopian tubes (falloposcopy); normally closed body cavities (through a small incision); abdominal or pelvic cavity (laparoscopy); interior of a joint (arthroscopy); or organs of the chest (thoracoscopy and mediastinoscopy).


In certain embodiments, the method provides for treating a patient with an HDAC inhibitor and CDK4/6 inhibitor or a combination thereof, or via ACT, wherein the patient is suffering from Sys. the method comprising the steps of: determining whether the patient expresses a gene signature, biological program or marker gene as described herein: obtaining or having obtained a biological sample from the patient; and performing or having performed an assay as described herein on the biological sample to determine if the patient expresses the gene signature, biological program or marker gene; and if the patient has a malignant gene signature, biological program or marker gene, then administering HDAC inhibitor and CDK4/6 inhibitor or a combination thereof to the patient, or treating the patient with ACT in an amount sufficient to selectively target synovial sarcoma cells, and if the patient does not have a malignant gene signature, biological program or marker gene, then not administering treatments to the patient, wherein a risk of having synovial sarcoma, and in some, instances, risk of metastatic disease, is increased if the patient has a malignant gene signature, biological program or marker gene. In an aspect, the administration of an effective amount of modulating agent reduces the malignant gene signature, treats the Synovial Sarcoma and/or tumor burden, and/or decreases the risk of malignancy.


In embodiments, methods of treatment may comprise administration of two or more agents, In particular embodiments, the administration of two or more modulating agents may provide a synergistic effect. A synergistic effect is defined herein as more than additive results of agents independently administered. In particular embodiments, the additive results may be measured by duration of repression/activation of one or more target genes, or by amount of repression/activation of one or more target genes, or, for example of tumor burden, immune resistance, or other indicator of treatment.


The present invention also may comprise a kit with a detection reagent that binds to one or more biomarkers or can be used to detect one or more biomarkers.


MS Methods

Biomarker detection may also be evaluated using mass spectrometry methods. A variety of configurations of mass spectrometers can be used to detect biomarker values. Several types of mass spectrometers are available or can be produced with various configurations. In general, a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and instrument-control system, and a data system. Difference in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, an inlet can be a capillary-column liquid chromatography source or can be a direct probe or stage such as used in matrix-assisted laser desorption. Common ion sources are, for example, electrospray, including nanospray and microspray or matrix-assisted laser desorption. Common mass analyzers include a quadrupole mass filter, ion trap mass analyzer and time-of-flight mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70:647R-716R (1998); Kinter and Sherman, New York (2000)).


Protein biomarkers and biomarker values can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF/TOF) technology, called ultraflex III TOF/TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS).sup.N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and APPI-(MS).sup.N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.


Sample preparation strategies are used to label and enrich samples before mass spectroscopic characterization of protein biomarkers and determination biomarker values. Labeling methods include but are not limited to isobaric tag for relative and absolute quantitation (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich samples for candidate biomarker proteins prior to mass spectroscopic analysis include but are not limited to aptamers, antibodies, nucleic acid probes, chimeras, small molecules, an F(ab′)2 fragment, a single chain antibody fragment, an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor, affybodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g. diabodies etc) imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acid, a hormone receptor, a cytokine receptor, and synthetic receptors, and modifications and fragments of these.


Immunoassays

Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect the analyte in a sample depending on the specific assay format. To improve specificity and sensitivity of an assay method based on immunoreactivity, monoclonal antibodies are often used because of their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays because of their increased affinity for the target as compared to monoclonal antibodies Immunoassays have been designed for use with a wide range of biological sample matrices Immunoassay formats have been designed to provide qualitative, semi-quantitative, and quantitative results.


Quantitative results may be generated through the use of a standard curve created with known concentrations of the specific analyte to be detected. The response or signal from an unknown sample is plotted onto the standard curve, and a quantity or value corresponding to the target in the unknown sample is established.


Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of an analyte/biomarker. This method relies on attachment of a label to either the analyte or the antibody and the label component includes, either directly or indirectly, an enzyme. ELISA tests may be formatted for direct, indirect, competitive, or sandwich detection of the analyte. Other methods rely on labels such as, for example, radioisotopes (I125) or fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and others (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).


Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescent, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time resolved-FRET (TR-FRET) immunoassays. Examples of procedures for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level discrimination, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.


Methods of detecting and/or quantifying a detectable label or signal generating material depend on the nature of the label. The products of reactions catalyzed by appropriate enzymes (where the detectable label is an enzyme; see above) can be, without limitation, fluorescent, luminescent, or radioactive or they may absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, without limitation, x-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.


Any of the methods for detection can be performed in any format that allows for any suitable preparation, processing, and analysis of the reactions. This can be, for example, in multi-well assay plates (e.g., 96 wells or 384 wells) or using any suitable array or microarray. Stock solutions for various agents can be made manually or robotically, and all subsequent pipetting, diluting, mixing, distribution, washing, incubating, sample readout, data collection and analysis can be done robotically using commercially available analysis software, robotics, and detection instrumentation capable of detecting a detectable label.


Hybridization Assays

Such applications are hybridization assays in which a nucleic acid that displays “probe” nucleic acids for each of the genes to be assayed/profiled in the profile to be generated is employed. In these assays, a sample of target nucleic acids is first prepared from the initial nucleic acid sample being assayed, where preparation may include labeling of the target nucleic acids with a label, e.g., a member of a signal producing system. Following target nucleic acid sample preparation, the sample is contacted with the array under hybridization conditions, whereby complexes are formed between target nucleic acids that are complementary to probe sequences attached to the array surface. The presence of hybridized complexes is then detected, either qualitatively or quantitatively. Specific hybridization technology which may be practiced to generate the expression profiles employed in the subject methods includes the technology described in U.S. Pat. Nos. 5,143,854, 5,288,644, 5,324,633, 5,432,049, 5,470,710, 5,492,806, 5,503,980, 5,510,270, 5,525,464, 5,547,839, 5,580,732, 5,661,028, 5,800,992, the disclosures of which are incorporated herein by reference, as well as WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP 373 203; and EP 785 280. In these methods, an array of “probe” nucleic acids that includes a probe for each of the biomarkers whose expression is being assayed is contacted with target nucleic acids as described above. Contact is carried out under hybridization conditions, e.g., stringent hybridization conditions as described above, and unbound nucleic acid is then removed. The resultant pattern of hybridized nucleic acids provides information regarding expression for each of the biomarkers that have been probed, where the expression information is in terms of whether or not the gene is expressed and, typically, at what level, where the expression data, i.e., expression profile, may be both qualitative and quantitative.


Optimal hybridization conditions will depend on the length (e.g., oligomer vs. polynucleotide greater than 200 bases) and type (e.g., RNA, DNA, PNA) of labeled probe and immobilized polynucleotide or oligonucleotide. General parameters for specific (i.e., stringent) hybridization conditions for nucleic acids are described in Sambrook et al., supra, and in Ausubel et al., “Current Protocols in Molecular Biology”, Greene Publishing and Wiley-interscience, NY (1987), which is incorporated in its entirety for all purposes. When the cDNA microarrays are used, typical hybridization conditions are hybridization in 5×SSC plus 0.2% SDS at 65 C for 4 hours followed by washes at 25° C. in low stringency wash buffer (1×SSC plus 0.2% SDS) followed by 10 minutes at 25° C. in high stringency wash buffer (0.1SSC plus 0.2% SDS) (see Shena et al., Proc. Natl. Acad. Sci. USA, Vol. 93, p. 10614 (1996)). Useful hybridization conditions are also provided in, e.g., Tijessen, Hybridization With Nucleic Acid Probes”, Elsevier Science Publishers B.V. (1993) and Kricka, “Nonisotopic DNA Probe Techniques”, Academic Press, San Diego, Calif. (1992).


Amplifying Target Molecules

Methods of screening can include amplification of target molecules of interest. The step of amplifying one or more target molecules can comprise amplification systems known in the art. In some embodiments, amplification is isothermal. In certain example embodiments, target RNAs and/or DNAs may be amplified prior to activating a CRISPR effector protein for detection, diagnosis or other uses as described herein. Any suitable RNA or DNA amplification technique may be used. In certain example embodiments, the RNA or DNA amplification is an isothermal amplification. In certain example embodiments, the isothermal amplification may be nucleic-acid sequenced-based amplification (NASBA), recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), strand displacement amplification (SDA), helicase-dependent amplification (HDA), or nicking enzyme amplification reaction (NEAR). In certain example embodiments, non-isothermal amplification methods may be used which include, but are not limited to, PCR, multiple displacement amplification (MDA), rolling circle amplification (RCA), ligase chain reaction (LCR), or ramification amplification method (RAM).


In certain example embodiments, the RNA or DNA amplification is NASBA, which is initiated with reverse transcription of target RNA by a sequence-specific reverse primer to create a RNA/DNA duplex. RNase H is then used to degrade the RNA template, allowing a forward primer containing a promoter, such as the T7 promoter, to bind and initiate elongation of the complementary strand, generating a double-stranded DNA product. The RNA polymerase promoter-mediated transcription of the DNA template then creates copies of the target RNA sequence. Importantly, each of the new target RNAs can be detected by the guide RNAs thus further enhancing the sensitivity of the assay. Binding of the target RNAs by the guide RNAs then leads to activation of the CRISPR effector protein and the methods proceed as outlined above. The NASBA reaction has the additional advantage of being able to proceed under moderate isothermal conditions, for example at approximately 41° C., making it suitable for systems and devices deployed for early and direct detection in the field and far from clinical laboratories.


In certain other example embodiments, a recombinase polymerase amplification (RPA) reaction may be used to amplify the target nucleic acids. RPA reactions employ recombinases which are capable of pairing sequence-specific primers with homologous sequence in duplex DNA. If target DNA is present, DNA amplification is initiated and no other sample manipulation such as thermal cycling or chemical melting is required. The entire RPA amplification system is stable as a dried formulation and can be transported safely without refrigeration. RPA reactions may also be carried out at isothermal temperatures with an optimum reaction temperature of 37-42° C. The sequence specific primers are designed to amplify a sequence comprising the target nucleic acid sequence to be detected. In certain example embodiments, a RNA polymerase promoter, such as a T7 promoter, is added to one of the primers. This results in an amplified double-stranded DNA product comprising the target sequence and a RNA polymerase promoter. After, or during, the RPA reaction, a RNA polymerase is added that will produce RNA from the double-stranded DNA templates. The amplified target RNA can then in turn be detected by the CRISPR effector system. In this way target DNA can be detected using the embodiments disclosed herein. RPA reactions can also be used to amplify target RNA. The target RNA is first converted to cDNA using a reverse transcriptase, followed by second strand DNA synthesis, at which point the RPA reaction proceeds as outlined above.


In an embodiment of the invention may comprise nickase-based amplification. The nicking enzyme may be a CRISPR protein. Accordingly, the introduction of nicks into dsDNA can be programmable and sequence-specific. FIG. 115 depicts an embodiment of the invention, which starts with two guides designed to target opposite strands of a dsDNA target. According to the invention, the nickase can be Cpf1, C2c1, Cas9 or any ortholog or CRISPR protein that cleaves or is engineered to cleave a single strand of a DNA duplex. The nicked strands may then be extended by a polymerase. In an embodiment, the locations of the nicks are selected such that extension of the strands by a polymerase is towards the central portion of the target duplex DNA between the nick sites. In certain embodiments, primers are included in the reaction capable of hybridizing to the extended strands followed by further polymerase extension of the primers to regenerate two dsDNA pieces: a first dsDNA that includes the first strand Cpf1 guide site or both the first and second strand Cpf1 guide sites, and a second dsDNA that includes the second strand Cpf1 guide site or both the first and second strand Cprf guide sites. These pieces continue to be nicked and extended in a cyclic reaction that exponentially amplifies the region of the target between nicking sites.


The amplification can be isothermal and selected for temperature. In one embodiment, the amplification proceeds rapidly at 37 degrees. In other embodiments, the temperature of the isothermal amplification may be chosen by selecting a polymerase (e.g. Bsu, Bst, Phi29, klenow fragment etc.). operable at a different temperature.


Thus, whereas nicking isothermal amplification techniques use nicking enzymes with fixed sequence preference (e.g. in nicking enzyme amplification reaction or NEAR), which requires denaturing of the original dsDNA target to allow annealing and extension of primers that add the nicking substrate to the ends of the target, use of a CRISPR nickase wherein the nicking sites can be programed via guide RNAs means that no denaturing step is necessary, enabling the entire reaction to be truly isothermal. This also simplifies the reaction because these primers that add the nicking substrate are different than the primers that are used later in the reaction, meaning that NEAR requires two primer sets (i.e. 4 primers) while Cpf1 nicking amplification only requires one primer set (i.e. two primers). This makes nicking Cpf1 amplification much simpler and easier to operate without complicated instrumentation to perform the denaturation and then cooling to the isothermal temperature.


Accordingly, in certain example embodiments the systems disclosed herein may include amplification reagents. Different components or reagents useful for amplification of nucleic acids are described herein. For example, an amplification reagent as described herein may include a buffer, such as a Tris buffer. A Tris buffer may be used at any concentration appropriate for the desired application or use, for example including, but not limited to, a concentration of 1 mM, 2 mM, 3 mM, 4 mM, 5 mM, 6 mM, 7 mM, 8 mM, 9 mM, 10 mM, 11 mM, 12 mM, 13 mM, 14 mM, 15 mM, 25 mM, 50 mM, 75 mM, 1 M, or the like. One of skill in the art will be able to determine an appropriate concentration of a buffer such as Tris for use with the present invention.


A salt, such as magnesium chloride (MgCl2), potassium chloride (KCl), or sodium chloride (NaCl), may be included in an amplification reaction, such as PCR, in order to improve the amplification of nucleic acid fragments. Although the salt concentration will depend on the particular reaction and application, in some embodiments, nucleic acid fragments of a particular size may produce optimum results at particular salt concentrations. Larger products may require altered salt concentrations, typically lower salt, in order to produce desired results, while amplification of smaller products may produce better results at higher salt concentrations. One of skill in the art will understand that the presence and/or concentration of a salt, along with alteration of salt concentrations, may alter the stringency of a biological or chemical reaction, and therefore any salt may be used that provides the appropriate conditions for a reaction of the present invention and as described herein.


Other components of a biological or chemical reaction may include a cell lysis component in order to break open or lyse a cell for analysis of the materials therein. A cell lysis component may include, but is not limited to, a detergent, a salt as described above, such as NaCl, KCl, ammonium sulfate [(NH4)2SO4], or others. Detergents that may be appropriate for the invention may include Triton X-100, sodium dodecyl sulfate (SDS), CHAPS (3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate), ethyl trimethyl ammonium bromide, nonyl phenoxypolyethoxylethanol (NP-40). Concentrations of detergents may depend on the particular application, and may be specific to the reaction in some cases. Amplification reactions may include dNTPs and nucleic acid primers used at any concentration appropriate for the invention, such as including, but not limited to, a concentration of 100 nM, 150 nM, 200 nM, 250 nM, 300 nM, 350 nM, 400 nM, 450 nM, 500 nM, 550 nM, 600 nM, 650 nM, 700 nM, 750 nM, 800 nM, 850 nM, 900 nM, 950 nM, 1 mM, 2 mM, 3 mM, 4 mM, 5 mM, 6 mM, 7 mM, 8 mM, 9 mM, 10 mM, 20 mM, 30 mM, 40 mM, 50 mM, 60 mM, 70 mM, 80 mM, 90 mM, 100 mM, 150 mM, 200 mM, 250 mM, 300 mM, 350 mM, 400 mM, 450 mM, 500 mM, or the like. Likewise, a polymerase useful in accordance with the invention may be any specific or general polymerase known in the art and useful or the invention, including Taq polymerase, Q5 polymerase, or the like.


In some embodiments, amplification reagents as described herein may be appropriate for use in hot-start amplification. Hot start amplification may be beneficial in some embodiments to reduce or eliminate dimerization of adaptor molecules or oligos, or to otherwise prevent unwanted amplification products or artifacts and obtain optimum amplification of the desired product. Many components described herein for use in amplification may also be used in hot-start amplification. In some embodiments, reagents or components appropriate for use with hot-start amplification may be used in place of one or more of the composition components as appropriate. For example, a polymerase or other reagent may be used that exhibits a desired activity at a particular temperature or other reaction condition. In some embodiments, reagents may be used that are designed or optimized for use in hot-start amplification, for example, a polymerase may be activated after transposition or after reaching a particular temperature. Such polymerases may be antibody-based or aptamer-based. Polymerases as described herein are known in the art. Examples of such reagents may include, but are not limited to, hot-start polymerases, hot-start dNTPs, and photo-caged dNTPs. Such reagents are known and available in the art. One of skill in the art will be able to determine the optimum temperatures as appropriate for individual reagents.


Amplification of nucleic acids may be performed using specific thermal cycle machinery or equipment, and may be performed in single reactions or in bulk, such that any desired number of reactions may be performed simultaneously. In some embodiments, amplification may be performed using microfluidic or robotic devices, or may be performed using manual alteration in temperatures to achieve the desired amplification. In some embodiments, optimization may be performed to obtain the optimum reactions conditions for the particular application or materials. One of skill in the art will understand and be able to optimize reaction conditions to obtain sufficient amplification.


In certain embodiments, detection of DNA with the methods or systems of the invention requires transcription of the (amplified) DNA into RNA prior to detection.


It will be evident that detection methods of the invention can involve nucleic acid amplification and detection procedures in various combinations. The nucleic acid to be detected can be any naturally occurring or synthetic nucleic acid, including but not limited to DNA and RNA, which may be amplified by any suitable method to provide an intermediate product that can be detected. Detection of the intermediate product can be by any suitable method including but not limited to binding and activation of a CRISPR protein which produces a detectable signal moiety by direct or collateral activity.


Helicase-Dependent Amplification


In helicase-dependent amplification, a helicase enzyme is used to unwind a double stranded nucleic acid to generate templates for primer hybridization and subsequent primer-extension. This process utilizes two oligonucleotide primers, each hybridizing to the 3′-end of either the sense strand containing the target sequence or the anti-sense strand containing the reverse-complementary target sequence. The HDA reaction is a general method for helicase-dependent nucleic acid amplification.


In combining this method with a CRISPR-SHERLOCK system, the target nucleic acid may be amplified by opening R-loops of the target nucleic acid using first and second CRISPR/Cas complexes. The first and second strand of the target nucleic acid may thus be unwound using a helicase, allowing primers and polymerase to bind and extend the DNA under isothermal conditions.


The term “helicase” refers here to any enzyme capable of unwinding a double stranded nucleic acid enzymatically. For example, helicases are enzymes that are found in all organisms and in all processes that involve nucleic acid such as replication, recombination, repair, transcription, translation and RNA splicing. (Kornberg and Baker, DNA Replication, W. H. Freeman and Company (2nd ed. (1992)), especially chapter 11). Any helicase that translocates along DNA or RNA in a 5′ to 3′ direction or in the opposite 3′ to 5′ direction may be used in present embodiments of the invention. This includes helicases obtained from prokaryotes, viruses, archaea, and eukaryotes or recombinant forms of naturally occurring enzymes as well as analogues or derivatives having the specified activity. Examples of naturally occurring DNA helicases, described by Kornberg and Baker in chapter 11 of their book, DNA Replication, W. H. Freeman and Company (2nd ed. (1992)), include E. coli helicase I, II, III, & IV, Rep, DnaB, PriA, PcrA, T4 Gp41helicase, T4 Dda helicase, T7 Gp4 helicases, SV40 Large T antigen, yeast RAD. Additional helicases that may be useful in HDA include RecQ helicase (Harmon and Kowalczykowski, J. Biol. Chem. 276:232-243 (2001)), thermostable UvrD helicases from T. tengcongensis (disclosed in this invention, Example XII) and T. thermophilus (Collins and McCarthy, Extremophiles. 7:35-41. (2003)), thermostable DnaB helicase from T. aquaticus (Kaplan and Steitz, J. Biol. Chem. 274:6889-6897 (1999)), and MCM helicase from archaeal and eukaryotic organisms ((Grainge et al., Nucleic Acids Res. 31:4888-4898 (2003)).


A traditional definition of a helicase is an enzyme that catalyzes the reaction of separating/unzipping/unwinding the helical structure of nucleic acid duplexes (DNA, RNA or hybrids) into single-stranded components, using nucleoside triphosphate (NTP) hydrolysis as the energy source (such as ATP). However, it should be noted that not all helicases fit this definition anymore. A more general definition is that they are motor proteins that move along the single-stranded or double stranded nucleic acids (usually in a certain direction, 3′ to 5′ or 5 to 3, or both), i.e. translocases, that can or cannot unwind the duplexed nucleic acid encountered. In addition, some helicases simply bind and “melt” the duplexed nucleic acid structure without an apparent translocase activity.


Helicases exist in all living organisms and function in all aspects of nucleic acid metabolism. Helicases are classified based on the amino acid sequences, directionality, oligomerization state and nucleic-acid type and structure preferences. The most common classification method was developed based on the presence of certain amino acid sequences, called motifs. According to this classification helicases are divided into 6 super families: SF1, SF2, SF3, SF4, SF5 and SF6. SF1 and SF2 helicases do not form a ring structure around the nucleic acid, whereas SF3 to SF6 do. Superfamily classification is not dependent on the classical taxonomy.


DNA helicases are responsible for catalyzing the unwinding of double-stranded DNA (dsDNA) molecules to their respective single-stranded nucleic acid (ssDNA) forms. Although structural and biochemical studies have shown how various helicases can translocate on ssDNA directionally, consuming one ATP per nucleotide, the mechanism of nucleic acid unwinding and how the unwinding activity is regulated remains unclear and controversial (T. M. Lohman, E. J. Tomko, C. G. Wu, “Non-hexameric DNA helicases and translocases: mechanisms and regulation,” Nat Rev Mol Cell Biol 9:391-401 (2008)). Since helicases can potentially unwind all nucleic acids encountered, understanding how their unwinding activities are regulated can lead to harnessing helicase functions for biotechnology applications.


The term “HDA” refers to Helicase Dependent Amplification, which is an in vitro method for amplifying nucleic acids by using a helicase preparation for unwinding a double stranded nucleic acid to generate templates for primer hybridization and subsequent primer-extension. This process utilizes two oligonucleotide primers, each hybridizing to the 3′-end of either the sense strand containing the target sequence or the anti-sense strand containing the reverse-complementary target sequence. The HDA reaction is a general method for helicase-dependent nucleic acid amplification.


The invention comprises use of any suitable helicase known in the art. These include, but are not necessarily limited to, UvrD helicase, CRISPR-Cas3 helicase, E. coli helicase I, E. coli helicase II, E. coli helicase III, E. coli helicase IV, Rep helicase, DnaB helicase, PriA helicase, PcrA helicase, T4 Gp41 helicase, T4 Dda helicase, SV40 Large T antigen, yeast RAD helicase, RecD helicase, RecQ helicase, thermostable T. tengcongensis UvrD helicase, thermostable T. thermophilus UvrD helicase, thermostable T. aquaticus DnaB helicase, Dda helicase, papilloma virus E1 helicase, archaeal MCM helicase, eukaryotic MCM helicase, and T7 Gp4 helicase.


An “individual discrete volume” is a discrete volume or discrete space, such as a container, receptacle, or other defined volume or space that can be defined by properties that prevent and/or inhibit migration of nucleic acids and reagents necessary to carry out the methods disclosed herein, for example a volume or space defined by physical properties such as walls, for example the walls of a well, tube, or a surface of a droplet, which may be impermeable or semipermeable, or as defined by other means such as chemical, diffusion rate limited, electro-magnetic, or light illumination, or any combination thereof. By “diffusion rate limited” (for example diffusion defined volumes) is meant spaces that are only accessible to certain molecules or reactions because diffusion constraints effectively defining a space or volume as would be the case for two parallel laminar streams where diffusion will limit the migration of a target molecule from one stream to the other. By “chemical” defined volume or space is meant spaces where only certain target molecules can exist because of their chemical or molecular properties, such as size, where for example gel beads may exclude certain species from entering the beads but not others, such as by surface charge, matrix size or other physical property of the bead that can allow selection of species that may enter the interior of the bead. By “electro-magnetically” defined volume or space is meant spaces where the electro-magnetic properties of the target molecules or their supports such as charge or magnetic properties can be used to define certain regions in a space such as capturing magnetic particles within a magnetic field or directly on magnets. By “optically” defined volume is meant any region of space that may be defined by illuminating it with visible, ultraviolet, infrared, or other wavelengths of light such that only target molecules within the defined space or volume may be labeled. One advantage to the used of non-walled, or semipermeable is that some reagents, such as buffers, chemical activators, or other agents maybe passed in Applicants' through the discrete volume, while other material, such as target molecules, maybe maintained in the discrete volume or space. Typically, a discrete volume will include a fluid medium, (for example, an aqueous solution, an oil, a buffer, and/or a media capable of supporting cell growth) suitable for labeling of the target molecule with the indexable nucleic acid identifier under conditions that permit labeling. Exemplary discrete volumes or spaces useful in the disclosed methods include droplets (for example, microfluidic droplets and/or emulsion droplets), hydrogel beads or other polymer structures (for example poly-ethylene glycol di-acrylate beads or agarose beads), tissue slides (for example, fixed formalin paraffin embedded tissue slides with particular regions, volumes, or spaces defined by chemical, optical, or physical means), microscope slides with regions defined by depositing reagents in ordered arrays or random patterns, tubes (such as, centrifuge tubes, microcentrifuge tubes, test tubes, cuvettes, conical tubes, and the like), bottles (such as glass bottles, plastic bottles, ceramic bottles, Erlenmeyer flasks, scintillation vials and the like), wells (such as wells in a plate), plates, pipettes, or pipette tips among others. In certain example embodiments, the individual discrete volumes are the wells of a microplate. In certain example embodiments, the microplate is a 96 well, a 384 well, or a 1536 well microplate.


Single Cell Sequencing

In certain embodiments, the invention involves single cell RNA sequencing (see, e.g., Kalisky, T., Blainey, P. & Quake, S. R. Genomic Analysis at the Single-Cell Level. Annual review of genetics 45, 431-445, (2011); Kalisky, T. & Quake, S. R. Single-cell genomics. Nature Methods 8, 311-314 (2011); Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Research, (2011); Tang, F. et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nature Protocols 5, 516-535, (2010); Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods 6, 377-382, (2009); Ramskold, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotechnology 30, 777-782, (2012); and Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification. Cell Reports, Cell Reports, Volume 2, Issue 3, p666-6′73, 2012).


In certain embodiments, the invention involves plate based single cell RNA sequencing (see, e.g., Picelli, S. et al., 2014, “Full-length RNA-seq from single cells using Smart-seq2” Nature protocols 9, 171-181, doi:10.1038/nprot.2014.006).


In certain embodiments, the invention involves high-throughput single-cell RNA-seq. In this regard reference is made to Macosko et al., 2015, “Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets” Cell 161, 1202-1214; International patent application number PCT/US2015/049178, published as WO2016/040476 on Mar. 17, 2016; Klein et al., 2015, “Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells” Cell 161, 1187-1201; International patent application number PCT/US2016/027734, published as WO2016168584A1 on Oct. 20, 2016; Zheng, et al., 2016, “Haplotyping germline and cancer genomes with high-throughput linked-read sequencing” Nature Biotechnology 34, 303-311; Zheng, et al., 2017, “Massively parallel digital transcriptional profiling of single cells” Nat. Commun. 8, 14049 doi: 10.1038/ncomms14049; International patent publication number WO2014210353A2; Zilionis, et al., 2017, “Single-cell barcoding and sequencing using droplet microfluidics” Nat Protoc. January; 12(1):44-73; Cao et al., 2017, “Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing” bioRxiv preprint first posted online Feb. 2, 2017, doi: dx.doi.org/10.1101/104844; Rosenberg et al., 2017, “Scaling single cell transcriptomics through split pool barcoding” bioRxiv preprint first posted online Feb. 2, 2017, doi: dx.doi.org/10.1101/105163; Rosenberg et al., “Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding” Science 15 Mar. 2018; Vitak, et al., “Sequencing thousands of single-cell genomes with combinatorial indexing” Nature Methods, 14(3):302-308, 2017; Cao, et al., Comprehensive single-cell transcriptional profiling of a multicellular organism. Science, 357(6352):661-667, 2017; and Gierahn et al., “Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput” Nature Methods 14, 395-398 (2017), all the contents and disclosure of each of which are herein incorporated by reference in their entirety.


In certain embodiments, the invention involves single nucleus RNA sequencing. In this regard reference is made to Swiech et al., 2014, “In vivo interrogation of gene function in the mammalian brain using CRISPR-Cas9” Nature Biotechnology Vol. 33, pp. 102-106; Habib et al., 2016, “Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons” Science, Vol. 353, Issue 6302, pp. 925-928; Habib et al., 2017, “Massively parallel single-nucleus RNA-seq with DroNc-seq” Nat Methods. 2017 October; 14(10):955-958; and International patent application number PCT/US2016/059239, published as WO2017164936 on Sep. 28, 2017, which are herein incorporated by reference in their entirety.


In certain embodiments, the invention involves the Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) as described. (see, e.g., Buenrostro, et al., Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nature methods 2013; 10 (12): 1213-1218; Buenrostro et al., Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486-490 (2015); Cusanovich, D. A., Daza, R., Adey, A., Pliner, H., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C. & Shendure, J. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015 May 22; 348(6237):910-4. doi: 10.1126/science.aab1601. Epub 2015 May 7; US20160208323A1; US20160060691A1; and WO2017156336A1).


Screening for Modulating Agents

A further aspect of the invention relates to a method for identifying an agent capable of modulating one or more phenotypic aspects of a cell or cell population as disclosed herein, comprising: a) applying a candidate agent to the cell or cell population; b) detecting modulation of one or more phenotypic aspects of the cell or cell population by the candidate agent, thereby identifying the agent. The phenotypic aspects of the cell or cell population that is modulated may be a gene signature or biological program specific to a cell type or cell phenotype or phenotype specific to a population of cells (e.g., an inflammatory phenotype or suppressive immune phenotype). In certain embodiments, steps can include administering candidate modulating agents to cells, detecting identified cell (sub)populations for changes in signatures, or identifying relative changes in cell (sub) populations which may comprise detecting relative abundance of particular gene signatures.


The term “modulate” broadly denotes a qualitative and/or quantitative alteration, change or variation in that which is being modulated. Where modulation can be assessed quantitatively—for example, where modulation comprises or consists of a change in a quantifiable variable such as a quantifiable property of a cell or where a quantifiable variable provides a suitable surrogate for the modulation—modulation specifically encompasses both increase (e.g., activation) or decrease (e.g., inhibition) in the measured variable. The term encompasses any extent of such modulation, e.g., any extent of such increase or decrease, and may more particularly refer to statistically significant increase or decrease in the measured variable. By means of example, modulation may encompass an increase in the value of the measured variable by at least about 10%, e.g., by at least about 20%, preferably by at least about 30%, e.g., by at least about 40%, more preferably by at least about 50%, e.g., by at least about 75%, even more preferably by at least about 100%, e.g., by at least about 150%, 200%, 250%, 300%, 400% or by at least about 500%, compared to a reference situation without said modulation; or modulation may encompass a decrease or reduction in the value of the measured variable by at least about 10%, e.g., by at least about 20%, by at least about 30%, e.g., by at least about 40%, by at least about 50%, e.g., by at least about 60%, by at least about 70%, e.g., by at least about 80%, by at least about 90%, e.g., by at least about 95%, such as by at least about 96%, 97%, 98%, 99% or even by 100%, compared to a reference situation without said modulation. Preferably, modulation may be specific or selective, hence, one or more desired phenotypic aspects of an immune cell or immune cell population may be modulated without substantially altering other (unintended, undesired) phenotypic aspect(s).


The term “agent” broadly encompasses any condition, substance or agent capable of modulating one or more phenotypic aspects of a cell or cell population as disclosed herein. Such conditions, substances or agents may be of physical, chemical, biochemical and/or biological nature. The term “candidate agent” refers to any condition, substance or agent that is being examined for the ability to modulate one or more phenotypic aspects of a cell or cell population as disclosed herein in a method comprising applying the candidate agent to the cell or cell population (e.g., exposing the cell or cell population to the candidate agent or contacting the cell or cell population with the candidate agent) and observing whether the desired modulation takes place.


Agents may include any potential class of biologically active conditions, substances or agents, such as for instance antibodies, proteins, peptides, nucleic acids, oligonucleotides, small molecules, or combinations thereof, as described herein.


The methods of phenotypic analysis can be utilized for evaluating environmental stress and/or state, for screening of chemical libraries, and to screen or identify structural, syntenic, genomic, and/or organism and species variations. For example, a culture of cells, can be exposed to an environmental stress, such as but not limited to heat shock, osmolarity, hypoxia, cold, oxidative stress, radiation, starvation, a chemical (for example a therapeutic agent or potential therapeutic agent) and the like. After the stress is applied, a representative sample can be subjected to analysis, for example at various time points, and compared to a control, such as a sample from an organism or cell, for example a cell from an organism, or a standard value. By exposing cells, or fractions thereof, tissues, or even whole animals, to different members of the chemical libraries, and performing the methods described herein, different members of a chemical library can be screened for their effect on immune phenotypes thereof simultaneously in a relatively short amount of time, for example using a high throughput method.


Aspects of the present disclosure relate to the correlation of an agent with the spatial proximity and/or epigenetic profile of the nucleic acids in a sample of cells. In some embodiments, the disclosed methods can be used to screen chemical libraries for agents that modulate chromatin architecture epigenetic profiles, and/or relationships thereof.


In some embodiments, screening of test agents involves testing a combinatorial library containing a large number of potential modulator compounds. A combinatorial chemical library may be a collection of diverse chemical compounds generated by either chemical synthesis or biological synthesis, by combining a number of chemical “building blocks” such as reagents. For example, a linear combinatorial chemical library, such as a polypeptide library, is formed by combining a set of chemical building blocks (amino acids) in every possible way for a given compound length (for example the number of amino acids in a polypeptide compound). Millions of chemical compounds can be synthesized through such combinatorial mixing of chemical building blocks.


In certain embodiments, the present invention provides for gene signature screening. The concept of signature screening was introduced by Stegmaier et al. (Gene expression-based high-throughput screening (GE-HTS) and application to leukemia differentiation. Nature Genet. 36, 257-263 (2004)), who realized that if a gene-expression signature was the proxy for a phenotype of interest, it could be used to find small molecules that effect that phenotype without knowledge of a validated drug target. The signatures or biological programs of the present invention may be used to screen for drugs that reduce the signature or biological program in cells as described herein. The signature or biological program may be used for GE-HTS. In certain embodiments, pharmacological screens may be used to identify drugs that are selectively toxic to cells having a signature.


The Connectivity Map (cmap) is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules and simple pattern-matching algorithms that together enable the discovery of functional connections between drugs, genes and diseases through the transitory feature of common gene-expression changes (see, Lamb et al., The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science 29 Sep. 2006: Vol. 313, Issue 5795, pp. 1929-1935, DOI: 10.1126/science.1132939; and Lamb, J., The Connectivity Map: a new tool for biomedical research. Nature Reviews Cancer January 2007: Vol. 7, pp. 54-60). In certain embodiments, Cmap can be used to screen for small molecules capable of modulating a signature or biological program of the present invention in silico.


MS Methods

Biomarker detection may also be evaluated using mass spectrometry methods. A variety of configurations of mass spectrometers can be used to detect biomarker values. Several types of mass spectrometers are available or can be produced with various configurations. In general, a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and instrument-control system, and a data system. Difference in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, an inlet can be a capillary-column liquid chromatography source or can be a direct probe or stage such as used in matrix-assisted laser desorption. Common ion sources are, for example, electrospray, including nanospray and microspray or matrix-assisted laser desorption. Common mass analyzers include a quadrupole mass filter, ion trap mass analyzer and time-of-flight mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman, New York (2000)).


Protein biomarkers and biomarker values can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF/TOF) technology, called ultraflex III TOF/TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS).sup.N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and APPI-(MS).sup.N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.


Sample preparation strategies are used to label and enrich samples before mass spectroscopic characterization of protein biomarkers and determination biomarker values. Labeling methods include but are not limited to isobaric tag for relative and absolute quantitation (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich samples for candidate biomarker proteins prior to mass spectroscopic analysis include but are not limited to aptamers, antibodies, nucleic acid probes, chimeras, small molecules, an F(ab′)2 fragment, a single chain antibody fragment, an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor, affybodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g. diabodies etc) imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acid, a hormone receptor, a cytokine receptor, and synthetic receptors, and modifications and fragments of these.


Immunoassays

Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect the analyte in a sample depending on the specific assay format. To improve specificity and sensitivity of an assay method based on immunoreactivity, monoclonal antibodies are often used because of their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays because of their increased affinity for the target as compared to monoclonal antibodies Immunoassays have been designed for use with a wide range of biological sample matrices Immunoassay formats have been designed to provide qualitative, semi-quantitative, and quantitative results.


Quantitative results may be generated through the use of a standard curve created with known concentrations of the specific analyte to be detected. The response or signal from an unknown sample is plotted onto the standard curve, and a quantity or value corresponding to the target in the unknown sample is established.


Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of an analyte/biomarker. This method relies on attachment of a label to either the analyte or the antibody and the label component includes, either directly or indirectly, an enzyme. ELISA tests may be formatted for direct, indirect, competitive, or sandwich detection of the analyte. Other methods rely on labels such as, for example, radioisotopes (I125) or fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and others (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).


Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescent, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time resolved-FRET (TR-FRET) immunoassays. Examples of procedures for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level discrimination, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.


Methods of detecting and/or quantifying a detectable label or signal generating material depend on the nature of the label. The products of reactions catalyzed by appropriate enzymes (where the detectable label is an enzyme; see above) can be, without limitation, fluorescent, luminescent, or radioactive or they may absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, without limitation, x-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.


Any of the methods for detection can be performed in any format that allows for any suitable preparation, processing, and analysis of the reactions. This can be, for example, in multi-well assay plates (e.g., 96 wells or 384 wells) or using any suitable array or microarray. Stock solutions for various agents can be made manually or robotically, and all subsequent pipetting, diluting, mixing, distribution, washing, incubating, sample readout, data collection and analysis can be done robotically using commercially available analysis software, robotics, and detection instrumentation capable of detecting a detectable label.


Hybridization Assays

Such applications are hybridization assays in which a nucleic acid that displays “probe” nucleic acids for each of the genes to be assayed/profiled in the profile to be generated is employed. In these assays, a sample of target nucleic acids is first prepared from the initial nucleic acid sample being assayed, where preparation may include labeling of the target nucleic acids with a label, e.g., a member of a signal producing system. Following target nucleic acid sample preparation, the sample is contacted with the array under hybridization conditions, whereby complexes are formed between target nucleic acids that are complementary to probe sequences attached to the array surface. The presence of hybridized complexes is then detected, either qualitatively or quantitatively. Specific hybridization technology which may be practiced to generate the expression profiles employed in the subject methods includes the technology described in U.S. Pat. Nos. 5,143,854; 5,288,644; 5,324,633; 5,432,049; 5,470,710; 5,492,806; 5,503,980; 5,510,270; 5,525,464; 5,547,839; 5,580,732; 5,661,028; 5,800,992; the disclosures of which are herein incorporated by reference; as well as WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP 373 203; and EP 785 280. In these methods, an array of “probe” nucleic acids that includes a probe for each of the biomarkers whose expression is being assayed is contacted with target nucleic acids as described above. Contact is carried out under hybridization conditions, e.g., stringent hybridization conditions as described above, and unbound nucleic acid is then removed. The resultant pattern of hybridized nucleic acids provides information regarding expression for each of the biomarkers that have been probed, where the expression information is in terms of whether or not the gene is expressed and, typically, at what level, where the expression data, i.e., expression profile, may be both qualitative and quantitative.


Optimal hybridization conditions will depend on the length (e.g., oligomer vs. polynucleotide greater than 200 bases) and type (e.g., RNA, DNA, PNA) of labeled probe and immobilized polynucleotide or oligonucleotide. General parameters for specific (i.e., stringent) hybridization conditions for nucleic acids are described in Sambrook et al., supra, and in Ausubel et al., “Current Protocols in Molecular Biology”, Greene Publishing and Wiley-interscience, NY (1987), which is incorporated in its entirety for all purposes. When the cDNA microarrays are used, typical hybridization conditions are hybridization in 5×SSC plus 0.2% SDS at 65 C for 4 hours followed by washes at 25° C. in low stringency wash buffer (1×SSC plus 0.2% SDS) followed by 10 minutes at 25° C. in high stringency wash buffer (0.1SSC plus 0.2% SDS) (see Shena et al., Proc. Natl. Acad. Sci. USA, Vol. 93, p. 10614 (1996)). Useful hybridization conditions are also provided in, e.g., Tijessen, Hybridization With Nucleic Acid Probes”, Elsevier Science Publishers B.V. (1993) and Kricka, “Nonisotopic DNA Probe Techniques”, Academic Press, San Diego, Calif. (1992).


In certain embodiments, the gene signature includes surface expressed proteins. In certain embodiments, surface proteins may be targeted for detection and isolation of cell types, or may be targeted therapeutically to modulate an immune response.


In one embodiment, the signature genes and/or cells may be detected or isolated by immunofluorescence, immunohistochemistry, fluorescence activated cell sorting (FACS), mass cytometry (CyTOF), RNA-seq, scRNA-seq (e.g., Drop-seq, InDrop, 10× Genomics), single cell qPCR, MERFISH (multiplex (in situ) RNA FISH) and/or by in situ hybridization. Other methods including absorbance assays and colorimetric assays are known in the art and may be used herein.


Sequencing and Nucleic Acid Analysis

In certain embodiments, the invention involves targeted nucleic acid profiling (e.g., sequencing, quantitative reverse transcription polymerase chain reaction, and the like) (see e.g., Geiss G K, et al., Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol. 2008 March; 26(3):317-25). In certain embodiments, a target nucleic acid molecule (e.g., RNA molecule), may be sequenced by any method known in the art, for example, methods of high-throughput sequencing, also known as next generation sequencing or deep sequencing. A nucleic acid target molecule labeled with a barcode (for example, an origin-specific barcode) can be sequenced with the barcode to produce a single read and/or contig containing the sequence, or portions thereof, of both the target molecule and the barcode. Exemplary next generation sequencing technologies include, for example, Illumina sequencing, Ion Torrent sequencing, 454 sequencing, SOLiD sequencing, and nanopore sequencing amongst others.


In certain embodiments, the invention involves single cell RNA sequencing (see, e.g., Kalisky, T., Blainey, P. & Quake, S. R. Genomic Analysis at the Single-Cell Level. Annual review of genetics 45, 431-445, (2011); Kalisky, T. & Quake, S. R. Single-cell genomics. Nature Methods 8, 311-314 (2011); Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Research, (2011); Tang, F. et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nature Protocols 5, 516-535, (2010); Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods 6, 377-382, (2009); Ramskold, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotechnology 30, 777-782, (2012); and Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification. Cell Reports, Cell Reports, Volume 2, Issue 3, p666-6′73, 2012).


In certain embodiments, the invention involves plate based single cell RNA sequencing (see, e.g., Picelli, S. et al., 2014, “Full-length RNA-seq from single cells using Smart-seq2” Nature protocols 9, 171-181, doi:10.1038/nprot.2014.006).


In certain embodiments, the invention involves high-throughput single-cell RNA-seq. In this regard reference is made to Macosko et al., 2015, “Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets” Cell 161, 1202-1214; International patent application number PCT/US2015/049178, published as WO2016/040476 on Mar. 17, 2016; Klein et al., 2015, “Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells” Cell 161, 1187-1201; International patent application number PCT/US2016/027734, published as WO2016168584A1 on Oct. 20, 2016; Zheng, et al., 2016, “Haplotyping germline and cancer genomes with high-throughput linked-read sequencing” Nature Biotechnology 34, 303-311; Zheng, et al., 2017, “Massively parallel digital transcriptional profiling of single cells” Nat. Commun. 8, 14049 doi: 10.1038/ncomms14049; International patent publication number WO2014210353A2; Zilionis, et al., 2017, “Single-cell barcoding and sequencing using droplet microfluidics” Nat Protoc. January; 12(1):44-73; Cao et al., 2017, “Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing” bioRxiv preprint first posted online Feb. 2, 2017, doi: dx.doi.org/10.1101/104844; Rosenberg et al., 2017, “Scaling single cell transcriptomics through split pool barcoding” bioRxiv preprint first posted online Feb. 2, 2017, doi: dx.doi.org/10.1101/105163; Rosenberg et al., “Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding” Science 15 Mar. 2018; Vitak, et al., “Sequencing thousands of single-cell genomes with combinatorial indexing” Nature Methods, 14(3):302-308, 2017; Cao, et al., Comprehensive single-cell transcriptional profiling of a multicellular organism. Science, 357(6352):661-667, 2017; and Gierahn et al., “Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput” Nature Methods 14, 395-398 (2017), all the contents and disclosure of each of which are herein incorporated by reference in their entirety.


In certain embodiments, the invention involves single nucleus RNA sequencing. In this regard reference is made to Swiech et al., 2014, “In vivo interrogation of gene function in the mammalian brain using CRISPR-Cas9” Nature Biotechnology Vol. 33, pp. 102-106; Habib et al., 2016, “Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons” Science, Vol. 353, Issue 6302, pp. 925-928; Habib et al., 2017, “Massively parallel single-nucleus RNA-seq with DroNc-seq” Nat Methods. 2017 October; 14(10):955-958; and International patent application number PCT/US2016/059239, published as WO2017164936 on Sep. 28, 2017, which are herein incorporated by reference in their entirety.


In certain embodiments, the invention involves the Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) as described. (see, e.g., Buenrostro, et al., Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nature methods 2013; 10 (12): 1213-1218; Buenrostro et al., Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486-490 (2015); Cusanovich, D. A., Daza, R., Adey, A., Pliner, H., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C. & Shendure, J. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015 May 22; 348(6237):910-4. doi: 10.1126/science.aab1601. Epub 2015 May 7; US20160208323A1; US20160060691A1; and WO2017156336A1).


The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.


EXAMPLES
Example 1—Single-Cell RNA-Seq Atlas of Synovial Sarcoma (SyS): Cell Type Inference from Expression and Genetic Features

Despite the relatively low number of secondary mutations, SyS tumors display different degrees of cellular differentiation and plasticity, and are classified accordingly as monophasic (mesenchymal cells), biphasic (mesenchymal and epithelial cells), or poorly differentiated (undifferentiated cells). The co-existence of distinct cellular phenotypes and morphologies in a single SyS tumor provides a unique opportunity to explore intratumor heterogeneity and cell state transitions. However, since human SyS has been studied primarily in established cellular models (Kadoch et al. Cell 153:71-85 (2013); McBride et al. Cancer Cell (2018) doi:10.1016/j.ccell.2018.05.002; Banito et al. Cancer Cell 33:527-541.e8 (2018)) and through bulk profiling of tumor tissues (Nakayama et al. Am J Surg Pathol 34:1599-1607 (2010); Lagarde et al. J Clin Oncol Of Am Soc Clin Oncol 31:608-615 (2013)), the molecular features of the different SyS subpopulations have so far remained elusive. In particular, it remains unclear how this malignant cellular diversity comes about, which malignant cell states drive tumor progression, and how to selectively target aggressive synovial sarcoma cells to blunt tumor growth and dissemination.


To address these questions, Applicants leveraged single-cell RNA-Seq (scRNA-Seq; FIG. 1A) and profiled 16,872 cells from 12 human SyS tumors. The data reveal a spectrum of cell states and a clear developmental hierarchy, where poorly differentiated cells cycle and replenish the tumor. Within each tumor they found a distinct subpopulation of malignant cells that express a core oncogenic program with features of immune evasion, resistance to apoptosis, oxidative metabolism, and poor differentiation. Applicants demonstrated that the program is associated with poor clinical outcomes and is controlled in part by the SS18-SSX oncoprotein and in part by the tumor microenvironment. Lastly, Applicants computationally modeled the program transcriptional regulation, highlighting HDAC3 and CDK4/6 as its key regulator and targets, respectively. In accordance with this, combining HDAC and CDK4/6 inhibitors Applicants were able to block the program and selectively target synovial sarcoma cells, while sparing non-malignant ones.


