WO2020091944A1

WO2020091944A1 - Genomic and immune infiltration differences between msi and mss gi tumors

Info

Publication number: WO2020091944A1
Application number: PCT/US2019/054567
Authority: WO
Inventors: Yulia NEWTON; Charles VASKE; Christopher Szeto; Sandeep K. REDDY; Stephen Charles BENZ
Original assignee: Nantomics, Llc
Priority date: 2018-10-31
Filing date: 2019-10-03
Publication date: 2020-05-07

Abstract

Contemplated systems and methods are directed to omics analysis for GI cancers with respect to MSI status. Certain cancers had significant differences in their pathway activity, checkpoint gene expression, and expression of TME genes, and the inventors contemplate that such differences can be used to further improve treatment outcome prediction of cancers with regard to immune therapy.

Description

GENOMIC AND IMMUNE INFILTRATION DIFFERENCES BETWEEN MSI AND

MSS GI TUMORS

[0001] This application claims priority to our copending US provisional patent application with the serial number 62/753,722, filed October 31, 2018, and which is incorporated by reference herein.

Field of the Invention

[0002] The field of the invention is omics analysis of tumors, especially as it relates to MSI (microsatellite instability) analysis of GI (gastrointestinal) tumors.

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] Cancer immunotherapies have led to remarkable responses in some patients, however, many patients failed to respond, despite having the apparent same type of cancer as those that responses positive to immunotherapy. A possible explanation for such failure is that various effector cells of the immune system can be blocked by compounds (checkpoint inhibitors) that interact with one or more inhibitory regulatory pathways. Notably, some tumor cells can make use of the inhibitory regulatory pathways to so evade detection and destruction by the immune system. Among other components, PD-l and CTLA-4 are the most studied receptors that are involved with inhibition of immune responses and specific drugs have now become available that block activation of these receptors. For example, antibodies directed to PD-l (e.g., nivolumab and pembrolizumab) and CTLA4 (e.g., ipilimumab) have yielded significant clinical responses in some cases of melanoma, renal cell carcinoma, non-small cell lung cancer, and various other tumor types. Unfortunately, not all types of cancers respond equally well to treatment with checkpoint inhibitors. Moreover, even within the same type of cancer, positive response predictability for checkpoint inhibitors has been elusive.

[0005] Loss of mismatch repair (MMR) often results in drug resistance directly by impairing the ability of the cell to detect DNA damage and activate apoptosis, and also indirectly by increasing the mutation rate throughout the genome. For example, MMR-deficient cells have been reported to be resistant to various methylating/alkylating agents, platinum-containing drugs, antimetabolites, and topoisomerase II inhibitors. Moreover, MMR deficient cells have an increased mutation rate, which is often expressed as microsatellite instability (MSI). The determination of MSI has become a routine and can be performed in various manners. For example, MSI can be determined using amplification of DNA from matching normal and test samples, and such amplification typically targets loci known in the art to identify MSI (e.g., US 6150100, US 7662595, US2003/0113723, WO 2017/112738, etc). Alternatively, MSI may also be inferred from NGS genomics data (e.g., US 2017/0032082, Clin Chem 2014, 60:9, pl 192-1199). As cells with MSI are often less sensitive to conventional drug treatment, immunotherapy would be desirable. However, efficacy of immunotherapy for MSI tumors has remained unpredictable as is treatment of MSI tumors with checkpoint inhibitors.

[0006] 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.

[0007] Therefore, while various methods are known in the art to characterize a tumor using MSI and other metrics to predict treatment outcome for immune therapy, all or almost all of them suffer from one or more disadvantages. Therefore, there is still a need for improved omics analysis as it relates to MSI status with regard to prediction of treatment outcome for immune therapy.

Summary of The Invention

[0008] The inventive subject matter is directed to various systems and methods of omics analysis of a tumor in which microsatellite status is used and/or qualified with additional omics data. In preferred aspects, such additional data help improve prediction of treatment outcome of the tumor with immune therapy.

