Compositions and methods of predicting responsiveness to an immunotherapy

Abstract

Disclosed are methods of predicting a. subject's responsiveness to an immunotherapy comprising detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAGS, NKG7, PDCD1, S100A4, and TIGIT; and comparing the presence and / or amount of the miR155 gene signature to a control sample or threshold, wherein the presence and / or an altered amount of the miR155 gene signature relative to the presence or amount in the control sample or threshold indicates the subject will be responsive or is responding to the immunotherapy.

Description

ATTORNEY DOCKET NO.21101.0481P1 COMPOSITIONS AND METHODS OF PREDICTING RESPONSIVENESS TO AN IMMUNOTHERAPY CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 705,808, filed on October 10, 2024, which is incorporated by reference herein in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under F30 CA260977 awarded by the National Institutes of Health. The government has certain rights in the invention. BACKGROUND

[0003] In the United States, colorectal cancer (CRC) is the third deadliest and most common type of cancer, despite significant reductions in incidence and mortality due to improved standardized screening. However, the incidence of metastatic CRC (mCRC) has been steadily increasing in younger individuals since 2010, and mCRC is among the most significant obstacles due to a survival rate of 14% with the current standard of care treatments, necessitating new therapeutic options. Immune checkpoint inhibitors (ICI) have drastically improved patient survival in many solid tumor types by inhibiting PD-1 and reinvigorating PD-1+CD8+T cells to resume their antitumor function. Pembrolizumab and Nivolumab, both of which are PD-1 inhibitors, induce robust T cell-mediated antitumor responses in mCRC with high microsatellite instability (MSI-H). MSI-H is often a result of a deficiency in mismatch repair (dMMR) enzymes, resulting in increased genetic instability, tumor mutational burden (TMB-H), and neoantigen load (NAL), which enhance T cell-mediated anti-tumor and ICI responses. Although dMMR / MSI-H status is the clinical molecular standard for ICI response prediction, 30-70% of dMMR / MSI-H patients do not respond to anti-PD-1 therapy, depending on the study. Additionally, dMMR / MSI-H patients represent a minority of CRC patients, and ICI response rates are disconcordant with dMMR / MSI-H status as a consequence of tumor heterogeneity and subsequent T cell infiltration. Rather than utilizing tumor intrinsic factors, the field has shifted towards and seen success with characterizing CD8+T cell-dependent metrics as predictors of positive patient and therapy outcomes. It is now clear that characterizing tumor-associated CD8+T cell-dependent metrics will be key determinants of ICI responses.

[0004] CD8+T cells promote anti-tumor immunity and ICI responses through many required states: stem-like, transitory, and effector. The stem-like Tcf-1+CD8+T cells in tumors and tumor-draining lymph nodes (tdLNs) serve as a reservoir for expanding primed anti-tumor CD8+ATTORNEY DOCKET NO.21101.0481P1 T cells in the tumor microenvironment (TME). These stem-like CD8+T cells enter a hyperproliferative Cx3cr1+transitory state before transitioning into anti-tumor Cxcr6+CD8+T cells. Cxcr6 expression on CD8+T cells is highly specific to the effector state and TME and necessary for the expansion of an intratumoral CD8+T cell niche required for anti-tumor CD8+T cell persistence and effector function. In this study, a single microRNA (miRNA) was identified and played a vital role in this entire CD8 T cell differentiation cascade, with each stage playing an indispensable role in anti-tumor immunity and ICI responses.

[0005] MicroRNAs (miRNAs) are short non-coding RNA molecules that have evolved to inhibit the stability or translation of target messenger RNAs (mRNAs) based on sequence complementarity with conserved 3’ UTR target sites. miRNAs are well known to regulate tumor cell biology, deemed “oncomirs” or tumor suppressors. Studies have found that miRNAs influence specific immune cells responding to solid tumors and that they also impact immunotherapy responses, ranging from ICI to CAR T-cells. Despite these advances, their true potential to influence CRC tumor immunity and immunotherapies remains unclear and requires additional investigation.

[0006] miR-155 has recently emerged as a regulator of tumor immunity due to its influence on a variety of immune cells, including T, NK, and myeloid cells. Previously, the loss of miR-155 in T cells decreased the accumulation of interferon-gamma (Ifn ) producing CD4+ andCD8+ T cells, correlating with increased tumor growth and burden, and the loss of Ifn -mediatedalterations to the immunological composition in melanoma, an immunologically inflammatory and ICI responsive cancer. However, the role of miR-155 in cancers located in immunologically suppressive environments such as the colon remains understudied. BRIEF SUMMARY

[0007] Disclosed are methods of predicting a subject’s responsiveness to an immunotherapy comprising detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; and comparing the presence and / or amount of the miR155 gene signature to a control sample or threshold, wherein the presence and / or an altered amount of the miR155 gene signature relative to the presence or amount in the control sample or threshold indicates the subject will be responsive or is responding to the immunotherapy.

[0008] Disclosed are methods for selecting a subject having cancer to treat with an immunotherapy comprising detecting the presence and / or amount of a miR155 gene signature inATTORNEY DOCKET NO.21101.0481P1 the subject or a sample from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; comparing the presence or amount of the miR155 gene signature to a control sample or threshold, and selecting the subject having the presence and / or an altered amount of the miR155 gene signature relative to the presence or amount in the control sample or threshold for treatment with an immunotherapy.

[0009] Disclosed are methods for identifying a subject currently responding to an immunotherapy comprising detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject prior to treatment with the immunotherapy; detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject during or after treatment with the immunotherapy; and identifying the subject as responding to the immunotherapy when the presence and / or amount of the miR155 gene signature has changed in the subject or a sample from the subject during or after treatment with the immunotherapy relative to the presence and / or amount of the miR155 gene signature in the subject or the sample from the subject prior to treatment with the immunotherapy; wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0010] Disclosed are methods of treating a subject having cancer comprising detecting the presence or altered amount of a miR155 gene signature in the subject or a sample obtained from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; and administering an immunotherapy to the subject.

[0011] Disclosed are methods of increasing T effector cells in a subject comprising administering, to the subject, an agent that increases the levels of miR155. In some aspects, the presence or upregulation of miR155 allows for the miR155 gene signature to be expressed or upregulated.

[0012] Disclosed are methods of identifying a cancer that can be effectively treated with an immunotherapy comprising detecting the presence and / or amount of a miR155 gene signature in a sample obtained from a subject having cancer, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; wherein the presence and / or amount of the miR155 gene signature identifies the sample as being from a subject having a cancer that can be effectively treated with an immunotherapy.

[0013] Disclosed are recombinant cells comprising altered expression of miR155, CD3E,ATTORNEY DOCKET NO.21101.0481P1 CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0014] Disclosed are nucleic acid arrays comprising at least fifteen probes immobilized on a solid support, each of said probes being between about 15 and about 500 nucleotides in length, each of said probes being derived from a sequence corresponding to, or complementary to, a transcript of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0015] Additional advantages of the disclosed method and compositions will be set forth in part in the description which follows, and in part will be understood from the description, or may be learned by practice of the disclosed method and compositions. The advantages of the disclosed method and compositions will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several embodiments of the disclosed method and compositions and together with the description, serve to explain the principles of the disclosed method and compositions.

[0017] FIGS.1A-1H shows a functional in vivo screen targeting miRNAs identifies miR- 155 as necessary for effector CD8+T cell-mediated anti-tumor immunity. Schematic of in vivo miRNA CRISPR-Cas9 screen. miRNAs selected based on miRNAs positively correlated to CD8A and COAD and TCGA projects with miRNA seq data, excluding leukemias and lymphomas, and with positive and negative prognosticating power (Cox and KM analysis in SKCM-immune or CIBERSORT-CD8a-high cohort).2E5 mCherry+(library transduced) cellswere adoptively transferred intravenously into Tcr KO mice 7 days post-challenge with 1E6MC38-Ova cells subcutaneously (FIG.1A). pCR amplified barcode enrichment was quantified, and log2fold change was normalized to input sgRNA barcode representation; one sample t and Wilcoxon test; dashed line p=0.05; n=4. (FIG.1B). Representative mCherry+FACs of transduced CD8+T cells with psMSCV-miR-155 sgRNAs (FIG.1C) relative expression of miR-155-5p in mCherry+ CD8+ T cells n=2 (FIG. 1D). Tcr KO mice challenged with 1E6 MC38-OVA cells and received 2E5 OT1 or OT1-miR155 KO CD8+T cells intravenously; n=6-8 per group; tumor growth kinetics (FIG.1E), tumor mass (FIG.1F), # of CD8+T cells / g of tumor(Fig. 1G), % of Ifn + CD8+ T cells (FIG. 1H); two-way ANOVA with multiple comparisons; t-ATTORNEY DOCKET NO.21101.0481P1 test with Welsh’s Correction; * p>0.05, ** p>0.01; bars represent the mean; error bars represent the SEM.

[0018] FIGS.2A-2U show T cell miR-155 promotes effector T-cell mediated antitumor immunity in two molecular subtypes of colon cancer. Tumor growth kinetics (FIG.2A) and tumor mass (Fig.2B) of miR-155fl / fl (WT) or miR-155 fl / fl CD4Cre+ / - (miR-155 TKO) mice challenged with 1E6 MC38-Ova cells subcutaneously and administered 2E5 OT1 CD8+T cells or PBS intravenously day three post-challenge; n=6-8. Frequency of intratumoral CD8+T cells(FIG. 2C,D) producing Ifn (FIG. 2E,G) and Gzmb (Fig. 2F,H) in miR-155fl / fl (WT) or miR-155 fl / fl CD4Cre+ / - (miR-155 TKO) mice challenged with 1E6 MC38 cells; n=7-11. Frequency of intratumoral effector CD44+CD62L- (eff) (I) and PD-1+(J) CD8+T cells in miR-155fl / fl (WT) or miR-155 fl / fl CD4Cre+ / - (miR-155 TKO) mice challenged with 1E6 MC38-Ova cells; n=5-6. Surface area measurements of tumors (FIG.2K) and polyp count (Fig.2L) of miR- 155fl / fl (WT) or miR-155 fl / fl CD4Cre+ / - (miR-155 TKO) mice challenged with AOM / DSS (Fig.2M); data pooled from two replicate experiments; n=17-20. Frequency and # of CD8+T cells / g of tumor (FIG.2N, O) and frequency of intratumoral stemlike CD44-CD62L+(Tscm) (P, R), central-memory like CD44+CD62L+(CM) (FIG.2P, S), effector CD44+CD62L- (eff) (FIG. 2P, T), and PD-1+(Fig.2Q, U) CD8+T cells; n=5-6. t-test with Welsh’s Correction; * p>0.05, ** p>0.01; bars represent the mean; error bars represent the SEM.

[0019] FIGS.3A-3P show miR-155 is necessary for an effective CD8+T cell-mediated anti- PD-1 response to CRC. Tumor growth kinetics (FIG.3A) and tumor mass (FIG.3B) of miR- 155fl / fl (WT) or miR-155 fl / fl CD4Cre+ / - (TKO) mice challenged with 1E6 MC38 cells subcutaneously and administered 300ug anti-Pd-1 mAb (+ ICI) or PBS intraperitoneally days four, seven, and ten post-tumor challenge (red arrows) (FIG.3A, B); data pooled from two replicate experiments n=11-22; ROUT outlier test (Q=1%). Representative frequency and # ofCD8+ T cells / g of tumor (FIG. 3C, D) and intratumoral frequency of Gzmb+ (FIG. 3E,G), Ifn +(FIG.3F, H), PD-1+(FIG.3I, K), Tim-3+(FIG.3J, L), stem-like CD44-CD62L+(Tscm) (FIG. 3M, N), central-memory like CD44+CD62L+(CM) (FIG.3M, O), effector CD44+CD62L- (eff) (FIG.3M, P) CD8+T cells; n=7-10. One-way ANOVA with multiple comparisons of the means of each group; * p>0.05, ** p>0.01, ***p>0.001, ****p>0.0001; bars represent the mean; error bars represent the SEM.

[0020] FIGS.4A-4U show scRNA-seq of tumor-associated immune cells reveals CD8+T cell miR-155 regulation of the Tcf-1 / Cxcr6 axis. UMAP analysis of tumor-associated CD45+cells from miR-155 fl / fl (WT) or miR-155fl / fl CD4Cre+ / - (miR-155 TKO) challenged with 1E6 MC38 cells subcutaneously and administered 300ug anti-Pd-1 mAb (+ ICI) or PBSATTORNEY DOCKET NO.21101.0481P1 intravenously day seven and ten post-challenge; teal arrow denotes CD8+T cell cluster (FIG. 4A). Log2fold change of gene expression in miR-155 fl / fl (WT) vs miR-155fl / fl CD4Cre+ / - (miR-155 TKO) CD8+T cell cluster+ / -anti-PD-1 mAb (FIG.4B); Wilcoxon rank sum test with Bonferroni correction. From miR-155+ / +(WT) or miR-155- / -(155-KO) mice challenged with 1E6 MC38 cells subcutaneously, frequency and # of tumor-associated CD8+T cells (FIG.4C-E) and frequency of intratumoral stem-like CD44-CD62L+(Tscm) (FIG.4F, G), central-memory like CD44+CD62L+(CM) (FIG.4F, H), effector CD44+CD62L- (eff) (FIG.4F, I), Ccr7+Cx3Cr1- (FIG.4M, N), Cx3cr1+(FIG.4M, O), Tcf1-Cxcr6+(FIG.4M, P), Tcf-1+Cxcr6- (FIG.4M, Q) CD8+T cells. Frequency of tdLN associated CD44-CD62L+(Naive) (FIG.4J), central-memory like CD44+CD62L+(CM) (FIG.4K), effector CD44+CD62L- (eff) (FIG.4L), Ccr7+Cx3Cr1- (FIG.4R), Cx3cr1+(FIG.4S), Tcf1-Cxcr6+(Fig.4T), Tcf+Cxcr6- (FIG.4U) CD8+T cells. t-test with Welsh’s Correction; * p>0.05, ** p>0.01, ***p>0.001, ****p>0.0001; n=10 per group.