The workflow was as follows: (1) Mapped the transcriptional landscape of synovial sarcoma cells: characterized differentiation trajectories, revealed that stem-like cells are those that cycle, and discovered this new core oncogenic program. (2) These aggressive features (poor differentiation, cell cycle, and the different core oncogenic features) are tightly co-regulated and predictive of clinical outcomes. (3) The fusion and (TNF/IFN-secreting) immune cells promote/repress the aggressive features, respectively. (4) Lastly, Applicants selectively targeted the different aggressive cells by combining HDAC (core oncogenic) and CDK4/6 (cell cycle) inhibitors.


Using full-length (Picelli et al. Nat Protoc 9:171-181 (2014)) and droplet-based (Zheng et al. Nat Commun 8:14049 (2017)) scRNA-Seq, Applicants profiled 16,872 high quality malignant, immune, and stromal cells from 12 human SyS tumors (FIGS. 1A, 1B, 2A, Tables 1-3). Cells were assigned to different cell types according to both genetic and transcriptional features (FIG. 1B, 2A-2D, Methods): (1) Detection of the SS18-SSX fusion transcripts (Haas eta 1. bioRxiv (2017) doi:10.1101/120295); (2) inference of copy number alterations (CNAs) from scRNA-Seq profiles (Patel et al. Science 344:1396-1401 (2014)), which was validated in four tumors by bulk whole-exome sequencing (WES) (FIG. 1C); (3) expression-based clustering and post hoc annotation of non-malignant clusters based on canonical cell type markers (FIG. 2A, Tables 4 and 5); and (4) similarity of cells to bulk expression profiles of Sys tumors (Abeshouse et al. Cell 171:950-965.e28 (2017)). The four approaches were highly congruent (FIG. 2A). For example, the fusion was detected in 58.6% of cells defined as malignant by other analyses, but only in 0.89% of non-malignant cells. Notably, SSX1/2 expression was also very specific to malignant cells (detection rate of 66.64% and 1.49% in the malignant and non-malignant cells, respectively; FIG. 2A “SSX1/2 detection”), and in one of the tumors Applicants identified another malignant-specific fusion (MEOX2-AGMO) (FIGS. 3B, 3C). Similarly, CNAs were detected only in cells that were assigned as malignant by the other analyses (FIG. 1B, 1C), and the Sys similarity scores distinguished between malignant and non-malignant cells (as defined by the other methods) with 100% accuracy (FIGS. 1B, 2B). Cells discordant across these criteria (<0.05%) were excluded from all downstream analyses.









TABLE 2A







Clinical characteristics of the patients and samples in the scRNA-seq cohort.























Metastatic/









Neoadjuvant
primary


Patient
Tumor
Mutation
Diagnostic
Sex
Age
Localization
Treatment
lesion


















P1
S1
SS18-SSX1
Biphasic
M
23
Knee
Chemotherapy
Primary









(limp perfusion









with Melphalan









and TNFalpha)


P2
S2
SS18-SSX1
Monophasic
M
52
Thigh
Chemotherapy
Primary









(AIM) +









Radiotherapy


P5
S5
SS18-SSX2
Monophasic
F
62
Lung
Chemotherapy
Metastatic









(Ifosfamide) +









Radiotherapy


P7
S7
SS18-SSX2
Poorly
M
45
Para-aortic
Radiotherapy
Primary





differentiated


P10
S10
SS18-SSX1
Monophasic
M
22
Lung
Chemotherapy
Metastatic









(AIM) +









Radiotherapy


P11
S11
SS18-SSX1
Monophasic
F
34
Lung (L
None
Primary



primary




inf lobe)


P11
S11
SS18-SSX1
Monophasic
F
34
Lung (L
None
Metastatic



Metastatic




sup lobe)


P12
S12
SS18-SSX1
Biphasic
M
24
Chest
None
Primary








wall


P12
S12 post
SS18-SSX1
Biphasic
M
24
Chest
Chemotherapy
Primary



treatment




wall
(AIM) +









Radiotherapy


P13
S13
SS18-SSX2
Monophasic
F
29
Thyroid
Chemotherapy
Metastatic









(AIM) +









Radiotherapy


P14
S14
SS18-SSX1
Monophasic
F
57
Knee
None
Primary


P16
S16
SS18-SSX2
Biphasic
M
















TABLE 2B







Quality measures of the scRNA-seq cohorts.


Synovial sarcoma human tumors












No. of
Median no. of
Median no. of



Cell type
cells
detected genes
aligned reads
Sequencing














B.cell
90
2558.5
241971.5
Smart-Seq2


CAFs
81
3046
517671
Smart-Seq2


Endothelial
80
3356.5
497703.5
Smart-Seq2


Macrophage
943
3720
362523
Smart-Seq2


Malignant
4371
4743
320982
Smart-Seq2


Mast
185
3222
350962
Smart-Seq2


NK
102
2601
177431.5
Smart-Seq2


CD4 T cell
235
2618
177160
Smart-Seq2


CD8 T cell
659
2527
153707
Smart-Seq2


T cell
206
2582.5
182173.5
Smart-Seq2


Inconsistent
157
5753
357370
Smart-Seq2


assignments


CAFs
158
2665.5
9263
10x


Endothelial
418
3650
14031.5
10x


Macrophage
275
2089
7855
10x


Malignant
8323
2850
9609
10x


Inconsistent
589
2792
10110
10x


assignments
















TABLE 3







Quality measures of the scRNA-seq cohorts.


Synovial sarcoma cell lines/cultures














No. of
Median no. of
Median no. of



Experiment
Cell type
cells
detected genes
aligned reads
Sequencing















TNF/IFN
SS11metaexp
185
7215
200412
Smart-Seq2



SSexp1
263
6925
485893
Smart-Seq2



SSexp2
256
6445
283369
Smart-Seq2



SSexp4
391
7468
244698
Smart-Seq2


SS18-SXKD
ASKA, shCt
1477
4743
27148
10x



(day 3)



ASKA, shCt
1301
5346
30837
10x



(day 7)



ASKA, shSSX
1503
4606
26029
10x



(day 3)



ASKA, shSSX
1554
5172.5
32017
10x



d(day 7)



SYO1, shCt
1284
3451.5
18852.5
10x



(day 3)



SYO1, shCt
1742
3462.5
16348
10x



(day 7)



SYO1, shSSX
2000
3295
17713
10x



(day 3)



SYO1, shSSX
1402
2700
10737.5
10x



(day 7)
















TABLE 4





Cell type signatures derived from the analysis of the synovial sarcoma scRNA-seq cohort.


Single-cell denovo signatures





















Endothelial




B cell
CAF
cell
Macrophage
Mastocyte





AFF3
ACAN
ACVRL1
ABCC3
ABCA1


BACH2
ACTA2
ADAM15
ABHD12
ABCC1


BANK1
ACTN1
ADCY4
ACSL1
ABCC4


BCL11A
ADAMTS12
AFAP1L1
ADAP2
ACER3


BLK
ADAMTS4
AIF1L
ADIPOR1
ACOT7


C12orf42
ADIRF
APLNR
ADORA3
ACSL4


CCR7
AEBP1
APOL3
AIF1
ADAM12


CD19
AGAP11
AQP1
AKR1B1
ADCYAP1


CD37
ANGPT1
ARAP3
ALCAM
ADRB2


CD55
ANGPTL4
ARHGAP29
ALDH2
AGTRAP


CD79A
ANO1
ARHGAP31
AMPD3
AHR


CD79B
ARHGAP1
ARHGEF15
AP1B1
ALDH1A1


CDCA7L
ARHGAP42
ARHGEF28
APOC1
ALOX5


CLEC17A
ARHGEF17
ARL15
ATP13A3
ALOX5AP


CXCR5
ASPN
ARRDC3
ATP6V0B
ALS2


CYBASC3
AVPR1A
BCAM
ATP6V1B2
AMHR2


DAPP1
AXL
BCL6B
B3GNT5
ANKRD27


EEF1B2
BGN
BDKRB2
BCAT1
AQP10


EZR
C11orf96
BMPR2
BCL2A1
ARHGAP18


FAM129C
C1orf198
BTNL9
BEST1
ARHGEF6


FCER2
C1QTNF1
C8orf4
BLVRB
ATP6V0A2


FCRL1
C1R
CA2
C10orf54
AURKA


FCRL2
C1S
CALCRL
C15orf48
B4GALT5


FCRL5
C21orf7
CAPN11
C1QA
BACE2


FCRLA
C4A
CARD10
C1QB
BATF


FGD2
C4B
CASKIN2
C1QC
BLVRA


GNG7
C4B_2
CCL14
C2
BMP2K


HLA-DOB
CALD1
CD200
C3
BTK


HLA-DQA2
CCDC102B
CD320
C3AR1
C1orf186


HVCN1
CCR10
CD34
C5AR1
C20orf118


ICOSLG
CD248
CDH5
C9orf72
C6orf25


IGJ
CDH6
CFI
CARD9
C8G


IGLL5
CFH
CLDN5
CASP1
CACNA2D4


IL16
CHN1
CLEC14A
CCL20
CADPS


IRF8
CLEC11A
CLEC1A
CCL3
CALB2


KDM4B
COL12A1
CLEC3B
CCL3L1
CAPG


KIAA0226
COL14A1
CLIC2
CCL3L3
CATSPER1


KIAA0226L
COL16A1
CLIC5
CCL4L1
CCDC115


LILRA4
COL18A1
CNTNAP3B
CCL4L2
CD274


LOC283663
COL1A1
COL15A1
CCR1
CD33


LY9
COL1A2
CRIM1
CCRL2
CD69


LYN
COL3A1
CSGALNACT1
CD14
CD82


MS4A1
COL5A2
CTNND1
CD163
CDCP1


MZB1
COL5A3
CTTNBP2NL
CD163L1
CDH12


NAPSB
COL6A1
CX3CL1
CD209
CDK15


NCF1
COL6A3
CXorf36
CD300C
CKS2


NCF1B
COX4I2
CYYR1
CD300E
CLCN3


NCF1C
CPE
DLL4
CD300LB
CLNK


NCOA3
CRISPLD2
DOCK6
CD302
CLU


PAX5
CRYAB
DOCK9
CD68
CMA1


RALGPS2
CSPG4
DYSF
CD80
CPA3


SELL
DAAM2
ECSCR
CD86
CPEB4


SNX2
DBNDD2
EFNA1
CEBPB
CPPED1


SNX29P1
DCN
EFNB2
CECR1
CRLF2


SPIB
DKK3
EGFL7
CFD
CSF2RB


ST6GAL1
DSTN
EHD4
CLDN1
CTNNBL1


TCL1A
EBF1
ELK3
CLEC10A
CTSG


TLR9
ECM2
ELTD1
CLEC4A
CTTNBP2


WDFY4
EDIL3
EMCN
CLEC4E
DDX26B



EDNRA
EMP1
CLEC5A
DDX3Y



EFEMP2
ENG
CLEC7A
DENNDIB



ENPEP
ENPP2
CPVL
DIP2C



EPS8
EPB41L4A
CREG1
DRD2



FHL5
ESAM
CRYL1
DUSP10



FILIP1L
ESM1
CSF1R
DUSP14



FN1
EXOC3L1
CSF2RA
DUSP6



FOXS1
EXOC3L2
CSF3R
EDEM2



GALNT16
FAM107A
CSTA
EFHC2



GEM
FAM167B
CTSB
ELL2



GJC1
FAM198B
CTSL1
ELOVL7



GPRC5C
FAM65A
CTSS
EMR2



GPX3
FCN3
CXCL16
ENPP3



GUCY1A2
FGD5
CXCL3
EPB41L1



GUCY1A3
FGF12
CYBB
ESYT1



GUCY1B3
FKBP1A
CYP2S1
EVPL



HEPH
FLI1
DAPK1
EXOSC8



HEYL
FLJ41200
DMXL2
FAIM2



HIGD1B
FLT1
DRAM2
FAM157B



HSPB2
FLT4
DSC2
FCER1A



ITGA7
GABRD
DSE
FER



KANK2
GALNT15
EBI3
FOSB



KCNE4
GALNT18
EPB41L3
GALC



KCNJ8
GIMAP6
EREG
GALNT6



KIRREL
GIMAP8
F13A1
GATA1



LDB3
GIPC2
FAM105A
GATA2



LGALS3BP
GJA1
FAM26F
GCSAML



LGI4
GNG11
FAM49A
GGT1



LHFP
GPRC5B
FCAR
GM2A



LMOD1
GRB10
FCGBP
GMPR



LPL
HECW2
FCGR1A
GRAP2



LPP
HEG1
FCGR1B
HDC



LPPR4
HID1
FCGR1C
HPGD



LTBP1
HLX
FCGR2A
HPGDS



LUM
HPCAL1
FCGR2B
HS3ST1



LURAP1L
HSPA12B
FCGR2C
HS6ST1



LZTS1
HSPG2
FCGRT
IL18R1



MAP2
HYAL2
FCN1
IL1RL1



MARK1
ICAM2
FGD4
IL5RA



MFGE8
IFI27
FGL2
JPH4



MGP
IGFBP3
FOLR2
KCNMA1



MIR143HG
IL3RA
FPR1
KCNQ1



MMP11
INPP1
FPR3
KIAA1522



MRGPRF
INSR
FUCA1
KIAA1549



MRVI1
IPO11-LRRC70
G0S2
KIT



MSRB3
IQCK
GAA
KREMEN1



MT1A
ITGA2
GAB2
KRT1



MT2A
ITGA5
GATM
LAT



MYH11
ITGA6
GCA
LAT2



MYL9
ITGB4
GGTA1P
LAX1



MYLK
JAG2
GK
LEO1



MYO1B
JAM2
GPR137B
LIF



NNMT
KANK3
GPR34
LOC100130264



NOTCH3
KCNJ2
GPR84
LOC284454



NRIP2
KCNK5
GRINA
LOC339524



NTRK2
KCNN3
GRN
LPCAT2



NUPR1
KDR
HCAR2
LTC4S



OLFML2B
KIAA0355
HCAR3
MAOB



PALLD
KIAA1462
HCK
MAPK6



PARM1
KIAA1671
HEIH
MAPRE1



PCDH18
KL
HEXA
MBOAT7



PCOLCE
LDB2
HEXB
MEIS2



PDE5A
LIMS2
HK3
MITF



PDGFA
LOC100505495
HLA-DMB
MLPH



PDGFRB
LUZP1
HLA-DPB2
MRGPRX2



PHLDA1
MALL
HMOX1
MS4A2



PLAC9
MANSC1
HPSE
MSRA



PLEKHH2
MECOM
HSD17B11
NDEL1



PLN
MGST2
HSPA6
NDST2



PODN
MKL2
HSPA7
NEK6



PPP1RUA
MMRN2
IER3
NFKBIZ



PRELP
MPZL2
IFI30
NMT2



PRKG1
MTMR9LP
IGF1
NRCAM



PTEN
MYCT1
IGSF21
NTM



PTGIR
NDST1
IGSF6
NTRK1



RASD1
NEDD9
IL10
OSBPL8



RASL12
NOS3
IL1A
P2RX1



RCN3
NOSTRIN
IL1B
PADI2



REM1
NOTCH4
IL1R2
PAK1



RERG
NOX4
ILIRAP
PAQR5



RGS16
NPDC1
IL1RN
PEPD



RGS5
NPR1
IL6R
PIGA



S1PR3
NRN1
IL8
PIK3R6



SELM
PALMD
INSIG1
PLAT



SEMA5A
PCDH12
IRAK2
PLGRKT



4-Sep
PCDH17
IRAK3
PLIN2



SERPINF1
PDE10A
KCTD12
PLXNA4



SERPING1
PDE2A
KLF4
PPM1H



SGCA
PECAM1
KYNU
PPP1R15B



SLC2A4
PIK3R3
LGMN
PRDX1



SLC7A2
PKN3
LILRA1
PRDX6



SMOC2
PLCB1
LILRA2
PRELID2



SOD3
PLVAP
LILRA3
PRG2



SORBS3
PLXNA2
LILRA6
PRKAB1



SSTR2
PLXND1
LILRB2
PRKCA



STEAP4
PODXL
LILRB3
PRR26



SUSD2
PPM1F
LILRB4
PTGS1



SYNPO2
PREX2
LILRB5
RAB27B



TAGLN
PRSS23
LOC100505702
RAB37



TFPI
PTPRB
LOC338758
RAB38



TGFB1I1
PTPRM
LOC731424
RAB44



TGFB3
PVR
LRRC25
RASGRP4



THBS2
RAMP2
LST1
RD3



THY1
RAMP3
LY86
RGS13



TINAGL1
RAPGEF3
LYZ
RHBDD2



TMEM119
RAPGEF4
MAFB
RPS6KA5



TNFAIP6
RAPGEF5
MAN2B1
SDPR



TPM1
RASA4
MANBA
SERPINB1



TPM2
RASGRF2
MB21D2
SIGLEC17P



TPPP3
RASGRP3
MCOLN1
SIGLEC6



TRPC6
RASIP1
ME1
SIGLEC8



VCL
RBP7
MERTK
SLC11A2



ZAK
RGS3
MFSD1
SLC18A2




RND1
MGAT1
SLC1A5




ROBO4
MKNK1
SLC24A3




RPS6KA2
MNDA
SLC26A2




S100A16
MPEG1
SLC2A6




S1PR1
MRC1
SLC39A11




SCARB1
MRO
SLC44A1




SCHIP1
MS4A14
SLC45A3




SEC14L1
MS4A4A
SLC4A8




SEMA3F
MS4A6A
SLC8A3




SEMA6B
MS4A7
SMIM3




SH3BGRL2
MSR1
SMYD3




SHANK3
MXD1
SSR4




SHE
MYO7A
ST3GAL4




SHROOM4
NAAA
STMN1




SLC29A1
NAGA
STX3




SLC9A3R2
NAIP
STXBP2




SLCO2A1
NAMPT
STXBP5




SMAGP
NCF2
STXBP6




SOCS2
NFAM1
SVOPL




SOX7
NINJ1
TBC1D14




SPRY1
NLRP3
TDRD3




SPTBN1
NPL
TEC




STC1
OGFRL1
TESPA1




SYNPO
OLR1
TMEM154




TEK
OR2B11
TMOD1




TGFBR2
OSCAR
TPSAB1




TGM2
OSM
TPSB2




THSD1
P2RX7
TPSD1




THSD7A
P2RY13
TPSG1




TIE1
PILRA
TPST2




TJP1
PLA2G7
TRPV2




TM4SF1
PLB1
TSC22D2




TM4SF18
PLBD1
TSNAX




TMEM204
PLEK
TSTD1




TNFAIP1
PLSCR1
TTI1




TNFAIP8L1
PLXDC2
UNC13D




TNFRSF10C
PPIF
VAT1




TNXB
PPT1
VWA5A




TSPAN18
PYGL
ZNF48




TSPAN7
RAB20




USHBP1
RASSF4




VAMP5
RBM47




VWF
RGS18




WWTR1
RNASE6




ZNF366
S100A9





SCIMP





SDS





SERPINA1





SERPINB8





SHMT1





SIGLEC1





SIGLEC10





SIGLEC9





SIRPA





SIRPB1





SIRPB2





SLAMF8





SLC11A1





SLC15A3





SLC16A10





SLC1A3





SLC31A2





SLC37A2





SLC40A1





SLC43A2





SLC7A7





SLC8A1





SLCO2B1





SNX8





SOD2





SPI1





SPP1





STAB1





TBXAS1





TFEC





TFRC





TGFBI





THEMIS2





TKT





TLR1





TLR2





TLR4





TM6SF1





TMEM106A





TMEM176A





TMEM176B





TMEM86A





TNF





TNFSF13





TNFSF13B





TOM1





TREM1





TREM2





TRPM2





VMO1





VSIG4





WDR91





ZNF267





ZNF385A

















CD4
CD8

Malignant



NK cell
T cell
T cell
T cell
cell







AOAH
ADAM19
CD8A
ADAM19
ABTB2



B3GNT7
CCR4
CD8B
BCL11B
AK4



C1orf21
CD28
GZMH
CAMK4
ALDH1A3



CD247
CD4
LAG3
CCR4
ALDH7A1



CD7
CD40LG

CCR5
ALKBH2



CMC1
CD5

CD2
ALX4



DENND2D
CTLA4

CD27
ANKRD20A12P



EFHD2
CXCR6

CD28
APLP1



FGFBP2
DPP4

CD3D
ARC



GK5
FLT3LG

CD3E
ARMCX2



GNLY
GPRIN3

CD3G
ATN1



GZMB
ICOS

CD4
ATXN7L3B



HIPK2
IL7R

CD40LG
BAI2



IL2RB
MAF

CD5
BAMBI



KIR2DL1
OXNAD1

CD6
BARX2



KIR2DL3
PBX4

CD8A
BEX2



KIR2DL4
PBXIP1

CD8B
BMP4



KIR2DS4
POLR3E

CDKN1B
BMP5



KIR3DL1
RCAN3

CLEC2D
BMP7



KIR3DL2
SPOCK2

CTLA4
BMPER



KLRC1
TNFRSF25

CXCR6
BNC2



KLRC2
TRAT1

DPP4
BRD8



KLRC3


DUSP4
C14orf39



KLRD1


EMB
C19orf48



KLRF1


EMBP1
C5orf4



KRT86


EML4
CA10



LGALS9C


FAM102A
CA11



MCTP2


FLT3LG
CACNA1G



MLC1


FYB
CAD



NCR1


GALM
CADM1



PRF1


GPR171
CBX1



S1PR5


GPRIN3
CCBE1



SH2D1B


GZMH
CCDC144B



SLFN13


GZMK
CCDC144C



SPON2


ICOS
CCDC171



TXK


IL32
CCNB1IP1



ZBTB16


IL7R
CDH11






LAG3
CDH3






LCK
CDON






LEPROTL1
CES4A






LIME1
CHST8






MAF
CILP2






MIAT
CKB






NLRC5
CKS1B






OXNAD1
CLUL1






PBX4
COL11A2






PBXIP1
COL2A1






PDCD1
COL4A5






PIK3IP1
COL9A2






POLR3E
COL9A3






RCAN3
COLEC12






SIRPG
CPT1C






SPOCK2
CRABP1






TC2N
CRABP2






THEMIS
CRISPLD1






TNFRSF25
CRLF1






TRAT1
CRNDE






UBASH3A
CRTAC1






WNK1
CSAD






ZFP36L2
CTAG1A







CTAG1B







CUL7







DHRS3







DLK1







DLX1







DLX2







DMKN







DNAH14







DNM3OS







DNPH1







DPEP1







EDN3







EFNA2







EFNA5







EGFR







EPCAM







EPS8L2







ETV4







FAHD2B







FAM115A







FBLN1







FBN2







FBXO2







FGF11







FGF19







FGF9







FGFR1







FGFR2







FGFRL1







FIBCD1







FKBP10







FKTN







FLNC







FLRT1







FLRT3







FOXD2-AS1







FOXF1







FSTL4







FUZ







FZD1







GADD45G







GATA6







GFRA1







GLT8D2







GPC4







GPR125







GRIK3







GRM4







GSTA4







HHAT







HIST1H2BK







HRNR







HS6ST2







IFT88







IGF2







IGF2BP2







IQCA1







KDM5B







KIF1A







KIF26B







KLK10







LINC00516







LINC00665







LOC100128881







LOC100506123







LOC101101776







LOC339166







LOC349196







LPHN1







LRIG3







LTBP4







MAGED4







MAGED4B







MDK







MEG3







MEG8







MEOX2







MFAP2







MIR100HG







MLLT11







MMP2







MRC2







MSLN







MSX2







MTMR11







MUC1







MUC6







NEFH







NET1







NGFRAP1







NIPSNAP1







NIT2







NKD2







NLGN3







NOG







NPTX2







NPW







NRP2







NSG1







NSMF







NTN1







OCA2







OLFM1







OSR1







PAFAH1B3







PAGR1







PAICS







PARP2







PCDHA6







PCDHB10







PCDHB14







PCDHB2







PCDHGA3







PCDHGC3







PCSK1N







PDGFRA







PHC1







PHGDH







PIGC







PIGP







PIP5K1A







PKD1







PNMAL1







PRAME







PSAT1







PSD3







PTCH1







PTPRF







PTPRU







RASL11B







RBP1







RIPK4







RNF212







ROBO2







ROR1







RTL1







RTN1







SCRN1







SCUBE1







SERPINE2







SERTAD4







SGCD







SHANK2







SHISA2







SIM2







SIX1







SIX4







SIX5







SLC16A4







SOHLH1







SOX15







SOX8







SOX9







SPDYE8P







SPOCK1







SSX1







SSX2







SSX2B







STEAP2







STRA6







SUCO







SV2A







TARBP1







TBX18







TBX3







TBX5







TCEAL7







TENM3







TET1







TGFB2







THBS3







THSD4







TIMM13







TLE1







TMED3







TMEM106C







TMEM25







TMEM254







TMEM30B







TMEM59L







TMEM67







TMTC2







TNC







TNNI3







TNNT1







TNPO2







TRO







TRPS1







TSPYL4







TUBB2B







TUSC3







UCHL1







USP46







WIF1







WNT5A







ZFHX4-AS1







ZIC2







ZNF512







ZNF608







ZNF692







ZNF711

















TABLE 5







Canonical markers used for the initial cell type assignments in Table 4.


Canonical markers













T cell
B cell
Macrophage
Mastocyte
NKcell
Endothelial cell
CAF





CD2
CD19
CD163
ENPP3
KLRA1
PECAM1
FAP


CD3D
CD79A
CD14
KIT
NKG2
VWF
THY1


CD3E
CD79B
CSF1R

KLRB1
CDH5
DCN


CD3G
BLK


KLRD1

COL1A1








COL1A2








COL6A1








COL6A2








COL6A3









Applicants assigned the cells to nine subsets: malignant cells, epithelial cells, Cancer Associated Fibroblasts (CAFs), CD8 and CD4 T cells, B cells, Natural Killer (NK) cells, macrophages, and mastocytes, and generated signatures for each subset (Tables 4, 5, FIGS. 1B, 3A). Malignant cells primarily grouped by their tumor of origin, while their non-malignant counterparts (immune and stroma) grouped primarily by cell type (FIG. 1B), as was observed in other tumor types (Puram et al. Cell 171:1611-1624.e24 (2017; Tirosh et al. Science 352:189-196 (2016); Venteicher et al. Science 355 (2017) doi:10.1126/science.aai8478). The malignant cells of each of the biphasic (BP) tumors (S1 and S12) formed two distinct subsets—epithelial and mesenchymal—which clustered together with malignant cells of the other biphasic tumors (FIG. 1B).


Example 2—Developmental Hierarchies and a Repeating Pattern of Intratumor Variation

In the malignant cells, Applicants identified three major patterns of intratumor variation that were shared across multiple tumors (FIG. 4A): (de)differentiations, cell cycle, and a new cellular modality that were termed the core oncogenic program. First, Applicants charted the developmental hierarchy of synovial sarcoma cells, revealing a spectrum of cell states along two differentiation trajectories. To uncover this pattern, they identified mesenchymal and epithelial lineage programs based on intratumor variation within biphasic tumors (FIG. 4A, 3A, Tables 1, 6, and 7). The programs overlapped previous signatures of epithelial to mesenchymal transition (Taube et al. PNAS 107:15449-15454 (2010); Gröger et al. PLOS ONE 7:e51136 (2012)) (P<1.55*10−10, hypergeometric test), and included canonical markers of mesenchymal (ZEB1, ZEB2, PDGFRA and SNAI2) and epithelial (MUC1 and EPCAM) cells (FIG. 5A). Next, Applicants scored each cell for the mesenchymal and epithelial programs, and computed differentiation scores based on the overall expression of both programs (FIG. 4C, Methods). This analysis suggests that the cells gradually acquire (or lose) mesenchymal or epithelial features, stemming from a subpopulation of poorly differentiated cells, which underexpressed both programs (FIG. 4C).


Cellular Plasticity and a Core Oncogenic Program Characterize Synovial Sarcoma Cells

To identify malignant cell functions that may impact immune cell infiltration, Applicants characterized the cellular programs in SyS malignant cells. Applicants identified three co-regulated gene modules, which repeatedly appeared across multiple tumors in Applicants' cohort (FIG. 4A, Table 6, METHODS). The first two modules reflected mesenchymal and epithelial cell states (FIG. 4A, 20A). These differentiation programs included canonical mesenchymal (ZEB1, ZEB2, PDGFRA and SNAI2) or epithelial (MUC1 and EPCAM) markers (36, 37) (P<1.55*10−10, hypergeometric test), and demonstrated that epithelial cells had a marked increase in antigen presentation and interferon (IFN) γ responses (P<8.49*10−6, hypergeometric test).


Among mesenchymal cells with a relatively low Overall Expression (METHODS) of the mesenchymal program, one subset also expressed epithelial markers, reminiscent of transitioning to/from an epithelial state, while another underexpressed both programs, reminiscent of a poorly differentiated state. These poorly differentiated cells were highly enriched with cycling cells (P=2.44*10−6°, mixed effects), indicating that they might function as the tumor progenitors, fueling tumor growth (FIG. 4C, FIG. 12B, 12C). Diffusion map analysis of the cells based on these two programs highlighted putative differentiation trajectories, and found structured differentiation patterns only in the biphasic tumors (FIG. 15A, METHODS). RNA velocity (38) demonstrated that epithelial to mesenchymal transitions may also take place (FIG. 20B), suggestive of cellular plasticity. Further supporting this hypothesis, the post-treatment sample of patient SyS12 includes a new subpopulation of mesenchymal cells, which was absent from the pre-treatment sample, and resembles the epithelial cells in terms of its CNAs (FIG. 20C).


The third module highlighted a new program present in a subset of cells in each tumor (25.2-84.7% per tumor, FIG. 4A, 15B, FIG. 21A-21C), which Applicants named the core oncogenic program. The program is characterized by expression of genes from respiratory carbon metabolism (oxidative phosphorylation, citric acid cycle, and carbohydrate/protein metabolism, P<1*10−8, hypergeometric test, Table 6), and repression of genes involved in TNF signaling, apoptosis, p53 signaling, and hypoxia processes (P<1*10−10, hypergeometric test, Table 6), including known tumor suppressors, such as p21 (CDKN1A) and KLF4. The program was expressed in a higher proportion of cycling and poorly differentiated cells (P<2.94*10−4, mixed-effects, FIG. 15C).


To test the clinical value of these transcriptional programs, Applicants reanalyzed two independent bulk gene expression cohorts (21, 22). Both dedifferentiation (METHODS) and the core oncogenic program were substantially more pronounced in the more aggressive poorly differentiated SyS tumors (P<2.76*10−4, one-sided t-test, FIG. 5A, METHODS), and were associated with increased risk of metastatic disease (P<1.36*10−3, Cox regression, FIG. 5B).









TABLE 6







Malignant programs identified in the clinical scRNA-Seq cohort.













Cell

Core oncogenic


Epithelial
Mesenchymal
cycle
Core oncogenic up
down
















ABCG1
LBH
AASS
ANLN
AFG3L1P
MRPL28
AKIRIN1


ABHD11
LECT1
ADAM33
ARHGAP11A
AGPAT2
MRPL35
AMD1


ABRACL
LGALS3BP
AKAP13
ATAD5
AGPAT5
MRPL4
ARC


ACOT7
LIME1
ANKRD44
BIRC5
AHCY
MRPL52
ATF3


ACP5
LLGL2
ARMCX3
BRCA2
AKR1B1
MRPS17
ATF4


ADAMTSL2
LOC100505761
ATP1B2
BUB1B
AKR1C3
MRPS21
BHLHE40


AES
LOC541471
BMP5
C21orf58
AKT1
MRPS26
BRD2


AGPAT2
LOC646329
C14orf37
CASC5
ALDH1A1
MRPS34
BTG1


AGRN
LPAR2
C14orf39
CCNA2
ALG3
MTG1
BTG2


AGTRAP
LPIN3
C16orf45
CCNB2
ALX4
MTRNR2L1
C12orf44


AHNAK2
LRRC16A
Clorf151-NBL1
CCNE2
ANAPC7
MTRNR2L10
C6orf62


AIG1
LSR
CACNB2
CDC6
ANKRD26P1
MTRNR2L2
CCNL1


AKR1C3
LY6E
CADM1
CDKN3
APEH
MTRNR2L6
CDKN1A


ALDH1A3
LYPD6B
CALD1
CENPE
APEX1
MTRNR2L8
CKS2


ALDH3A2
MAG11
CCBE1
CENPF
APP
MYBBP1A
CLK1


ALDH4A1
MAL2
CCDC88A
CENPH
APRT
MZT2B
COQ10B


ALOX15
MAP7
CD302
CENPK
ARF5
NACA
CSRNP1


ANK3
MBOAT1
CLIP3
CENPW
ARL6IP4
NAT14
CYCS


ANO9
MCAM
CNRIP1
CHAF1B
ARL6IP5
NDUFA1
DDIT3


ANXA11
MDK
CNTLN
CLSPN
ASB13
NDUFA11
DDX3X


ANXA3
MFSD3
COL1A2
DHFR
ATF7IP
NDUFA13
DDX3Y


AP1M2
MGAT4B
COL21A1
DNA2
ATIC
NDUFA3
DDX5


APOE
MIF4GD
COL4A1
DTL
ATP5A1
NDUFA4
DLX2


APP
MLXIPL
COL4A2
EZH2
ATP5C1
NDUFA7
DNAJA1


ARHGAP8
MPZL2
COL5A1
FANCA
ATP5E
NDUFA8
DNAJA4


ARID5A
MSLN
COL5A2
FANCD2
ATP5G2
NDUFAB1
DNAJB1


ARRDC1
MSMO1
COL6A3
FANCI
ATP5I
NDUFB10
DNAJB9


ASS1
MSX2
COL8A1
FOXM1
ATP5J
NDUFB11
DUSP1


ATHL1
MUC1
CPXM1
GINS2
ATP5J2
NDUFB2
DUSP2


ATP6V0E2
MX1
CRTAP
HELLS
ATP5O
NDUFB3
EGR1


BAIAP2L1
MYH9
CXCL12
KIAA0101
ATR
NDUFB4
EGR2


BARX2
MYO6
CYGB
KIF11
ATRAID
NDUFB7
EGR3


BCAM
NCOA7
DAB2
KIF14
AUP1
NDUFB9
EIF1


BSCL2
NDUFA4L2
DCN
KIF18A
AURKAIP1
NDUFS6
EIF4A3


C14orf1
NDUFS8
DEGS1
KIF20B
BCAP31
NDUFS8
EIF5


C19orf21
NET1
DNAJA4
KIF2C
BCL7C
NEDD8
ERF


C19orf33
NPNT
DNAJC12
KNSTRN
BMP1
NEFL
ETF1


C1GALT1C1
NSMF
DNM3OS
KNTC1
BOP1
NHP2
FAM53C


C1orf210
NT5DC1
DZIP1
MAD2L1
BRK1
NIPSNAP3A
FOS


CAP2
NT5E
EDNRA
MCM2
BSG
NKAIN4
FOSB


CAPN6
NUDT14
EGFR
MCM3
BTF3
NME1
FOSL1


CARD16
OAS1
EMP1
MCM4
C11orf48
NME2
FOSL2


CARNS1
OCIAD2
F2R
MCM5
C14orf2
NNT
GADD45B


CBLC
OCLN
FBXO32
MKI67
C16orf88
NOMO1
GEM


CCDC153
ORMDL2
FERMT2
MLF1IP
C17orf76-AS1
NOMO2
GTF2B


CCDC24
P4HTM
FGF1O
NCAPD2
C1QBP
NPEPL1
H3F3B


CCND1
PARD6B
FHL1
NCAPG2
C2orf68
NRBP2
HBP1


CD151
PARP8
FKBP7
NUSAP1
C4orf48
NREP
HERPUD1


CD55
PARP9
FLJ42709
OAS3
C7orf73
NSMF
HES1


CD59
PARVG
FLNB
OIP5
C9orfl6
NSUN5
HSP90AA1


CD7
PCBD1
FN1
ORC6
CAD
NSUN5P1
HSP90AB1


CD74
PDGFB
FOSL2
PRC1
CALML3
NSUN5P2
HSPA1A


CD9
PDHX
FRZB
PSMC3IP
CAPNS1
NT5DC2
HSPA1B


CDCP1
PDLIM1
FSTL1
PTTG1
CBX6
NUBP2
HSPA8


CDH1
PDLIM2
GALNT18
RACGAP1
CCDC137
NUDT5
HSPH1


CDH3
PERP
GEM
RFC4
CCDC140
NUTF2
ICAM1


CDH4
PHYHD1
GFPT2
RNASEH2A
CCT3
OBSL1
ID1


CDK2AP2
PIGV
GFRA1
RRM2
CD320
OGG1
ID2


CHST9
PIM1
GPM6B
SGOL2
CD63
OST4
ID3


CKB
PKP3
GPX7
SMC4
CD7
OXLD1
IER2


CLDN3
PKP4
GSTA4
SPAG5
CDK2AP1
PAFAH1B3
IER3


CLDN4
PLEKHB1
GSTM5
SPDL1
CECR5
PARK7
IFRD1


CLDN7
PLEKHG1
GYPC
STIL
CHCHD1
PATZ1
IRF1


CLIC3
PLEKHN1
HAAO
TCF19
CHCHD2
PAX3
JUN


CLU
PLLP
HCG11
TIMELESS
CIAPIN1
PAX9
JUNB


COL12A1
PLXDC2
HENMT1
TK1
CKAP5
PCDHA3
JUND


CRB3
PLXNA2
HMGCLL1
TOP2A
CLDN4
PDCD11
KLF10


CRIP1
PLXNB1
HOXC10
TPX2
CLNS1A
PDCD5
KLF4


CRIP2
PNOC
HOXC9
TYMS
CNPY2
PDIA4
KLF6


CXADR
PNP
HSD17B11
UBE2C
COA5
PEBP1
KLHL15


CXCL1
PPL
IFFO1
UBE2T
COL18A1
PET100
LMNA


CYB561
PPP1CA
IL17RD
UHRF1
COL5A1
PFKL
LOC284454


CYBA
PPP1R16A
IL1R1
WDHD1
COL6A2
PFKP
MAFF


CYFIP2
PPP1R1B
INHBA
ZWINT
COL9A3
PFN1
MCL1


CYHR1
PPP1R9A
INPP4B

COX4I1
PFN1P2
MIR22HG


CYP39A1
PRKCG
ITPRIPL2

COX5A
PGD
MLF1


CYP4X1
PRPH
KIF26B

COX5B
PGLS
MXD1


CYSTM1
PRR15
LAMA2

COX6A1
PHF14
MYADM


DBNDD2
PRR15L
LAMB1

COX6B1
PIGM
NFATC1


DCXR
PRSS8
LEF1

COX6C
PIGQ
NFATC2


DDR1
PSME1
LEPRE1

COX7C
PIGT
NFKBIA


DDX58
PSME2
LOXL2

CRIP1
PKD2
NFKBIZ


DHCR7
PTGER4
LRP1

CRLF1
PLP2
NR4A1


DMKN
PTGES
LUM

CRMP1
PMS2P5
NR4A2


DRD1
PTN
MEF2A

CSAG3
POLD2
NR4A3


DSP
PTPRF
MEOX2

CSE1L
POLR1B
PAFAH1B2


EFCAB4A
PTRH1
MFAP4

CSRP2BP
POLR2F
PER1


EFNA5
RAB3IP
MLF1

CST3
PPIA
PER2


ELOVL1
RALGPS1
MMP2

CSTB
PPIB
PPP1R15A


ELOVL7
RASSF7
MSN

CSTF3
PPIP5K2
RGS16


EMB
RBM47
MSRB3

CTAG1A
PPP1R16A
RHOB


ENO2
REC8
MXRA5

CTAG1B
PRDX2
RIPK4


ENPP5
REEP2
MYL9

CYC1
PRDX4
RRP12


ENTPD3
RGL3
NCAM1

CYHR1
PRELID1
SAT1


EPB41L5
RHBDF2
NDNF

DAD1
PRKDC
SELK


EPCAM
RHBDL1
NDOR1

DANCR
PSMA5
SERTAD1


EPHA2
RIPK4
NEDD4

DBNDD1
PSMA7
SF1


EPS8L2
ROBO3
NEFH

DCHS1
PSMB7
SIK1


ERBB2
RTN3
NID1

DCP1B
PSMD4
SLC25A25


ERBB3
S100A16
NID2

DCTPP1
PSMG3
SLC25A44


ESRP1
S100A4
NR4A2

DCXR
PTPRF
SOCS3


ESRP2
S100A6
NUDT11

DGCR6L
PTPRS
SRSF3


EZR
SAMD12
OXER1

DHFR
PUS7
TNFAIP3


F11R
SCG5
PALLD

DNMT3A
PXDN
TNFRSF12A


F2RL1
SCNN1A
PDGFRA

DPEP3
PYCR1
TOB1


FAAH
SCRN2
PDIA5

DPYSL2
RABAC1
TRIB1


FAAH2
SEC11C
PDLIM4

DYNLRB1
RABL6
TSPYL1


FAM111A
SECTM1
PDZRN3

DYNLT1
RANBP1
TSPYL2


FAM167A
SELENBP1
PLIN2

EDF1
RBM26
TUBA1A


FAM213A
SEMA3B
PLK1S1

EEF1B2
RBM6
TUBA1B


FAM221A
SGPL1
PLSCR4

EEF1D
RBX1
TUBB2A


FAM65C
SH3YL1
PMP22

EEF1G
REST
TUBB4B


FAM84B
SHANK2
PPP1R15B

EIF2AK1
RGMA
UBB


FBXO2
SHANK2-AS3
PROS1

EIF3C
RGS10
UBC


FBXO44
SIM2
QKI

EIF3H
RHOBTB3
XBP1


FGF19
SLC11A2
QPRT

EIF3K
RNASEK
YWHAG


FGFRL1
SLC12A2
RAB31

EIF4EBP1
RNPC3
ZBTB21


FMO2
SLC16A5
RAI14

ELAC2
RNPEP
ZFAND5


FXYD3
SLC25A25
RASL11B

ELOVL1
ROMO1
ZFP36


FXYD5
SLC25A29
RBMS3

EML3
RUVBL1


FZD6
SLC29A1
RCBTB2

ENO1
RUVBL2


GALNT3
SLC35F2
RCN3

EPRS
SARS2


GAS6
SLC50A1
RGL1

ERGIC3
SELENBP1


GCHFR
SLC6A9
RGS3

ETAA1
SEMA3A


GPR56
SLC7A5
RHOJ

EXOSC4
SERF2


GPRC5A
SLC7A8
RUNX1T1

EXOSC7
SERTAD4


GPRC5C
SLFN5
SEMA6A

FADD
SETD4


GRB7
SLPI
SERTAD1

FADS2
SFN


GSDMD
SMAD1
SESN1

FAM178A
SGK196


HERC6
SMPDL3B
SH3PXD2A

FAM19A5
SH2D4A


HIGD2A
S0RT1
SIX1

FAM213B
SH3PXD2B


HLA-B
SOX14
SLC2A10

FAM50B
SHMT2


HMGA1
SPINT1
SNAI2

FARSA
SIGIRR


HOOK2
SPINT2
SPARC

FARSB
SIM2


HPN
ST14
ST3GAL3

FBN3
SIX1


HSPB2
ST3GAL5
STARD13

FGF19
SLC25A23


IFITM1
STAP2
TCF12

FGF9
SLC25A6


IFITM2
STRA13
TCF4

FLAD1
SLC35B4


IFITM5
STRA6
TGFB1I1

FMO1
SLC6A15


IGFBP6
STXBP2
TMEM30B

FRG1B
SMARCA4


IGSF9
SULF1
TMEM45A

FSD1
SMC2


INADL
SULF2
TNFRSF19

G6PC3
SMC3


INF2
SUMF1
TSC22D3

GABPB1-AS1
SNHG6


IQGAP1
SVIP
UBE2E2

GADD45GIP1
SNRPD2


IRF6
SYNGR2
UBL3

GAPDH
SNRPD3


IRF7
SYTL1
UNC5B

GCN1L1
SNRPF


ISLR
TACSTD2
WIF1

GDI2
SOX11


ITGA3
TAPBPL
WNT16

GEMIN7
SPCS1


ITGB4
TCF7L2
ZEB1

GGH
SPDYE8P


ITGB8
TENM1
ZEB2

GLB1L
SRI


ITPR2
TFAP2B
ZFHX4

GLB1L2
SRM


ITPR3
TFAP2C
ZNF3O2

GLI1
SRSF9


JUP
TLE2


GNAS
SSNA1


KIAA1522
TLE6


GNB2L1
SSR4


KIAA1598
TM4SF1


GNPTAB
SSX2


KIF1A
TM7SF2


GOLM1
SSX2B


KLF5
TMC4


GPR124
STAG3L1


KLK1
TMCC3


GPR126
STAG3L2


KLK10
TMEM125


GPRC5B
STAG3L3


KLK11
TMEM176B


GSTO2
STAG3L4


KLK7
TNFAIP2


GUSB
STARD4-AS1


KLK8
TNFRSF12A


H19
SULF2


KRT18
TNFRSF14


HERC2
SULT1A1


KRT19
TNFRSF21


HERC2P7
SUMF2


KRT7
TNFSF13


HIGD2A
SYNPR


KRT8
TNKS1BP1


HINT1
TBCD


KRTCAP3
TNNI3


HMG20B
TCEB2



TNNT1


HN1L
TELO2



TOM1L1


HNRNPD
TFAP2A



TPD52


HOXD11
THY1



TSPO


HOXD9
TIGD1



TUBB2B


HSD17B10
TIMM13



TUBB3


HYAL2
TIMM8B



UCP2


HYLS1
TKT



VAMP8


ICT1
TMA7



WDR34


IFT81
TMC6



WDR54


IMP3
TMEM101



WFDC2


ING4
TMEM147



XAF1


IRS4
TMEM177



ZDHHC12


ITM2C
TMSB10



ZMAT1


ITPA
TMTC2



ZNF165


JMJD8
TOMM40



ZNF423


KDM1A
TOMM6



ZNF664


KIAA0020
TOMM7






KIF1A
TRAPPC1






KRT14
TSPAN3






KRT15
TSR3






KRT8
TSTA3






KRTCAP2
TTYH3






LAMA2
TUBG1






LARP1
TUFM






LDHB
TUSC3






LECT1
TWIST2






LGALS1
TXN






LINC00115
TXNDC17






LINC00116
TXNDC5






LINC00516
TXNDC9






LINC00665
UBA52






LOC100131234
UBE2T






LOC100272216
UBE3B






LOC101101776
UCK2






LOC202781
UCP2






LOC375295
UPK3B






LOC441081
UQCR10






LOC654433
UQCR11






LOXL1
UQCRB






LSM4
UQCRC1






LSM7
UQCRQ






LUC7L3
USMG5






LY6E
USP5






MAB21L1
VARS






MAGEA4
VCAN






MAGEA9
VKORC1






MAGEC2
VPS28






MAP1B
VPS72






MATN3
VSNL1






MBD6
WDR12






MDH2
YWHAB






MDK
ZNF212






METTL3
ZNF605






MFSD3






MGC21881






MGST1






MGST3






MIF






MIS18A






MKKS






MMP14






MRPL12






MRPL15






MRPL17
















TABLE 7







Malignant programs enrichment with pre-defined gene


sets (hypergeometric p-values: −log10 transformed).









