WO2021263115A1 - Compositions et méthodes pour traiter le cancer et surmonter la résistance au blocage pd-1/pd-l1 et pour déterminer la résistance à un traitement par inhibiteur de point de contrôle - Google Patents

Compositions et méthodes pour traiter le cancer et surmonter la résistance au blocage pd-1/pd-l1 et pour déterminer la résistance à un traitement par inhibiteur de point de contrôle Download PDF

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WO2021263115A1
WO2021263115A1 PCT/US2021/039107 US2021039107W WO2021263115A1 WO 2021263115 A1 WO2021263115 A1 WO 2021263115A1 US 2021039107 W US2021039107 W US 2021039107W WO 2021263115 A1 WO2021263115 A1 WO 2021263115A1
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cancer
cells
pro
inflammation
checkpoint inhibitor
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Matthew GALSKY
Jun Zhu
Li Wang
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Icahn School Of Medicine At Mount Sinai
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • the present disclosure relates generally to compositions and methods for treating cancer, overcoming PD-1/PD-L1 blockade resistance, and determining resistance to checkpoint inhibitor treatment.
  • Standard treatment for metastatic urothelial cancer (UC) of the bladder has historically been limited to platinum-based chemotherapy.
  • the treatment landscape has experienced a major shift with the introduction of several PD-1/PD-L1 immune checkpoint inhibitors (CPI) into the armamentarium.
  • CPI immune checkpoint inhibitors
  • Pro-tumorigenic inflammation a “Hallmark of Cancer” pathogenesis (Hanahan, D. & Weinberg, R.A., “Hallmarks of Cancer: The Next Generation,” Cell 144(5):646-674 (2011)), involves a TME shaped by activated fibroblasts, endothelial cells and innate immune cells, particularly myeloid phagocytic cells, which promote cancer growth and progression at least in part by impairing antitumor immunity.
  • Antitumor immunity and pro-tumorigenic inflammation coexist in delicate spatiotemporal balance in individual TMEs complicating identification of tumors in the clinic which are resistant to CPI due to the latter and obfuscating identification of cellular populations or signaling interactions for prioritization as therapeutic targets to overcome such resistance.
  • a first aspect of the present disclosure relates to a method of treating cancer.
  • the method includes obtaining or having obtained a biological sample from a subject; determining or having determined an adaptive immunity gene panel expression level in the sample; determining or having determined a pro-tumorigenic inflammation gene panel expression level in the sample; calculating or having calculated a checkpoint inhibitor treatment sensitivity score from the adaptive immunity gene panel expression level and the pro-tumorigenic inflammation gene panel expression level; and administering an anti-cancer treatment to the subject, wherein the anti-cancer treatment comprises a checkpoint inhibitor treatment, if the checkpoint inhibitor sensitivity score is at or above a critical value.
  • a second aspect of the present disclosure relates to a method of overcoming PD-
  • the method includes obtaining or having obtained a biological sample from a subject; determining or having determined an adaptive immunity gene panel expression level in the sample; determining or having determined a pro-tumorigenic inflammation gene panel expression level in the sample; calculating or having calculated a checkpoint inhibitor treatment sensitivity score from the adaptive immunity gene panel expression level and the pro-tumorigenic inflammation gene panel expression level; and administering an anti-cancer treatment to the subject, wherein the anti -cancer treatment comprises a checkpoint inhibitor treatment, if the subject is determined a target for overcoming PD-1/PD-L1 blockade resistance.
  • a third aspect of the present disclosure relates to a method for determining whether a subj ect is resistant to checkpoint inhibitor treatment for cancer.
  • the method includes obtaining or having obtained a biological sample from a subject; determining or having determined an adaptive immunity gene panel expression level in the sample; determining or having determined a pro-tumorigenic inflammation gene panel expression level in the sample; calculating or having calculated a checkpoint inhibitor treatment sensitivity score from the adaptive immunity gene panel expression level and the pro-tumorigenic inflammation gene panel expression level; and determining whether the subject is resistant to checkpoint inhibitor treatment for cancer based on said comparison.
  • a fourth aspect of the present disclosure relates to a checkpoint inhibitor treatment sensitivity score associated with checkpoint inhibitor treatment that includes an adaptive immunity gene panel and a pro-tumorigenic inflammation gene panel.
  • gene signatures related to adaptive immunity and pro-tumorigenic inflammation associated with sensitivity or resistance to PD-1/PD-L1 blockade were identified and validated in patients with metastatic urothelial cancer. The balance of these signatures was defined, and the 2IR score was coined, in individual urothelial cancer tumor microenvironments best correlated with clinical outcomes and defined cellular states of single myeloid cells linked to these microenvironments and PD-1/PD-L1 blockade resistance.
  • Integrating these bulk and single cell RNA signatures into clinical trials seeking to overcome myeloid cell-related PD-1/PD-L1 blockade resistance, and further credentialing therapeutic targets linked pro-tumorigenic inflammation, may help facilitate extension of the benefits of PD- 1/PD-Ll blockade to additional patients with urothelial cancer.
  • the present disclosure seeks to, inter alia , define dominant molecular and cellular features associated with PD-1/PD-L1 blockade resistance in metastatic urothelial cancer.
  • RNA sequencing data was then generated from muscle-invasive bladder cancer specimens to dissect the cellular composition underlying the identified gene signatures.
  • An adaptive immune response gene signature associated with response and a pro- tumorigenic inflammation gene signature associated with resistance to PD-1/PD-L1 blockade was identified.
  • the adaptive immune response:pro-tumorigenic inflammation signature expression ratio coined the 2IR score, best correlated with clinical outcomes and was externally validated. Mapping these bulk gene signatures onto single-cell RNA sequencing data uncovered their underlying cellular diversity with prominent expression of the pro-tumorigenic inflammation signature by myeloid phagocytic cells. However, heterogeneity in expression of adaptive immune and pro-tumorigenic inflammation genes was observed among single myeloid phagocytic cells quantified as the M SC 2IR score.
  • Single myeloid phagocytic cells with low M S 2IR scores demonstrated upregulation of proinflammatory cytokines/chemokines and downregulation of antigen presentation genes, were unrelated to Ml versus M2 polarization, and were enriched in pre-treatment blood from patients with PD-L1 blockade-resistant metastatic urothelial cancer.
  • the adaptive immune response:pro-tumorigenic inflammation gene expression ratio coined the 2IR score (adaptive Immune: pro-tumorigenic Inflammation Ratio), had the largest effect on clinical outcomes and was validated in an independent UC clinical trial cohort.
  • Single-cell RNA sequencing (scRNA-seq) data was then generated from UC bladder specimens to uncover the cellular composition underlying the gene signatures revealing diverse cellular populations contributed to both the adaptive immune response and pro-tumorigenic inflammation gene signatures.
  • pro-tumorigenic inflammation signature was expressed prominently by myeloid phagocytic cells as a whole, diverse expression of the adaptive immune response and pro-tumorigenic signature genes were observed across individual macrophages/monocytes and neutrophils leading to application of the 2IR score to each individual cell (Myeloid Single Cell Immune:protumorigenic Inflammation Ratio or M SC 2IR score).
  • proinflammatory cytokines e.g, II I B
  • chemokines e.g., CCL20
  • downregulation of antigen presentation genes could not be discerned based on Ml versus M2 classification, and were enriched in the pretreatment blood of patients with CPI-resistant metastatic UC.
  • the balance of adaptive immunity and pro- tumorigenic inflammation is associated with CPI outcomes in UC and resistance associated with the latter may be driven by interactions among diverse cell types in the TME and linked to a proinflammatory cellular state of myeloid phagocytic cells detectable in both the TME and peripheral blood.
  • FIGS. 1A-1D show cohorts and workflow for discovery of gene signatures associated with sensitivity and resistance to anti-PD-l/PD-Ll treatment in metastatic urothelial cancer.
  • IMvigor 210 was a single-arm phase 2 study investigating PD-L1 inhibition with atezolizumab in patients with metastatic urothelial cancer.
  • the illustration depicts the numbers of patients with available pre-PD-Ll inhibition RNA-sequencing (RNA-seq) data, tumor mutational burden (TMB) data, or both, derived from archival tumor specimens available for the current analysis.
  • RNA-seq RNA-sequencing
  • TMB tumor mutational burden
  • FIG IB shows a step-wise approach to the identification of consistently co-expressed gene modules, conditioned on TMB, associated with better overall survival or worse overall survival with PD-L1 blockade treatment in patients with metastatic urothelial cancer.
  • Data from The Cancer Genome Atlas (TCGA) urothelial bladder cancer dataset was used to identify consistently co-expressed gene modules (see Examples 1 and 8).
  • FIG. 1C shows hallmark pathways enriched in the adaptive immune response, pro-tumorigenic inflammation, and stromal gene signatures using Fisher’s exact test (nominal two-sided p-value ⁇ le-5). Color corresponds to the -loglO of the p-value.
  • TCGA Cancer Genome Atlas
  • checkmate 275 was a single-arm phase 2 study investigating PD-1 inhibition with nivolumab in patients with metastatic urothelial cancer.
  • the illustration depicts the number of patients with available pre-PD-1 inhibition RNA- sequencing data, TMB data, or both derived from archival tumor specimens used for validation of the association between the adaptive immune response, pro-tumorigenic inflammation, and stromal gene signatures and outcomes with PD-1/PD-L1 blockade in metastatic urothelial cancer.
  • FIGS. 2A-2G show the adaptive immune response and pro-tumorigenic inflammation gene signatures, and the ratio of signature expression termed the 2IR score, are associated with clinical outcomes with PD-1/PD-L1 blockade in patients with metastatic urothelial cancer.
  • OS overall survival
  • TMB tumor mutational burden
  • the plot indicates log HRs while annotation provides HRs.
  • OS overall survival
  • FIG. 2C shows objective response rate with PD-L1 blockade in the IMvigor 210 cohort according to the 2IR score (cut at tertiles). For each 2IR score tertile, bar graphs depict the percentage of patients achieving a complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD) as the best objective response with PD-L1 blockade.
  • FIG. 2D shows the association between each biomarker (or biomarker combination) and overall survival (OS) in the IMvigor 210 cohort was evaluated using the Z-score by univariate Cox regression analysis and the p-value by log likelihood ratio test (left).
  • FIG. 2F shows objective response rate with PD-1 blockade in the Checkmate 275 cohort according to the 2IR score (cut at tertiles).
  • FIG. 2G shows that the association between each biomarker (or biomarker combination) and overall survival (OS) in the Checkmate 275 cohort was evaluated using the Z-score by univariate Cox regression analysis and the p-value by log likelihood ratio test (left).
  • FIGS. 3A-3G show the adaptive immune response and pro-tumorigenic inflammation gene signatures are associated with spatial organization of immune cells in the tumor microenvironment.