[0021] FIGS.5A-5P show miR-155 represses Ship-1, indirectly inhibiting Tcf-1 and enhancing anti-tumor immunity. From miR-155+ / +(WT) or miR-155- / -(155-KO) mice challenged with 1E6 MC38 cells subcutaneously, frequency of intratumoral Tcf1+Ship-1+CD8+T cells (FIG.5A, B) and within CD44+CD62L- (CM) (FIG.5C), CD44+CD62L- (eff) (FIG. 5D), Ccr7+Cx3cr1-(FIG.5F), and Cx3cr1+(FIG.5G) subsets. Histogram and MFI of Ship-1 expression in Cxcr6+Tcf1-Ship-1+(FIG.5E, H) CD8+T cells; n=10 per group; t-test with Welsh’s Correction. Histogram p-Akt+cells (FIG.5I), frequency of p-Akt+Ship-1+, and MFI of p-Akt and Ship-1 in vitro anti-CD3 / CD28 activated CD8+T cells (FIG.5J) and within Ccr7+Cx3cr1- (FIG.5K), Cx3cr1+(FIG.5L), Cxcr6+(Fig.5M) CD8+T cell subsets; n=3 per group; t-test with Welsh’s Correction. Tumor growth kinetics (Fig.5N) and mass (FIG.5O) inTcr KO mice challenged with 1E6 MC38 OVA cells subcutaneously and received 2E5 OT-1-Cas9, OT1-Cas9-155KO, or OT1-Cas9-miR155- / - / Ship-1KO (OT1-Cas9-155 / Ship-1KO) CD8+T cells intravenously; One-way ANOVA with multiple comparisons of the means of each group; * p>0.05, ** p>0.01, ***p>0.001, ****p>0.0001; bars represent the mean; error bars represent the SEM; n = 6-7; Grubbs’ outlier test Alpha=0.01. Frequency of CD8+T cells (Fig.5P); n=5-7; Brown-Forsythe and Welch ANOVA test with multiple comparisons of means of each group. Grubbs’ outlier test Alpha = 0.05; * p>0.05, ** p>0.01, ***p>0.001, ****p>0.0001.

[0022] FIGS.6A-6F show SHIP-1 predicts poor patient outcomes in CRC. Kaplan Meier's survival probability of COAD patients from TCGA with top and bottom 30% INPP5D expressing patients; n=150 per group (FIG.6A) The top line of FIG.6A is SHIP-1-low. The bottom line of FIG.6A is SHIP-1-high. INPP5D expression within MSI-H, MSI-L, and MSSATTORNEY DOCKET NO.21101.0481P1 TCGA COAD patient subsets (FIG.6B); t-test with pairwise comparisons; * p>0.05, ** p>0.01. Gene set enrichment analysis of hallmark pathways in top vs bottom 50% of INPP5D expressing patients; Top 10 and bottom 10 hallmark pathways are shown (FIG.6C). Relative expression of INPP5D (SHIP-1) / L32 (FIG.6D) and hsa-miR-155-5p / L32 (FIG.6E); t-test with Welsh’s Correction; * p>0.05; n=7-11 per group. Computed Pearson correlation coefficients between relative expression of hsa-miR-155-5p / L32 and INPP5D (Ship-1) / L32 stratified N / M status (Fig. 6F).

[0023] FIGS.7A-7H show miR-155 expression in CD8+T cells is predictive ofImmunoscore and IFN responses in COAD and ICI responses in human cancer. Log2 foldchange of miRNA expression in CD8A high vs low COAD patients from TCGA (FIG.7A); Wilcoxon rank sum test with Bonferroni correction. Computed Pearson correlation between CD8A and hsa-miR-155-5p and MLH1 (FIG.7B). hsa-miR-155-5p expression within MSI-H, MSI-L, and MSS TCGA COAD patient subsets (Fig.7C); C1-C6 immune landscapes (FIG. 7D); t-test with pairwise comparisons; **** p>0.0001. Gene set enrichment analysis of hallmark pathways in top vs bottom 50% of hsa-miR-155-5p expressing patients; Top 10 and bottom 10 hallmark pathways are shown (FIG.7E). scRNAseq scaled mean expression and frequency of MIR155HG, IFNG, GZMB, PRF1, and CD8A within tumor-associated cell types. Overlapping significantly upregulated genes between WT vs. miR-155 TKO and WT + ICI vs. miR-155 TKO + ICI in the CD8+T cell cluster from scRNAseq data set from Figure 4 (FIG.7G). Median area under the receiver operating characteristic curve value of individual gene signatures across 36 patient datasets and 12 cancers; the top and bottom 10 gene signatures of 48 gene signatures are shown (Fig.7H).

[0024] FIGS.8A-8G show miR-155 expression in tumor-antigen-specific CD8+T cells promotes intratumoral effector function. Representative flow plots of tumor-associated CD45+CD8a+CD3e+T cells (FIG.8A), Ifnγ+CD8+T cells (FIG.8B), and stem (CD62L+CD44-), central memory-like (CD62L+CD44+), and effector (CD62L-CD44+) CD8+T cells (FIG.8C) inTcr KO mice challenged with 1E6 MC38-OVA cells receiving 2E5 OT1 or OT1-miR155 KOCD8+T cells intravenously. Frequency of tumor-associated CD45+CD8a+CD3e+T cells (FIG. 8D) and stem (CD62L+CD44-), central memory-like (CD62L+CD44+), and effector (CD62L- CD44+) CD8+T cells (FIG.8E). Frequency of tdLN CD45+CD8a+CD3e+T cells (FIG.8F) and stem (CD62L+CD44-), central memory-like (CD62L+CD44+), and effector (CD62L-CD44+) CD8+T cells (FIG.8G). n=5-8 per group; t-test with Welsh’s Correction; * p>0.05, ** p>0.01; Grubbs Outlier test Alpha=0.05.

[0025] FIGS.9A-9P show miR-155 enhances tumor-specific effector CD8+T cellATTORNEY DOCKET NO.21101.0481P1 differentiation. Representative flow plots of tumor-associated stem (CD62L+CD44-), central memory-like (CD62L+CD44+), effector (CD62L-CD44+) (FIG.9A), and PD-1+(FIG.9B) CD8+T cells in miR-155 fl / fl (WT) or miR-155fl / fl CD4Cre+ / - (miR-155 TKO) challenged with 1E6 MC38-OVA cells subcutaneously. Frequency of tumor-associated central memory-like (CD62L+CD44+) (FIG.9C) and stem-like (CD62L+CD44-) (FIG.9D) CD8+T cells. Frequencyof splenic CD3+CD8+ T cells (FIG. 9E) and of Gzmb+ (Fig. 9F), Ifn + (FIG. 9G), PD-1+ (FIG.9H), naive (CD62L+CD44-) (FIG.9I), central memory-like (CD62L+CD44+) (FIG.9J), and effector (CD62L-CD44+) (FIG.9K) CD8+T cells in miR-155 fl / fl (WT) or miR-155fl / fl CD4Cre+ / - (miR-155 TKO) challenged with 1E6 MC38-OVA cells subcutaneously. Frequency of tumor-associated CD3+CD8+T cells (FIG.9L) and mesenteric tdLN-associated CD8+T cells (FIG.9M) and of naive (CD62L+CD44-) (FIG.9N), central memory-like (CD62L+CD44+) (FIG. 9O), and effector (CD62L-CD44+) (FIG.9P) CD8+T cells in miR-155 fl / fl (WT) or miR-155fl / fl CD4Cre+ / - (miR-155 TKO) challenged with AOM / DSS. t-test with Welsh’s Correction; * p>0.05, ****p>0.0001; n=5-10 per group.

[0026] FIGS.10A-10I show miR-155 does not significantly impact effector CD8+T cell differentiation in tdLNs with ICI. miR-155fl / fl (WT) or miR-155 fl / fl CD4Cre+ / - (miR-155 TKO) mice challenged with 1E6 MC38 cells subcutaneously and administered 300ug anti-Pd-1 mAb (+ICI) or PBS intravenously day four, seven, and ten post-challenge. Frequency of tumor- associated CD45+CD3+CD8+T cells (FIG.10A) and tdLN-associated frequency of CD45+CD3+CD8+T cells (FIG.10B) and CD44-CD62L+(Naive) (FIG.10C), central-memorylike CD44+CD62L+ (CM) (FIG. 10D), effector CD44+CD62L- (eff) (FIG. 10E), Ifn + (FIG.10F), Gzmb+(FIG.10G), Tim-3+(FIG.10H), and PD-1+(FIG.10I) CD8+T cells; t-test with Welsh’s Correction; * p>0.05, ** p>0.01, ***p>0.001, ****p>0.0001; n=6-10.

[0027] FIGS.11A-11E show miR-155 expression in T cells has the largest impact on CD8+T cell frequency and differentiation. Frequency of each cluster from UMAP analysis of tumor- associated CD45+cells from miR-155 fl / fl (WT) or miR-155fl / fl CD4Cre+ / - (miR-155 TKO) challenged with 1E6 MC38 cells subcutaneously and administered 300ug anti-Pd-1 mAb (w / ICB) or PBS intraperitoneally day seven and ten post-challenge (FIG.11A), and violin plots of log2expression values of Cxcr6 and Tcf7 in the CD8+T cell cluster (FIG.11B). Tumor growth kinetics (FIG.11C) and weight (FIG.11D) as well as frequency of tdLN-associated live CD45+CD3+CD8+T cells (FIG.11E) from miR-155+ / +(WT) or miR-155- / - (155-KO) mice challenged with 1E6 MC38 cells subcutaneously. t-test with Welsh’s Correction; ** p>0.01. two-way ANOVA with multiple comparisons; ****p>0.0001; n=10 per group.

[0028] FIGS.12A-12F show miR-155 expression inhibits Foxo1 and its downstreamATTORNEY DOCKET NO.21101.0481P1 pathways to promote CD8+T cell differentiation. Violin plots of log2expression values of Foxo1 (FIG.12A), Eomes (FIG.12B), Sell (CD62L) (FIG.12C), Lef1 (FIG.12D), and Klf2 (FIG.12E) in the CD8+T cell cluster of UMAP analysis of tumor-associated CD45+cells from miR-155 fl / fl (WT) or miR-155fl / fl CD4Cre+ / - (miR-155 TKO) challenged with 1E6 MC38 cells subcutaneously and administered 300ug anti-Pd-1 mAb (w / ICI) or PBS intraperitoneally day seven and ten post-challenge. Western blot of Ship-1 protein expression with Ship-1 sgRNA1 and 2 transduced OT1-Cas9-miR-155KO CD8+T cells (FIG.12F).

[0029] FIGS.13A-13C show a unique miR-155-dependant CD8+T cell transcriptomic profile predicts ICI responses. Computed Pearson correlation between CD8A and MSH2, MSH6, PMS2, and MUTYH (FIG.13A). Average area under the receiver operating characteristic curve in each of the 36 datasets; 48 previously published gene signatures are listed in descending median area under the receiver operating characteristic curve value (FIG.13B). Clustering of the percent gene overlap among the 48 previously published gene signatures (FIG.13C); two major gene signature clusters are identified, the first related to immune activation signature (red dendrogram), and the second gene signatures are related to immune suppressive signatures (blue dendrogram). DETAILED DESCRIPTION

[0030] The disclosed method and compositions may be understood more readily by reference to the following detailed description of particular embodiments and the Example included therein and to the Figures and their previous and following description.

[0031] It is to be understood that the disclosed method and compositions are not limited to specific synthetic methods, specific analytical techniques, or to particular reagents unless otherwise specified, and, as such, may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0032] Disclosed are materials, compositions, and components that can be used for, can be used in conjunction with, can be used in preparation for, or are products of the disclosed method and compositions. These and other materials are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc. of these materials are disclosed that while specific reference of each various individual and collective combinations and permutation of these compounds may not be explicitly disclosed, each is specifically contemplated and described herein. For example, if a peptide is disclosed and discussed and a number of modifications that can be made to a number of molecules including the amino acids are discussed, each and every combination and permutation of the peptide and the modifications thatATTORNEY DOCKET NO.21101.0481P1 are possible are specifically contemplated unless specifically indicated to the contrary. Thus, if a class of molecules A, B, and C are disclosed as well as a class of molecules D, E, and F and an example of a combination molecule, A-D is disclosed, then even if each is not individually recited, each is individually and collectively contemplated. Thus, is this example, each of the combinations A-E, A-F, B-D, B-E, B-F, C-D, C-E, and C-F are specifically contemplated and should be considered disclosed from disclosure of A, B, and C; D, E, and F; and the example combination A-D. Likewise, any subset or combination of these is also specifically contemplated and disclosed. Thus, for example, the sub-group of A-E, B-F, and C-E are specifically contemplated and should be considered disclosed from disclosure of A, B, and C; D, E, and F; and the example combination A-D. This concept applies to all aspects of this application including, but not limited to, steps in methods of making and using the disclosed compositions. Thus, if there are a variety of additional steps that can be performed it is understood that each of these additional steps can be performed with any specific embodiment or combination of embodiments of the disclosed methods, and that each such combination is specifically contemplated and should be considered disclosed. A. Definitions

[0033] It is understood that the disclosed method and compositions are not limited to the particular methodology, protocols, and reagents described as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention which will be limited only by the appended claims.

[0034] It must be noted that as used herein and in the appended claims, the singular forms "a ", "an", and "the" include plural reference unless the context clearly dictates otherwise. Thus, for example, reference to "an immunotherapy" includes a plurality of such immunotherapies, reference to "the immunotherapy” is a reference to one or more immunotherapies and equivalents thereof known to those skilled in the art, and so forth.

[0035] The word “or” as used herein means any one member of a particular list and also includes any combination of members of that list.