Hypergeometric p-values (−log10 transformed)
















Core
Core





Cell
oncogenic
oncogenic


Gene set
Epithelial
Mesenchymal
cycle
up
down















HALLMARK TNFA SIGNALING
0.46
1.24
0.00
0.00
17.00


VIA NFKB


HALLMARK APOPTOSIS
1.99
2.61
0.27
0.01
12.10


HALLMARK HYPOXIA
0.59
1.61
0.00
0.31
9.74


HALLMARK P53 PATHWAY
2.50
0.16
0.00
0.15
9.41


GO CELL CYCLE PROCESS
0.00
0.01
17.00
0.05
2.86


GO NUCLEOSIDE
0.08
0.00
0.19
17.00
1.36


TRIPHOSPHATE METABOLIC


PROCESS


GO GLYCOSYL COMPOUND
0.21
0.00
0.34
17.00
1.17


METABOLIC PROCESS


EMT Up Groger et al. 2012)
0.00
10.84
0.00
0.38
0.33


EMT Up (Taube et al. 2010)
0.00
9.81
0.00
0.10
0.27


GO OXIDATIVE
0.04
0.00
0.00
17.00
0.25


PHOSPHORYLATION


HALLMARK E2F TARGETS
0.00
0.00
17.00
1.13
0.23


HALLMARK OXIDATIVE
0.01
0.00
0.00
17.00
0.05


PHOSPHORYLATION


EMT Down (Groger et al. 2012)
17.00
0.32
0.00
0.06
0.00


EMT Down (Taube et al. 2010)
17.00
0.18
0.00
0.21
0.00


GO OXIDOREDUCTASE
0.34
0.00
0.43
11.46
0.00


COMPLEX


GO POSITIVE REGULATION OF
0.73
2.58
0.05
0.13
17.00


GENE EXPRESSION


GO POSITIVE REGULATION OF
0.29
2.65
0.15
0.40
17.00


TRANSCRIPTION FROM RNA


POLYMERASE II PROMOTER


GO REGULATION OF
0.05
1.21
0.18
0.14
17.00


TRANSCRIPTION FROM RNA


POLYMERASE II PROMOTER


GO RNA POLYMERASE II
0.25
2.03
0.06
0.04
17.00


TRANSCRIPTION FACTOR


ACTIVITY SEQUENCE SPECIFIC


DNA BINDING


GO NEGATIVE REGULATION OF
0.14
0.29
0.66
0.30
15.65


NITROGEN COMPOUND


METABOLIC PROCESS


GO REGULATION OF CELL
0.58
1.68
0.03
1.13
15.35


DEATH


GO TRANSCRIPTION FACTOR
0.43
2.98
0.00
0.06
15.35


ACTIVITY RNA POLYMERASE II


CORE PROMOTER PROXIMAL


REGION SEQUENCE SPECIFIC


BINDING


GO NEGATIVE REGULATION OF
0.11
0.30
0.45
0.52
14.81


GENE EXPRESSION


GO TRANSCRIPTIONAL
0.32
2.55
0.00
0.24
14.72


ACTIVATOR ACTIVITY RNA


POLYMERASE II


TRANSCRIPTION REGULATORY


REGION SEQUENCE SPECIFIC


BINDING


GO POSITIVE REGULATION OF
0.81
1.70
0.10
0.15
14.31


BIOSYNTHETIC PROCESS


GO NEGATIVE REGULATION OF
0.10
0.63
0.50
0.20
13.59


TRANSCRIPTION FROM RNA


POLYMERASE II PROMOTER


GO RESPONSE TO ABIOTIC
1.40
2.28
0.53
0.83
13.31


STIMULUS


GO SEQUENCE SPECIFIC DNA
0.07
0.93
0.89
0.15
13.07


BINDING


GO RESPONSE TO
2.37
4.09
0.66
0.85
12.58


ENDOGENOUS STIMULUS


GO REGULATORY REGION
0.09
0.60
0.10
0.04
12.49


NUCLEIC ACID BINDING


GO NUCLEIC ACID BINDING
0.01
0.92
0.22
0.00
12.36


TRANSCRIPTION FACTOR


ACTIVITY


GO RESPONSE TO NITROGEN
1.80
1.88
0.86
1.13
12.26


COMPOUND


GO DOUBLE STRANDED DNA
0.31
0.63
0.50
0.10
11.87


BINDING


GO TRANSCRIPTION FACTOR
0.01
0.57
0.05
0.42
11.80


BINDING


GO CELLULAR RESPONSE TO
4.08
4.86
0.05
0.18
11.78


ORGANIC SUBSTANCE


GO TRANSCRIPTIONAL
0.27
2.87
0.00
0.14
11.62


ACTIVATOR ACTIVITY RNA


POLYMERASE II CORE


PROMOTER PROXIMAL REGION


SEQUENCE SPECIFIC BINDING


HALLMARK UV RESPONSE UP
0.04
0.00
0.29
0.40
11.40


GO REGULATION OF SEQUENCE
0.71
0.02
0.13
0.04
11.38


SPECIFIC DNA BINDING


TRANSCRIPTION FACTOR


ACTIVITY


GO NEGATIVE REGULATION OF
0.40
2.56
0.07
0.94
10.88


CELL DEATH


GO RESPONSE TO
0.08
0.07
0.00
0.00
10.71


TOPOLOGICALLY INCORRECT


PROTEIN


GO RESPONSE TO ORGANIC
1.88
2.32
1.54
0.35
10.58


CYCLIC COMPOUND


GO TRANSCRIPTION FROM
0.01
0.66
0.11
0.05
10.45


RNA POLYMERASE II


PROMOTER


GO REGULATION OF CELL
0.01
0.19
14.45
0.13
10.33


CYCLE


GO RESPONSE TO EXTERNAL
7.02
1.11
0.10
0.21
10.21


STIMULUS


GO NEGATIVE REGULATION OF
1.61
2.13
0.02
0.47
10.20


RESPONSE TO STIMULUS


GO POSITIVE REGULATION OF
0.47
0.17
0.04
1.28
10.18


CELL DEATH


GO RESPONSE TO OXYGEN
2.62
4.80
0.81
1.50
10.14


CONTAINING COMPOUND


GO NEGATIVE REGULATION OF
0.72
2.36
0.03
0.56
10.13


CELL COMMUNICATION


GO CORE PROMOTER
0.27
1.52
0.14
0.02
9.85


PROXIMAL REGION DNA


BINDING


GO NEGATIVE REGULATION OF
0.67
0.39
1.25
0.57
9.42


PROTEIN METABOLIC PROCESS


GO TISSUE DEVELOPMENT
5.77
9.95
0.12
0.80
9.34


GO RESPONSE TO PEPTIDE
0.29
0.37
0.38
0.71
9.07


GO RHYTHMIC PROCESS
0.28
0.14
1.67
0.33
9.02


GO RESPONSE TO OXIDATIVE
0.54
2.58
0.36
2.79
8.80


STRESS


GO CIRCADIAN RHYTHM
0.11
0.00
1.03
0.54
8.64


GO RESPONSE TO INORGANIC
4.05
2.06
0.60
1.93
8.50


SUBSTANCE


GO REGULATION OF CELL
3.76
5.75
0.02
0.27
8.26


DIFFERENTIATION


GO REGULATION OF DNA
0.08
0.27
0.00
0.72
8.17


TEMPLATED TRANSCRIPTION


IN RESPONSE TO STRESS


GO REGULATION OF CELL
4.40
3.02
1.53
0.18
8.03


PROLIFERATION


GO RESPONSE TO
1.53
0.17
0.37
0.35
7.98


EXTRACELLULAR STIMULUS


GO NEGATIVE REGULATION OF
0.17
0.13
0.93
0.33
7.96


PROTEIN MODIFICATION


PROCESS


GO CELLULAR RESPONSE TO
0.65
0.08
0.00
0.11
7.91


EXTRACELLULAR STIMULUS


GO POSITIVE REGULATION OF
1.69
0.35
0.00
0.16
7.80


IMMUNE SYSTEM PROCESS


GO PROTEIN REFOLDING
0.00
0.67
0.00
0.00
7.78


GO REGULATION OF PROTEIN
0.57
1.41
1.71
0.05
7.73


MODIFICATION PROCESS


GO CELLULAR RESPONSE TO
2.02
4.61
0.06
0.39
7.71


ENDOGENOUS STIMULUS


GO REGULATION OF
0.07
1.12
0.00
0.70
7.61


APOPTOTIC SIGNALING


PATHWAY


GO RESPONSE TO CAMP
1.84
0.33
0.63
0.94
7.61


GO ENZYME BINDING
0.13
0.30
2.50
0.59
7.61


GO NEGATIVE REGULATION OF
2.69
0.09
1.06
0.28
7.57


MOLECULAR FUNCTION


GO CELLULAR RESPONSE TO
0.01
0.03
4.16
0.51
7.50


STRESS


GO NEGATIVE REGULATION OF
0.83
0.00
0.40
0.06
7.49


SEQUENCE SPECIFIC DNA


BINDING TRANSCRIPTION


FACTOR ACTIVITY


GO RESPONSE TO RADIATION
0.80
0.76
0.33
0.65
7.48


GO NEGATIVE REGULATION OF
0.18
0.20
0.08
0.08
7.46


INTRACELLULAR SIGNAL


TRANSDUCTION


GO CELLULAR RESPONSE TO
0.69
0.15
0.00
0.13
7.27


EXTERNAL STIMULUS


GO RESPONSE TO HORMONE
0.72
1.79
1.04
0.55
7.27


GO RESPONSE TO PURINE
1.13
0.20
0.47
0.79
7.22


CONTAINING COMPOUND


GO RESPONSE TO LIPID
1.14
3.32
0.49
0.25
7.18


GO NEGATIVE REGULATION OF
0.24
0.25
0.30
0.02
7.11


PHOSPHORYLATION


GO REGULATION OF CELLULAR
0.06
0.72
0.09
0.49
6.75


RESPONSE TO STRESS


GO RESPONSE TO
1.42
0.25
1.34
0.66
6.71


ORGANOPHOSPHORUS


GO RESPONSE TO STARVATION
0.17
0.11
0.00
0.19
6.66


GO UNFOLDED PROTEIN
0.03
0.17
0.42
0.61
6.66


BINDING


GO RESPONSE TO
0.43
0.00
0.00
0.35
6.58


CORTICOSTERONE


GO REGULATION OF
3.14
6.59
0.03
0.80
6.54


MULTICELLULAR ORGANISMAL


DEVELOPMENT


GO RESPONSE TO REACTIVE
0.54
1.35
0.71
1.53
6.50


OXYGEN SPECIES


GO RESPONSE TO
0.00
1.51
0.40
0.01
6.48


TEMPERATURE STIMULUS


GO RESPONSE TO HYDROGEN
0.03
0.15
0.40
0.15
6.39


PEROXIDE


GO REGULATION OF RESPONSE
0.90
1.05
0.04
0.14
6.35


TO STRESS


GO INTRINSIC APOPTOTIC
0.51
0.00
0.00
0.47
6.33


SIGNALING PATHWAY


GO TRANSCRIPTION FACTOR
0.17
0.29
0.04
0.36
6.33


ACTIVITY PROTEIN BINDING


GO NEGATIVE REGULATION OF
0.16
1.80
0.00
0.41
6.31


APOPTOTIC SIGNALING


PATHWAY


GO REGULATION OF
0.18
1.24
0.28
0.02
6.23


INTRACELLULAR SIGNAL


TRANSDUCTION


GO REGULATION OF DNA
0.05
0.21
0.00
1.69
6.22


BINDING


GO RESPONSE TO
0.47
0.64
0.00
0.26
6.19


MECHANICAL STIMULUS


GO REGULATION OF IMMUNE
2.43
0.67
0.01
0.02
6.08


SYSTEM PROCESS


GO SKELETAL MUSCLE CELL
0.20
0.00
0.00
0.43
6.06


DIFFERENTIATION


GO CELL DEATH
1.16
1.30
0.25
0.52
6.06


GO NEGATIVE REGULATION OF
0.37
0.28
0.49
0.04
6.05


PHOSPHORUS METABOLIC


PROCESS


GO MUSCLE STRUCTURE
1.37
4.79
0.10
0.71
6.05


DEVELOPMENT


GO VASCULATURE
1.99
8.85
0.00
0.09
5.95


DEVELOPMENT


GO CIRCULATORY SYSTEM
2.69
8.13
0.00
0.44
5.63


DEVELOPMENT


GO LOCOMOTION
7.80
8.09
0.01
0.26
5.30


GO NEGATIVE REGULATION OF
0.09
0.34
7.54
0.12
5.06


CELL CYCLE


HALLMARK ESTROGEN
11.52
0.86
2.02
0.27
3.80


RESPONSE LATE


GO BLOOD VESSEL
1.43
8.47
0.00
0.07
3.66


MORPHOGENESIS


GO CELL MOTILITY
6.24
7.45
0.03
0.14
3.55


HALLMARK EPITHELIAL
0.57
17.00
0.00
1.44
3.41


MESENCHYMAL TRANSITION


GO MOVEMENT OF CELL OR
6.60
6.93
0.63
0.16
3.20


SUBCELLULAR COMPONENT


GO ANGIOGENESIS
0.43
6.61
0.00
0.06
3.06


GO CELL CYCLE
0.01
0.00
17.00
0.02
3.04


HALLMARK INTERFERON
6.14
0.00
0.24
0.03
3.02


GAMMA RESPONSE


GO CELL CYCLE PHASE
0.00
0.02
15.65
0.18
2.63


TRANSITION


GO EPITHELIUM
4.11
6.29
0.20
0.59
2.60


DEVELOPMENT


GO ANATOMICAL STRUCTURE
1.07
6.38
0.06
0.02
2.37


FORMATION INVOLVED IN


MORPHOGENESIS


GO DNA CONFORMATION
0.01
0.00
17.00
0.10
2.34


CHANGE


GO PROTEIN DNA COMPLEX
0.02
0.05
7.62
0.19
2.12


SUBUNIT ORGANIZATION


GO ENERGY DERIVATION BY
0.04
0.04
0.00
15.65
1.96


OXIDATION OF ORGANIC


COMPOUNDS


GO REGULATION OF CELL
7.12
2.03
0.00
0.04
1.87


ADHESION


GO RESPONSE TO WOUNDING
6.31
2.97
0.27
0.43
1.75


GO REGULATION OF MITOTIC
0.07
0.12
11.69
0.57
1.70


CELL CYCLE


GO REGULATION OF CELL
0.15
0.32
6.66
0.77
1.67


CYCLE PHASE TRANSITION


GO MITOTIC CELL CYCLE
0.16
0.00
6.67
0.08
1.50


CHECKPOINT


GO GENERATION OF
0.08
0.09
0.00
15.65
1.49


PRECURSOR METABOLITES


AND ENERGY


GO CHROMATIN ASSEMBLY OR
0.05
0.00
7.70
0.07
1.43


DISASSEMBLY


GO DNA PACKAGING
0.04
0.00
14.22
0.05
1.36


GO NUCLEOSIDE
0.08
0.00
0.55
17.00
1.34


MONOPHOSPHATE METABOLIC


PROCESS


GO ORGAN MORPHOGENESIS
1.71
6.40
0.02
0.48
1.33


GO NUCLEAR CHROMOSOME
0.00
0.12
7.64
0.07
1.30


GO ADENYL NUCLEOTIDE
0.00
0.00
6.63
0.18
1.29


BINDING


GO PURINE CONTAINING
0.34
0.00
0.00
17.00
1.19


COMPOUND METABOLIC


PROCESS


GO MICROTUBULE
0.08
0.02
10.01
0.42
1.19


CYTOSKELETON


GO MITOTIC CELL CYCLE
0.00
0.00
17.00
0.12
1.10


GO CELL CYCLE CHECKPOINT
0.06
0.00
11.53
0.12
1.08


GO NEGATIVE REGULATION OF
0.13
0.05
7.73
0.21
1.06


MITOTIC CELL CYCLE


GO CELLULAR RESPONSE TO
0.01
0.00
6.20
0.14
1.05


DNA DAMAGE STIMULUS


GO CYTOSKELETAL PART
0.94
0.46
7.88
0.25
1.00


GO CELL MORPHOGENESIS
4.13
7.91
0.00
0.02
0.89


INVOLVED IN


DIFFERENTIATION


GO MITOCHONDRIAL
0.00
0.00
0.00
7.71
0.85


ELECTRON TRANSPORT


CYTOCHROME C TO OXYGEN


GO CYTOSKELETON
1.24
0.66
7.67
0.09
0.83


GO NEGATIVE REGULATION OF
6.41
0.61
0.00
0.05
0.82


CELL ADHESION


GO CELLULAR RESPIRATION
0.04
0.00
0.00
17.00
0.80


GO MICROTUBULE
0.46
0.01
6.49
0.39
0.79


GO REGULATION OF CELL
0.16
0.16
11.87
0.70
0.77


CYCLE PROCESS


HALLMARK MYC TARGETS V1
0.00
0.00
3.82
6.78
0.77


GO CHROMOSOME
0.00
0.00
17.00
0.02
0.74


ORGANIZATION


GO SPINDLE MIDZONE
0.91
0.63
7.19
0.26
0.72


GO BIOLOGICAL ADHESION
10.74
5.62
0.00
0.19
0.70


GO NUCLEOBASE CONTAINING
0.29
0.02
0.78
17.00
0.68


SMALL MOLECULE METABOLIC


PROCESS


GO REGULATION OF CELLULAR
6.19
7.06
0.03
0.14
0.63


COMPONENT MOVEMENT


GO MEMBRANE REGION
10.84
1.70
0.02
0.00
0.61


GO BASOLATERAL PLASMA
8.42
0.87
0.00
0.05
0.58


MEMBRANE


GO CELL CYCLE G1 S PHASE
0.02
0.14
11.42
0.14
0.58


TRANSITION


HALLMARK G2M CHECKPOINT
0.18
0.04
17.00
0.01
0.54


GO ENVELOPE
0.30
0.00
0.14
14.40
0.54


GO CELL SURFACE
7.57
1.03
0.06
0.14
0.53


GO RECEPTOR ACTIVITY
7.72
4.16
0.00
0.02
0.52


GO AMIDE BIOSYNTHETIC
0.01
0.01
0.04
6.93
0.50


PROCESS


GO ORGANONITROGEN
0.55
0.14
0.03
17.00
0.46


COMPOUND METABOLIC


PROCESS


GO DNA REPLICATION
0.14
0.00
6.98
0.30
0.45


INDEPENDENT NUCLEOSOME


ORGANIZATION


GO MITOCHONDRIAL
0.03
0.00
0.02
17.00
0.42


ENVELOPE


GO MITOCHONDRION
0.00
0.00
0.03
9.53
0.41


ORGANIZATION


GO PEPTIDE METABOLIC
0.01
0.05
0.00
6.25
0.40


PROCESS


GO CHROMOSOME
0.00
0.02
17.00
0.01
0.40


SEGREGATION


GO MULTICELLULAR
0.35
7.87
0.00
1.36
0.39


ORGANISMAL


MACROMOLECULE METABOLIC


PROCESS


GO PHOSPHATE CONTAINING
0.19
0.16
0.20
9.55
0.39


COMPOUND METABOLIC


PROCESS


GO CHROMOSOME
0.00
0.05
17.00
0.04
0.38


GO APICAL PLASMA
9.04
0.57
0.00
0.04
0.38


MEMBRANE


GO MULTICELLULAR
0.30
7.46
0.00
1.20
0.36


ORGANISM METABOLIC


PROCESS


GO ORGANONITROGEN
0.10
0.13
0.08
7.92
0.35


COMPOUND BIOSYNTHETIC


PROCESS


GO REGULATION OF SISTER
0.00
0.00
6.08
0.04
0.34


CHROMATID SEGREGATION


GO CELL JUNCTION
8.40
0.62
0.00
0.03
0.33


GO PLASMA MEMBRANE
12.18
0.64
0.04
0.00
0.32


REGION


GO MACROMOLECULAR
0.02
0.00
6.21
1.91
0.27


COMPLEX ASSEMBLY


GO REGULATION OF
0.00
0.00
9.59
0.02
0.27


CHROMOSOME SEGREGATION


GO RESPIRATORY CHAIN
0.17
0.00
0.00
17.00
0.27


GO SMALL MOLECULE
1.56
0.24
0.05
12.25
0.26


METABOLIC PROCESS


GO REGULATION OF
0.03
0.44
6.15
0.05
0.26


ORGANELLE ORGANIZATION


GO CELL DIVISION
0.08
0.00
17.00
0.01
0.25


GO APICAL PART OF CELL
9.64
0.37
0.00
0.24
0.25


GO EXTRACELLULAR SPACE
7.39
5.48
0.01
1.55
0.23


GO MITOCHONDRION
0.00
0.02
0.00
12.65
0.23


GO EXTRACELLULAR
4.58
12.06
0.00
3.21
0.22


STRUCTURE ORGANIZATION


GO ELECTRON TRANSPORT
0.12
0.00
0.00
17.00
0.22


CHAIN


GO OXIDATION REDUCTION
0.77
0.97
0.07
14.18
0.22


PROCESS


GO CELL CELL ADHESION
6.70
1.30
0.00
0.12
0.21


GO PHOSPHORYLATION
0.04
0.19
0.07
6.13
0.21


GO MEIOTIC CELL CYCLE
0.27
0.00
7.40
0.30
0.21


PROCESS


GO TRANSLATIONAL
0.00
0.00
0.00
6.01
0.20


TERMINATION


GO ORGANELLE INNER
0.06
0.00
0.04
17.00
0.17


MEMBRANE


GO MEIOTIC CELL CYCLE
0.17
0.00
7.98
0.19
0.16


GO SKIN DEVELOPMENT
5.05
7.51
0.00
0.54
0.16


GO MITOCHONDRIAL PART
0.00
0.00
0.00
17.00
0.14


GO REGULATION OF NUCLEAR
0.13
0.00
11.48
0.14
0.14


DIVISION


GO MESENCHYME
0.72
6.07
0.00
0.91
0.13


DEVELOPMENT


GO ENDOPLASMIC RETICULUM
0.23
8.64
0.00
1.09
0.12


LUMEN


GO PROTEIN COMPLEX
0.11
0.01
7.23
1.32
0.12


BIOGENESIS


GO CELL JUNCTION
7.55
0.97
0.00
0.04
0.12


ORGANIZATION


GO CARBOHYDRATE
0.68
0.02
0.24
17.00
0.12


DERIVATIVE METABOLIC


PROCESS


GO DNA METABOLIC PROCESS
0.00
0.00
17.00
0.35
0.12


GO ORGANOPHOSPHATE
0.58
0.24
0.34
17.00
0.11


METABOLIC PROCESS


GO PROTEIN COMPLEX
0.12
0.08
8.57
3.24
0.08


SUBUNIT ORGANIZATION


GO NUCLEAR CHROMOSOME
0.01
0.04
17.00
0.01
0.07


SEGREGATION


GO REGULATION OF CELL
0.28
0.00
10.53
0.32
0.06


DIVISION


GO SINGLE ORGANISM
0.99
0.38
0.76
6.33
0.05


BIOSYNTHETIC PROCESS


GO CELL CELL JUNCTION
11.12
0.10
0.00
0.18
0.04


GO ORGANELLE FISSION
0.00
0.00
17.00
0.02
0.04


GO SPINDLE
0.06
0.02
10.38
0.32
0.04


GO EXTRACELLULAR MATRIX
1.08
15.65
0.00
1.70
0.03


GO CHROMOSOMAL REGION
0.02
0.00
17.00
0.21
0.03


GO MITOTIC NUCLEAR
0.00
0.01
17.00
0.05
0.02


DIVISION


GO MEMBRANE PROTEIN
0.94
0.02
0.00
6.76
0.00


COMPLEX


GO INTRINSIC COMPONENT OF
15.65
1.45
0.00
0.07
0.00


PLASMA MEMBRANE


GO APICAL JUNCTION
8.93
0.19
0.00
0.09
0.00


COMPLEX


GO APICOLATERAL PLASMA
7.63
0.81
0.00
0.40
0.00


MEMBRANE


GO ATP DEPENDENT
0.08
0.00
6.08
0.70
0.00


CHROMATIN REMODELING


GO BASEMENT MEMBRANE
0.88
9.21
0.00
1.40
0.00


GO CELL CELL JUNCTION
6.40
0.00
0.00
0.06
0.00


ASSEMBLY


GO CENTROMERE COMPLEX
0.17
0.00
10.84
0.38
0.00


ASSEMBLY


GO CHROMOSOME
0.03
0.00
17.00
0.03
0.00


CENTROMERIC REGION


GO CHROMOSOME
0.00
0.00
6.95
0.23
0.00


CONDENSATION


GO COLLAGEN BINDING
1.20
6.63
0.00
0.23
0.00


GO COLLAGEN TRIMER
0.13
11.23
0.00
1.06
0.00


GO COMPLEX OF COLLAGEN
0.00
12.16
0.00
0.27
0.00


TRIMERS


GO CONDENSED
0.02
0.00
17.00
0.03
0.00


CHROMOSOME


GO CONDENSED
0.14
0.00
17.00
0.00
0.00


CHROMOSOME CENTROMERIC


REGION


GO CYTOCHROME COMPLEX
0.00
0.00
0.00
6.02
0.00


GO DNA DEPENDENT DNA
0.14
0.00
10.67
0.08
0.00


REPLICATION


GO DNA REPLICATION
0.11
0.04
15.05
0.04
0.00


GO DNA REPLICATION
0.00
0.00
10.56
0.00
0.00


INITIATION


GO ENDODERMAL CELL
0.28
6.26
0.00
0.21
0.00


DIFFERENTIATION


GO ENERGY COUPLED PROTON
0.00
0.00
0.00
6.39
0.00


TRANSPORT DOWN


ELECTROCHEMICAL GRADIENT


GO EXTRACELLULAR MATRIX
0.83
14.61
0.00
0.82
0.00


COMPONENT


GO HISTONE EXCHANGE
0.15
0.00
7.19
0.71
0.00


GO HYDROGEN ION
0.19
0.00
0.00
13.81
0.00


TRANSMEMBRANE


TRANSPORT


GO HYDROGEN TRANSPORT
0.54
0.16
0.00
13.65
0.00


GO INNER MITOCHONDRIAL
0.02
0.00
0.00
17.00
0.00


MEMBRANE PROTEIN


COMPLEX


GO INORGANIC CATION
0.73
0.12
0.00
6.26
0.00


TRANSMEMBRANE


TRANSPORTER ACTIVITY


GO KINETOCHORE
0.09
0.00
17.00
0.00
0.00


GO KINETOCHORE
0.55
0.00
6.66
0.46
0.00


ORGANIZATION


GO LATERAL PLASMA
7.75
0.00
0.00
0.45
0.00


MEMBRANE


GO MCM COMPLEX
0.00
0.00
6.88
0.00
0.00


GO MITOCHONDRIAL ATP
0.00
0.00
0.00
6.85
0.00


SYNTHESIS COUPLED PROTON


TRANSPORT


GO MITOCHONDRIAL
0.00
0.06
0.00
17.00
0.00


MEMBRANE PART


GO MITOCHONDRIAL PROTEIN
0.01
0.00
0.00
17.00
0.00


COMPLEX


GO MITOCHONDRIAL
0.06
0.00
0.00
10.25
0.00


RESPIRATORY CHAIN COMPLEX


ASSEMBLY


GO MITOCHONDRIAL
0.09
0.00
0.00
9.97
0.00


RESPIRATORY CHAIN COMPLEX


I BIOGENESIS


GO MITOTIC SISTER
0.00
0.00
10.78
0.08
0.00


CHROMATID SEGREGATION


GO MONOVALENT INORGANIC
0.63
0.08
0.00
9.34
0.00


CATION TRANSMEMBRANE


TRANSPORTER ACTIVITY


GO MONOVALENT INORGANIC
0.71
0.36
0.00
9.33
0.00


CATION TRANSPORT


GO NADH DEHYDROGENASE
0.13
0.00
0.00
14.12
0.00


COMPLEX


GO NUCLEOSIDE
0.00
0.00
0.62
6.63
0.00


TRIPHOSPHATE BIOSYNTHETIC


PROCESS


GO OXIDOREDUCTASE
1.07
1.32
0.13
13.28
0.00


ACTIVITY


GO OXIDOREDUCTASE
0.00
0.00
0.00
6.18
0.00


ACTIVITY ACTING ON A HEME


GROUP OF DONORS


GO OXIDOREDUCTASE
0.73
0.20
0.00
10.49
0.00


ACTIVITY ACTING ON NAD P H


GO OXIDOREDUCTASE
0.72
0.00
0.00
11.71
0.00


ACTIVITY ACTING ON NAD P H


QUINONE OR SIMILAR


COMPOUND AS ACCEPTOR


GO PROTEINACEOUS
0.51
15.65
0.00
1.76
0.00


EXTRACELLULAR MATRIX


GO PROTON TRANSPORTING
0.00
0.00
0.00
8.77
0.00


ATP SYNTHASE COMPLEX


GO REGULATION OF EXIT
0.00
0.00
6.46
0.00
0.00


FROM MITOSIS


GO RENAL SYSTEM PROCESS
6.80
0.87
0.00
0.92
0.00


GO RIBONUCLEOSIDE
0.00
0.00
0.00
7.80
0.00


TRIPHOSPHATE BIOSYNTHETIC


PROCESS


GO SISTER CHROMATID
0.02
0.00
14.18
0.04
0.00


COHESION


GO SISTER CHROMATID
0.00
0.00
17.00
0.02
0.00


SEGREGATION


GO SPINDLE CHECKPOINT
0.00
0.00
6.84
0.00
0.00


GO SPINDLE MICROTUBULE
0.09
0.00
7.79
0.05
0.00


GO SPINDLE POLE
0.02
0.00
8.32
0.23
0.00


GO TRANSLATIONAL
0.00
0.00
0.00
7.31
0.00


ELONGATION


HALLMARK MITOTIC SPINDLE
0.04
0.68
12.22
0.04
0.00










Second, transcriptional module analysis across all three tumor subtypes (using weighted-PCA via PAGODA (Fan et al. Nat Methods 13:241-244 (2016)) and clustering of gene-gene co-expression networks), also identified a cell cycle program that distinguished cycling from non-cycling cells (P<1*10−30, mixed-effects test, Tables 6, 7, FIG. 4C). Overall, 8.6% of malignant cells were cycling (1.1-23.6% per tumor), such that cycling cells were more frequent in treatment-naïve vs. post-treatment tumors (P=5.21*10−11, hypergeometric test; P=1.33*10−6 logistic mixed-effects test). Interestingly, the cycling cells where substantially less differentiated (P=2.44*10−60, mixed effects), revealing a tumor structure where the poorly differentiated (or stem-like) cells substantially more prone to cycle and replenish the tumor (FIGS. 4B-4F). These findings support a model of malignant cell differentiation, as opposed to dedifferentiation in SyS.


The third module identified a new core oncogenic program present in a subset of cells in each tumor, and characterized by the modulation of several cancer-promoting pathways (FIGS. 4A, 4B). The program induced genes from respiratory carbon metabolism (oxidative phosphorylation, citric acid cycle, and carbohydrate/protein metabolism, P<1*10−8, hypergeometric test), and repressed genes involved in TNF signaling, apoptosis, p53 signaling, and hypoxia processes (P<1*10−10, hypergeometric test, Tables 6 and 7), including known tumor suppressors (e.g., CDKN1A and KLF6). The program was enhanced in cycling and poorly differentiated cells (P<2.94*10−4, mixed-effects, FIG. 4D), and significantly overlapped an immunotherapy resistance program that Applicants recently found in melanoma (Jerby-Arnon et al. Cell 175:984-997.e24 (2018)) (P<7.16*10−10, hypergeometric test). Applicants confirmed the presence of the program in situ at the protein level by immunohistochemistry (IHC) and multiplexed immunofluorescence (t-CyCIF) (Lin et al. eLife 7:e31657 (2018)) (FIGS. 4E-4F); and detected its expression and variation across bulk RNA-Seq data of 64 primary SyS tumors (McBride et al. Cancer Cell (2018) doi:10.1016/j.ccell.2018.05.002) (FIG. 6). Taken together, the core oncogenic program captures intra- and inter-tumor variation, manifests multiple cancer hallmarks, and highlights a yet unappreciated subpopulation of cells in SyS.


Example 3—the Core Oncogenic Program is Associated with Poor Clinical Outcomes

To test the generalizability and clinical relevance of the above findings, Applicants analyzed two independent bulk RNA-Seq cohorts (Nakayama et al. Am J Surg Pathol 34:1599-1607 (2010); Lagarde et al. J Clin Oncol Off J Am Soc Clin Oncol 31:608-615 (2013)). The first cohort included 34 SyS tumors (Nakayama et al. Am J Surg Pathol 34:1599-1607 (2010)), spanning monophasic, biphasic and poorly differentiated morphologies (FIG. 5A). Whereas the epithelial program was significantly higher in biphasic compared to monophasic tumors (P=4.14*10−6, one-sided t-test, FIG. 5A), poorly differentiated tumors had lower differentiation scores and higher proliferation and core oncogenic scores (P<2.76*10−4, one-sided t-test, FIG. 5A), consistent with their poor clinical prognosis. Next, Applicants examined the prognostic value of the programs in another independent cohort of 58 primary SyS tumors from treatment naïve patients with metastasis-free survival information (Lagarde et al. J Clin Oncol Off J Am Soc Clin Oncol 31:608-615 (2013)). The differentiation scores were associated with higher metastasis-free survival rates (P=1.49*10−4, Cox regression, FIG. 5B), while cell cycle and the core oncogenic programs were associated with the risk of metastatic disease (P=5.89*10−6 and 1.36*10−3, respectively, Cox regression, FIG. 5B). These findings support the notion that poor differentiation features and the core oncogenic program mark the aggressive subpopulation of malignant cells, which are more prone to metastasize.


Example 4—SS18-SSX Sustains the Core Oncogenic Program and Blocks Differentiation

To decouple the intrinsic and extrinsic factor determining the malignant cell states in SyS Applicants first tested whether the core oncogenic and other programs were co-regulated by the genetic fusion driving SyS. Applicants turned to explore the potential regulators of these cellular programs, starting from the genetic driver. To this end, they depleted SS18-SSX in two SyS cell lines (SYO1 and Aska) using shRNA and profiled 12,263 cells with scRNA-Seq. The fusion KD led to massive and highly consistent transcriptional alternation in both cell lines (FIG. 7A, Tables 8, 9). It substantially repressed the core oncogenic program and cellular proliferation (P<8.05*10-107, t-test, FIG. 7A-7C), while inducing mesenchymal differentiation programs and markers, including ZEB1 and VIM (P<1*10−50, t-test and likelihood-ratio test FIGS. 7A-7B, 8A). Leveraging the single-cell readout Applicants confirmed that the KD impact on the core oncogenic and differentiation programs was decoupled from, and not secondary to, the repression of cellular proliferation (FIG. 7B), such that the impact on the core oncogenic and differentiation programs was observed even when controlling for the cycling status of the cells, and when considering only cycling or non-cycling cells (P<1.54*10−13, t-test, FIG. 5B, METHODS). Thus, the fusion's impact on cell cycle may be secondary or downstream to its impact on the core oncogenic program. In addition, the fusion KD led to an induction of antigen presentation and cell autonomous immune responses, such as TNF and IFN signaling (P<1*10−30, mixed-effects, FIG. 8A).









TABLE 8







The SS18-SSX fusion program.








Fusion UP
Fusion DOWN










Direct targets
Indirect targets
Direct targets
Indirect targets















ABLIM1
ACADVL
H1F0
ADM
AAED1
LRRC59


ADD3
ADAM9
H1FX
ANXA2
ABL2
LRRFIP2


ALDH1A3
ADIPOR1
H2AFJ
ATF3
ABRACL
MAFB


APBB2
AGPAT5
H2AFV
ATOH8
AC093673
MAGED1


ARC
AIMP1
H2AFZ
BAMBI
ACAN
MAGED2


ARID5B
AKAP9
H3F3A
BCL2L11
ACTA1
MALL


AUTS2
AKR7A2
HCFC1R1
CCND1
ACTA2
MALT1


BMP7
AKTIP
HDDC2
CDH2
ACTB
MAP1A


CADM1
ANAPC16
HDGF
CHST2
ACTG2
MAP1B


CASC10
ANP32A
HELLS
CRIP2
ACTN1
MAP1LC3B


CCDC140
ANP32B
HIST1H4C
CRLF1
ADAM19
MARCKS


CCND2
ANP32E
HMGB1
CTSB
ADRM1
MDFIC


CDK6
ARL6IP1
HMGB2
CTSD
AK5
MED19


CDX2
ARPC1A
HMGN1
CXCR4
AKAP12
MEST


CELF2
ART5
HMGN2
DKK2
AKR1B1
METTL9


CKB
ASF1B
HMGN3
DUSP4
AMD1
MGLL


CLMN
ATAD2
HNRNPA2B1
DUSP5
ANGPT2
MGP


COL8A1
ATP5A1
HNRNPC
EGFR
ANKH
MICAL2


COL9A3
ATP5G1
HNRNPH1
ETV4
ANKRD11
MIR4435-1HG


COLEC12
ATP5G2
HNRNPU
FLNA
ANXA1
MMP1


COPS8
ATP5I
HNRNPUL1
FSCN1
ANXA5
MMP10


CRABP2
ATP5L
HOXA3
GADD45B
AP2S1
MMP24-AS1


CRNDE
ATP6V0B
HOXC10
GAS1
APCDD1
MMP3


CRTAC1
ATRAID
HOXC6
HTRA1
ARF4
MORF4L2


CTNNB1
AURKAIP1
HPS4
IGFBP4
ARF6
MPC2


DUT
AURKB
HSP90AB1
INSIG1
ARG2
MRPL13


FGFR1
BCL7C
IFIH1
KLF4
ARHGAP22
MRPL36


FJX1
BIRC5
IGFBP5
KLF6
ARHGDIA
MRPS6


FLRT2
BLOC1S1
IMPDH2
LGALS3
ARL2BP
MSN


FOXC1
BMP4
INSIG2
LMO4
ARL4C
MSRA


GAMT
BOLA3
IPO7
LMO7
ARPC1B
MT1E


GAS6
BRD2
ISG20L2
LPAR1
ARPC2
MT1F


GPM6B
BRD7
JPH4
MAP2K3
ARPC5L
MT1X


HAND2
BTBD1
KCNQ1OT1
MSX1
ARRDC3
MT2A


HES1
C11orf31
KIAA0101
MYC
ASAP1
MYL12A


HES4
C14orf2
KIF1A
NR3C1
ASPH
MYL12B


HEY2
C19orf53
KIF23
NR4A2
ATF4
MYL6


HHIP
C1QBP
KPNA2
NSG1
ATOX1
MYL9


HMCN1
C20orf24
KPNB1
PHLDA1
ATP1B1
MYLIP


HOTAIRM1
C6orf48
LAGE3
PHLDA2
ATP1B3
MYLK


HOXA10
C7orf50
LAMTOR2
PLOD2
ATP6V0E1
MYO10


HOXC8
CA11
LAPTM4B
PMP22
ATP6V1G1
MYO1B


HOXD1
CAPNS1
LDHB
PTEN
AXL
MYOF


IGF2
CBFB
LINC01116
RND3
B2M
NAA10


IRX3
CBX1
LIX1
SAMD11
BACE2
NABP1


ITM2C
CBX3
LNPEP
SIRPA
BCAR1
NANS


JAG1
CBX5
LSM3
SLC35D3
BID
NBL1


LIMCH1
CCDC137
LSM4
SOCS3
BNIP3L
NDRG1


LSAMP
CCDC85B
LSMD1
SOX4
BPGM
NEAT1


LTBP4
CCL2
LUC7L3
SPHK1
BRI3
NETO2


LYPLA1
CCNB1
LYAR
TBX3
BTG1
NF2


MEOX1
CCNE1
MATR3
TCF4
C11orf96
NFKBIA


MEOX2
CCT3
MCTP2
TNMD
C12orf75
NHP2L1


METRN
CCT6A
MDK
VGF
C16orf45
NNMT


MRGBP
CDC20
MET
WLS
C19orf24
NOP10


NFIA
CDCA5
MIF
XYLT1
C1orf198
NPC2


NFIB
CENPA
MKI67
ZFP36L1
CALD1
NPTX2


NR2F2
CENPF
MLF2
ZNF704
CALM1
NQO1


NRP2
CEP112
MMADHC

CALU
NR1H2


OSR2
CEP78
MOB3B

CAP1
NREP


PAX3
CHCHD2
MRPS26

CAPN2
NT5E


PCDH9
CHD1
MT-CO1

CAV1
NTMT1


PDZRN3
CHD9
MT-CO2

CAV2
NUPR1


PEX2
CIRBP
MT-CO3

CBLN1
OAF


PIGC
CKLF
MT-ND1

CCDC71L
OASL


PIM3
CKS1B
MT-ND2

CCDC80
OLFM2


PRRT2
CKS2
MT-ND3

CCDC92
PAWR


PTCH1
CLPTM1L
MT-ND4

CCL5
PCOLCE


PTHLH
CLSPN
MT-ND5

CCL7
PDCD6


RBM20
CMTM6
MTERF

CD44
PDLIM2


RBMS3
CNOT7
MTF2

CD59
PDLIM4


REEP3
COX6C
MTRNR2L10

CD63
PDLIM7


ROBO1
COX8A
MTRNR2L8

CD9
PEA15


SERTAD4
CPSF6
MZT2A

CD99
PELO


SERTAD4-AS1
CRELD2
MZT2B

CDC25B
PERP


SHISA2
CSDE1
NAV2

CDC37
PGF


SMARCD3
CTDSPL
NBEAL1

CDC42EP3
PHC2


SULF2
CXCL14
NCAM1

CDC42EP5
PHLDA3


TENM3
CYCS
NCL

CDKN1A
PIM1


TLE1
CYP24A1
NDUFA4

CDV3
PKIG


TLE3
DAXX
NDUFB1

CEBPB
PKM


TLE4
DBF4
NDUFB10

CEBPD
PLAU


TMEM47
DBP
NDUFC1

CFL1
PLAUR


TRPS1
DCBLD2
NME3

CHCHD10
PLIN2


WNT16
DCTPP1
NME4

CHMP1B
PLOD1


ZFHX3
DCUN1D4
NOLC1

CITED2
PMAIP1


ZFHX4
DCXR
NOP56

CITED4
PNP


ZFHX4-AS1
DDX18
NRAS

CKAP4
POMP


ZIC1
DDX60L
NT5C3B

CLIC1
PPFIBP1


ZNF385D
DEK
NTM

CLIC4
PPME1



DENR
NUCKS1

CLMP
PPP1R14B



DFFA
NUDT1

CLTA
PPP1R15A



DHRS3
NUDT3

CMTM3
PRR13



DKC1
ODC1

CNN3
PRRX1



DMKN
ORC6

COL12A1
PRRX2



DNAJC2
PA2G4

COL1A1
PRSS23



DNMT1
PABPC1

COL3A1
PSMA7



DNPH1
PAFAH1B3

COL5A1
PSME1



DUSP9
PAMR1

COL5A2
PSME2



EBF1
PARP1

COL5A3
PTGDS



EDNRA
PBK

COL6A1
PTN



EEF2
PCBP2

COL6A2
PTRF



EFHD2
PCSK1N

COPRS
PTTG1IP



EI24
PFN2

CORO1C
PXDC1



EIF3L
PHF19

COTL1
RAB32



EIF4B
PHGDH

CPE
RAB3B



EIF4G2
PIGP

CREB3L1
RAB7A



ERH
PIN1

CREM
RABAC1



ERVMER34-1
PLK1

CSNK1A1
RAC1



ESF1
PMVK

CSRP1
RAI14



ETF1
PNISR

CSRP2
RALA



ETFB
PNN

CST3
RAP1B



F12
POLR2I

CTA-29F11
RASD1



FAM3C
POLR2J3

CTGF
RBM8A



FAM49B
POLR2L

CTHRC1
RBMS1



FAM64A
PON2

CTSA
RCN3



FBN1
POP7

CTSC
RECK



FDPS
PPDPF

CTSL
REXO2



FHL1
PPIC

CXCL3
RGCC



FSD1
PPP2CB

CXXC5
RGMB



FUS
PPP2R5C

CYB5R3
RGS10



FZD10
PRDX3

CYBA
RGS16



GCSH
PRKDC

CYR61
RGS2



GGCT
PRPF4

CYSTM1
RHOBTB3



GLO1
PTK2

CYTL1
RHOC



GLTSCR2
PTMS

DAB2
RIPK2



GMFB
PTRHD1

DAP
RNF114



GNL1
RAB34

DBI
RNF115



GPR125
RAC3

DCUN1D5
RNF149



GTF2I
RAD21

DDIT4
RP11-395G23




RANBP1

DDR2
RPL10




RBM39

DIRAS3
RPL10A




RBMX

DKK3
RPL11




RBP1

DNAJB6
RPL13A




RHNO1

DOK1
RPL15




RNF187

DSTN
RPL27




RNPS1

DSTYK
RPL28




RP11-357H14

DUS1L
RPL6




RP11-410K21

DUSP1
RPL7




RPL12

DUSP14
RPL7A




RPL17

DYNLRB1
RPS13




RPL21

EBNA1BP2
RPS15A




RPL31

ECM1
RPS18




RPL36A

EEF1A1
RPS27A




RPL37A

EEF1B2
RPS3




RPL39L

EFEMP2
RPS4X




RPS16

EHD2
RPS5




RPS19BP1

EID1
RPS7




RPS2

EIF2AK4
RRBP1




RPS26

EIF4EBP1
RSU1




RPS27

EMP2
RTN4




RPS27L

EMP3
S100A10




RPS28

ENO1
S100A11




RPS29

EPN1
S100A13




RPSA

ERCC1
S100A2




RPSAP58

ERRFI1
S100A4




RRAS

EVA1A
SAA1




RRM2

F3
SAT1




RRP1B

FABP4
SDC2




RUSC1

FABP5
11-Sep




SCD

FAM107B
SERINC2




SCT

FAM114A1
SERPINE1




SELT

FAM127A
SERPINE2




SEPW1

FAM171B
SERPINH1




SF3B14

FAM43A
SESTD1




SFPQ

FAM46A
SFRP1




SGCB

FAM89A
SFRP4




SKA2

FBXO32
SGK1




SLBP

FGF1
SGK223




SLC25A13

FHL2
SH3BGRL3




SLC25A39

FN1
SH3GL1




SLC39A6

FOS
SH3GLB1




SLC50A1

FOSL1
SLC16A3




SLIT3

FOSL2
SLC16A6




SMC2

FST
SLC17A5




SMC4

FSTL1
SLC18A2




SMIM19

FTH1
SLC20A2




SNHG7

FTL
SLC3A2




SNRNP70

FUCA2
SLC4A7




SNRPB

FXYD5
SLC52A2




SNRPD1

G0S2
SLN




SOCS1

GADD45A
SMAGP




SOX8

GDF15
SMAP2




SQLE

GEM
SMYD3




SRGAP3

GIPC1
SNAI2




SRRM1

GLIPR1
SNHG15




SRRM2

GLIPR2
SNX3




SRSF1

GLIS3
SNX9




SRSF11

GLRX
SOCS2




SRSF2

GNG11
SPARC




SRSF3

GNG12
SPATS2L




SRSF6

GNG5
SPOCD1




SRSF7

GPR56
SPOCK1




SSRP1

GSN
SPRY2




STARD3NL

GSTO1
SRGAP1




STARD7

HBEGF
SRM




STMN1

HERPUD1
SSR2




STOML2

HEXIM1
SSR3




STRA13

HEY1
STAT1




SUCO

HIC1
STC1




SUMO2

HIST1H2AC
STC2




SUPT16H

HLA-A
STK17A




SURF2

HLA-C
STK38L




SUZ12

HM13
STRAP




TAF9

HMOX1
SUGCT




TCEA1

HOMER3
TAF7




TCEB2

HSF1
TAGLN




TEX30

HSPA8
TAGLN2




TGFBR1

HSPB1
TAX1BP1




THAP5

ID1
TBCC




TMA7

ID2
TCEB1




TMEM100

ID3
TGFB1




TMEM106C

ID4
TGFB1I1




TMEM134

IER3
TGFBI




TMEM147

IFI44
TGIF1




TMEM14A

IFI6
TGM2




TMEM14B

IFIT1
THBS1




TMEM160

IFIT2
THY1




TMEM184C

IFIT3
TIMP2




TMEM256

IFITM3
TIMP3




TMEM30B

IFRD1
TIPARP




TNFAIP2

IGFBP3
TMEM123




TOMM22

IGFBP7
TMEM173




TOMM40

IGFL2
TMEM45A




TOP2A

IL11
TMEM70




TPD52L1

IL8
TMSB10




TPM1

IMP4
TMSB4X




TPX2

INA
TNC




TRAPPC1

INHBA
TNFRSF12A




TRMT112

IQCD
TNFRSF1A




TSEN54

ISG15
TNFRSF21




TSHZ2

ITGA10
TP53I11




TYMS

ITGA5
TPBG




U2SURP

ITGAV
TPM2




UBE2C

JUN
TPM3




UBE2Q1

JUNB
TPM4




UBE2S

KCNG1
TPT1




UCHL1

KCNMA1
TRAM1




UHMK1

KDELR2
TRIB1




UNKL

KDELR3
TSC22D1




UQCR10

KIAA0040
TSPAN5




UQCRB

KIFC3
TSPO




USP1

KISS1
TUBA1A




USP46

KLHDC3
TUBA1C




VMA21

KLHL42
TWSG1




WDR34

KRT10
TXNDC17




WDR45B

KRT19
TXNRD1




WIF1

KRT81
UACA




WRB

KRTAP2-3
UAP1




WWC3

KXD1
UBL3




XRCC6

LAMA4
UBTD1




YBX1

LAMTOR1
UXT




YRDC

LAPTM4A
VASN




YWHAH

LARP6
VAT1




ZDHHC12

LBH
VIM




ZFP36

LDHA
VMP1




ZNF22

LGALS1
VOPP1




ZWINT

LIMA1
WBP5






LINC00152
WDR1






LINC00704
WIPI1






LMNA
WISP1






LOX
WWTR1






LOXL1
XBP1






LOXL2
YES1







YIF1A







YIPF3







YPEL5







YWHAB







ZC3HAV1







ZEB1







ZFAND5







ZFP36L2







ZNF259







ZNF503







ZNF706







ZYX
















TABLE 9







The SS18-SSX fusion program enrichment with pre-defined gene sets


(hypergeometric p-values: −log10 transformed, capped at 17)










