Provided herein are compositions, systems, kits, and methods for performing an activity based on detecting, in a sample from a cancer patient, the presence of elevated levels of Lactotransferrin (LTF) mRNA or protein, or detecting the presence in the MIF promoter region of at least one of: −173C and −794 CATT5-8, and treating a patient with immunotherapy, or generating a report that the subject should be treated with immunotherapy.
Immunotherapeutic strategies to stimulate anti-cancer immune responses have provided new treatment options in multiple advanced cancers (1-4). However, the efficacy of these approaches is variable, and in some tumors, such as glioblastoma (GBM), immunotherapy success has been limited (5-7). The obstacles to immunotherapy effectiveness in GBM include a highly suppressive myeloid cell-driven tumor microenvironment and systemic immune suppression, which limits T cell infiltration and activation, and the anatomical limitations of the blood-brain and blood-tumor barriers (8-12). Accordingly, identifying how resistance to these therapies is regulated is essential for developing effective next-generation immunotherapeutic strategies for GBM and other refractory cancers.
Within the GBM microenvironment, a series of cell-cell interactions concomitantly drive tumor growth and immune suppression (10). An immune-suppressive pathway in GBM was identified that is driven by macrophage migration inhibitory factor (MIF) secreted by cancer stem cells (CSCs) that in turn activates myeloid-derived suppressor cells (MDSCs) (13). Recent has shown that MDSCs are increased in the circulation and tumor microenvironment (12), they portend a poor prognosis (8), their expansion can be driven by CSC-derived MIF (8,13), and they can be reduced by MIF neutralization (either genetically or pharmacologically) (9,14). Furthermore, MIF has been studied in a variety of cancers in the context of inflammation and has been found to regulate immune activity (15-32). However, MIF has not been explored in the context of immunotherapy.
Provided herein are compositions, systems, kits, and methods for performing an activity based on detecting, in a sample from a cancer patient, the presence of elevated levels of Lactotransferrin (LTF) mRNA or protein, or detecting the presence in the MIF promoter region of at least one of: −173C and −794 CATT5-8, and treating a patient with immunotherapy, or generating a report that the subject should be treated with immunotherapy.
In some embodiments, provided herein are methods of performing an activity based on the presence of at least one polymorphism in the DNA of a patient with glioblastoma comprising: a) performing a nucleic acid detection assay on a DNA sample from a subject, or receiving results from the assay, wherein the assay detects the presence in the MIF promoter region of at least one polymorphism selected from: −173C and −794 CATT5-8, and wherein the subject has symptoms of glioblastoma; and b) performing at least one of the following activities: i) treating the subject with: a glioblastoma therapeutic agent, an immune modulating therapy for glioblastoma, pembrolizumab, ipilimumab, nivolumab, a viral therapy for glioblastoma, or a CAR-T cell therapy for glioblastoma; ii) generating and/or transmitting a report that indicates the presence of the at least one polymorphism and that the subject has increased recurrence risk and/or more rapid decline in KPS status risk and/or decreased survival risk compared to glioblastoma patient's without one or both of the polymorphisms; and iii) generating and/or transmitting a report that indicates the presence of the at least one polymorphism, and that the subject should be treated with a glioblastoma therapeutic agent, an immune modulating therapy for glioblastoma, pembrolizumab, ipilimumab, nivolumab, a viral therapy for glioblastoma, or a CAR-T cell therapy for glioblastoma.
In certain embodiments, the subject's genotype is determined to be −173G/C or −173C/C. In further embodiments, the subject's genotype is determined to be −794 CATT5-8/CATT4 or CATT5-8/CATT5-8. In additional embodiments, the subject has both of the polymorphisms. In particular embodiments, the report indicates that the subject has both of the polymorphisms. In further embodiments, the −794 CATT5-8 is −794 CATT7. In other embodiments, the detecting is conducted by a method comprising sequencing (e.g., next generation sequencing, such as Illumina SBS technology).
In certain embodiments, step a) is receiving results from the assay, and wherein the at least one of the following activities is treating the subject. In additional embodiments, the treating is with the immune modulating therapy for glioblastoma. In other embodiments, the treating is with the CAR-T cell therapy for glioblastoma.
In some embodiments, provided herein are methods for performing an activity based on elevated levels of Lactotransferrin (LTF) in a biological sample from a patient with cancer comprising: a) performing a detection assay on a biological sample from a subject, or receiving results from the assay, wherein the assay detects an increased level of Lactotransferrin (LTF) mRNA and/or LTF protein compared to control levels, and wherein the subject has symptoms of cancer (e.g., a non-thyroid cancer); and b) performing at least one of the following activities: i) treating the subject with: an immune modulating therapy, pembrolizumab, ipilimumab, nivolumab, or a CAR-T cell therapy; ii) generating and/or transmitting a report that indicates the increased level of LTF mRNA and/or protein and that the subject should be treated with an immune modulating therapy, pembrolizumab, ipilimumab, nivolumab, or a CAR-T cell therapy.