[0036] As used herein, the phrase “miR-155 gene signature” refers to a group of genes in a cell with a uniquely characteristic pattern of gene expression that occurs as a result of an altered or unaltered biological process or pathogenic medical condition, such as cancer. The miR155 gene signature disclosed herein comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature comprises all of miR155, CD3E, CD3G,ATTORNEY DOCKET NO.21101.0481P1 CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature always comprises miR155 and at least one or more of CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0037] As used herein, the term "effectively treated" means an amount of treatment that is sufficient, when administered to a subject suffering from or susceptible to a disease, disorder, and / or condition, to treat, alleviate, ameliorate, relieve, alleviate symptoms of, prevent, delay onset of, inhibit progression of, reduce severity of, and / or reduce incidence of the disease, disorder, and / or condition.

[0038] As used herein, the term "treating" refers to partially or completely alleviating, ameliorating, relieving, delaying onset of, inhibiting progression of, reducing severity of, and / or reducing incidence of one or more symptoms or features of a particular disease, disorder, and / or condition. For example, "treating" cancer may refer to inhibiting survival, growth, and / or spread of the cancer. Treatment may be administered to a subject who does not exhibit signs of a disease, disorder, and / or condition and / or to a subject who exhibits only early signs of a disease, disorder, and / or condition for the purpose of decreasing the risk of developing pathology associated with the disease, disorder, and / or condition.

[0039] As used herein, “sample” is meant to mean a biological specimen from an animal; a tissue or organ from an animal; a cell (either within a subject, taken directly from a subject, or a cell maintained in culture or from a cultured cell line); a cell lysate (or lysate fraction) or cell extract; or a solution containing one or more molecules derived from a cell or cellular material (e.g. a polypeptide or nucleic acid), which is assayed as described herein. A sample may also be any body fluid or excretion (for example, but not limited to, blood, urine, stool, saliva, tears, bile) that contains cells or cell components.

[0040] As used herein, “subject” refers to the target of administration, e.g. an animal. Thus, the subject of the disclosed methods can be a vertebrate, such as a mammal. For example, the subject can be a human. The term does not denote a particular age or sex. Subject can be used interchangeably with “individual” or “patient”.

[0041] Ranges may be expressed herein as from "about" one particular value, and / or to "about" another particular value. When such a range is expressed, also specifically contemplated and considered disclosed is the range from the one particular value and / or to the other particular value unless the context specifically indicates otherwise. Similarly, when valuesATTORNEY DOCKET NO.21101.0481P1 are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another, specifically contemplated embodiment that should be considered disclosed unless the context specifically indicates otherwise. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint unless the context specifically indicates otherwise. Finally, it should be understood that all of the individual values and sub-ranges of values contained within an explicitly disclosed range are also specifically contemplated and should be considered disclosed unless the context specifically indicates otherwise. The foregoing applies regardless of whether in particular cases some or all of these embodiments are explicitly disclosed.

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of skill in the art to which the disclosed method and compositions belong. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present method and compositions, the particularly useful methods, devices, and materials are as described. Publications cited herein and the material for which they are cited are hereby specifically incorporated by reference. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such disclosure by virtue of prior invention. No admission is made that any reference constitutes prior art. The discussion of references states what their authors assert, and applicants reserve the right to challenge the accuracy and pertinency of the cited documents. It will be clearly understood that, although a number of publications are referred to herein, such reference does not constitute an admission that any of these documents forms part of the common general knowledge in the art.

[0043] Throughout the description and claims of this specification, the word “comprise” and variations of the word, such as “comprising” and “comprises,” means “including but not limited to,” and is not intended to exclude, for example, other additives, components, integers or steps. In particular, in methods stated as comprising one or more steps or operations it is specifically contemplated that each step comprises what is listed (unless that step includes a limiting term such as “consisting of”), meaning that each step is not intended to exclude, for example, other additives, components, integers or steps that are not listed in the step. B. Methods 1. Predicting Responsiveness to Immunotherapy

[0044] Disclosed are methods of predicting a subject’s responsiveness to an immunotherapy comprising detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject, wherein the miR155 gene signature comprises one or more of miR155,ATTORNEY DOCKET NO.21101.0481P1 CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; and comparing the presence and / or amount of the miR155 gene signature to a control sample or threshold, wherein the presence and / or an altered amount of the miR155 gene signature relative to the presence or amount in the control sample or threshold indicates the subject will be responsive or is responding to the immunotherapy. In some aspects, an immunotherapy can be any therapy / therapeutic that mediates T cell differentiation. In some aspects, an immunotherapy can be, but is not limited to, immune checkpoint inhibitors (ICI), vaccines, CAR T cells, STING agonists, co-stimulatory molecule agonists (e.g., OX-40, 4-1BB, or IL-2 receptor agonists) or T cell-based therapies. Thus, for example, disclosed are methods of predicting a subject’s responsiveness to an ICI comprising detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; and comparing the presence and / or amount of the miR155 gene signature to a control sample or threshold, wherein the presence and / or an altered amount of the miR155 gene signature relative to the presence or amount in the control sample or threshold indicates the subject will be responsive or is responding to the ICI.

[0045] In some aspects, the immune checkpoint inhibitor is ipilimumab (Yervoy), pembrolizumab (Keytruda), nivolumab (Opdivo), cemiplimab (trade name Libtayo), and dostarlimab (Jemperli), atezolizumab (Tecentriq), durvalumab (Imfinzi), avelumab (Bavencio) or Tiragolumab. In some aspects, the ICI can be, but is not limited to, anti-PD-1, anti-PD-L1, anti-LAG3, anti-CTLA4, or anti-Tigit.

[0046] In some aspects, the presence or amount of a miR155 gene signature relative to the presence in a control sample or relative to a threshold indicates the subject will be responsive or is responding to an immunotherapy (e.g., ICI). Thus, in some aspects, a control sample can have no detection of the miR155 gene signature. Thus, in some aspects, any amount of the genes from the miR155 gene signature can indicate the subject will be responsive or is responding to an immunotherapy. In some aspects, the threshold at which one or more of the signature genes are considered to be "expressed" or "elevated" can depend on the implemented assay and the overall levels of the gene products. Some approaches that can be used in this context include, but are not limited to, 1) Ranking of the signature gene transcripts in comparison to the rest of the transcriptome. Using existing or new data to be collected, the gene(s) can be considered "expressed" or "elevated" if the said gene(s) are found in a certain quantile in the data; 2) Direct transcript counting approaches. Technologies including (but not limited to) Nanostring, digitalATTORNEY DOCKET NO.21101.0481P1 droplet PCR, single molecule imaging, in situ hybridization, immunofluorescence microscopy can quantify the actual transcript and protein amounts in the cells and tissues. An absolute or relative threshold can be determined after normalizing the gene expression signal to the mass of tissue sample and / or total RNA; 3) Amplification based approaches. Technologies including (but not limited to) quantitative reverse transcription PCR and RNA sequencing can identify transcript levels in a sample. A relative threshold can be determined based on Ct values (for qRT-PCR) and normalized transcript counts such as transcript-per-million (for RNAseq); and 4) Receiving operating characteristics (ROC) analysis. Using the existing immunotherapy transcriptomics datasets, the optimal threshold can be identified by selecting the expression level of the gene(s) that corresponds to the highest "true positive rate" and the "lowest false positive rate" in binary classification scheme involving responders and nonresponders. This threshold level corresponds to the elbow of the curve which is the closest point to the upper left corner of the ROC space (1x1 square).

[0047] In some aspects, an altered amount of the miR155 gene signature can refer to an increase or decrease in the amount of one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0048] In some aspects, the amount of a miR155 gene signature relative to the amount in a control sample or relative to a threshold indicates the subject will be responsive or is responding to an immunotherapy (e.g., ICI). Thus, in some aspects, a control sample can have an amount of the miR155 gene signature, wherein any amount higher than what is in the control sample can indicate the subject will be responsive or is responding to an ICI. Thus, in some aspects, detection of the miR155 gene signature that is over a certain threshold can indicate the subject will be responsive or is responding to an ICI. In some aspects, a threshold can be previously established based on a set of control samples wherein the control samples are from subjects that where known not to respond to ICI. In some aspects, an altered amount of the miR155 gene signature is an amount that is at least 1x, 1.5x or 2x the amount of the miR155 gene signature in a control sample.

[0049] In some aspects, a control sample is a sample obtained from a subject that does not have cancer, did not respond to an ICI or did respond to an ICI. In some aspects, a control sample is a sample can be adjacent or distant healthy samples from the same subject.

[0050] In some aspects, a sample from a subject can be, but is not limited to, blood, plasma, saliva, serum, urine, cerebrospinal fluid, feces, or tissue. In some aspects, the sample is from a tumor biopsy. Thus, in some aspects, the sample comprises total tumor RNA or DNA.

[0051] In some aspects, the presence and / or amount of the miR155 gene signature isATTORNEY DOCKET NO.21101.0481P1 detected at the DNA or RNA level.

[0052] In some aspects, the miR155 gene signature comprises the presence or an altered amount of miR155 and one or more of CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature comprises miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature comprises the presence or an altered amount of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0053] In some aspects, detecting the presence and / or amount of a miR155 gene signature can be performed using any known technique for detecting the presence and / or amount of a gene. In some aspects, the nucleic acid array disclosed herein can be used. For example, a nucleic acid array can comprise at least fifteen probes immobilized on a solid support, each of said probes being between about 15 and about 500 nucleotides in length, each of said probes being derived from a sequence corresponding to, or complementary to, a transcript of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0054] In some aspects, detecting the presence and / or amount of a miR155 gene signature can be performed using qPCR, nanostring, or next-generation RNA sequencing.

[0055] In some aspects, the miR155 gene signature is detected in CD8+ T cells. Thus, in some aspects, the sample obtained from the subject comprises CD8+ T cells. In some aspects, CD8+ T cells can be isolated from a sample obtained from the subject and then the miR155 gene signature can be detected in the isolated CD8+ T cells.

[0056] In some aspects, the miR155 gene signature is detected in total tumor RNA. Thus, in some aspects, total tumor RNA can be isolated from a sample obtained from the subject and then the miR155 gene signature can be detected in the total tumor RNA. In some aspects, CD8+ T cell total RNA can be isolated from a sample and then the miR155 gene signature can be detected in the CD8+ T cell total RNA.

[0057] In some aspects, the subject has cancer. In some aspects, the cancer can be, but is not limited to, colon cancer, kidney cancer, malignant pleural mesothelioma, non-small cell lung cancer, gastric cancer, esophageal cancer, melanoma, breast cancer, bladder cancer, colorectal cancer, pancreatic adenocarcinoma, head and neck squamous cell carcinoma, or thymic carcinoma.ATTORNEY DOCKET NO.21101.0481P1 2. Selecting a Subject to Treat with an Immunotherapy

[0058] Disclosed are methods for selecting a subject having cancer to treat with an immunotherapy comprising detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; comparing the presence or amount of the miR155 gene signature to a control sample or threshold, and selecting the subject having the presence and / or an altered amount of the miR155 gene signature relative to the presence or amount in the control sample or threshold for treatment with an immunotherapy.

[0059] In some aspects, an immunotherapy can be any therapy / therapeutic that mediates T cell differentiation. In some aspects, an immunotherapy can be, but is not limited to, immune checkpoint inhibitors (ICI), vaccines, CAR T cells, STING agonists, co-stimulatory molecule agonists (e.g., OX-40, 4-1BB, or IL-2 receptor agonists) or T cell-based therapies. Thus, for example, disclosed are methods for selecting a subject having cancer to treat with an immune checkpoint inhibitor (ICI) comprising detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; comparing the presence or amount of the miR155 gene signature to a control sample or threshold, and selecting the subject having the presence and / or an altered amount of the miR155 gene signature relative to the presence or amount in the control sample or threshold for treatment with an immune checkpoint inhibitor (ICI) .

[0060] In some aspects, the immune checkpoint inhibitor is ipilimumab (Yervoy), pembrolizumab (Keytruda), nivolumab (Opdivo), cemiplimab (trade name Libtayo), and dostarlimab (Jemperli), atezolizumab (Tecentriq), durvalumab (Imfinzi), Tiragolumab, or avelumab (Bavencio). In some aspects, the ICI can be, but is not limited to, anti-PD-1, anti-PD- L1, anti-LAG3, anti-CTLA4, or anti-Tigit.

[0061] In some aspects, the presence or amount of a miR155 gene signature relative to the presence in a control sample or relative to a threshold indicates the subject will be responsive to an immunotherapy (e.g., ICI) and therefore should be selected for treatment with an immunotherapy. Thus, in some aspects, a control sample can have no detection of the miR155 gene signature. Thus, in some aspects, any amount of the genes from the miR155 gene signature can indicate the subject should be selected for treatment with an immunotherapy. In some aspects, the threshold at which one or more of the signature genes are considered to beATTORNEY DOCKET NO.21101.0481P1 "expressed" or "elevated" can depend on the implemented assay and the overall levels of the gene products. Some approaches that can be used in this context include, but are not limited to, 1) Ranking of the signature gene transcripts in comparison to the rest of the transcriptome. Using existing or new data to be collected, the gene(s) can be considered "expressed" or "elevated" if the said gene(s) are found in a certain quantile in the data; 2) Direct transcript counting approaches. Technologies including (but not limited to) Nanostring, digital droplet PCR, single molecule imaging, in situ hybridization, immunofluorescence microscopy can quantify the actual transcript and protein amounts in the cells and tissues. An absolute or relative threshold can be determined after normalizing the gene expression signal to the mass of tissue sample and / or total RNA; 3) Amplification based approaches. Technologies including (but not limited to) quantitative reverse transcription PCR and RNA sequencing can identify transcript levels in a sample. A relative threshold can be determined based on Ct values (for qRT-PCR) and normalized transcript counts such as transcript-per-million (for RNAseq); and 4) Receiving operating characteristics (ROC) analysis. Using the existing immunotherapy transcriptomics datasets, the optimal threshold can be identified by selecting the expression level of the gene(s) that corresponds to the highest "true positive rate" and the "lowest false positive rate" in binary classification scheme involving responders and nonresponders. This threshold level corresponds to the elbow of the curve which is the closest point to the upper left corner of the ROC space (1x1 square).