  • FIGS. 3A-3D show representative images of multiplexed immunohistochemical consecutive staining on a single slide (MICSSS) demonstrating abundance of CD8+ T cells (FIGS. 3 A, 3B) and tertiary lymphoid-like structures (FIG. 3B) in specimens with high 2IR scores and a paucity of CD8+ T cells and prominent macrophages and stroma (FIGS. 3C, 3D) in specimens with a low 2IR scores.
  • Yellow outline in panel A represents demarcation of cancer cell nests.
  • FIG. 3E shows representative image of urothelial cancer specimen demonstrating region of interest (ROI), designated by the square, and machine learning-based segmentation of cancer cell nest and stromal zones to define T cell localization in the tumor microenvironment using pancytokeratin immunohistochemical staining, designated by the yellow outline bordering cytokeratin-expressing cells.
  • FIG. 3F shows Spearman’s correlation between enumeration of CD8+ T cells localized to cancer cell nests or stromal zones and adaptive immune response gene signature, pro-turn origenic inflammation gene signature, or 2IR score. The results are based on analysis of 76 ROIs across 19 specimens with both immunohistochemistry and RNA sequencing data from the Checkmate 275 cohort.
  • FIG. 3G shows correlation between enumeration of CD8+ T cells localized to cancer cell nests and the 2IR score. The results are based on analysis of 76 ROIs across 19 specimens with both immunohistochemistry and RNA sequencing data from the Checkmate 275 cohort. Spearman’s correlation was used to determine the correlation coefficient R and p value.
  • FIGS. 4A-4F depict defining the cellular origins of adaptive immune response, pro-tumorigenic inflammation, and stromal gene signature expression using single-cell RNA sequencing.
  • FIG. 4A shows a schematic representation of projection of gene signatures identified using bulk RNA sequencing data linked to outcomes with anti-PD-l/PD-Ll treatment onto single-cell RNA sequencing data generated from a separate cohort of invasive urothelial bladder cancer specimens.
  • the illustration depicts nine major cell clusters visualized using Uniform Manifold Approximation and Projection (UMAP) across eight urothelial cancer specimens and two adjacent normal urothelial cancer specimens profiled using droplet-based encapsulation single-cell RNA sequencing.
  • UMAP Uniform Manifold Approximation and Projection
  • FIG. 4B shows single cell expression of top 10 overexpressed genes in each major cell cluster. Heatmap visualization color-coding the scaled gene expression level for selected marker genes (rows). Visualized are 500 randomly selected cells per cluster.
  • FIG 4C shows frequency of cell populations in individual samples included in the single-cell RNA sequencing cohort. For each sample, bar graphs depict the percentage of cells in clusters associated with each population. Samples were ranked according to T/NK cell frequency. Normal indicates samples obtained for urothelial tissue that was considered grossly normal by visual inspection adjacent to site of harvested tumor tissue.
  • FIG. 4D shows a heatmap of overlap between genes comprising the adaptive immune response, pro-tumorigenic inflammation, and stromal gene signatures and genes overexpressed in each of the major cell clusters in the single-cell RNA sequencing cohort.
  • the number in each cell corresponds to the odds ratio for the corresponding overlap between genes, the color corresponds to the -loglO p-value (for enrichment) or loglO p value (for depletion) by two-sided Fisher’s exact test.
  • FIG. 4E shows a heatmap visualizing the expression of adaptive immune response, pro-tumorigenic, and stromal signature genes across each of the major and minor cell clusters in the single-cell RNA sequencing cohort.
  • FIG. 4F shows expression level of pro-tumorigenic inflammation signature genes per cell (left) and adaptive immune response signature genes per cell (right) as assessed by the AddModuleScore() function in the Seurat package across major cell populations.
  • FIGS. 5A-5H show that the pro-tumorigenic inflammation gene signature is expressed prominently by myeloid phagocytic cells and low M SC 2IR score myeloid phagocytic cells are characterized by increased expression of proinflammatory genes and decreased expression of antigen presentation genes.
  • FIG. 5A shows eight minor myeloid phagocytic cell clusters visualized using Uniform Manifold Approximation and Projection (UMAP) across eight urothelial cancer specimens and two adjacent normal urothelial cancer specimens profiled using droplet-based encapsulation single-cell RNA sequencing.
  • FIG. 5B shows myeloid phagocytic cell populations in the single-cell RNA sequencing cohort.
  • UMAP Uniform Manifold Approximation and Projection
  • FIG. 5C shows expression level of Ml and M2 macrophage polarization signature genes in the myeloid phagocytic cell populations as assessed by the AddModuleScore() function in the Seurat package.
  • FIG. 5D shows expression of pro-tumorigenic inflammation signature genes versus adaptive immune response genes in single myeloid phagocytic cells in the urothelial cancer tumor microenvironment and classification of single myeloid phagocytic cells by myeloid single cell 2IR (M SC 2IR) score.
  • M SC 2IR myeloid single cell 2IR
  • FIG. 5E shows schematic representation of the relationship between the 2IR score in the urothelial cancer tumor microenvironment based on bulk RNA sequencing and the M SC 2IR score in individual myeloid phagocytic cells based on single cell RNA sequencing.
  • FIG. 5F shows the frequency of cells with low, intermediate and high M SC 2IR score within each myeloid phagocytic cell minor population.
  • FIG. 5G shows volcano plot of genes differentially expressed between myeloid phagocytic cells with high versus low M S 2IR scores. P-value was calculated by Wilcoxon rank-sum test and then adjusted by Bonferroni correction. Genes with log fold change (FC) >0.1 and adjusted p-value ⁇ 0.05 were considered as significant.
  • 5H shows top-ranking ligands inferred to regulate genes upregulated in low M SC 2IR score myeloid phagocytic cells according to NicheNet. Heatmap visualization of ligand activity and downstream target genes inferred to be regulated by each respective ligand.
  • FIGS. 6A-6E show that low M SC 2IR score monocytes are enriched in the pre treatment peripheral blood of patients with metastatic urothelial cancer resistant to anti-PD-Ll treatment.
  • CPI anti-PD-Ll immune checkpoint inhibition
  • FIG. 6D shows volcano plot of genes differentially expressed between peripheral blood monocytes with high and low 2IR score. P- value was calculated by Wilcoxon rank-sum test and then adjusted by Bonferroni correction. Genes with log fold change (FC) >0.1 and adjusted p-value ⁇ 0.05 were considered as significant.
  • FIG. 6E shows top-ranking ligands inferred to regulate genes upregulated in low M SC 2IR score peripheral blood monocytes according to NicheNet. Heatmap visualization of ligand activity and downstream target genes inferred to be regulated by each respective ligand.
  • FIG. 7 shows a step-wise approach to the identification of consistently co expressed gene modules, conditioned on TMB, associated with better overall survival or worse overall survival with PD-L1 blockade treatment in patients with metastatic urothelial cancer.
  • FIG. 8 shows canonical pathways enriched in the adaptive immune response, pro-turn origenic_inflammation_signature, and stromal gene signatures using Fisher’s exact test (adjusted p-value (BH method) ⁇ le-3).
  • FIG. 9 shows a correlation among adaptive immune response, pro- tumorigenic_inflammation_signature, stromal signature and tumor mutational burden. Pairwise scatter plots for the adaptive_immune_response signature, pro- tumorigenic_inflammation_signature, and stromal signature expression and TMB in the IMvigor 210 cohort. Numbers represent Spearman’s correlation coefficient (*, p ⁇ 0.05; **, p ⁇ 0.01; ***, pO.001). Abbreviations: TMB, tumor mutational burden.
  • FIG. 10 shows association between gene expression and overall survival in
  • IMvigor 210 study with and without conditioning on adaptive_immune_response signature.
  • Z- score representing the association between each gene and OS was obtained from the Cox regression model where only one covariate of logTMB was included (x-axis) or two covariates, i.e., logTMB and signature expression of adapative_immune_response, were included.
  • Color encodes the signature to which each gene belongs.
  • FIGS. 1 lA-1 ID show selection of top-ranking genes in the immune signatures and association with clinical outcomes in the IMvigor 210 cohort.
  • FIG. 11 A shows selecting the optimum number of top-ranked genes for the adaptive_immune_response signature. The red dashed line indicates the cutoff for the top-ranked genes.
  • the X-axis represents the top 200 genes in the adaptive_immune_response signature (ranked based on their association with overall survival), Y-axis represents the association of 2IR score with overall survival, where the adaptive_immune_response score was calculated using top-ranked N genes (x-axis) and the pro- tumorigenic inflammation signature score was calculated using top-ranked M genes (M was enumerated from 1 to 200), and the boxplot is a summarization of the 200 associations corresponding to the 200 different M values.
  • FIG. 1 IB shows selecting the optimum number of top-ranked genes in pro-tumorigenic inflammation signature.
  • the X-axis and Y-axis are analogous to the panel in FIG. 11A.
  • FIG. 11C shows Kaplan-Meier curve for overall survival stratified by tertile expression of the 2IR scores in IMvigor 210 cohort where the 2IR scores were calculated using the selected top-ranked genes.
  • FIG. 1 ID shows objective response rate with PD-L1 blockade in the IMvigor 210 cohort according to the 2IR scores divided by tertiles where the 2IR scores were calculated using the selected top-ranked genes.
  • FIG. 12 shows association between the 2IR score and overall survival in TCGA bladder cancer cohort.
  • FIG. 13 shows correlation among the 2IR score and other biomarkers previously correlated with outcomes with anti-PD-l/PD-Ll treatment. Pairwise scatter plots for the 2IR score and other biomarkers in the IMvigor 210 cohort. Numbers represent Spearman’s correlation coefficient (*, p ⁇ 0.05; **, p ⁇ 0.01; ***, p ⁇ 0.001). Abbreviations: TMB, tumor mutational burden.
  • FIG. 14 shows tertiary lymphoid-like structures in specimens with high 2IR scores. Representative images from high 2IR score urothelial cancer specimens demonstrated occasional tertiary lymphoid-like structures as indicated by the arrows.
  • FIG. 15 shows quality control plots for single-cell RNA sequencing cohort.
  • Percentage of mitochondrial gene abundance, number of unique genes detected and UMI count for each cell stratified by major cell cluster are shown. Color indicates the sample identity. The number of cells per sample were shown in the bracket.
  • FIGS. 16A-16E show high resolution characterization of fibroblast-related cells.
  • FIG. 16A shows minor fibroblast-related cell clusters visualized using Uniform Manifold Approximation and Projection (UMAP).
  • FIG. 16B shows frequency of cell populations in individual samples included in the single-cell RNA sequencing cohort. For each sample, bar graphs depict the percentage of cells in each minor Fibroblast-related cluster.
  • FIG. 16C shows single-cell expression of top 10 overexpressed genes in fibroblast-related cell populations.
  • FIG. 16D shows pathways enriched in each of the fibroblast cell populations using Fisher’s exact test. The color corresponds to the -loglO p-value of two-sided Fisher’s exact test (adjusted for multiple testing using BH method). Only pathways that are significantly enriched in at least one cell population are shown (adjusted p ⁇ le-4).