In certain embodiments, the cancer is glioblastoma or melanoma. In particular embodiments, the LTF mRNA or LTF protein is the LTF mRNA. In some embodiments, the LTF mRNA or LTF protein is the LTF protein. In additional embodiments, step a) is receiving results from the assay, and wherein the at least one of the following activities is treating the subject. In certain embodiments, the treating is with the immune modulating therapy. In other embodiments, the immune modulating therapy is immune modulating therapy for glioblastoma or melanoma. In additional embodiments, the treating is with the CAR-T cell therapy. In other embodiments, the CAR-T cell therapy is CAR-T cell therapy for glioblastoma or melanoma.
Provided herein are compositions, systems, kits, and methods for performing an activity based on detecting, in a sample from a cancer patient, the presence of elevated levels of Lactotransferrin (LTF) mRNA or protein, or detecting the presence in the MIF promoter region of at least one of: −173C and −794 CATT5-8, and treating a patient with immunotherapy, or generating a report that the subject should be treated with immunotherapy.
In certain embodiments, the present disclosure relates to detecting a migration inhibitory factor (MIF) −794 CATT repeat and/or MIF rs755622 single nucleotide polymorphism (SNP) as prognostic factors and response prediction in immunotherapy treatment in glioblastoma (GBM), where rs755622=MIF-173G/C rs5844572=−794CATT5-8.
In work conducted during the development of embodiments herein, a ‘The Cancer Genome Atlas’ (TCGA) data screen was performed on GBM patients to evaluate the promoter region of MIF. Subsequently, peripheral nucleated blood from 200 GBM patients was obtained and analyzed for the two unique MIF markers. MIF promoter variations in patients were compared to frequencies in the general population. The TCGA data screen indicated that younger GBM patients harbor the variant MIF SNP. In an initial study demonstrating feasibility of the screen and MIF nucleotide variability, it was found that patients with GBM tended to have increased MIF CATT repeats when compared to proportions in the general population, and the repeat numbers were approaching levels seen in other diseased immunogenic populations. Within GBM subsets, patients with increased CATT repeats had earlier time to recurrence, more rapid declines in Kamofsky Performance Status (KPS), and decreased survival. Individuals who have variant MIF microsatellite loci develop GBM at an earlier age, and harbor more aggressive tumors. Analysis of MIF promoter sequences in the peripheral blood may be used as a screening tool for both the development of GBM and as a marker for increased aggressiveness and/or response to immune therapies in patients with glioblastoma.
In work conducted during the development of embodiments herein, it was found that Intra-tumoral MIF expression leads to a worse prognosis in GBM. Genomic presence of minor allele SNPs confers a 3.1 month decrease in progression free survival and a 4.1 month decrease in overall survival in GBM despite standard of care therapies. Using a multivariate analysis, minor allele SNP presence is an independent prognostic indicator for worse outcomes in GBM Minor allele SNP presence is more prognostic than KPS, MGMT status, sex, 1p19q co-deletion MDSC signature genes are overexpressed in patients with genomic presence of minor allele SNPs
The present disclosure is not limited with regard to how the MIF promoter regions variants (−173C and −794 CATT5-8) are detected. In some embodiments, detection involves measurement or detection of a characteristic of a non-amplified nucleic acid, amplified nucleic acid, a component comprising amplified nucleic acid, or a byproduct of the amplification process, such as a physical, chemical, luminescence, or electrical aspect, which correlates with amplification (e.g. fluorescence, pH change, heat change, etc.). In some embodiments, fluorescence detection methods are provided for detection of amplified or non-amplified MIF promoter region nucleic acid. In certain embodiments, various detection reagents, such as fluorescent and non-fluorescent dyes and probes are employed. For example, the protocols may employ reagents suitable for use in a TaqMan reaction, such as a TaqMan probe; reagents suitable for use in a SYBR Green fluorescence detection; reagents suitable for use in a molecular beacon reaction, such as molecular beacon probes; reagents suitable for use in a scorpion reaction, such as a scorpion probe; reagents suitable for use in a fluorescent DNA-binding dye-type reaction, such as a fluorescent probe; and/or reagents for use in a LightUp protocol, such as a LightUp probe. In some embodiments, provided herein are methods and compositions for detecting and/or quantifying a detectable signal (e.g. fluorescence) from MIF promoter region target nucleic acid. Thus, for example, methods may employ labeling (e.g. during amplification, post-amplification) amplified nucleic acids with a detectable label, exposing partitions to a light source at a wavelength selected to cause the detectable label to fluoresce, and detecting and/or measuring the resulting fluorescence. Fluorescence emitted from label can be tracked during amplification reaction to permit monitoring of the reaction (e.g., using a SYBR Green-type compound), or fluorescence can be measure post-amplification.