Fusion UP
Fusion DOWN












Direct
Indirect
Direct
Indirect


Gene set
targets
targets
targets
targets














GO_TISSUE_DEVELOPMENT
13.87
0.02
13.22
17.00


HALLMARK_TNFA_SIGNALING_VIA_NFKB
1.39
0.62
17.00
17.00


EMT_Up (Groger et al. 2012)
0.65
0.51
1.90
17.00


HALLMARK_HYPOXIA
0.00
0.07
7.40
17.00


EMT_Up (Taube et al. 2010)
0.00
0.39
5.63
17.00


HALLMARK_APOPTOSIS
0.90
1.27
4.26
12.36


GO_ORGAN_MORPHOGENESIS
17.00
0.48
6.85
9.38


GO_NEUROGENESIS
12.98
0.32
5.10
5.47


GO_EMBRYO_DEVELOPMENT
13.77
0.95
8.38
4.89


GO_SKELETAL_SYSTEM_DEVELOPMENT
10.86
0.64
2.99
3.36


GO_STEM_CELL_DIFFERENTIATION
11.13
1.00
1.07
1.87


HALLMARK_MYC_TARGETS_V1
0.27
17.00
0.40
1.02


HALLMARK_G2M_CHECKPOINT
0.27
17.00
1.04
0.50


GO_PATTERN_SPECIFICATION_PROCESS
13.71
0.40
1.05
0.20


HALLMARK_E2F_TARGETS
0.27
17.00
0.40
0.02


GO_REGULATION_OF_CELL_DIFFERENTIATION
8.35
0.22
9.55
17.00


HALLMARK_INTERFERON_GAMMA_RESPONSE
0.76
0.37
0.40
8.77


GO_REGULATION_OF_MULTICELLULAR_ORGANISMAL_DEVELOPMENT
9.57
0.03
7.86
17.00


GO_REGULATION_OF_CELL_PROLIFERATION
8.32
0.91
11.38
17.00


GO_REGULATION_OF_ANATOMICAL_STRUCTURE_MORPHOGENESIS
5.22
0.27
5.10
17.00


GO_EXTRACELLULAR_STRUCTURE_ORGANIZATION
4.69
0.24
0.74
17.00


GO_EXTRACELLULAR_MATRIX
3.67
0.03
2.35
17.00


GO_REGULATION_OF_CELL_DEATH
2.92
2.30
11.52
17.00


GO_NEGATIVE_REGULATION_OF_CELL_COMMUNICATION
2.76
0.11
8.45
17.00


GO_NEGATIVE_REGULATION_OF_RESPONSE_TO_STIMULUS
2.76
0.23
8.45
17.00


GO_POSITIVE_REGULATION_OF_RESPONSE_TO_STIMULUS
2.66
0.33
9.24
17.00


GO_CELL_JUNCTION
2.39
0.14
2.63
17.00


GO_REGULATION_OF_CELLULAR_COMPONENT_MOVEMENT
2.05
0.83
5.49
17.00


GO_EXTRACELLULAR_SPACE
1.84
0.36
3.22
17.00


GO_POSITIVE_REGULATION_OF_CELL_DEATH
1.56
1.32
5.58
17.00


GO_CELLULAR_RESPONSE_TO_ORGANIC_SUBSTANCE
1.05
1.12
6.42
17.00


GO_RESPONSE_TO_ENDOGENOUS_STIMULUS
1.01
1.55
7.19
17.00


GO_ANCHORING_JUNCTION
0.94
1.45
2.10
17.00


GO_RESPONSE_TO_ORGANIC_CYCLIC_COMPOUND
0.87
2.49
5.58
17.00


GO_RESPONSE_TO_OXYGEN_CONTAINING_COMPOUND
0.82
1.82
4.48
17.00


HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION
0.76
0.93
10.12
17.00


GO_RESPONSE_TO_LIPID
0.61
2.39
4.15
17.00


GO_CELL_SUBSTRATE_JUNCTION
0.35
1.78
2.47
17.00


GO_ACTIN_CYTOSKELETON
0.30
0.21
0.99
17.00


GO_POSITIVE_REGULATION_OF_DEVELOPMENTAL_PROCESS
8.16
0.27
5.36
15.95


GO_REGULATION_OF_CELL_ADHESION
2.01
0.08
1.67
15.95


GO_RESPONSE_TO_EXTERNAL_STIMULUS
1.72
1.05
3.85
15.95


GO_RESPONSE_TO_ABIOTIC_STIMULUS
0.26
0.27
5.09
15.95


GO_POSITIVE_REGULATION_OF_CELL_COMMUNICATION
2.75
0.18
9.35
15.65


GO_RESPONSE_TO_NITROGEN_COMPOUND
0.65
0.72
5.05
15.26


GO_CELLULAR_RESPONSE_TO_ENDOGENOUS_STIMULUS
0.74
1.97
3.69
15.18


GO_RESPONSE_TO_HORMONE
0.60
2.36
5.70
15.05


GO_NEGATIVE_REGULATION_OF_MOLECULAR_FUNCTION
1.68
0.36
2.83
14.75


GO_CELLULAR_RESPONSE_TO_OXYGEN_CONTAINING_COMPOUND
0.73
1.81
2.40
14.42


GO_STRUCTURAL_MOLECULE_ACTIVITY
0.28
3.21
0.00
14.35


GO_BIOLOGICAL_ADHESION
3.88
0.02
2.36
14.15


GO_NEGATIVE_REGULATION_OF_MULTICELLULAR_ORGANISMAL
6.87
0.54
3.11
14.12


PROCESS


GO_PROTEIN_LOCALIZATION
0.61
1.94
1.43
14.03


GO_MOVEMENT_OF_CELL_OR_SUBCELLULAR_COMPONENT
4.09
0.19
4.15
14.00


GO_POSITIVE_REGULATION_OF_MULTICELLULAR
7.43
0.42
2.61
13.80


ORGANISMAL_PROCESS


GO_CELLULAR_MACROMOLECULE_LOCALIZATION
1.02
2.54
1.44
13.77


GO_PROTEINACEOUS_EXTRACELLULAR_MATRIX
3.39
0.07
0.22
13.58


GO_RESPONSE_TO_STEROID_HORMONE
1.39
2.36
4.46
13.53


GO_RESPONSE_TO_WOUNDING
0.44
0.83
1.26
13.47


GO_RESPONSE_TO_INORGANIC_SUBSTANCE
0.26
0.55
2.14
13.44


GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION
0.53
1.46
1.57
13.33


GO_CELL_DEATH
1.45
3.13
3.72
13.23


GO_INTERSPECIES_INTERACTION_BETWEEN_ORGANISMS
0.14
7.08
0.63
13.21


GO_POSITIVE_REGULATION_OF_PROTEIN_METABOLIC
1.99
3.48
7.83
13.12


PROCESS


GO_NEGATIVE_REGULATION_OF_CELL_PROLIFERATION
5.27
0.20
5.35
13.11


GO_VASCULATURE_DEVELOPMENT
7.63
1.02
5.58
13.10


GO_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM
0.00
6.83
0.00
13.04


GO_CIRCULATORY_SYSTEM_DEVELOPMENT
10.04
0.77
5.40
13.01


GO_CYTOSKELETON
0.20
1.09
0.13
12.99


GO_ENZYME_BINDING
0.68
6.19
1.94
12.88


GO_REGULATION_OF_PROTEIN_MODIFICATION_PROCESS
1.92
2.56
7.70
12.85


GO_POSITIVE_REGULATION_OF_CELL_DIFFERENTIATION
6.37
0.09
5.22
12.77


GO_POSITIVE_REGULATION_OF_MOLECULAR_FUNCTION
1.12
2.65
5.20
12.54


GO_REGULATION_OF_INTRACELLULAR_SIGNAL_TRANSDUCTION
1.32
0.92
8.76
12.37


GO_CYTOSKELETAL_PROTEIN_BINDING
0.42
0.41
1.76
12.32


GO_REGULATION_OF_HYDROLASE_ACTIVITY
0.05
1.08
0.61
12.17


HALLMARK_COAGULATION
0.00
0.39
2.28
11.98


GO_RESPONSE_TO_BIOTIC_STIMULUS
0.07
0.64
1.13
11.96


GO_CYTOSOLIC_RIBOSOME
0.00
9.18
0.00
11.82


GO_RESPONSE_TO_OXYGEN_LEVELS
0.16
0.23
4.87
11.69


GO_NEGATIVE_REGULATION_OF_DEVELOPMENTAL_PROCESS
8.16
1.10
5.34
11.65


GO_NEGATIVE_REGULATION_OF_PROTEIN_METABOLIC_PROCESS
0.64
2.58
5.60
11.61


GO_ACTIN_BINDING
1.21
0.48
1.75
11.58


GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO
0.00
7.71
0.00
11.52


ENDOPLASMIC_RETICULUM


GO_REGULATION_OF_CELL_DEVELOPMENT
6.29
0.08
4.37
11.44


GO_ENZYME_LINKED_RECEPTOR_PROTEIN_SIGNALING_PATHWAY
3.56
0.75
1.52
11.43


GO_POSITIVE_REGULATION_OF_CATALYTIC_ACTIVITY
0.45
3.32
4.06
11.42


GO_REGULATION_OF_RESPONSE_TO_STRESS
0.31
1.14
2.99
11.31


GO_NEGATIVE_REGULATION_OF_CELL_DEATH
2.78
2.20
7.58
11.23


GO_RECEPTOR_BINDING
2.02
1.64
1.94
11.19


GO_POSITIVE_REGULATION_OF_CELLULAR_COMPONENT
2.39
2.21
3.23
10.97


ORGANIZATION


GO_CELL_MOTILITY
3.51
0.19
5.16
10.95


GO_RESPONSE_TO_METAL_ION
0.14
0.32
1.28
10.90


GO_REGULATION_OF_PHOSPHORUS_METABOLIC_PROCESS
1.73
1.69
8.93
10.84


GO_PROTEIN_LOCALIZATION_TO_MEMBRANE
0.38
3.67
2.58
10.81


GO_RESPONSE_TO_EXTRACELLULAR_STIMULUS
0.63
0.50
4.83
10.76


GO_NEGATIVE_REGULATION_OF_LOCOMOTION
0.58
1.13
0.84
10.66


GO_RESPONSE_TO_ALCOHOL
1.91
2.92
4.45
10.61


GO_INTRACELLULAR_VESICLE
0.47
0.02
2.94
10.60


GO_NEGATIVE_REGULATION_OF_CATALYTIC_ACTIVITY
0.41
0.65
2.31
10.57


GO_WOUND_HEALING
0.27
0.58
0.47
10.56


GO_BLOOD_VESSEL_MORPHOGENESIS
7.90
1.07
3.50
10.54


GO_PROTEIN_TARGETING
2.35
5.28
0.19
10.53


GO_PROTEIN_COMPLEX_BINDING
0.55
1.38
3.96
10.53


GO_NEGATIVE_REGULATION_OF_CELL_DIFFERENTIATION
7.18
0.85
3.84
10.52


GO_INTRACELLULAR_PROTEIN_TRANSPORT
1.12
2.50
0.23
10.51


GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS_NONSENSE
0.00
10.90
0.00
10.51


MEDIATED_DECAY


GO_RESPONSE_TO_CYTOKINE
0.29
0.28
0.97
10.35


HALLMARK_MYOGENESIS
0.76
0.93
1.04
10.30


GO_POSITIVE_REGULATION_OF_CELL_ADHESION
1.85
0.22
0.21
10.17


GO_IMMUNE_SYSTEM_PROCESS
0.47
0.69
4.03
10.07


GO_CELLULAR_RESPONSE_TO_EXTRACELLULAR_STIMULUS
0.29
0.22
1.91
10.07


GO_EXTRACELLULAR_MATRIX_COMPONENT
1.09
0.06
0.56
10.06


GO_PROTEIN_TARGETING_TO_MEMBRANE
0.92
6.39
0.48
9.98


GO_ACTIN_FILAMENT_BASED_PROCESS
0.09
0.06
0.97
9.90


GO_CELLULAR_RESPONSE_TO_EXTERNAL_STIMULUS
1.10
0.35
2.34
9.90


GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_MEMBRANE
0.58
5.62
2.34
9.90


GO_REGULATION_OF_PROTEOLYSIS
0.29
2.69
1.47
9.90


GO_RESPONSE_TO_CORTICOSTEROID
0.00
3.11
2.98
9.84


GO_MACROMOLECULAR_COMPLEX_BINDING
2.20
5.42
2.60
9.81


GO_POSITIVE_REGULATION_OF_INTRACELLULAR_SIGNAL
1.75
0.53
9.48
9.70


TRANSDUCTION


GO_REGULATION_OF_PEPTIDASE_ACTIVITY
0.36
0.48
1.11
9.69


GO_APOPTOTIC_SIGNALING_PATHWAY
0.17
0.45
0.28
9.69


GO_OSSIFICATION
2.57
1.59
0.32
9.67


GO_RIBOSOMAL_SUBUNIT
0.00
7.81
0.00
9.67


GO_VIRAL_LIFE_CYCLE
0.52
7.68
0.77
9.66


GO_ENZYME_REGULATOR_ACTIVITY
0.15
0.91
1.02
9.65


GO_CYTOPLASMIC_VESICLE_PART
0.71
0.03
0.34
9.62


GO_ANGIOGENESIS
4.81
1.59
0.77
9.55


GO_CELLULAR_RESPONSE_TO_ORGANIC_CYCLIC_COMPOUND
1.50
1.60
2.19
9.51


GO_EPITHELIUM_DEVELOPMENT
10.45
0.06
12.07
9.43


GO_LOCOMOTION
4.77
0.23
5.48
9.37


GO_NEGATIVE_REGULATION_OF_GENE_EXPRESSION
6.26
4.69
2.39
9.35


GO_ESTABLISHMENT_OF_LOCALIZATION_IN_CELL
0.34
7.06
0.09
9.33


GO_MULTI_ORGANISM_METABOLIC_PROCESS
0.00
7.96
0.53
9.30


GO_RESPONSE_TO_GROWTH_FACTOR
3.36
1.25
4.60
9.26


GO_ANATOMICAL_STRUCTURE_FORMATION_INVOLVED_IN
12.18
0.14
8.93
9.23


MORPHOGENESIS


GO_RIBOSOME
0.00
6.56
0.00
9.20


GO_CELLULAR_RESPONSE_TO_STRESS
0.07
5.14
4.57
9.19


GO_POSITIVE_REGULATION_OF_HYDROLASE_ACTIVITY
0.06
1.09
0.70
9.19


GO_CELLULAR_RESPONSE_TO_LIPID
1.02
1.66
2.22
9.14


GO_CELLULAR_RESPONSE_TO_NITROGEN_COMPOUND
0.52
0.85
1.41
9.09


GO_REGULATION_OF_CELLULAR_RESPONSE_TO_GROWTH_FACTOR
3.63
0.27
1.69
9.08


STIMULUS


GO_POSITIVE_REGULATION_OF_PROTEIN_COMPLEX_ASSEMBLY
0.28
0.19
1.05
8.89


GO_TRANSLATIONAL_INITIATION
0.00
11.31
0.00
8.88


GO_MEMBRANE_ORGANIZATION
0.18
1.79
2.13
8.84


GO_REGULATION_OF_MULTI_ORGANISM_PROCESS
0.00
1.02
0.47
8.81


HALLMARK_P53_PATHWAY
0.27
1.29
3.81
8.77


GO_NEGATIVE_REGULATION_OF_CELL_DEVELOPMENT
3.81
0.13
1.38
8.57


GO_CYTOSKELETAL_PART
0.10
2.01
0.30
8.54


GO_PROTEIN_LOCALIZATION_TO_ORGANELLE
1.22
6.09
0.38
8.48


GO_POSITIVE_REGULATION_OF_CELL_PROLIFERATION
4.26
1.73
7.96
8.46


GO_REGULATION_OF_CELL_SUBSTRATE_ADHESION
1.55
0.26
1.14
8.40


GO_REGULATION_OF_RESPONSE_TO_EXTERNAL_STIMULUS
0.16
0.42
2.06
8.40


GO_CELLULAR_RESPONSE_TO_INORGANIC_SUBSTANCE
0.35
0.32
0.00
8.39


GO_HEPARIN_BINDING
2.52
1.32
0.00
8.35


GO_REGULATION_OF_VASCULATURE_DEVELOPMENT
1.92
0.71
2.54
8.22


HALLMARK_UV_RESPONSE_DN
2.66
0.36
8.52
8.16


GO_TRANSMEMBRANE_RECEPTOR_PROTEIN_TYROSINE_KINASE
2.55
0.35
0.44
8.16


SIGNALING_PATHWAY


GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO
1.32
5.50
0.22
8.13


ORGANELLE


GO_POSITIVE_REGULATION_OF_CELLULAR_COMPONENT
0.70
0.09
1.71
8.12


BIOGENESIS


GO_REGULATION_OF_TRANSFERASE_ACTIVITY
2.02
3.89
5.44
8.09


GO_REGULATION_OF_APOPTOTIC_SIGNALING_PATHWAY
0.80
0.40
3.51
8.07


GO_CELL_SURFACE
0.26
0.70
1.37
8.06


GO_REGULATION_OF_CATABOLIC_PROCESS
0.00
2.87
0.94
8.05


GO_REGULATION_OF_CELL_MORPHOGENESIS
1.23
0.15
1.89
8.05


HALLMARK_KRAS_SIGNALING_UP
1.39
0.37
1.84
8.04


HALLMARK_IL2_STAT5_SIGNALING
0.76
0.37
4.94
8.04


GO_RESPONSE_TO_MOLECULE_OF_BACTERIAL_ORIGIN
0.00
0.77
0.71
8.03


GO_RESPONSE_TO_KETONE
0.30
2.45
4.00
8.02


GO_IDENTICAL_PROTEIN_BINDING
1.80
0.80
1.07
8.02


GO_VESICLE_MEDIATED_TRANSPORT
0.29
0.01
0.21
8.01


GO_SINGLE_ORGANISM_CELLULAR_LOCALIZATION
0.90
3.25
1.11
7.97


GO_CELLULAR_RESPONSE_TO_CYTOKINE_STIMULUS
0.17
0.10
0.34
7.97


GO_COLLAGEN_FIBRIL_ORGANIZATION
0.87
0.33
1.04
7.97


GO_POSITIVE_REGULATION_OF_GENE_EXPRESSION
11.55
5.54
7.61
7.96


GO_NEGATIVE_REGULATION_OF_CELLULAR_COMPONENT
0.93
3.27
3.50
7.95


ORGANIZATION


GO_NEGATIVE_REGULATION_OF_NITROGEN_COMPOUND
6.15
4.84
2.35
7.93


METABOLIC_PROCESS


GO_REGULATION_OF_CELLULAR_COMPONENT_BIOGENESIS
0.78
0.13
2.50
7.89


GO_CYTOSOLIC_PART
0.00
9.73
0.00
7.89


GO_CATABOLIC_PROCESS
0.09
6.96
0.53
7.87


GO_CELL_ADHESION_MOLECULE_BINDING
2.26
0.09
0.00
7.86


GO_GLYCOSAMINOGLYCAN_BINDING
2.96
1.25
0.00
7.84


GO_REGULATION_OF_IMMUNE_SYSTEM_PROCESS
1.08
0.19
0.84
7.84


GO_NEGATIVE_REGULATION_OF_PROTEIN_MODIFICATION
0.69
1.52
4.63
7.78


PROCESS


GO_REGULATION_OF_TRANSMEMBRANE_RECEPTOR_PROTEIN
1.36
0.58
1.80
7.77


SERINE_THREONINE_KINASE_SIGNALING_PATHWAY


GO_RNA_CATABOLIC_PROCESS
0.00
11.26
0.36
7.75


GO_RESPONSE_TO_VIRUS
0.21
0.64
0.89
7.74


GO_REGULATION_OF_KINASE_ACTIVITY
2.04
2.23
6.34
7.74


GO_NEGATIVE_REGULATION_OF_PHOSPHORYLATION
0.67
1.59
4.97
7.73


GO_REGULATION_OF_NERVOUS_SYSTEM_DEVELOPMENT
6.09
0.03
3.23
7.72


GO_POSITIVE_REGULATION_OF_PEPTIDASE_ACTIVITY
0.35
0.59
1.23
7.70


GO_INTEGRIN_BINDING
0.48
0.28
0.00
7.70


GO_ENZYME_ACTIVATOR_ACTIVITY
0.27
0.28
0.00
7.70


GO_RESPONSE_TO_NUTRIENT
0.79
1.00
1.07
7.66


GO_CYTOSKELETON_ORGANIZATION
0.41
0.78
0.45
7.62


GO_RUFFLE
0.35
0.13
1.22
7.61


GO_ACTIN_FILAMENT_ORGANIZATION
0.00
0.02
1.14
7.61


GO_STRUCTURAL_CONSTITUENT_OF_RIBOSOME
0.00
6.96
0.00
7.59


GO_COLLAGEN_BINDING
0.00
0.18
0.82
7.55


GO_CELLULAR_RESPONSE_TO_HORMONE_STIMULUS
0.80
1.35
1.89
7.54


GO_CELLULAR_RESPONSE_TO_AMINO_ACID_STIMULUS
0.00
1.23
0.90
7.53


GO_MULTICELLULAR_ORGANISM_METABOLIC_PROCESS
0.53
0.00
1.63
7.52


GO_MULTICELLULAR_ORGANISMAL_MACROMOLECULE
0.59
0.00
1.76
7.50


METABOLIC_PROCESS


GO_POSITIVE_REGULATION_OF_PROTEOLYSIS
0.12
2.48
1.19
7.49


GO_NEGATIVE_REGULATION_OF_TRANSPORT
1.52
0.01
2.22
7.45


GO_POSITIVE_REGULATION_OF_PROTEIN_MODIFICATION
1.98
2.57
7.01
7.45


PROCESS


GO_REGULATION_OF_MAPK_CASCADE
1.41
1.06
8.09
7.44


GO_CELLULAR_RESPONSE_TO_OXYGEN_LEVELS
0.38
0.37
1.29
7.42


GO_VESICLE_MEMBRANE
0.24
0.03
0.43
7.34


GO_INTRACELLULAR_SIGNAL_TRANSDUCTION
0.26
0.61
3.90
7.32


GO_RESPONSE_TO_AMINO_ACID
0.00
1.95
0.60
7.31


GO_PERINUCLEAR_REGION_OF_CYTOPLASM
0.65
0.56
1.10
7.30


GO_CELL_SUBSTRATE_ADHESION
0.89
0.03
0.47
7.27


GO_CELL_LEADING_EDGE
1.36
0.16
3.59
7.24


GO_RESPONSE_TO_VITAMIN
1.27
2.22
0.66
7.23


GO_REGULATION_OF_TRANSPORT
1.41
0.07
3.89
7.22


GO_ACTIN_FILAMENT_BUNDLE
0.71
0.61
0.87
7.18


GO_REGULATION_OF_SYMBIOSIS_ENCOMPASSING
0.00
0.59
0.39
7.15


MUTUALISM_THROUGH_PARASITISM


GO_MACROMOLECULE_CATABOLIC_PROCESS
0.06
9.35
0.38
7.14


HALLMARK_ANDROGEN_RESPONSE
2.17
1.05
1.56
7.06


GO_REGULATION_OF_CELL_MORPHOGENESIS_INVOLVED_IN
1.41
0.31
1.98
7.00


DIFFERENTIATION


GO_RESPONSE_TO_DRUG
1.62
0.73
1.62
6.99


GO_RESPONSE_TO_MECHANICAL_STIMULUS
1.34
0.33
0.38
6.97


GO_EXTRINSIC_COMPONENT_OF_MEMBRANE
0.21
0.10
1.58
6.93


GO_TISSUE_MORPHOGENESIS
7.90
0.10
8.12
6.93


GO_SINGLE_ORGANISM_CELL_ADHESION
1.52
0.02
2.21
6.91


GO_RESPONSE_TO_ACID_CHEMICAL
0.15
1.37
0.71
6.88


GO_NEGATIVE_REGULATION_OF_INTRACELLULAR_SIGNAL
0.64
0.71
3.94
6.85


TRANSDUCTION


GO_SULFUR_COMPOUND_BINDING
1.92
1.01
0.00
6.85


GO_CONNECTIVE_TISSUE_DEVELOPMENT
5.04
0.39
1.06
6.85


GO_RRNA_METABOLIC_PROCESS
0.00
8.66
0.00
6.84


GO_POSITIVE_REGULATION_OF_CYTOSKELETON_ORGANIZATION
0.00
0.77
0.44
6.84


GO_ACTOMYOSIN
0.68
0.56
0.84
6.79


GO_PROTEIN_COMPLEX_SUBUNIT_ORGANIZATION
0.66
6.51
0.46
6.79


GO_NEGATIVE_REGULATION_OF_PHOSPHORUS_METABOLIC
0.47
1.41
5.08
6.77


PROCESS


GO_GROWTH_FACTOR_BINDING
1.94
0.47
1.41
6.76


GO_POSITIVE_REGULATION_OF_IMMUNE_SYSTEM_PROCESS
1.34
0.23
0.42
6.76


GO_REGULATION_OF_OSSIFICATION
7.60
0.46
0.00
6.73


GO_RESPONSE_TO_ESTROGEN
2.84
0.80
2.64
6.71


GO_MEMBRANE_REGION
0.86
0.15
2.12
6.70


GO_POSITIVE_REGULATION_OF_LOCOMOTION
0.67
0.78
2.37
6.70


GO_CELLULAR_RESPONSE_TO_NUTRIENT
0.86
0.86
0.00
6.69


GO_MUSCLE_SYSTEM_PROCESS
1.04
0.16
0.79
6.69


GO_NEGATIVE_REGULATION_OF_TRANSFERASE_ACTIVITY
0.83
1.46
2.71
6.65


GO_NEGATIVE_REGULATION_OF_NERVOUS_SYSTEM
3.31
0.19
1.54
6.64


DEVELOPMENT


GO_BASEMENT_MEMBRANE
1.31
0.10
0.68
6.64


GO_MOLECULAR_FUNCTION_REGULATOR
0.12
0.72
0.59
6.64


GO_REGULATION_OF_WOUND_HEALING
0.00
0.45
0.56
6.62


GO_REGULATION_OF_EPITHELIAL_CELL_PROLIFERATION
7.97
0.98
6.23
6.60


GO_REGULATION_OF_OSTEOBLAST_DIFFERENTIATION
6.64
0.54
0.00
6.49


GO_POSITIVE_REGULATION_OF_EXTRINSIC_APOPTOTIC
0.74
0.00
2.09
6.49


SIGNALING_PATHWAY


GO_HOMEOSTATIC_PROCESS
0.24
0.07
1.72
6.47


GO_REGULATION_OF_EPITHELIAL_CELL_MIGRATION
1.60
1.22
1.18
6.47


GO_POSITIVE_REGULATION_OF_PHOSPHORUS_METABOLIC
2.24
1.18
8.43
6.44


PROCESS


HALLMARK_TGF_BETA_SIGNALING
0.73
0.22
0.00
6.41


GO_GROWTH
1.15
0.28
1.69
6.39


GO_NEGATIVE_REGULATION_OF_KINASE_ACTIVITY
1.16
1.23
3.37
6.37


GO_REGULATION_OF_CELLULAR_RESPONSE_TO_TRANSFORMING
0.50
0.64
1.58
6.32


GROWTH_FACTOR_BETA_STIMULUS


GO_DEFENSE_RESPONSE
0.00
0.17
1.92
6.28


GO_POSITIVE_REGULATION_OF_BIOSYNTHETIC_PROCESS
8.86
4.71
8.11
6.27


GO_REGULATION_OF_EXTRINSIC_APOPTOTIC_SIGNALING
0.94
0.14
2.16
6.24


PATHWAY


GO_REGULATION_OF_METAL_ION_TRANSPORT
0.15
0.52
0.70
6.16


GO_CELL_PROJECTION
2.16
0.29
1.09
6.14


GO_CELLULAR_RESPONSE_TO_ACID_CHEMICAL
0.00
1.13
1.14
6.14


GO_LEUKOCYTE_MIGRATION
0.20
0.58
1.55
6.12


HALLMARK_IL6_JAK_STAT3_SIGNALING
0.00
0.11
0.70
6.11


GO_REGULATION_OF_PROTEIN_COMPLEX_ASSEMBLY
0.38
0.36
1.82
6.10


GO_CELL_CORTEX
1.21
0.25
2.50
6.10


GO_REGULATION_OF_NEURON_DIFFERENTIATION
5.11
0.09
3.31
6.07


GO_ENDOPLASMIC_RETICULUM
0.79
0.12
0.40
6.06


GO_RESPONSE_TO_BACTERIUM
0.00
0.18
0.82
6.06


GO_SIDE_OF_MEMBRANE
0.00
0.03
0.53
6.02


GO_RIBOSOME_BIOGENESIS
0.00
8.61
0.00
6.01


GO_REGULATION_OF_TRANSCRIPTION_FROM_RNA_POLYMERASE
10.46
3.16
5.22
5.86


II_PROMOTER


GO_PEPTIDE_METABOLIC_PROCESS
0.05
9.79
0.00
5.34


GO_POLY_A_RNA_BINDING
0.08
17.00
0.76
5.25


GO_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT
0.00
6.24
0.00
5.17


GO_RNA_BINDING
0.14
17.00
0.42
5.17


GO_RIBONUCLEOPROTEIN_COMPLEX
0.00
17.00
0.07
4.82


GO_EPITHELIAL_CELL_DIFFERENTIATION
6.45
0.23
3.60
4.82


GO_TUBE_DEVELOPMENT
6.81
0.36
6.92
4.79


GO_POSITIVE_REGULATION_OF_TRANSCRIPTION_FROM_RNA
6.74
1.74
5.98
4.70


POLYMERASE_II_PROMOTER


GO_ORGANIC_CYCLIC_COMPOUND_CATABOLIC_PROCESS
0.09
12.54
0.18
4.61


GO_NCRNA_PROCESSING
0.00
8.70
0.00
4.41


GO_HEAD_DEVELOPMENT
8.95
0.86
6.76
4.24


GO_AMIDE_BIOSYNTHETIC_PROCESS
0.00
9.24
0.00
4.22


GO_CENTRAL_NERVOUS_SYSTEM_DEVELOPMENT
6.06
0.68
7.58
4.12


GO_CELL_DEVELOPMENT
7.98
0.37
3.69
4.10


GO_TRANSCRIPTION_FACTOR_BINDING
6.19
1.81
5.19
3.98


GO_RIBONUCLEOPROTEIN_COMPLEX_BIOGENESIS
0.00
14.41
0.00
3.97


GO_NEGATIVE_REGULATION_OF_TRANSCRIPTION_FROM_RNA
8.67
0.59
2.58
3.88


POLYMERASE_II_PROMOTER


GO_CELLULAR_CATABOLIC_PROCESS
0.05
9.01
0.18
3.85


GO_MESENCHYME_DEVELOPMENT
9.83
0.67
1.07
3.83


GO_CELLULAR_AMIDE_METABOLIC_PROCESS
0.03
7.66
0.00
3.69


GO_ORGANONITROGEN_COMPOUND_BIOSYNTHETIC_PROCESS
0.12
8.02
0.93
3.58


GO_MUSCLE_STRUCTURE_DEVELOPMENT
6.15
0.73
3.11
3.28


GO_EMBRYONIC_MORPHOGENESIS
8.79
0.10
10.27
3.06


GO_GLAND_DEVELOPMENT
5.61
1.17
6.20
3.00


GO_ORGANONITROGEN_COMPOUND_METABOLIC_PROCESS
0.28
7.88
0.31
2.98


GO_MORPHOGENESIS_OF_AN_EPITHELIUM
6.48
0.10
7.23
2.94


GO_MESENCHYMAL_CELL_DIFFERENTIATION
10.06
1.13
0.54
2.94


GO_EMBRYONIC_ORGAN_DEVELOPMENT
10.49
0.17
4.14
2.86


GO_PLACENTA_DEVELOPMENT
2.72
0.39
7.23
2.84


GO_REPRODUCTIVE_SYSTEM_DEVELOPMENT
4.62
1.09
9.42
2.84


GO_NUCLEOLUS
0.40
10.02
1.69
2.83


GO_UROGENITAL_SYSTEM_DEVELOPMENT
7.76
0.42
3.02
2.75


GO_HEART_DEVELOPMENT
7.67
0.80
3.76
2.51


GO_NCRNA_METABOLIC_PROCESS
0.00
8.13
0.00
2.46


GO_MRNA_METABOLIC_PROCESS
0.17
17.00
0.34
2.33


GO_DEVELOPMENTAL_PROCESS_INVOLVED_IN_REPRODUCTION
3.34
0.52
7.53
2.13


GO_HEART_MORPHOGENESIS
6.95
0.84
1.78
1.96


GO_SENSORY_ORGAN_DEVELOPMENT
9.32
0.36
2.08
1.94


GO_TUBE_MORPHOGENESIS
4.51
0.10
6.93
1.72


GO_EMBRYO_DEVELOPMENT_ENDING_IN_BIRTH_OR_EGG
9.60
0.86
5.00
1.71


HATCHING


GO_RNA_PROCESSING
0.08
17.00
0.05
1.69


GO_TRANSCRIPTION_FACTOR_ACTIVITY_RNA_POLYMERASE
4.46
0.10
8.04
1.67


II_CORE_PROMOTER_PROXIMAL_REGION_SEQUENCE_SPECIFIC


BINDING


GO_MRNA_BINDING
0.35
9.91
0.49
1.55


GO_NEURON_DIFFERENTIATION
8.43
0.84
2.81
1.46


GO_REGULATION_OF_ORGAN_FORMATION
6.89
0.38
0.00
1.43


GO_CELL_CYCLE_PROCESS
0.65
10.70
2.24
1.35


HALLMARK_HEDGEHOG_SIGNALING
8.36
0.00
0.00
1.30


GO_SENSORY_ORGAN_MORPHOGENESIS
7.62
0.25
0.91
1.28


GO_MESONEPHROS_DEVELOPMENT
7.30
0.11
1.66
1.24


GO_CELL_CYCLE
0.90
10.00
1.76
1.16


GO_NUCLEAR_TRANSPORT
0.82
6.20
0.00
1.15


GO_MACROMOLECULAR_COMPLEX_ASSEMBLY
0.56
11.35
0.55
1.14


GO_FOREBRAIN_DEVELOPMENT
6.00
0.07
6.57
1.13


GO_EYE_DEVELOPMENT
6.36
0.34
0.70
1.10


GO_GLYCOSYL_COMPOUND_METABOLIC_PROCESS
0.39
7.16
0.00
1.05


GO_NUCLEOSIDE_MONOPHOSPHATE_METABOLIC_PROCESS
0.22
6.92
0.00
0.98


GO_NEPHRON_DEVELOPMENT
6.56
0.24
0.59
0.89


GO_NEGATIVE_REGULATION_OF_CELL_CYCLE
3.62
6.49
2.32
0.87


GO_NUCLEOSIDE_TRIPHOSPHATE_METABOLIC_PROCESS
0.24
9.55
0.00
0.79


GO_KIDNEY_EPITHELIUM_DEVELOPMENT
7.60
0.21
1.39
0.78


GO_MITOTIC_CELL_CYCLE
0.10
10.28
1.35
0.75


GO_CELLULAR_MACROMOLECULAR_COMPLEX_ASSEMBLY
0.03
12.19
0.07
0.75


GO_CANONICAL_WNT_SIGNALING_PATHWAY
0.52
0.10
6.82
0.75


GO_CELL_DIVISION
1.01
7.15
0.16
0.74


GO_NUCLEOSIDE_TRIPHOSPHATE_BIOSYNTHETIC_PROCESS
0.00
7.00
0.00
0.73


GO_REGULATION_OF_RNA_SPLICING
0.51
7.11
0.00
0.73


GO_ENVELOPE
0.01
6.38
0.52
0.72


GO_TRANSCRIPTIONAL_ACTIVATOR_ACTIVITY_RNA
6.50
0.22
7.03
0.70


POLYMERASE_II_TRANSCRIPTION_REGULATORY_REGION


SEQUENCE_SPECIFIC_BINDING


GO_EYE_MORPHOGENESIS
6.07
0.40
0.00
0.68


GO_RNA_POLYMERASE_II_TRANSCRIPTION_FACTOR_ACTIVITY
9.75
0.04
5.43
0.67


SEQUENCE_SPECIFIC_DNA_BINDING


GO_CHROMATIN
2.18
9.40
0.51
0.65


GO_REGULATORY_REGION_NUCLEIC_ACID_BINDING
6.41
0.30
6.10
0.57


GO_METANEPHROS_DEVELOPMENT
6.19
0.82
0.00
0.54


GO_REGULATION_OF_MRNA_METABOLIC_PROCESS
0.00
7.96
0.00
0.53


GO_RIBONUCLEOSIDE_TRIPHOSPHATE_BIOSYNTHETIC_PROCESS
0.00
6.75
0.00
0.47


GO_NUCLEOBASE_CONTAINING_SMALL_MOLECULE
0.48
6.96
0.13
0.44


METABOLIC_PROCESS


GO_DOUBLE_STRANDED_DNA_BINDING
6.78
0.53
7.34
0.39


GO_CAMERA_TYPE_EYE_MORPHOGENESIS
6.95
0.30
0.00
0.38


GO_REGULATION_OF_MRNA_SPLICING_VIA_SPLICEOSOME
0.00
7.14
0.00
0.37


GO_NEGATIVE_REGULATION_OF_RNA_SPLICING
0.00
6.90
0.00
0.35


GO_REGULATION_OF_CHROMOSOME_ORGANIZATION
1.05
6.61
0.80
0.33


GO_RIBONUCLEOPROTEIN_COMPLEX_SUBUNIT_ORGANIZATION
0.00
8.15
0.00
0.31


HALLMARK_OXIDATIVE_PHOSPHORYLATION
0.00
8.92
0.00
0.31


GO_NUCLEIC_ACID_BINDING_TRANSCRIPTION_FACTOR
7.81
0.03
5.90
0.29


ACTIVITY


GO_SEQUENCE_SPECIFIC_DNA_BINDING
9.71
0.33
4.29
0.27


HALLMARK_WNT_BETA_CATENIN_SIGNALING
6.28
0.00
0.99
0.20


GO_DNA_TEMPLATED_TRANSCRIPTION_TERMINATION
0.00
6.92
0.00
0.17


GO_HYDROGEN_ION_TRANSMEMBRANE_TRANSPORT
0.00
6.64
0.00
0.15


GO_APPENDAGE_DEVELOPMENT
9.06
0.11
3.05
0.12


GO_SPLICEOSOMAL_COMPLEX
0.00
10.96
0.45
0.12


GO_TERMINATION_OF_RNA_POLYMERASE_II_TRANSCRIPTION
0.00
7.14
0.00
0.12


GO_NUCLEAR_CHROMOSOME
2.43
7.73
0.41
0.11


GO_REGULATION_OF_GENE_EXPRESSION_EPIGENETIC
0.67
6.48
0.00
0.11


GO_MRNA_3_END_PROCESSING
0.00
7.54
0.00
0.09


GO_DNA_METABOLIC_PROCESS
0.49
9.85
0.07
0.07


GO_MITOTIC_NUCLEAR_DIVISION
0.13
7.96
0.63
0.07


GO_REGIONALIZATION
12.14
0.58
1.35
0.07


GO_RNA_SPLICING_VIA_TRANSESTERIFICATION_REACTIONS
0.00
17.00
0.00
0.06


GO_CHROMOSOME
1.74
12.88
0.41
0.06


GO_CATALYTIC_STEP_2_SPLICEOSOME
0.00
9.61
0.00
0.06


GO_DNA_CONFORMATION_CHANGE
0.00
8.87
0.00
0.06


GO_ORGANELLE_FISSION
0.07
7.07
0.88
0.05


GO_RNA_3_END_PROCESSING
0.00
8.03
0.00
0.05


GO_NUCLEAR_BODY
0.42
6.32
0.65
0.04


GO_MITOCHONDRIAL_MEMBRANE_PART
0.00
6.65
0.00
0.04


GO_CHROMOSOME_CENTROMERIC_REGION
0.00
10.86
0.00
0.04


GO_RNA_SPLICING
0.12
15.18
0.21
0.03


GO_RIBONUCLEOPROTEIN_COMPLEX_LOCALIZATION
0.00
6.17
0.00
0.03


GO_DNA_PACKAGING
0.00
8.32
0.00
0.03


GO_CONDENSED_CHROMOSOME
0.77
8.28
0.00
0.03


GO_CHROMOSOME_SEGREGATION
0.00
6.09
0.00
0.02


GO_NUCLEAR_CHROMOSOME_SEGREGATION
0.00
7.23
0.00
0.01


GO_MRNA_PROCESSING
0.31
14.65
0.17
0.01


GO_CHROMOSOMAL_REGION
0.00
9.44
0.00
0.01


GO_ORGANELLE_INNER_MEMBRANE
0.00
7.13
0.13
0.01


GO_SISTER_CHROMATID_SEGREGATION
0.00
8.15
0.00
0.01


GO_HISTONE_BINDING
0.00
8.96
0.00
0.01


GO_ANTERIOR_POSTERIOR_PATTERN_SPECIFICATION
9.73
0.97
1.88
0.00


GO_CHROMOSOME_ORGANIZATION
0.27
12.92
0.13
0.00


GO_CHROMATIN_ORGANIZATION
0.34
8.12
0.30
0.00


GO_CARDIAC_RIGHT_VENTRICLE_MORPHOGENESIS
6.47
0.00
1.40
0.00


GO_MITOCHONDRIAL_PROTEIN_COMPLEX
0.00
8.05
0.00
0.00


GO_INNER_MITOCHONDRIAL_MEMBRANE_PROTEIN_COMPLEX
0.00
7.61
0.00
0.00


GO_MITOCHONDRIAL_ATP_SYNTHESIS_COUPLED_PROTON
0.00
6.66
0.00
0.00


TRANSPORT


GO_NEGATIVE_REGULATION_OF_MRNA_SPLICING_VIA
0.00
6.33
0.00
0.00


SPLICEOSOME


GO_ELECTRON_TRANSPORT_CHAIN
0.00
6.32
0.00
0.00


GO_CELLULAR_COMPONENT_DISASSEMBLY_INVOLVED_IN
0.00
6.32
0.00
0.00


EXECUTION_PHASE_OF_APOPTOSIS


GO_RNA_STABILIZATION
0.00
6.20
0.00
0.00


GO_KINETOCHORE_ORGANIZATION
0.00
6.05
0.00
0.00









Using these SS18-SSX KD experiments Applicants defined the SS18-SSX program, which Applicants then stratified to direct and indirect fusion targets based on available SS18-SSX ChIP-Seq profiles (13, 28) (Methods; FIGS. 22A-22B, Table 8). Analyzing these functional single-cell data Applicants identified SS18-SSX transcriptional targets/program (FIG. 7A; Tables 8 and 9). Reassuringly, the SS18-SSX program captured bulk transcriptional alterations that followed SS18-SSX KD in another cell line (HS-SY-II, P<1*10−17, hypergeometric test) (Banito et al. Cancer Cell 33:524-541.e8 (2018)). It was enriched with SS18-SSX direct targets (McBride et al. Cancer Cell (2018) doi:10.1016/j.ccell.2018.05.002; Banito et al. Cancer Cell 33:524-541.e8 (2018)) (P=6.66*10−16, hypergeometric test), and repressed genes which are suppressed by the ATF2-SS18/SSX-TLE1 complex (P=2.94*10−8, hypergeometric test) (Su et al. Cancer Cell 21:333-347 (2012)). It was overexpressed in SyS malignant cells compared to non-malignant cells (P<1*10−30, t-test), and in SyS tumors compared to other cancer and sarcoma types (Baird et al. Cancer Res 65:9226-9235 (2005)) (FIGS. 7D-7E). It included IGF2, which is critical for SyS tumorigenesis (Sun et al. Oncogene 25:1042-1052 (2006)), and TLE1, a diagnostic marker of SyS (Banito et al. Cancer Cell 33:524-541.e8 (2018)).


Then, using available SS18-SSX ChIP-Seq profiles (McBride et al. Cancer Cell (2018) doi:10.1016/j.ccell.2018.05.002; Banito et al. Cancer Cell 33:524-541.e8 (2018)), Applicants stratified the SS18-SSX program to its direct and indirect targets and found that the fusion directly dysregulates developmental programs (P<5.28*10−7, hypergeometric test), while its impact on cell cycle is mostly indirect (P<1.2*10−9, hypergeometric test, Tables 8 and 9, FIG. 7E), and mediated by cyclin D2 (CCND2) and CDK6—the only cell cycle genes that are members of the direct SS18-SSX program. Taken together, Applicants' findings support a model in which SS18-SSX directly promotes the core oncogenic program, blocks differentiations, and drives cell cycle progression.


The oncoprotein also directly promotes the core oncogenic program, by directly dysregulating many of its genes (P=2.51*10−5, hypergeometric test) and gene modules, including TNF signaling, hypoxia, apoptosis, and p53 signaling (P<1.4*10−5, FIGS. 7E, 8A, Tables 8 and 9). Lastly, modeling the transcriptional regulation of the core oncogenic program (Methods), Applicants found that it includes a large number of transcription factors (TFs, 47 of 119 repressed genes, P=1.89*10−15, hypergeometric test), with 193 TF-target interactions between its genes. The fusion directly dysregulated the most dominant TFs in the resulting regulatory network, including JUN, ATF4, EGR1, and ATF3.


Example 5—TNF and IFNγ Synergistically Repress the Core Oncogenic Program and SS18-SSX Program

The association between the core oncogenic program and the cold phenotype suggest that the program promotes T cell exclusion in SyS. Another (non-mutually exclusive) hypothesis is that, despite their low numbers, the immune cells in the tumor microenvironment may nonetheless impact the state of the malignant cells, for example, through the secretion of different molecules and cytokines. To test this, Applicants implemented a mixed-effects inference approach that uses scRNA-Seq data to find associations between the expression of secreted molecules and ligands in immune cells and the state of the malignant cells, as described below. First, Applicants used single-cell immune signatures to estimate the composition of bulk SyS tumors in two published cohorts (Banito et al. Cancer Cell 33:524-541.e8 (2018); Lagarde et al. J Clin Oncol Off J Am Soc Clin Oncol 31:608-615 (2013)) (Methods), and stratified them into “hot” or “cold”, based on their relative inferred proportions of immune cells. “Hot” tumors, with relatively high levels of immune cells, showed repression of the core oncogenic and proliferation programs and had significantly higher differentiation scores (P<5.34*10−3, r=−0.44, −0.36 and 0.48, respectively, partial Pearson correlation, conditioning on inferred tumor purity; FIG. 8B).


Supporting the generalizability of these findings, the core oncogenic program overlapped a transcriptional signature Applicants recently associated with T cell exclusion in melanoma (32) (P<7.16*10−10, hypergeometric test). Among the overlapping genes Applicants find the induction of the CTAMA GEA4, the BAF complex unit SMARCA4 and genes involved in oxidative phosphorylation, as well as the repression of apoptosis and p53 signaling (e.g., ATF3, JUN, KLF4, and SAT1). The melanoma T cell exclusion signature also overlapped the mesenchymal state defined here, inducing SNAI2 and repressing 23 epithelial genes, including CDH1 (P=6.33*10−8, hypergeometric test).