[0062] In some aspects, an altered amount of the miR155 gene signature can refer to an increase or decrease in the amount of one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0063] In some aspects, the amount of a miR155 gene signature relative to the amount in a control sample or relative to a threshold indicates the subject will be responsive to an immunotherapy (e.g., ICI) and therefore should be selected for treatment with an immunotherapy. Thus, in some aspects, a control sample can have an amount of the miR155 gene signature, wherein any amount higher than what is in the control sample can indicate the subject should be selected for treatment with an immunotherapy. Thus, in some aspects, detection of the miR155 gene signature that is over a certain threshold can indicate the subject should be selected for treatment with an immunotherapy. In some aspects, a threshold can be previously established based on a set of control samples wherein the control samples are from subjects that where known not to respond to ICI. In some aspects, an altered amount of the miR155 gene signature is an amount that is at least 1x, 1.5x or 2x the amount of the miR155 gene signature in a control sample.ATTORNEY DOCKET NO.21101.0481P1

[0064] In some aspects, a control sample is a sample obtained from a subject that does not have cancer, did not respond to an ICI or did respond to an ICI. In some aspects, a control sample is a sample can be adjacent or distant healthy samples from the same subject.

[0065] In some aspects, a sample from a subject can be, but is not limited to, blood, plasma, saliva, serum, urine, cerebrospinal fluid, feces, or tissue. In some aspects, the sample is from a tumor biopsy. Thus, in some aspects, the sample comprises total tumor RNA or DNA.

[0066] In some aspects, the presence and / or amount of the miR155 gene signature is detected at the DNA or RNA level.

[0067] In some aspects, the miR155 gene signature comprises the presence or an altered amount of miR155 and one or more of CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature comprises miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature comprises the presence or an altered amount of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0068] In some aspects, detecting the presence and / or amount of a miR155 gene signature can be performed using any known technique for detecting the presence and / or amount of a gene. In some aspects, the nucleic acid array disclosed herein can be used. For example, a nucleic acid array can comprise at least fifteen probes immobilized on a solid support, each of said probes being between about 15 and about 500 nucleotides in length, each of said probes being derived from a sequence corresponding to, or complementary to, a transcript of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0069] In some aspects, detecting the presence and / or amount of a miR155 gene signature can be performed using qPCR, nanostring, or next-generation RNA sequencing.

[0070] In some aspects, the miR155 gene signature is detected in CD8+ T cells. Thus, in some aspects, the sample obtained from the subject comprises CD8+ T cells. In some aspects, CD8+ T cells can be isolated from a sample obtained from the subject and then the miR155 gene signature can be detected in the isolated CD8+ T cells.

[0071] In some aspects, the miR155 gene signature is detected in total tumor RNA. Thus, in some aspects, total tumor RNA can be isolated from a sample obtained from the subject and then the miR155 gene signature can be detected in the total tumor RNA. In some aspects, CD8+ T cell total RNA can be isolated from a sample and then the miR155 gene signature can beATTORNEY DOCKET NO.21101.0481P1 detected in the CD8+ T cell total RNA.

[0072] In some aspects, the cancer can be, but is not limited to, colon cancer, kidney cancer, malignant pleural mesothelioma, non-small cell lung cancer, gastric cancer, esophageal cancer, melanoma, breast cancer, bladder cancer, colorectal cancer, pancreatic adenocarcinoma, head and neck squamous cell carcinoma, or thymic carcinoma. 3. Identifying a Subject Responding to Immunotherapy

[0073] Disclosed are methods for identifying a subject currently responding to an immunotherapy comprising detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject prior to treatment with the immunotherapy; detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject during or after treatment with the immunotherapy; and identifying the subject as responding to the immunotherapy when the presence and / or amount of the miR155 gene signature has changed in the subject or a sample from the subject during or after treatment with the immunotherapy relative to the presence and / or amount of the miR155 gene signature in the subject or the sample from the subject prior to treatment with the immunotherapy; wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, an increase or high levels of the gene signature indicate the subject is responding to the immunotherapy.

[0074] In some aspects, an immunotherapy can be any therapy / therapeutic that mediates T cell differentiation. In some aspects, an immunotherapy can be, but is not limited to, immune checkpoint inhibitors (ICI), vaccines, CAR T cells, STING agonists, co-stimulatory molecule agonists (e.g., OX-40, 4-1BB, or IL-2 receptor agonists), or T cell-based therapies. Thus, for example, disclosed are methods for identifying a subject currently responding to an ICI comprising detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject prior to treatment with the ICI; detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject during or after treatment with the immunotherapy; and identifying the subject as responding to the immunotherapy when the presence and / or amount of the miR155 gene signature has changed (e.g., increased) in the subject or a sample from the subject during or after treatment with the immunotherapy relative to the presence and / or amount of the miR155 gene signature in the subject or the sample from the subject prior to treatment with the immunotherapy; wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.ATTORNEY DOCKET NO.21101.0481P1

[0075] In some aspects, the immune checkpoint inhibitor is ipilimumab (Yervoy), pembrolizumab (Keytruda), nivolumab (Opdivo), cemiplimab (trade name Libtayo), and dostarlimab (Jemperli), atezolizumab (Tecentriq), durvalumab (Imfinzi), Tiragolumab, or avelumab (Bavencio). In some aspects, the ICI can be, but is not limited to, anti-PD-1, anti-PD- L1, anti-LAG3, anti-CTLA4, or anti-Tigit.

[0076] In some aspects, an increase in the presence or amount of a miR155 gene signature during or after treatment with an immunotherapy relative to the presence of the miR155 gene signature in the subject or the sample from the subject prior to treatment with the immunotherapy indicates the subject is responding to the immunotherapy

[0077] In some aspects, a decrease in the presence or amount of a miR155 gene signature during or after treatment with an immunotherapy relative to the presence of the miR155 gene signature in the subject or the sample from the subject prior to treatment with the immunotherapy indicates the subject is responding to the immunotherapy.

[0078] In some aspects, instead of comparing the presence of the miR155 gene signature during or after treatment with an immunotherapy to the presence of the miR155 gene signature prior to treatment with the immunotherapy, the presence of the miR155 gene signature during or after treatment with an immunotherapy can be compared to a control sample or a threshold. In some aspects, a control sample can have an amount of the miR155 gene signature known to indicate a subject would be a responder to immunotherapy. Thus, in some aspects, any increase in the amount of the genes from the miR155 gene signature seen in the subject or sample from the subject during or after treatment can indicate the subject should be identified as responding to the immunotherapy. In some aspects, the threshold at which one or more of the signature genes are considered to be "expressed" or "elevated" can depend on the implemented assay and the overall levels of the gene products. Some approaches that can be used in this context include, but are not limited to, 1) Ranking of the signature gene transcripts in comparison to the rest of the transcriptome. Using existing or new data to be collected, the gene(s) can be considered "expressed" or "elevated" if the said gene(s) are found in a certain quantile in the data; 2) Direct transcript counting approaches. Technologies including (but not limited to) Nanostring, digital droplet PCR, single molecule imaging, in situ hybridization, immunofluorescence microscopy can quantify the actual transcript and protein amounts in the cells and tissues. An absolute or relative threshold can be determined after normalizing the gene expression signal to the mass of tissue sample and / or total RNA; 3) Amplification based approaches. Technologies including (but not limited to) quantitative reverse transcription PCR and RNA sequencing can identify transcript levels in a sample. A relative threshold can be determined based on Ct values (forATTORNEY DOCKET NO.21101.0481P1 qRT-PCR) and normalized transcript counts such as transcript-per-million (for RNAseq); and 4) Receiving operating characteristics (ROC) analysis. Using the existing immunotherapy transcriptomics datasets, the optimal threshold can be identified by selecting the expression level of the gene(s) that corresponds to the highest "true positive rate" and the "lowest false positive rate" in binary classification scheme involving responders and nonresponders. This threshold level corresponds to the elbow of the curve which is the closest point to the upper left corner of the ROC space (1x1 square).

[0079] In some aspects, instead of comparing the amount of the miR155 gene signature during or after treatment with an immunotherapy to the amount of the miR155 gene signature prior to treatment with the immunotherapy, the amount of the miR155 gene signature during or after treatment with an immunotherapy can be compared to a control sample or a threshold.

[0080] In some aspects, an altered amount of the miR155 gene signature can refer to an increase or decrease in the amount of one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0081] In some aspects, an increase in the amount of a miR155 gene signature during or after treatment with an immunotherapy relative to the amount in a control sample or relative to a threshold indicates the subject can be identified as responding to the immunotherapy (e.g., ICI). Thus, in some aspects, a control sample can have an amount of the miR155 gene signature known to be present in a subject that will be a responder to an immunotherapy, wherein any amount higher than what is in the control sample can indicate the subject should be identified as responding to treatment with the immunotherapy. In some aspects, an altered amount of the miR155 gene signature is an amount that is at least 1x, 1.5x or 2x higher than the amount of the miR155 gene signature in a control sample.

[0082] In some aspects, a control sample is a sample obtained from a subject having cancer, did not respond to an ICI or did respond to an ICI. In some aspects, a control sample is a sample can be adjacent or distant healthy samples from the same subject.

[0083] In some aspects, a sample from a subject can be, but is not limited to, blood, plasma, saliva, serum, urine, cerebrospinal fluid, feces, or tissue. In some aspects, the sample is from a tumor biopsy. Thus, in some aspects, the sample comprises total tumor RNA or DNA.

[0084] In some aspects, the presence and / or amount of the miR155 gene signature is detected at the DNA or RNA level.

[0085] In some aspects, the miR155 gene signature comprises the presence or an altered amount of miR155 and one or more of CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155ATTORNEY DOCKET NO.21101.0481P1 gene signature comprises miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature comprises the presence or an altered amount of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature comprises the presence or an altered amount of miR155 and one or more of CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, and TIGIT.

[0086] In some aspects, detecting the presence and / or amount of a miR155 gene signature can be performed using any known technique for detecting the presence and / or amount of a gene. In some aspects, the nucleic acid array disclosed herein can be used. For example, a nucleic acid array can comprise at least fifteen probes immobilized on a solid support, each of said probes being between about 15 and about 500 nucleotides in length, each of said probes being derived from a sequence corresponding to, or complementary to, a transcript of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0087] In some aspects, detecting the presence and / or amount of a miR155 gene signature can be performed using qPCR, nanostring, or next-generation RNA sequencing.

[0088] In some aspects, the miR155 gene signature is detected in CD8+ T cells. Thus, in some aspects, the sample obtained from the subject comprises CD8+ T cells. In some aspects, CD8+ T cells can be isolated from a sample obtained from the subject and then the miR155 gene signature can be detected in the isolated CD8+ T cells.

[0089] In some aspects, the miR155 gene signature is detected in total tumor RNA. Thus, in some aspects, total tumor RNA can be isolated from a sample obtained from the subject and then the miR155 gene signature can be detected in the total tumor RNA. In some aspects, CD8+ T cell total RNA can be isolated from a sample and then the miR155 gene signature can be detected in the CD8+ T cell total RNA.

[0090] In some aspects, the subject has cancer. In some aspects, the cancer can be, but is not limited to, colon cancer, kidney cancer, malignant pleural mesothelioma, non-small cell lung cancer, gastric cancer, esophageal cancer, melanoma, breast cancer, bladder cancer, colorectal cancer, pancreatic adenocarcinoma, head and neck squamous cell carcinoma, or thymic carcinoma. 4. Treating

[0091] Disclosed are methods of treating a subject having cancer comprising detecting the presence or altered amount of a miR155 gene signature in the subject or a sample obtained fromATTORNEY DOCKET NO.21101.0481P1 the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; and administering an immunotherapy to the subject.

[0092] In some aspects, an immunotherapy can be any therapy / therapeutic that mediates T cell differentiation. In some aspects, an immunotherapy can be, but is not limited to, immune checkpoint inhibitors (ICI), vaccines, CAR T cells, STING agonists, co-stimulatory molecule agonists (e.g., OX-40, 4-1BB, or IL-2 receptor agonists), or T cell-based therapies. Thus, for example, disclosed are methods of treating a subject having cancer comprising detecting the presence or altered amount of a miR155 gene signature in the subject or a sample obtained from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; and administering an ICI to the subject.

[0093] In some aspects, the immune checkpoint inhibitor is ipilimumab (Yervoy), pembrolizumab (Keytruda), nivolumab (Opdivo), cemiplimab (trade name Libtayo), and dostarlimab (Jemperli), atezolizumab (Tecentriq), durvalumab (Imfinzi), Tiragolumab, or avelumab (Bavencio). In some aspects, the ICI can be, but is not limited to, anti-PD-1, anti-PD- L1, anti-LAG3, anti-CTLA4, or anti-Tigit.

[0094] In some aspects, the presence or amount of a miR155 gene signature relative to the presence in a control sample or relative to a threshold indicates the subject will be responsive or is responding to an immunotherapy (e.g., ICI). Thus, in some aspects, a control sample can have no detection of the miR155 gene signature. Thus, in some aspects, any amount of the genes from the miR155 gene signature can indicate the subject will be responsive or is responding to an immunotherapy. In some aspects, the threshold at which one or more of the signature genes are considered to be "expressed" or "elevated" can depend on the implemented assay and the overall levels of the gene products. Some approaches that can be used in this context include, but are not limited to, 1) Ranking of the signature gene transcripts in comparison to the rest of the transcriptome. Using existing or new data to be collected, the gene(s) can be considered "expressed" or "elevated" if the said gene(s) are found in a certain quantile in the data; 2) Direct transcript counting approaches. Technologies including (but not limited to) Nanostring, digital droplet PCR, single molecule imaging, in situ hybridization, immunofluorescence microscopy can quantify the actual transcript and protein amounts in the cells and tissues. An absolute or relative threshold can be determined after normalizing the gene expression signal to the mass of tissue sample and / or total RNA; 3) Amplification based approaches. Technologies including (but not limited to) quantitative reverse transcription PCR and RNA sequencing can identifyATTORNEY DOCKET NO.21101.0481P1 transcript levels in a sample. A relative threshold can be determined based on Ct values (for qRT-PCR) and normalized transcript counts such as transcript-per-million (for RNAseq); and 4) Receiving operating characteristics (ROC) analysis. Using the existing immunotherapy transcriptomics datasets, the optimal threshold can be identified by selecting the expression level of the gene(s) that corresponds to the highest "true positive rate" and the "lowest false positive rate" in binary classification scheme involving responders and nonresponders. This threshold level corresponds to the elbow of the curve which is the closest point to the upper left corner of the ROC space (1x1 square).