[0009] In one aspect of the inventive subject matter, the inventors contemplate a method of predicting treatment response of a tumor (e.g., gastrointestinal tumor) to immune therapy that includes a step of obtaining omics data from a tumor sample, wherein the omics data comprise DNA sequence data and RNA sequence expression data. In another step, the omics data are then used to determine expression levels for a checkpoint related gene, and/or a TME related gene, and in a further step the omics data are used to determine up- or downregulation of a pathway in the tumor sample relative to a non-tumor sample. In yet another step the omics data are used to determine MSI status. Treatment response is then predicted using the expression levels for a checkpoint related gene, and/or a TME related gene, the up- or downregulation of the pathway, and the MSI status.

[0010] For example, contemplated checkpoint related gene may be PDL2, PDL1, LAG3, and/or TIM3, and TME related genes include those expressed in an immune cell (e.g., 0X40, IDO, and/or TIGIT). In other examples, the pathway (and especially the up-regulated pathway) is an EMT transition pathway, a TNF-alpha signaling via NF-kappa B pathway, an allograft rejection pathway, an IL6-JAK-STAT3 signaling pathway, an inflammatory response pathway, a KRAS signaling pathway, an interferon alpha response pathway, a hypoxia pathway, an IL2-STAT signaling pathway, and/or or a complement pathway.

[0011] In another example, treatment response is predicted to be positive when the tumor has a MSI status and/or wherein at least one of the checkpoint related gene is overexpressed, and/or treatment response is positive when the pathway is an over-enriched immune pathway, and/or wherein the TME related gene is a gene that is expressed in an eosinophil cell, a Thl7 cell, a TFH cell, a neutrophil cell, a macrophage, a T cell, a CD8⁺ T cell, and an NK cell.

[0012] In another aspect of the inventive subject matter, the inventors contemplate a method of inferring MSI status of a cancer tissue that includes a step of obtaining omics data from a tumor sample, wherein the omics data comprise DNA sequence data and RNA sequence expression data. The omics data are then used to determine expression levels for a checkpoint related gene, and/or a TME related gene, and to determine up- or downregulation of a pathway in the tumor sample relative to a non-tumor sample. Microsatellite status is then inferred using the expression levels for the checkpoint related gene, and/or the TME related gene, and the up- or downregulation of the pathway.

[0013] While not limiting to the inventive subject matter, suitable checkpoint related gene include PDL2, PDL1, LAG3, and/or TIM3, while preferred TME related genes are genes that is expressed in an immune cell (e.g., 0X40, IDO, and/or TIGIT). Contemplated pathway especially include an EMT transition pathway, a TNF-alpha signaling via NF-kappa B pathway, an allograft rejection pathway, an IL6-JAK-STAT3 signalling pathway, an inflammatory response pathway, a KRAS signaling pathway, an interferon alpha response pathway, a hypoxia pathway, an IL2-STAT signaling pathway, and/or or a complement pathway, particularly where such pathways are up-regulated.

[0014] Therefore, in some embodiment the microsatellite status may be inferred as MSI when at least one of the checkpoint related gene is overexpressed and when the TME related gene is indicative of immune cell enrichment in the TME. Alternatively, or additionally, the microsatellite status may be inferred as MSI when the pathway is up-regulated and when the pathway is an EMT transition pathway, a TNF-alpha signaling via NF-kappa B pathway, an allograft rejection pathway, an IL6-JAK-STAT3 signaling pathway, an inflammatory response pathway, a KRAS signaling pathway, an interferon alpha response pathway, a hypoxia pathway, an IL2-STAT signaling pathway, and/or or a complement pathway.

[0015] In a further aspect of the inventive subject matter, the inventors contemplate a method of improving MSI-based treatment outcome prediction for a treatment response of a tumor to immune therapy that includes the steps of determining MSI status of the tumor, obtaining omics data from a tumor sample, wherein the omics data comprise DNA sequence data and RNA sequence expression data, using the omics data to determine expression levels for a checkpoint related gene, and/or a TME related gene, using the omics data to determine up- or downregulation of a pathway in the tumor sample relative to a non-tumor sample, and using the expression levels for the checkpoint related gene and/or the TME related gene, and the up- or downregulation of the pathway to qualify the MSI status.