In some embodiments, detection of MIF promoter regions variants (−173C and −794 CATT5-8) employs one or more of fluorescent labeling, fluorescent intercalation dyes, FRET-based detection methods (U.S. Pat. No. 5,945,283; PCT Publication WO 97/22719; both of which are incorporated by reference in their entireties), quantitative PCR, real-time fluorogenic methods (U.S. Pat. No. 5,210,015 to Gelfand, U.S. Pat. No. 5,538,848 to Livak, et al., and U.S. Pat. No. 5,863,736 to Haaland, as well as Heid, C. A., et al., Genome Research, 6:986-994 (1996); Gibson, U. E. M, et al., Genome Research 6:995-1001 (1996); Holland, P. M., et al., Proc. Natl. Acad. Sci. USA 88:7276-7280, (1991); and Livak, K. J., et al., PCR Methods and Applications 357-362 (1995), each of which is incorporated by reference in its entirety), molecular beacons (Piatek, A. S., et al., Nat. Biotechnol. 16:359-63 (1998); Tyagi, S. and Kramer, F. R., Nature Biotechnology 14:303-308 (1996); and Tyagi, S. et al., Nat. Biotechnol. 16:49-53 (1998); herein incorporated by reference in their entireties), Invader assays (Third Wave Technologies, (Madison, Wis.)) (Neri, B. P., et al., Advances in Nucleic Acid and Protein Analysis 3826:117-125, 2000; herein incorporated by reference in its entirety), nucleic acid sequence-based amplification (NASBA; (See, e.g., Compton, J. Nucleic Acid Sequence-based Amplification, Nature 350: 91-91, 1991; herein incorporated by reference in its entirety), Scorpion probes (Thelwell, et al. Nucleic Acids Research, 28:3752-3761, 2000; herein incorporated by reference in its entirety), partially double-stranded linear probes (Luk, K.-C., et al, J. Virological Methods 144:1-11, 2007; herein incorporated by reference in its entirety), capacitive DNA detection (See, e.g., Sohn, et al. (2000) Proc. Natl. Acad. Sci. U.S.A. 97:10687-10690; herein incorporated by reference in its entirety), etc.
Target MIF promoter nucleic acid molecules may be analyzed by any number of techniques to determine the presence of, amount of, or identity of the molecule. Non-limiting examples include sequencing, mass determination, and base composition determination. The analysis may identify the sequence of all or a part of the amplified nucleic acid (e.g., MIP promoter region containing position −173C and −794) or one or more of its properties or characteristics to reveal the desired information.
Illustrative non-limiting examples of nucleic acid sequencing techniques include, but are not limited to, chain terminator (Sanger) sequencing and dye terminator sequencing, as well as “next generation” sequencing techniques. A number of DNA sequencing techniques are known in the art, including fluorescence-based sequencing methodologies (See, e.g., Birren et al., Genome Analysis: Analyzing DNA, 1, Cold Spring Harbor, N.Y.; herein incorporated by reference in its entirety). In some embodiments, automated sequencing techniques understood in that art are utilized. In some embodiments, the systems, devices, and methods employ parallel sequencing of partitioned amplicons (PCT Publication No: WO2006084132 to Kevin McKeman et al., herein incorporated by reference in its entirety). In some embodiments, DNA sequencing is achieved by parallel oligonucleotide extension (See, e.g., U.S. Pat. No. 5,750,341 to Macevicz et al., and U.S. Pat. No. 6,306,597 to Macevicz et al., both of which are herein incorporated by reference in their entireties). Additional examples of sequencing techniques include the Church polony technology (Mitra et al., 2003, Analytical Biochemistry 320, 55-65; Shendure et al., 2005 Science 309, 1728-1732; U.S. Pat. Nos. 6,432,360, 6,485,944, 6,511,803; herein incorporated by reference in their entireties) the 454 picotiter pyrosequencing technology (Margulies et al., 2005 Nature 437, 376-380; US 20050130173; herein incorporated by reference in their entireties), the Solexa single base addition technology (Bennett et al., 2005, Pharmacogenomics, 6, 373-382; U.S. Pat. Nos. 6,787,308; 6,833,246; herein incorporated by reference in their entireties), Illumina Single base sequencing technology, the Lynx massively parallel signature sequencing technology (Brenner et al. (2000). Nat. Biotechnol. 18:630-634; U.S. Pat. Nos. 5,695,934; 5,714,330; herein incorporated by reference in their entireties) and the Adessi PCR colony technology (Adessi et al. (2000). Nucleic Acid Res. 28, E87; WO 00018957; herein incorporated by reference in its entirety).
