WO2020092589A1 - Immune checkpoint therapeutic methods - Google Patents

Abstract

Contemplated systems and methods are directed to omics analysis of various tumors, especially as it relates to prediction of treatment outcomes using immune checkpoint therapy of colorectal cancer. More particularly, patients with tumors having high TMB and CMS2 and that exhibit Wnt pathway activation can be excluded from immune therapy due to an immunosuppressive TME.

Description

IMMUNE CHECKPOINT THERAPEUTIC METHODS

[0001] This application claims priority to our copending US provisional patent application with the serial number 62/753,680, which was filed 10/31/2018, and which is incorporated by reference herein.

Field of the Invention

[0002] The field of the invention is omics analysis of tumor tissue, especially as it relates to response prediction to immunotherapy.

Background of the Invention

[0003] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

[0004] All publications and patent applications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

[0005] Tumor mutational burden (TMB) has emerged as a potential biomarker for immune therapy, following quantification of PD-L1 expression of PD-L1 in tumor tissue. However, TMB does not represent direct evidence of immunogenicity and does not accurately predict the dynamic immune response. Therefore, TMB alone cannot accurately assess the dynamic immune microenvironment. As biologic links between TMB and immunogenicity are not well understood, any cutoff score remains arbitrary and should be at best applied with caution in the clinic ( Journal of Clinical Oncology, Vol 36, No 30, 2018: pp 2978-2979).

[0006] Consensus molecular subtypes (CMS) classification of colorectal cancer has recently been shown as a predictive tool for chemotherapeutic efficacy against metastatic colorectal cancer using specific drug treatments. For example, irinotecan (IRI)-based chemotherapy was significantly superior to oxaliplatin (OX)-based chemotherapy for progression-free survival (PFS) and overall survival (OS) in CMS4, while for anti-epidermal growth factor receptor therapy, CMS1 showed particularly worse PFS and OS. On the other hand, CMS2 showed particularly good PFS and OS compared with the other subtypes (See Oncotarget, 2018, Vol. 9, (No. 27), pp: 18698-18711). However, these studies failed to provide any general guidance as to CRC subtyping and immune therapy.

[0007] Still further studies established that patients with CMS 1 subtype CRC, and especially those with a high microsatellite instability (MSI-H) genotype were the suitable candidates for immunotherapy, which led to the regulatory approval of the checkpoint inhibitors nivolumab and pembrolizumab. Unfortunately, MSI-H only represents a small fraction of patients with metastatic CRC ( J Cancer Metastasis Treat 20l8;4:28). Moreover, MSI classification was not reported to help exclude patients from anticipated immune therapy.

[0008] While numerous tumors with high mutational burden often have increased treatment success with checkpoint inhibitors, immune suppressive tumor microenvironments frequently reduce or even negate treatment success. Therefore, there is still a need to provide systems and methods of stratifying tumors with high tumor mutational burden into treatment sensitive and resistant groups.

Summary of The Invention

[0009] The inventive subject matter is directed to various methods of selecting patients for immune therapy of a tumor with high tumor mutational burden, and especially where such tumors are gastrointestinal tumors. Most typically, patient selection for these and other cancers will not rely on univariate analyses considering TMB, CMS, or specific mutations per se, but will take into consideration multiple factors in concert to so arrive at a more likely responder population with respect to immune therapy.

[0010] In one aspect of the inventive subject matter, the inventors contemplate a method of treating a patient with immune checkpoint therapy that includes a step of obtaining omics data from a tumor sample, and a further step of classifying the data as having high tumor mutational burden (TMB). In another step, the data are classified as belonging to a consensus molecular subtype (CMS1, CMS2, CMS3, or CMS4), and immune checkpoint therapy is administered to the patient when the TMB is high and when the CMS subtype is not CMS2. [0011] Moreover, it is contemplated that such methods may also include a step of determining a pathogenic mutation in a gene where that gene encodes TP53, KRAS, APC, NBPF1, ZNF117, NBPF10, KMT2C, CSMD3, PABPC1, BCLAF1, PIK3CA, ZNF479, SMAD4, CDC27, and/or HMCN1, and wherein checkpoint therapy is administered to the patient when at least one, or at least three, or at least five pathogenic mutation are present.

[0012] Alternatively, or additionally, contemplated methods may also include a step of determining an inferred immune infiltrate activity, and checkpoint therapy is administered to the patient when the inferred immune infiltrate activity is classified as hot. For example, inferred immune infiltrate activity may be based on at least three, or at least ten, or at least fifteen different immune cells such as Tgd cells, Tern cells, pDC cells, Tern cells, NK cells, TFH cells, B Cells, T-cells, CD8 T-cells, Thl-cells, Th2-cells, Helper T Cells, aDC cells, NK CD56dim cells, Treg cells, Thl7 cells, NK CD56bright cells, mast cells, eosinophils, macrophages, dendritic cells, and neutrophils. Most typically the inferred immune infiltrate activity will be based on at least effector memory cells.