  • FIG. 16E shows the frequency of cells with low, intermediate and high stromal signature expression score within each fibroblast-related population.
  • FIGS. 17A-17D show high resolution characterization of T/NK cells.
  • FIG. 17A shows minor T/NK cell clusters visualized using Uniform Manifold Approximation and Projection (UMAP).
  • FIG. 18B shows expression level of immune regulation, naive/central memory, cytotoxic and tissue resident signature genes in the minor T cell populations as assessed by the AddModuleScore() function in the Seurat package.
  • FIG. 17C shows frequency of cell populations in individual samples included in the single-cell RNA sequencing cohort.
  • FIG. 17D shows single-cell expression of top 10 overexpressed genes in T/NK cell populations in the single-cell RNA sequencing cohort.
  • Heatmap visualization color-coding the scaled gene expression level for selected marker genes (rows). Visualized are 200 randomly selected cells per cluster or all cells when the cell cluster contained ⁇ 200 cells.
  • FIGS. 18A-18B show expression of immune checkpoint, cytokine, and chemokine genes across all T cell metaclusters. Single-cell expression of immune checkpoint, cytokine and chemokine genes expressed in each major cell cluster. Heatmap visualization color-coding the scaled gene expression level for selected marker genes (rows).
  • FIGS. 19A-19C shows high resolution characterization of dendritic cells (DCs).
  • FIG. 19A shows minor DC cell clusters visualized using Uniform Manifold Approximation and Projection (UMAP).
  • FIG. 19B shows expression level of cDCl, cDC2 and pDC signature genes in the minor DC cell populations as assessed by the AddModuleScore() function in the Seurat package.
  • FIG. 19C single-cell expression of top 10 overexpressed genes in each minor cell cluster. Heatmap visualization color-coding the scaled gene expression level for selected marker genes (rows). Visualized are 500 randomly selected cells per cluster or all cells when the cell cluster contains ⁇ 500 cells.
  • FIGS. 20A-20D show high resolution characterization of epithelial cells.
  • FIG. 20A shows eight minor epithelial cell clusters visualized using Uniform Manifold Approximation and Projection (UMAP).
  • FIG. 20B shows UMAP plot of epithelial cells colored by sample origins.
  • FIG. 20C shows single-cell expression of top 10 overexpressed genes in each minor cell cluster. Heatmap visualization color-coding the scaled gene expression level for selected marker genes (rows). Visualized are 500 randomly selected cells per cluster or all cells when the cell cluster contains ⁇ 500 cells.
  • FIG. 20D shows frequency of cell populations in individual samples included in the single-cell RNA sequencing cohort. For each sample, bar graphs depict the percentage of cells in each minor cell cluster.
  • FIGS. 21A-21E show high resolution characterization of endothelial cells.
  • FIG. 21 A shows five minor endothelial cell clusters visualized using Uniform Manifold Approximation and Projection (UMAP).
  • FIG. 2 IB shows single-cell expression of top 10 overexpressed genes in each minor cell cluster. Heatmap visualization color-coding the scaled gene expression level for selected marker genes (rows). Visualized are 500 randomly selected cells per cluster or all cells when the cell cluster contains ⁇ 500 cells.
  • FIG. 21C shows expression level of Stalk-like and Tip-like endothelial cell signature genes in the minor endothelial populations as assessed by the AddModuleScore() function in the Seurat package.
  • FIG. 2 ID shows pathways enriched in each of the endothelial cell populations using Fisher’s exact test.
  • FIG. 21E shows frequency of cell populations in individual samples included in the single-cell RNA sequencing cohort. For each sample, bar graphs depict the percentage of cells in each minor cell cluster.
  • FIGS. 22A-22B show high resolution characterization of B cells.
  • FIG. 22A shows four minor B cell clusters visualized using Uniform Manifold Approximation and Projection (UMAP).
  • FIG. 22B shows single-cell expression of top 10 overexpressed genes in each minor cell cluster.
  • Heatmap visualization color-coding the scaled gene expression level for selected marker genes (rows). Visualized are 500 randomly selected cells per cluster or all cells when the cell cluster contains ⁇ 500 cells.
  • FIGS. 23A-23B show high resolution characterization of myeloid phagocytic cell populations.
  • FIG. 23A shows frequency of myeloid phagocytic cell populations in individual samples included in the single-cell RNA sequencing cohort. For each sample, bar graphs depict the percentage of cells in each minor cell cluster.
  • FIG. 23B shows expression level of Ml and M2 macrophage polarization signature genes per cell in the myeloid phagocytic cells with low, intermediate and high M C 2IR score as assessed by the AddModuleScore() function in the Seurat package.
  • FIG. 24 shows canonical pathways and hallmark pathways enriched in the genes up-regulated in M S 2IR high or low myeloid phagocytic cells in UC tissue specimens using Fisher’s exact test (adjusted p-value ⁇ le-4 using BH method).
  • FIG. 25 shows expression of top-ranked ligands inferred to regulate genes upregulated in low M C 2IR myeloid phagocytic cells in the urothelial tumor tissue single cell RNA sequencing cohort.
  • the heatmap indicates expression of the gene encoding each ligand averaged over single cells belonging to each cluster.
  • the highest ranked ligands inferred to regulate expression of genes upregulated in low M SC 2IR monocytes/macrophages, ILIA and IL1B, are most highly expressed by monocytes/macrophages.
  • FIGS. 26A-26D show high resolution characterization of monocytes in PBMC of patients with metastatic urothelial cancer pre-treatment with anti-PD-Ll immune checkpoint inhibition.
  • FIG. 26A shows four minor monocyte cell clusters visualized using Uniform Manifold Approximation and Projection (UMAP).
  • FIG. 26B shows single-cell expression of top 10 overexpressed genes in each minor cell cluster.
  • Heatmap visualization color-coding the scaled gene expression level for selected marker genes (rows). Visualized are 200 randomly selected cells per cluster or all cells when the cell cluster contains ⁇ 200 cells.
  • FIG. 26C shows scatter plot of expression level of adaptive_immune_response signature and pro- tumorigenic_inflammation_signature for each monocyte cell colored by the M SC 2IR score category.
  • FIG. 26D shows frequency of cells with low, intermediate and high M SC 2IR score within each monocyte population.
  • FIG. 27A which shows the frequency of high Ml_signature cells
  • FIG. 27B shows low Ml_signature cells
  • FIG. 27C shows high M2_signature cells
  • FIG. 27D shows low M2_signature cells.
  • a first aspect of the present disclosure relates to a method of treating cancer.
  • the method includes obtaining or having obtained a biological sample from a subject; determining or having determined an adaptive immunity gene panel expression level in the sample; determining or having determined a pro-tumorigenic inflammation gene panel expression level in the sample; calculating or having calculated a checkpoint inhibitor treatment sensitivity score from the adaptive immunity gene panel expression level and the pro-tumorigenic inflammation gene panel expression level; and administering an anti-cancer treatment to the subject, wherein the anti-cancer treatment comprises a checkpoint inhibitor treatment, if the checkpoint inhibitor sensitivity score is at or above a critical value.
  • the term “about” means that the numerical value is approximate and small variations would not significantly affect the practice of the disclosed embodiments. Where a numerical limitation is used, unless indicated otherwise by the context, “about” means the numerical value can vary by ⁇ 1 or ⁇ 10% , or any point therein, and remain within the scope of the disclosed embodiments.
  • cancer and “cancerous” refer to or describe the physiological condition or disorder in mammals that is typically characterized by unregulated cell growth.
  • a “tumor” comprises one or more cancerous cells.
  • the cancer is selected from the group consisting of urothelial cancer, bladder cancer, estrogen receptor-dependent breast cancer, estrogen receptor-independent breast cancer, hormone receptor-dependent prostate cancer, hormone receptor-independent prostate cancer, cervical cancer, brain cancer, renal cancer, glioblastoma, colon cancer, familial adenomatous polyposis (FAP), colorectal cancer, pancreatic cancer, esophageal cancer, stomach cancer, genitourinary cancer, gastrointestinal cancer, uterine cancer, ovarian cancer, astrocytomas, gliomas, skin cancer, squamous cell carcinoma, Keratoakantoma, Bowen disease, cutaneous T-Cell Lymphoma, melanoma, basal cell carcinoma, actinic keratosis; ichti
  • the method is performed on a selected subject having a tumor.
  • the tumor is fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing’s tumor, leiomyosarcoma, rhabdomyosarcoma, colon carcinoma, pancreatic cancer, breast cancer, ovarian cancer, prostate cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma
  • the terms “subject”, “individual”, or “patient,” are used interchangeably, and mean any animal, including mammals, such as mice, rats, other rodents, rabbits, dogs, cats, swine, cattle, sheep, horses, or primates, such as humans.
  • a subject as used herein includes both humans and other animals, particularly mammals. Thus, the methods are applicable to both human therapy and veterinary applications. Examples of a subject include, but are not limited to, a human, rat, mouse, guinea pig, monkey, pig, goat, cow, horse, dog, cat, bird, and fowl.
  • the patient is a mammal, for example, a primate.
  • the subject is a human.
  • the subject is an infant, a juvenile, or an adult.
  • the subject has been diagnosed with cancer, is at risk of having cancer, or is suspected of having cancer.
  • the terms “cell”, “cell line,” and “cell culture” are used interchangeably and all such designations include progeny.
  • the words “transformants” and “transformed cells” include the primary subject cell and cultures or progeny derived therefrom without regard for the number of transfers. It is also understood that all progeny may not be precisely identical in DNA content, due to deliberate or inadvertent mutations. Mutant progeny that have the same function or biological activity as screened for in the originally transformed cell are included. Where distinct designations are intended, it will be clear from the context.
  • the term “sample” is a biological sample obtained from a subject.
  • the biological sample can be any sample that contains genes. Those skilled in the art will recognize that plasma, whole blood, or a sub-fraction of whole blood, may be used.
  • the biological sample may also be serum or bone marrow. These various biological samples may be obtained using standard procedures for the recovery of the particular sample.
  • the biological sample is selected from the group consisting of tumor tissue, whole blood, serum, urine, and nasal excretion.
  • a blood or serum sample may be obtained by use of a standard blood draw, as disclosed in U.S. Pat. No. 4,263,922 to White, the disclosure of which is incorporated herein by reference in its entirety.
  • a standard blood draw blood is drawn through a needle assembly and handle system into a collection tube. Subsequent to the blood draw, the needle assembly and the handle are removed from an end of the tube and a separate cap is fitted over each end of the tube to retain the blood sample in the tube for analysis.
  • a finger prick with a lancet or a blood draw via standard venipuncture is also a convenient method to obtain a body fluid sample.
  • the drawn blood may be exposed immediately to an anticoagulant to preclude coagulation thereof.