In certain embodiments, the LTF mRNA is detected by Northern blot analysis, nuclease protection assays (NPA), in situ hybridization, reverse transcription-polymerase chain reaction (RT-PCR), or RNA-seq sequencing methods.
In particular embodiments, the Lactotransferrin (LTF) protein is detected by a protein detection assay. In some embodiments, the LTF protein detection assays include, but are not limited to: 1) a sandwich immunoassay (e.g., monoclonal, polyclonal and/or DVD-Ig sandwich immunoassays or any variation thereof (e.g., monoclonal/DVD-Ig or DVD-Ig/polyclonal), including chemiluminescence detection, radioisotope detection (e.g., radioimmunoassay (RIA)) and enzyme detection (e.g., enzyme immunoassay (EIA) or enzyme-linked immunosorbent assay (ELISA) (e.g., Quantikine ELISA assays, R&D Systems, Minneapolis, Minn.))), 2) a competitive inhibition immunoassay (e.g., forward and reverse), 3) a fluorescence polarization immunoassay (FPIA), 4) an enzyme multiplied immunoassay technique (EMIT), 5) a bioluminescence resonance energy transfer (BRET), 6) a homogeneous chemiluminescent assay, 7) a SELDI-based immunoassay, 8) chemiluminescent microparticle immunoassay (CMIA) and 9) a clinical chemistry colorimetric assay (e.g., IMA, creatinine for eGFR determination and LC-MS/MS). (See, e.g., Tietz Textbook of Clinical Chemistry and Molecular Diagnostics. 4th Edition, edited by C A Burtis, E R Ashwood and D E Bruns, Elsevier Saunders, St. Louis, Mo., 2006).
Further, if an immunoassay is being utilized, any suitable detectable label as is known in the art can be used. For example, the detectable label can be a radioactive label (such as 3H, 1251, 35S, 14C, 32P, and 33P), an enzymatic label (such as horseradish peroxidase, alkaline peroxidase, glucose 6-phosphate dehydrogenase, and the like), a chemiluminescent label (such as acridinium esters, thioesters, or sulfonamides; luminol, isoluminol, phenanthridinium esters, and the like), a fluorescent label (such as fluorescein (e.g., 5-fluorescein, 6-carboxyfluorescein, 3′6-carboxyfluorescein, 5(6)-carboxyfluorescein, 6-hexachloro-fluorescein, 6-tetrachlorofluorescein, fluorescein isothiocyanate, and the like)), rhodamine, phycobiliproteins, R-phycoerythrin, quantum dots (e.g., zinc sulfide-capped cadmium selenide), a thermometric label, or an immuno-polymerase chain reaction label. An introduction to labels, labeling procedures and detection of labels is found in Polak and Van Noorden, Introduction to Immunocytochemistry, 2nd ed., Springer Verlag, N.Y. (1997), and in Haugland, Handbook of Fluorescent Probes and Research Chemicals (1996), which is a combined handbook and catalogue published by Molecular Probes, Inc. Eugene, Oreg. A fluorescent label can be used in FPIA (see, e.g., U.S. Pat. Nos. 5,593,896, 5,573,904, 5,496,925, 5,359,093, and 5,352,803, which are hereby incorporated by reference in their entireties). An acridinium compound can be used as a detectable label in a homogeneous or heterogeneous chemiluminescent assay (see, e.g., Adamczyk et al., Bioorg. Med. Chem. Lett. 16: 1324-1328 (2006); Adamczyk et al., Bioorg. Med. Chem. Lett. 4: 2313-2317 (2004); Adamczyk et al., Biorg. Med. Chem. Lett. 14: 3917-3921 (2004); and Adamczyk et al., Org. Lett. 5: 3779-3782 (2003)).
Identification of a MIF SNPs, and Lactotransferrin, that Alters the Immune Landscape in the Tumor Microenvironment and Informs Immunotherapy Response
While immunotherapies have shown durable responses for multiple tumors, their efficacy remains limited in some advanced cancers, including glioblastoma. This may be due to differences in the immune landscape, as the glioblastoma microenvironment strongly favors immunosuppressive myeloid cells, which are linked to an elevation in immune-suppressive cytokines, including macrophage migration inhibitory factor (MIF). We now find that a single-nucleotide polymorphism (SNP) rs755622 in the MIF promoter associates with increased leukocyte infiltration in glioblastoma and can be leveraged to predict immunotherapy response across multiple cancers. Furthermore, we identified lactotransferrin expression as being associated with the SNP, which could also be used as a biomarker for immune infiltrated tumors with a higher response rate to immunotherapy. These findings provide the first example in glioblastoma of a germline SNP that underlies differences in the immune microenvironment and identifies high lactotransferrin expression as a biomarker of immunotherapy response.