[0095] In some aspects, an altered amount of the miR155 gene signature can refer to an increase or decrease in the amount of one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0096] In some aspects, the amount of a miR155 gene signature relative to the amount in a control sample or relative to a threshold indicates the subject will be responsive or is responding to an immunotherapy (e.g., ICI). Thus, in some aspects, a control sample can have an amount of the miR155 gene signature, wherein any amount higher than what is in the control sample can indicate the subject will be responsive or is responding to an ICI. Thus, in some aspects, detection of the miR155 gene signature that is over a certain threshold can indicate the subject will be responsive or is responding to an ICI. In some aspects, a threshold can be previously established based on a set of control samples wherein the control samples are from subjects that where known not to respond to ICI. In some aspects, an altered amount of the miR155 gene signature is an amount that is at least 1x, 1.5x or 2x the amount of the miR155 gene signature in a control sample.

[0097] In some aspects, a control sample is a sample obtained from a subject that does not have cancer, did not respond to an ICI or did respond to an ICI. In some aspects, a control sample is a sample can be adjacent or distant healthy samples from the same subject.

[0098] In some aspects, the presence and / or amount of the miR155 gene signature is detected at the DNA or RNA level.

[0099] In some aspects, the miR155 gene signature comprises the presence or an altered amount of miR155 and one or more of CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature comprises miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the miR155 gene signature comprises the presence or an altered amount of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2,ATTORNEY DOCKET NO.21101.0481P1 IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0100] In some aspects, disclosed are methods of treating a subject having cancer comprising detecting the presence or altered amount of a miR155 gene signature in the subject or a sample obtained from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; administering an immunotherapy to the subject and further administering a second cancer therapeutic to the subject. In some aspects, the second cancer therapeutic is different than the immunotherapy. In some aspects, the second cancer therapeutic can be, but is not limited to, chemotherapy, radiation, targeted therapy, or other an immunotherapy different from the initial immunotherapy, including STING agonists, vaccines, ICIs, CAR-T cells, and other immune-modulatory agents.

[0101] In some aspects, the miR155 gene signature is detected in CD8+ T cells. Thus, in some aspects, the sample obtained from the subject comprises CD8+ T cells. In some aspects, CD8+ T cells can be isolated from a sample obtained from the subject and then the miR155 gene signature can be detected in the isolated CD8+ T cells.

[0102] In some aspects, the miR155 gene signature is detected in total tumor RNA. Thus, in some aspects, total tumor RNA can be isolated from a sample obtained from the subject and then the miR155 gene signature can be detected in the total tumor RNA. In some aspects, CD8+ T cell total RNA can be isolated from a sample and then the miR155 gene signature can be detected in the CD8+ T cell total RNA.

[0103] In some aspects, the cancer can be, but is not limited to, colon cancer, kidney cancer, malignant pleural mesothelioma, non-small cell lung cancer, gastric cancer, esophageal cancer, melanoma, breast cancer, bladder cancer, colorectal cancer, pancreatic adenocarcinoma, head and neck squamous cell carcinoma, or thymic carcinoma. 5. Increasing T Effector Cells

[0104] Disclosed are methods of increasing T effector cells in a subject comprising administering, to the subject, an agent that increases the levels of miR155. In some aspects, the presence or upregulation of miR155 allows for the miR155 gene signature to be expressed or upregulated.

[0105] In some aspects, the agent that increase the levels of miR155 is a composition comprising miR-155. In some aspects, the agent that increase the levels of miR155 is a composition that regulates expression of miR155.

[0106] In some aspects, the agent can be, but is not limited to, a nanoparticle comprising miR-155, an exosome comprising miR155, a miR155 mimic, CAR T cells capable ofATTORNEY DOCKET NO.21101.0481P1 overexpressing miR155, a viral vector capable of overexpressing miR-155, a secondary immunotherapy agent (e.g., secondary ICI agents), vaccine, co-infection, tumor cell death / lysis through secondary therapies (e.g., chemotherapy / radiation / oncolytic viruses / targetted therapies or other therapies that cause cancer cell death). In some aspects, vaccines and tumor cell death / lysis can cause a release of cancer antigens thus stimulating CD8 T cell effector responses and miR-155.

[0107] In some aspects, the agent causes an increase in differentiation of T stem-cell-like memory (Tscm) or T central memory-like (Tcm) cells to T effector (Teff) cells.

[0108] In some aspects, the subject has cancer. In some aspects, the cancer can be, but is not limited to, colon cancer, kidney cancer, malignant pleural mesothelioma, non-small cell lung cancer, gastric cancer, esophageal cancer, melanoma, breast cancer, bladder cancer, colorectal cancer, pancreatic adenocarcinoma, head and neck squamous cell carcinoma, or thymic carcinoma. 6. Identifying a Cancer to be Treated with Immunotherapy

[0109] Disclosed are methods of identifying a cancer that can be effectively treated with an immunotherapy comprising detecting the presence and / or amount of a miR155 gene signature in a sample obtained from a subject having cancer, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; wherein the presence and / or amount of the miR155 gene signature identifies the sample as being from a subject having a cancer that can be effectively treated with an immunotherapy.

[0110] In some aspects, the sample comprises cancer cells or nucleic acids derived from cancer cells. In some aspects, the cancer cells comprise CD8+ cells.

[0111] In some aspects, the amount of the miR155 gene signature is equivalent to, or within a range, of the amount of the miR155 gene signature from a sample from a subject having cancer, wherein the cancer is known to be effectively treated with the immunotherapy.

[0112] In some aspects, the amount of the miR155 gene signature can be an increase or decrease in the amount of one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0113] In some aspects, the amount of a miR155 gene signature is an increase relative to the amount in a control sample or relative to a threshold which indicates the cancer can be effectively treated with the immunotherapy (e.g., ICI). Thus, in some aspects, a control sample can have an amount of the miR155 gene signature, wherein any amount higher than what is in the control sample can indicate the cancer can be effectively treated with the immunotherapy.ATTORNEY DOCKET NO.21101.0481P1 Thus, in some aspects, detection of the miR155 gene signature that is over a certain threshold can indicate the cancer can be effectively treated with the immunotherapy. In some aspects, a threshold can be previously established based on a set of control samples wherein the control samples are from subjects that where known not to respond to immunotherapy. In some aspects, an altered amount of the miR155 gene signature is an amount that is at least 1x, 1.5x or 2x the amount of the miR155 gene signature in a control sample.

[0114] In some aspects, a sample from a subject can be, but is not limited to, blood, plasma, saliva, serum, urine, cerebrospinal fluid, feces, or tissue. In some aspects, the sample is from a tumor biopsy. Thus, in some aspects, the sample comprises total tumor RNA or DNA.

[0115] In some aspects, detecting the presence and / or amount of a miR155 gene signature can be performed using any known technique for detecting the presence and / or amount of a gene. In some aspects, the nucleic acid array disclosed herein can be used. For example, a nucleic acid array can comprise at least fifteen probes immobilized on a solid support, each of said probes being between about 15 and about 500 nucleotides in length, each of said probes being derived from a sequence corresponding to, or complementary to, a transcript of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0116] In some aspects, detecting the presence and / or amount of a miR155 gene signature can be performed using qPCR, nanostring, or next-generation RNA sequencing.

[0117] In some aspects, the cancer can be, but is not limited to, colon cancer, kidney cancer, malignant pleural mesothelioma, non-small cell lung cancer, gastric cancer, esophageal cancer, melanoma, breast cancer, bladder cancer, colorectal cancer, pancreatic adenocarcinoma, head and neck squamous cell carcinoma, or thymic carcinoma. C. Recombinant Cells

[0118] Disclosed are recombinant cells comprising altered expression of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. Disclosed are recombinant cells comprising altered expression of one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. Disclosed are recombinant cells comprising altered expression of three or more miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0119] Disclosed are recombinant cells comprising an agent capable of altering the expression of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. Disclosed are recombinant cellsATTORNEY DOCKET NO.21101.0481P1 comprising an agent capable of altering the expression of one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT. In some aspects, the agent can be, but is not limited to, a nanoparticle comprising miR-155, an exosome comprising miR155, a miR155 mimic, CAR T cells capable of overexpressing miR155, a viral vector capable of overexpressing miR-155, a secondary immunotherapy agent (e.g., secondary ICI agents), vaccine, co-infection, tumor cell death / lysis through secondary therapies (e.g., chemotherapy / radiation / oncolytic viruses / targetted therapies or other therapies that cause cancer cell death). In some aspects, vaccines and tumor cell death / lysis can cause a release of cancer antigens thus stimulating CD8 T cell effector responses and miR-155.

[0120] In some aspects, the recombinant cell is a T cell. In some aspects, the T cell is a CD8+ T cell. D. Nucleic Acid Array

[0121] Disclosed are nucleic acid arrays comprising at least fifteen probes immobilized on a solid support, each of said probes being between about 15 and about 500 nucleotides in length, each of said probes being derived from a sequence corresponding to, or complementary to, a transcript of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0122] In some aspects, the nucleic acid arrays can be used for expression-based assessment for predicting a subject’s responsiveness and / or determining a subject’s current response to an immunotherapy. Thus, the disclosed nucleic acid arrays can be used to detect the presence and / or amount of miR155 gene signature in any of the disclosed methods. E. Kits

[0123] The compositions and materials described above as well as other materials can be packaged together in any suitable combination as a kit useful for performing, or aiding in the performance of, the disclosed method. It is useful if the kit components in a given kit are designed and adapted for use together in the disclosed method. For example disclosed are kits comprising one or more oligonucleotides, wherein the one or more oligonucleotides specifically bind to one or more of the genes miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

[0124] In some aspects, one or more oligonucleotides are primers, probes, or a combination thereof. ExamplesATTORNEY DOCKET NO.21101.0481P1 1. Introduction

[0125] Through this study on CRC, a classically ICI-resistant cancer, the findings reveal a mechanism underlying the role of CD8+T cell-expressed miR-155 in driving intratumoral CD8+T cell stem to effector differentiation, and defining a CD8+T cell state that predicts and controls responses to immunotherapy.

[0126] miRNAs, including miR-155, hold promise as key regulators of immune cell behavior that can improve current immunotherapies, including those used to treat CRC. Here, using a novel, in vivo CRISPR-Cas9-based miRNA functional screen, miR-155 was identified in CD8+T cells as a key regulator of CRC immunity. Loss of miR-155 in murine T cells results in increased CRC tumor burden and a complete loss of ICI responses. Furthermore, a dependence on miR-155 for an intratumor CD8+T cell differentiation cascade from Tcf-1+stem-like to the Cxcr6+effector state was discovered, which relies on miR-155 repressing Ship-1 and, subsequently, Tcf-1. Analysis of human CRC data revealed miR-155 was primarily expressed in CD8+T cells, correlated to multiple emerging molecular parameters of ICI responses, and is potentially more prognostic than dMMR / MSI-H status, the current clinical standard. Finally, a novel miR-155-dependent CD8+T cell 15 gene signature, “the miR-155-15”, was identified as a top gene signature among 48 publicly available predictive gene signatures across 12 cancer types and 36 patient data sets in predicting ICI responses. Together, this work clearly identifies miR-155 as a master regulator of intratumor CD8+T cell responses to CRC tumors, including their capacity to respond to ICI, and these findings have groundbreaking translational implications for diagnosing and treating CRC. 2. Results i. miR-155 is the most functionally significant miRNA in CD8+T cells in mouse colon cancer

[0127] To identify miRNA determinants of CD8+T cell-mediated colon cancer immunity and ICI responses, an in vivo tumor antigen-specific CD8+T cell CRISPR-Cas9 miRNA screen was performed. Candidate miRNAs were selected that served as negative or positive prognostic factors and positively correlated to CD8A in colon adenocarcinoma (COAD) and cancer types with miRNA sequencing data from The Cancer Genome Atlas (TCGA) projects, excluding cancers of immunologic or hematopoietic origin.41 candidate miRNAs were identified, and a custom library with single guide RNAs (sgRNAs) targeting these miRNAs, 5 protein controls, and non-targeting scrambled controls was created. The library was transduced into OT1-Cas9- CD8+T cells, which have a transgenic T cell receptor (Tcr) recognizing ovalbumin (OVA). Thetransduced CD8+ T cells were adoptively transferred into Tcr KO mice, which lack CD4+ andATTORNEY DOCKET NO.21101.0481P1 CD8+T cells, seven days post-challenge with MC38 (syngeneic COAD tumor cell line) OVA expressing (MC38-OVA) cells. Functional data pertaining to CD8+T cell persistence / infiltration was then interpreted from the frequency of sgRNA barcodes targeting each miRNA in tumors compared to input barcode frequencies in the adoptively transferred CD8+T cells (FIG.1A). The sgRNAs targeting anti-CD8+T cell fitness genes become enriched while those targeting pro-CD8+T cell fitness genes become depleted (FIG.1A-B). Pdcd1 (PD-1) sgRNA barcodes, an inhibitor of T cell function and the primary clinical target of ICI, showed the highest degree of statistically significant enrichment. In contrast, miR-155 sgRNA barcodes showed the highest degree of statistically significant depletion (FIG.1B). The similarity in the magnitude of change of miR-155 and Pdcd1 sgRNA barcode representation highlights the importance of miR-155 as the most impactful miRNA positive regulator of CD8+T cell accumulation in the TME. Within this system, transduction efficiency was ~30% (FIG.1C) and target genes, such as miR-155,were successfully deleted in mCherry+ transduced sorted cells (FIG. 1D). In Tcr KO micechallenged with MC38-OVA cells, miR-155+CD8+OT1 cells (WT) elicited nearly complete tumor clearance, while OT1-miR-155KO-CD8+T cell recipients could not control tumor growth or burden (FIG..1E-F), validating miR-155 as an intrinsic regulator of tumor-antigen specific CD8+T cells. Loss of miR-155 in OT1 CD8+T cells also reduced the frequency and number ofintratumoral CD8+ T cells and Ifn -producing CD8+ T cells (FIG. 1G-H, 8A-B, 8D). Theseexpansion and functional deficits were restricted to the TME and not the tdLNS, and reflected an intratumoral reduction in effector (eff) CD44+CD62L- CD8+T cells and corresponding expansion of central memory-like (CM) CD44+CD62L+CD8+T cells (FIG.8C, 8E-G), which have stem-like properties and are precursors for expanding effector CD8+T cells. Despite this increased CD8+CM pool, miR-155 KO CD8+T cells failed to expand and terminally differentiate into anti-tumor effector CD8+T cells. Altogether, these findings identify miR-155 as the most significant miRNA positively regulating CD8+T cell anti-tumor immunity against CRC, which occurs selectively in tumors. ii. T cell miR-155 is necessary for anti-tumor immunity in pre-clinical colon cancer models