To examine whether immune cells impact SyS cells through physical interactions (ligand-receptor bindings) and the secretion of certain cytokines, Applicants developed a mixed-effects inference approach that uses scRNA-Seq data to find associations between the expression of ligands in immune cells and the state of the malignant cells (Methods). This analysis revealed that the expression of IFNγ and TNF in CD8 T cells and macrophages, respectively (FIG. 9A), was strongly associated with the repression of the core oncogenic program in the malignant cells (P<9.4*10−39, mixed-effects). In accordance with these findings, TNF receptors and multiple genes involved in TNF and IFN responses were repressed in the core oncogenic program, and according to the connectivity map (CMap) (Subramanian et al. Cell 171:1437-1452.e17 (2017))—the overexpression of IFNs and TNF receptors repressed the program in various cancer cell lines. Applicants further stratified the core oncogenic program to its predicted TNF/IFNy-dependent and -independent components, by the association of each gene's expression in the malignant cells with the TNF and IFNy expression levels in the corresponding macrophages and CD8 T cells, respectively (Methods, Table 10A).


To examine these associations, Applicants treated primary SyS cell cultures with TNF and IFNγ, both separately and in combination, and profiled 1,050 cells by scRNA-Seq. As predicted, combined TNF and IFNγ treatment repressed the core oncogenic program (P=6.66*10−18, mixed-effects, FIG. 9B) in a synergistic manner (P=9.49*10−4, interaction term, mixed-effects). Moreover, the treatment repressed the SS18-SSX program (P<3.12*10−16, both direct and indirect components, including TLE1; FIG. 9B, Tables 10 and 11), while inducing multiple genes from the epithelial program (P=1.95*10−9, hypergeometric test, Tables 10 and 11). Short-term (4-6 hours) treatment with TNF alone substantially repressed homeobox genes (e.g., MEOX2, Tables 10 and 11), which are directly bound by SS18-SSX (McBride et al. Cancer Cell (2018) doi:10.1016/j.ccell.2018.05.002; Banito et al. Cancer Cell 33:524-541.e8 (2018)) (P<1*10−17, hypergeometric test). It also repressed the core oncogenic program, but only temporarily (P=8.73*10−18, mixed-effects; FIG. 8C), suggesting that IFNγ is require to sustain the effect. Interestingly, TNF also induced TNF expression in the Sys cells (P<5.57*10−8, mixed-effects, FIG. 22C), suggesting that autocrine signaling might induce the effect as well. Taken together these findings demonstrate that macrophages and T cells can suppress the SS18-SSX program by secreting TNFγ and IFNγ.









TABLE 10A







Predicted TNF/IFN-dependent and independent components of the core


oncogenic program according to the cell-cell interaction analyses.









Core oncogenic


Core oncogenic TNF/IFN-independent
TNF/IFN-dependent










UP
DOWN
UP
DOWN















AFG3L1P
HERC2
PFN1
AMD1
AHCY
ATF3


AGPAT2
HIGD2A
PFN1P2
ATF4
BTF3
BHLHE40


AGPAT5
HINT1
PGD
BRD2
DBNDD1
CDKN1A


AKR1B1
HMG20B
PGLS
BTG1
EEF1G
CSRNP1


AKR1C3
HN1L
PHF14
C12orf44
FADS2
DDX5


AKT1
HNRNPD
PIGQ
C6orf62
FGF9
DUSP1


ALG3
HOXD11
PIGT
CCNL1
GLB1L2
DUSP2


ALX4
HOXD9
PKD2
CKS2
GNB2L1
FOSL1


ANAPC7
HSD17B10
PLP2
CLK1
LDHB
JUND


ANKRD26P1
HYAL2
PMS2P5
COQ10B
MDH2
KLF4


APEH
HYLS1
POLD2
CYCS
NACA
KLF6


APRT
ICT1
POLR1B
DDX3X
PPIA
LMNA


ARF5
IFT81
POLR2F
DDX3Y
PTPRS
MAFF


ARL6IP4
IRS4
PPIB
DLX2
PXDN
MIR22HG


ARL6IP5
ITM2C
PPIP5K2
DNAJA1
SEMA3A
NFKBIA


ATF7IP
ITPA
PPP1R16A
DNAJA4
SLC25A6
NFKBIZ


ATP5A1
JMJD8
PRDX4
DNAJB1
TKT
NR4A1


ATP5E
KDM1A
PRELID1
DNAJB9
UBA52
PER1


ATP5J
KIAA0020
PSMA5
EGR1
VCAN
SAT1


ATP5J2
KRT14
PSMA7
EGR2

SIK1


ATR
KRT15
PSMB7
EGR3

TNFAIP3


ATRAID
KRT8
PSMD4
EIF4A3

TNFRSF12A


AUP1
KRTCAP2
PSMG3
EIF5

UBC


AURKAIP1
LAMA2
PTPRF
ERF


BCAP31
LARP1
PUS7
FAM53C


BCL7C
LECT1
RABAC1
FOS


BMP1
LGALS1
RABL6
FOSB


BOP1
LINC00115
RANBP1
GADD45B


BRK1
LINC00116
RBM26
GEM


BSG
LOC100272216
RBM6
GTF2B


C11orf48
LOC202781
RBX1
H3F3B


C16orf88
LOC375295
REST
HBP1


C2orf68
LOC654433
RGMA
HERPUD1


C4orf48
LOXL1
RGS10
HES1


C7orf73
LSM4
RHOBTB3
HSP90AA1


C9orf16
LSM7
RNASEK
HSP90AB1


CALML3
LUC7L3
RNPC3
HSPA1A


CAPNS1
LY6E
RNPEP
HSPA1B


CBX6
MAB21L1
ROMO1
HSPA8


CCDC137
MAGEA4
RUVBL1
HSPH1


CCDC140
MAGEA9
SARS2
ICAM1


CD63
MAGEC2
SELENBP1
ID1


CD7
MAP1B
SERF2
ID2


CDK2AP1
MATN3
SERTAD4
ID3


CECR5
MBD6
SETD4
IER2


CHCHD1
MDK
SH2D4A
IRF1


CHCHD2
METTL3
SH3PXD2B
JUN


CIAPIN1
MFSD3
SIM2
JUNB


CKAP5
MGC21881
SLC25A23
KLHL15


CLNS1A
MGST1
SLC35B4
LOC284454


CNPY2
MGST3
SLC6A15
MCL1


COL18A1
MIS18A
SMARCA4
MLF1


COL5A1
MKKS
SMC2
MXD1


COL6A2
MMP14
SMC3
NR4A2


COL9A3
MRPL12
SNRPD3
NR4A3


COX4I1
MRPL17
SNRPF
PAFAH1B2


COX5A
MRPL28
SPCS1
RGS16


COX5B
MRPL35
SRI
RIPK4


COX6A1
MRPL4
SRM
RRP12


COX6B1
MRPL52
SRSF9
SERTAD1


COX6C
MRPS17
SSNA1
SF1


CRIP1
MRPS21
SSR4
SLC25A25


CRLF1
MRPS34
SSX2
SLC25A44


CRMP1
MTG1
SSX2B
SOCS3


CSAG3
MTRNR2L1
STAG3L2
SRSF3


CSRP2BP
MTRNR2L10
STAG3L3
TOB1


CST3
MTRNR2L2
STAG3L4
TRIB1


CSTB
MTRNR2L6
STARD4-
TSPYL1




AS1


CSTF3
MTRNR2L8
SULF2
TUBA1A


CTAG1A
MYBBP1A
SULT1A1
TUBA1B


CTAG1B
NAT14
SUMF2
TUBB2A


CYHR1
NDUFA1
SYNPR
TUBB4B


DAD1
NDUFA13
TBCD
UBB


DANCR
NDUFA4
TCEB2
YWHAG


DCP1B
NDUFA7
TELO2


DCXR
NDUFA8
TFAP2A


DGCR6L
NDUFAB1
THY1


DHFR
NDUFB10
TIGD1


DNMT3A
NDUFB11
TIMM8B


DPEP3
NDUFB2
TMA7


DYNLRB1
NDUFB3
TMC6


DYNLT1
NDUFB4
TMEM101


EDF1
NDUFB7
TMEM147


EEF1D
NDUFB9
TMEM177


EIF2AK1
NDUFS6
TOMM40


EIF3K
NDUFS8
TOMM6


ELAC2
NEDD8
TOMM7


ELOVL1
NEFL
TRAPPC1


EML3
NIPSNAP3A
TSR3


EPRS
NKAIN4
TSTA3


ERGIC3
NME1
TTYH3


ETAA1
NNT
TUFM


EXOSC4
NOMO1
TUSC3


EXOSC7
NOMO2
TWIST2


FADD
NPEPL1
TXN


FAM178A
NRBP2
TXNDC17


FAM19A5
NSMF
TXNDC5


FAM213B
NSUN5
TXNDC9


FAM50B
NSUN5P1
UBE2T


FARSA
NSUN5P2
UBE3B


FARSB
NT5DC2
UPK3B


FBN3
NUBP2
UQCR10


FLAD1
NUDT5
UQCR11


FRG1B
NUTF2
UQCRC1


G6PC3
OBSL1
UQCRQ


GADD45GIP1
OGG1
USMG5


GCN1L1
OST4
USP5


GEMIN7
OXLD1
VARS


GLB1L
PATZ1
VKORC1


GLI1
PAX3
VPS28


GNAS
PCDHA3
VPS72


GNPTAB
PDCD11
VSNL1


GOLM1
PDCD5
WDR12


GPR124
PDIA4
YWHAB


GPR126
PEBP1
ZNF212


GPRC5B
PET100
ZNF605


GSTO2
PFKL


GUSB
PFKP


H19
















TABLE 10B







Differentially expressed genes following TNF and IFN-gamma treatment.
















TNF
TNF &
IFN
TNF
TNF short
TNF & IFN


IFN up
TNF up
short up
IFN up
down
down
down
down





A2M
ABTB2
ABCA5
ABCG1
ACTG1
ACTG1
AASDHPPT
ABI2


ABHD16A
ACAT2
ABCF1
ABHD16A
AKAP9
ADAMTS9
ABHD10
ACTB


ACSL1
ACHE
ABR
ABTB1
APCDD1L
ANO1
ACN9
ACTG1


ACTA2
ACOX1
ALCAM
ACSL1
BTF3
APCDD1L
ADAMTS14
ACTR2


ACY3
ADAR
ALOX15B
ACTA2
CD248
ARHGAP10
ADH5
ADH5


ADAMTS3
AIFM2
ARID4B
ADAMTSL4
CYGB
ARL5A
AGPAT1
AGAP2


ADAR
AKIP1
ATF3
ADRB2
DAZ2
BAIAP2
AGPAT2
ALDH18A1


ADC
ALCAM
ATP13A3
AFAP1L2
DAZ4
BCHE
AGPAT5
ALDOA


ALPK1
ANO9
BAZ1A
AHRR
DCBLD2
BOP1
AHNAK2
ANP32E


APOBEC3D
ANXA7
BCL3
AIFM2
DHRS3
BZW2
AJUBA
ANXA6


APOBEC3F
APBA3
BHLHE40
AIM2
FSTL1
C17orf76-AS1
ALDH3A2
AP2M1


APOBEC3G
APLF
BID
AKAP2
FUS
C6orf48
ALDOA
AP3S1


APOL1
APOBEC3F
BIRC3
ALOX5AP
GAL
CA10
ALG1
APEX1


APOL2
APOBEC3G
BTG2
ALPK1
GATA6
CALCRL
ALKBH2
ARCN1


APOL3
APOL2
BTG3
ANPEP
GSE1
CCDC178
AMZ2P1
ARF1


APOL4
APOL6
C10orf10
AP1G2
HOXA6
CD248
ANKZF1
ARF4


APOL6
ATXN2L
C11orf96
AP5Z1
HOXB5
CHODL
ANP32A
ARF5


ARHGAP18
B2M
C15orf48
APOBEC3C
IPO5
CHST8
AP2M1
ARPC1A


ATF3
BARX2
C20orf111
APOBEC3D
JMJD1C
CIRBP
ARAP1
ARPC2


B2M
BCL6B
C2CD4B
APOBEC3F
KIAA1467
COL25A1
ARHGEF3
ARPC3


BATF2
BHLHE41
CAV1
APOBEC3G
LAMA2
CXCL12
ARMCX6
ARPC4


BATF3
BID
CBR3
APOD
NID1
DANCR
ARSK
ARPP19


BST2
BIRC2
CCL1
APOL1
OSBPL8
DAZ2
ARV1
ATG12


BTN2A2
BIRC3
CCL2
APOL2
PCDH11X
DLX1
ASB13
ATP5A1


BTN3A1
BST2
CCL5
APOL3
PKM
EBF1
ATIC
ATP5B


BTN3A2
BTG2
CCNL1
APOL6
RBM3
EBF3
ATP5G2
ATP5C1


BTN3A3
BTN2A2
CCRN4L
AREG
RPL10
EEF1B2
ATPIF1
ATP5F1


C14orf159
BTN3A1
CD40
ARHGEF2
RPL6
EGR1
B3GALNT1
ATP5G2


C19orf12
BTN3A2
CD82
ARIH2OS
SCN4B
EIF3L
B3GNT1
ATP5G3


C19orf66
C10orf10
CD83
ARRDC2
SHISA2
EIF4EBP1
BAG4
ATP5J


C1R
C16orf46
CDK6
ATAD3C
SLC35F2
EMP1
BCOR
ATP5L


C1RL
C19orf66
CEBPD
ATF3
SMAD9
ENAH
BIVM
ATP5O


C15
C1R
CFLAR
ATF5
SSBP2
EPHA3
BMP3
ATP6V1D


C5orf56
C1S
CHST15
ATG2A
SVEP1
EPHA7
BMP4
ATP6V1G1


CALCOC02
CASP4
CNKSR3
ATHL1
TAGLN
ERCC1
BNIP3L
ATXN10


CARD16
CAV1
COTL1
ATP6V0D2
TMSB15A
ETV1
BOP1
AZIN1


CASP1
CCDC88C
CREB5
ATXN2L
TNFRSF10D
F2R
BRAT1
BAI3


CASP4
CCL2
CSF2
B2M
TOMM20
FAM49A
BRK1
BANF1


CASP7
CCL20
CX3CL1
BATF2
TSPAN13
FARP1
C11orf48
BGN


CCDC178
CCL5
CXCL1
BATF3
U2SURP
FHL2
C12orf52
BMP5


CCL2
CCND1
CXCL2
BCAR1
UNC5C
FLI1
C12orf73
BNC2


CD200
CCR10
CXCL3
BCL3

FLJ41200
C14orf1
BNIP3L


CD274
CD40
CXCR7
BDKRB2

FOS
C17orf58
BRK1


CD40
CD44
CYB5A
BIRC3

FZD1
C18orf32
BSG


CD44
CD47
CYLD
BST2

GAS2
C19orf24
BTF3


CD47
CD58
DENND4A
BTG1

GAS5
C20orf112
BTF3L4


CD74
CD59
DOT1L
BTN3A1

GNAI1
C22orf29
BZW1


CDKN2A
CD70
DSEL
BTN3A2

GNB2L1
C6orf57
BZW2


CEACAM1
CD82
DUSP5
BTN3A3

GRIK3
CABIN1
C11orf58


CFH
CD83
DYSF
C10orf10

H19
CACYBP
C14orf166


CIITA
CDC42EP4
ECE1
C14orf159

HOXA10
CALHM2
C17orf76-AS1


CLDN1
CDH13
EFNA1
C15orf48

HOXD-AS1
CAPRIN1
C19orf10


CMPK2
CFLAR
EGR3
C19orf12

ID2
CASP2
C1orf43


CNN3
CHST15
EIF5
C19orf66

IMPDH2
CASP6
C1QBP


CPQ
CNTNAP1
ELF3
C1R

ITGA4
CBFB
C4orf3


CTSO
COTL1
ELL2
C1RL

ITM2C
CBR1
C9orf16


CTSS
CPXM2
ELOVL7
C1RL-AS1

KAL1
CBX2
CALM2


CX3CL1
CREB3
EPC1
C1S

KAZALD1
CBX3
CALR


CXCL1
CRIM1
ETS1
C2

KIAA1467
CBX8
CALU


CXCL10
CRYAA
EVA1A
C3

KIF26B
CCDC8
CASP2


CXCL11
CSF1
EXT1
C5orf56

KLF10
CCT8
CCND2


CXCL16
CSPG4
F3
C8orf4

KLHL14
CECR5
CCT2


CXCL6
CTSS
FAM107B
C9orf3

LDHB
CES2
CCT3


CXCL9
CX3CL1
FAM19A3
CA4

LHX8
CHD7
CCT4


DDX58
CXCL1
FAM65A
CALCOCO2

LINC00478
CHD9
CCT5


DDX60
CXCL2
FJX1
CARD16

LOC100506474
CHST8
CCT6A


DDX60L
CXCL3
FMNL3
CASP1

LOC644961
CISD2
CCT7


DHX58
CXCR4
FNDC3B
CASP4

LPAR1
CLASP1
CCT8


DNPEP
CXCR7
FOSB
CAV1

LRIG3
CLIP3
CD248


DPYD
CYB5A
FOSL1
CAV2

LSP1
CMBL
CD63


DSC2
CYFIP2
FTH1
CCDC130

LZTS1
CNP
CD99


DSG2
DAG1
GADD45A
CCDC88C

LZTS1-AS1
COMMD3
CDC42


DTX3L
DBI
GADD45B
CCL2

MEG3
COX20
CDK4


EBI3
DCLK1
GFPT2
CCL20

MITF
CRYZ
CELF2


EDARADD
DDX58
GPATCH2L
CCL5

MLLT11
CSNK1G3
CFDP1


ENG
DDX60
HAS2
CCL8

MMP16
CSTB
CFL1


ENOX1
DHCR7
HIVEP1
CCNL1

MYC
CXXC5
CHCHD2


EPSTI1
DHRS3
HIVEP2
CCRL1

MYL9
DAG1
CHMP3


ERAP1
DHX58
HLA-A
CD274

NDUFA4L2
DBN1
CIRBP


ERAP2
DMD
HLA-B
CD38

NFIB
DCTD
CNBP


ETV7
DPYSL3
HLA-C
CD40

NR2F2
DLAT
CNN3


EYA4
DTX3L
HLA-F
CD47

NR5A2
DLL1
COL5A2


FAM111A
DTX4
HLA-H
CD70

NRP1
DLX1
COPA


FAM129A
EBI3
HMOX1
CD74

OLFM3
DLX2
COPZ1


FBXO6
ECE1
HSPG2
CD82

PAFAH1B3
DNAAF2
CORO1A


FIBIN
EDARADD
ICAM1
CDCP1

PAG1
DNAJC4
COX5A


FLT3LG
EFNA1
ICOSLG
CDK11A

PAR-SN
DOLK
COX6C


FTH1
ELF3
IER3
CDK11B

PCDH18
DPYSL2
COX7A2


GBP1
EMP3
IER5
CDKN2A

PCDH7
DTWD1
COX7A2L


GBP1P1
EPS8L2
IFIH1
CEACAM1

PCDH9
EEF2K
CRABP2


GBP2
EPSTI1
IL15
CEBPB

PCOLCE2
EGFLAM
CRIP2


GBP3
ERAP1
IL18R1
CFB

PCSK1
EHMT2
CRTAP


GBP4
ETV7
IL32
CFD

PDLIM7
ENAH
CSNK1A1


GBP5
EVA1A
IL34
CFLAR

PPIC
ENDOD1
CSNK2A1


GIMAP2
EXOC3L4
IL6
CH25H

PRICKLE1
ENO2
CSNK2B


GPC3
FADS2
IL7
CHKA

RASL11B
EPHB3
DAD1


GRM4
FAM129A
ING3
CHRNA1

REEP2
EPN2
DANCR


GSDMD
FAM65A
INHBA
CIITA

RGMB
EXOSC4
DARS


GSTO1
FMNL3
IRAK2
CLCN7

RND3
FAM115A
DAZ2


GTPBP1
FNDC3B
IRF1
CLDN1

RPL10
FAM131A
DAZ4


HAPLN3
FRMD4A
ITGAV
CLIP1

RPL10A
FAM136A
DCAF13


HCP5
FSTL3
JUN
CLSTN3

RPL11
FAM156A
DCBLD2


HEXDC
FTH1
JUNB
CMPK2

RPL12
FAM175A
DCTN3


HEY2
FXYD6
KDM6B
COL15A1

RPL13
FAM210A
DDB1


HLA-A
GBP1
KLF5
CPT1B

RPL14
FAM217B
DDOST


HLA-B
GBP3
KLF6
CPZ

RPL15
FANCF
DDX39B


HLA-C
GBP4
KLF7
CSF1

RPL17
FARSA
DPYSL2


HLA-DMA
GFPT2
KLF9
CTSC

RPL18
FBXO17
DSTN


HLA-DMB
GFRA1
LACC1
CTSD

RPL18A
FDFT1
EBF1


HLA-DOA
GPR133
LAMC2
CTSO

RPL19
FOXK1
EBF2


HLA-DOB
GPX4
LIF
CTSS

RPL22
FRMD8
EBF3


HLA-DPA1
GRAMD1A
LOC100126784
CX3CL1

RPL22L1
FTSJ2
EDNRA


HLA-DPB1
GRINA
LOC100862671
CXCL1

RPL23
FZD1
EEF1A1


HLA-DQA1
GSDMD
LOC387895
CXCL10

RPL23A
FZD3
EEF1B2


HLA-DQB1
HAPLN3
LOC440896
CXCL11

RPL24
GALNT2
EEF1G


HLA-DRA
HAS2
MAFF
CXCL16

RPL26
GAPDH
EEF2


HLA-DRB1
HERC6
MAML2
CXCL9

RPL27
GIT2
EFNA5


HLA-DRB5
HIP1
MAP2K3
CYBA

RPL27A
GLG1
EID1


HLA-DRB6
HIPK2
MAP3K8
DDIT4L

RPL28
GNPDA1
EIF1


HLA-E
HLA-A
MASTL
DDX58

RPL29
GPI
EIF2S3


HLA-F
HLA-B
MIR155HG
DDX60

RPL3
GRSF1
EIF3D


HLA-H
HLA-C
MIR22HG
DENND3

RPL30
GTF3C2
EIF3E


HRH1
HLA-E
MTRNR2L1
DHX58

RPL31
GTF3C6
EIF3F


HS3ST1
HLA-F
MTRNR2L6
DLGAP1

RPL32
GTPBP3
EIF3H


ICAM1
HLA-H
MTRNR2L8
DNPEP

RPL34
HLTF
EIF3I


IDO1
HORMAD1
NDRG4
DOCK9

RPL35A
HMG20A
EIF3K


IFI27
HSPG2
NEDD4L
DPP7

RPL36A
HMOX2
EIF3L


IFI30
HYAL3
NFATC2
DRAM1

RPL37
HOXA10
EIF3M


IFI35
ICAM1
NFE2L2
DTX2

RPL37A
HOXA6
EIF4A1













IFI44
ICOSLG
NFKB1
DTX2P1-UPK3BP1-
RPL38
HOXA9
EIF4A2





PMS2P11














IFI44L
ID4
NFKB2
DTX3L

RPL39
HOXB5
EIF4B


IFI6
IER3
NFKBIA
DUSP5

RPL4
HOXB6
EIF4H


IFIH1
IFI27
NFKBIB
DYRK2

RPL41
HOXB7
EIF5A


IFIT2
IFI27L2
NFKBID
EBI3

RPL5
HOXC10
EMC4


IFIT3
IFI30
NFKBIE
ECE1

RPL6
HOXD-AS1
ENAH


IFIT5
IFI35
NFKBIZ
EPSTI1

RPL7A
HPCAL1
ENO1


IFITM1
IFI6
NINJ1
ERAP1

RPL8
HSD11B1L
EPHA3


IFITM3
IFIH1
NPAS2
ERAP2

RPL9
HSPA8
EPHA4


IL15
IFIT1
OPTN
ETV7

RPLP0
HSPE1
ERCC1


IL17RC
IFIT3
PIM1
EZH1

RPLP1
HTRA1
ERGIC3


IL18BP
IFITM1
PIM3
FADS3

RPS10
ID1
ESD


IL32
IFITM3
PLA2G4C
FAM111A

RPS11
ID3
FAM162A


IL3RA
IGF2
PLAU
FAM129A

RPS12
IDH1
FBL


IL8
IKBKE
PMAIP1
FAM193A

RPS13
IFT88
FIBP


IRF1
IL18R1
PPP1R15A
FAS

RPS14
IMP3
FKBP1A


IRF2
IL27RA
PPP4R4
FBXL19-AS1

RPS15A
IMPDH1
FLJ41200


IRF3
IL32
PPRC1
FBXO32

RPS16
INSIG2
FSTL1


IRF7
IL34
PSMB8
FBXO6

RPS17
INTS3
FUS


IRF8
IL4I1
PSMB9
FENDRR

RPS17L
IVD
FZD1


IRF9
IL8
PSME2
FLJ14186

RPS18
KANK2
G3BP2


ISG15
INHBA
RAPH1
FLJ39739

RPS2
KDM4B
GABARAP


ISG20
IRF1
RARB
FLJ45340

RPS20
KIAA1430
GANAB


JAK2
IRF2
REL
FLT3LG

RPS23
KIF26B
GAPDH


LAP3
IRF7
RELB
FNDC1

RPS24
KLHDC3
GAS2


LGALS17A
IRF9
RGS16
FOXF1

RPS25
L3HYPDH
GATA6


LGALS3BP
ISG15
RHOB
FRMD4A

RPS27
LAGE3
GCSH


LGALS9
ITGA5
S1PR1
FRMD6-AS1

RPS28
LDHA
GDI2


LITAF
ITGAV
SDC4
FTH1

RPS3
LINC00094
GNAI1


LOC100507463
ITIH5
SELE
FTL

RPS3A
LINC00516
GNAS


LRP10
JAK2
SEMA4C
GATM-AS1

RPS4X
LOC100294145
GNB2L1


LRRTM2
JAM2
SERPINA3
GBP1

RPS5
LOC101101776
GNG10


LY6E
KCNQ3
SGPP2
GBP1P1

RPS6
LOC441081
GNL3


MAN2B2
KIF25
SLC12A7
GBP2

RPS7
LOXL4
GPI


MARCKS
KLF7
SLC41A2
GBP3

RPS8
LRRFIP1
GPX7


MDK
KLHL5
SLC7A1
GBP4

RPS9
LSM2
GSTA4


MEST
LAD1
SLC7A2
GBP5

RRP15
LYPLA1
H19


MFSD12
LAMC1
SNAP25
GDF15

RUNX1T1
LZTS2
H2AFZ


MIA
LAMC2
SOCS2
GGT5

SCN4B
MALSU1
H3F3AP4


MICB
LAP3
SOD2
GIMAP2

SEMA3E
MAP4K5
HADHA


MKX
LDLR
SOX9
GIMAP5

6-Sep
MAT2B
HADHB


MLKL
LGALS1
SQSTM1
GLI1

SETBP1
MBLAC2
HDAC2


MOV10
LGALS3
SRCAP
GPR133

SIX1
MEA1
HDLBP


MR1
LGALS3BP
ST5
GPX4

SLC25A6
MEAF6
HMGCLL1


MT2A
LITAF
STAT5A
GRIPAP1

SMAD9
MEOX2
HMGN1


MVP
LOC100130093
STK40
GSDMD

SNAI2
METAP1
HMGN2


MX1
LOC400043
SUSD4
GTPBP1

SNHG16
METTL5
HNRNPA1


MYD88
LOC440896
TAP1
GTPBP2

SNHG6
MINA
HNRNPA1P10


NINJ2
LRRC32
TAPBP
GYPC

SNHG8
MLH3
HNRNPA2B1


NLRC5
LRRN3
TBC1D10A
HAPLN3

SOX11
MLLT11
HNRNPA3


NMI
LSR
TFPI2
HAS2

SPOCK1
MLLT3
HNRNPC


NQO1
MAN2B1
TGIF1
HCFC1

SPRED1
MLX
HNRNPD


NRN1
MAP4K2
TIFA
HCP5

SPRY2
MMACHC
HNRNPK


NUB1
MATN2
TNF
HDAC10

STEAP2
MMP2
HNRNPR


OAS1
MIA
TNFAIP1
HERC2P2

SYTL5
MRP63
HNRPDL


OAS2
MIA-RAB4B
TNFAIP2
HERC6

TAG LN
MRPL15
HOXC10


OAS3
MIR155HG
TNFAIP3
HIST1H2BJ

TIMM13
MRPL17
HSP90AB1


OASL
MMP10
TNFAIP8
HLA-A

TMEFF2
MRPL34
HSP90B1


OCA2
MMP9
TNFRSF10B
HLA-B

TMEM100
MRPL55
HSPD1


OGFR
MOV10
TNFRSF18
HLA-C

TMEM130
MRPS23
ILF2


OLFML2B
MSRB1
TNFRSF6B
HLA-DMA

TMPRSS15
MRPS26
IMPDH2


OPTN
MT2A
TP53INP2
HLA-DMB

TPBG
MRPS27
IP6K2


PARP10
MTMR4
TRAF1
HLA-DOA

TRIB1
MRPS34
IPO5


PARP12
MTRNR2L1
TRAF3
HLA-DOB

TRIL
MRPS6
ITGB1


PARP14
MTRNR2L2
TRIO
HLA-DPA1

TSPAN13
MTA2
ITM2B


PARP3
MTRNR2L8
TUBB2A
HLA-DPB1

UBE2E3
MTCH2
ITM2C


PARP9
MVD
TUBB2B
HLA-DQA1

UNC5C
MTX2
KDELR1


PDCD1LG2
MVP
TYK2
HLA-DQB1

ZC3HAV1L
MUM1
KDELR2


PDE4B
MX1
VCAM1
HLA-DRA

ZEB1
NAE1
KLHDC3


PLSCR1
NBEAL1
ZBTB21
HLA-DRB1

ZFHX4
NARF
LAMA2


PML
NCCRP1
ZC3H12A
HLA-DRB5


NCKIPSD
LAPTM4A


PPP2R2B
NCKAP5
ZEB2
HLA-DRB6


NDFIP2
LAPTM4B


PSMA3
NFE2L3
ZFP36
HLA-E


NDRG3
LDHA


PSMA4
NFKB2
ZFP36L1
HLA-F


NDUFA8
LDHB


PSMA5
NFKBIA
ZNF217
HLA-H


NDUFAF6
LMAN1


PSMB10
NFKBIB
ZNF267
HMGA1


NDUFS6
LSM7


PSMB8
NFKBIZ
ZNFX1
HMHA1


NELFCD
LTA4H


PSMB9
NINJ1

HORMAD1


NET1
LZTS1


PSME1
NLRC5

HPS3


NFATC3
MAGED1


PSME2
NMI

HTRA3


NGLY1
6-Mar


PTHLH
NMNAT2

ICAM1


NISCH
MCTP2


PTN
NOD2

ICOSLG


NKX2-5
MDH1


PYCARD
NPAS2

IDO1


NOL3
MDH2


RARRES3
NPL

IFI27


NR2F2
MEOX2


RBCK1
NR0B1

IFI27L2


NR2F6
METAP2


RBMXL1
NRP2

IFI30


NR5A2
MGST3


RFX5
NUAK2

IFI35


NSD1
MIF


RNF213
OAS1

IFI6


NSF
MINOS1


RSAD2
OAS2

IFIH1


NUDT22
MLF2


RTP4
OAS3

IFIT3


NUMA1
MMADHC


RUFY4
OASL

IFITM1


NYNRIN
MORF4L1


SAMD9
OCA2

IFITM3


OGFOD2
MORF4L2


SAMD9L
ODF3B

IFNLR1


PAFAH1B2
MRFAP1


SAMHD1
OPTN

IGF2


PAFAH1B3
MRPL15


SDSL
PAPSS2

IGFBP4


PAGR1
MRPL21


SECTM1
PARP10

IKBKE


PAQR4
MRPL51


SEMA4D
PARP12

IKZF2


PARP16
MRPS21


SERPING1
PARP14

IL15


PDCD2L
MTDH


SHISA5
PARP4

IL15RA


PDCL3
MYH10


SLC15A3
PARP9

IL18BP


PDS5B
MYL12A


SLC37A1
PDE4B

IL32


PFKFB4
MYL12B


SLC6A15
PDPN

IL3RA


PFN2
MYL9


SLFN5
PFKL

IL4I1


PIGP
NACA


SMIM14
PHF11

IL7


PIP4K2C
NAP1L1


SOAT1
PHLDA3

IL8


PKN1
NCBP2


SOCS1
PIK3IP1

INGX


PMS1
NCL


SP100
PIM1

INHBA


PNMA2
NDUFA12


SP110
PLA2G4C

IRAK2


PNMAL1
NDUFA4


SP140L
PLAT

IRF1


PODNL1
NDUFA4L2


SSPN
PLCB4

IRF2


POLD4
NDUFAB1


SSTR2
PLEKHA1

IRF3


POLR1B
NDUFB11


STAT1
PLEKHG1

IRF7


POLR2G
NDUFB6


STAT2
PLSCR1

IRF8


POLR3H
NDUFB8


TAP1
PPP1R14C

IRF9


POLR3K
NDUFB9


TAP2
PRCP

IRX6


POLRMT
NDUFV2


TAPBP
PRDM1

ISG15


PPAT
NFIB


TAPBPL
PSMA3

ISG20


PPCS
NGFRAP1


TCIRG1
PSMA5

ITGA1


PPIP5K2
NHP2


TMEM140
PSMB10

ITGA2


PPP1CA
NHP2L1


TMSB4X
PSMB8

ITGB2


PPP2R4
NIDI


TNFAIP2
PSMB9

ITK


PRAME
NME1


TNFRSF14
PSME1

JAK2


PRICKLE1
NME2


TNFRSF1B
PSME2

JUNB


PRMT6
NOB1


TPP1
PTGER4

KAT2A


PRUNE
NONO


TRAFD1
PTGES

KIAA1217


PTEN
NPM1


TRIL
QPCT

KIAA1462


PTPLA
NR2F2


TRIM21
RAI14

KIAA1755


PTPLAD1
NR5A2


TRIM22
RANGAP1

KIF1A


PTPRG
NREP


TRIM25
RARB

KLHDC7B


PYCR2
NRP1


TRIM38
RARRES3

KYNU


PYGB
NUCB2


TRIM56
RASSF4

LAMP3


RAD50
NUCKS1


TYMP
REL

LAP3


RAI1
NUTF2


UBA7
RELB

LBX2-AS1


RASSF7
OCIAD1


UBB
RGS16

LGALS17A


REEP2
OST4


UBD
RGS3

LGALS3


RFT1
OSTC


UBE2L6
RIPK2

LGALS3BP


RGS19
P4HB


UBR2
RNF19A

LGALS9


RIN1
PABPC1













USF1
RNF213

LOC100132247

RMDN1
PAFAH1B2


USP18
RQCD1

LOC100132891

RNF135
PAFAH1B3


USP30-AS1
RTP4

LOC100133331

RNF168
PAICS


VAMP5
RUNX3

LOC100288069

RNF216
PAIP2


VCAM1
SALL4

LOC100289019

RNH1
PAPSS1


WARS
SAMD9

LOC100507463

RPP21
PARK7














XAF1
SAMD9L

LOC284260


RPP25
PCDH7


XIRP1
SCARF1

LOC399744


RPS19BP1
PCDH9


XRN1
SCD

LOC731275


RRP1B
PCMT1


ZNF672
SCN1B

LOC732275


RUNX1T1
PCOLCE


ZNFX1
SDC4

LTBP2


SAMD11
PCOLCE2



SEC23B

LY6E


SCAMP1
PDHB



SELE

LYZ


SCCPDH
PDIA3



SELM

MALAT1


SDHAF1
PDIA4



SERPINA3

MAP3K8


SERTM1
PDIA6



SGPP2

MAST3


SET
PDLIM7



SLC11A2

MBOAT1


SFXN1
PFN2



SLC12A7

MFSD12


SGK196
PGAM1



SLC2A6

MGLL


SHISA2
PGK1



SLC37A1

MICAL1


SIKE1
PHB



SLC43A2

MICB


SIX1
PHB2



SLC7A2

MIR155HG


SLBP
PLOD1



SLFN5

MKNK2


SLC16A3
PLP2



SMG7

MLKL


SLC20A1
PPA2



SOCS2

MLL2


SLC29A2
PPIA



SORCS1

MMP14


SLC2A10
PPIB



SOX9

MMP17


SLC35B2
PPME1



SP100

MOB3C


SLC35B4
PPP1CA



SPAG1

MOV10


SMA4
PPP1CB



SPIB

MSC


SMAP1
PPP2R1A



SPPL2A

MT2A


SMIM15
PPT1



SQSTM1

MTRNR2L1


SNAP29
PRDX2














SRR

MTRNR2L10

SNAPC2
PRDX4















SSPN

MTRNR2L2


SNRNP25
PRDX6



ST5

MTRNR2L6


SNX2
PRKAR1A



ST6GAL2

MTRNR2L8


SNX27
PSMB1



STAP2

MVP


SPICE1
PSMD8



STARD10

MX1


SPRY2
PTDSS1



STAT1

MYEOV


SSR1
PTGES3



STAT5A

MYO10


ST13
PTMA



STRA6

MYO1B


STEAP2
PTPLAD1



SYNGR2

MYO9B


SUOX
RAB1A



SYNGR3

NAAA


SUPT20H
RAB2A



TAGLN2

NDOR1


TAPT1-AS1
RAN



TANK

NEAT1


TBC1D5
RBBP4



TAP1

NETO1


TBC1D9B
RBFOX2



TAP2

NEURL3


TBX18
RBM3



TAPBP

NFE2L3


TCEAL8
RBM4



TAPBPL

NFKB2


TET1
RBMX



TBC1D17

NFKBIA


TFCP2
RBPJ



THY1

NFKBIZ


THNSL1
RCBTB2



TIFA

NLRC5


THYN1
RHOA



TMEM123

NMI


TIA1
RHOC



TMEM173

NNMT


TIMM21
RNF5P1



TMEM205

NOD2


TIMM22
RPL10



TMPRSS2

NPIPL3


TIMM9
RPL10A



TNF

NPTX2


TMEM134
RPL11



TNFAIP1

NRP2


TMEM216
RPL12



TNFAIP2

NT5E


TMEM223
RPL14



TNFAIP3

NUAK2


TNRC6B
RPL15



TNFAIP6

NUB1


TPI1
RPL17



TNFAIP8

OAS1


TRAM2
RPL18



TNFRSF14

OAS2


TRAPPC2L
RPL22



TNFRSF18

OAS3


TRERF1
RPL22L1



TNFRSF4

OGFR


TRIL
RPL23



TNFRSF6B

OPTN


TRPT1
RPL24



TNIP1

OSGIN1


TSEN54
RPL26



TNKS1BP1

P2RX4


TSPAN3
RPL27



TNN

PABPC1L


TTC3
RPL29



TRADD

PACS2


TUBB
RPL3



TRAF1

PAPPA


TUBB3
RPL32



TRIM21

PARP10


TXNDC15
RPL34



TRIM25

PARP12


UBL4A
RPL35A



TYK2

PARP14


UBXN2B
RPL36A



TYMP

PARP3


UCK2
RPL39



UBA7

PARP8


VAT1
RPL4



UBD

PARP9


VDAC3
RPL5



UBE2L6

PAWR


VPS35
RPL6



USP18

PCGF5


WDR12
RPL7



VAMP5

PDCD1LG2


WDR41
RPL7A



VCAM1

PDGFA


WNT5B
RPL7L1



WARS

PERP


XYLB
RPL8



WWC3

PHLDA1


YEATS2
RPL9



XAF1

PHLDA2


ZC3HAV1L
RPLP0



XRN1

PILRA


ZFHX4
RPN2



ZBTB5

PIM1


ZNF174
RPS10



ZC3H7B

PKD1


ZNF232
RPS13



ZEB2

PKD1P1


ZNF280D
RPS14



ZFP36L1

PLA1A


ZNF32
RPS15A



ZNFX1

PLA2G16


ZNF395
RPS17





PLA2G4C


ZNF532
RPS17L





PLAUR


ZNF692
RPS2





PLD1


ZNF74
RPS23





PLD2


ZNF816
RPS24





PLEC


ZSWIM7
RPS27A





PLSCR1



RPS3





PML



RPS3A





POM121L9P



RPS4X





POU5F1



RPS4Y1





PP7080



RPS6





PRDM1



RPS7





PRLR



RPS8





PSMB10



RPSA





PSMB8



RPSAP58





PSMB9



RSL1D1





PSME1



RSL24D1





PSME2



RSU1





PTHLH



RTN3





PTPRJ



RUNX1T1





PYCARD



SAP18





RAB38



SARNP





RAMP1



SDCBP





RARRES1



SEC11A





RARRES3



SEC13





RASD1



SEC22B





RBCK1



SEC31A





RELB



SEC61B





RGCC



SEC61G





RGS11



SEMA3E





RGS16



SEMA6D





RHBDF2



SEP15





RHEBL1



SEP2





RHOB



SEP7





RIPK2



SEPW1












RNF144A-AS1


SERBP1













RNF213



SERINC1



ROBO3



SERP1



RPLP0P2



SERPINH1



RSAD2



SET



RTP4



SF3B14



RUFY4



SHISA2



RUNX3



SIX1



SAA1



SKP1



SAMD9L



SLC25A3



SAT1



SLC25A5



SCARA5



SLC25A6



SCARF1



SLIT2



SCO2



SMAD9



SCRIB



SMARCA1



SECTM1



SND1



SEMA4D



SNHG16



SERPINA1



SNRPE



SERPING1



SNRPF



SIGIRR



SNRPN



SLC15A3



SNURF



SLC25A28



SOX11



SLC37A1



SPARC



SLC7A2



SPCS1



SLC9A3



SPCS2



SLFN5



SPIN1



SOCS1



SPOCK1



SOD2



SRP14



SOX8



SRP72



SP100



SRP9



SP110



SRSF1



SP140L



SRSF2



SQRDL



SRSF3



SQSTM1



SRSF6



SSH1



SSB



SSTR2



SSBP2



SSUH2



SSR1



STAT1



SSR2



STAT2



SSR3



STAT5A



ST13



SUSD2



STMN1



TAC3



STRAP



TAP1



STT3A



TAP2



SUB1



TAPBP



SUMO1



TAPBPL



SUMO2



TBX2



SURF4



TCIRG1



SYNCRIP



TEP1



TCEAL8



TF



TCEB1



TIMP3



TCP1



TLCD2



TFPI



TMEM140



TIMM13



TMEM158



TMA7



TMEM194A



TMBIM6



TMEM205



TMED10



TMEM8A



TMED2



TMPRSS3



TMED3



TNFAIP2



TMEM100



TNFAIP3



TMEM258



TNFAIP8



TMEM59



TNFRSF14



TMEM66



TNFSF10



TMEM70



TNFSF9



TMEM98



TNS3



TNFRSF10D



TRAF1



TOMM20



TRAFD1



TOMM22



TREX1



TOMM6



TRIM14



TPI1



TRIM21



TPM1



TRIM22



TPM2



TRIM25



TPM4



TRIM38



TRAP1



TRIM56



TRPS1



TRIM69



TSPAN13



TRPM4



TUBA1A



TXNIP



TUBA1B



TYMP



TUBB



UACA



TWISTNB



UBA7



TXN2



UBD



TXNL1



UBE2L6



TXNL4A



UBR4



U2AF1



ULK1



UBE2E3



UNKL



UBE2V1



UPP1



UBE2V2



USF1



UFC1



USP18



UGDH



USP30-AS1



UNC5C



UTRN



UQCRC1



VAMP5



UQCRH



VCAM1



VAPA



VPS13C



VDAC1



WARS



VDAC3



WASH1



VIM



WASH7P



VKORC1



WDR25



VPS28



WDR81



WASF1



XAF1



WDR61



XIRP1



WDR83OS



XRN1



XRCC5



ZBP1



XRCC6



ZBTB3



YIPF3



ZC3H3



YWHAE



ZC3HAV1



YWHAQ



ZFC3H1



YWHAZ



ZFP36L1



ZC3H15



ZFYVE26



ZFHX4



ZNFX1



ZNF652



ZSWIM8



ZNF706

















TABLE 11







The TNF/IFN programs enrichment with pre-defined gene sets (hypergeometric p-values: -log10 transformed, capped at 17).



