For Cleveland Clinic, peripheral blood samples from 451 patients with GBM were collected through the Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center under approved IRB protocol 2559. White blood cells from each blood sample were isolated via Ficoll gradient and then snap frozen and stored at −80° C. for research use. For this study, we selected all available GBM samples. For Moffitt Cancer Center, salivary DNA samples collected using Origene kits was available for 386 recently diagnosed GBM patients under IRB protocol MCC 15004. DNA was extracted and stored in aliquot pellets at −80° C. for future research use. For Case Western Reserve University (CWRU)/University Hospitals of Cleveland, peripheral blood samples from 131 patients with GBM were collected through the Ohio Brain Tumor Study (OBTS) at Case Western Reserve University (CWRU), under approval from University Hospitals IRB CC296. Clinical and pathological data were gathered for each patient. Patient blood samples were collected and processed at the time of consent.
Genomic DNA was extracted from the peripheral blood of GBM patients using a Qiagen DNeasy Blood & Tissue Kit following the manufacturer's protocols. DNA purity and concentration were measured using a ThermoFisher NanoDrop spectrophotometer.
MIF SNP genotyping was performed using PCR amplification and subsequent restriction enzyme digestion with AluI. PCR was performed using Accuprime Pfx DNA polymerase (ThermoFisher, catalog number 12344-024) using 0.2 μmol forward primer (5′-CCCAAAGACAGGAGGTAC-3′, SEQ ID NO:1) and 0.2 μmol reverse primer (5′-ATGATGGCAGAAGGACCAG-3′, SEQ ID NO:2). PCR was run as follows: 95° C. for 5 minutes, followed by 35 cycles of 95° C. for 30 seconds, 60° C. for 45 seconds, and 68° C. for 1 minute. Following the 35 cycles, there was a final 68° C. elongation step for 5 minutes, followed by storage at 4° C. After amplification, the PCR product was confirmed on a 1% agarose gel by identification of an approximately 500 bp product. After confirmation, 10 μl of PCR product was mixed with 2 μl 10× CutSmart buffer. 1 μl AluI, and 7 μl water and digested at 37° C. for 1 hour. After digestion, the alleles containing a G (non SNP) produced a 450 bp fragment, while the alleles with a C (rs755622) produced a 270 bp fragment.
Raw BAM files from the TCGA_GBM cohort were utilized to analyze the rs2096525 MIF SNP from whole exome sequencing data aligned by the TCGA. Aligned the SNP rs2096525 genotype was identified via use of HaplotypeCaller, where samples with alternative counts at the reference position chr22: 23894632 were identified. After classification of the samples by genotype, the phenotype data was downloaded via TCGA, and survival analysis was performed using log-rank test via R version 4.1.0.
Flash-frozen tissue was requested from the Rose Ella Burkhardt Brain Tumor Center at the Cleveland Clinic under IRB 2559 and corresponded to n=34 patients with previously identified rs755622 SNP status from matched white blood cell pellets (n=17 C/*, and n=17 G/G). Within each group, patients were selected who underwent full Stupp protocol standard-of-care treatment and were evenly divided by sex and prognosis (<6 months progression-free survival (PFS) or >6 months PFS). Samples were processed and sequenced by Genewiz. Briefly, RNA was extracted by Qiagen RNeasy kit, and then the library was prepared using True-seq library preparation. Average sequencing depth was 40 Mbp per sample.
FASTQ files were aligned to the hg19 using STAR aligner with default parameters. Fragments were counted using Rsamtools with UCSC.hg19.knownGene transcript database. Raw counts were used in DESeq2 downstream for differential expression comparing the rs75662 SNP status groups.
Processed count data from TCGA_GBM mRNA dataset was downloaded from the Broad Firehose “http://gdac_broadinstitute.org/runs/stddata_2016_01_28/data/GBM/20160128/”, where the raw count file GBM.uncv2.mRNAseq_raw_counts.txt was utilized for downstream analysis.