  • anticoagulants include without limitation heparin, EDTA, D-Phe-Pro-Arg chloromethyl ketone dihydrochloride (“PPACK”), and sodium citrate.
  • Bone marrow samples can be obtained according to standard procedures known in the art. For example, bone marrow samples can be obtained using needle aspiration or other known techniques. In certain instances, cells can be isolated from a bone marrow sample using a Ficoll-Hypaq density gradient. Other procedures for obtaining bone marrow samples include bone biopsy devices such as a hollow cannula or needle. Regardless of the tool used to acquire the sample, the bone marrow sample can then be prepared for subsequent analysis (e.g., fixation and labeling). Samples may be obtained in accordance with the methods described in U.S.
  • Patent Publ. No. 2014/0038177 to Silva et al. the disclosure of which is incorporated herein by reference in their entirety.
  • a tumor cell sample may be obtained by any known method to those skilled in the art.
  • a tumor cell sample may be obtained through a tissue biopsy including but not limited to a needle biopsy (e.g., a fine needle aspiration or a core needle biopsy), a surgical biopsy (e.g., a incisional biopsy or a excisional biopsy), or a combination thereof.
  • a tumor cell sample may alternatively be obtained by a liquid biopsy (e.g., from cell-free circulating tumor DNA or circulating tumor cells).
  • a tumor cell may, in one embodiment, be obtained by transurethral resection.
  • the biological sample comprises cells in a tumor microenvironment (TME).
  • TAE tumor microenvironment
  • the biological sample comprises cells selected from tumor cells and peripheral blood cells.
  • the cells are selected from the group consisting of myeloid cells, myeloid lineage cells, macrophages, monocytes, neutrophils, fibroblasts, endothelial cells, innate immune cells, epithelial cells, non-hematopoietic stromal cells, B cells, T cells, natural killer cells, and any combination thereof.
  • T and NK cell minor clusters, dendritic cell minor clusters, epithelial cell minor clusters, endothelial cell minor clusters, B cell minor clusters are examples of cells that may be included in a biological sample described herein.
  • the method of the present aspect includes determining an adaptive immunity gene panel expression level in the sample.
  • An adaptive immunity gene panel as described herein includes one or more genes associated with adaptive immunity.
  • An expression level of adaptive immunity is determined by measuring and/or detecting expression of one or more genes associated with adaptive immunity in a sample.
  • the adaptive immunity gene panel comprises one or more of IL21, RAB33A, HSF5, KCNJ10, C2, APOL6, CBFA2T3, CXCLJ3, BHLHE22 , CXCL9, or any combination thereof.
  • the adaptive immunity gene panel comprises one or more of IL21 , RAB33A, FISF5 , KCNJ10, C2, APOL6, CBFA2T3, CXCL13, BHLHE22, CXCL9.
  • the adaptive immunity gene panel comprises two or more of IL21, RAB33A , HSF5, KCNJ10 , C2, APOL6, CBFA2T3, CXCL13, BHLHE22 , CXCL9.
  • the adaptive immunity gene panel comprises three or more of IL21, RAB33A, HSF5, KCNJ10, C2, APOL6, CBFA2T3, CXCL13 , BHLHE22 , CXCL9.
  • the adaptive immunity gene panel comprises four or more of IL2J, RAB33A , HSF5, KCNJ10 , C2, APOL6, CBFA2T3, CXCL13, BHLHE22, CXCL9. In one embodiment, the adaptive immunity gene panel comprises five or more of IL21, RAB33A, HSF5, KCNJ10 , C2, APOL6, CBFA2T3, CXCLJ3, BHLHE22, CXCL9. In one embodiment, the adaptive immunity gene panel comprises six or more of IL21 , RAB33A , HSF5 , KCNJ10, C2, APOL6, CBFA2T3, CXCL13, BHLHE22, CXCL9.
  • the adaptive immunity gene panel comprises seven or more of IL21, RAB33A , HSF5 , KCNJ10 , C2, APOL6, CBFA2T3, CXCL13, BHLHE22 , CXCL9. In one embodiment, the adaptive immunity gene panel comprises eight or more of IL21, RAB33A, HSF5, KCNJ10, C2, APOL6, CBFA2T3, CXCL13 , BHLHE22 , CXCL9. In one embodiment, the adaptive immunity gene panel comprises nine or more of IL21, RAB33A, HSF5 , KCNJ10 , C2, APOL6, CBFA2T3, CXCL13, BHLHE22, CXCL9. In one embodiment, the adaptive immunity gene panel comprises ten of IL21, RAB33A, HSF5, KCNJ10 , C2, APOL6 , CBFA2T3 , CXCL13 , BHLHE22, CXCL9.
  • the method of the present aspect includes determining a pro- tumorigenic inflammation gene panel expression level in the sample.
  • a pro-tumorigenic inflammation gene panel as described herein includes one or more genes associated with tumorigenic inflammation.
  • An expression level of tumorigenic inflammation is determined by measuring and/or detecting expression of one or more genes associated with tumorigenic inflammation in a sample.
  • the pro-tumorigenic inflammation gene panel comprises one or more of DENND1C, DOCK2, DOK2, TLR6 , PRAM1, IL10RA , CELF2, AMICA1, WAS, BTK, WDFY4, MYOIG, AOAH, SASH3, VENTX, NLRP3, MTL1, HCST, ADCY7, CYTIP, MSN, ACAP1, LAT2, CD86, HBEGF, AREG, LST1, FMR4P, SRGN, RASGRP4, CD300LB, GPR183, TMCC3, SLA, CPNE8,MPEG1, BPI, STX11, TLR2, or any combination thereof.
  • the pro-tumorigenic inflammation gene panel comprises one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty -two or more, twenty -three or more, twenty-four or more, twenty-five or more, twenty-six of more, twenty-seven or more, twenty-eight or more, twenty-nine or more, thirty or more, thirty-one or more, thirty-two or more, thirty -three or more, thirty -four or more, thirty-five or more, thirty-six or more, thirty- seven or more, thirty-eight or more, or thirty-nine of DENND1C, DOCK2, DOK2, TLR6 , PRAM1, IL10RA, CELF2, AMICA1, WAS
  • the adaptive immune response and pro-tumorigenic inflammation gene signatures are contributed to by diverse cell types within the TME and may be linked to underlying cellular states.
  • a checkpoint inhibitor treatment sensitivity score is calculated from the adaptive immunity gene panel expression level and the pro-tumorigenic inflammation gene panel expression level.
  • the checkpoint inhibitor treatment sensitivity score is a ratio of an adaptive immune response to pro-tumorigenic inflammation in the biological sample.
  • the adaptive immune response:pro-tumorigenic inflammation signature expression ratio (referred to herein interchangeably as “2IR” score) best correlates with clinical outcomes.
  • the checkpoint inhibitor treatment sensitivity score may alternatively, or in addition to, comprise a checkpoint inhibitor treatment sensitivity score of single myeloid phagocytic cells of the sample (referred to herein interchangeably as “Msc2IR” score).
  • the anti-cancer treatment does not comprise the checkpoint inhibitor treatment.
  • the critical value (referred to interchangeably herein as “cut-point”) may be optimized or may be used as a continuous variable.
  • the critical value may be optimized to a particular value or range of values, where if the checkpoint inhibitor sensitivity score is below a particular critical value or range of critical values, the checkpoint inhibitor treatment is not administered.
  • the checkpoint inhibitor sensitivity score is at or above a particular critical value or range of critical values, the checkpoint inhibitor treatment is administered.
  • the critical value of the checkpoint inhibitor sensitivity score may be any suitable score. In one embodiment, the checkpoint inhibitor sensitivity score is a value within the range of between about 0.01 and about 1,000.
  • the checkpoint inhibitor sensitivity score may cut 2IR and Msc2IR as described herein into tertiles to predict outcomes.
  • a 2IR score is discretized into tertiles, the R package survminor used to plot the Kaplan Meier curve, and the significance testing for differences in OS performed using the log-rank test.
  • Logistic regression models may be performed to evaluate association between the gene modules and TMB with objective response. In a logistic regression, a complete response or partial response may be treated as 1, and stable disease or progressive disease may be treated as 0.
  • the signature expression and TMB may be similarly standardized before entering the logistic regression model to estimate the coefficient, and the significance testing may be performed by Wald’s test for example.
  • the tertiles may be classified as High Checkpoint Inhibitor Sensitivity Score (HCISS), Middle Checkpoint Inhibitor Sensitivity Score (MCISS), and/or Low Checkpoint Inhibitor Sensitivity Score (LCISS) and may be measured based on time (e.g., between zero to 40 months) and survivor probability (e.g., between 0.00 to 1.00).
  • the response rate with PD-L1 blockade according to the checkpoint inhibitor sensitivity score (e.g., 2IR and/or Msc2IR scores) divided by tertiles may be calculated, and classified as CR (complete response); PR (partial response); SD (stable disease); PD (progressive disease); and OS (overall survival).
  • the CR, PR, SD, PD, and OS may vary between subjects having a HCISS, MCISS, or LCISS.
  • the proportion of patients having PD is largest and ranges, for example, from 0.00 to at or below 0.75; and the proportion of patients having SD is next largest and ranges, for example, from at or below 0.75 to below 1.00; the proportion of patients having PR is next largest and ranges, for example, between 0.75 to below 1.00; and the proportion of patients having CR is smallest ranging from, for example, between 0.75 and 1.00.
  • the proportion of patients having PD is largest and ranges, for example, from 0.00 to below or above 0.50; the proportion of patients having SD is next largest and ranges, for example, from below or above 0.5 to below 1.00; the proportion of patients having PR is next largest and ranges, for example, between 0.75 to below 1.00; and the proportion of patients having CR is smallest ranging from, for example, between 0.75 and 1.00.
  • the proportion of patients having PD is largest or similarly sized to patients having PR, and ranges, for example, from 0.00 to above 0.40 or from 0.00 to above 0.25; the proportion of patients having PR is next largest and ranges, for example, from above 0.5 to below 1.00; the proportion of patients having SD is next largest and ranges, for example, between 0.40 to above 0.50 or from 0.25 to above 0.50; and the proportion of patients having CR is of similar size to SD or smaller than SD and ranges from, for example, between 0.75 and 1.00.
  • the checkpoint inhibitor treatment sensitivity score (2IR) is calculated using formula (I):
  • M adaptive immune comprises the adaptive immunity gene panel expression level in the sample
  • M pr o-tumorigenic inflammation comprises the pro-tumorigenic inflammation gene panel expression level in the sample
  • w comprises weight
  • w is estimated by one or more relative coefficient in formula
  • w represents a relative contribution of Survival Outcome.
  • the checkpoint inhibitor treatment sensitivity score comprises a checkpoint inhibitor treatment sensitivity score of single myeloid phagocytic cells (Msc2IR) of the sample, and Msc2IR is calculated using formula (IV):
  • w is estimated by one or more relative coefficient w * in formula (V):
  • the adaptive immunity gene panel expression level is obtained by bulk RNA sequencing or single-cell RNA sequencing.