[0013] In further embodiments, contemplated methods may also include a step of determining expression of at least checkpoint gene, and checkpoint therapy is administered to the patient when at least one checkpoint gene (e.g., CTLA4, PDL1, PDL2, TIM3, LAG3) is overexpressed relative to normalized expression.

[0014] With respect to the TMB it is generally contemplated that a high TMB classification is assigned when the TMB is an average normalized TMB of at least 7 and/or when the omics data have > 200 non-synonymous exonic mutations. Most preferably, checkpoint therapy is administered to the patient when the CMS subtype is CMS1 or CMS4, particularly where the tumor is a gastrointestinal tumor. Immune checkpoint therapy will typically include administration of a checkpoint inhibitor targeting CTLA4 or PD-l. Where desired, a cancer vaccine composition (e.g., viral vaccine in which a recombinant virus encodes a plurality of neoantigens) may be administered to the patient. Additionally, or alternatively, an immune stimulatory cytokine or cytokine analog may be administered to the patient (e.g., IL-2, IL-12, IL-15, IL-21 or ALT- 803).

[0015] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

Brief Description of The Drawing

[0016] Fig.l exemplarily illustrates GI cancer types and CMS subtypes within each cancer type and age distribution of CMS subtypes.

[0017] Fig.2 depicts exemplary results showing CMS types as a function of TMB.

[0018] Fig.3 depicts exemplary results showing checkpoint expression as a function of CMS types.

[0019] Fig.4 depicts exemplary results for expression levels and expression correlations for selected TME/checkpoint genes for CMS1 and CMS2 subtypes.

[0020] Fig.5 depicts exemplary results for expression levels and expression correlations for selected TME/checkpoint genes for CMS3 and CMS4 subtypes.

[0021] Fig.6 is another graph depicting exemplary comparative results for expression levels for selected TME/checkpoint genes for CMS 1-4 subtypes.

[0022] Fig.7 depicts an exemplary heat map depicting hot/cold clustering based on immune deconvolution and CMS types.

[0023] Fig.8 depicts exemplary results for inferred immune infiltrate activity for CMS 1-4 subtypes.

[0024] Fig.9 depicts Z-score data for CMS 1-4 subtypes.

[0025] Fig.10 depicts exemplary results for selected germline mutations for CMS 1-4 subtypes.

[0026] Fig.ll depicts exemplary results for selected somatic mutation signatures for CMS 1-4 subtypes.

[0027] Fig.12 depicts exemplary results for selected somatic mutation signatures for CMS 1-4 subtypes. [0028] Fig.13 depicts exemplary results for selected pathogenic Wnt family APC mutations for CMS2.

[0029] Fig. 14 depicts exemplary photomicrographs with high specificity between deep-net generated tumor masks and pathologist annotations·

[0030] Fig. 15 depicts exemplary graphs related to alignment of purity and stromal estimates with expectations from DNA & RNA.

[0031] Fig. 16 depicts exemplary results for RNAseq-based immune deconvolution sorted by sTIL levels.

[0032] Fig. 17 depicts exemplary results for RNAseq-based lymphocyte score vs. various deep-net assessments.

[0033] Fig. 18 depicts exemplary results for checkpoint expression patterns using different methods of bifurcating patients.

[0034] Fig. 19 depicts exemplarily results illustrating which immune-cell types are most associated with sTIL levels.

Detailed Description

[0035] Tumor mutational burden (TMB) has emerged as an important biomarker for immune checkpoint therapy (ICT) response. Yet even in the context of high TMB, ICT is frequently ineffective in an immuno-suppressed microenvironment. The inventors have now discovered systems and methods of detecting an immunosuppressive TME despite high TMB. Among other parameters, CMS2 for colorectal cancer, associated with Wnt pathway activation, low checkpoint expression, one or more pathogenic mutants, and/or inferred immune infiltrate activity were found to be indicative of an immunosuppressive TME despite a high TMB. Conversely, it should be recognized that such multivariate analysis can also be employed to improve accuracy of treatment prediction for various cancers to immune therapy, and particularly GI cancers. Thus, and viewed from a different perspective, contemplated systems and methods will also use, CMS (or other subtyping such as PAM50) typing, Wnt pathway activation, checkpoint expression, one or more pathogenic mutants (both germline and/or somatic), and/or inferred immune infiltrate activity to predict treatment response to immune therapy. [0036] Most typically, contemplated analyses will be based on omics data from the tumor and/or matched normal tissue to so arrive at patient and tumor specific mutations. Thus, the omics data will include DNA data, and especially whole genome sequencing data and/or whole exome sequencing data. For example, suitable omics data may include tumor/normal- paired DNAseq (WGS or WES) data and deep RNAseq (~200xl0⁶reads) data. As will be readily appreciated, all manners of DNA and RNA data collection are deemed suitable and may include‘fresh’ data from a tumor sample as well as previously obtained and stored data from a database. As such, mutational analysis can be performed versus a reference sequence or in a tumor- versus-normal manner using systems and methods as described in US

2012/0066001 and US20150094963, incorporated by reference herein.