TNF



TNF





TNF
&


TNF
&



IFN
TNF
short
IFN
IFN
TNF
short
IFN


Gene Set
up
up
up
up
down
down
down
down


















HALLMARK_TNFA_SIGNALING_VIA_NFKB
5.54
17.00
17.00
17.00
0.00
0.97
0.00
0.00


HALLMARK_INTERFERON_GAMMA_RESPONSE
17.00
17.00
14.02
17.00
0.00
0.00
0.00
0.00


HALLMARK_APOPTOSIS
2.57
5.61
13.97
5.93
0.00
0.14
0.51
0.12


HALLMARK_P53_PATHWAY
0.86
2.03
6.18
5.59
0.00
1.46
0.00
0.10


HALLMARK_HYPOXIA
0.42
1.83
12.68
0.35
0.00
0.10
1.09
1.51


Homeobox
0.00
0.07
0.17
0.03
1.68
3.15
7.31
0.54


HALLMARK_OXIDATIVE_PHOSPHORYLATION
0.02
0.01
0.01
0.00
0.00
0.16
1.55
15.35


HALLMARK_MYC_TARGETS_V1
0.00
0.00
0.00
0.00
0.28
5.56
0.20
17.00


GO_CELLULAR_RESPONSE_TO_ORGANIC_SUBSTANCE
17.00
17.00
11.94
17.00
0.62
0.93
0.00
2.75


GO_POSITIVE_REGULATION_OF_RESPONSE_TO_STIMULUS
17.00
17.00
17.00
17.00
0.09
0.12
0.10
1.70


GO_ACTIVATION_OF_IMMUNE_RESPONSE
15.95
9.14
3.80
17.00
0.23
0.20
0.02
1.61


GO_POSITIVE_REGULATION_OF_IMMUNE_RESPONSE
17.00
13.32
6.46
17.00
0.16
0.09
0.01
1.14


GO_IMMUNE_EFFECTOR_PROCESS
17.00
15.65
1.97
17.00
0.20
0.15
0.30
1.03


GO_REGULATION_OF_IMMUNE_RESPONSE
17.00
17.00
10.46
17.00
0.08
0.13
0.01
0.78


GO_IMMUNE_SYSTEM_PROCESS
17.00
17.00
17.00
17.00
0.11
0.39
0.19
0.77


GO_REGULATION_OF_MULTI_ORGANISM_PROCESS
17.00
17.00
3.83
17.00
0.00
0.04
0.12
0.71


GO_REGULATION_OF_IMMUNE_SYSTEM_PROCESS
17.00
17.00
17.00
17.00
0.09
0.44
0.01
0.68


GO_RESPONSE_TO_EXTERNAL_STIMULUS
17.00
17.00
17.00
17.00
0.30
1.55
0.10
0.62


GO_REGULATION_OF_CELL_ADHESION
12.20
10.72
10.21
17.00
0.39
0.86
0.16
0.60


GO_ANTIGEN_BINDING
17.00
4.66
5.53
17.00
0.00
0.23
0.09
0.42


GO_POSITIVE_REGULATION_OF_IMMUNE_SYSTEM_PROCESS
17.00
17.00
15.95
17.00
0.07
0.38
0.00
0.38


GO_CELLULAR_RESPONSE_TO_CYTOKINE_STIMULUS
17.00
17.00
17.00
17.00
0.12
0.09
0.00
0.37


GO_RESPONSE_TO_VIRUS
17.00
17.00
4.06
17.00
0.00
0.09
0.09
0.29


GO_RESPONSE_TO_CYTOKINE
17.00
17.00
17.00
17.00
0.09
0.09
0.00
0.26


GO_REGULATION_OF_INNATE_IMMUNE_RESPONSE
17.00
17.00
7.69
17.00
0.00
0.01
0.01
0.19


GO_REGULATION_OF_DEFENSE_RESPONSE
17.00
17.00
10.24
17.00
0.00
0.02
0.01
0.18


GO_NEGATIVE_REGULATION_OF_MULTI_ORGANISM_PROCESS
17.00
17.00
2.54
17.00
0.00
0.07
0.01
0.17


GO_RESPONSE_TO_BACTERIUM
17.00
15.05
17.00
17.00
0.21
0.33
0.00
0.15


GO_RESPONSE_TO_BIOTIC_STIMULUS
17.00
17.00
15.65
17.00
0.07
0.29
0.00
0.15


GO_REGULATION_OF_LEUKOCYTE_PROLIFERATION
13.06
7.55
4.99
17.00
0.45
0.06
0.05
0.11


GO_POSITIVE_REGULATION_OF_CYTOKINE_PRODUCTION
15.65
9.63
6.73
17.00
0.00
0.01
0.19
0.09


GO_ADAPTIVE_IMMUNE_RESPONSE
15.05
9.09
4.60
17.00
0.00
0.25
0.01
0.07


GO_CYTOKINE_MEDIATED_SIGNALING_PATHWAY
17.00
17.00
17.00
17.00
0.20
0.02
0.00
0.06


GO_REGULATION_OF_CYTOKINE_PRODUCTION
17.00
17.00
10.94
17.00
0.15
0.03
0.20
0.06


GO_CELLULAR_RESPONSE_TO_INTERFERON_GAMMA
17.00
17.00
7.48
17.00
0.00
0.00
0.03
0.05


GO_INNATE_IMMUNE_RESPONSE
17.00
17.00
7.15
17.00
0.00
0.04
0.05
0.05


GO_RESPONSE_TO_TYPE_I_INTERFERON
17.00
17.00
5.33
17.00
0.00
0.21
0.00
0.04


GO_IMMUNE_RESPONSE
17.00
17.00
17.00
17.00
0.07
0.15
0.01
0.03


GO_DEFENSE_RESPONSE
17.00
17.00
17.00
17.00
0.17
0.03
0.01
0.03


GO_RESPONSE_TO_INTERFERON_GAMMA
17.00
17.00
6.75
17.00
0.00
0.00
0.02
0.03


GO_INFLAMMATORY_RESPONSE
13.60
17.00
17.00
17.00
0.71
0.12
0.00
0.03


GO_POSITIVE_REGULATION_OF_CELL_CELL_ADHESION
12.96
9.85
8.66
17.00
0.00
0.17
0.01
0.03


GO_REGULATION_OF_HOMOTYPIC_CELL_CELL_ADHESION
15.00
7.69
7.05
17.00
0.00
0.09
0.00
0.02


GO_REGULATION_OF_CELL_CELL_ADHESION
14.27
8.56
7.98
17.00
0.00
1.09
0.01
0.02


GO_REGULATION_OF_CELL_ACTIVATION
12.52
9.03
9.36
17.00
0.61
0.16
0.00
0.01


GO_DEFENSE_RESPONSE_TO_OTHER_ORGANISM
17.00
17.00
2.76
17.00
0.00
0.03
0.10
0.01


GO_DEFENSE_RESPONSE_TO_VIRUS
17.00
17.00
1.76
17.00
0.00
0.00
0.31
0.00


GO_INTERFERON_GAMMA_MEDIATED_SIGNALING_PATHWAY
17.00
17.00
5.08
17.00
0.00
0.00
0.00
0.00


GO_MHC_PROTEIN_COMPLEX
17.00
5.58
4.47
17.00
0.00
0.00
0.00
0.00


GO_MHC_CLASS_II_PROTEIN_COMPLEX
17.00
0.00
0.00
17.00
0.00
0.00
0.00
0.00


HALLMARK_INTERFERON_ALPHA_RESPONSE
17.00
17.00
4.32
17.00
0.00
0.00
0.02
0.00


HALLMARK_INFLAMMATORY_RESPONSE
14.18
17.00
17.00
17.00
0.42
1.10
0.00
0.01


HALLMARK_COMPLEMENT
9.99
9.65
4.43
17.00
0.00
0.04
0.00
0.00


HALLMARK_ALLOGRAFT_REJECTION
17.00
14.88
9.58
17.00
0.00
1.28
0.00
1.16


GO_EXTRACELLULAR_SPACE
11.88
10.41
6.41
15.95
0.39
0.50
0.06
2.53


GO_NEGATIVE_REGULATION_OF_VIRAL_PROCESS
17.00
15.65
1.38
15.95
0.00
0.00
0.00
0.08


GO_REGULATION_OF_T_CELL_PROLIFERATION
13.44
5.97
3.22
15.65
0.00
0.00
0.03
0.06


GO_POSITIVE_REGULATION_OF_CELL_ACTIVATION
11.99
7.48
5.85
15.48
0.00
0.00
0.00
0.03


GO_PEPTIDE_ANTIGEN_BINDING
17.00
6.64
7.02
15.18
0.00
0.00
0.00
0.00


GO_REGULATION_OF_SYMBIOSIS_ENCOMPASSING_MUTUALISM_THROUGH_PARASITISM
17.00
14.81
2.06
14.81
0.00
0.03
0.05
0.77


GO_ANTIGEN_PROCESSING_AND_PRESENTATION
17.00
8.50
4.88
14.75
0.00
0.00
0.00
0.47


GO_POSITIVE_REGULATION_OF_CELL_ADHESION
9.81
11.70
9.27
14.57
0.26
0.12
0.08
0.41


GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN
17.00
8.32
4.17
14.48
0.00
0.00
0.00
0.71


GO_NEGATIVE_REGULATION_OF_VIRAL_GENOME_REPLICATION
17.00
15.65
1.37
14.48
0.00
0.00
0.00
0.07


GO_REGULATION_OF_CELL_PROLIFERATION
7.07
9.67
14.19
14.03
0.36
1.02
0.03
1.80


GO_CELL_SURFACE
9.46
13.95
8.60
13.95
0.13
0.71
0.00
2.20


GO_LUMENAL_SIDE_OF_MEMBRANE
17.00
6.15
5.29
13.93
0.00
0.00
0.25
0.16


GO_CYTOKINE_ACTIVITY
8.54
7.93
11.45
13.51
0.00
0.10
0.27
0.12


GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_ENDOGENOUS_PEPTIDE_ANTIGEN
17.00
12.49
6.30
13.49
0.00
0.00
0.00
0.00


GO_HUMORAL_IMMUNE_RESPONSE
11.01
5.64
4.91
13.40
0.00
0.18
0.07
0.56


GO_POSITIVE_REGULATION_OF_DEFENSE_RESPONSE
14.22
13.52
8.21
13.31
0.00
0.04
0.00
0.16


GO_NEGATIVE_REGULATION_OF_IMMUNE_SYSTEM_PROCESS
14.06
9.72
6.64
13.11
0.24
0.65
0.06
0.15


GO_POSITIVE_REGULATION_OF_LEUKOCYTE_PROLIFERATION
9.41
5.37
5.01
13.02
0.00
0.00
0.00
0.07


GO_ADAPTIVE_IMMUNE_RESPONSE_BASED_ON_SOMATIC_RECOMBINATION_OF_IMMUNE_RECEPTORS_BUILT_FROM
10.05
3.51
3.59
13.02
0.00
0.15
0.05
0.22


IMMUNOGLOBULIN_SUPERFAMILY_DOMAINS


GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_ENDOGENOUS_ANTIGEN
15.65
10.67
5.61
12.98
0.00
0.00
0.00
0.00


GO_POSITIVE_REGULATION_OF_MULTICELLULAR_ORGANISMAL_PROCESS
8.12
10.57
9.25
12.57
0.10
1.01
0.11
1.23


GO_REGULATION_OF_VIRAL_GENOME_REPLICATION
17.00
13.06
0.92
12.44
0.00
0.16
0.20
1.57


GO_SIDE_OF_MEMBRANE
11.05
10.76
9.66
12.43
0.00
0.14
0.01
0.23


GO_REGULATION_OF_RESPONSE_TO_EXTERNAL_STIMULUS
8.01
6.44
6.48
12.33
0.00
0.40
0.10
0.29


GO_NEGATIVE_REGULATION_OF_CYTOKINE_PRODUCTION
13.58
9.57
8.14
12.23
0.44
0.00
0.25
0.09


GO_REGULATION_OF_TYPE_I_INTERFERON_PRODUCTION
11.10
11.17
4.32
11.96
0.00
0.00
0.75
0.23


GO_CELL_ACTIVATION
5.92
6.61
6.92
11.85
0.18
1.41
0.13
0.94


GO_REGULATION_OF_IMMUNE_EFFECTOR_PROCESS
12.53
9.75
5.60
11.58
0.00
0.03
0.17
0.11


GO_RECEPTOR_BINDING
8.21
9.18
6.87
11.50
0.78
0.17
0.09
0.72


GO_REGULATION_OF_INTERFERON_GAMMA_PRODUCTION
11.28
5.52
4.12
11.46
0.00
0.00
0.00
0.27


GO_RESPONSE_TO_MOLECULE_OF_BACTERIAL_ORIGIN
11.81
11.59
17.00
11.34
0.29
0.17
0.01
0.12


GO_LEUKOCYTE_ACTIVATION
7.08
7.06
7.81
11.29
0.00
1.05
0.16
0.30


GO_POSITIVE_REGULATION_OF_CELL_COMMUNICATION
9.48
7.75
11.53
11.07
0.34
0.04
0.09
1.23


GO_REGULATION_OF_RESPONSE_TO_STRESS
12.49
9.26
8.62
11.02
0.00
0.04
0.04
0.54


GO_POSITIVE_REGULATION_OF_T_CELL_PROLIFERATION
8.82
4.73
3.61
10.92
0.00
0.00
0.00
0.05


GO_POSITIVE_REGULATION_OF_RESPONSE_TO_EXTERNAL_STIMULUS
8.16
7.01
5.53
10.81
0.00
0.23
0.04
0.46


GO_ER_TO_GOLGI_TRANSPORT_VESICLE
11.37
4.18
2.42
10.70
0.00
0.00
0.00
1.40


GO_NEGATIVE_REGULATION_OF_TYPE_I_INTERFERON_PRODUCTION
11.16
11.19
4.63
10.56
0.00
0.00
0.55
0.11


GO_ER_TO_GOLGI_TRANSPORT_VESICLE_MEMBRANE
13.10
5.06
2.89
10.51
0.00
0.00
0.00
1.87


GO_RESPONSE_TO_TUMOR_NECROSIS_FACTOR
12.57
15.95
12.57
10.45
0.34
0.00
0.00
0.03


GO_LYMPHOCYTE_MEDIATED_IMMUNITY
8.59
1.34
0.98
10.01
0.00
0.17
0.22
0.85


HALLMARK_IL6_JAK_STAT3_SIGNALING
10.67
6.28
10.08
9.87
0.00
0.17
0.00
0.00


GO_LEUKOCYTE_CELL_CELL_ADHESION
5.27
8.92
7.67
9.80
0.00
0.67
0.01
0.26


GO_PLASMA_MEMBRANE_PROTEIN_COMPLEX
10.95
6.04
3.86
9.64
0.75
0.29
0.04
0.33


GO_EXTERNAL_SIDE_OF_PLASMA_MEMBRANE
6.53
9.47
7.07
9.55
0.00
0.10
0.00
0.24


GO_NEGATIVE_REGULATION_OF_INNATE_IMMUNE_RESPONSE
12.47
8.45
3.99
9.46
0.00
0.00
0.00
0.00


GO_NEGATIVE_REGULATION_OF_CELL_ACTIVATION
5.59
1.57
3.54
9.36
1.35
0.65
0.10
0.04


GO_CYTOKINE_RECEPTOR_BINDING
7.24
11.81
13.07
9.36
0.00
0.34
0.18
0.36


GO_POSITIVE_REGULATION_OF_LEUKOCYTE_MIGRATION
7.44
8.84
5.93
9.26
0.00
0.41
0.00
0.18


GO_RESPONSE_TO_LIPID
7.71
6.74
15.65
9.21
0.53
0.49
0.15
0.76


GO_CELL_DEATH
1.74
8.74
10.95
9.19
0.13
0.21
0.54
0.35


GO_ENDOCYTIC_VESICLE_MEMBRANE
9.94
3.10
2.11
9.17
0.00
0.00
0.44
0.21


GO_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN
6.84
0.95
1.36
8.99
0.00
0.00
0.00
0.25


GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN_VIA_MHC_CLASS_I
17.00
12.52
6.73
8.95
0.00
0.00
0.00
0.85


GO_VACUOLE
6.15
2.91
1.99
8.87
0.00
0.01
0.01
0.21


GO_REGULATION_OF_RESPONSE_TO_WOUNDING
4.10
4.82
8.66
8.85
0.00
0.05
0.00
0.13


GO_RESPONSE_TO_INTERLEUKIN_1
3.42
6.46
10.44
8.81
0.00
0.14
0.00
0.02


GO_REGULATION_OF_INFLAMMATORY_RESPONSE
5.44
5.36
7.92
8.80
0.00
0.03
0.00
0.19


GO_B_CELL_MEDIATED_IMMUNITY
6.87
0.99
0.86
8.78
0.00
0.33
0.16
0.68


GO_REGULATION_OF_LEUKOCYTE_MIGRATION
6.47
7.63
7.01
8.63
0.00
0.32
0.00
0.41


GO_REGULATION_OF_ANTIGEN_PROCESSING_AND_PRESENTATION
8.76
3.54
0.73
8.62
0.00
0.00
0.00
0.00


GO_MHC_CLASS_II_RECEPTOR_ACTIVITY
10.35
0.00
0.00
8.55
0.00
0.00
0.00
0.00


GO_POSITIVE_REGULATION_OF_NF_KAPPAB_TRANSCRIPTION_FACTOR_ACTIVITY
3.32
4.44
5.99
8.46
0.00
0.09
0.00
0.39


GO_NEGATIVE_REGULATION_OF_MULTICELLULAR_ORGANISMAL_PROCESS
8.67
6.14
7.13
8.36
0.49
2.05
0.77
2.10


GO_AMIDE_BINDING
9.13
4.12
3.01
8.34
0.91
0.47
0.10
2.13


GO_POSITIVE_REGULATION_OF_INTRACELLULAR_SIGNAL_TRANSDUCTION
6.86
8.68
12.31
8.25
0.06
0.13
0.03
0.65


GO_ENDOSOME
6.17
2.59
2.81
8.24
0.00
0.04
0.04
0.19


HALLMARK_IL2_STAT5_SIGNALING
3.55
8.16
9.74
8.19
0.38
0.31
0.15
0.00


GO_REGULATION_OF_RESPONSE_TO_CYTOKINE_STIMULUS
6.48
8.25
3.84
8.15
0.00
0.07
0.35
0.29


GO_POSITIVE_REGULATION_OF_CELL_PROLIFERATION
3.99
5.50
9.32
8.15
0.26
0.98
0.19
2.06


GO_LYMPHOCYTE_COSTIMULATION
7.56
1.45
1.45
8.11
0.00
0.00
0.00
0.08


GO_POSITIVE_REGULATION_OF_INNATE_IMMUNE_RESPONSE
9.73
10.64
4.83
8.11
0.00
0.02
0.01
0.19


GO_CHEMOKINE_ACTIVITY
7.97
5.86
6.34
8.01
0.00
0.41
0.00
0.00


GO_NEGATIVE_REGULATION_OF_LEUKOCYTE_PROLIFERATION
6.33
1.41
0.28
7.96
0.86
0.29
0.42
0.26


GO_NEGATIVE_REGULATION_OF_DEFENSE_RESPONSE
8.32
5.31
4.40
7.92
0.00
0.10
0.02
0.04


GO_MHC_CLASS_I_PROTEIN_COMPLEX
11.91
8.94
6.60
7.91
0.00
0.00
0.00
0.00


GO_LYMPHOCYTE_CHEMOTAXIS
4.34
2.74
3.64
7.89
0.00
0.00
0.00
0.00


GO_REGULATION_OF_LEUKOCYTE_MEDIATED_CYTOTOXICITY
8.90
6.64
4.36
7.89
0.00
0.00
0.00
0.00


GO_REGULATION_OF_I_KAPPAB_KINASE_NF_KAPPAB_SIGNALING
7.24
8.20
7.70
7.88
0.00
0.09
0.31
0.29


GO_POSITIVE_REGULATION_OF_LOCOMOTION
4.32
7.36
8.24
7.79
0.00
0.94
0.01
0.35


GO_VACUOLAR_PART
7.75
1.82
0.70
7.78
0.00
0.00
0.09
0.10


GO_LEUKOCYTE_MEDIATED_IMMUNITY
7.88
0.91
1.18
7.74
0.00
0.11
0.12
0.51


GO_ENDOSOMAL_PART
8.14
2.43
1.27
7.66
0.00
0.00
0.19
0.03


GO_TRANSPORT_VESICLE_MEMBRANE
8.76
4.18
2.35
7.65
0.00
0.00
0.03
0.76


GO_LYMPHOCYTE_MIGRATION
4.21
2.65
4.82
7.61
0.00
0.00
0.00
0.00


GO_RESPONSE_TO_INTERFERON_BETA
6.84
8.90
0.54
7.61
0.00
0.00
0.00
0.00


GO_NEGATIVE_REGULATION_OF_IMMUNE_RESPONSE
9.18
8.73
4.54
7.60
0.00
0.00
0.00
0.00


GO_CELL_CHEMOTAXIS
7.67
4.79
5.43
7.48
0.00
0.71
0.00
0.04


GO_CYTOPLASMIC_VESICLE_PART
4.73
3.11
0.71
7.43
0.00
0.02
0.06
2.41


GO_POSITIVE_REGULATION_OF_SEQUENCE_SPECIFIC_DNA_BINDING_TRANSCRIPTION_FACTOR_ACTIVITY
4.10
5.32
5.46
7.41
0.00
0.13
0.02
0.46


GO_NEGATIVE_REGULATION_OF_CELL_KILLING
7.35
5.13
3.16
7.40
0.00
0.00
0.00
0.00


GO_ENDOCYTIC_VESICLE
7.06
2.28
2.54
7.36
0.00
0.02
0.12
0.23


GO_NEGATIVE_REGULATION_OF_HOMOTYPIC_CELL_CELL_ADHESION
7.64
0.90
1.00
7.35
0.00
0.18
0.06
0.03


GO_REGULATION_OF_CYTOKINE_SECRETION
9.96
3.46
2.04
7.30
0.00
0.15
0.00
0.08


GO_INTRINSIC_COMPONENT_OF_PLASMA_MEMBRANE
5.49
9.36
5.47
7.29
1.72
2.07
0.01
0.04


GO_CHEMOKINE_MEDIATED_SIGNALING_PATHWAY
6.17
8.81
7.16
7.27
0.00
0.37
0.00
0.00


GO_RESPONSE_TO_OXYGEN_CONTAINING_COMPOUND
4.54
4.93
13.64
7.26
0.48
0.94
0.07
1.71


GO_REGULATION_OF_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN
8.80
2.98
0.91
7.25
0.00
0.00
0.00
0.00


GO_REGULATION_OF_CELL_KILLING
8.31
6.15
4.08
7.24
0.00
0.00
0.00
0.00


GO_LYMPHOCYTE_ACTIVATION
4.54
4.94
5.38
7.22
0.00
1.43
0.30
0.59


GO_PEPTIDASE_ACTIVITY
4.91
4.13
0.64
7.13
0.00
0.09
0.05
0.12


GO_INTERSPECIES_INTERACTION_BETWEEN_ORGANISMS
8.67
5.32
2.93
7.12
0.49
17.00
0.00
17.00


GO_CELLULAR_RESPONSE_TO_INTERLEUKIN_1
2.66
6.04
9.66
7.12
0.00
0.20
0.00
0.04


GO_LEUKOCYTE_CHEMOTAXIS
5.17
4.31
3.32
7.12
0.00
0.00
0.00
0.04


GO_REGULATION_OF_INTERFERON_BETA_PRODUCTION
6.06
5.24
3.54
7.11
0.00
0.00
0.55
0.00


GO_COMPLEMENT_ACTIVATION
4.63
1.08
0.61
7.10
0.00
0.00
0.00
0.30


GO_CELLULAR_RESPONSE_TO_BIOTIC_STIMULUS
6.75
5.45
8.38
7.06
0.00
0.00
0.00
0.02


GO_POSITIVE_REGULATION_OF_INTERFERON_GAMMA_PRODUCTION
6.83
4.88
2.90
7.05
0.00
0.00
0.00
0.15


GO_ENDOPEPTIDASE_ACTIVITY
6.14
4.04
0.49
6.96
0.00
0.31
0.10
0.02


GO_POSITIVE_REGULATION_OF_IMMUNE_EFFECTOR_PROCESS
6.96
4.95
3.09
6.95
0.00
0.00
0.00
0.14


GO_ANTIGEN_RECEPTOR_MEDIATED_SIGNALING_PATHWAY
10.38
4.17
1.72
6.86
0.00
0.06
0.01
0.23


GO_VACUOLAR_MEMBRANE
6.18
0.89
0.59
6.85
0.00
0.01
0.21
0.15


GO_T_CELL_RECEPTOR_SIGNALING_PATHWAY
11.12
3.95
1.36
6.84
0.00
0.07
0.01
0.30


GO_CHEMOKINE_RECEPTOR_BINDING
6.99
6.06
5.61
6.80
0.00
0.34
0.00
0.00


GO_REGULATION_OF_LEUKOCYTE_MEDIATED_IMMUNITY
6.51
6.18
5.93
6.76
0.00
0.00
0.00
0.01


GO_DEFENSE_RESPONSE_TO_BACTERIUM
6.20
5.14
2.12
6.76
0.00
0.16
0.00
0.24


GO_NEGATIVE_REGULATION_OF_NATURAL_KILLER_CELL_MEDIATED_IMMUNITY
6.30
5.76
3.48
6.67
0.00
0.00
0.00
0.00


GO_REGULATION_OF_PEPTIDASE_ACTIVITY
5.78
6.29
4.50
6.65
0.00
1.22
0.05
0.96


GO_CELLULAR_RESPONSE_TO_OXYGEN_CONTAINING_COMPOUND
3.49
2.89
9.14
6.62
0.28
1.08
0.02
0.92


GO_REGULATION_OF_INTERFERON_ALPHA_PRODUCTION
5.19
4.66
0.69
6.62
0.00
0.00
0.00
0.37


GO_REGULATION_OF_ALPHA_BETA_T_CELL_PROLIFERATION
6.72
6.08
1.70
6.62
0.00
0.00
0.00
0.00


GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_PEPTIDE_ANTIGEN_VIA_MHC_CLASS_I
12.19
10.53
5.90
6.60
0.00
0.00
0.00
0.12


GO_REGULATION_OF_INTERLEUKIN_6_PRODUCTION
3.18
2.62
2.94
6.57
0.00
0.27
0.00
0.23


GO_NEGATIVE_REGULATION_OF_T_CELL_PROLIFERATION
7.66
0.62
0.00
6.52
0.00
0.00
0.21
0.13


GO_REGULATION_OF_INTERLEUKIN_1_PRODUCTION
3.62
3.04
0.84
6.51
0.00
0.32
0.00
0.09


GO_REGULATION_OF_SEQUENCE_SPECIFIC_DNA_BINDING_TRANSCRIPTION_FACTOR_ACTIVITY
3.49
5.60
9.27
6.48
0.00
0.37
0.02
0.19


GO_POSITIVE_REGULATION_OF_INTERLEUKIN_1_PRODUCTION
3.66
2.24
0.00
6.47
0.00
0.00
0.00
0.18


GO_NEGATIVE_REGULATION_OF_CELL_CELL_ADHESION
7.16
0.96
0.74
6.43
0.00
0.77
0.03
0.01


GO_INTRINSIC_COMPONENT_OF_ENDOPLASMIC_RETICULUM_MEMBRANE
10.38
4.74
3.27
6.35
0.00
0.00
0.38
0.08


GO_POSITIVE_REGULATION_OF_PHOSPHORUS_METABOLIC_PROCESS
2.31
5.53
11.29
6.35
0.20
0.23
0.07
0.44


GO_NEGATIVE_REGULATION_OF_IMMUNE_EFFECTOR_PROCESS
7.47
5.42
3.84
6.31
0.00
0.00
0.06
0.11


GO_ACTIVATION_OF_INNATE_IMMUNE_RESPONSE
7.68
8.91
4.79
6.27
0.00
0.03
0.00
0.31


GO_REGULATION_OF_LEUKOCYTE_DIFFERENTIATION
2.43
4.40
8.09
6.24
0.00
2.17
0.01
0.01


GO_NEGATIVE_REGULATION_OF_CELL_PROLIFERATION
5.82
2.75
6.16
6.21
0.10
1.16
0.06
0.30


GO_STAT_CASCADE
5.05
3.40
3.61
6.21
0.00
0.37
0.00
0.00


GO_NEGATIVE_REGULATION_OF_LEUKOCYTE_MEDIATED_IMMUNITY
5.73
3.95
5.29
6.11
0.00
0.00
0.00
0.00


GO_SINGLE_ORGANISM_CELL_ADHESION
3.78
10.11
6.08
6.10
0.22
0.54
0.00
0.63


GO_IMMUNE_RESPONSE_REGULATING_CELL_SURFACE_RECEPTOR_SIGNALING_PATHWAY
8.12
3.48
2.10
6.02
0.30
0.37
0.06
1.99


GO_MHC_CLASS_II_PROTEIN_COMPLEX_BINDING
7.77
0.00
0.00
6.02
0.00
0.00
0.43
0.89


GO_MHC_PROTEIN_COMPLEX_BINDING
7.77
0.00
0.00
6.02
0.00
0.00
0.43
0.89


GO_LEUKOCYTE_MIGRATION
6.21
9.24
6.18
6.01
0.00
0.17
0.03
0.29


GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_OR_POLYSACCHARIDE_ANTIGEN_VIA_MHC_CLASS_II
8.41
0.15
0.00
5.99
0.00
0.00
0.04
0.60


GO_REGULATION_OF_INTRACELLULAR_SIGNAL_TRANSDUCTION
3.79
9.53
17.00
5.95
0.01
0.19
0.03
0.21


GO_BIOLOGICAL_ADHESION
4.45
12.38
6.00
5.91
1.40
1.64
0.00
0.89


GO_NEGATIVE_REGULATION_OF_RESPONSE_TO_STIMULUS
6.59
11.06
9.95
5.89
0.01
0.37
0.04
0.06


HALLMARK_KRAS_SIGNALING_UP
1.78
6.77
7.76
5.85
1.08
3.25
0.03
0.43


GO_CELL_CELL_ADHESION
4.24
7.59
4.97
5.80
0.52
1.06
0.00
0.44


GO_RESPONSE_TO_ORGANIC_CYCLIC_COMPOUND
1.96
3.85
8.72
5.79
0.90
0.23
0.09
1.53


GO_COATED_VESICLE_MEMBRANE
7.67
3.48
1.43
5.73
0.00
0.00
0.39
2.79


GO_REGULATION_OF_EXTRINSIC_APOPTOTIC_SIGNALING_PATHWAY
1.96
4.48
10.09
5.72
0.00
0.43
0.12
0.51


GO_POSITIVE_REGULATION_OF_I_KAPPAB_KINASE_NF_KAPPAB_SIGNALING
6.70
5.26
2.47
5.69
0.00
0.16
0.36
0.40


GO_REGULATION_OF_CELLULAR_COMPONENT_MOVEMENT
5.04
7.59
8.36
5.66
1.00
0.81
0.15
0.60


GO_PATTERN_RECOGNITION_RECEPTOR_SIGNALING_PATHWAY
4.24
6.54
3.51
5.63
0.00
0.14
0.04
0.41


GO_SIGNAL_TRANSDUCER_ACTIVITY
6.90
6.41
2.33
5.55
0.88
1.83
0.08
0.12


GO_POSITIVE_REGULATION_OF_BIOSYNTHETIC_PROCESS
1.24
2.89
11.90
5.50
0.38
1.27
0.01
3.55


GO_TAXIS
3.70
5.71
7.34
5.47
0.62
3.20
0.51
0.97


GO_CELLULAR_RESPONSE_TO_VIRUS
7.05
5.01
2.45
5.31
0.00
0.00
0.00
0.00


GO_REGULATION_OF_CELL_DEATH
2.65
6.21
15.35
5.29
0.53
0.97
0.73
5.01


GO_POSITIVE_REGULATION_OF_MOLECULAR_FUNCTION
2.55
6.64
8.09
5.22
0.11
0.03
0.00
0.80


GO_REGULATION_OF_PROTEIN_SECRETION
7.13
3.30
2.00
5.20
0.00
0.17
0.00
0.43


GO_REGULATION_OF_HEMOPOIESIS
1.76
3.53
8.87
5.04
0.00
1.31
0.07
0.01


GO_REGULATION_OF_INTERLEUKIN_10_SECRETION
6.30
0.64
0.00
5.03
0.00
0.00
0.00
0.00


GO_LEUKOCYTE_DIFFERENTIATION
3.36
2.44
6.84
4.79
0.00
0.86
0.13
0.08


GO_RESPONSE_TO_MECHANICAL_STIMULUS
0.99
3.07
10.66
4.77
0.00
0.39
0.03
0.03


GO_POSITIVE_REGULATION_OF_PROTEIN_MODIFICATION_PROCESS
2.87
6.35
11.09
4.74
0.13
0.14
0.05
0.45


HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION
2.05
5.81
6.34
4.74
1.63
1.52
0.10
5.26


GO_RECEPTOR_ACTIVITY
6.03
6.50
1.26
4.69
0.68
1.66
0.08
0.04


GO_POSITIVE_REGULATION_OF_PROTEIN_METABOLIC_PROCESS
2.84
6.23
13.42
4.57
0.16
0.26
0.03
2.51


GO_REGULATION_OF_ERK1_AND_ERK2_CASCADE
2.21
4.63
8.51
4.54
0.00
0.66
0.09
0.14


GO_LOCOMOTION
3.51
10.07
7.56
4.52
0.40
2.84
0.46
1.21


GO_CLATHRIN_COATED_ENDOCYTIC_VESICLE_MEMBRANE
6.29
0.20
0.00
4.40
0.00
0.00
1.14
0.12


GO_TUMOR_NECROSIS_FACTOR_MEDIATED_SIGNALING_PATHWAY
9.20
11.26
5.20
4.32
0.55
0.00
0.02
0.23


GO_REGULATION_OF_PHOSPHORUS_METABOLIC_PROCESS
1.74
5.39
8.64
4.23
0.16
0.14
0.19
0.37


GO_POSITIVE_REGULATION_OF_LEUKOCYTE_DIFFERENTIATION
1.01
3.41
6.22
4.21
0.00
1.43
0.00
0.02


GO_POSITIVE_REGULATION_OF_HEMOPOIESIS
1.06
3.68
6.80
4.11
0.00
1.05
0.02
0.01


GO_INTRACELLULAR_SIGNAL_TRANSDUCTION
2.60
8.52
10.04
4.09
0.06
0.05
0.00
0.23


GO_REGULATION_OF_GRANULOCYTE_CHEMOTAXIS
3.48
6.15
5.29
4.06
0.00
0.00
0.00
0.99


GO_RESPONSE_TO_INTERFERON_ALPHA
6.84
4.85
0.00
4.02
0.00
0.00
0.00
0.00


GO_BLOOD_VESSEL_MORPHOGENESIS
1.38
6.59
8.37
4.01
0.00
0.57
1.56
0.87


GO_POSITIVE_REGULATION_OF_MAPK_CASCADE
1.88
5.02
10.14
3.97
0.00
0.30
0.07
0.56


GO_REGULATION_OF_MONONUCLEAR_CELL_MIGRATION
1.46
6.08
4.29
3.95
0.00
0.00
0.00
1.00


GO_CELLULAR_RESPONSE_TO_LIPID
3.72
3.66
6.71
3.91
0.00
0.64
0.17
1.13


GO_REGULATION_OF_MAPK_CASCADE
1.24
5.22
10.69
3.89
0.00
0.60
0.05
0.18


GO_POSITIVE_REGULATION_OF_ERK1_AND_ERK2_CASCADE
2.22
3.99
6.24
3.87
0.00
0.69
0.26
0.45


GO_INTRINSIC_COMPONENT_OF_ORGANELLE_MEMBRANE
6.31
3.20
2.42
3.65
0.28
0.01
0.33
0.10


GO_REGULATION_OF_PROTEIN_MODIFICATION_PROCESS
2.37
6.08
8.67
3.60
0.11
0.12
0.07
0.49


GO_CXCR_CHEMOKINE_RECEPTOR_BINDING
6.23
3.09
2.70
3.58
0.00
0.65
0.00
0.00


GO_REGULATION_OF_APOPTOTIC_SIGNALING_PATHWAY
1.70
3.28
6.76
3.57
0.00
0.65
0.05
2.21


GO_RESPONSE_TO_ALCOHOL
1.05
1.07
6.31
3.57
0.00
0.27
0.08
1.21


GO_POSITIVE_REGULATION_OF_PEPTIDYL_TYROSINE_PHOSPHORYLATION
2.90
6.32
4.56
3.38
0.00
0.10
0.00
0.47


GO_REGULATION_OF_CYTOKINE_BIOSYNTHETIC_PROCESS
1.08
2.73
6.15
3.38
0.00
0.00
0.13
0.00


GO_POSITIVE_REGULATION_OF_CATALYTIC_ACTIVITY
1.47
3.85
6.57
3.33
0.06
0.04
0.01
0.53


GO_NEGATIVE_REGULATION_OF_CELL_COMMUNICATION
2.75
8.62
7.08
3.31
0.02
0.64
0.11
0.11


GO_ENDOPLASMIC_RETICULUM_PART
3.52
1.35
0.61
3.30
0.27
0.00
0.47
6.86


GO_CELLULAR_RESPONSE_TO_EXTERNAL_STIMULUS
2.22
1.48
8.95
3.24
0.00
0.19
0.15
0.04


GO_POSITIVE_REGULATION_OF_CELL_DEATH
1.48
0.90
6.64
3.21
0.70
0.88
0.86
1.63


GO_CELL_MOTILITY
3.68
8.12
7.58
3.07
0.08
1.16
0.19
1.19


HALLMARK_UV_RESPONSE_UP
0.76
2.33
11.28
3.07
0.45
0.05
0.73
0.55


GO_VASCULATURE_DEVELOPMENT
1.27
5.47
8.37
2.98
0.16
0.68
1.04
0.74


GO_ANGIOGENESIS
1.64
5.44
7.76
2.96
0.00
0.34
1.01
1.02


GO_POSITIVE_REGULATION_OF_GENE_EXPRESSION
0.72
2.15
11.03
2.87
0.39
1.14
0.05
1.81


GO_I_KAPPAB_KINASE_NF_KAPPAB_SIGNALING
0.45
6.40
6.53
2.78
0.00
0.00
0.09
0.40


GO_ENDOPLASMIC_RETICULUM
3.34
1.40
0.36
2.76
0.27
0.01
0.47
9.20


GO_NEGATIVE_REGULATION_OF_TRANSPORT
2.86
5.33
6.10
2.73
0.00
0.18
0.09
0.36


GO_RNA_POLYMERASE_II_TRANSCRIPTION_FACTOR_ACTIVITY_SEQUENCE_SPECIFIC_DNA_BINDING
1.04
3.13
8.28
2.73
0.44
3.32
0.81
0.31


GO_REGULATION_OF_MULTICELLULAR_ORGANISMAL_DEVELOPMENT
2.76
4.77
8.32
2.60
0.15
2.70
0.26
1.71


GO_MEMBRANE_PROTEIN_COMPLEX
4.70
1.93
1.14
2.57
0.44
0.05
0.37
7.77


GO_PROTEIN_COMPLEX_BINDING
1.24
1.07
0.07
2.53
0.05
0.01
0.86
8.93


GO_POSITIVE_REGULATION_OF_TRANSCRIPTION_FROM_RNA_POLYMERASE_II_PROMOTER
0.66
1.98
10.21
2.45
0.70
1.02
0.06
0.84


GO_REGULATION_OF_SMOOTH_MUSCLE_CELL_PROLIFERATION
0.60
2.18
7.29
2.43
0.00
0.89
0.05
0.42


GO_POSITIVE_REGULATION_OF_DEVELOPMENTAL_PROCESS
0.89
2.80
6.52
2.43
0.16
1.93
0.07
1.80


GO_REGULATION_OF_TYROSINE_PHOSPHORYLATION_OF_STAT_PROTEIN
2.01
4.07
6.82
2.42
0.00
0.00
0.00
0.34


GO_NIK_NF_KAPPAB_SIGNALING
4.09
6.32
5.11
2.27
0.00
0.00
0.00
0.38


GO_NEGATIVE_REGULATION_OF_CELL_DEATH
0.61
6.24
11.35
2.08
0.18
0.72
0.23
4.13


GO_RESPONSE_TO_ABIOTIC_STIMULUS
0.53
1.55
7.93
1.83
0.17
0.35
0.13
0.27


GO_CELLULAR_RESPONSE_TO_EXTRACELLULAR_STIMULUS
1.33
0.61
6.03
1.81
0.00
0.38
0.09
0.03


GO_REGULATION_OF_CELL_DIFFERENTIATION
0.49
3.08
8.25
1.71
0.08
3.11
0.41
1.55


GO_REGULATION_OF_STAT_CASCADE
1.10
2.97
6.53
1.57
0.00
0.16
0.05
0.25


GO_REGULATION_OF_MACROPHAGE_DERIVED_FOAM_CELL_DIFFERENTIATION
0.00
1.95
6.77
1.56
0.00
0.00
0.00
0.00


GO_REGULATION_OF_TRANSCRIPTION_FROM_RNA_POLYMERASE_II_PROMOTER
0.42
1.97
11.60
1.53
0.39
0.81
1.36
0.35


GO_MACROMOLECULAR_COMPLEX_BINDING
0.29
1.10
0.61
1.50
0.18
0.07
1.51
10.34


GO_NEGATIVE_REGULATION_OF_GENE_EXPRESSION
0.85
1.98
6.28
1.32
0.01
0.84
1.70
1.92


GO_RESPONSE_TO_INORGANIC_SUBSTANCE
0.12
1.00
8.85
1.30
0.17
0.04
0.07
0.56


GO_CIRCULATORY_SYSTEM_DEVELOPMENT
0.85
3.63
7.13
1.24
0.24
0.88
0.51
0.78


GO_TRANSCRIPTION_FACTOR_ACTIVITY_RNA_POLYMERASE_II_CORE_PROMOTER_PROXIMAL_REGION_SEQUENCE
0.76
1.46
6.53
1.15
0.00
5.27
0.41
0.69


SPECIFIC_BINDING


GO_POSITIVE_REGULATION_OF_CELL_DIFFERENTIATION
0.40
2.03
6.78
1.10
0.08
1.65
0.01
1.46


GO_ANATOMICAL_STRUCTURE_FORMATION_INVOLVED_IN_MORPHOGENESIS
0.06
3.20
6.37
1.09
0.19
1.19
0.55
1.42


GO_RESPONSE_TO_ALKALOID
0.57
2.08
7.10
0.97
0.00
0.14
0.04
0.65


GO_CATABOLIC_PROCESS
3.19
1.65
0.19
0.95
0.02
17.00
0.48
6.78


GO_NUCLEIC_ACID_BINDING_TRANSCRIPTION_FACTOR_ACTIVITY
0.51
1.34
8.70
0.85
0.58
1.55
1.66
0.01


GO_CELLULAR_CATABOLIC_PROCESS
3.26
1.25
0.42
0.82
0.06
17.00
0.03
7.22


GO_TRANSCRIPTIONAL_ACTIVATOR_ACTIVITY_RNA_POLYMERASE_II_TRANSCRIPTION_REGULATORY_REGION_SEQUENCE
0.20
1.26
7.62
0.78
0.87
4.82
0.93
1.58


SPECIFIC_BINDING


GO_CELLULAR_RESPONSE_TO_HYDROGEN_PEROXIDE
0.15
0.33
6.61
0.75
0.00
0.23
0.32
0.75


GO_RESPONSE_TO_HYDROGEN_PEROXIDE
0.05
0.11
7.12
0.68
0.00
0.10
0.26
0.68


GO_ANCHORING_JUNCTION
0.60
0.77
0.55
0.55
0.38
17.00
0.05
17.00


GO_VIRAL_LIFE_CYCLE
0.80
1.30
0.52
0.51
0.64
17.00
0.00
17.00


GO_CELL_JUNCTION
0.63
1.60
0.15
0.49
0.63
17.00
0.01
12.04


GO_CELL_SUBSTRATE_JUNCTION
0.22
1.36
0.76
0.34
0.45
17.00
0.06
17.00


GO_REGULATION_OF_PROTEIN_LOCALIZATION_TO_CHROMOSOME_TELOMERIC_REGION
0.52
0.00
0.00
0.32
0.00
0.00
0.43
7.41


GO_MACROMOLECULAR_COMPLEX_ASSEMBLY
0.32
0.37
0.28
0.29
0.49
0.44
0.01
7.99


GO_MACROMOLECULE_CATABOLIC_PROCESS
2.79
1.70
0.78
0.28
0.13
17.00
0.00
8.37


GO_ORGANIC_CYCLIC_COMPOUND_CATABOLIC_PROCESS
0.59
0.51
0.16
0.26
0.49
17.00
0.05
17.00


GO_CYTOSOLIC_PART
0.18
0.08
0.07
0.24
0.84
17.00
0.03
17.00


GO_HYDROGEN_TRANSPORT
0.18
0.26
0.09
0.24
0.00
0.00
0.10
7.15


GO_POSTTRANSCRIPTIONAL_REGULATION_OF_GENE_EXPRESSION
1.67
1.09
2.88
0.23
0.13
0.91
0.27
11.85


GO_ENERGY_COUPLED_PROTON_TRANSPORT_DOWN_ELECTROCHEMICAL_GRADIENT
0.40
0.00
0.00
0.22
0.00
0.00
0.31
7.10


GO_HYDROGEN_ION_TRANSMEMBRANE_TRANSPORT
0.08
0.17
0.13
0.20
0.00
0.00
0.04
7.85


GO_TRANSCRIPTIONAL_ACTIVATOR_ACTIVITY_RNA_POLYMERASE_II_CORE_PROMOTER_PROXIMAL_REGION_SEQUENCE
0.09
0.70
4.78
0.19
0.00
6.56
0.98
1.66


SPECIFIC_BINDING


GO_PROTEIN_STABILIZATION
0.33
0.02
0.00
0.17
0.00
0.00
0.63
6.99


GO_TRANSLATION_FACTOR_ACTIVITY_RNA_BINDING
0.06
0.00
0.36
0.15
0.00
0.76
0.03
9.61


GO_ATP_BIOSYNTHETIC_PROCESS
0.30
0.00
0.00
0.15
0.00
0.00
0.66
6.86


GO_PIGMENT_GRANULE
0.22
0.03
0.00
0.15
0.00
0.00
0.13
13.19


GO_RIBONUCLEOSIDE_TRIPHOSPHATE_BIOSYNTHETIC_PROCESS
0.20
0.00
0.00
0.08
0.00
0.00
0.87
7.09


GO_NAD_METABOLIC_PROCESS
0.00
0.45
0.00
0.08
0.00
0.29
3.63
6.12


GO_REGULATION_OF_CELLULAR_AMIDE_METABOLIC_PROCESS
0.04
0.17
2.23
0.05
0.19
1.54
0.16
12.66


GO_PROTEIN_LOCALIZATION_TO_MEMBRANE
0.01
0.05
0.22
0.05
1.08
17.00
0.16
17.00


GO_INTRACELLULAR_PROTEIN_TRANSPORT
0.02
0.02
0.09
0.05
0.72
17.00
0.10
17.00


GO_SINGLE_ORGANISM_CELLULAR_LOCALIZATION
0.00
0.00
0.12
0.05
1.07
17.00
0.16
17.00


GO_NUCLEOBASE_CONTAINING_SMALL_MOLECULE_METABOLIC_PROCESS
0.49
0.07
0.00
0.04
0.00
0.02
2.62
6.14


GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION
0.00
0.00
0.00
0.03
0.73
17.00
0.00
17.00


GO_PROTEIN_TARGETING
0.03
0.07
0.22
0.03
1.46
17.00
0.11
17.00


GO_GLYCOSYL_COMPOUND_METABOLIC_PROCESS
0.10
0.03
0.00
0.02
0.00
0.00
3.87
7.59


GO_PROTEIN_LOCALIZATION
0.00
0.03
0.04
0.02
0.73
17.00
0.01
15.48


GO_REGULATION_OF_TRANSLATIONAL_INITIATION
0.00
0.17
1.35
0.02
0.00
0.43
0.16
8.71


GO_PEPTIDE_METABOLIC_PROCESS
0.04
0.01
0.01
0.01
0.28
17.00
0.12
17.00


GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ORGANELLE
0.02
0.11
0.68
0.01
1.59
17.00
0.06
17.00


GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_MEMBRANE
0.04
0.08
0.24
0.01
0.72
17.00
0.25
17.00


GO_SINGLE_ORGANISM_BIOSYNTHETIC_PROCESS
0.21
0.19
0.01
0.01
0.00
0.00
6.15
1.41


GO_UNFOLDED_PROTEIN_BINDING
0.06
0.13
0.11
0.01
0.58
0.00
0.90
10.39


GO_MITOCHONDRION
0.03
0.00
0.01
0.01
0.00
0.00
3.81
6.32


GO_NUCLEOLUS
0.12
0.00
0.00
0.01
0.13
7.16
0.60
8.75


GO_RNA_CATABOLIC_PROCESS
0.05
0.19
0.28
0.01
0.76
17.00
0.04
17.00


GO_ORGANONITROGEN_COMPOUND_METABOLIC_PROCESS
0.15
0.22
0.09
0.01
0.02
17.00
0.80
17.00


GO_ESTABLISHMENT_OF_LOCALIZATION_IN_CELL
0.00
0.00
0.01
0.01
0.17
17.00
0.43
17.00


GO_PROTEIN_TARGETING_TO_MEMBRANE
0.01
0.00
0.00
0.01
1.04
17.00
0.03
17.00


GO_NUCLEOSIDE_MONOPHOSPHATE_METABOLIC_PROCESS
0.00
0.00
0.00
0.00
0.00
0.02
3.04
10.56


GO_CYTOSOLIC_RIBOSOME
0.00
0.00
0.00
0.00
1.26
17.00
0.00
17.00


GO_CELLULAR_AMIDE_METABOLIC_PROCESS
0.01
0.00
0.01
0.00
0.20
17.00
0.12
17.00


GO_PURINE_CONTAINING_COMPOUND_METABOLIC_PROCESS
0.30
0.03
0.00
0.00
0.00
0.02
2.19
7.96


GO_MITOCHONDRIAL_ENVELOPE
0.00
0.00
0.00
0.00
0.06
0.00
1.95
7.10


GO_ENVELOPE
0.00
0.01
0.00
0.00
0.22
0.00
1.92
6.13


GO_CELLULAR_MACROMOLECULE_LOCALIZATION
0.00
0.04
0.28
0.00
0.61
17.00
0.07
17.00


GO_CELLULAR_MACROMOLECULAR_COMPLEX_ASSEMBLY
0.04
0.00
0.01
0.00
1.24
1.30
0.00
10.21


GO_MRNA_BINDING
0.00
0.01
0.69
0.00
0.00
1.60
0.11
11.14


GO_NUCLEOSIDE_TRIPHOSPHATE_METABOLIC_PROCESS
0.09
0.00
0.00
0.00
0.00
0.00
1.91
11.03


GO_PROTEIN_FOLDING
0.03
0.01
0.00
0.00
0.00
0.08
0.46
11.03


GO_MYELIN_SHEATH
0.02
0.10
0.04
0.00
0.42
0.17
1.22
14.07


GO_MULTI_ORGANISM_METABOLIC_PROCESS
0.01
0.00
0.04
0.00
1.05
17.00
0.00
17.00


GO_ORGANELLE_INNER_MEMBRANE
0.00
0.00
0.00
0.00
0.00
0.01
1.30
7.51


GO_PROTEIN_LOCALIZATION_TO_ORGANELLE
0.00
0.02
0.48
0.00
1.61
17.00
0.10
17.00


GO_RRNA_METABOLIC_PROCESS
0.03
0.00
0.00
0.00
0.65
17.00
0.42
17.00


GO_ORGANONITROGEN_COMPOUND_BIOSYNTHETIC_PROCESS
0.04
0.03
0.02
0.00
0.10
17.00
0.91
17.00


GO_MEMBRANE_ORGANIZATION
0.00
0.01
0.04
0.00
0.69
17.00
0.31
17.00


GO_STRUCTURAL_MOLECULE_ACTIVITY
0.00
0.03
0.02
0.00
1.70
17.00
0.19
17.00


GO_RIBONUCLEOPROTEIN_COMPLEX_SUBUNIT_ORGANIZATION
0.03
0.01
0.02
0.00
0.86
6.94
0.00
11.60


GO_RNA_BINDING
0.22
0.01
0.00
0.00
1.24
17.00
0.06
17.00


GO_STRUCTURAL_CONSTITUENT_OF_RIBOSOME
0.00
0.00
0.00
0.00
0.83
17.00
0.83
17.00


GO_AMIDE_BIOSYNTHETIC_PROCESS
0.00
0.00
0.00
0.00
0.33
17.00
0.28
17.00


GO_RIBONUCLEOPROTEIN_COMPLEX
0.02
0.01
0.01
0.00
0.68
17.00
0.85
17.00


GO_RIBOSOME
0.00
0.00
0.01
0.00
1.38
17.00
1.09
17.00


GO_GENERATION_OF_PRECURSOR_METABOLITES_AND_ENERGY
0.00
0.01
0.04
0.00
0.00
0.04
3.12
9.14


GO_RIBOSOME_BIOGENESIS
0.00
0.00
0.00
0.00
0.54
17.00
0.33
17.00


GO_POLY_A_RNA_BINDING
0.01
0.00
0.00
0.00
1.07
17.00
0.04
17.00


GO_MRNA_METABOLIC_PROCESS
0.00
0.00
0.00
0.00
0.51
17.00
0.01
17.00


GO_RIBONUCLEOPROTEIN_COMPLEX_BIOGENESIS
0.00
0.00
0.00
0.00
0.35
17.00
0.11
17.00


GO_NCRNA_PROCESSING
0.00
0.00
0.00
0.00
0.42
17.00
0.75
14.81


GO_NCRNA_METABOLIC_PROCESS
0.00
0.00
0.00
0.00
0.28
17.00
1.02
11.58


GO_RNA_PROCESSING
0.00
0.00
0.00
0.00
0.89
17.00
0.12
17.00


GO_POLYSOME
0.00
0.16
0.31
0.00
0.90
2.42
0.00
6.51


GO_RIBOSOMAL_SUBUNIT
0.00
0.00
0.00
0.00
1.71
17.00
0.40
17.00


GO_LARGE_RIBOSOMAL_SUBUNIT
0.00
0.00
0.00
0.00
2.37
17.00
0.25
17.00


GO_TRANSLATIONAL_INITIATION
0.00
0.00
0.13
0.00
1.03
17.00
0.00
17.00


GO_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM
0.00
0.00
0.05
0.00
1.16
17.00
0.00
17.00


GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS_NONSENSE_MEDIATED_DECAY
0.00
0.01
0.00
0.00
1.17
17.00
0.00
17.00


GO_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_ENDOPLASMIC_RETICULUM
0.00
0.00
0.00
0.00
1.27
17.00
0.00
17.00


GO_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT
0.00
0.00
0.00
0.00
1.75
17.00
0.00
17.00


GO_CYTOPLASMIC_TRANSLATION
0.00
0.00
0.00
0.00
0.86
14.45
0.00
17.00


GO_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT
0.00
0.00
0.00
0.00
0.00
17.00
0.00
12.03


GO_MITOCHONDRIAL_PROTEIN_COMPLEX
0.00
0.00
0.00
0.00
0.43
0.18
1.28
10.36


GO_SMALL_RIBOSOMAL_SUBUNIT
0.00
0.00
0.00
0.00
0.00
17.00
0.44
10.32


GO_MITOCHONDRIAL_MEMBRANE_PART
0.00
0.00
0.03
0.00
0.38
0.03
0.70
10.23


GO_FORMATION_OF_TRANSLATION_PREINITIATION_COMPLEX
0.00
0.00
0.00
0.00
0.00
0.53
0.00
10.10


GO_INNER_MITOCHONDRIAL_MEMBRANE_PROTEIN_COMPLEX
0.00
0.00
0.00
0.00
0.00
0.08
0.98
8.64


GO_REGULATION_OF_TELOMERASE_RNA_LOCALIZATION_TO_CAJAL_BODY
0.00
0.00
0.00
0.00
0.00
0.00
0.41
8.54


GO_TRANSLATION_INITIATION_FACTOR_ACTIVITY
0.00
0.00
0.24
0.00
0.00
0.25
0.00
8.11


GO_RRNA_BINDING
0.00
0.00
0.00
0.00
0.00
17.00
0.27
7.91


GO_MITOCHONDRIAL_ATP_SYNTHESIS_COUPLED_PROTON_TRANSPORT
0.00
0.00
0.00
0.00
0.00
0.00
0.37
7.89


GO_RIBOSOME_ASSEMBLY
0.00
0.00
0.00
0.00
1.83
12.90
0.00
7.59


GO_RIBOSOMAL_LARGE_SUBUNIT_BIOGENESIS
0.00
0.00
0.00
0.00
1.92
12.21
0.36
7.26


GO_EPHRIN_RECEPTOR_SIGNALING_PATHWAY
0.00
0.22
0.16
0.00
0.69
1.00
0.48
7.15


GO_TRANSLATION_PREINITIATION_COMPLEX
0.00
0.00
0.00
0.00
0.00
0.62
0.00
7.10


GO_EUKARYOTIC_TRANSLATION_INITIATION_FACTOR_3_COMPLEX
0.00
0.00
0.00
0.00
0.00
0.62
0.00
7.10


GO_SPERM_EGG_RECOGNITION
0.00
0.00
0.00
0.00
0.00
0.00
0.51
6.98


GO_BINDING_OF_SPERM_TO_ZONA_PELLUCIDA
0.00
0.00
0.00
0.00
0.00
0.00
0.51
6.98


GO_REGULATION_OF_ESTABLISHMENT_OF_PROTEIN_LOCALIZATION_TO_CHROMOSOME
0.00
0.00
0.00
0.00
0.00
0.00
0.51
6.98


GO_PROTON_TRANSPORTING_ATP_SYNTHASE_COMPLEX
0.00
0.00
0.00
0.00
0.00
0.00
0.83
6.88


GO_CELL_CELL_RECOGNITION
0.00
0.00
0.00
0.00
0.00
0.00
0.37
6.55


GO_COPI_COATED_VESICLE
0.00
0.00
0.00
0.00
0.00
0.00
0.00
6.32


GO_NADH_METABOLIC_PROCESS
0.00
0.26
0.00
0.00
0.00
0.00
4.02
6.28


GO_CATALYTIC_STEP_2_SPLICEOSOME
0.00
0.00
0.00
0.00
0.00
0.00
0.03
6.13


GO_RIBOSOMAL_LARGE_SUBUNIT_ASSEMBLY
0.00
0.00
0.00
0.00
2.57
10.40
0.00
5.70


GO_RIBOSOMAL_SMALL_SUBUNIT_BIOGENESIS
0.00
0.00
0.00
0.00
0.00
9.84
0.00
3.89


GO_RIBOSOMAL_SMALL_SUBUNIT_ASSEMBLY
0.00
0.00
0.00
0.00
0.00
6.48
0.00
2.21









Example 6—Evidence of Antitumor Immune Activity Despite Low Immune Infiltration

Having mapped the malignant cell states, Applicants turned to characterize immune cells within SyS tumors. Single-cell data revealed diverse cell states indicative of antitumor immunity (FIG. 9C, Table 12): analyzing macrophages Applicants observed M1-like and M2-like states, reminiscent of pro- and anti-inflammatory properties, respectively (FIGS. 9C, 10A-10C; Table 12). Applicants also observed various T cell subsets, including naïve, cytotoxic, exhausted, and regulatory T cells (FIG. 9C-9D).