[0016] For example, suitable checkpoint related gene include PDL2, PDL1, LAG3, and/or TIM3, while TME related genes include a gene that is expressed in an immune cell.

Moreover, it is contemplated that the pathway is an EMT transition pathway, a TNF-alpha signaling via NF-kappa B pathway, an allograft rejection pathway, an IL6-JAK-STAT3 signaling pathway, an inflammatory response pathway, a KRAS signaling pathway, an interferon alpha response pathway, a hypoxia pathway, an IL2-STAT signaling pathway, and/or or a complement pathway, especially where such pathway is up-regulated. Therefore, the step of qualifying the MSI status pathway may be a qualification that the MSI is likely responsive to immune therapy.

[0017] 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

[0018] Fig.l depicts the types of cancers found in the data set of GI cancers used herein.

[0019] Fig.2 shows the proportions of MSS and MSI samples within each type of GI cancer of the data set of Fig.1.

[0020] Fig.3 is a graph comparing gene expression of selected genes in samples with MSI high versus MSI low status for colorectal tumors.

[0021] Fig.4 shows an exemplary list of up-regulated pathways in MSI-high colorectal tumor samples.

[0022] Fig.5 shows further up-regulated pathways in MSI-high colorectal tumor samples.

[0023] Fig.6 shows an exemplary list of up-regulated pathways in MSI-low colorectal tumor samples.

[0024] Fig.7 is a graph comparing gene expression of selected genes in samples with MSI high versus MSI low status for gastric tumors.

[0025] Fig.8 shows an exemplary list of up-regulated pathways in MSI-high gastric tumor samples.

[0026] Fig.9 shows an exemplary list of up-regulated pathways in MSI-low gastric tumor samples.

[0027] Fig.10 graphically depicts pathway overlaps between different types of GI cancers for MSI high and MSI low samples.

[0028] Fig.ll illustrates somatic variants for colorectal cancers.

[0029] Fig.12 illustrates a heat map for GI cancers.

[0030] Fig.13 illustrates exemplary results of a Z-score analysis depicting immune infiltrates summarized on MSI status. [0031] Fig.14 depicts correlations (left) and expression (right) of checkpoint and TME genes.

[0032] Fig.15 illustrates expression differences between MSI and MSS samples for selected genes in GI cancers.

[0033] Fig.16 illustrates expression differences between MSI and MSS samples for selected genes in colorectal cancers.

[0034] Fig.17 illustrates immune cell enrichment in MSI and MSS samples of GI cancers. [0035] Fig.18 graphically illustrates MSI versus MSS differential pathway enrichments.

Detailed Description

[0036] Dysregulation of DNA mismatch repair pathway can lead to microsatellite instability in many gastrointestinal (GI) tumors, and microsatellite instability (MSI) has been viewed as an important diagnostic and prognostic marker, particularly in the context of immune therapy MSI tumors comprise about 15% of colorectal malignancies and can also be found in other GI tumor types. However, prior analyses focusing on MSI only were often inconclusive or had relatively low predictive value with respect to the immune status and/or prediction of treatment success with immune therapy. To further refine prediction of treatment outcome and diagnoses, the inventors analyzed genomic and immune infiltration differences between MSI and microsatellite stable (MSS) GI tumors spanning multiple cancer types. In addition, the inventors also investigated up-/down-regulation of pathway activities in the context of MSI and MSS tumors.

[0037] More particularly, and as is described in more detail below, a total of 521 GI cancer patient data were evaluated using deep whole exome sequencing (WES) of tumor and blood samples, as well as whole transcriptomic sequencing (RNA-Seq., at ~200M reads per tumor). Data were obtained for analysis from a commercial database. Variant calling was performed through joint probabilistic analysis of tumor and normal DNA reads, with germline status of variants being determined by heterozygous or homozygous alternate allele fraction in the germline sample.