[0037] As will be readily appreciated, the omics data may also be used to determined tumor mutational burden and specific mutations, and particularly know pathogenic mutations (that may or may not tumor driver genes). TMB can be determined in numerous manners, and in preferred aspects it is contemplated that the TMB is established by observing all somatic- specific non-synonymous exonic mutations (see e.g., Science 348.6230 (2015): 124-128). Thus, and viewed from another perspective, TMB may be ascertained form omics data when the omics data have > 200 non-synonymous exonic mutations. Alternatively, TMB may be ascertained from omics data when the TMB is an average normalized TMB of at least 7. As is shown, for example, in more detail below, TMB was higher in CMS1 and CMS2 subtypes, which at least conceptually would represent a more likely treatment success with immune therapy. On the other hand, CMS4 would not have been viewed as a good candidate for immune therapy when looking at TMB only. Notably, CMS classification and TMB for CMS2 type tumors provide contradictory results (CMS2 less likely treatment success but TMB high indicative of likely treatment success).

[0038] Moreover, it should be noted that the omics data (and particularly RNAseq data) also allow for determination of the checkpoint expression. Notably, and as is shown in more detail below, checkpoint expression was associated with selected CMS subtypes. Here, despite a high TMB, CMS2 tumors had a significantly reduced checkpoint expression. As such, immune treatment of TMB high CMS2 tumors would not be indicated. Interestingly, CMS2 was also associated with certain pathogenic mutations as can be seen below, which further disqualified CMS2 TMB high tumors from immune therapy. [0039] In addition, it was discovered that the RNAseq data are also especially informative of inferred immune infiltration activity. Immune infiltration activity can be performed by immune deconvolution of genes specifically expressed in specific immune cells as is shown in more detail below. Among other cells, especially suitable cells include Tgd cells, Tern cells, pDC cells, Tcm cells, NK cells, TFH cells, B Cells, T-cells, CD8 T-cells, Thl-cells, Th2-cells, Helper T Cells, aDC cells, NK CD56dim cells, Treg cells, Thl7 cells, NK

CD56bright cells, mast cells, eosinophils, macrophages, dendritic cells, neutrophils, and especially effector memory cells.

[0040] As is shown below, CMS2 TMB high tumors exhibited an immunologically cold profile, which once more disqualified CMS2 TMB high tumors from immune therapy. Thus, it should be appreciated that high TMB alone is not a suitable marker for prediction of treatment success. Indeed, it is contemplated that a multi-factorial approach that takes into account (1) a molecular subtype (e.g. , CMS, PAM50, etc.), (2) one or more pathogenic mutations, and especially mutations in TP53, KRAS, APC, NBPF1, ZNF117, NBPF10, KMT2C, CSMD3, PABPC1, BCLAF1, PIK3CA, ZNF479, SMAD4, CDC27, and HMCN1, (3) high TMB, (4) inferred immune infiltrate activity, and/or (5) checkpoint expression will produce a more robust metric for prediction of treatment success with immune therapy.

[0041] Based on the inventors’ findings, it is therefore contemplated that treatment success with immune therapy will be more likely where the molecular subtype is CMS1 or CMS4, where the tumor has a high TMB, where inferred immune infiltrate activity is reflective of an immunologically‘hot’ cluster, and where a relatively low count of pathogenic mutations is present (especially in TP53, KRAS, APC, NBPF1, ZNF117, NBPF10, KMT2C, CSMD3, PABPC1, BCLAF1, PIK3CA, ZNF479, SMAD4, CDC27, and/or HMCN1). Moreover, treatment success for CMS1 and CMS4 tumors with immune therapy will be more likely when checkpoint expression is high, particularly for LAG3, TIM3, PDL1, PDL2, and CTLA4.

[0042] Conversely, treatment success for high TMB CMS2 tumors with immune therapy will be less likely when checkpoint expression (especially PDL-2, LAG3, PDL1, CTLA4, IDO) is low, when the inferred immune infiltrate activity is reflective of an immunologically‘cold’ cluster, and/or when specific pathogenic mutations are present in selected genes (especially in TP53, KRAS, APC, NBPF1, ZNF117, NBPF10, KMT2C, CSMD3, PABPC1, BCLAF1, PIK3CA, ZNF479, SMAD4, CDC27, and/or HMCN1). [0043] As will be readily appreciated, immune therapy of a patient can include various forms of treatment, and especially contemplated treatments include treatments with one or more checkpoint inhibitors, vaccine compositions, and /or immune stimulatory cytokines.