  • the pro-tumorigenic inflammation gene panel expression level is obtained by bulk RNA sequencing or single-cell RNA sequencing.
  • the anti-cancer treatment comprises a PD-1/PD-L1 blockade.
  • PD-1/PD-L1 blockade therapy is a useful and potent cancer treatment strategy; however, only a minority of subjects experience a positive response to PD-1/PD-L1 blockade therapy. Many subjects experience primary or acquired resistance that may eventually lead to cancer progression in patients with clinical responses. Accordingly, the resistance to PD-1/PD-L1 blockade remains a significant challenge hindering its further application.
  • the anti-cancer treatment comprises one or more immune checkpoint inhibitors (e g., PD-1/PD-L1 blockade).
  • the method described herein further includes detecting the presence or absence of one or more additional markers.
  • the additional markers are selected from the group consisting of IL1B, CCL20, CLCL13, TGFB1, CXCL8, CXCL9, CXCL10, APOE, C1QA, C1QB, SLC40A1, TREM2, THBS1, S100A8, FCN1, VCAN, SPP1, one or more antigen presentation genes, and any combination thereof.
  • low Msc2IR scores reveal upregulation of proinflammatory pathways and top- ranking genes such as IL1B , CXCL8 (IL8), SPP1, and CCL20.
  • high Msc2IR scores demonstrate upregulation of genes and pathways related to antigen presentation and the T cell recruiting chemokines CXCL9 and CXCL10.
  • IL-la and IL- 1b are ligands inferred to regulate genes overexpressed in low M SC 2IR score cells.
  • ILIA and IL1B are expressed by myeloid phagocytic cells.
  • Low M SC 2IR score monocytes/macrophages and neutrophils, with upregulation of proinflammatory genes and downregulation of antigen presentation genes and not delineated by classical Ml versus M2 polarization or graph-based unsupervised cell clustering, may define a cellular state of myeloid phagocytic cells contributing to CPI resistance.
  • low M S 2IR score myeloid cells may be present in both the TME and peripheral blood of patients with UC and may be associated with CPI resistance.
  • the method may include administering an anti -cancer treatment to the subject if the checkpoint inhibitor sensitivity score is at or above a critical value, wherein the anti-cancer treatment comprises a checkpoint inhibitor treatment.
  • the terms “treat” or “treatment”, unless otherwise indicated by context, refer to therapeutic treatment and prophylactic measures to prevent relapse, wherein the object is to inhibit or slow down (lessen) an undesired physiological change or disorder, such as the development or spread of cancer or an associated infection.
  • beneficial or desired clinical results include, but are not limited to, alleviation of symptoms, diminishment of extent of disease, stabilized (i.e., not worsening) state of disease, delay or slowing of disease progression, amelioration or palliation of the disease state, and remission (whether partial or total), whether detectable or undetectable.
  • Treatment can also mean prolonging survival as compared to expected survival if not receiving treatment.
  • Those in need of treatment include those already having the condition or disorder as well as those prone to have the condition or disorder, and those who are suspected of having the condition or disorder.
  • the term “treating” may include any or all of inhibiting growth of tumor cells, cancer cells, or of a tumor; inhibiting replication of tumor cells or cancer cells; lessening of overall tumor burden or decreasing the number of cancerous cells; and ameliorating one or more symptoms associated with the disease.
  • Treatment can involve administering a compound described herein to a subject diagnosed with a disease, and may involve administering the compound to a subject who does not have active symptoms. Conversely, treatment may involve administering the compositions to a subject at risk of developing a particular disease, or to a subject reporting one or more of the physiological symptoms of a disease, even though a diagnosis of this disease may not have been made.
  • administer refers to the act of introducing the dosage form into the system of subject in need of treatment.
  • administration and its variants are each understood to include concurrent and/or sequential introduction of the dosage form and the other active agents.
  • Administration of any of the described dosage forms includes parallel administration, co-administration or sequential administration.
  • the therapies are administered at approximately the same time, e.g., within about a few seconds to a few hours of one another.
  • a “therapeutically effective” amount of the compounds described herein is typically one which is sufficient to achieve the desired effect and may vary according to the nature and severity of the disease condition, and the potency of the compound. It will be appreciated that different concentrations may be employed for prophylaxis than for treatment of an active disease. A therapeutic benefit is achieved with the amelioration of one or more of the physiological symptoms associated with the underlying disorder such that an improvement is observed in the subject, notwithstanding that the subject may still be afflicted with the underlying disorder.
  • a therapeutically effective amount of a drug may reduce the number of cancer cells; reduce the tumor size; inhibit (i.e., slow to some extent and preferably stop) cancer cell infiltration into peripheral organs; inhibit (i.e., slow to some extent and preferably stop) tumor metastasis; inhibit, to some extent, tumor growth; and/ or relieve to some extent one or more of the symptoms associated with the cancer.
  • the drug may inhibit the growth of and/or kill existing cancer cells, it may be cytostatic and/or cytotoxic.
  • efficacy can, for example, be measured by assessing the time to disease progression (TTP) and/or determining the response rate (RR).
  • the therapeutic effect can be a decrease in the severity of symptoms associated with the disorder and/or inhibition (partial or complete) of progression of the disorder, or improved treatment, healing, prevention or elimination of a disorder, or side-effects.
  • the amount needed to elicit the therapeutic response can be determined based on the age, health, size, and sex of the subject. Optimal amounts can also be determined based on monitoring of the subject’s response to treatment.
  • treatment or “treat” may include effective inhibition, suppression or cessation of symptoms so as to prevent or delay the onset, retard the progression, or ameliorate the symptoms of a condition.
  • the treatment may be administered using any method known to those skilled in the art of diagnosing and/or treating cancer and associated conditions.
  • the administering is carried out intraperitoneally, orally, parenterally, nasally, subcutaneously, intravenously, intramuscularly, intracerebroventricularly, intraparenchymally, by inhalation, intranasal instillation, by implantation, by intracavitary or intravesical instillation, intraocularly, intraarterially, intralesionally, transdermally, topically, intradermally, intrapleurally, intrathecally, or by application to mucous membranes.
  • the method further includes repeating said administering.
  • the method described herein may, in one embodiment, further include administering an additional treatment.
  • the additional treatment may be any suitable to for treating cancer and associated conditions.
  • An additional treatment may be particularly useful for subjects that having low 2IR sample scores.
  • the additional treatment is selected from surgery, cryosurgery, laser, hyperthermia, photodynamic therapy, external beam radiation therapy, internal radiation therapy, oral chemotherapy, intravenous chemotherapy, injection chemotherapy, intrathecal chemotherapy, intraperitoneal chemotherapy, intra-arterial chemotherapy, topical chemotherapy, targeted therapy, immunotherapy, immune checkpoint inhibitors, t-cell transfer therapy, monoclonal antibodies, treatment vaccines, immune system modulators, small molecule drugs, hormone therapy, stem cell transplant, or any combination thereof.
  • the additional treatment is a cytokine target or a chemokine target.
  • the cytokine target or the chemokine target is a target for IL-1, IL-8, or any combination thereof.
  • the additional treatment is a target for one or more inflammasome or one or more upstream receptors leading to inflammasome activation including TLR1, TLR2, TLR6, TLR 8, CLEC5a, or any combination thereof.
  • a particularly useful additional treatment may include one that targets pro-tumorigenic myeloid cells.
  • a second aspect of the present disclosure relates to a method of overcoming PD-
  • the method includes obtaining or having obtained a biological sample from a subject; determining or having determined an adaptive immunity gene panel expression level in the sample; determining or having determined a pro-tumorigenic inflammation gene panel expression level in the sample; calculating or having calculated a checkpoint inhibitor treatment sensitivity score from the adaptive immunity gene panel expression level and the pro-tumorigenic inflammation gene panel expression level; and administering an anti-cancer treatment to the subject, wherein the anti -cancer treatment comprises a checkpoint inhibitor treatment, if the subject is determined a target for overcoming PD-1/PD-L1 blockade resistance.
  • the subject is determined a target for overcoming PD-l/PD-
  • a third aspect of the present disclosure relates to a method for determining whether a subject is resistant to checkpoint inhibitor treatment for cancer.
  • the method includes obtaining or having obtained a biological sample from a subject; determining or having determined an adaptive immunity gene panel expression level in the sample; determining or having determined a pro-tumorigenic inflammation gene panel expression level in the sample; calculating or having calculated a checkpoint inhibitor treatment sensitivity score from the adaptive immunity gene panel expression level and the pro-tumorigenic inflammation gene panel expression level; and determining whether the subject is resistant to checkpoint inhibitor treatment for cancer based on said comparison.
  • checkpoint inhibitor sensitivity score is below a critical value, then a checkpoint inhibitor treatment is not administered. In another embodiment, if the checkpoint inhibitor sensitivity score is at or above a critical value, then a checkpoint inhibitor treatment is administered.
  • a fourth aspect of the present disclosure relates to a checkpoint inhibitor treatment sensitivity score associated with checkpoint inhibitor treatment that includes an adaptive immunity gene panel and a pro-tumorigenic inflammation gene panel.
  • Example 1 Materials and Methods For Examples 2-7.
  • RNAseq bulk RNA sequencing
  • IMvigor 210 was a single arm phase 2 study investigating PD-L1 inhibition with atezolizumab (1200 mg intravenously every 3 weeks) in patients with metastatic urothelial cancer (NCT01208652, NCT02951767). The primary endpoint of the trial was the objective response rate according to Response Evaluation Criteria In Solid Tumors vl.l. Patients with metastatic urothelial cancer progressing despite prior platinum-based chemotherapy, or chemotherapy-naive patients who were not eligible for cisplatin-based chemotherapy, were eligible. The results of IMvigor 210 have previously been reported.
  • RNAseq data TMB (“FMOne mutation burden per MB”)
  • objective response rate 348 unique patients were extracted using the R package IMvigor210CoreBiologies (Nickles et al., “TGF-b Attenuates Tumor Response to PD-L1 Blockade by Contributing to Exclusion of T Cells,” IMvigor210CoreBiologies Package, which is hereby incorporated by reference in its entirety).
  • the Cancer Genome Atlas bladder cancer dataset includes patients with clinically localized muscle-invasive urothelial cancer of the bladder who underwent radical cystectomy. This cohort has previously been described in detail (Robertson et al., “Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer,” Cell 171(3):540-556.e25 (2017), which is hereby incorporated by reference in its entirety) and RNAseq data
  • Level_3_RSEM_genes_normalized for 408 unique patients was downloaded from Firehose (2016_01_28) at the Broad Institute.
  • the updated clinical data were downloaded from an integrated TCGA pan-cancer clinical data resource Liu et al., “An Integrated TCGA Pan- Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics,” Cell 173(2):400-416.el 1 (2016), which is hereby incorporated by reference in its entirety.