[0038] Most notably, and as is also discussed in more detail below, the gene expression and pathway analysis found significantly higher immune signaling and upregulation of structural cellular integrity pathways in the MSI cohort, while higher metabolic signaling was observed in the MSS cohort. In addition, a per-sample deconvolution of immune infiltration using cell type gene markers showed some MSI samples with high CD8 T-cells.

[0039] Co-expression analysis of checkpoint and tumor microenvironment (TME) associated genes showed higher correlation of FOXP3 and CTLA4 in the MSS cohort compared to the MSI samples, whereas correlation between FOXP3 and PDL1 is decreased. Moreover, TIM3, LAG3, and 0X40 were significantly more expressed in MSI samples than in MSS samples. Within the subset of colorectal tumors, additional checkpoints are significantly differentially overexpressed in MSI malignancies. Indeed, the inventors noted that 50 somatic variants are significantly differential in MSI tumors.

[0040] Consequently, the inventors contemplate that patient samples can be further stratified using in addition to MSI status omics analysis to determine immune infiltration and genomic differences that are relevant to immune therapy. MSI tumors that demonstrably exhibit higher immune signaling, with many immune and checkpoint markers expressed at higher levels in MSI tumors may be particularly suitable for immune therapy. In addition, it was found that some cellular integrity pathways appeared to be upregulated in the MSI cohort, and a number of potentially important somatic variants were associated with MSI samples. Viewed form a different perspective, the inventors noted that among MSI tumors various other factors may be predictive or improve prediction accuracy for treatment outcome. As is shown in more detail below, such other factors particularly included expression level of specific checkpoint genes, up-or down-regulation of specific pathways, and presence or absence of immune infiltration of various immune cells. In yet further contemplated aspects, one or more of these other factors may even be used as a proxy measure for microsatellite status.

[0041] Therefore, the inventors contemplate that a method of predicting treatment response of a tumor to immune therapy may include a step of obtaining omics data that will typically include DNA sequence data (such as those from NGS sequencing) and RNA sequence expression data (such as those from RNAseq). These omics data can then be used to determine expression levels for a checkpoint related gene and/or a TME related gene.

Moreover, the same omics data can be used to determine up- or downregulation of a pathway in the tumor sample per se or relative to a non-tumor sample. For example, the pathway activity can be inferred form the omics data as is described, for example in WO2011/139345 and WO/2013/062505, and/or via gene set enrichment analysis as is well known in the art. Furthermore, it should be appreciated that the omics data may also be used to determine the microsatellite status as is known from US 2017/0032082, incorporated by reference herein. Once the microsatellite (or MSI) status is determined, a treatment response may be predicted on the basis of the microsatellite (or MSI) status, the expression levels for one or more of the checkpoint related gene, the expression level of one or more TME related genes (or immune infiltration status), and/or the up- or down-regulation of one or more pathways. As will be readily appreciated, where the microsatellite status is determined to be MSI, but where the expression levels for the checkpoint related gene and/or the expression level of the TME related genes (or immune infiltration status) is low, the prediction of treatment success based on MSI status may be downgraded. On the other hand, where pathways associated with MSI are upregulated, the prediction of treatment success based on MSI status may be upgraded.

[0042] Where MSI status was not identified, it is also contemplated that various other analyses may be employed to infer MSI status, and especially contemplated analyses include determine expression of levels for one or more checkpoint related gene, expression levels of TME related genes (or immune infiltration status), and up- or downregulation of a pathway in the tumor. In such case, MSI can be inferred if expression of levels for one or more selected checkpoint related genes are increased relative to normal or MSS samples, expression of levels for one or more selected TME related genes (or immune infiltration status) are increased relative to normal or MSS samples, and/or selected pathways associated with MSI are upregulated and/or selected pathways with MSS are down-regulated.