[0128] Next, the relevance of T cell miR-155 status was examined in multiple mutationally distinct colon cancer mouse models representative of two of four molecular subtypes of CRC.Both MSI-H and colitis-associated CRC, which is uniquely characterized by a TGF- dominantimmune-suppressive TME, was examined. To test the necessity of miR-155, a CD4-Cre+miR- 155fl / fl mice (miR-155 TKO) model, which knocks out miR-155 in both CD4+and CD8+T cells, was utilized. miR-155fl / fl (WT) littermates were used as controls for the syngeneic MC38ATTORNEY DOCKET NO.21101.0481P1 model (MSI-H) or the azoxymethane (AOM) / dextran sodium sulfate (DSS) model (primary colitis-associated COAD). miR-155 TKO mice challenged with MC38-OVA could not control tumor growth (FIG.2A) and burden by mass (FIG.2B). Tumor control in mir-155 TKO mice was restored to WT levels upon receiving an adoptive transfer of miR-155+OT1-CD8+T cells(FIG. 2A-B). A lower frequency of intratumoral CD8+ T cells and Ifn + and Gzmb+ CD8+ Tcells in miR155 TKO mice was observed (FIG.2C-H). Additionally, miR-155 TKO mice had a decreased frequency of PD-1+and eff CD8+T cells, CD8+T cell subsets indicating antigen- experienced tumor-associated T cells and effector anti-tumor responses (FIG.2I, 2J, 9A-B). This decreased effector population is likely due to a failure in differentiation from the T stem-cell-like memory (Tscm) or CM states, both of which are expanded in miR-155 TKO mice (FIG.9C-D).

[0129] Similarly to the MC38 model, when challenged with AOM / DSS, miR-155 TKO mice have an increased tumor burden and polyp number (FIG.2K-M). These mice also have a decreased number and frequency of CD8+T cells, PD-1+CD8+T cells, and eff CD8+T cells and an increased frequency of CM CD8+T cells (FIG.2N-U, 9L). Notably, these stark immunologic differences were restricted to tumors and not seen in the spleens of either MC38- and tdLNs of AOM / DSS-challenged mice (FIG.9E-K, M-P). Regardless of the colon cancer subtype, the data indicate that T cell miR-155 is crucial in promoting the expansion of intratumoral effector CD8+T cells necessary for antitumor immunity and that CD8+T cells expressing miR-155 are sufficient in mediating this antitumor response. iii. T cell miR-155 is necessary for ICI responses in a pre-clinical colon cancer model

[0130] T cell miR-155 promotes anti-tumor immunity mediated by effector CD8+T cell differentiation, function, and accumulation in tumors; however, it remains unclear whether T cell miR-155 impacts ICI responses. To determine if miR-155 regulates ICI responses in CRC, WT and miR-155 TKO mice were challenged with MC38 cells and administered either PBS or anti-PD-1 monoclonal antibody (mAb) therapy (ICI). miR-155 TKO mice treated with PBS or ICI exhibited equivalent tumor growth kinetics and mass (FIG.3A-B) consistent with an unchanged frequency and number of CD8+T cells / g of tumor (FIG.3C-D, 10A). Meanwhile, WT mice that received ICI therapy exhibited significantly less growth and had nearly complete tumor regression compared to WT mice treated with PBS (FIG.3A-B). ICI therapy could not rescue the defective effector CD8+T cell numbers, differentiation, and effector responses of miR-155 TKO mice to the ones seen in WT mice (FIG.3D-P, 10A). However, ICI therapy marginally improved features of effector CD8+T cell differentiation in miR-155 TKO mice, such as Gzmb production (FIG.3E, 3G), PD-1 expression (FIG.3I-K), contraction of the highlyATTORNEY DOCKET NO.21101.0481P1 proliferative CD8+Tscm cells (FIG.3M-N), and expansion of effector CD8+T cells in the TME (FIG.3M, 3P) as well as Gzmb production in tdLNs (FIG.10G) compared to miR-155 TKO mice treated with PBS. Despite some functional responses, miR-155KO CD8+T cells could not effectively persist in the tumor and / or transition into antigen-experienced PD-1+(FIG.3K), Tim- 3+(FIG.3L), or effector CD8+T states (FIG.3P) necessary for high CD8+T cell infiltration and an effective ICI response. This was corroborated by an expanded pool of Tscm and CM CD8+T cells in the miR-155 KO mice (FIG.3M-P). In the tdLNs of WT and miR-155 TKO mice, there were little to no differences (<2%) in the frequency of total CD8+T cells as well as naïve, CM,eff, PD-1+, Tim-3+, Ifn +, and Gzmb+ CD8+ T cell (FIG. 10B-I). These results show that miR-155 is necessary for intratumoral effector CD8+T cell differentiation and persistence to elicit proper antitumor and ICI responses. iv. T cell miR-155 enables the intratumoral CD8+ T cell stem-like to effector differentiation in colon cancer

[0131] To better understand the T cell states within colon cancer, single-cell RNA-Seq (scRNAseq) was performed on CD45+immune cells that were sorted from MC38-challenged WT or miR-155 TKO mice with or without ICI treatment (FIG.4A). The most dramatic change was in the frequency of cells from the CD8+T cell cluster, approximately 5-fold lower in miR- 155 TKO immune cells (FIG.4A, 11A). Upon differential gene analysis, effector-related genes in miR-155+CD8+T cells, such as Id2, Pdcd1, Lag3, and Nkg7, among many others were enriched. Most notable was the increase in Cxcr6 expression, as recent literature illustrates its novel role in effector CD8+T cell persistence in the TME, CTL-mediated antitumor immunity, and necessity for ICI responses. Of the down-regulated genes, Tcf7, which encodes Tcf1 and defines stem-like T cells, was the most significantly down-regulated in WT CD8+T cells+ / - ICI (FIG.4B, 11B). These findings were validated in miR-155 KO or WT animals challenged with MC38 cells. Again, the frequency and number of tumor-associated miR-155KO CD8+T cells were decreased, which was associated with a greater tumor burden (FIG.4C-E, 11C-D). Considering intratumoral differentiation of CD8+T cells from Tscm and CM to eff, miR-155KO CD8+T cells were seemingly stuck in the Tscm and CM states with stem-like properties and unable to differentiate into an effector state (FIG.4F-I). Indeed, these miR-155KO CD8+T cells had expanded stem-like Ccr7+Cxc3r1- and Tcf-1+Cxcr6- populations (FIG. 4M-N, 4Q), and WT CD8+T cells were more differentiated and had expanded proliferative transitory Cx3cr1+and the effector Tcf-1-Cxcr6+populations (FIG.4M, 4O-P). This stem-like to transitory to effector state was tumor-specific, as no differences were seen in the tdLNs (FIG.4R-U), corroborating the little (<2%) to no changes in frequency for total CD8+T cell and (naïve), CM, and effATTORNEY DOCKET NO.21101.0481P1 populations across multiple models of murine colon cancer (FIG.4J-L, 11E, 9M-P). Considering TCR clonality in stem-like and terminally differentiated CD8 T cells in human cancer, These results are consistent with miR-155 promoting CD8+T cell differentiation of antigen- experienced tdLN primed CD8+T cells necessary for CD8+T cell persistence, tumor immunity, and ICI responses. v. T cell miR-155 represses SHIP-1 (INPP5D), an inhibitor of effector-like CD8+ T cell differentiation and antitumor immunity

[0132] To understand miR-155-mediated stem-transitory-effector differentiation, potential miR-155 targets that could enhance Tcf-1 and have clinical indicators of affecting clinical CRC survival and immunity were examined. The canonical miR-155 target, Ship-1 (Inpp5d) was identified, as it met both molecular and clinical criteria (FIG.6). Ship-1 also known as inositol polyphosphate-5-phosphatase, inhibits the phosphorylation of Akt (p-Akt) in CD8+T cells and subsequently effector function. In the absence of p-Akt, nuclear localization and function of Foxo1 are enhanced, promoting Tcf-1 associated stem-like states in CD8+T cells. In the colon TME, miR-155 KO CD8+T cells, including total, CM, eff, stem-like Ccr7+Cx3cr1-, and transitory Cx3cr1+, had a dramatic increase in the frequency of Tcf-1+Ship-1+populations (FIG. 5A-D, 5F-G). Additionally, Ship-1 was de-repressed in effector Tcf-1-Cxcr6+miR-155 KO CD8+T cells (FIG.5E, 5H). The relationship between Ship-1 and p-Akt within CD8+T cells throughout the differentiation cascade was examined further. Within the activated total, Ccr7+stem-like, Cx3cr1+transitory, and Cxcr6+effector miR-155 KO CD8+T cells, the frequency and expression of p-Akt was decreased while Ship-1 expression was increased (FIG.5I, 5J-M). Complementing the enhancement of Ship-1 and Tcf-1 and inhibition of p-Akt, Foxo1 expression was upregulated in miR-155 KO CD8+T cells+ / - ICI at the mRNA level (FIG.12A). To skew CD8+T cells towards stem / memory-like states, Foxo1 acts as a transcriptional activator of Tcf- 1(Tcf7), Eomes, CD62L(Sell), Lef1, and Klf2, all of which were upregulated in miR-155 KO CD8+T cells+ / - ICI (FIG.11B, 12B-E). These findings show that miR-155 promotes the effector differentiation cascade for anti-tumor CD8+T cells by inhibiting Ship-1, which subsequently increases p-Akt. In turn, p-Akt suppresses Foxo1, which lowers the expression of its target, Tcf-1(Tcf7), and reduced Tcf-1 enables the expression of effector genes such as Cxcr6.

[0133] Next, CD8+T cells rescue of defective responses to CRC tumors in vivo was tested by deleting Ship1 in miR-155- / - CD8+T cells. Using the CD8+T cell CRISPR-Cas9 platform, Ship1 was deleted in OT1-Cas9-miR-155KO CD8+T cells (FIG.12F). Then, OT1-Cas9, OT1- Cas9-miR-155KO, or OT1-Cas9-miR-155 / Ship-1KO CD8+T cells were adoptively transferredinto Tcr KO mice challenged with MC38-OVA cells. Compared to mice receiving OT1-miR-ATTORNEY DOCKET NO.21101.0481P1 155KO-CD8+T cells, mice receiving OT1-miR-155 / Ship-1KO CD8+T cells exhibited greater tumor regression (FIG.5N) and decreased mass (FIG.5O), partially and yet significantly restoring the CD8+T cell-mediated tumor control back to mice receiving miR-155 replete OT1- CD8+ T cells (FIG.5N, 5O). This phenotype was complemented by the partial rescue of CD8+T cell infiltration into the TME (FIG.5P), demonstrating a functional consequence for derepressed Ship1 in tumor-associated miR-155KO CD8+T cells. Taken together, this data establishes the in vivo miR-155-dependent repression of Ship1 in the colon TME that governs a proper CD8+T cell differentiation cascade. vi. Clinical data implicates SHIP-1 (INPP5D) as a miR-155 target and indicator of poor CRC patient and immunologic outcomes

[0134] To identify clinically relevant miR-155 targets, Kaplan-Meier survival analysis was performed on all T cell miR-155 biochemically validated targets, stratifying patients from TCGA into the top and bottom 30% expressing populations. SHIP-1 (INPP5D) was among the rare miR-155 targets that conferred a survival advantage when lowly expressed (FIG.6A) and is expressed at different levels across CRC subtypes, with the lowest expression in MSI-H patients (FIG.6B). Additionally, data from The Human Protein Atlas show that lymphocytes express SHIP-1 protein, making SHIP-1 a clinically relevant CRC-related miR-155 target in lymphocytes (data not shown). Gene set enrichment analysis (GSEA) was performed on all hallmark pathways and found that INPP5D (SHIP-1)-high patients were enriched for the Wnt / B- catenin pathway, which is a molecular subtype of CRC known to be ICI-resistant. Notably,INPP5D (SHIP-1)-low patients were enriched for IFN response genes among multipleinflammatory hallmark pathways enriched in miR-155-high patients (FIG. 6C, 7E). To confirm this data further, an independent CRC cohort was examined. Using qPCR, expression of SHIP-1 was increased (FIG.6D), and expression of miR-155 in mCRC (N / M positive) was decreased (FIG.6E). In non-metastatic CRC (N / M negative), miR-155 was more highly expressed and anti-correlated with INPP5D, but this correlation is lost in metastatic cases, potentially due to the loss of miR-155-mediated tumor control (FIG.6F). Given the converse relationships between miR-155 and SHIP-1 with GSEA enriched / depleted pathways, MSI-H patient populations (FIG.6B, 7C), and expression in non-metastatic CRC, CD8+T cell expression of miR-155 is likely repressing SHIP-1 in human CRC since miR-155 is primarily restricted to CD8+T cell in CRC (FIG.7F). vii. miR-155 expression defines an anti-tumor CD8+ T cell state in colon cancer predictive of pan-cancer ICI responses

[0135] Given the recent success of Immunoscore and IFN gene signatures in predicting ICIATTORNEY DOCKET NO.21101.0481P1 responses, differentially expressed miRNAs in colon cancer were identified using these molecular criteria. miR-155 was the most significantly enriched miRNA in colon cancer patients expressing high CD8A, the primary feature of Immunoscore (FIG.7A). Next, correlations were compared between CD8A and miR-155 versus MLH1 (FIG.7B), an MMR enzyme that is the most prevalent cause of dMMR when hypermethylated. As expected, MLH1 negatively correlated with CD8A. However, a bimodal distribution of patients, one group with MLH1 high (proficient-pMMR / MSS) and another with MLH1 low (dMMR / MSI-H), did not allow MLH1 expression to resolve CD8A status within MSI classifiers (FIG.6B). Unlike MLH1, a more significant and stronger positive correlation existed between miR-155 and CD8A in all patients and MSI classifiers (FIG.7B). All other clinically tested MMR enzymes were tested, and there was no correlation with CD8A (FIG.13A). Although dMMR / MSI-H patients had the highest expression of miR-155 (FIG.7C), these results implicate miR-155 as a better indicator of Immunoscore compared to dMMR / MSI-H status since miR-155 may provide improved resolution in predicting CD8+T cell status irrespective of the tumor cell defining feature, dMMR / MSI-H status.