  • Checkmate 275 was a single arm phase 2 study investigating PD-1 inhibition with nivolumab (3 mg/kg intravenously every 3 weeks) in patients with metastatic urothelial cancer (NCT02387996). The primary endpoint of the trial was the objective response rate according to Response Evaluation Criteria In Solid Tumors vl.1. Patients with metastatic urothelial cancer progressing despite prior platinum-based chemotherapy were eligible. The results of Checkmate 275 have previously been reported. Sharma et al., “Nivolumab in Metastatic Urothelial Carcinoma After Platinum Therapy (CheckMate 275): A Multi centre, Single- Arm, Phase 2 Trial,” The Lancet Oncology 18(3) (2017), which is hereby incorporated by reference in its entirety.
  • RNAseq and tumor mutational burden data was provided by Bristol Myers-Squibb and the latter was calculated as the missense mutation count.
  • RNAseq expression datasets For the IMvigor 210 dataset, only genes with a read count >1 in more than 10% of the samples were considered.
  • the raw read count data from the IMvigor and Checkmate 275 datasets were first transformed to RPKM and then scaled patient-wise such that the 75% quantile of each sample was equal to 1000 (similar to the RSEM normalization (Li, Bo & Dewey Colin N., “RSEM: Accurate Transcript Quantification from RNA-Seq Data With or Without a Reference Genome,” BMC Bioinformatics 12:323 (2011), which is hereby incorporated by reference in its entirety)).
  • genes nominally associated with better overall survival outcomes in the IMvigor 210 dataset were first identified.
  • a bivariable Cox regression model was used to estimate the association between the expression of each gene, Gene with the overall survival conditional on TMB: Surv(Event, Time) ⁇ Genei + log (TMB). 1193 genes were identified for which higher expression was associated with better survival outcomes (nominal P-value of two-sided Wald’s test ⁇ 0.05).
  • a lenient P-value cutoff was employed to be as inclusive as possible at this initial gene selection step, and then identified Consistently Co-expressed Gene Modules (CCGMs) to enrich for true signals and filter out possible noise.
  • CCGMs Consistently Co-expressed Gene Modules
  • a CCGM is defined as a list of genes that are co-regulated in multiple datasets.
  • WGCNA Weighted correlation network analysis
  • genes associated with worse survival outcomes conditioned on both TMB and the adaptive immune response signature genes were identified. Specifically, the association of each gene (Genei) with overall survival was assessed using a multivariable Cox regression model Surv Event,Time) ⁇ Genei + M adaptive immune + log (TMB)), where M adaptive immune was calculated for each sample by averaging expression of the adaptive immune response signature genes. A total of 1498 genes were associated with worse survival outcomes (nominal P-value of two-sided Wald’s test ⁇ 0.05).
  • the weighted correlation network analysis was conducted for the 1498 genes in both the IMvigor 210 and TCGA datasets, followed by the overlapping analysis analogous to the methodology described for derivation of the adaptive immune response signature which resulted in two CCGMs, i.e. the “pro-tumorigenic inflammation” signature (437 genes) and stromal signature (287 genes).
  • a third CCGM (50 genes) enriched with HALLMARK_MYC_targets was not further pursued in this study given its small size.
  • the “adaptive immune response” signature was further updated by excluding genes associated with worse survival in this three variate Cox regression model (Z>1.5), resulting in 483 genes in the module.
  • Sample collection and specimen processing Primary urothelial bladder cancer tumor tissue was obtained after obtaining informed consent in the context of an institutional review board approved genitourinary cancer clinical database and specimen collection protocol (IRB #10-1180) at the Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai.
  • Tissue specimens were processed immediately upon receipt and dissociated into single cell suspensions using the GentleMACS Octodissociator with kit matched to the tissue type (Miltenyi Biotech) following the manufacturer’s instructions.
  • Single-cell RNA sequencing was performed on these samples using the Chromium platform (lOx Genomics, Desion, CA) with the 3’ gene expression (3’ GEX) V3 kit, using an input of -10,000 cells.
  • GEMs Gel-Bead in Emulsions
  • Barcoded cDNA was extracted from the GEMs by Post-GEM RT-cleanup and amplified for 12 cycles.
  • Amplified cDNA was fragmented and subjected to end-repair, poly A-tailing, adapter ligation, and lOX-specific sample indexing following the manufacturer’s protocol.
  • Libraries were quantified using Bioanalyzer (Agilent) and QuBit (Thermofisher) analysis. Libraries were sequenced in paired end mode on a NovaSeq instrument (Illumina, San Diego, CA) targeting a depth of 50,000-100,000 reads per cell. Sequencing data was aligned and quantified using the Ceil Ranger Single-Cell Software Suite (version 3.0, lOx Genomics) against the provided GRCh38 human reference genome.
  • T/NK T/NK
  • B/plasma MS4A1”, MZB1”, “CD79A”
  • DC HLA-DQAl”, “HLA-DQBl”
  • Mast MS4A2
  • C1QA Macrophage/Monocyte
  • PLVAP Endothelial
  • DCN Fibroblast-related
  • ACTA2 Epithelial
  • NNAT Neuronal cells
  • Peripheral blood mononuclear cell single-cell RNA sequencing cohort Single cell RNA sequencing data for 10 frozen PBMC samples derived from pre-treatment peripheral blood of 5 patients with metastatic UC who achieved an objective response to treatment with atezolizumab and 5 patients with metastatic urothelial cancer who did not achieve an objective response to treatment with atezolizumab in the setting of the IMvigor 210 study were downloaded from GEO (GSE145281). The peripheral blood single-cell RNA sequencing cohort and analysis are detailed in the Methods in Example 8.
  • Example 2 Gene Signatures Independently Associated With CPI Outcomes in Patients With UC.
  • RNA sequencing and TMB data were available for 348 and 272 patients, respectively.
  • Mariathasan et al. “TGFp Attenuates Tumour Response to PD-L1 Blockade by Contributing to Exclusion of T Cells,” Nature 554(7693):544-548 (2016), which is hereby incorporated by reference in its entirety.
  • Step-wise identification of consistently co-expressed gene modules was pursued, which focused on identifying gene modules associated with overall survival (OS) and utilizing gene modularity to enrich for true signals (FIG. IB, FIG. 7; see Methods in Examples 1 and 8).
  • the IMvigor 210 dataset was further analyzed to identify genes associated with survival conditioning on both TMB and the adaptive immune response signature (FIG. IB). 1498 genes associated with shorter OS were identified. Meta-analysis of co expression patterns (Wang et al., “Meta-analysis of Inter-species Liver Co-expression Networks Elucidates Traits Associated with Common Human Diseases,” PLoS Computational Biology 5(12):el000616 (2009) and Narayanan et al., “Common Dysregulation Network in the Human Prefrontal Cortex Underlies Two Neurodegenerative Diseases,” Molecular Systems Biology 10(7):743-743 (2014), both of which are hereby incorporated by reference in their entirety) in the IMvigor 210 and TCGA UC datasets was again applied and two consistently co-expressed gene modules were identified for further analysis.
  • the first module consisting of 437 genes, was enriched in inflammation and innate immune genes (FIGS. 1C and 8) and associated with shorter OS and was therefore labeled the pro-tumorigenic inflammation signature.
  • the second module associated with shorter OS consisting of 287 genes, was enriched in epithelial mesenchymal transition (EMT)- and extracellular matrix (ECM)-related genes (FIG. 1C) and consistent with the prior work (Wang et al., “EMT- and Stroma-Related Gene Expression and Resistance to PD-1 Blockade in Urothelial Cancer,” Nature Communications 9(1):3503 (2016), which is hereby incorporated by reference in its entirety) was named the stromal signature .
  • EMT epithelial mesenchymal transition
  • ECM extracellular matrix
  • Table 2 shows univariable and multivariable models of the association of the adaptive_immune_response, pro-tumorigenic_inflammation_signature, and stromal signatures, and tumor mutational burden with clinical outcomes in the IMvigor 210 cohort.
  • * is a s subset of patients with available objective response rate data.
  • HR hazard ratio
  • TMB tumor mutational burden.
  • Signature expression and TMB were standardized before entering the Cox-regression model
  • RNA sequencing and TMB data were utilized from the Checkmate 275 study, a single-arm phase 2 trial evaluating the PD-1 inhibitor, nivolumab, in patients with metastatic UC (FIG. ID).
  • Sharma et al “Nivolumab in Metastatic Urothelial Carcinoma After Platinum Therapy (CheckMate 275): A Multicentre, Single-Arm, Phase 2 Trial,” The Lancet Oncology 18(3) (2017), which is hereby incorporated by reference in its entirety.
  • This cohort has been previously described, with further detail provided in Table 1; RNA sequencing and TMB data were available for 72 and 139 patients, respectively.
  • TUMORLOC location of primary tumor
  • ECOGPSLT Eastern Cooperative Oncology Group performance status
  • LIVMET liver metastases
  • PDL1 VAL PD-L1 immunohistochemical testing value
  • VISMET visceral metastases
  • CNSMET CNS metastases
  • HBN hemoglobin
  • NBELLMRF Bellmunt prognostic factor score
  • the 2IR score demonstrated favorable performance characteristics relative to such features including PD-L1 protein expression, the tumor immune dysfunction and exclusion (TIDE) and CD8 effector T cell gene signatures, ARID1A mutation status, and CXCL13 , TGFB1, or CXCL8 (IL8) gene expression (FIGS. 2D and 2G; see FIG. 13 for correlation between these features and the 2IR score) in both the IMvigor 210 and Checkmate 275 cohorts.
  • TIDE tumor immune dysfunction and exclusion
  • IL8 CXCL13 , TGFB1, or CXCL8
  • the 2IR score representing the balance of expression of the adaptive immune response and pro-tumorigenic inflammation gene signatures within individual TMEs, is associated with objective response and OS in CPI-treated patients with metastatic UC in two clinical trial cohorts and conveys information beyond that achieved with previously identified features.
  • a tissue profiling approach was employed known as multiplexed immunohistochemical consecutive staining on a single slide (MICSSS) (Remark et al., “In-Depth Tissue Profiling Using Multiplexed Immunohistochemical Consecutive Staining on Single Slide,” Science Immunology l(l):aaf6925 (2016) and Akturk et al., “Multiplexed Immunohistochemical Consecutive Staining on Single Slide (MICSSS): Multiplexed Chromogenic IHC Assay for High-Dimensional Tissue Analysis,” Methods in Molecular Biology 2055 :497-519 (2020), both of which are hereby incorporated by reference in their entirety) on a subset of 19 specimens from the Checkmate 275 cohort with matched RNA sequencing data.
  • MISSS Multiplexed Immunohistochemical Consecutive Staining on Single Slide
  • MICSSS revealed that specimens with higher 2IR scores exhibited occasional tertiary lymphoid-like structures (FIGS. 3 A, 3B, and 14) consistent with prior findings linking such structures with improved CPI outcomes.