[0043] In still further embodiments, a method of improving MSI-based treatment outcome prediction for a treatment response of a tumor to immune therapy is contemplated where the omics data are used to determine expression levels for one or more checkpoint related gene, and/or for one or more of a TME related gene. The omics data are also used to determine up- or down-regulation of a pathway in the tumor sample relative to a non-tumor sample. Upon these determinations, a previously or concurrently determined MSI status may then be qualified (e.g., as suitable for immune therapy or likely to be responsive to immune therapy).

[0044] With respect to the omics data it should be appreciated that the data may be obtained from various sources, and especially contemplated sources include various service providers (e.g., sequencing centers), sequence databases, and other data sources storing patient and tumor specific sequence data. Consequently, the omics data will include DNA sequencing data, and especially whole genome sequencing data and exome sequencing data, where the data are typically generated using NGS sequencing technologies. Likewise, contemplated omics data will also include RNA data, and particularly RNA sequence and expression data that will not only provide sequence and splicing information, but will also information as to the level of gene expression. As will be readily appreciated, such information may be used to identify patient and tumor specific mutations (where matched normal information from the patient is available), and such information may be further used to infer pathway activity using known pathway modeling algorithms (e.g., PARADIGM as described in WO2011/139345 or WO/2013/062505) and/or gene set enrichment analysis. Furthermore, it is noted that MSI status may be directly determined using PCR-based tests well known in the art, or from the omics data as is described, for example, in US 2017/0032082, incorporated by reference herein.

[0045] 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, 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.

Example

[0046] The following description provides exemplary guidance to the skilled artisan with respect to omics analysis of omics data from MSI/MSS tumor samples but is not intended to limit the subject matter of this disclosure. More particularly, to investigate the association of gene expression of one or more genes with MSI status, the inventors used a dataset that had omics data from 592 GI tumor samples with a tumor type distribution as shown in FIG.l. As can be seen from the Table in FIG.2, not every tumor type in the data set had one or more MSI high samples. Thus, subsequent analysis was performed within individual tumor types. [0047] For example, FIG.3 depicts a graph for a comparison of gene expression between MSI and MSS tumor data sets for colon, rectal, and anal tumors of selected genes in samples with MSI high versus MSI low status (8 samples MSI high and 83 MSI low). As can be readily seen from the graph, while most genes are expressed in substantially similar levels, other genes had significant increase or decrease in expression.

[0048] Based on the analyses and differentially expressed genes, the inventors found selected pathways upregulated in MSI High colorectal cancers as is shown in the table of FIG.4. Here an exemplary collection of the top 20 are shown (while there were 54 pathways with FWER p-val <= 0.05). FIG.5 lists further exemplary pathway enrichments for the same tumors. Similarly, the table in FIG.6 depicts pathways upregulated in MSS/MSI Low colorectal samples by GSEA analysis (21 pathways with FWER p-val <= 0.05).

[0049] Likewise, where gastric cancer was investigated, the inventors found that while most genes are expressed in substantially similar levels, other genes had a significant increase or decrease in expression, and FIG.7 shows exemplary results for differential expression in the gastric cancer samples (2MSI High, 22 MSI Low). The table in FIG.8 depicts up-regulated pathways in MSI High gastric cancer samples as seen by GSEA analysis, while the table in FIG.9 depicts upregulated pathways in MSS/MSI Low gastric cancer samples as seen by GSEA analysis. Notably, there was an overlap between colorectal and gastric cancers as determined by GSEA analysis as is exemplarily depicted in FIG.10. As such, it should be appreciated that the up-regulated and/or down-regulated pathways identified herein can be used to further qualify or even predict MSI status.