Therefore, exemplary therapies include those with antibodies against checkpoint inhibitors and their cognate receptors, while vaccine treatments particularly include those that provide recombinant neoantigens to a subject (e.g., via recombinant bacterial, yeast, and/or viral vectors and viruses/cells). Preferred immune stimulatory cytokines or cytokine analogs will comprise IL-2, IL-12, IL-15, IL-21 or ALT- 803.

Examples

[0044] In one exemplary aspect of the inventive subject matter, sequence analyses were performed on 464 GI tumors from a commercial database, and DNA sequences were based on tumor/normal-paired DNAseq (WGS or WES) and RNA sequences were based on deep RNAseq (about 200xl0⁶reads per tumor). The samples were classified as high TMB if they had > 200 non-synonymous exonic mutations as previously established (see e.g., Science 348.6230 (2015): 124-128). Each sample was assigned to one of the colorectal Consensus Molecular subtypes (CMS) based on RNA classification. A curated panel of 109 genes that discriminate between 22 immune subsets was identified. For each of these immune signatures, a database containing 1880 unselected tumors was used to define a distribution of expression. The study samples were then scored for their deviances within such distributions. Somatic-specific pathogenic/likely pathogenic mutations were identified using ClinVar annotations. Significant enrichment was analyzed between immune subsets, CMS types,

TMB status, and somatic mutational status.

[0045] CMS1 & CMS2 have significantly higher TMB; CMS1 (MSI-enriched) expresses selected TME markers more than other subtypes. Perplexingly, CMS2 had significantly lower expression of 6 targetable checkpoint markers. As expected, CMS2 tumors were significantly enriched for likely pathogenic variants in the Wnt-associated gene APC. Immune- deconvolution indicated substantial exclusion of Tern cells from CMS2 tumors, in line with Wnt/ -catenin blockade of Tern to Tern maturation for immunoreactivity.

[0046] Compared to other subtypes, CMS1 & CMS2 were significantly high- TMB (adj. p < 3.8E-4 and p < 4.7E-3, respectively). Perplexingly, CMS2 had significantly lower expression of 11 well-established checkpoint and TME markers including LAG3 and PDL1 (adj. p 1.5E-2 and 2.9E-9 respectively), while CMS1 (MSI-enriched) expressed selected TME markers more than other subtypes (PDL1 adj. p < 4.0E-6 and LAG3 adj. p < 1.0E-6). As expected, CMS2 tumors were significantly enriched for likely pathogenic variants in the Wnt-associated gene APC (adj. p < 1.3E-8). Immune-deconvolution indicated substantial exclusion of Tern cells from CMS2 tumors, in line with Wnt/b-catenin blockade of Tern to Tern maturation for immunoreactivity.

[0047] The most common subtype of CRC, CMS2 (-37%), is highly immunosuppressive despite high TMB. ICT is only effective in an immunologic ally active microenvironment. Therefore, TMB alone as a biomarker likely is insufficient to indicate the effectiveness of immunotherapy and should be supplemented with CMS classification, and particularly with CMS2 classification to exclude patients with TMB+CMS2 from ICT.

[0048] More specifically, FIG.l depicts the distribution of tumor types across samples and the CMS subtype for each tumor type, along with a distribution of patient age and CMS subtype. The inventors also determined whether there are apparent differences in checkpoint or TME markers across various CMS subtypes, and exemplary results are shown in FIG.4 (CMS1 and CMS2) and FIG.5 (CMS3 and CMS4). As can be seen from the correlation and expression data, no substantial difference is observed. Notably, however, expression levels in the CMS2 subtype were significantly lower as compared to the other subtypes, while expression levels in the CMS4 subtype were higher in more than half of the observed genes as can be seen from FIG.6. Tables 1 and 2 below show numerical and statistical differences of CMS4 (table 1) and CMS2 (table 2) subtypes versus the rest of the subtypes.

Table 1

Table 2

[0049] As can be seen from FIG.2, TMB-high was associated with CMS1 and CMS2, while moderate TMB was seen for CMS3, and a low TMB was observed for CMS4. Notably, the checkpoint expression was significantly distinct between CMS1 and CMS2, despite CMS1 and CMS2 being TMB high as is shown in FIG3.

[0050] The inventors then performed an immune-deconvolution on the tumor tissues and observed that CMS2 is significant in the immune-suppressed cluster, while CMS4 associated with immune activation. Table 3 provides further statistical differences between CMS4 vs. CMS2 and CMS2 vs. CMS1.