  • Helmink et al. “B Cells and Tertiary Lymphoid Structures Promote Immunotherapy Response,” Nature 577(7791):549-555 (2020) and Cabrita et al., “Tertiary Lymphoid Structures Improve Immunotherapy and Survival in Melanoma,” Nature 577(7791):561-565 (2020), both of which are hereby incorporated by reference in their entirety.
  • cancer cell and stromal zones were defined based on pan-cytokeratin staining using a machine learning segmentation tool and examined CD8+ expression in 76 regions of interest across the 19 specimens (see Example 1 and FIG. 3E).
  • Example 4 Diverse Cellular Populations Underlie the Adaptive Immune Response, Pro- Tumorigenic Inflammation, and Stromal Gene Signatures.
  • BCG Bacillus Calmette-Guerin
  • NAT neoadjuvant therapy
  • UC urothelial cancer
  • NA not applicable
  • TURBT transurethral resection of bladder tumor
  • NE neuroendocrine
  • Canonical marker genes revealed nine major cell populations identified by scRNA-seq including T- and NK cells, B-cells, myeloid-lineage cells, non-hematopoietic stromal cells, and epithelial cells (FIGS. 4B and 4C). To determine the origins of the adaptive immune response, pro-tumorigenic inflammation, and stromal signatures, the expression pattern of the signature genes was assessed among these major cell populations (FIG. 4D).
  • each major cluster was subjected to a second round of partitioning revealing a total of 50 minor cell clusters (described in detail in Examples 8-10 and FIGS. 16-22).
  • expression of both adaptive immune response and pro-tumorigenic signature genes were observed within most minor cell populations (FIG. 4E).
  • the adaptive immune response and pro-tumorigenic inflammation gene signatures are contributed to by diverse cell types within the TME and may be linked to underlying cellular states rather than discrete cellular subpopulations.
  • Example 5 Individual Myeloid Phagocytic Cells Demonstrate Heterogeneous Expression of Adaptive Immune Response and Pro-Tumorigenic Inflammation Signature Genes.
  • Myeloid phagocytic cells demonstrated the most prominent expression of the pro- tumorigenic inflammation signature genes that were associated with CPI resistance in the clinical trial cohorts, yet also expressed some adaptive immune response signature genes (FIG. 4E). Consequently, the expression level of pro-tumorigenic inflammation signature per cell (as assessed by the AddModuleScore() function in the Seurat package) was highest in myeloid phagocytic cells (FIG. 4F).
  • myeloid phagocytic cells had much higher variance in the expression of the pro- tumorigenic inflammation signature genes (aka. heterogeneity of molecular state, FIG.
  • the macrophage populations resembled previously described “TAM-like macrophages” with increased expression of APOE , C1QA, C1QB , SLC40A1 and TREM2.
  • Zhang et al. “Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma,” Cell 179(4): 829-845. e20 (2019) and Lavin et al., “Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses,” Cell 169(4):750-765.el7 (2017), both of which are hereby incorporated by reference in their entirety.
  • Myeloid phagocytic cells are highly plastic, educated by cellular and signaling interactions in the TME, and play diverse roles in promoting and restraining anticancer immunity. Locati et al., “Diversity, Mechanisms, and Significance of Macrophage Plasticity,” Annual Review of Pathology: Mechanisms of Disease 15:123-147 (2020), which is hereby incorporated by reference in its entirety.
  • the single cell characterization of UC specimens revealed diversity in expression of the pro-tumorigenic inflammation and adaptive immune response signature genes across individual macrophages/monocytes and neutrophils (FIG. 5D).
  • M SC 2IR score myeloid single cell 2IR score
  • NicheNet (Browaeys et al., “NicheNet: Modeling Intercellular Communication by Linking Ligands to Target Genes,” Nature Methods 17(2): 159— 162 (2020), which is hereby incorporated by reference in its entirety) was used, an approach that predicts ligands that modulate target gene expression by leveraging prior knowledge of signaling pathways and transcriptional regulatory networks (see Example 1).
  • the M S 2IR score reflecting the balance of adaptive immune response and pro-tumorigenic inflammation gene expression in individual myeloid phagocytic cells, may reflect the plasticity of these cells in the TME (FIG. 5E).
  • Low M SC 2IR score monocytes/macrophages and neutrophils, with upregulation of proinflammatory genes and downregulation of antigen presentation genes and not delineated by classical Ml versus M2 polarization or graph-based unsupervised cell clustering, may define a cellular state of myeloid phagocytic cells contributing to CPI resistance.
  • Example 6 - Monocytes With Low M SC 2IR Scores are Enriched in the Pre-Treatment Peripheral Blood of Patients With CPI-Resistant Metastatic UC.
  • M S 2IR scores were calculated in individual monocytes identifying low, intermediate, and high M SC 2IR score populations. Monocytes with low M SC 2IR scores were significantly enriched in the peripheral blood of patients with CPI-resistant versus CPI-responsive metastatic UC (FIG. 6A; p value
  • Pro-tumorigenic inflammation is recognized as a “Hallmark of Cancer” pathogenesis.
  • Hanahan, D. & Weinberg, R.A. “Hallmarks of Cancer: The Next Generation,” Cell 144(5):646-674 (2011); Coussens et al., “Neutralizing Tumor-Promoting Chronic Inflammation: A Magic Bullet?,” Science 339(6117):286-291 (2013); and Shalapour, S. & Karin, M., “Pas denier: Control of Anti-tumor Immunity by Cancer-Associated Inflammation,” Immunity 51(1): 15—26 (2019), all of which are hereby incorporated by reference in their entirety.
  • the overarching goal was to identify dominant clinically relevant features correlated with CPI resistance that might be linked to underlying immunobiology and associated therapeutic targets for prioritization for further preclinical and clinical testing as CPI-based combination strategies. Additionally, with further refinement and validation, the identified tissue and blood-based features could prove valuable in establishing proof-of-concept in early phase clinical development of combination regimens targeting myeloid-related CPI resistance, through associations with clinical outcomes and/or pharmacodynamic monitoring (e g., serial changes in low Msc2IR score monocytes in peripheral blood).
  • pharmacodynamic monitoring e g., serial changes in low Msc2IR score monocytes in peripheral blood.
  • the stromal gene signature was no longer independently associated with CPI outcomes when the pro-tumorigenic inflammation signature was included in multivariable models suggesting the former may play a more indirect role.
  • Multiple studies have correlated T cell gene signatures, or related measures of adaptive immune resistance, with sensitivity to CPI.
  • Myeloid phagocytic cells have been linked to suppression of antitumor immunity across a range of malignancies via a variety of mechanisms though clinically tractable approaches to target myeloid cell-related CPI resistance have remained elusive.
  • Mantovani et al. “Tumour-Associated Macrophages as Treatment Targets in Oncology,” Nature Reviews.
  • IL-1 was among the top-ranked ligands inferred to regulate the low M SC 2IR score myeloid phagocytic cell gene program in line with prior experimental data demonstrating that inflammatory cytokine and chemokine production from pro-tumorigenic monocytes in patients with renal carcinoma was IL- 1 b-dependent.
  • Chittezhath et al. “Molecular Profiling Reveals a Tumor-Promoting Phenotype of Monocytes and Macrophages in Human Cancer Progression,” Immunity 41 (5):815—829 (2014), which is hereby incorporated by reference in its entirety.
  • IL-Ib has been considered a “master regulator” of inflammation involved in the tumor-promoting and immune suppressive function of myeloid cells, anti-IL-Ib combined with anti -PD- 1 therapy abrogated tumor growth in model systems, and anti-IL-Ib has been associated with lower cancer mortality in human studies.
  • Chittezhath et al. “Molecular Profiling Reveals a Tumor-Promoting Phenotype of Monocytes and Macrophages in Human Cancer Progression,” Immunity 41 (5):815—829 (2014); Condamine T.
  • IL-1 may reverse the inflammatory phenotype of low M SC 2IR score myeloid phagocytic cells and may represent a rational combination strategy to overcome CPI resistance in a defined subset of patients with UC. Additional studies are required to refine the role of IL-la versus IL-Ib in this context though IL- 1b is not present in cells from healthy individuals, and is a product of limited cell types such as myeloid-phagocytic cells, whereas IL-la is more ubiquitously expressed. Clinical trials combining CPI and anti-IL-1 therapies have already been initiated (NCT03631199, NCT03742349).
  • the present disclosure identifies and validates key gene signatures associated with sensitivity or resistance to CPI in patients with metastatic UC related to adaptive immunity and pro-tumorigenic inflammation, defines the 2IR score as reflecting such balance in individual UC TMEs, establishes the M SC 2IR score as reflecting the cellular state of myeloid phagocytic cells linked to CPI resistance, and identifies putative therapeutic targets to overcome resistance.
  • Example 8 Materials and Methods for Examples 9 and 10.
  • w can also be interpreted as accounting for the partial variation of M adaptive immune explained by M pro -i um0 rigenic inflammation score.
  • M a daptive immune M pr 0 _ i umor i g enic m/iam at ion using linear regression
  • Identifying the top-ranked genes in each signature To select a shorter list of genes in the adaptive immune response and pro-tumorigenic inflammation signatures with equivalent predictive power, signature expression was calculated using average expression of the top N genes in each signature (ranked based on their individual association with OS), from which the 2IR score was then calculated (noted as 2IR topN). To select the optimal number of genes from each signature, the association of 2IR_topN with OS was plotted against N, where N ranged from the top 1 gene in the list to the top 200 genes in the signature. The “elbow point” of the curve was then selected as the optimal number of top-ranked genes for each signature (FIG. 10). This resulted in 10, and 39 genes in the adaptive immune response and pro-tumorigenic inflammation signatures, respectively. Similarly, 25 top genes in the stromal signature were selected by evaluating the association of adaptive immune response YS stromal signature expression ratio with OS.
  • the TIDE score was calculated using a web tool from Harvard University.
  • the CD8 effector score was calculated by averaging the CD8 effector genes as previously described.
  • Mariathasan et al. “TGFp Attenuates Tumour Response to PD-L1 Blockade by Contributing to Exclusion of T cells,” Nature 554(7693):544-548 (2016), which is hereby incorporated by reference in its entirety.
  • To derive a combinatory score incorporating both the CD8 effector score and TGFB1 expression a similar strategy was used to the calculation of 2IR score.
  • the bivariate cox regression model Surv(Event, T ime ) ⁇ T GFB 1 + CDQeffector was used to learn the optimal weight for the two variables in IMvigor 210 dataset, and applied the same weight to the validation dataset of Checkmate275.
  • a similar strategy was used to combine CXCL13 expression and ARID 1 A mutation.