TABLE 12







scRNA-Seq-based M1 and M2 signatures.










M1
M2
M1 (top 50)
M2 (top 50)















ABRACL
LIMD2
A2M
KCNMA1
ALDH2
A2M


ACAP1
LIMS1
ABCC5
KCTD12
ANPEP
AP1B1


ACOT9
LIPN
ABCD4
KIAA1683
ANXA2
C1QA


ACSL5
LOC100288069
ABL2
KIF1B
AQP9
C1QB


ACTN1
LOC100506801
ACTR8
KLF2
BCL2A1
C1QC


ACTN4
LPCAT1
ADAM9
KLHL24
C15orf48
CCDC152


ACTR3
LPPR2
ADAP2
LAIR1
CAPN2
CCL3


ADA
LPXN
ADORA3
LAMB2
CD300E
CD163L1


ADAM19
LSP1
ADRB2
LEPREL1
CD44
CD209


ADAM8
LST1
AFF1
LGALS3BP
CD48
CD59


ADD3
LTA4H
AIG1
LGMN
CD52
CTSC


ADORA2A
LUZP6
AKR1B1
LHFPL2
CD55
CTSD


AGO2
LYN
ALOX5AP
LILRB5
CFP
DAB2


AGPAT9
LYPD3
AMDHD2
LIPA
CLEC12A
DNAJB1


AGTPBP1
LYST
ANKH
LMAN1
CORO1A
EGR1


AGTRAP
LYZ
ANKRD36

COTL1
F13A1


AHNAK
MAP2K1
ANKRD36B
LOXL3
CRIP1
FOLR2


AKAP2
MAP2K3
ANTXR1
LPAR5
CYTIP
FOS


ALDH2
MAPK1IP1L
AP1B1
LPAR6
EMP3
FRMD4A


ALOX5
MAPKAPK3
AP2A2
LTC4S
EREG
FUCA1


AMICA1
MARCO
APOC1
LYVE1
FAM65B
GADD45B


AMPD2
MBOAT7
APOE
MAF
FCN1
GPR34


ANPEP
MCOLN2
APPL2
MAMDC2
FGR
GYPC


ANXA1
MGST1
ARHGAP12
ME1
FLNA
HSPA1B


ANXA2
MNDA
ARHGAP18
MEF2C
G0S2
IER2


ANXA6
MOB3A
ARHGAP21
MERTK
GLIPR2
IGF1


AOAH
MPHOSPH6
ARHGAP24
MGAT4A
ITGAX
JUN


AP1S2
MTHFS
ARMCX1
MGAT5
KYNU
LGMN


APOBEC3A
MTMR11
ATF3
MITF
LCP1
LILRB5


AQP9
MTPN
ATF6
MKNK1
LGALS2
MAF


AREG
MVP
ATP1B1
MMD
LIMD2
MAMDC2


ARF5
MX2
ATP2C1
MMP14
LIPN
ME1


ARFGAP3
MXD1
AXL
MMP2
LSP1
MERTK


ASGR2
MYADM
BAG3
MPC2
LST1
MRC1


ATG3
MYD88
BAIAP2
MRC1
LYZ
MS4A4A


ATP2B1
MYL12B
BCL2L1
MRO
OLR1
MS4A7


B4GALT5
MYO1G
BEX4
MS4A4A
P2RX1
MSR1


BACH1
NAAA
BLNK
MS4A7
PLP2
NRP1


BASP1
NAP1L1
BMP2K
MSR1
S100A10
PLD3


BCL11A
NAPSB
C1orf85
MTMR9LP
S100A4
PLTP


BCL2A1
NBEAL2
C1QA
MTSS1
S100A6
PLXDC1


BCL3
NBPF1O
C1QB
MTUS1
S100A8
RNASE1


BID
NCF2
C1QC
MVB12B
SERPINA1
SEPP1


BIRC3
NDEL1
C2
MYLIP
SH3BGRL3
SIGLEC1


BST1
NEDD9
C20orf194
MYO5A
TIMP1
SLC40A1


C11orf21
NFAT5
C3
NAA20
TKT
SLC7A8


C15orf39
NFKB1
C5orf4
NAIP
UPP1
SLCO2B1


C15orf48
NOD2
CADM1
NASP
VCAN
STAB1


C19orf38
NOTCH2
CARD11
NCF4
VDR
TMEM176B


C19orf59
NOTCH2NL
CCDC152
NCKAP5
WARS
WLS


C1orf162
NUP210
CCL2
NEK6


C9orf72
OGDH
CCL3
NEU1


CAMKK2
OLR1
CCL3L1
NFATC2


CAPN2
OPTN
CCL3L3
NFIA


CARD16
OXSR1
CCL4
NGFRAP1


CASP9
P2RX1
CCL4L1
NISCH


CCDC69
P2RY1
CCL4L2
NMRK1


CCL20
PARM1
CCL8
NPL


CCND3
PCBP1
CCND1
NR4A2


CCR2
PDE4A
CD14
NRP1


CCR5
PDE4D
CD163
NRP2


CCRN4L
PDLIM7
CD163L1
NUPR1


CCT5
PFKP
CD200R1
NXF1


CD101
PGAM1
CD209
OLFML2B


CD1C
PGLS
CD276
OLFML3


CD1D
PIM3
CD28
P2RY13


CD1E
PLAC8
CD59
P4HA1


CD244
PLP2
CD81
PCDH12


CD300E
PNPLA8
CD84
PDGFA


CD37
PPA1
CD9
PDGFB


CD38
PPIF
CDKN2AIP
PDGFC


CD44
PPP1CA
CH25H
PDIA4


CD48
PRELID1
CHD7
PDK4


CD52
PRKCB
CHID1
PDPN


CD55
PSEN1
CITED2
PEAK1


CD58
PSMA6
CKS2
PEBP1


CD97
PSMB8
CLDN1
PER3


CDA
PSMB9
CMKLR1
PIK3IP1


CDC42EP2
PSME1
CNRIP1
PIK3R1


CDCA4
PSME2
COLEC12
PLA2G15


CEACAM4
PSTPIP2
CPEB4
PLAU


CECR1
PTGER2
CPED1
PLD3


CFP
PTGES
CPM
PLEKHG5


CHST15
PTP4A2
CREB3L2
PLTP


CKAP4
QPCT
CREG1
PLXDC1


CLCF1
RAB11FIP1
CSGALNACT1
PLXND1


CLEC10A
RAB24
CTSC
PMP22


CLEC12A
RAB27A
CTSD
PRDM1


CLEC4A
RAB3D
CTSL1
PROS1


CLEC4D
RAC2
CXCL12
RASGRP3


CLEC4E
RAP1B
CXCL3
RASSF4


CLIP4
RARA
CYB5R1
RB1


CNN2
RASSF5
CYBRD1
RCAN1


CORO1A
RHOF
CYFIP1
RGL1


COTL1
RILPL2
CYTL1
RGPD5


CPPED1
RIPK2
DAB2
RGS1


CRIP1
RNF19B
DHRS3
RGS10


CRLF2
RUNX3
DHRS7
RGS16


CSF3R
S100A10
DIP2C
RHOB


CSK
S100A12
DNAJA4
RNASE1


CST7
S100A4
DNAJB1
RND3


CSTA
S100A6
DNAJB4
RNF15O


CYB5R3
S100A8
DOCK4
SCARB1


CYFIP2
S100A9
DPP7
SCARB2


CYP1B1
SAMHD1
DSC2
SCD


CYP27A1
SAMSN1
DST
SDC3


CYTIP
SDC4
DTNA
SEPP1


DAPP1
SELL
DTNB
11-Sep


DDX21
SEMA6B
EBI3
SERPING1


DDX60L
9-Sep
EGFL7
SESN1


DENND5A
SERPINA1
EGR1
SGK1


DESI1
SERPINB1
EGR2
SIGLEC1


DIAPH1
SERPINB2
EGR3
SIGLEC8


DOCK5
SERPINB8
EIF4A2
SLC16A10


DYSF
SH2D3C
EMB
SLC18B1


EAF1
SH3BGRL3
ENG
SLC1A3


ECE1
SH3BP2
ENPP2
SLC29A1


EFHD2
SHKBP1
EPAS1
SLC2A5


EHD1
SIDT2
EPB41L2
SLC35F6


EIF4E2
SIRPB1
EPS15
SLC36A1


EIF6
SLAMF7
ERO1LB
SLC37A4


ELF4
SLC22A4
ETV5
SLC38A6


EMP3
SLC25A37
F13A1
SLC38A7


EMR1
SLC2A3
FABP3
SLC40A1


EREG
SLC2A6
FAM105A
SLC41A1


EVI2B
SLC35E4
FAM13A
SLC4A7


FAM157B
SLC38A1
FAM174B
SLC7A8


FAM65B
SLC6A6
FAM213A
SLC9A9


FBP1
SLC9A3R1
FAM46A
SLCO2B1


FCAR
SLCO4A1
FARP1
SMA5


FCER1A
SNAI1
FCGBP
SNHG12


FCN1
SNHG16
FCGR1A
SNX6


FFAR2
SNN
FCGR1B
SORBS3


FGR
SNX10
FCGR1C
SPATS2L


FKBP1A
SNX20
FCGR3A
SPIN1


FLNA
SPATA13
FCGR3B
SPP1


FLT1
SPN
FCHO2
SPSB2


FPR2
SRC
FHIT
SPTLC3


FYN
STAT6
FMNL2
SRGAP1


G0S2
STK10
FMNL3
ST3GAL6


G6PD
STK17B
FOLR2
ST6GAL1


GALNT3
STK38
FOS
STAB1


GBP5
STX11
FRMD4A
STMN1


GCH1
STXBP2
FRMD4B
STOM


GK
SUB1
FSCN1
SWAP70


GLIPR2
SULF2
FUCA1
TBC1D9B


GMFG
SYAP1
GAA
TCEAL3


GPCPD1
SYTL1
GADD45B
TCF12


GPR132
TAGLN2
GAL3ST4
TCF4


GPR35
TBC1D7
GAS6
TEX14


GPSM3
TBC1D8
GATM
TGFBR1


GST01
TCIRG1
GGTA1P
TLR1


GSTP1
TES
GIMAP5
TLR7


GTPBP1
TESC
GNPDA1
TM6SF1


H2AFY
TET2
GOLIM4
TMEM176A


H3F3A
TGM2
GPNMB
TMEM176B


HCK
THBS1
GPR155
TMEM198B


HCST
TICAM1
GPR34
TMEM2


HIGD2A
TIMP1
GSN
TMEM37


HK3
TKT
GYPC
TMEM86A


HLA-F
TMEM120A
HADH
TNF


HMGA1
TMEM71
HERC2P2
TNFRSF21


ICAM2
TNFAIP6
HERPUD1
TNS3


ICAM3
TNFRSF1B
HES1
TP53I11


IFITM1
TNFSF14
HEXA
TPCN1


IL18R1
TNIP1
HGF
TREM2


IL1R1
TNIP2
HIST2H2BF
TRPM2


IL1R2
TNIP3
HLA-DOA
TSPAN15


IL1RN
TP53BP2
HMOX1
TSPAN4


IL2RG
TRA2A
HNMT
TTYH3


IL3RA
TRAF1
HPGDS
ULK3


IMPDH1
TRAF3IP3
HRH1
USP53


IQSEC1
TREM1
HSD17B14
VAT1


IRAK3
TRIM25
HSPA1A
VSIG4


IRG1
TRMT6
HSPA1B
WBP5


ISG20
TSPAN32
HSPB1
WDR91


ITGA5
TUBA1A
HSPH1
WFS1


ITGAL
TUBA4A
HTRA1
WLS


ITGAX
TYMP
ICA1
ZFP36L1


ITGB2-AS1
UBE2D1
IDH1
ZFP36L2


KCNN4
UBE2J1
IER2
ZNF812


KCTD20
UBXN11
IER5
LOC100505702


KMO
UPP1
IFI16


KYNU
UXT
IFITM10


LCP1
VAMP5
IGF1


LDLR
VCAN
IGFBP4


LDLRAD3
VCL
IGSF21


LGALS1
VDR
IL2RA


LGALS2
VNN2
ING1


LGALS3
VRK2
ISCU


LILRA1
WARS
ISYNA1


LILRA2
WDR1
ITGAV


LILRA3
ZAK
ITGB5


LILRA5
ZC3H12A
ITPR2


LILRA6
ZDHHC20
ITSN1


LILRB2
ZFC3H1
JKAMP


LILRB3
ZYX
JUN









Evidence of Antitumor Immune Activity Despite Low Immune Infiltration

The lack of effective antitumor immunity in SyS may results from: either the inactivity of immune cells, limiting their recognition of or response to SyS malignant cells, or hampered immune cell infiltration and recruitment into the tumor parenchyma. To test the first possibility, Applicants examined CD8 T cell states (FIG. 14A, Table 1F), and found clear hallmarks of antitumor immunity and recognition. T cell subsets span naïve, cytotoxic, exhausted, and regulatory T cells (FIG. 14B; METHODS), with evidence of expansion based on TCR reconstruction (31) (showing 57 clones, all patient-specific, with shared clones between matched samples from the same patient). While cytotoxic and exhaustion markers were generally co-expressed in T cells (FIG. 14B, consistent with previous reports (29)), clonally expanded T cells had unique transcriptional features (Methods, Table 12), suggestive of an effector-like non-exhausted state (FIG. 14B, P<6.6*10−12, mixed-effects). These expanded T cells might respond to SyS-specific CTAs, which were specifically expressed in large fractions of the malignant cell populations (FIG. 4A). Moreover, CD8 T cells in SyS have features suggesting they are even more active than those in melanoma tumors, where anti-tumor immunity is relatively pronounced. First, compared to CD8 T cells from melanoma (32), CD8 T cells in SyS tumors overexpressed a program characterizing T cells in melanoma tumors that were responsive to immune checkpoint blockade (33) (FIG. 14C bottom, P=1.22*10−10, mixed-effects). In addition, compared to melanoma CD8 T cells, the SyS CD8 T cells also overexpressed effector and cytotoxic gene modules (34, 35) (e.g., GZMB, CX3CR1, P=6.36*10−9, mixed-effects), and repressed exhaustion markers (P=6.36*10−3, mixed-effects), including LAYN (34), and multiple checkpoint genes (CTLA4, HAVCR2, LAGS, PDCD1, and TIGIT; P=7.69*10−7, mixed-effects, FIG. 14C, top).


Other immune cells in the tumor microenvironment also showed features of antitumor immunity. Macrophages span M1-like and M2-like states, suggestive of both pro- and anti-inflammatory properties, respectively (FIG. 10A-10C; Methods, Table 12), and expressed relatively high levels of TNF (P=1.13*10−7, mixed-effects, >4 fold more compared to melanoma macrophages). However, mastocytes show regulatory features, with 39% of them expressing PD-L1 (as opposed to only 2% PD-L1 expressing malignant cells).


Applicants next examined the alternative hypothesis that T cell abundance might be a limiting factor in SyS, despite these favorable T cell states. Applicants compared SyS to 30 other cancer and sarcoma types. SyS tumors showed extremely low levels of immune cells, which cannot be explained by variation in the mutational load (FIG. 14D; P=2.58*10−11, mixed effects when conditioning on the tumor mutational load), and despite the malignant-cell specific expression of immunogenic CTAs (FIG. 3C). In addition, unlike melanoma (FIG. 10D, left), T cell levels were not correlated with prognosis in SyS (FIG. 10D, right), indicating that they may not cross the critical threshold to impact clinical outcomes. Only mastocytes had a moderate positive association with improved prognosis (P=0.012, Cox regression). These findings suggest that the lack of proper immune cell recruitment and infiltration is a key immune evasion mechanism in SyS, potentially mediated by the SyS cells.


Among CD8 T cells, TCR reconstruction (Stubbington et al. Nat Methods 13:329-332 (2016)) identified 57 clones, all patient-specific (with 6 shared clones between the primary and metastatic lesions of patient S11, and 7 shared clones between the pre- and post-treatment samples of patient S12). Clonally expanded T cells had unique features that Applicants characterized with an expansion program (Methods, Table 12). Interestingly, while cytotoxic and exhaustion markers were generally co-expressed (FIG. 9D, consistent with previous reports (Tirosh et al. Science 352:189-196 (2016)), the expansion program was particularly high in non-exhausted and highly cytotoxic T cells (FIG. 9D, P<6.6*10−12, mixed-effects). It was also associated with non-exhausted cytotoxic T cells in hepatocellular carcinoma (Puram et al. Cell 171:1611-1624.e24 (2017)) and melanoma (Jerby-Arnon et al. Cell 175:984-997.e24 (2018)) (P<4.89*10−19, mixed effects).


To further evaluate CD8 T cells in SyS, Applicants compared them to T cells from melanoma tumors (Jerby-Arnon et al. Cell 175:984-997.e24 (2018)) where anti-tumor immunity is relatively pronounced. In comparison to melanoma, CD8 T cells in SyS tumors overexpressed a program that was recently found to characterize T cells in tumors responsive to immune checkpoint blockade (Sade-Feldman et al. Cell 175:998-1013.e20 (2018)) (FIG. 9E). The SyS T cells also overexpressed effector and cytotoxic gene modules (Zheng et al. Cell 169:1342-1356.e16 (2017); Böttcher et al. Nat Comun 6:8306 (2015)) (e.g., GZMB, CX3CR1, P=6.36*10−9, mixed-effects), and repressed exhaustion markers (P=6.36*10−3, mixed-effects), including LAYN (Zheng et al. Cell 169:1342-1356.e16 (2017)), and multiple checkpoint genes (CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT; P=7.69*10−7, mixed-effects, FIG. 9E). These findings suggest that T cells in SyS tumors have a cytotoxic potential, which might be unleashed by immune checkpoint blockade.


Further analyses demonstrated that despite these favorable T cell states, T cell abundance might be a limiting factor in SyS. Comparing SyS tumors to 30 other cancer and sarcoma types demonstrated that SyS tumors have extremely low levels of immune cells, beyond those expected by their relatively low mutational load (FIG. 9F; P=2.58*10−11, mixed effects when conditioning on the tumor mutational load). In addition, unlike melanoma (FIG. 10D), T cell levels were not correlated with prognosis in SyS (FIG. 10D), and only mastocytes had a moderate positive association with prognosis (P=0.012, Cox regression).


Example 7—HDAC and CDK4/6 Inhibitors Synergistically Repress the Immune Resistant Features of Synovial Sarcoma Cells

Given the aggressive features of the core oncogenic program, its association with poor clinical outcome and T cell exclusion, and its dependency on the oncoprotein expression, Applicants set to identify pharmacological interventions that could block the program, aiming to selectively target synovial sarcoma cells. Here Applicants describe: (1) the computational model that led to the selection of HDAC/CDK inhibitors, (2) the results of the ongoing experiments, hopefully confirming predictions (FIGS. 11A-11C).


Applicants examined whether pharmacological agents could potentially repress the core oncogenic program and induce more immunogenic cell states in SyS cells. Computational modeling of the core oncogenic regulatory network (METHODS) highlighted the SSX-SS18-HDAC1 complex (20) as the program's master regulator (FIG. 18A), and the tumor suppressor CDKN1A (p21) as its most repressed target. The latter indicates that the core oncogenic program is regulating, rather than regulated by, cell cycle genes through the p21-CDK2/4/6 axis, potentially reinforcing the direct induction of cyclin D and CDK6 by SS18-SSX (FIG. 18B). According to this model (FIG. 18B), modulators of cell cycle (e.g., CDK4/6 inhibitors) and SS18-SSX (e.g., HDAC inhibitors) could synergistically target the immune resistance features of SyS cells, especially in the presence of tumor microenvironment cytokines as TNF. To test these predictions, Applicants treated SyS lines and primary mesenchymal stem cells (MSCs) with low doses of HDAC and CDK4/6 inhibitors, in order to avoid global toxicity-related effects, and examined their impact on the transcriptional state of the cells. As predicted, the HDAC inhibitor panobinostat markedly repressed the core oncogenic program (P=3.34*10−14, mixed-effects; FIG. 18C) and selectively induced CDKN1A in SyS cells (P=2.13*10−8) (FIG. 23A). Panobinostat also repressed the SS18-SSX program (P=5.32*10−72; FIG. 18D), decreased the expression of cell cycle genes (P<1.78*10−20), and induced an immunogenic phenotype (32) with enhanced antigen presentation and IFNγ responses (P<9.53*10−31; FIGS. 18E, 18F, FIG. 23B, 23CC). The CDK4/6 inhibitor abemaciclib repressed cell cycle gene expression (P=3.63*10−8), without impacting the core oncogenic program (P>0.1; FIG. 18C), supporting the notion that cell cycle regulation is down-stream of the core oncogenic program. Lastly, a low dose combination of panobinostat, abemaciclib and TNF synergistically repressed the core oncogenic program (P=1.72*10−37, FIG. 18C, FIG. 23A) and multiple immune resistant features, while inducing antigen presentation, IFN responses, and induced-self antigens as MICA/B (P=3.12*10−76; FIG. 18E, 18F, 23B, 23C). It also repressed MIF (Macrophage Migration Inhibitory Factor), a member of the core oncogenic and SS18-SSX programs, which has been previously shown to hamper T cell recruitment into the tumor (40). The effect of the drug combination on these programs and genes in viable SyS cells significantly exceeded the expected additive effect (P<0.01, mixed-effects interaction term, METHODS), and could potentially help both T cells (MHC-1) and NK cells (MICA/B) bind to and eliminate SyS cell. Consistent with the transcriptional changes, the drug combination displayed a significantly higher detrimental effect on the SyS cells compared to primary MSCs (P=5.7*10−13; FIG. 18F, 18G).


Discussion

Here, Applicants describe mapping of malignant and immune cell states and interactions in human SyS tumors, through integrative analyses of clinical and functional data. By leveraging scRNA-Seq Applicants mapped cell states in human SyS tumors, revealing active antitumor immunity in this relatively cold tumor, alongside malignant cellular plasticity and immune excluding features, centered around a core oncogenic program—a yet unappreciated cell modality that captures intra- and inter-tumor heterogeneity and is associated with aggressive disease (FIG. 11).


This program is regulated by the tumor's primary genetic driver and may hamper proper immune recruitment and infiltration. Nonetheless, immune cells can impact the malignant cells through TNF and IFNγ secretion, counteracting the transcriptional alterations induced by the oncoprotein. Targeting the oncogenic program and its downstream effects with HDAC and CDK4/6 inhibitors induced cell autonomous immune responses, repressed immune resistant features, and was selectively detrimental to SyS cells, thus providing a basis for the development of specific therapeutic strategies, which are currently lacking.


The findings demonstrate that different cancer hallmarks are co-regulated in SyS. The associations between the different malignant programs identified (FIG. 4A) demonstrate the connection between stem-like properties, cellular proliferation and the core oncogenic program (FIGS. 4C, 4D). In accordance with this, the repression of SS18-SSX blocked the core oncogenic program, arrested cell cycle and triggered cellular differentiation, suggesting that these three cellular features are co-regulated by the oncoprotein. The core oncogenic program itself couples different aggressive cellular characteristics, and associates aerobic metabolism with the repression of immune responses.


The metabolic features of the core oncogenic program may also impact the tumor microenvironment. Supporting this notion, recent studies have shown that malignant cells use oxidative phosphorylation to create a hypoxic niche and promote T cell dysfunction (41). These metabolic features might reflect the conserved role of the SWI/SNF complex in regulating carbon metabolism and sucrose non-fermenting phenotypes in the yeast Saccharomyces cerevisiae (42). These connections might generalize to other cancer types, as mutations in the BAF complex have been recently shown to induce a targetable dependency on oxidative phosphorylation in lung cancer (43).


Despite the extremely cold phenotypes displayed by SyS (FIG. 14D), expanded effector T cells are present in SyS tumors (FIGS. 14B-C), potentially responding to the CTAs expressed specifically in the malignant cells, including NY-ESO-1 and PRAME (FIG. 3C). Consistently, vaccines triggering dendritic-cells to prime NY-ESO-1 specific T cells can lead to durable responses in SyS patients (7), further supporting the notion that SyS immune evasion operates primarily through impaired T cell or dendritic cell recruitment (44). The latter may also be mediated through Wnt/β-catenin signaling pathway, which has been previously shown to interfere with CD8 T cell recruitment to tumors by dendritic cells (44), and is indeed active in all the malignant SyS cells and directly induced by SS18-SSX (FIG. 22A, Tables 5, 8). The core oncogenic program itself includes several CTAs, linking between malignant immune evasion and testicular immune privileges.


The analyses demonstrate that SyS tumors manifest extremely cold phenotypes, despite the overexpression of several cancer-testis antigens (FIG. 3C) and antitumor T cell reactivity (FIGS. 9D-9E). Indicating that the malignant cells may promote this cold phenotype, the core oncogenic program overlaps a transcriptional program that was previously linked to T cell exclusion in melanoma (Jerby-Arnon et al. Cell 175:984-997.e24 (2018)). In addition, Applicants found that Wnt/β-catenin signaling, which has been shown to drive T cell exclusion in mouse models (Spranger et al. Nature 523:231-235 (2015)), is directly upregulated by S S18-SSX (FIG. 7E) and is activated in all the SyS cells in the tumor (Tables 4 and 5). Further studies are needed to examine the underlying mechanisms of the different cancer-immune associations mapped here. Such mechanisms might be relevant in other cancer types given the role of PBAF genes in determining immune checkpoint blockade responses in melanoma and renal cancer (Pan et al. Science 359:770-775 (2018); Miao et al. Science 359:801-806 (2018)).


The association between the core oncogenic program and T cell exclusion is observed in situ in the SyS samples from Applicants' single-cell cohort. Applicants measured in situ expression of 12 proteins across 4,310,120 cells in 9 samples using multiplexed immunofluorescence (t-CyCIF) (39) (FIGS. 4E,F; METHODS), and profiled the in situ expression of 1,412 genes in 24 spatially distinct areas in two samples using the GeoMx high plex RNA Assay (early version for Next-Generation Sequencing; METHODS). Both approaches showed that CD45+ immune cells were exceptionally low in SyS (<0.4%, compared to >8.7% in melanoma samples (32)). Moreover, the malignant cells in the more immune infiltrated areas show a marked decrease in the core oncogenic program (r=−0.53, P=6.9*10−3, Pearson correlation, and P<1*10−10, mixed effects; METHODS). This suggests that the status of the malignant cells and the composition of the tumor microenvironment might be interconnected in SyS.


The findings also demonstrate that immune resistance, metabolic processes, cell cycle and de-differentiation are tightly co-regulated in SyS. Thus, beyond the targeted cytotoxicity of the adoptive immune system, CD8 T cells and macrophages may alleviate some of the aggressive features of SyS cells through the secretion of TNF and IFNγ, also impacting malignant cells with repressed antigen presentation or unrecognized antigens.


While the core oncogenic program shares some similar features with a T cell exclusion program we recently identified in melanoma (Jerby-Arnon et al., 2018), there are also substantial distinctions between the two programs, and >90% do not overlap between the two, likely reflecting the dramatic differences in driving events, cell of origin and tissue environment of the two tumors. This emphasizes the importance of understanding immune evasion for each tumor context. In particular, unlike the melanoma program, the core oncogenic program highlights a metabolic shift and is strongly connected to the genetic driver. In SyS tumors (but not in melanoma) Applicants successfully decoupled, through computational inference, the intrinsic and extrinsic signals which modulate this transcriptional program, facilitating the reconstruction of multicellular circuits. This new approach revealed a bi-directional interaction between malignant and immune cells where CD8 T cells and macrophages can in turn repress the core oncogenic program through the secretion of TNF and IFNγ. Thus, beyond their direct cytotoxic activity, immune cells can alleviate some of the aggressive features of SyS cells through cytokine secretion, targeting also malignant cells with repressed antigen presentation or unrecognized epitopes.


The tight co-regulation of processes indicate targeted therapies may be able to sensitize the tumor to immune surveillance. Supporting this notion, Applicants demonstrate that the combined inhibition of HDAC and CDK4/6, two known repressors of SS18-SSX (45, 46) and cellular proliferation (47), respectively, trigger immunogenic cell states even at sub-cytotoxic doses. This combinatorial treatment is also selectively cytotoxic to SyS cells, consistent with previous reports where HDAC and CDK4/6 inhibitors were used separately to induce cell death in SyS (45, 47). The basal antitumor immune response reported, and the ability of T cells and macrophages to repress the core oncogenic and SS18-SSX programs support the potential of exploiting HDAC and CDK4/6 inhibitors together with immunotherapy.


The epithelial and mesenchymal programs defined here might also be relevant in other cancer settings, given the role of the epithelial to mesenchymal transition (EMT) in drug resistance and metastatic disease. Interestingly, Applicants found a strong connection between TNF and IFNγ responses and the epithelial program (FIG. 12A, P<8.49*10−6, hypergeometric test), suggesting that EMT may also promote immune evasion capabilities, as previously suggested (Datar et al. Clin Cancer Res 22:3422 (2016); Terry et al. Mol Oncol 11:824-846 (2017)).


The programs identified by Applicants are tightly linked to clinical outcomes. While additional prospective data are needed to further examine their predictive value, the results shown here demonstrate that the overall expression of the programs in bulk tumors could be used for patient stratification. Alternatively, specific genes within the programs could potentially be used as biomarkers. For example, ALDH1A1 is a stem-cell marker which is among the top genes in the core oncogenic program. Its protein levels have been previously shown to be predictive of poor prognosis and metastatic disease in SyS patients (Zhou et al. Oncol Rep 37:3351-3360 (2017)).


Taken together, this study comprehensively maps and interrogates cell states in SyS, along with their regulatory circuits and clinical implications. Applicants demonstrated that the SS18-SSX oncoprotein and the tumor microenvironment coordinately shape cell states in SyS, setting the basis for the development of more effective treatment strategies.


Applicants demonstrated that the SS18-SSX oncoprotein and the tumor microenvironment coordinately shape cell states in SyS, resulting in the establishment of an immune privileged environment (FIG. 18I). The possibility to selectively target the underlying mechanisms to reverse immune evasion offers a new perspective for the clinical management of SyS, and potentially other malignancies driven by similar genetic events.


Materials and Methods

Human Tumor Specimen Collection and Dissociation


Patients at Massachusetts General Hospital and University Hospital of Lausanne were consented preoperatively in all cases according to their respective Institutional Review Boards (protocol numbers: CER-VD 260/15, DF/HCC 13-416). Fresh tumors were collected directly from the operating room at the time of surgery and presence of malignancy was confirmed by frozen section. Tumor tissues were mechanically and enzymatically dissociated using a human tumor dissociation kit (Miltenyi Biotec, Cat. No. 130-095-929), following the manufacturers recommendations. Clinical annotations are provided in Table 1.


Tissue Handling and Tumor Disaggregation


Resected tumors were transported in DMEM (ThermoFisher Scientific, Waltham, Mass.) on ice immediately after surgical procurement. Tumors were rinsed with PBS (Life Technologies, Carlsbad, Calif.). A small fragment was stored in RNA-Protect (Qiagen, Hilden, Germany) for bulk RNA and DNA isolation. Using scalpels, the remainder of the tumor was minced into tiny cubes <1 mm3 and transferred into a 50 ml conical tube (BD Falcon, Franklin Lakes, N.J.) containing 10 ml pre-warmed M199-media (ThermoFisher Scientific), 2 mg/ml collagenase P (Roche, Basel, Switzerland) and 10 U/μl DNase I (Roche). Tumor pieces were digested in this media for 10 minutes at 37° C., then vortexed for 10 seconds and pipetted up and down for 1 minute using pipettes of descending sizes (25 ml, 10 ml and 5 ml). As needed, this was repeated twice more until a single-cell suspension was obtained. This suspension was then filtered using a 70 μm nylon mesh (ThermoFisher Scientific) and residual cell clumps were discarded. The suspension was supplemented with 30 ml PBS (Life Technologies) with 2% fetal calf serum (FCS) (Gemini Bioproducts, West Sacramento, Calif.) and immediately placed on ice. After centrifuging at 580 g at 4° C. for 6 minutes, the supernatant was discarded and the cell pellet was re-suspended in PBS with 1% FCS and placed on ice prior to staining for FACS.


Fluorescence-Activated Cell Sorting (FACS)


Tumor cells were kept in Phosphate Buffered Saline with 1% bovine serum albumin (PBS/BSA) while staining. Cells were stained using calcein AM (Life Technologies) and TO-PRO-3 iodide (Life Technologies) to identify viable cells. For all tumors, Applicants used CD45-VioBlue (human antibody, clone REA747, Miltenyi Biotec) to identify immune cells and in few cases, Applicants also used CD3-PE to specifically identify lymphocytes (human antibody, clone BW264/56, Miltenyi Biotec). For all the samples, Applicants used unstained cells as control. Standard, strict forward scatter height versus area criteria were used to discriminate doublets and gate only single cells. Viable single cells were identified as calcein AM positive and TO-PRO-3 negative. Sorting was performed with the FACS Aria Fusion Special Order System (Becton Dickinson) using 488 nm (calcein AM, 530/30 filter), 640 nm (TO-PRO-3, 670/14 filter), 405 nm (CD45-VioBlue, 450/50 filter) and 561 nm (PE, 586/15 filter) lasers. Applicants sorted individual, viable, immune and non-immune single cells into 96-well plates containing TCL buffer (Qiagen) with 1% beta-mercaptoethanol. Plates were snap frozen on dry ice right after sorting and stored at −80° C. prior to whole transcriptome amplification, library preparation and sequencing.


Library Construction and Sequencing


For plate-based scRNA-seq, Whole transcriptome amplification was performed using the Smart-seq2 protocol (Picelli et al Nat Protoc 9:171-181 (2014)), with some modifications as previously described (Tirosh et al. Nature 539, 309-313 (2016); Venteicher et al. Science. 355 (2017), doi:10.1126/science.aai8478; Fisher et al. Genome Biol. 12, R1 (2011)). The Nextera XT Library Prep kit (Illumina) with custom barcode adapters (sequences available upon request) was used for library preparation. Libraries from 384 to 768 cells with unique barcodes were combined and sequenced using a NextSeq 500 sequencer (Illumina).


In addition to SMART-seq2, cells from three samples (SS12pT, SS13 and SS14) were also sequenced using droplet-based scRNA-Seq with the 10× genomics platform. The samples were partitioned for SMART-seq2 and 10× genomics after dissociation. For each tumor, approximately two thirds of the sample was used for SMART-seq2 and one third for droplet based scRNA-seq (10× genomics). Applicants sorted viable cells using MACS (Dead Cell Removal Kit, Miltenyi Biotec) and ran up to 2 channels per sample with a targeted number of cell recovery of 2,000 cells per channel. The samples were processed using the 10× Genomics Chromium 3′ Gene Expression Solution (version 2) based on manufacturer instructions and sequenced using a NextSeq 500 sequencer (Illumina).


Whole Exome Sequencing (WES)


DNA and RNA were extracted from fresh frozen tissue or Formalin-Fixed Paraffin-Embedded (FFPE) blocks for each patient (obtained according to their respective Institutional Review Board—approved protocols) using the AllPrep DNA/RNA extraction kit (Qiagen). Applicants used tumor tissue and matched normal muscle tissue from the same patient as reference. Library construction was performed as previously described (Fisher et al. Genome Biol. 12, R1 (2011)), with the following modifications: initial genomic DNA input into shearing was reduced from 3 μg to 20-250 ng in 50 μL of solution. For adapter ligation, Illumina paired end adapters were replaced with palindromic forked adapters, purchased from Integrated DNA Technologies, with unique dual-indexed molecular barcode sequences to facilitate downstream pooling. Kapa HyperPrep reagents in 96-reaction kit format were used for end repair/A-tailing, adapter ligation, and library enrichment PCR. In addition, during the post-enrichment SPRI cleanup, elution volume was reduced to 30 μL to maximize library concentration, and a vortexing step was added to maximize the amount of template eluted. After library construction, libraries were pooled into groups of up to 96 samples. Hybridization and capture were performed using the relevant components of Illumina's Nextera Exome Kit and following the manufacturer's suggested protocol, with the following exceptions: first, all libraries within a library construction plate were pooled prior to hybridization. Second, the Midi plate from Illumina's Nextera Exome Kit was replaced with a skirted PCR plate to facilitate automation. All hybridization and capture steps were automated on the Agilent Bravo liquid handling system. After post-capture enrichment, library pools were quantified using qPCR (automated assay on the Agilent Bravo), using a kit purchased from KAPA Biosystems with probes specific to the ends of the adapters. Based on qPCR quantification, libraries were normalized to 2 nM. Cluster amplification of DNA libraries was performed according to the manufacturer's protocol (Illumina) using exclusion amplification chemistry and flowcells. Flowcells were sequenced utilizing Sequencing-by-Synthesis chemistry. The flowcells are then analyzed using RTA v.2.7.3 or later. Each pool of whole exome libraries was sequenced on paired 76 cycle runs with two 8 cycle index reads across the number of lanes needed to meet coverage for all libraries in the pool.


RNA In Situ Hybridization

Paraffin-embedded tissue sections from human tumors from Massachusetts General Hospital and and University Hospital of Lausanne were obtained according to their respective Institutional Review Board-approved protocols. Sections were mounted on glass slides and stored at −80° C. Slides were stained using the RNAscope 2.5 HD Duplex Detection Kit (Advanced Cell Technologies, Cat. No. 322430), as previously described (2, 3, 6): slides were baked for 1 hour at 60° C., deparaffinized and dehydrated with xylene and ethanol. The tissue was pretreated with RNAscope Hydrogen Peroxide (Cat. No. 322335) for 10 minutes at room temperature and RNAscope Target Retrieval Reagent (Cat. No. 322000) for 15 minutes at 98° C. RNAscope Protease Plus (Cat. No. 322331) was then applied to the tissue for 30 minutes at 40° C. Hybridization probes were prepared by diluting the C2 probe (red) 1:50 into the C1 probe (green). Advanced Cell Technologies RNAscope Target Probes used included Hs-EGR1 (Cat. No. 457671-C2) and Hs-IGF2 (Cat. No. 594361). Probes were added to the tissue and hybridized for 2 hours at 40° C. A series of 10 amplification steps was performed using instructions and reagents provided in the RNAscope 2.5 HD Duplex Detection Kit. Tissue was counterstained with Gill's hematoxylin for 25 seconds at room temperature followed by mounting with VectaMount mounting media (Vector Laboratories).


In Situ Immunofluorescence Imaging

Formalin-fixed, paraffin-embedded (FFPE) tissue slides, 5 μm in thickness, were generated at the at the Massachusetts General Hospital from tissue blocks collected from patients under IRB-approved protocols (DF/HCC 13-416). Multiplexed, tissue cyclic immunofluorescence (t-CyCIF) was performed as described recently (5). For direct immunofluorescence, Applicants used the following antibodies (manufacturer, clone, dilution): c-Jun-Alexa-488 (Abcam, Clone E254, 1:200), CD45-PE (R&D, Clone 2D1, 1:150), p21-Alexa-647 (CST, Clone 12D1, 1:200), Hes1-Alexa-488 (Abcam, Clone EPR4226, 1:500), FoxP3-Alexa-570 (eBioscience, Clone 236A/E7, 1:150), NF-κB (Abcam, Clone E379, 1:200), E-Cadherin-Alexa-488 (CST, Clone 24E10, 1:400), pRB-Alexa-555 (CST, Clone D20B12, 1:300), COXIV-Alexa-647 (CST, Clone 3E11, 1:300), β-catenin-Alexa-488 (CST, Clone L54E2, 1:400), HSP90-PE (Abcam, polyclonal, lot #GR3201402-2, 1:500) and vimentin-Alexa-647 (CST, Clone D21H3, 1:200). Stained slides from each round oft-CyCIF were imaged with a CyteFinder slide scanning fluorescence microscope (RareCyte Inc. Seattle Wash.) using either a 10× (NA=0.3) or 40× long-working distance objective (NA=0.6). Imager5 software (RareCyte Inc.) was used to sequentially scan the region of interest in 4 fluorescence channels. Image processing, background subtraction, image registration, single-cell segmentation and quantification were performed as previously described (Lin et al. eLife. 7 (2018), doi:10.7554/eLife.31657).