Table 3

[0051] FIG.9 provides further Z-score data indicating that CMS2 excludes effector memory cells (Tern) very often, and that CMS1&4 seem to tolerate immune-infiltration. With respect to germline mutation, it was observed that there are numerous APC and BRCA2 pathogenic mutations are present in GI and exemplary results are shown in FIG.10. APC and BRCA2 germline pathogenic mutations are especially frequent in this cohort, but are not significantly distinct from previously reported CRC cohorts. FIG.ll depicts exemplary results for somatic mutations. Here, it was observed that CMS2 has more somatic-specific mutations in APC than others. It should be noted that APC is a component of the Wnt pathway. Seeing increased APC mutations is supportive of Tern depletion within CMS2 samples: APC mutations increase Wnt activation which reduces Tern maturation into Tern cells. With respect to mutational signatures, exemplary results are shown in FIG.12. Here, CMS2 was significantly associated with mutational signatures 10 (enriched in POL-E mutated CRC patients), 18 (stomach, unknown etiology), and 9 (associated with activation of induced deaminase activity in response to somatic hypermutation, especially in CLL), while CMS4 was significantly associated with sig 16 (typically associated with liver cancer but with unknown mechanism). As can be seen from FIG.7, CMS2 was significantly associated with lower inferred immune infiltrate activity, and especially effector memory cells, and FIG.8 depicts a table that further illustrates low inferred immune infiltrate activity in CMS2 tumors. In yet further analyses, the inventors also discovered that CMS2 is significantly associated with likely pathogenic Wnt family APC mutations as is exemplarily illustrated in FIG.13. Such mutational pattern once more is counter-indicative for likely treatment success with of such tumors immune therapy.

[0052] Finally, when comparing CMS2 subtype versus the remaining subtypes, CMS2 had the highest average TMB of the subtypes, yet the lowest immune infiltration and checkpoint expression. This subtype could spuriously be put as the best candidates for IO therapy when they are the least likely to respond. In view of the above, it should be appreciated that despite the CMS2 subtype having the highest TMB (and high TMB typically being indicative of positive treatment response to immune oncotherapy), patients with high TMB and belonging to CMS2 are the least likely to respond to immune oncotherapy. As such, patients with such profile should not be regarded candidates to immune oncotherapy.

[0053] In another exemplary method, the inventors combined digital masking using deep- neural nets with transcriptomic deconvolution to infer where immune- subpopulations may reside in the TME. More specifically, an unselected set of 187 clinical samples from the ImmunityBio database were analyzed. Each sample had H&E stained diagnostic slides with pathologist-annotated tumor regions, as well as deep whole-transcriptomic sequencing (>200M reads). Deep neural networks previously trained on TCGA slide images were used to generate digital spatial masks for 3 characteristics: tumor-content, lymphocytes, and stroma. Patients were scored based on the presence of intratumoral lymphocytes (iTIL) and stromal lymphocytes (sTILs). Immune subpopulations were then inferred from RNAseq expression of published immune-cell- specific genesets (Bindea, 2013 & Danaher, 2011), as was Wnt- signaling level (Slattery, 2018). Significant associations between immune subpopulations and level of infiltration were analyzed.

[0054] Using such approach, manually annotated positive tumor regions were accurately digitally masked as >83% tumor or lymphocyte. Wnt signaling was strongly associated with overall stromal content (Rho=0.47, p<0.0001). Strong anti-correlation was observed between levels of sTILs and iTILs (Rho=-0.42, p<0.0001), and remained significant when including overall stroma area as a covariate. Digital lymphocyte masks somewhat correlated with RNAseq-based deconvolution of lymphocyte classes (Rho=0.30, p=0.0001) in line with reports from others (Rosenthal, 2019), however, this decreased when comparing lymphocyte count within annotated tumor regions only (Rho=0.17, p=0.03), despite high concordance of lymphocyte counts within and outside of annotated regions overall (Rho=0.82, p<0.0001). RNAseq-based lymphocyte levels were more associated with sTILs than iTILs (Rho=0.19 vs. -0.28, p<0.01 respectively). It was found that adaptive response effectors such as NK and T- cells were found more resident in surrounding stromal tissue than infiltrating tumor tissue. Moreover, increased Wnt/B-catenin signaling in stromal regions, reported by others as immunosuppressive, may sequester immune effectors and aid in immune escape.

[0055] FIG.14 depicts example images with high specificity between deep-net generated tumor masks and pathologist annotations. Tumor predictions are marked in orange, lymphocytes in green, stroma in purple, pathologist annotations in blue/red circles. FIG.15 shows that purity and stromal estimates align with expectations from DNA & RNA. More specifically, as can be seen in the left graph a comparison of 243 NGS-based purity estimates and deep-net-based purity estimates is illustrated where the X-axis denotes percentage of tiles classified as tumor, and the y-axis denotes purity estimates from GPSCancer. Lines indicate min-max range of estimates from DNA sequencing. Notably, overall DNA purity estimates are higher than image-based estimates, most likely because of macrodissection prior to sequencing as well as different cell-density between tumor and non- tumor regions. The table to the right provides summary statistics for DNA and deep-net based purity. On average DNA purity is -24% higher. Despite overall having much lower average purity estimation from deep nets, regions marked as at least 80% tumor by pathologists in 184 images were also masked as 83% tumor regions on average by deep-nets (when allowing lymphocytes to be marked as correct). The smaller graph demonatrates that stromal tissue is reported to express Wnt-pathway genes. As can be taken from the graph, there is a highly significant correlation between Wnt geneset activation (inferred based on 32 Wnt-associated genes in URL: www.ncbi.nlm.nih.gov/pmc /articles/PMC58l4l96/) and deep-net predicted stromal area.