[0136] Most colon cancer patients have a wound-healing immune landscape (C1), excluding CD8+T cells from the TME, whereas a smaller proportion of colon cancer patientshave an IFN dominant immune landscape (C2), associated with high levels of CTLs. Given theprevious preclinical findings, C2 patients unsurprisingly had the highest miR-155 expression(FIG. 7D), and upon GSEA, miR-155 high patients had the highest enrichment score for IFNresponse pathway genes among multiple pathways associated with an inflammatory and ICI- responsive TME (FIG.7E). Conversely, the Wnt / B-catenin pathway, associated with ICI resistances, was enriched in miR-155-low patients (FIG.6E). Of note, this anticorrelates with observations for SHIP1 that are consistent with a functionally relevant miRNA-target relationship (FIG.6C). MIR155HG expression is primarily restricted to CD8+T cells, whichwere also the primary producers of IFN and major producers of GZMB and PRF1 (FIG. 7F)through the Human Colon Cancer Atlas scRNAseq data set. All these findings suggest that miR-155 is CD8+ T cell-specific, corresponds to a high Immunscore and Ifn responses, and mayplay a role in the CD8 T differentiation cascade necessary for ICI responses.

[0137] The impact of miR-155 on ICI responses through a novel T cell gene signature identified through the scRNAseq data set (FIG.4B) was identified and dissected further. A list of 15 genes was generated, including the overlapping upregulated genes between WT vs. miR- 155KO and WT+ICI vs. miR-155KO+ICI in the CD8+T cell cluster plus MIR155HG (FIG. 7G). These genes, referred to as the “miR-15515” (miR-155_UP), represent a gene set that isATTORNEY DOCKET NO.21101.0481P1 highly expressed in miR-155-replete CD8+T cells prior to and during a positive ICI response in the MC38 model. The miR-15515 was among the top predictive gene signatures of a positive ICI response among 48 publicly available ICI predictive gene signatures across 12 cancer types and 36 patient data sets (Table X). With a median area under the receiving operating characteristic (AUROC) curve value of 0.70, miR-155-15 can correctly classify a positive ICI response in 70% of cases. The predictive accuracy of miR-15515 is nearly equivalent to thewell-documented 18 IFN- gene signature from a landmark study, and overall, the miR-15515is a stronger predictor compared to other publicly available ICI response gene signatures (FIG. 7H, 13B) and clinically used ICI criteria. Additionally, the novel gene set is unique, with minimal overlapping genes compared to other gene signatures, providing entirely separate means of stratifying a patient’s ICI candidacy, which could be used in tandem with other known gene signatures (FIG.13C). Together, the correlation of miRNAs to CD8A identifies miR-155as the top CD8+ T cell-specific predictor of Immunoscore, an IFN dominant landscape, effectorCD8+T cells, and ICI correlates. With the novel miR-15515 gene signature ranking among the top predictors of ICI responses across 12 cancer types, miR-155 expression in CD8+T cellsdefines a key biological requirement for an effector CD8+ T cell differentiation driven IFNsculpted immune landscape conducive for ICI responses in human cancers. 3. Discussion

[0138] Through a functional in vivo miRNA CRISPR-Cas9 screen based on multiple clinical parameters, miR-155 was identified as the most impactful positive regulator of tumor-antigen- specific CD8+T cells in colon cancer (FIG.1). To understand the clinical implications of CD8+T cell expressed miR-155, the role of T cell miR-155 was studied in two preclinical models, each representing a unique TME, and in the context of human patients. In this study, T cell miR- 155 was a biological requirement for anti-tumor and ICI responses as well as intratumoral CD8+T cell effector differentiation, function, and Cxcr6 expression in immune-promoting and - suppressive TMEs (FIG.1-4). MiR-155 also defined a CD8+T cell state needed for anti-tumor and ICI responses by multiple molecular parameters in human CRC cohorts. Thus, miR-155 acts as a master regulator of CD8+T cells, directing differentiation towards an effector anti-tumor state in multiple colon TMEs.

[0139] CD8+ T cell effector function is often defined as the ability to produce Ifn andGzmb, among many other cytolytic molecules. Within the TME, these effector CD8+T cells are needed for anti-tumor and ICI responses, and the persistence of effector CD8+T cells is dependent on antigen-experienced-stem-like populations from the tumor that originate from primed naïve / stem-like CD8+T cells in secondary lymphoid organs. The data indicated thatATTORNEY DOCKET NO.21101.0481P1 miR-155 expression in CD8+T cells is necessary for their effector function, expansion, and ICI sensitivity in colon cancer (FIG.1-4), primarily by facilitating the CD8+T cell differentiation from a reservoir of Tscm or CM to an effector CD8+T cells all within the tumor itself. Through scRNAseq and flow cytometric analysis of intratumoral CD8+T cells, miR-155 expression in tumor-associated CD8+T cells is a requirement to transition from a Tcf-1+stem-like to a Cx3cr1+transitory and then to the recently described terminal Cxcr6+effector state (FIG.4). Although the primary and sustained anti-tumor responses are dependent on Tcf-1+CD8+T cell priming in secondary lymphoid organs, little to no difference was observed in the seeding and persistence of adoptively transferred miR-155KO CD8+T cells in tdLNs of T cell-deficient tumor-challenged mice (FIG.8). Additionally, CD8+T cell states in the spleens and tdLNs of miR-155 TKO tumor-challenged mice were unchanged compared to their WT counterparts (FIG.2-4). Instead, the miR-155-dependent differentiation cascade was largely limited to the TME (FIG.4). Without miR-155 expression in CD8+T cells, tumor-associated CD8+T cells could no longer downregulate Tcf-1 to initiate differentiation into an anti-tumor effector state (FIG.4). Given that Tcf-1 can directly bind to the Cxcr6 locus to repress expression, miR-155 is a negative regulator of Tcf-1 that, in turn, promotes effector differentiation and the expression of Cxcr6 for proper expansion and persistence of intratumoral CD8+T cells.

[0140] Each phase of the Tcf-1 stem, Cx3cr1 transitory, and the Cxcr6 effector intratumoral CD8+T cell states is crucial for anti-tumor immunity and ICI responses in multiple preclinical models. Thus, a complete reliance on miR-155 in T cells to elicit an ICI response was observed (FIG.3), as miR-155 facilitated the transition between each CD8+T cell state from the Tcf-1+stem to Cx3cr1+transitory to Cxcr6+effector phases (FIG.4). Although Tcf-1 is enhanced in miR-155 KO CD8+T cells and widely considered a necessity for anti-tumor and ICI responses, it has recently been reported that Tcf-1 deficient CD8+T cells still properly respond to ICI due to an expansion of transitory effector CD8+T cells in highly but not lowly antigenic tumors. Considering the situational Tcf-1 and mandatory miR-155 requirement for an ICI response in a highly antigenic setting, miR-155 may supersede or complement the biological need for Tcf-1 in ICI in both low and high antigen / affinity tumor settings, as miR-155 boosts CD8 T cell- mediated tumor immunity against low-affinity tumor antigens. miR-155 may also sensitize tumors to anti-PD-1 therapy as miR-155 expression promoted the expansion of PD-1 expressing tumor-associated CD8+T cells (FIG.2) and has been reported to promote the persistence of, and sustained immunity from, PD-1+exhausted CD8+T cells. Since PD-1 engagement and loss of miR-155 inhibit the stem-to-effector transition, anti-PD-1 therapy and miR-155 expression are likely synergistic in mediating and maintaining a CD8+T cell infiltrated TME for durable ICIATTORNEY DOCKET NO.21101.0481P1 responses. These findings support and expand on a landmark clinical study showing tumor regression after ICI requiring pre-existing TME residing CD8+T cells inhibited by the PD-1 / PD- L1 axis.

[0141] Mechanistically, the data shows that miR-155 promotes the critical effector differentiation cascade for anti-tumor CD8+T cells by inhibiting Ship-1, subsequently increasing p-Akt, and in turn, suppressing Foxo1 and lowering expression of its target, Tcf-1(Tcf7). In tumors of miR-155 KO mice, Ship1 / Tcf-1 double positive populations were increased across all CD8+ T cell states, and Ship1 was derepressed in the small number of Cxcr6+Tcf1- differentiated miR-155 KO CD8+ T cells observed. Additionally, all miR-155 KO CD8+ T cell states exhibited increased Ship-1 expression and decreased p-Akt (FIG.5), which was complemented by enhanced Foxo1 expression (FIG.12A) and all Foxo1 enhanced stem / memory factors, including Tcf-1 (Tcf7) (FIG.4U, 11B, 12B-E). The data demonstrated that in vivo repression of Ship-1 through miR-155 expands tumor-associated CD8+ T cells to promote anti- tumor immunity (FIG.5N-P). Not only did miR-155 repression of Ship-1 molecularly regulate tumor-associated CD8 T cell stemness and immunity, but clinical CRC data implicates miR-155 repression of SHIP-1 in CD8+ T cells in improving anti-tumor immunity and patient outcomes (FIG.6, 7). Through direct and indirect regulations of CD8+ T cell stemness, miR-155 promotes CD8+ T cell infiltrated tumors, necessary for ICI responses.

[0142] Corroborating preclinical models, miR-155 was strongly associated with multiple emerging effector CD8+T cell-driven parameters of ICI responses, specifically Immunoscoreand Ifn responses, and was more correlated to a CD8+ T cell infiltrated tumor compared to allclinically tested mismatch repair deficiency (dMMR) enzymes that identify ICI candidates (FIG. 7). A novel 15 gene signature was identified, the miR-155-15, which defines an ICI-responsive CD8+T cell state. The miR-155-15 was comparable to the top predictor of a positive ICI response, as the miR-155-15 can correctly classify an ICI response in 70% of cases with the statistical modeling (FIG.7G-H). As a gene signature, the miR-155-15 delineates basic CD8+T cell biology that is a pre-requisite for a positive ICI therapy response and can thus serve as a biomarker for successful treatment of mCRC among other cancers. The sensitivity and specificity of biomarkers are becoming increasingly vital for patient care, as cancer treatments inherently cause many undesirable side effects. With gene signatures like the miR-155-15, patient stratification for ICI can improve, subsequently improving patient outcomes and, most importantly, quality of life.

[0143] The miRNA field has made tremendous progress toward the understanding of how miRNAs are produced and function and in the development of technologies that can be used toATTORNEY DOCKET NO.21101.0481P1 target and therapeutically manipulate miRNAs. However, miRNAs have been underutilized as a means to boost cancer immunotherapy approaches. By overexpressing or augmenting specific miRNA levels within cells that comprise the TME or inhibiting miRNAs that restrict efficient antitumor immunity, miRNAs can improve current and future immunotherapy regimens. The current study provides key evidence that miR-155 has therapeutic potential in the context of the prevalent and deadly colon cancer and many other cancer types. Future studies will be critical to identify the synergistic potential of miR-155 with current anti-tumor regimens, including immunotherapy and novel anti-cancer mRNA vaccines. T cell-produced miRNAs which findings have linked to tumor immunity and immunotherapy in the setting of CRC, act to instruct T cells and influence the nature of the TME, both critical and interrelated determinants of effective immunotherapies. Thus, the findings pave the way for the future development of diagnostics and immune therapeutics that target miR-155 in the setting of CRC and other solid tumor types. 4. Methods i. EXPERIMENTAL MODEL DETAILS a. Mice

[0144] All mice in this study were on a C57BL / 6 genetic background, used between 8 and 14 weeks old, and housed in the animal facility at the University of Utah. All animal care / husbandry practices and use procedures were in compliance with and approved by the Institutional Animal Care and Use Committee (IACUC) at the University of Utah and by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) international. Mice were age and sex-matched, and both sexes were incorporated into each experiment. The mouse strains used in this study were the following: miR-155fl / fl-CD4Cre+ / -,OT1-Cas9, OT1-Cas9-miR-155KO, miR-155- / -, and Tcr KO. miR-155fl / fl-CD4Cre+ / - weregenerated by crossing miR-155fl / fl and CD4Cre mice to create miR-155fl / fl-CD4Cre+ / -. OT1- Cas9 mice were generated by crossing OT-1 mice (Jackson strain #003831) and Rosa26-Cas9 knock-in mice (Jackson strain #026179). Lastly, OT1-Cas9-miR-155KO animals were generatedby crossing OT1-Cas9 mice with miR-155- / - mice. Tcr KO mice were purchased from TheJackson Laboratory (Strain #002116). For all mice utilizing CD4Cre-recombinase driven miR- 155 fl / fl knockout, littermate CD4Cre- / - mice were used as wildtype controls. Additionally, all Cre-recombinase drivers were maintained as heterozygous expression.