  • Fab fragments (AffiniPure Fab Fragment Donkey anti-mouse (715-007-003) or anti-rabbit IgG (711-007-003)) against that primary antibody species were used for blocking any carry-over staining from previous immunostaining cycles using the same species of primary antibody whenever there was a repeat of same primary antibody species.
  • Primary antibody was incubated for a certain period of time depending on its optimized protocol and a polymer detection system (Bond Polymer Refine Detection Kit [DS9800], Leica Biosystems) was used afterwards for the secondary antibody and horse radish peroxidase (HRP) binding.
  • Air-dried slides were scanned by a slide scanner (NanoZoomer S60, Hamamatsu, Japan) and whole slide images were generated and stored on a server.
  • coverslips were removed by placing the slides in a rack and immersed in hot tap water (56°C) until the mounting media dissolved. Chemical destaining was performed by immersing the slides in HC1 (1%) and gradually diluted EtOH solutions after coverslip removal.
  • the immunostaining methodology described here were repeated for all markers in the panel including PD-L1 (1:100, E1L3N, Cell Signaling), CD3 (RTU, 2GV6, Ventana), CD8 (1:100, C8/144B, Dako), CD68 (1:100, KP1, Dako), Fibronectin (1:100, FI, Abeam), and PanCK (1:50, AE1/AE3, Dako).
  • More than 30 features including minimum, maximum, mean, and standard deviation values of intensity and shape-based features were calculated and recorded during the segmentation on nucleus, cytoplasm, and total cellular compartmental area for each cell.
  • Positive cell detection for CD8 was done by training a random forest algorithm. Intensity-based features were used with this algorithm and training was done by marking a set of segmented cells as positive or negative and feeding this training data into the random forest algorithm for the automatic classification of remainder of the segmented cells.
  • w was estimated by the coefficient w in the linear regression X M p ro - tumor igenic in f lammation among myeloid phagocytic cells in the tissue scRNA-seq dataset.
  • the M SC 2IR score was then discretized into tertiles to identify myeloid phagocytic cells with low, intermediate and high M S 2IR score.
  • the average gene expression profile was obtained for each of the 9 major cell clusters using the tumor scRNA-seq dataset (a 22,175 by 9 expression matrix) and a receptor defined as expressed in myeloid phagocytic cells if its corresponding expression value was larger than the median value of the expression matrix. 183 (out of the 510) receptors present in myeloid phagocytic cells were obtained in this manner. A list of 254 ligands interacting with these receptors, and also present in one of the 9 major cell clusters, were then considered as potential ligands.
  • Ligand activity scores were then calculated for each ligand according to its potential to regulate expression of the genes up-regulated in myeloid phagocytic cells with low M SC 2IR score. The top 20 ligands were shown in the results.
  • Peripheral blood mononuclear cell single-cell RNA sequencing cohort The major cell types for each cell were annotated using the automatic annotation algorithm implemented in R package SingleR and the reference profiles obtained from Absolute Deconvolution.
  • Aran et al. “Reference-Based Analysis of Lung Single-Cell Sequencing Reveals a Transitional Profibrotic Macrophage,” Nature Immunology 20(2): 163-172 (2019) and Monaco et ah, “RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types,” Cell Reports 26(6): 1627-1640. e7 (2019), both of which are hereby incorporated by reference in their entirety.
  • Monocytes (including both CD 14 and CD 16 monocytes) were further annotated into minor clusters using the strategy of unsupervised cell clustering similar to the above analysis of the tumor tissue scRNA-seq dataset. Unlike the tumor tissue scRNA-seq dataset, substantial batch effect was observed within the frozen PBMC scRNA-seq dataset: cells were clustered according to samples rather than cell types. To remove potential batch effect and focus on cell subtypes shared across samples, the standard workflow was used for data integration as recommended by the Seurat package where “anchors” were used to harmonize different datasets. Stuart et ah, “Comprehensive Integration of Single-Cell Data,” Cell 177(7): 1888-1902. e21 (2019), which is hereby incorporated by reference in its entirety. Monocytes were then clustered into 4 minor clusters based on the integrated data.
  • Monocytes with low, intermediate and high M SC 2IR score were identified following the similar strategy used in the tumor tissue scRNA-seq analysis. Notably, the M SC 2IR score was calculated for each cell using the gene expression value before batch correction or integration, such that the score was not affected by any integration procedures. Differentially expressed genes between monocytes with high and low M S 2IR score were also derived using the original expression before integration. Ligands regulating genes upregulated in low M SC 2IR score monocytes were identified using NicheNet in the manner as described for the tumor tissue scRNA-seq dataset.
  • NELLMRF score Bellmunt prognostic score
  • Fibroblast-related cell minor clusters The major fibroblast-related cell cluster was further partitioned into four fibroblast clusters with overexpression of fibroblast markers LUM and DCN , as well as three non-fib roblast clusters (FIGS. 15 A, 15B).
  • fibroblast markers LUM and DCN overexpression of fibroblast markers
  • FIGS. 15 A, 15B Three non-fib roblast clusters.
  • CAF heterogeneity implicate distinct CAF populations including those involved in shaping stromal architecture (i.e., myofibroblastic CAFs) and those associated with maintaining an inflammatory milieu (i.e., inflammatory CAFs). Kalluri, R., “The Biology and Function of Fibroblasts in Cancer,” Nature Reviews. Cancer 16(9): 582-598 (2016), which is hereby incorporated by reference in its entirety.
  • fibroblast-eCAF-C7 with overexpression of C7 and TIMP1
  • fibroblast-TNF-CXCL2 a subset most consistent with inflammatory CAFs
  • fibroblast-ECM-LRRC15 a subset most consistent with myofibroblastic CAFs
  • fibroblast-ECM-LRRC15 a subset most consistent with myofibroblastic CAFs characterized by expression of the marker gene LRRC15 and EMT- and ECM-related genes
  • T and NK cell minor clusters T and NK cell clusters were identified across all patient samples (FIGS. 16A-16E).
  • Two CD8+ T cell clusters were annotated as T-CD8-effector and T-CD8-Trm-ITGA1.
  • the T-CD8-effector cluster was characterized by higher expression of cytotoxic T cell genes such as GZMA, GZMB, GZMK, PRF1, and IFNG.
  • the CD8-Trm-ITGA1 cluster expressed ITGA1, a marker of tissue resident memory (Trm) T cells as well as other Trm signature genes.
  • PDCD1 which encodes PD-1
  • PDCD1 was expressed by a small subset of CD8+ and CD4+ T cells with the highest expression observed in the T-CD8-effector cluster.
  • Regulatory T cells expressed high levels of CTLA4, TIGIT, and ICOS relative to other T cells.
  • Innate lymphocytes, including NK cells and a population resembling innate lymphoid cells lacking lineage markers and expressing CD127 ( IL-7Ra ) and CD 117 (KIT) were also identified.
  • Dendritic cell minor clusters Dendritic cell (DC) clusters were annotated according to previously defined signature genes and included conventional DC1 (cDCl), DC2 (cDC2), and plasmacytoid DC (pDC) (FIGS. 18A-18B).
  • DC1 cDCl
  • DC2 cDC2
  • pDC plasmacytoid DC
  • Balan et al. “Large-Scale Human Dendritic Cell Differentiation Revealing Notch-Dependent Lineage Bifurcation and Heterogeneity,” Cell Reports 24(7): 1902-1915. e6 (2018); Martin et al., “Single-Cell Analysis of Crohn’s Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy,” Cell 178(6): 1493-1508.
  • a fourth DC cluster expressed the activation marker, LAMP 3, consistent with a high resolution scRNA-seq analysis of hepatocellular cancer in which LAMP3+ DCs were shown to represent a mature form of conventional DCs with the potential to migrate from tumor to lymph nodes.
  • Zhang et al. “Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma,” Cell 179(4):829-845.e20 (2019), which is hereby incorporated by reference in its entirety.
  • Epithelial cell minor clusters The epithelial cells were further clustered into 8 minor clusters. Three subsets were annotated as basal cells, and the remaining five as luminal cells according to basal and luminal markers (FIGS. 19A-19C). Most subsets consisted of cells from one sample, reflecting the inter-patient cancer cell heterogeneity. One exception was the epithelial-luminal-UPK subset which was detected in the majority of specimens. This subset expressed higher uroplakins and other genes expressed in terminally differentiated urothelial umbrella cells ( KRT20 , SNX31), and were present in both tumor and adjacent normal tissues. In contrast, the epithelial-luminal-CRTACl population was only found in adjacent normal tissue.
  • Endothelial cell minor clusters Endothelial cells (ECs) were clustered into 5 subsets (FIGS. 20A-20D).
  • ECs Endothelial cells
  • tip cells a blood vessel
  • stalk cells a blood vessel
  • Zhao et al. “Single-Cell Transcriptome Analyses Reveal Endothelial Cell Heterogeneity in Tumors and Changes Following Antiangiogenic Treatment,” Cancer Research 78(9):2370- 2382 (2018), which is hereby incorporated by reference in its entirety.
  • the two tip-like subsets overexpressed genes related to distinct pathways: the endothelial_Tip_TNF subset was enriched with TNF- and IL6-related pathways while the endothelial Stalk ECM subset was enriched with ECM, EMT and Integrin related pathways. These findings are analogous to the myofibroblastic and inflammatory CAF dichotomy. A fourth subset expressed FBLN5. Interestingly, this endothelial subset overexpressed IFN pathways compared with other subsets.
  • B cell minor clusters B-cells were further split into plasma (CD38+), naive B (IGHM+ and IGHD+) and memory B cells (CD27+) as detailed in FIGS. 21A-21E. Two memory B subsets were identified with overexpression of CCR7 and CD24, respectively.

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Abstract

La présente divulgation concerne une méthode de traitement du cancer. Le procédé comprend l'obtention d'un échantillon biologique à partir d'un sujet ; la détermination d'un niveau d'expression de panel de gènes d'immunité adaptative dans l'échantillon ; la détermination d'un niveau d'expression d'un panel de gènes d'inflammation pro-tumorigène dans l'échantillon ; le calcul d'un score de sensibilité au traitement par inhibiteur de point de contrôle à partir du niveau d'expression d'un panel de gènes d'immunité adaptative et du niveau d'expression du panel de gènes d'inflammation pro-tumorigène ; et l'administration d'un traitement anticancéreux au sujet, le traitement anticancéreux comprenant un traitement par inhibiteur de point de contrôle si le score de sensibilité d'inhibiteur de point de contrôle est au niveau d'une valeur critique ou au-dessus de celle-ci. Des méthodes pour surmonter la résistance au blocage de PD-1/PD-L1 chez un sujet et des procédés pour déterminer si un sujet est résistant au traitement par inhibiteur de point de contrôle pour le cancer sont également divulgués. Un score de sensibilité de traitement par inhibiteur de point de contrôle associé à un traitement par inhibiteur de point de contrôle qui comprend un panel de gènes d'immunité adaptative et un panel de gènes d'inflammation pro-tumorigène est également divulgué.
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