[0056] The inventors further performed RNAseq-based immune deconvolution sorted by sTIL levels. Here, analyzed samples were filtered from 184 to the 166 samples that had >15% tumor area as an image quality filter, and FIG.16 depicts exemplary results. Panel A) depicts the percentage of lymphocyte regions also classified as within stromal regions (sorted). Panel B) depicts the percentage of lymphocyte regions also classified as within tumor regions. Note these are somewhat anti-correlated with sTILs. Panel C) shows the percentage of all slide patches that classify as lymphocyte-rich. Note that variance in % lymphocyte increases as sTIL decreases. Panel D) shows the percentage of all slide patches that classify as stroma. Note this is somewhat correlated with sTIL levels, and Panel E) depicts Wnt geneset activation. Note that this (like % stroma) is also somewhat correlated with sTIL. Panel F) shows a Heatmap of inferred activities (z-scores) for 23 immune-cell types, based on comparison to a background population. In general the‘hotter’ samples are associated with higher sTILs. Finally, Panel G) depicts the sum of Z-score across all immune-cell types (i.e. sum of columns in F). Note that activation level somewhat correlates with sTILs.

[0057] FIG.17 illustrates exemplary results for RNAseq-based lymphocyte score vs. various deep-net assessments. RNA-based estimate is the mean z-score for NK,T, and B cells. More specifically, Panel A shows the correlation between RNA estimates of total lymphocyte and image estimates is -0.35, in line with what others have presented (e.g. Rosenthal et al, 2019). Panel B) depicts the correlation between RNA and lymphocyte count goes down when only assessing pathologist-annotated tumor regions, suggesting the positive correlation in A) is mainly driven by areas outside tumor regions (as annotated by pathologists). Panel C) shows RNA-based estimates and iTIL is signifnantly anticorrelated, suggesting RNA levels aren’t driven by lymphocytes in tumor-regions. Panel D) illustrates that RNA estimates are somewhat correlated with sTILs. This statistically supports that RNA-based estimates of immune infiltration are potentially driven by stromal or non-tumor-infiltrating lymphocytes. Table E) shows coefficients and p- values for a bivariate linear regression model relating iTIL ~ sTIL + image_stroma. Notably, even when taking overall stroma level into consideration, sTIL percentage is still a strong contraindicator of tumor infiltration.

[0058] FIG.18 demonstrates exemplary results for checkpoint expression patterns using different methods of bifurcating patients. RNA expression of 8 key immunoregulatory (IO) molecules, split on either RNAseq-based lymphocyte score median (blue/green), image % lymphocyte patches (red/purple), or sTIL (i.e. percentage of lymphocytes within stroma) (yellow, cyan) t-test results for each gene between these 3 methods to group patients are presented in the table on the right, with p-values adjusted using Benjamini-Hochberg multiple hypothesis correction. Higher immune infiltration by RNA-based deconvolution is significantly associated with elevated levels of all IO genes. sTIL level is significantly associated with differential expression of most IO genes. Lymphocyte area is the least informative of the three grouping methods shown here, although it is significantly associated with 4/8 IO genes analyzed.

[0059] FIG.19 exemplarily illustrates which immune-cell types are most associated with sTIL levels (and expression of most checkpoints). Shown on the left are violin plots contrasting inferred activity levels of each immune cell set with high sTIL (top 50%) vs. low sTIL (bottom 50%). Shown on the right is a table of the associations between sTIL and immune-cell type that remain significant after Benjamini-Hochberg adjustment. All cell types were higher in high sTIL vs. low sTIL (split by median sTIL score). Mast cells are most significantly associated with high sTILs, and are known to reside in connective tissue which stroma resembles. Very diverse immune cell types seem to be associated with sTIL levels, suggesting this is a general measure of immune-competency rather than a specific response- type, although perhaps independent of PDL1 -mediated evasion.