Raw counts from TCGA_GBM were analyzed using the R package DESeq2 version 1.29 in R version 4.0.1. After identification of the germline SNP status of rs2096525, the patient samples containing the minor allele were compared to patients homozygous for the major allele for differential expression.
ssGSEA
The nCounter PanCancer Immune Profiling Panel gene set was used for immune infiltration deconvolution signatures. Each signature was used with ssGSEA using Gene Set Variation Analysis (GSVA) R package version 1.40.134. Microenvironment score, stromal score and immune score were generated using xCell R package version 1.1.035. Comparing signature scores between groups was performed using ANOVA with the p values shown for each comparison in the heatmaps above deconvolution heatmaps. All heatmaps of deconvolution and p-values were generated using pheatmap version 1.0.12.
Tumor Immune Dysfunction and Exclusion biomarker evaluation was performed at http://tide.dfci.harvard.edu/. Using LTF as a single-gene signature, the AUC for 25 immunotherapy studies was performed from RNA-sequencing data evaluating clinical response. Of the 25 signatures, 9 had an AUC greater than random36.
Melanoma RNAseq datasets were utilized from published reports37-40 sGSEA was utilized to score the LTF gene in all samples, and then a top quartile cutoff was selected to group the samples into high and low groups. Univariate analysis was performed using the LTF score, and log rank p-values were obtained along with survival curves using the R package Survival.
Serial sections (7 μm thick) from each sample (formalin-fixed paraffin-embedded tumor biopsies) were stained with 3 different sets of markers and indicated below:
Set 1 (triple immunofluorescence staining): DAPI, CD3 (ab11089, abcam, 1:50), CD8 (85336, cell signaling, 1:50), CD107a (NBP2-52721, novus, 1:50)
Set 2 (double immunofluorescence staining): DAPI, MIF (MAB2892, R&D systems, 1:500), LTF (HPA059976, Atlas, 1:100)
Set 3 (multiplex staining): DAPI, CD74 (ab1794,Abcam/1:200), CD11b (ab133357,Abcam/1:200 P2RY12 (NBP2-33870,Novus Bio/1:200), HLA-DR (ab20181/Abcam/1:200), CD68 (790-2931, Ventana/1:200), CD4 (ab133616, Abcam/1:200)
For staining, slides were baked at 60C prior to deparrifinization. Slides were then deparaffinized using a Leica Autostaine XL and antigen retrieval was performed using a sodium citrate buffer (pH6) with slides steamed in pressure cooker at 110C for 10 minutes. Slides were then cooled to room temperature and transferred to water for 5 minutes prior to TBST for 15 minutes. Primary antibody placed at above concentrations and incubated in a humid chamber overnight at 4C. Slides placed on Biocare Intellipath Staining platform for blocking (3% donkey serum). secondary antibody incubations and Hoechst/DAPI staining (Thermo Scientific H1399/1 mg/ml). The following secondary antibodies were used: Rabbit Cy3 (Jackson Immuno 711-165-152, 1:250), Mouse 488 (Jackson Immuno 715-545-151, 1:250), Rat Cy3 (Jackson Immuno 712-165-153, 1:250), Rabbit Cy5 (Jackson Immuno 711-175-152, 1:250). Slides manually cover slipped with an aqueous mounting medium.
Whole tissue sections were imaged with multispectral capabilities of the Vectra Polaris Automated Quantitative Pathology Imaging System (Akoya Bioscience Inc.). Multispectral images were then unmixed in inForm (Akoya Biosciences Inc., version 2.5) to obtain component images for each individual marker and tissue autofluorescence. Component image tiles were stitched and saved as OME-TIFF (Open Microscopy Environment) format for analysis, storage, and archival.
The open source image analysis software QuPath41 was used for the detection and classification of cells. For each slide, 10 to 20 regions of interest (ROI) were selected to represent the different parts of the whole section while avoiding imaging, staining and sectioning artifacts. StarDist42, a deep-learning algorithm, along with a pretrained model was used within QuPath for detecting cell nuclei from the DAPI channel. For each cell, intensity measurements were used to determine its positivity for each marker in the panel. A custom script with a manual decision tree43 was implemented in QuPath to classify cells based on their positivity. Representative images were extracted using QuPath, and single-cell data for each sample were exported as .csv files for further analysis and charting in R version 4.0.1.
Demographic and clinical characteristics were evaluated between clinical cohorts. Analysis of variance (ANOVA) and chi squared tests were performed to assess differences in continuous and categorical variables, respectively. Additionally, these characteristics were assessed for MIF SNP rs755622. For this assessment, T tests were performed to assess differences in continuous data. All statistics were generated in R version 4.0.1.