RNA Profiling In Situ Hybridization (ISH)

DNA oligo probes were designed to bind mRNA targets. From 5′ to 3′, they each comprised of a 35-50 nt target complementary sequence, a UV photocleavable linker, and a 66 nt indexing oligo sequence containing a unique molecular identifier (UMI), RNA ID sequence, and primer binding sites. Up to 10 RNA detection probes were designed per target mRNA. RNA detection probes were provided by Nanostring Technologies. To perform the ISH, 5 um FFPE tissue sections from two patients were mounted on positively charged histology slides. Sections were baked at 65 C for 45 minutes in a HybEZ II hybridization oven (Advanced Cell Diagnostics, INC.), Slides were deparaffinized using Citrsolv (Decon Labs, Inc., 1601) rehydrated in an ethanol gradient, and washed in 1× phosphate-buffered saline pH 7.4 (PBS: Invitrogen, AM9625). Slides were incubated for 15 minutes in 1× Tris-EDTA pH 9.0 buffer (Sigma Aldrich, SRE0063) at 100 C with low pressure in a TintoRetriever Pressure cooker (bioSB 7008). Slides were washed then incubated in 1 ug/mL proteinase K (Thermo Fisher Scientific, Inc., AM2546) in PBS for 15 minutes at 37° C. and washed again in PBS. Tissues were then fixed in 10% neutral-buffered formalin (Thermo Fisher Scientific, 15740) for 5 minutes, incubated in NBF stop buffer (0.1M Tris Base, 0.1M Glycine, Sigma) for 5 minutes twice, then washed for 5 minutes in PBS. Tissues were then incubated overnight at 37° C. with GeoMx™ RNA detection probes in Buffer R (Nanostring Technologies) using a Hyb EZ II hybridization oven (Advanced cell Diagnostics, Inc). During incubation, slides were covered with HybriSlip Hybridization Covers (Grace BioLabs, 714022). Following incubation, HybriSlip covers were gently removed and 25-minute stringent washes were performed twice in 50% formamide and 2×SSC at 37° C. Tissues were washed for 5 minutes in 2×SSC then blocked in Buffer W (Nanostring Technologies) for 30 minutes at room temperature in a humidity chamber. 500 nM Syto13 and antibodies targeting PanCK and CD45 (Nanostring technologies) in Buffer W were applied to each section for 1 hour at room temperature. Slides were washed twice in fresh 2×SSC then loaded on the GeoMx™ Digital Spatial Profiler (DSP) (7). In brief, entire slides were imaged at 20× magnification and 12 circular regions of interest (ROI) with 200-300 μm diameter were selected per sample. The DSP then exposed ROIs to 385 nm light (UV) releasing the indexing oligos and collecting them with a microcapillary. Indexing oligos were then deposited in a 96-well plate for subsequent processing. The indexing oligos were dried down overnight and resuspended in 10 μL of DEPC-treated water.


Sequencing libraries were generated by PCR from the photo-released indexing oligos and ROI-specific Illumina adapter sequences and unique i5 and i7 sample indices were added. Each PCR reaction used 4 μL of indexing oligos, 1 μL of indexing PCR primers, 2 μL of Nanostring 5×PCR Master Mix, and 3 μL PCR-grade water. Thermocycling conditions were 37° C. for 30 min, 50° C. for 10 min, 95° C. for 3 min; 18 cycles of 95° C. for 15 sec, 65° C. for 1 min, 68° C. for 30 sec; and 68° C. 5 min. PCR reactions were pooled and purified twice using AMPure XP beads (Beckman Coulter, A63881) according to manufacturer's protocol. Pooled libraries were sequenced at 2×75 base pairs and with the single-index workflow on an Illumina NextSeq to generate 458M raw reads.


Primary Cell Cultures and Cell Lines

Human primary synovial sarcoma (SyS) spherogenic cultures (SScul1, SScul2 and SScul3) were derived from patients undergoing surgery at Massachusetts General Hospital and University Hospital of Lausanne according to their respective Institutional Review Board-approved protocols. Directly after dissociation (as above), the dissociated bulk tumor cells were put in culture and were grown as spheres using ultra-low attachment cell culture flasks in IMDM 80% (Gibco, Cat. No. 1244053), KnockOut Serum Replacement 20% (Gibco, Cat. No. 10828028), Recombinant Human EGF Protein 10 ng/mL (R&D systems, Cat. No. 236-EG-200), Recombinant Human FGF basic, 145 aa (TC Grade) Protein long/mL (R&D systems, Cat. No. 4114-TC-01M) and Penicillin-Streptomycin (Gibco, Cat. No. 15140122). Cells were expanded by mechanical and enzymatic dissociation every week using TrypLE Express Enzyme (ThermoFisher, Cat. No. 12605010).


The SyS cell lines used in the SS18-SSX KD experiments, and the functional drug assays include: Aska, a generous gift from Kazuyuki Itoh, Norifumi Naka, and Satoshi Takenaka (Osaka University, Japan), and SYO1, a generous gift from Akira Kawai (National Cancer Center Hospital, Japan), and HS-SY-II (purchased from RIKEN Bio Resource Center, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan). All three cell lines were cultured using standard protocols in DMEM medium (Gibco) supplemented with 10-20% fetal bovine serum, 1% Glutamax (Gibco), 1% Sodium Pyruvate (Gibco) and 1% Penicillin-Streptomycin (Gibco) and grown in a humidified incubator at 37° C. with 5% CO2.


Human primary pediatric Mesenchymal Stem Cells (MSCs) were isolated from healthy donors undergoing corrective surgery in agreement with the Institutional Review Board-approved protocol of the University Hospital of Lausanne (Protocol number 2017-0100). Samples were deidentified prior to culture and analysis. Cells were expanded in 90% IMDM (Gibco, Cat. No. 1244053) containing 10% Fetal Bovine Serum (Gibco), 1% Penicillin-Streptomycin (Gibco) and long/mL Platelet-Derived Growth Factor BB (PDGF-BB, PeproTech) as previously described.


SS18-SSX Knockdown in Aska and SYO1 Cell Lines

The SyS cell lines Aska and SYO1 were cultured using standard protocols in DMEM medium (Gibco) supplemented with 10-20% fetal bovine serum, 1% Glutamax (Gibco), 1% Sodium Pyruvate (Gibco) and 1% Penicillin-Streptomycin (Gibco) and grown in a humidified incubator at 37° C. with 5% CO2. Cells expressing a pLKO.1 vector with a scrambled shRNA hairpin control (5′-CCTAAGGTTAAGTCGCCCTCGCTCGAGCGAGGGCGACTTAAC CTTAGG-3′) (SEQ ID NO: 5) or a shSSX hairpin targeting SSX of the SS18-SSX fusion (5′-CAGTCACTGACAGTTAATAAA-3′) (SEQ ID NO: 6) were prepared by lentiviral infection. In brief, lentivirus was prepared by transfection of HEK293T cells with gene delivery vector and the packaging vectors pspax2 and pMD2.G, filtration of media followed by ultracentrifugation, and then resuspension of viral pellet in PBS. Aska and SYO1 cells were infected with lentivirus for 48 hours and then underwent 5 days of selection with puromycin (2 μg/mL) prior to collection for single cell RNA-seq analysis.


In Vitro IFN/TNF Experiment

Cells were dissociated 12 hours before adding the drugs at the concentrations indicated directly to the growing media and cells were collected at different time point (ranging from 4 hours to 4 days) for SMART-seq2. Viability was determined by CellTiter-Glo Luminescent Cell Viability Assay (Promega) after 5 to 7 days of treatment. TNF-alpha (Miltenyi Biotec, Human TNF-α, Cat. No. 130-094-014) IFN-gamma (R&D systems, Recombinant Human IFN-gamma Protein, Cat. No. 285-IF-100) were suspended in deionized sterile-filtered water.


In Vitro Drug Assay and Cell Proliferation Measurements

For the functional drug assay, 200,000 SYO-1 cells and HSSYII cells, and 100,000 MSCs were seeded in 60×15 mm plates (Falcon). Cells were stimulated for five days with the following compounds: 100 or 200 nM Abemaciclib (Selleckchem, U.S.A.), 15 or 30 ng/ml TNF (Miltenyi Biotech, Germany) or a combination of the two. Compounds were refreshed at days three and four, and the solvent (DMSO) was used as control. At day 4, 12.5 or 25 nM Panobinostat (Selleckchem, U.S.A.) was added to the cultures, and the cells were harvested 24 hours later for proliferation scoring. To assessment cellular proliferation, cells were detached with trypsin, washed in PBS, and re-suspended in 1 ml of complete medium. After diluting 1:2 with Trypan blue (Invitrogen) viable cells were counted using the Automated Cell Counter Countess II FL (Thermo Fisher Scientific). Each experimental condition was measured in triplicate.


Computational Analysis Methods

scRNA-Seq Pre-Processing and Gene Expression Quantification


BAM files were converted to merged, demultiplexed FASTQ files. The paired-end reads obtained with SMART-Seq2 were mapped to the UCSC hg19 human transcriptome using Bowtie (9), and transcript-per-million (TPM) values were calculated with RSEM v1.2.8 in paired-end mode (10). The paired-end reads obtained with droplet scRNA-Seq (10× Genomics) were mapped to the UCSC hg19 human transcriptome using STAR (11), and gene counts/TPM values were obtained using CellRanger (cellranger-2.1.0, 10× Genomics).


For bulk RNA-Seq data, expression levels were quantified as E=log 2(TPM+1). For scRNA-seq data, expression levels were quantified as E=log 2(TPMi,j/10+1). TPM values were divided by 10 because the complexity of the single-cell libraries is estimated to be within the order of 100,000 transcripts. The 10-1 factoring prevents counting each transcript ˜10 times and overestimating the differences between positive and zero TPM values. The average expression of a gene i across a population of N cells, denoted here as P, was defined as







E

i
,
p


=


log
2



(

1
+



Σ

j

P



T

P


M

i
,
j



N


)






For each cell, Applicants quantified the number of genes with at least one mapped read, and the average expression level of a curated list of housekeeping genes (Tirosh et al. Science. 352, 189-196 (2016)). Applicants excluded all cells with either fewer than 1,700 detected genes or an average housekeeping expression (E, as defined above) below 3 (Table 2B). For the remaining cells, Applicants calculated the average expression of each gene (Ep), and excluded genes with an average expression below 4, which defined a different set of genes in different analyses depending on the subset of cells included. In cases where Applicants analyzed different cell subsets together, genes were removed only if they had an average Ep below 4 in each of the different cell subsets included in the analysis. Different cell types and malignant cells from different tumors were considered as different cell subsets in this regard.


WES Data Pre-Processing

BAM file was produced with the Picard pipeline (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 (broadinstitute.org/cancer/cga/Firehose). Quality control modules within Firehose were applied to all sequencing data for comparison of the origin for tumor and normal genotypes and to assess fingerprinting concordance. Cross-contamination of samples was estimated using ContEst (13).


Somatic Alteration Assessment

MuTect (14) was applied to identify somatic single-nucleotide variants. Indelocator (broadinstitute.org/cancer/cga/indelocator), Strelka (15), and MuTect2 (broadinstitute.org/gatk/documentation/tooldocs/current/org_broadinstitute_gatk_tools_walkers_cancer_m2_MuTect2) were applied to identify small insertions or deletions. A voting scheme was used with inferred indels requiring a call by at least 2 out of 3 algorithms.


Artifacts introduced by DNA oxidation during sequencing were computationally removed using a filter-based method (16). In the analysis of primary tumors that are formalin-fixed, paraffin-embedded samples (FFPE) Applicants further applied a filter to remove FFPE-related artifacts (17). Reads around mutated sites were realigned with Novoalign (www.novocraft.com/products/novoalign/) to filter out false positive that are due to regions of low reliability in the reads alignment. At the last step, Applicants filtered mutations that are present in a comprehensive WES panel of 8,334 normal samples (using the Agilent technology for WES capture) aiming to filter either germline sites or recurrent artifactual sites. Applicants further used a smaller WES panel of 355 normal samples that are based on Illumina technology for WES capture, and another panel of 140 normal samples sequenced without Applicants' cohort (18) to further capture possible batch-specific artifacts. Annotation of identified variants was done using Oncotator (19) (broadinstitute.org/cancer/cga/oncotator).


Copy Number and Copy Ratio Analysis

To infer somatic copy number from WES, Applicants used ReCapSeg (on gatk forums available at broadinstitute.org/categories/recapseg-documentation), calculating proportional coverage for each target region (i.e., reads in the target/total reads) followed by segment normalization using the median coverage in a panel of normal samples. The resulting copy ratios were segmented using the circular binary segmentation algorithm (20). To infer allele-specific copy ratios, Applicants mapped all germline heterozygous sites in the germline normal sample using GATK Haplotype Caller (21) and then evaluated the read counts at the germline heterozygous sites in order to assess the copy profile of each homologous chromosome. The allele-specific copy profiles were segmented to produce allele specific copy ratios.


Gene Sets Overall Expression

Applicants used the following scheme to compute the overall expression (OE) of a gene set, namely, a signature. The OE metric filters technical variation and highlights biologically meaningful patterns. The procedure is based on the notion that the measured expression of a specific gene is correlated with its true expression (signal), but also contains a technical (noise) component. The latter may be due to various stochastic processes in the capture and amplification of the gene's transcripts, sample quality, as well as variation in sequencing depth. OE of a gene signature is computed in a way that accounts for the variation in the signal-to-noise ratio across genes and cells.


Given a gene signature and a gene expression matrix E (as defined above), Applicants first binned the genes into 50 expression bins according to their average expression across the cells or samples. The average expression of a gene across a set of cells within a sample is Ei,p (see: scRNA-seq pre-processing and gene expression quantification) and the average expression of a gene across a set of N tumor samples was defined as:








𝔼
j



[

E
ij

]


=


Σ
j





E
ij

N

.






Given a gene signature S that consists of K genes, with kb genes in bin b, Applicants sample random S-compatible signatures for normalization. A random signature is S-compatible with signature S if it consists of overall K genes, such that in each bin b it has exactly kb genes. The OE of signature Sin cell or sample j is then defined as:







O






E
j


=



Σ

i

S




C
ij




𝔼

S
¯




[


Σ

i


S
¯





C
ij


]







where {tilde over (S)} is a random S-compatible signature, and Cij is the centered expression of gene i in cell or sample j, defined as Cij=Eij−E[Eij]. Because the computation is based on the centered gene expression matrix C, genes that generally have a higher expression compared to other genes will not skew or dominate the signal. Applicants found that 100 random S-compatible signatures are sufficient to yield a robust estimate of the expected value custom-character{tilde over (S)}i∈{tilde over (S)}Cij]. The distribution of the OE values was normal or a mixture of normal distributions, facilitating subsequent analyses.


The term transcriptional program (e.g., the core oncogenic program) is used to denote cell states defined by a pair of signatures, such that one (S-up) is overexpressed and the other (S-down) is underexpressed. The OE of a program is then the OE of S-up minus the OE of S-down.


In cases where the OE of a given signature has a bimodal distribution across the cell population, it can be used to naturally separate the cells into two subsets. To this end, Applicants applied the Expectation Maximization (EM) algorithm for mixtures of normal distributions to define the two underlying normal distributions. Applicants then assigned cells to the two subsets, depending on the distribution (high or low) that they were assigned to. Applicants use the term a transcriptional program (e.g., the core oncogenic program) to characterize cell states which are defined by a pair of signatures, such that one (S-up) is overexpressed and the other (S-down) is underexpressed. Applicants define the OE of the program as the OE of S-up minus the OE of S-down.


Cell Type Assignments

Cell type assignments were performed on the basis of genetic and transcriptional features, according to the four analyses described below.


(1) Fusion detection. Fusion detection was performed with STAR-Fusion (Haas et al. bioRxiv (2017), doi:10.1101/120295), to detect any transcript that indicates the fusion of two genes.


(2) Copy Number Alterations (CNA) inference. To infer CNAs from the scRNA-seq data Applicants used the approach described in (Tirosh et al. Science. 352, 189-196 (2016)) as implemented in the R code provided in github.com/broadinstitute/inferCNA with the default parameters. To identify malignant cells based on CNA patterns, Applicants defined the overall CAN level of a given cell as the sum of the absolute CNA estimates across all genomic windows. Within each tumor, Applicants identified CD45− cells with the highest overall CNA level (top 10%), and considered their average CNA profile as the CAN profile of the pertaining tumor. For each cell Applicants then computed a CNA-R-score, that is, the Spearman correlation coefficient obtained when comparing its CNA profile to the CNA profile of its tumor. Cells with a high CNAV-R-score (greater than the 25% of the CD45− cell population) were considered as malignant according to the CNA criterion. As certain tumors/malignant cells have a stable genome, Applicants did not use the CNA criterion to identify non-malignant cells. Large-scale CNAs were visualized (FIG. 13F) using a Bayesian approach, as described in github.com/broadinstitute/infercnv/wiki/infercnv-i6-HMM-type.


(3) Differential similarity to bulk tumors. Applicants compared the scRNA-Seq profiles to those of bulk sarcoma tumors (Abeshouse et al. Cell. 171, 950-965.e28 (2017)). RNA-Seq of bulk sarcoma tumors was downloaded from TCGA (xena.ucsc.edu). For each cell in Applicants' scRNA-Seq cohort Applicants: (1) computed the spearman correlation between its expression profile and the expression profiles of the bulk sarcoma tumors, and (2) examined if the rs coefficients obtained when comparing the cell to SyS tumors were higher compared to those obtained when comparing the cell to non-SyS sarcoma tumors, using a one-sided Wilcoxon ranksum test. Cells with a ranksum p-value <0.05 were considered as potentially malignant, and as potentially non-malignant otherwise.


(4) Transcription-based clustering. Applicants clustered the cells by applying a shared nearest neighbor (SNN) modularity optimization algorithm (Waltman et al. Eur Phys J B. 86 (2013), doi:10.1140/epjb/e2013-40829-0), as implemented in the Seurat R package. First Principle Component Analysis (PCA) was preformed and k-nearest neighbors were calculated to construct the SNN graph. The latter was used to identify clusters that optimize the modularity function. Next, Applicants assigned clusters to cell types. Clusters where the majority of cells had the SS18-SSX1/2 fusion were considered malignant clusters. Non-malignant clusters were assigned to cell types by computing the overall expression of well-established cell type markers across the non-malignant cells (Tables 4 and 5). The OE of each of these cell type signature had a bimodal distribution across the cell population. Applying Expectation Maximization (EM) algorithm for mixtures of normal distributions, Applicants defined the two underlying normal distributions, and assigned cells to cell types. Each non-malignant cluster was enriched with cells of a particular cell type, and was assigned to the pertaining cell type.


Applicants used these four converging criteria to assign the cells to nine cell subss: malignant cells, epithelial cells, CAFs, CD8 and CD4 T cells, B cells, NK cells, macrophages, and mastocytes. More specifically, a cell was classified as malignant if it was CD45- and classified as malignant according to analyses (3) and (4) above. A cell was classified as non-malignant if it was classified as non-malignant according to analyses (1), (3)-(4) above. Non-malignant cells were then further assigned to cell types based on their cluster assignment. Cells with inconsistent assignments were removed from further analyses. Lastly, within malignant cells Applicants identified epithelial cells by clustering each of the biphasic tumors into two clusters.


Cell type assignments were preformed separately for the Smart-Seq2 cohort and the 10× Genomics (Zheng et al. Nat. Commun. 8, 14049 (2017)) cohort, such that fusion detection was used only in the former, where full length transcripts were sequenced.


Malignant Epithelial and Mesenchymal Differentiation Programs

First, Applicants performed intra-tumor analyses to identify differentially expressed genes when comparing the epithelial malignant cells to the mesenchymal malignant cells. Applicants performed this analysis for each of the three biphasic tumor samples (S1, and S12 pre- and post-treatment). The fourth biphasic tumor (S16) was not included in this analysis as its sample did not include epithelial malignant cells. Genes that were overexpressed in the epithelial cells compared to the mesenchymal cells in all three samples were defined as epithelial genes, and likewise for mesenchymal genes. When using these signatures in the analysis of bulk gene expression profiles Applicants removed genes that were included in the non-malignant cell type signatures.


Using these signatures Applicants defined: (1) the epithelial vs. mesenchymal differentiation score as the OE of the epithelial signature minus the OE of the mesenchymal signature, and (2) the differentiation score as the OE of the epithelial signature plus the OE of the mesenchymal signature.


Cell Type Signatures

Cell type signatures were generated based on pairwise comparisons between identified cell subtypes: malignant cells, epithelial cells, CAFs, CD8 and CD4 T cells, B cells, NK cells, macrophages, and mastocytes. For each pair of cell subtypes Applicants identified differentially expressed genes using the likelihood-ratio test (26), as implemented in the Seurat package (satijalab.org/seurat). Genes were considered as cell type specific if they were overexpressed in a particular cell subtype compared to all other cell subtypes (log-fold change >0.25 and p-value <0.05, following Bonferroni correction). Applicants defined a general T cell signature for both CD4 and CD8 cells by identifying genes that were overexpressed in both CD4 and CD8 compared to all other (non T) cells. A more permissive version of this generic T cell signature includes genes which were overexpressed in CD4 or CD8 T cells compared to all other (non T) cells.


Inferring Tumor Composition

Tumor composition was assessed based on the Overall Expression of the different cell type specific signatures Applicants identified from the scRNA-seq data (Table 5). For example, the CD8 T cell signature was used to infer the level of CD8 T cells in the tumor, and likewise for other cell types. To estimate tumor purity Applicants used the malignant SyS signature identified here (Table 5), which consists of genes that are exclusively expressed by malignant SyS cells compared to non-malignant cells in SyS tumors.


To evaluate the performance of this approach, Applicants simulated 200 bulk RNA-Seq profiles. For each simulated bulk RNA-Seq profile we: (1) randomly chose one of the tumors in the cohort; (2) sampled 100 cells from different cell types profiled in this tumor—these cells include a mix of immune, stroma and malignant cells, at a randomly chosen composition; (3) summed the scRNA-Seq profiles of this randomly chosen population (P) of 100 cells, such that the bulk expression of


gene i across this population was defined as







E

i
,
P


=


log
2



(

1
+



Σ

j

P



T

P


M

i
,
j



100


)






Applicants also used cell type signatures Applicants previously derived from melanoma scRNA-Seq data (22) to predict the tumor composition of the simulated SyS bulk RNA-Seq profiles, and vice versa. Applicants then compared the predictions to the known cell type composition. The predicted composition was highly correlated with the known composition (r>0.9, P<1*10−30, Spearman correlation) for all cell types.


Multilevel Mixed-Effects Models

To examine the association between two cell features, denoted here as x and y, across different patients or experiments Applicants used multilevel mixed-effects regression models (random intercepts models). The models include patient/experiment-specific intercepts to control for the dependency between the scRNA-seq profiles of cells that were obtained from the same patient/experiment. The models also control for data quality by providing the number of reads (log-transformed) that were detected in each cell as a covariate. To compute the association between features x and y Applicants provided x as another covariate and used y as the dependent variable. The models were implemented using the lme4 and lmerTest R packages (CRAN.R-project.org/package=lme4, CRAN.R-project.org/package=lmerTest).


For example, to test if malignant cycling cells were more frequent in treatment naïve samples, Applicants used a logistic mixed-effects model as described above. The dependent variable y was the cycling status of the malignant cells. The independent covariate x was a binary variable denoting if the sample was obtained before or after treatment. Only malignant cells were included in this model.


T Cell Receptor (TCR) Reconstruction and T Cell Expansion Program

TCR reconstruction was performed using TraCeR (27), with the Python package in github.com/Teichlab/tracer. To characterize the transcriptional state of clonally expanded T cells, Applicants first identified the clonality level of the T cells in Applicants' cohort. T cell that were obtained from tumors with a larger number of T cells with reconstructed TCRs were more likely to be


defined as expanded. To control for this confounder Applicants performed the following down-sampling procedure. First, Applicants removed T cells without a reconstructed alpha or beta TCR chain, and samples with less than 20 T cells with a reconstructed TCR. Next, Applicants computed the probability that a given cell will be a part of a clone when subsampling 20 T cells from each tumor. T cells with a high probability to be a part of a clone (above the median) were considered expanded, and non-expanded otherwise. To identify the genes differentially expressed in expanded CD8 T cells Applicants used mixed-effects models with a binary covariate, denoting if the cell was classified as expanded or not.


CD8 T Cell Analyses

The analysis of T cell exhaustion vs. T cell cytotoxicity was performed as previously described (12), with the exhaustion signature provided in (12). First, Applicants computed the cytotoxicity and exhaustion scores of each CD8 T cell. Next, to control for the association between the expression of exhaustion and cytotoxicity markers, Applicants estimated the relationship between the cytotoxicity and exhaustion scores using locally-weighted polynomial regression (LOWESS, black line in FIG. 2B). Based on these values Applicants classified the CD8 T cells into four groups: Cells with a low cytotoxicity score (below the 25th percentile) were classified as naïve or memory-like cells, while the others were considered effector or exhausted if their cytotoxicity scores were significantly higher or lower than expected given their exhaustion scores, respectively. According to this classification, Applicants examined if the clonal expansion program was higher in the effector-like cells. In addition, Applicants compared the SyS CD8 T cells to CD8 T cells from human melanoma tumors (22) using mixed-effects models with a sample-level covariate denoting if the sample was obtained from a SyS or melanoma tumor.


Malignant Epithelial and Mesenchymal Differentiation Programs

The epithelial and mesenchymal signatures were obtained through intra-tumor differential expression analysis, using the likelihood-ratio test for single cell gene expression (26), as implemented in the Seurat package (satijalab.org/seurat). Applicants compared the mesenchymal to epithelial cells in each of the three biphasic tumor samples (SyS1, SyS12 and SyS12pt). The tumor SyS16 was not included in this analysis (although it was annotated as partially biphasic according to its histology), because its scRNA-Seq sample did not include any epithelial malignant cells. Genes that were up-regulated in the epithelial cells compared to the mesenchymal cells in all three samples were defined as epithelial genes, and likewise for mesenchymal genes. When using the epithelial and mesenchymal signatures in the analysis of bulk gene expression Applicants removed from these signatures those genes that are also part of non-malignant cell type signatures.


Using these signatures Applicants defined: (1) the epithelial vs. mesenchymal differentiation score as the OE of the epithelial signature minus the OE of the mesenchymal signature, and (2) the differentiation score as the OE of the epithelial signature plus the OE of the mesenchymal signature. An alternative way to define the differentiation score of a particular cell is first to assign it to the epithelial or mesenchymal subset, and then use only the pertaining signature to estimate its differentiation level. However, this approach will not distinguish between poorly-differentiated mesenchymal cells, and mesenchymal cells which have begun to transition to an epithelial state. Hence, Applicants used the inclusive definition of differentiation.


Based on the genes in the epithelial and mesenchymal signatures Applicants then generated diffusion maps (28) for each one of the tumors in the cohort, using the density R package (bioconductor.org/packages/release/bioc/html/destiny) with the default parameters.


Identifying Co-Regulated Gene Modules

To identify co-regulated gene modules that capture intra-tumor heterogeneity Applicants analyzed each tumor separately. To identify patterns that explain the cell-cell variation both in epithelial and in mesenchymal malignant cells, Applicants further divided the biphasic samples (SyS1, SyS12, and SyS12pt) to their epithelial and mesenchymal compartments. Applicants used PAGODA (29) as implemented in github.com/hms-dbmi/scde to filter technical variation and identify co-regulated gene modules in each sample. To identify genes that were repeatedly co-regulated Applicants then constructed a gene-gene co-regulation graph. In this graph, an edge between two genes denotes that the two genes appeared together in the same gene module in at least five samples. Next, Applicants identified dense clusters in the graph using the Newman-Girvan (30) community clustering as previously implemented (31). Applicants filtered out small gene clusters (<20 genes). Lastly, for each gene cluster Applicants identified the opposing gene module by identifying genes that were negatively correlated with its Overall Expression (OE) across the malignant cells. Correlation was computed using partial Spearman correlation, when controlling for the number of genes and (log-transformed) reads detected per cells, and correcting for multiple hypotheses testing using the Benjamini-Hochberg procedure (32).


For comparison Applicants applied another complementary approach, LIGER (33), which identifies repeating gene modules in the malignant cells using integrative non-negative matrix factorization (NMF) (34). Integrative NMF learns a low-dimensional space, where cells are defined by one set of dataset-specific factors (denoted as Vi), and another set of shared factors (denoted as W). Each factor, or metagene, represents a distinct pattern of gene co-regulation. To find these metagenesit


solves the following optimization problem





argminHi,Vi,W≥0Σi∥Ei−Hi(W+Vi)∥F2+λΣi∥HiViF2


Where Ei denotes the expression matrix (log-transformed TPM) of the malignant cells in sample i, Vi denotes sample-specific metagenes and W denotes the shared metagenes across all samples. For this analysis, each biphasic tumor was again split to two “samples”, of epithelial and mesenchymal cells. Applicants used the top 100 genes of each metagene in Was the iNMF signatures, and then computed the overall expression of these signatures in the malignant cells. The resulting signatures and their expression across the malignant cells matched the signatures identified with the PCA-based approach, and specifically the core-oncogenic program was re-discovered (FIG. 21A).


Quantifying RNA Velocity

Estimates of RNA velocity were computed using the Velocyto toolkit (velocyto.org/). Applicants applied Velocyto with the default parameters, using a gene-relative model. To explore the potential transitions between the epithelial and mesenchymal cell states and avoid confounders, Applicants used only the genes from these differentiation programs (Table 6) for the analysis.


Predicting Patient Prognosis

To test if a given program predicts metastasis free-survival or overall survival, Applicants first computed the OE of the program in each tumor based on the bulk gene expression data. Next, Applicants used a Cox regression model with censored data to compute the significance of the association between the expression values and survival. To visualize the predictions of a specific signature in a Kaplan Meier (KM) plot, Applicants stratified the patients into three groups according to the program expression: high or low expression correspond to the top or bottom 20% of the population, respectively, and intermediate otherwise. Applicants used a log-rank test to examine if there was a significant difference between the survival rates of the three patient groups.


Analysis of In Situ Immunofluorescence Imaging

Immune cells were detected based on the protein level of CD45 (>7.5 log-transformed). Malignant cells were identified based on histological morphology, and high protein levels of Hes1. High protein expression was detected by applying the EM algorithm for mixtures of normal distributions. The core oncogenic program score was computed only in the malignant cells based the combined expression of its repressed protein markers: Hsp90, p21, NFkB, and cJun (minus sum of centered log-transformed values). Each image—corresponding to a specific sample in the scRNA-Seq cohort—was divided to frames of 100 cells. The average expression of the core oncogenic program in the malignant cells and the fraction of immune cells in each frame was computed. Using these frame-level values Applicants examined the association between the expression of the core oncogenic program in the malignant cells and the fraction of the immune cells, using a mixed-effects model, with a sample-level intercept (see Multilevel mixed-effects models). The mixed-effect model accounts for the nested structure of the data (frames are associated with samples), and ensures the pattern repeatedly appears across different samples.


Analysis of In Situ RNA Profiling

FASTQ files from multiple lanes were merged to generate single files for processing and insure proper removal of PCR duplicates later in the pipeline. Illumina adapter sequences were trimmed using Trim Galore (version 0.4.5) with a minimum base pair overlap stringency of four bases and a base quality threshold of 20. Paired end reads were stitched using Paired-End reAd mergeR (PEAR, version 0.9.10) specifying a minimum stitched read length of 24 bp and a maximum stitched read length of 28 bp. The 14 bp UMI sequence was extracted from the stitched FASTQ files from the 5′ end of the sequence reads using umi tools (version 0.5.3). The FASTQ files with extracted UMIs were then aligned to a genome containing the 12 bp reference sequence tags using bowtie2 (version 2.3.4.1) in end-to-end mode with a seed length of four. Using a custom python


function, the generated SAM files were split into multiple SAM files based on the tag to which they aligned to limit memory usage when removing PCR duplicates. The split SAM files were converted to bam files, sorted, and index using samtools (version 1.9) with the import, sort, and index options respectively. PCR duplicates were removed from the sorted and indexed bam files using the dedup command from umi tools with an edit distance threshold of three. An edit distance threshold of three was used. Using custom python functions, the SAM files with PCR duplicates removed were merged for each sample and used to generate digital counts of the tags.


Outlier counts were removed before generating a consensus count for each target. Outlier tags were identified as those with counts 90% below the mean of the probe group in at least 20% of the ROIs analyzed and removed them from the analysis. Subsequently, Applicants removed tags from the analysis if they were flagged as outliers in at least 20% of the AOIs analyzed. This was done using the Rosner Test if there were at least 10 probes for the target (k=0.2*Number of Probes, alpha=0.01), or the Grubbs test if there were less than 10 probes for the target. Probes flagged as outliers in less than 20% of the ROIs analyzed were only removed from the analysis for the ROIs in which they were flagged. Count reported for each target transcript were calculated as the geometric mean of the remaining probes.


The counts for each target transcript were then normalized to the count of the house keeper genes (C1orf43, GPI, OAZ1, POLR2A, PSMB2, RAB7A, SDHA, SNRPD3, TBC1D10B, TPM4, TUBB,


UBB). The geometric mean of the house keeper gene counts was calculated for each ROI. These geometric means were then divided by the geometric mean of the geometric mean of the house keeper genes to generate a normalization factor for each ROI. The counts of the transcripts in each AOI were than multiplied by the associated normalization factor.


The normalized in situ RNA measures were used to compute: (1) the T cell levels as described in the Inferring tumor composition section; (2) the overall expression of the malignant programs in each of the regions of interest (ROI), as described in the Gene sets Overall Expression section;

    • and (3) the differentiation scores, as described in the Malignant epithelial and mesenchymal differentiation programs section.


Identifying SS18-SSX Targets

The fusion program consists of genes that were differentially expressed in the Aska or SYO1 cells with the SS18-SSX shRNA (shSSX) compared to those with control shRNA (shCt) after 3 or 7 days post-infection. Gene that were previously reported (35, 36) to be bound by the SS18-SSX oncoprotein in at least two SyS cell lines were defined as direct SS18-SSX targets, and were used to stratify the SS18-SSX program to direct and indirect targets.


Mapping Cancer-Immune Interactions

The association between the core oncogenic program in the malignant cells and the expression of different ligands/cytokines in the immune cells was examined using the multilevel mixed-effects regression model described above, using the scRNA-Seq data collected from SyS tumors. The dependent variable y was the OE of the core oncogenic program and the covariate x was the average expression of a certain ligand/cytokine in a specific type of immune cells (e.g., macrophages) that were profiled from the same tumor. The model also corrected for inter-patient dependencies using the patient-specific intercepts and for cell complexity (log(number of reads)). Applicants restricted the analysis to ligands/cytokines that can physically bind to proteins expressed by the malignant cells (37). The immune cells were either macrophages or CD8 T cells, as other immune cell types were not sufficiently represented in the data.


Applicants used a similar approach to further stratify the program to its TNF/IFN-dependent and independent components. Applicants repeated the same analysis described above, using each one of the genes in the core oncogenic program as the dependent variable. Genes which were associated with both TNF and IFN (P<0.05, following Bonferroni correction) were considered as TNF/IFN dependent, and genes which were not associated with both cytokines (P>0.05) were considered as TNF/IFN-independent.


TNF and IFNγ Impact on SyS Cell Cultures

SyS cell cultures were treated with TNF and IFNγ, separately and in combination (see In vitro IFN/TNF experiment section), and profiled with scRNA-Seq. Given this data, differentially expressed genes and gene sets were identified using mixed-effects regression models (Multilevel mixed-effects models section), with experiment-specific intercepts. The dependent variable was the expression of a gene or the OE of a gene set. The model included three treatment covariates: only TNF, only IFN, and a combination of TNF and IFN. Another binary covariate denoted the duration of the treatment (1 for <24 h duration and 0 otherwise). The model corrected for differences between the different SyS cultures and experiments, and identified patterns that repeatedly appeared across the different experiments. The effect-size and significance of the combination covariate denotes the effect of the combination, and not the synergy between the two cytokines.


To examine if the combined treatment with TNF and IFNγ had synergistic effects, Applicants used only the control cells and the cells treated for 4 days with one or two of the cytokines. This model also included 3 binary treatment covariates (TNF, IFN, and the combination), but this time cells that were treated with the combination were positive for all three treatment covariates. The effect-size and significance of the combination covariate hence denotes the synergistic effect of the combination.


Reconstructing Regulatory Networks

To reconstruct the gene regulatory network controlling the core oncogenic program Applicants assembled a database of transcription factor (TF) to target mapping based on four sources: JASPAR (38), HTRIdb (39), MSigDB (40, 41), and TRRUST (42), and augmented it with the direct SS18-SSX targets identified here (Table 8) and TF-target pairs Applicants identified in a cis-regulatory motif analysis of the core oncogenic program. Specifically, for the cis-regulatory analysis, Applicants used RcisTarget (43) (a R/Bioconductor implementation of icisTarget (44) and iRegulon (45)) to identify cis-regulatory elements significantly overrepresented in a window of 500 bp around the transcription start site of the core oncogenic genes (normalized enrichment score >3.0) along with their cognate TFs.


Applicants pruned the resulting network to include only core oncogenic program genes (and SS18-SSX) (i.e., all TFs and targets aside from SS18-SSX are program genes). An edge in the network between a TF and its target denotes that: (1) the TF is regulating the target according to at least one of the sources described above, and (2) there is an association between their expression levels in the scRNA-Seq data of SyS tumors. Edges are weighted 1 and −1 to reflect positive and negative associations. Applicants used pageRank (46) (with the R implementation as provided in igraph (igraph.org/r/)) as a measure of TF and target importance in the network. To compute TF importance Applicants first flipped the direction of the edges in the network, going from target to TFs. Consistent with the network weights, targets from the up- or down-regulated side of the network were considered induced or repressed, respectively. Likewise, TFs from the up- or down-regulated side of the network were considered activators and repressors, respectively.


Selectivity and Synergy in Drug Experiments

To evaluate the impact of each drug on the expression of a certain program or gene in a specific cell lines (SYO1, HSSYII, or MSCs), Applicants used a regression model with four binary treatment covariates: abemaciclib, TNF, panobinostat, and the combination of all three drugs. As in the case of TNF/IFN analysis, to examine the synergy of the combination, the cells treated with the combination were positive for all four treatment covariates. The model also included the number of reads detected in each cell (log-transformed) to control for technical variation. When examining the impact on the two SyS cell lines together, Applicants used a mixed-effects model with a cell line specific intercept, to control for cell line specific baseline states. Drug selectivity was examined by using a mixed-effects model that accounts for all three cell lines and has another covariate to denote if the treated cells were SyS or not.


Data Availability

Processed scRNA-seq data and interactive plots generated for this study are provided through the Single Cell Portal available at broadinstitute.org/single_cell/study/synovial-sarcoma. The processed scRNA-seq data is provided via the Gene Expression Omnibus (GEO), accession number GSE131309 (available at National Library of Medicine of the NCBI; nih.gov/geo/query/acc.cgi?acc=GSE131309); access currently requires a secure token avcjkioijjylryp. Raw scRNA-Seq data will be deposited in DUOS (duos is available at broadinstitute.org/#/home).


Various modifications and variations of the described methods, pharmaceutical compositions, and kits of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific embodiments, it will be understood that it is capable of further modifications and that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in the art are intended to be within the scope of the invention. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure come within known customary practice within the art to which the invention pertains and may be applied to the essential features herein before set forth.


REFERENCES



  • 1. T. O. Nielsen, N. M. Poulin, M. Ladanyi, Synovial sarcoma: recent discoveries as a roadmap to new avenues for therapy. Cancer Discov. 5, 124-134 (2015).

  • 2. C. Kadoch, G. R. Crabtree, Reversible disruption of mSWI/SNF (BAF) complexes by the SS18-SSX oncogenic fusion in synovial sarcoma. Cell. 153, 71-85 (2013).

  • 3. M. Ayyoub et al., CD4+ T Cell Responses to SSX-4 in Melanoma Patients. J. Immunol. 174, 5092 (2005).

  • 4. M. Ayyoub et al., Tumor-reactive, SSX-2-specific CD8+ T Cells Are Selectively Expanded during Immune Responses to Antigen-expressing Tumors in Melanoma Patients. Cancer Res. 63, 5601 (2003).

  • 5. H. A. Smith, D. G. McNeel, The SSX Family of Cancer-Testis Antigens as Target Proteins for Tumor Therapy. Clin. Dev. Immunol. 2010, 18 (2010).

  • 6. H. A. Smith, D. G. McNeel, Vaccines targeting the cancer-testis antigen SSX-2 elicit HLA-A2 epitope-specific cytolytic T cells. J. Immunother. Hagerstown Md. 1997. 34, 569-580 (2011).

  • 7. M. J. McBride et al., The SS18-SSX Fusion Oncoprotein Hijacks BAF Complex Targeting and Function to Drive Synovial Sarcoma. Cancer Cell (2018), doi:10.1016/j.ccell.2018.05.002.

  • 8. A. Banito et al., The SS18-SSX Oncoprotein Hijacks KDM2B-PRC1.1 to Drive Synovial Sarcoma. Cancer Cell. 33, 527-541.e8 (2018).

  • 9. L. Su et al., Deconstruction of the SS18-SSX fusion oncoprotein complex: insights into disease etiology and therapeutics. Cancer Cell. 21, 333-347 (2012).

  • 10. R. Nakayama et al., Gene expression profiling of synovial sarcoma: distinct signature of poorly differentiated type. Am. J Surg. Pathol. 34, 1599-1607 (2010).

  • 11. P. Lagarde et al., Chromosome instability accounts for reverse metastatic outcomes of pediatric and adult synovial sarcomas. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 31, 608-615 (2013).

  • 12. S. Picelli et al., Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171-181 (2014).

  • 13. G. X. Y. Zheng et al., Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

  • 14. B. Haas et al., STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq. bioRxiv (2017), doi:10.1101/120295.

  • 15. A. P. Patel et al., Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 344, 1396-1401 (2014).

  • 16. Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas. Cell. 171, 950-965.e28 (2017).

  • 17. S. V. Puram et al., Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer. Cell. 171, 1611-1624.e24 (2017).

  • 18. I. Tirosh et al., Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 352, 189-196 (2016).

  • 19. A. S. Venteicher et al., Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science. 355 (2017), doi:10.1126/science.aai8478.

  • 20. A. Tsherniak et al., Defining a Cancer Dependency Map. Cell. 170, 564-576.e16 (2017).

  • 21. J. H. Taube et al., Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes. Proc. Natl. Acad. Sci. U.S.A. 107, 15449-15454 (2010).

  • 22. C. J. Gröger, M. Grubinger, T. Waldhör, K. Vierlinger, W. Mikulits, Meta-Analysis of Gene Expression Signatures Defining the Epithelial to Mesenchymal Transition during Cancer Progression. PLOS ONE. 7, e51136 (2012).

  • 23. J. Fan et al., Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods. 13, 241-244 (2016).

  • 24. L. Jerby-Arnon et al., A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell. 175, 984-997.e24 (2018).

  • 25. J.-R. Lin et al., Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife. 7, e31657 (2018).

  • 26. K. Baird et al., Gene expression profiling of human sarcomas: insights into sarcoma biology. Cancer Res. 65, 9226-9235 (2005).

  • 27. Y. Sun et al., IGF2 is critical for tumorigenesis by synovial sarcoma oncoprotein SYT-SSX1. Oncogene. 25, 1042-1052 (2006).

  • 28. A. Subramanian et al., A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell. 171, 1437-1452.e17 (2017).

  • 29. M. J. T. Stubbington et al., T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods. 13, 329-332 (2016).

  • 30. M. Sade-Feldman et al., Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma. Cell. 175, 998-1013.e20 (2018).

  • 31. C. Zheng et al., Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. Cell. 169, 1342-1356.e16 (2017).

  • 32. J. P. Böttcher et al., Functional classification of memory CD8+ T cells by CX3CR1 expression. Nat. Commun. 6, 8306 (2015).

  • 33. N. E. Scharping, A. V. Menk, R. D. Whetstone, X. Zeng, G. M. Delgoffe, Efficacy of PD-1 Blockade Is Potentiated by Metformin-Induced Reduction of Tumor Hypoxia. Cancer Immunol. Res. 5, 9-16 (2017).

  • 34. N. E. Scharping, A. V. Menk, R. D. Whetstone, X. Zeng, G. M. Delgoffe, Efficacy of PD-1 Blockade Is Potentiated by Metformin-Induced Reduction of Tumor Hypoxia. Cancer Immunol. Res. 5, 9-16 (2017).

  • 35. S. Spranger, R. Bao, T. F. Gajewski, Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature. 523, 231-235 (2015).

  • 36. D. Pan et al., A major chromatin regulator determines resistance of tumor cells to T cell-mediated killing. Science. 359, 770-775 (2018).

  • 37. D. Miao et al., Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science. 359, 801-806 (2018).

  • 38. I. Datar, K. A. Schalper, Epithelial-Mesenchymal Transition and Immune Evasion during Lung Cancer Progression: The Chicken or the Egg? Clin. Cancer Res. 22, 3422 (2016).

  • 39. S. Terry et al., New insights into the role of EMT in tumor immune escape. Mol. Oncol. 11, 824-846 (2017).

  • 40. Y. Zhou et al., Evaluation of expression of cancer stem cell markers and fusion gene in synovial sarcoma: Insights into histogenesis and pathogenesis. Oncol. Rep. 37, 3351-3360 (2017).

  • 41. A. Butler, P. Hoffman, P. Smibert, E. Papalexi, R. Satija, Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411 (2018).


Claims
  • 1. A method of detecting an expression signature in synovial sarcoma (Sys) tumor comprising detecting in tumor cells obtained from a subject the expression or activity of a malignant cell gene signature comprising one or more biomarkers selected from the group consisting of a) epithelial malignant signature as defined in Table 1E;b) mesenchymal malignant cell signature as defined in Table 1D;c) cell cycle signature as defined in Table 1C;d) core oncogenic signature as defined in Table 1A.1;e) a fusion signature as defined in Table 8; orf) a combination thereof
  • 2. The method of claim 1, wherein detection of the cell cycle signature indicates an increased risk of metastatic disease compared to a sample not expressing the cell cycle signature.
  • 3. The method of claim 2, wherein the one or more biomarkers comprise cyclin D2 (CND2), CDK6, or both CND2 and CDK6.
  • 4. The method of claim 1, wherein detection of the core oncogenic signature indicates an increased risk of metastatic disease compared to a sample not expressing the core oncogenic signature.
  • 5. The method of claim 1, wherein absence of the core oncogenic signature indicates higher progression free survival.
  • 6. A method of diagnosing a subject with synovial sarcoma, comprising detecting one or more signatures of claim 1.
  • 7. A method of diagnosing a subject with increased risk of metastatic disease, comprising detecting one or more signatures of claim 1.
  • 8. A method of treating SyS in a subject in need thereof comprising administering inhibitor of HDAC, CDK4/6, or a combination thereof to selectively target synovial sarcoma cells.
  • 9. The method of claim 7, further comprising administration with immune checkpoint inhibitors.
  • 10. A method of monitoring a cancer in a subject in need thereof comprising detecting the expression or activity of one or more expression signatures of claim 1 in tumor samples obtained from the subject for at least two time points.
  • 11. The method of claim 10, wherein at least one sample obtained before treatment.
  • 12. The method of claim 10, wherein the tumor sample obtained after treatment.
  • 13. A method of treatment comprising targeting one or more genes or polypeptides of one or expression signatures of claim 1.
  • 14. A method of treatment for Synovial Sarcoma comprising treatment with TNF and IFN-gamma, the treatment providing a synergistic effect.
  • 15. A method of treatment comprising administration of a modulator of one or more genes of cell cycle signature as defined in Table 1C, a SS18-SSX signature as defined in Table 8, or a combination thereof.
  • 16. The method of treatment of claim 15, wherein a combination of a modulator of cell cycle signature and SS18-SSX signature are administered and provide a synergistic effect.
  • 17. An isolated CD8+ T cell characterized by expression of one or more biomarkers of an expression signature as defined in Table 1F.
  • 18. An isolated or engineered CD8+ T cell characterized by increased expression of TNF alpha and/or interferon gamma.
  • 19. A method of treating a subject with SyS comprising administration of the isolated or engineered CD8+ T cell of claim 17 or 18 to a subject in need thereof.
  • 20. A method of treating Synovial Sarcoma (Sys) in a subject comprising: i) detecting the expression or activity of a malignant cell gene signature is a sample from a subject, the signature comprising one or more biomarkers selected from the group consisting of: a) epithelial malignant signature as defined in Table 1E;b) mesenchymal malignant cell signature as defined in Table 1D;c) cell cycle signature as defined in Table 1C;d) core oncogenic signature as defined in Table 1A.1;e) a fusion signature as defined in Table 8; orf) a combination thereof; andii) administering an effective amount of a modulating agent of the signature.
  • 21. The method of claim 20, wherein the modulating agent is inhibitor of HDAC, CDK4/6, or a combination thereof, to selectively target synovial sarcoma cells.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 62/817,545 filed Mar. 12, 2019 and U.S. Provisional Application 62/880,438 filed Jul. 30, 2019. The entire contents of the above-identified applications are fully incorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under grant numbers CA180922, CA202820, CA14051 granted by the National Institutes of Health. The government has certain rights in the invention.

PCT Information
Filing Document Filing Date Country Kind
PCT/US2020/022466 3/12/2020 WO 00
Provisional Applications (2)
Number Date Country
62817545 Mar 2019 US
62880438 Jul 2019 US