[0060] Based on the above, it should therefore be recognized that deep-net tumor/normal mask performs as expected against pathologist’s gold-standard, and that RNA deconvolution and that deep-net lymphocyte scores agree as much as expected, in line with results from others. Moreover, it was shown that Wnt signaling (RNA) correlates with deep-net stromal content, that RNAseq-based‘immune-hot’ scores correlate with stromal content, specifically sTILs, and not iTILs, and that patients with high sTILs appear to have a wide variety of immune-cell modalities elevated. Finally, it can be concluded that increased Wnt/B-catenin signaling in stromal regions, reported by others as immunosuppressive, may sequester immune effectors and aid in immune evasion. [0061] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term“about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein.

[0062] It should be noted that any language directed to a computer should be read to include any suitable combination of computing devices, including servers, interfaces, systems, databases, agents, peers, engines, modules, controllers, or other types of computing devices operating individually or collectively. One should appreciate the computing devices comprise a processor configured to execute software instructions stored on a tangible, non- transitory computer readable storage medium (e.g., hard drive, solid state drive, RAM, flash, ROM, etc.). The software instructions preferably configure the computing device to provide the roles, responsibilities, or other functionality as discussed below with respect to the disclosed apparatus. In especially preferred embodiments, the various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPS, AES, public -private key exchanges, web service APIs, known financial transaction protocols, or other electronic information exchanging methods. Data exchanges preferably are conducted over a packet- switched network, the Internet, LAN, WAN, VPN, or other type of packet switched network.

[0063] As used herein, the term“administering” a pharmaceutical composition or drug refers to both direct and indirect administration of the pharmaceutical composition or drug, wherein direct administration of the pharmaceutical composition or drug is typically performed by a health care professional (e.g., physician, nurse, etc.), and wherein indirect administration includes a step of providing or making available the pharmaceutical composition or drug to the health care professional for direct administration (e.g., via injection, infusion, oral delivery, topical delivery, etc.). It should further be noted that the terms“prognosing” or “predicting” a condition, a susceptibility for development of a disease, or a response to an intended treatment is meant to cover the act of predicting or the prediction (but not treatment or diagnosis of) the condition, susceptibility and/or response, including the rate of progression, improvement, and/or duration of the condition in a subject.

[0064] All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g.“such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

[0065] As used in the description herein and throughout the claims that follow, the meaning of“a,”“an,” and“the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of“in” includes“in” and“on” unless the context clearly dictates otherwise. As also used herein, and unless the context dictates otherwise, the term "coupled to" is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with" are used synonymously.

[0066] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein.

The inventive subject matter, therefore, is not to be restricted except in the scope of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms“comprises” and“comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C .... and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

Claims

CLAIMS What is claimed is:

1. A method of treating a patient with immune checkpoint therapy, comprising:

obtaining omics data from a tumor sample;

classifying the data as having high tumor mutational burden (TMB);

classifying the data as belonging to a consensus molecular subtype (CMS) selected from the group consisting of CMS1, CMS2, CMS3, and CMS4; and administering immune checkpoint therapy to the patient when the TMB is high and when the CMS subtype is not CMS2.

2. The method of claim 1, further comprising a step of determining a pathogenic mutation in a gene encoding a protein selected from the group consisting of TP53, KRAS, APC, NBPF1, ZNF117, NBPF10, KMT2C, CSMD3, PABPC1, BCLAF1, PIK3CA, ZNF479, SMAD4, CDC27, and HMCN1, and further wherein checkpoint therapy is administered to the patient when at least one and less than four pathogenic mutation is present.

3. The method of claim 1, further comprising a step of determining a pathogenic mutation in a gene encoding a protein selected from the group consisting of TP53, KRAS, APC, NBPF1, ZNF117, NBPF10, KMT2C, CSMD3, PABPC1, BCLAF1, PIK3CA, ZNF479, SMAD4, CDC27, and HMCN1, and further wherein checkpoint therapy is administered to the patient when at least three and less than six pathogenic mutations are present.

4. The method of claim 1, further comprising a step of determining a pathogenic mutation in a gene encoding a protein selected from the group consisting of TP53, KRAS, APC, NBPF1, ZNF117, NBPF10, KMT2C, CSMD3, PABPC1, BCLAF1, PIK3CA, ZNF479, SMAD4, CDC27, and HMCN1, and further wherein checkpoint therapy is administered to the patient when less than 5 pathogenic mutations are present.

5. The method of any one of claims 1-4, further comprising a step of determining an inferred immune infiltrate activity, and further wherein checkpoint therapy is administered to the patient when the inferred immune infiltrate activity is classified as hot.

6. The method of claim 5, wherein the inferred immune infiltrate activity is based on at least three of Tgd cells, Tern cells, pDC cells, Tcm cells, NK cells, TFH cells, B Cells, T-cells,

CD8 T-cells, Thl-cells, Th2-cells, Helper T Cells, aDC cells, NK CD56dim cells, Treg cells, Thl7 cells, NK CD56bright cells, mast cells, eosinophils, macrophages, dendritic cells, and neutrophils.