Overall and progression-free survival of each clinical cohort was assessed for MIF snp rs755622. Kaplan Meier (KM) analysis was performed to evaluate the difference in survival and recurrence between GG genotype patients and CC or CG genotype patients. These analyses were also performed among only those cases who had received standard of care. Log rank tests were performed to assess differences in KM curves. Univariate and multivariable Cox proportional hazards models were generated to assess the impact of MIF snp rs755622 on overall and progression free survival. The proportional hazards assumption was assessed and not found in violation. Multivariable models were adjusted for age, sex, surgery, and standard of care. Hazard ratios (HR) and 95% confidence intervals (95% CI) are reported. All statistics were generated in R version 4.0.1.
Patients with the MIF SNP Rs755622 have an Increase in Lactotransferrin (LTF) and Increased Immune Microenvironment Signaling
Based on our previous assessments of MIF as a driver of CSC-MDSC-mediated communication13, we assessed overall MIF expression levels across brain tumors and found elevated MIF in isocitrate dehydrogenase (IDH) wild-type GBM patient tumor samples when compared to that of patients with lower-grade (IDH mutant astrocytomas and oligodendrogliomas) gliomas (
We assessed the rs755622 MIF SNP in three separate, annotated clinical cohorts of GBM patients (total of 966 patients including 449 from Cleveland Clinic, 386 from Moffitt Cancer Center, and 131 from Case Western Reserve University/University Hospitals of Cleveland). Our analysis of individual and combined cohort statistics revealed significant differences in the frequency of key prognostic indicators between cohorts (Karnofsky Performance Score (KPS), total surgical resection, receipt of standard of care (SOC), and recurrence) across the 3 cohorts.
We observed no significant difference in GBM incidence or patient survival between the rs755622 MIF SNP genotypes, but we hypothesized that there may be differences in tumor and microenvironment interactions between genotypes given the association of the rs755622 MIF SNP with inflammation in non-oncologic conditions (Table 1).
indicates data missing or illegible when filed
To explore this possibility, we selected 17 patients with primary, untreated GBM from each MIF genotype in our Cleveland Clinic cohort with similar clinical parameters and outcomes (Table 2) and subjected their tumor tissue to bulk RNA-sequencing.
1n (%); Median (IQR)
Differential gene expression analysis revealed that the rs755622 minor allele patients (e.g., −173 C SNP) had an enrichment in immune cell related genes (
Immunofluorescence Confirms Enhanced T Cell Infiltration and CD8 T Cell Activation in GBM Patients with the MIF SNP Rs755622
To further interrogate the immune cell differences between MIF genotypes, we utilized matched tissue from 22 patients (11 C/* and 11 G/G) from the same cohort subjected to RNA-sequencing. Staining for LTF confirmed the RNA-sequencing analysis and identified that LTF protein was increased in minor allele patients (
While we observed changes in the lymphoid compartment from the RNA-sequencing studies and validated these in human patient samples, these initial assessments did not focus on specific myeloid cell subtypes that are known to be involved in GBM immune suppression (including microglia, monocytes, macrophages, and MDSCs). Using matched samples from the RNA sequencing study, we next stained for CD4+ T cells and for myeloid cell subtypes known to be involved in GBM immune suppression (e.g., microglia, monocytes, macrophages, and MDSCs (n=11 C/* and n=11 G/G). Individual cell subtypes were identified using the top quartile for lineage-specific markers (CD4+ T cells: CD4+, CD11b−; macrophage: CD11b+, CD68+, HLA-DR+; microglia: P2RY12+; MDSCs: CD11b+, CD74+, CD68−, HLA-DR−; monocytes: CD11b+, HLA-DRA+, P2RY12−, CD68−) (
GBM Patients with High LTF Expression are Immunologically Activated
Seeking to expand on these initial observations, we sought to identify the rs755622 SNP in The Cancer Genome Atlas (TCGA) dataset but were unsuccessful because this marker is too far upstream of transcription to have read coverage in whole exome sequencing data. Given the strong correlation between the MIF minor allele with LTF expression, we used LTF expression as a surrogate of MIF genotype and interrogated immune changes associated with LTF. Notably, we found a similar differential expression profile between LTF-high (top 25%) and LTF-low (bottom 25%) patients in the TCGA as we did between genotypes in our dataset (
To determine whether these findings with respect to LTF signature recapitulate the rs755622 genotype, we examined possible associations with the rs2096525 SNP, which is in linkage disequilibrium with rs75562250,51 but located within the first or second MIF intron. We interrogated the TCGA GBM whole exome sequencing dataset for the rs2096525 SNP and we found the minor allele to be present in approximately 12% of GBM patients (
Our queried GBM datasets are limited by the circumstance that overall clinical responses are weaker than studies of other solid tumors, including melanoma and non-small cell lung cancer. To determine whether the influence of MIF genotype may extend beyond GBM, we interrogated four melanoma immunotherapy clinical trials with available RNA-sequencing data. We separated high LTF-expressing patients based on the top 25% of expression (