[0050] Further analysis of germ line variations showed no significant germline variants when looking at Frame Shift, Missense, and Nonsense variants using Clinical Significance, Likely pathogenic, Pathogenic, Pathogenic/Likely pathogenic, and drug response as Clinvar flags. With respect to somatic variants, the inventors analyzed Frame Shift, Missense, Nonsense, Splice Site variants for selected cancer genes only (COSMIC). One-sided Fisher exact test with multiple hypothesis adjustment was performed, and analysis was done separately for colorectal and stomach tumor types. Exemplary results for colorectal cancer are shown in FIG.ll, while gastric cancer had no significant hits after multiple hypothesis p- value adjustment. [0051] In still further investigations, the inventors performed unsupervised clustering on immune-infiltrate scores: Here, Z-scores were determined for cell-type enrichment in 464 patients with RNAseq performed. Scores are calculated by comparison to all Nant clinical samples. MSI-status is annotated above (light grey=MSI, grey=MSS). Silhouette analysis was performed to find k=2 as best number of classes (see labels below). As can be seen from

FIG.12, there is a relatively immuno-active subset of GI cancers (cluster 1, see label below). Notably, MSI-H is not significantly associated with either cluster (p=0.28).

[0052] FIG.13 depicts immune infiltrates summarized on MSI status: Here, average Z- scores (left), percentage of patients called high (middle) and percentage called low (right) for immune cell-types in 464 patients with RNAseq performed as depicted. Scores are calculated by comparison to all clinical samples. 8/22 immune cell markers were considered high in at least one MSI sample, and CD8 T-cells were high in 2/12 MSI samples. No cell types were lower than expected in MSI.

[0053] FIG.14 depicts correlations (left) and expression (right) of checkpoint and TME genes. Expression is log2(TPM+l), blue=0, red=7. Overall patterns are difficult to discern with only 12 MSI samples. However, within MSI samples clear correlation between FOXP3 and CTLA4 is diminished, whereas correlation between FOXP3 and PDL1 is increased.

[0054] When analyzing expression differences between checkpoint and TME genes, the inventors noted that TIM3, LAG3, and 0X40 are significantly more expressed in MSI samples as shown in FIG.15. Here, expression box-and-scatters (left) and t-test results (right) of checkpoint and TME genes are shown in MSI vs. MSS for all GI clinical samples. Genes are sorted left-to-right by significance of differential expression. Limiting the analysis to colorectal samples provides a similar picture as shown in FIG.16. Here, expression box-and- scatters (left) and t-test results (right) of checkpoint and TME genes is shown in MSI vs.

MSS in just CRC clinical samples. Genes are sorted left-to-right by significance of differential expression. As can be readily taken from the figure, more checkpoints are significantly differentially expressed between MSS and MSI in CRC, and the differential is larger. Unadjusted P- values are significant for the majority of the markers observed. With the exception of VEGFA and VEGFB, all markers are higher in MSI samples. Thus, it should be noted that these checkpoint genes may be used to qualify MSI status or even to predict MSI. [0055] In still further experiments, expression analysis and immune gene deconvolution tests were performed on RNA expression data to identify immune cell infiltration in the tumor tissue. FIG.17 depicts results from immune cell enrichment analysis using RNA expression and gene deconvolution of MSI tumor samples and MSS tumor samples. As can be readily seen, MSI samples were generally enriched in immune cells, while MSS samples had less immune cell infiltration. However, it should be noted that such infiltration will vary from tumor to tumor, and as such the analysis can be performed on a specific sample to further detect whether or not the sample exhibits immune cell infiltration (which is predictive for a positive treatment outcome).

[0056] FIG.18 exemplarily illustrates pathway up- and down-regulation for MSI and MSS GI tumors. As can be seen from FIG.18, MSI was strongly associated with up-regulation of selected pathways, while MSS was associated with up-regulation of other pathways. As such, pathway up-and down-regulation can be employed as a tool to predict MSI status or as a tool to qualify MSI status. For example, where a tumor sample is determined to be MSI, a likely positive treatment outcome may be associated with up-regulation of the MSS linked pathways as shown in FIG.18.

[0057] 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.

[0058] 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. 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.

[0059] 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.

[0060] 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 predicting treatment response of a tumor to immune therapy, comprising: obtaining omics data from a tumor sample, wherein the omics data comprise DNA sequence data and RNA sequence expression data;

using the omics data to determine expression levels for a checkpoint related gene, and/or a TME related gene;

using the omics data to determine up- or downregulation of a pathway in the tumor sample relative to a non-tumor sample;

using the omics data to determine MSI status; and

predicting treatment response using the expression levels for a checkpoint related gene, and/or a TME related gene, the up- or down-regulation of the pathway, and the MSI status.