7. The method of claim 5, wherein the inferred immune infiltrate activity is based on at least ten of Tgd cells, Tern cells, pDC cells, Tcm cells, NK cells, TFH cells, B Cells, T-cells, CD8 T-cells, Thl-cells, Th2-cells, Helper T Cells, aDC cells, NK CD56dim cells, Treg cells, Thl7 cells, NK CD56bright cells, mast cells, eosinophils, macrophages, dendritic cells, and neutrophils.

8. The method of claim 5, wherein the inferred immune infiltrate activity is based on at least effector memory cells.

9. The method of any one of claims 1-8, further comprising a step of determining expression of at least checkpoint gene, and further wherein checkpoint therapy is administered to the patient when at least one checkpoint gene is overexpressed relative to normalized expression.

10. The method of claim 9, wherein the checkpoint gene is CTLA4, PDL1, PDL2, TIM3, and LAG3.

11. The method of any one of claims 1-10, wherein the classification as high TMB is assigned when the TMB is an average normalized TMB of at least 7.

12. The method of any one of claims 1-10, wherein the classification as high TMB is assigned when the omics data have > 200 non- synonymous exonic mutations.

13. The method of any one of claims 1-12, wherein checkpoint therapy is administered to the patient when the CMS subtype is CMS1.

14. The method of any one of claims 1-12, wherein checkpoint therapy is administered to the patient when the CMS subtype is CMS4.

15. The method of any one of claims 1-14, wherein the tumor sample is a gastrointestinal tumor sample.

16. The method of any one of claims 1-15, wherein the immune checkpoint therapy comprises administering a checkpoint inhibitor targeting CTLA4 or PD-l.

17. The method of any one of claims 1-16, further comprising a step of administering a cancer vaccine composition to the patient.

18. The method of claim 17 wherein the cancer vaccine is a viral vaccine in which a recombinant virus encodes a plurality of neoantigens.

19. The method of any one of claims 1-18, further comprising a step of administering an immune stimulatory cytokine or cytokine analog.

20. The method of claim 19 wherein the immune stimulatory cytokine or cytokine analog comprises IL-2, IL-12, IL-15, IL-21 or ALT-803.

21. The method of claim 1, further comprising a step of determining an inferred immune infiltrate activity, and further wherein checkpoint therapy is administered to the patient when the inferred immune infiltrate activity is classified as hot.

22. The method of claim 21, wherein the inferred immune infiltrate activity is based on at least three of Tgd cells, Tern cells, pDC cells, Tern cells, NK cells, TFH cells, B Cells, T- cells, CD8 T-cells, Thl-cells, Th2-cells, Helper T Cells, aDC cells, NK CD56dim cells, Treg cells, Thl7 cells, NK CD56bright cells, mast cells, eosinophils, macrophages, dendritic cells, and neutrophils.

23. The method of claim 21, wherein the inferred immune infiltrate activity is based on at least ten of Tgd cells, Tern cells, pDC cells, Tern cells, NK cells, TFH cells, B Cells, T-cells, CD8 T-cells, Thl-cells, Th2-cells, Helper T Cells, aDC cells, NK CD56dim cells, Treg cells, Thl7 cells, NK CD56bright cells, mast cells, eosinophils, macrophages, dendritic cells, and neutrophils.

24. The method of claim 21, wherein the inferred immune infiltrate activity is based on at least effector memory cells.

25. The method of claim 1, further comprising a step of determining expression of at least checkpoint gene, and further wherein checkpoint therapy is administered to the patient when at least one checkpoint gene is overexpressed relative to normalized expression.

26. The method of claim 25, wherein the checkpoint gene is CTLA4, PDL1, PDL2, TIM3, and LAG3.

27. The method of claim 1, wherein the classification as high TMB is assigned when the TMB is an average normalized TMB of at least 7.

28. The method of claim 1, wherein the classification as high TMB is assigned when the omics data have > 200 non- synonymous exonic mutations.

29. The method of claim 1, wherein checkpoint therapy is administered to the patient when the CMS subtype is CMS1.

30. The method of claim 1, wherein checkpoint therapy is administered to the patient when the CMS subtype is CMS4.

31. The method of claim 1, wherein the tumor sample is a gastrointestinal tumor sample.

32. The method of claim 1, wherein the immune checkpoint therapy comprises administering a checkpoint inhibitor targeting CTLA4 or PD-l.

33. The method of claim 1, further comprising a step of administering a cancer vaccine composition to the patient.

34. The method of claim 33 wherein the cancer vaccine is a viral vaccine in which a recombinant virus encodes a plurality of neoantigens.

35. The method of claim 1, further comprising a step of administering an immune stimulatory cytokine or cytokine analog.

36. The method of claim 35 wherein the immune stimulatory cytokine or cytokine analog comprises IL-2, IL-12, IL-15, IL-21 or ALT-803.