To determine the cellular composition changes that occur in the LTF high vs low immunotherapy patients we utilized deconvolition analyses. Deconvolution analysis of the melanoma cohorts analyzed in univariate analyses for
MIF, considered the first cytokine activity52, has been extensively studied in the context of immune activation and the inflammatory response, as well as in tumor biology, where it has been shown to drive cancer cell proliferation and the generation of a tumor-promoting immune microenvironment53. In GBM, MIF functions include enhancement of CSC maintenance54,55, resistance to therapies including standard-of-care therapy temozolomide56 as well as the anti-angiogenic agent bevacizumab57, as well as altering growth factor receptor signaling48. However, the potential functional consequences of common MIF promoter polymorphisms, such as the −173 SNP (rs755622), have not been well studied in inflammation associated with malignancies. While the rs755622 SNP is associated with numerous inflammatory conditions and certain cancers, particularly those sensitive to immunotherapies19,53,58,59, we found that in GBM, there was no correlation with incidence or prognosis in response to standard-of-care therapy across three studied cross-institutional cohorts. This finding extended to the functional MIF promoter −794 CATT microsatellite (rs5844572) that is in linkage disequilibrium with the rs755622 SNP. We also observed no major difference in MIF level between genotypes, likely due to the elevated level of MIF in GBM and is further confound by many GBM patients being treated with dexamethasone, which increased MIF production60. However, we found evidence for distinct tumor immune microenvironment between genotypes, with a heightened increase in CD8+ T cells in the minor allele patients. This enhancement in immune response parameters correlated with an enhancement in LTF expression in the minor allele patients, which further predicted immunotherapy response in GBM patients as well as those with melanoma and non-small cell lung cancer.
Our initial assessment of MIF genotypes revealed an association between the minor SNP allele and LTF, which has not been previously described. In non-pathophysiological conditions, LTF is an iron binding glycoprotein that functions to protect against pathogens and has been shown to have anti-inflammatory activity. LTF has been described in cancer to function in an anti-proliferative manner. In GBM, LTF expression is reduced compared to lower-grade brain tumors61 and can inhibit GBM cell proliferation62. LTF also has also been studied as a nanoparticle carrier for a variety of pre-clinical cancer therapies, including in GBM, where it has brain penetrance63. In the context of MDSCs, we found that MIF enhances MDSC function in GBM and that LTF can induce MDSCs in pathological neonatal inflammatory conditions64.
Utilizing both RNA-sequencing and matched tissue samples, we identified that MIF minor SNP allele patients who had increased LTF expression, also had an increase in CD8+ T cells and a reduction in macrophages, with no change in MDSCs. We also not observe a consistent change in tumor associated macrophages between MIF SNP patients and LTF expression, which could be due to a limitation of deconvolution methods in distinguishing myeloid subtypes. However, taken together, this immune microenvironment is likely more conductive to immune-activating strategies, and this was confirmed in ongoing data from a small clinical trial in GBM and multiple melanoma clinical trials. The utility of LTF as a marker of a more inflammatory microenvironment with potential consequences for immunotherapy response was further demonstrated with the TIDE database platform. While our initial analysis revealed a high correlation between the MIF minor allele and elevated LTF, the association between the MIF SNP minor allele and LTF was made indirectly, due to the inability to efficiently identify the MIF SNP status in large genomic datasets based on its location in the promoter region, which is not covered by whole exome sequencing. Nonetheless, utilizing LTF at a median cut-off does not yield the same results as the top quartile of LTF, which more closely represents the frequency of the MIF SNP minor allele. Another limitation of our findings is that the MIF SNP has not been functionally characterized but is in linkage disequilibrium with the MIF −794 CATT repeat, which is associated with an increase in MIF production in immune cells and brain tissues.
The known genetic determinants of immunotherapy response in gliomas are currently limited to somatic mutations in IDH65,66, and the present findings identify a common germline SNP with clear immunologic significance that may be utilized to improve clinical decision-making and support development of more effective immunotherapies.
All publications and patents mentioned in the specification and/or listed below are herein incorporated by reference. Various modifications and variations of the described method and system 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 should be understood 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 relevant fields are intended to be within the scope described herein.
The present application claims priority to U.S. Provisional application Ser. No. 63/112,461, filed Nov. 11, 2020, which is herein incorporated by reference in its entirety as if fully set forth herein.
Filing Document | Filing Date | Country | Kind |
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PCT/US21/58961 | 11/11/2021 | WO |
Number | Date | Country | |
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63112461 | Nov 2020 | US |