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

3. The method of claim 1, wherein the checkpoint related gene is PDL2, PDL1, LAG3, and/or TIM3.

4. The method of claim 1, wherein the TME related gene is a gene that is expressed in an immune cell.

5. The method of claim 4, wherein the TME related gene is 0X40, IDO, or TIGIT.

6. The method of claim 1, wherein the pathway is an EMT transition pathway, a TNF-alpha signaling via NF-kappa B pathway, an allograft rejection pathway, an IL6-JAK-STAT3 signaling pathway, an inflammatory response pathway, a KRAS signaling pathway, an interferon alpha response pathway, a hypoxia pathway, an IL2-STAT signaling pathway, and/or or a complement pathway.

7. The method of claim 1, wherein the treatment response is positive when the tumor has a MSI status and/or wherein at least one of the checkpoint related gene is overexpressed.

8. The method of claim 1, wherein the treatment response is positive when the pathway is an over-enriched immune pathway, and/or wherein the TME related gene is a gene that is expressed in an eosinophil cell, a Thl7 cell, a TFH cell, a neutrophil cell, a macrophage, a T cell, a CD8⁺ T cell, and an NK cell.

9. A method of inferring MSI status of a cancer tissue, comprising:

obtaining omics data from a tumor sample, wherein the omics data comprise DNA sequence data and RNA sequence expression data;

inferring microsatellite status using the expression levels for the checkpoint related gene, and/or the TME related gene, and the up- or downregulation of the pathway.

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

11. The method of claim 9 wherein the TME related gene is a gene that is expressed in an immune cell.

12. The method of claim 11, wherein the TME related gene is 0X40, IDO, or TIGIT.

13. The method of claim 9, wherein the pathway is an EMT transition pathway, a TNF-alpha signaling via NF-kappa B pathway, an allograft rejection pathway, an IL6-JAK-STAT3 signalling pathway, an inflammatory response pathway, a KRAS signaling pathway, an interferon alpha response pathway, a hypoxia pathway, an IL2-STAT signaling pathway, and/or or a complement pathway.

14. The method of claim 9, wherein the microsatellite status is inferred as MSI when at least one of the checkpoint related gene is overexpressed and when the TME related gene is indicative of immune cell enrichment in the TME.

15. The method of claim 14, wherein the microsatellite status is inferred as MSI when the pathway is up-regulated and when the pathway is an EMT transition pathway, a TNF- alpha signaling via NF-kappa B pathway, an allograft rejection pathway, an IL6-JAK- STAT3 signaling pathway, an inflammatory response pathway, a KRAS signaling pathway, an interferon alpha response pathway, a hypoxia pathway, an IL2-STAT signaling pathway, and/or or a complement pathway.

16. A method of improving MSI-based treatment outcome prediction for a treatment response of a tumor to immune therapy, comprising:

determining MSI status of the tumor;

using the omics data to determine up- or down-regulation of a pathway in the tumor sample relative to a non-tumor sample; and

using the expression levels for the checkpoint related gene and/or the TME related gene, and the up- or downregulation of the pathway to qualify the MSI status.

17. The method of claim 16 wherein the checkpoint related gene is PDL2, PDL1, LAG3, and/or TIM3.

18. The method of claim 16 wherein the TME related gene is a gene that is expressed in an immune cell.

19. The method of claim 16 wherein the pathway is an EMT transition pathway, a TNF-alpha signaling via NF-kappa B pathway, an allograft rejection pathway, an IL6-JAK-STAT3 signaling pathway, an inflammatory response pathway, a KRAS signaling pathway, an interferon alpha response pathway, a hypoxia pathway, an IL2-STAT signaling pathway, and/or or a complement pathway.

20. The method of claim 16 wherein the step of qualifying the MSI status pathway is

qualifying the MSI as likely to be responsive to immune therapy.