[0145] The average male mouse weight ranged between 26-30g, and the average female mouse weight ranged between 18-22g. Mice were housed in cages with no more than 5 mice and single-housed if deemed experimentally / health status necessary. The mice were fed on theATTORNEY DOCKET NO.21101.0481P1 Teklad Global Soy Protein-Free Extruded diet. Mice were maintained as specific pathogen-free with regular testing of sentinel cages / mice on each rack. b. Tumor cell lines

[0146] The MC38 cell line was obtained from Kerafast (cat # ENH204-FP; female; RRID:CVCL_B288) and cultured in MC38 media. MC38 cells were recovered from cryopreservation for all experiments and cultured for 7 to 14 days before challenging mice ectopically. Retroviral transduction was used to generate the MC38-Ovalbumin (Ova) expressing cell line. ii. METHOD DETAILS a. Tumor models

[0147] For the MC38 syngeneic ectopic model, 5E5 to 1E6 MC38 or MC38-OVA cells were transplanted subcutaneously in 100uL of 1X DPBS into the left flank of mice. The mice were monitored for tumor growth by surface area caliper measurements of length and width. The mice were sacrificed on days 12-14 or if tumors ulcerated / exceeded the length of 2cm in any direction. Tumors were excised, then weighed for mass, and manual disruption between the frosted end of glass slides was performed in complete RPMI culture media prior to processing into a single cell suspension.

[0148] For the primary model, the Azoxymethane (AOM) (Sigma-Aldrich; cat: A5486- 25MG) / Dextran Sodium Sulfate (DSS) (MP Biomedicals; cat:0216011080) model of colon cancer was used.10mg of AOM per kg of mouse mass at a concentration of 2mg / uL in 1X DPBS was administered intraperitoneally. Following the administration of AOM, mice were given 2.5% DSS in drinking water for three cycles every two weeks. Each cycle was 5 days long, and mice were sacrificed on day 80 post-administration of AOM to assess tumor burden and immunologic composition. The colons were harvested from mice, and the colon processing procedure is as follows. All epiploic appendages were removed from the peritoneal side of the colon. The colons were then dissected lengthwise. Tumors were counted after adding 0.04% Trypan blue (VWR; cat: VWRVK940-100ML) diluted in 1X DPBS to the luminal surface of the colon. Tumor burden was assessed by surface area caliper measurements of length and width. The tumors were excised and chopped with a razor blade, digested in 2mL of 50% Accumax diluted with complete RPMI culture media for 10-25 minutes prior to single cell processing.

[0149] Secondary lymphoid organs, including spleens, tumor-draining mesenteric lymph nodes (AOM / DSS), and tumor-draining inguinal lymph nodes (MC38), were collected upon sacrificing the animals and processed into a single cell suspension.

[0150] For single-cell suspensions, tissues were mashed and filtered through a 40uM cellATTORNEY DOCKET NO.21101.0481P1 strainer. With spleens, an additional RBC lysis (Biolegend; cat: 420301) step per the manufacturer’s recommendation was included before flow cytometric staining and analysis. All single-cell processing steps were performed on ice or at 4C.

[0151] For cytokine analysis of tissue samples, the cells were washed with complete RPMI culture media prior to plating in a tissue-culture treated 96-well plate in 250uL of T cell culture media with 50ng / mL PMA (Sigma-Aldrich; cat: P1585-1MG), 500ng / mL ionomycin (Sigma- Aldrich; cat: 10634-1MG), and 1 uL GolgiPlugTM(BD; cat: BDB555029) for 5-6 hours at 37C prior to washing twice with FACS buffer and staining for flow cytometric staining and analysis. b. Administration of anti-PD-1

[0152] 300μg anti-PD-1 antibody (BioXCell; Clone: RMP1-14; cat: BE0146) in 200uL 1X DPBS was administered intraperitoneally in MC38 tumor-challenged mice. c. Single-cell RNA sequencing and sample preparation

[0153] Mice were challenged with 1E6 MC38 cells and were administered anti-PD-1 antibody on days 7 and 10. Tumor cells were pooled by sex and treatment / genotype (n=6-8). The tumor cells were excised and processed then into a single-cell suspension prior to staining with DAPI and APC-conjugated anti-CD45, washed twice with 1X DPBS with 0.4% BSA prior (Miltenyi; cat: 130-091-376), and FACS sorted on the BD FACSAria Cell Sorter. Sorted cells were washed in 1X DPBS with 0.4% BSA prior to quality control, and processing for scRNAseq performed by the High-throughput Genomics Core. The scRNAseq was performed on the 10X platform and performed according to manufacturer recommendations and sequenced on the Novaseq 6000. The gene reads were processed with the 10X Genomics Cell Ranger pipeline. Mitochondrial gene representation and the variance of unique molecular identifier (UMI) counts were regressed out prior to analysis. The Fastq files were aligned to the refdata-gex-mm10- 2020-A mouse reference dataset from 10X genomics using CellRanger count version 5.0.0, and feature-barcode matrices were generated. All subsequent analyses were performed using Seurat R package (v4.2.0). Data were preprocessed to remove low-quality cells based on the following criteria: cells with less than 300 features (considered low-quality), or more than 6000 features (potentially representing duplicates), or more than 5% mitochondrial gene fraction (indicative of stressed or dying cells) were excluded from further analysis. After removing unwanted cells from the dataset, the Seurat’s sctransform method was used to normalize and integrate data sets. After integration, dimensionality reduction was performed through Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) with 12 principal components. Single cells were clustered based on nearest-neighbor graph construction via FindNeighbors and FindClusters Seurat functions, and 14 clusters were identified at theATTORNEY DOCKET NO.21101.0481P1 resolution level of 0.6. Marker genes defining each cluster were determined using Seurat’s FindAllMarkers function with default parameters. The output of this function (i.e. differentially expressed gene information) was used to name clusters using log fold-change dot product method of CIPR with the ImmGen database as the reference. Additionally, annotations of T cell clusters were verified against T cell reference datasets using ProjectTil package (v3.3). The overall cluster-level differences were visualized by calculating the proportions of each cluster in individual samples. Differential expression analyses were performed between sample groups using the FindMarkers() function of Seurat, where genes that are expressed in at least 10% of the analyzed cells were considered, and the minimum fold-change threshold was set to 0.1. These analyses were performed for each cluster separately or by considering all Cd3e+Cd8a+ cells as one cluster (defined as non-zero expression of both genes). Analysis results were depicted in volcano plots and / or violin plots. d. qPCR analysis of miR-155-5p and INPP5D

[0154] De-identified RNA from human colon tumors was used (generously donated by Dr. Ellen J. Beswick, Associate Professor, Internal Medicine, University of Kentucky). The total cellular RNA was isolated with RNAqueous (Ambion) per the manufacturer’s recommendations and was quantified.

[0155] For all qPCR reactions, 400 ng of RNA was used to produce cDNA with qScript cDNA SuperMix (QuantaBio; cat:101414-106) for mRNA. PowerUp SYBR Green Master Mix (Applied Biosciences) was used according to the manufacturer’s recommendations.100ng of RNA was used for miRNA reactions per miRNA LNA RT kit (Qiagen; cat: 339306) manufacturer’s recommendations. All reactions were run on an Applied Biosciences QS6 Thermocycler (Thermo). e. Human colon cancer scRNAseq analysis

[0156] ScRNAseq data was analyzed through the Broad Institute’s Single Cell Portal, specifically the Human Colon Cancer Atlas (c295) ( / / singlecell.broadinstitute.org / single_cell / study / SCP1162 / human-colon-cancer-atlas- c295#study-summary). Cell cluster analysis from CRC patient samples was defined by the authors of the original manuscript. f. Analysis of SHIP-1 protein expression in human cancer patients

[0157] The proportion of cancer patients of each type that were SHIP-1 positive (Ab Clone: HPA070455) were adapted from The Human Protein Atlas online portal under patient pathology ( / / www.proteinatlas.org / ENSG00000168918-INPP5D / pathology). Representative antibody staining of CRC samples with antibody clone HPA070455 was accessed through the proteinATTORNEY DOCKET NO.21101.0481P1 expression navigator for CRC patients ( / / www.proteinatlas.org / ENSG00000168918- INPP5D / pathology / colorectal+cancer#img). g. CD8 T cell antitumor miRNA CRISPR target collection

[0158] A curated collection of miRNA CRISPRs was generated from bioinformatic analysis of human cancer data sets from The Cancer Genome Atlas. Candidate miRNAs were identified based on positive correlation to CD8A in multiple datasets, including ones overlapping between COAD, SCKM, and 1 / 3 of TCGA projects. Glioblastoma multiform (GMB) was excluded because it lacked miRNA sequencing data. Acute myeloid leukemia (LAML) and lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) were excluded due to their hematopoietic origins. miRNAs with positive and negative prognosticating power (Cox and KM analysis in SKCM-immune or CIBERSORT-CD8a-high cohort) were then included in the custom collection. Additionally, non-conserved miRNAs between mice and humans or miRNAs with fewer than 4 sgRNA targeting sequences were excluded. Protein controls involved in T cell biology and 8 scrambled controls were also included, resulting in 192 sgRNAs. Mutation phenotypes of these selected miRNAs were simultaneously evaluated in parallel using a single targeting pool of these sgRNAs expressed from the pMSCV.hU6.stuff.v1_EFs.mCh (p545) retrovector marked with mCherry. Oligonucleotides encoding each of four validated Crispr targeting guides for each miRNA and controls were individually ligated into Bbs I-digested p545. The resulting 192 plasmid clones were amplified and pooled at identical concentrations for viral packaging and integrating transduction to reduce sampling bias, allowing for more complete coverage with fewer transduced cells. h. Amplicon seq of integrated CRISPR library barcode sequences.

[0159] Seven days status post challenging TcrBKO mice with MC38-OVA cells, 2E5 CRISPR target collection containing CD8 T cells was transplanted intravenously and tumors were collected after 14 days of tumor growth. Total tumor DNA was isolated, and the abundance of stably integrated CRISPR guide vector sequences was quantified. DNA from each isolated tumor was used as a template for 8 independent aliquots of sub-saturating PCR using primers o2011-o2012, which flank sgRNA sites in the p545-based integrants. These reactions were then re-pooled to generate amplicon libraries containing HTS adapters surrounding guide sequences. Each amplicon library was subjected to HTS amplicon sequencing (AMPLICON-EZ, Azenta Life Sciences). The relative frequency of each guide in the sequencing data from endpoint tumors is modeled as inversely correlated to the corresponding miRNA's requirement for T-cell persistence in the tumor and is reported as log2fold change relative to the frequency in T-cells prior to transplantation.ATTORNEY DOCKET NO.21101.0481P1 i. Retroviral generation and viral transduction

[0160] To generate the retrovirus, HEK293T / 17 (ATCC; cat: CRL-11268; female; RRID:CVCL_1926) cells were transfected through lipofection with BioT (BiolandScientific LLC; cat: B015) with packaging plasmids encoding gag, pol, env and plasmids of interest. PLAT-E cells (Cell Biolabs Inc; cat: RV-101; female; RRID:CVCL_B488), were also used and transfected with BioT and plasmids of interest. The transfected cells were cultured in HEK293T / 17 medium, which was used to generate clarified viral supernatant.

[0161] CD8 T cells were infected with clarified viral supernatant with 8ug / mL of Polybrene Infection / Transfection Reagent (EMD MilliporeTM; cat: TR1003G) and centrifuged at 2000rpm for 2 hours at 32C in tissue culture treated 6 well plates. For MC38 cells, the viral transduction process was repeated a second time. Alternatively, CD8 T cells were also subject to transduction in non-tissue culture treated 24 well plates coated with RetroNectin (Takara Bio; cat: T100B) with LentiXTMConcentrator (Takara Bio; cat: 631232) concentrated viral supernatant and centrifuged at 1500g for 45 minutes at 4C before an overnight incubation. The CD8 T cells were subject to a second round of viral transduction and expansion prior to use. After target cell transduction, the transduced cells were FACs sorted for selection fluorophores, including GFP or mCherry, on the BD FACSAria Cell Sorter. j. CD8 T cell isolation and preparation for in vivo and in vitro use

[0162] CD8 T cells were purified from splenocytes by negative selection from OT1-Cas9 or OT1-Cas9-miR-155KO mice by MACS with a CD8+ T cell isolation kit (Miltenyi Biotec; cat: 130-104-075) per manufacturer’s recommendations. After every isolation, a small sample of CD8 T cells was stained with APC-Cy7 conjugated anti-CD3e, and BV785 conjugated anti- CD8a and checked for purity by flow cytometry on the BD LSRFortessa. The purity of the isolated CD8+ T cells was >90% for all experiments. Isolated CD8 T cells were subject to in vivo and in vitro experiments.

[0163] Purified CD8+ T cells were activated with plate-bound anti-CD3 / CD28 antibody (Biolegend; cat: 100340, 102116) coated onto tissue culture treated 6 well plates with 8μg / mL anti-CD3 / CD28 antibody in T cell media.24 hours later, activated CD8 T cells were transduced with viral supernatant. Alternatively, 1E6 CD8+ T cells were activated with Gibco Dynabeads Mouse T cell activator CD3 / CD28 for T cell Expansion and Activation (Thermo Fisher Scientific; cat: 11452D) per manufacturer’s instructions in 24 well non-tissue culture treated plates (Nunc; cat: 144530) and subject to viral transduction with LentiXTMConcentrator concentrated viral supernatant. Transduced mCherry+ CD8 T cells were washed 2 times in 1XDPBS prior to adoptively transferring 2E5 CD8 T cells intravenously into TcrBKO miceATTORNEY DOCKET NO.21101.0481P1 challenged with MC38-Ova cells.

[0164] For in vitro use, CD8 T cells were activated with plate-bound anti-CD3 / CD28 antibody (coated onto tissue culture treated 6 well plates with 1μg / mL anti-CD3 / CD28 antibody in T cell media. k. Flow cytometric analysis of single cells from lymphoid organs and tumors:

[0165] Single cell suspension from tumors, lymphoid organs, and in vitro cultures were subject to the following protocol. Single cells were stained for GhostDye780 per the manufacturer’s recommendations before staining with antibodies (1:200 dilution factor) against surface antigens in FACS buffer. If no intracellular antigens were analyzed, fixation was performed in eBioscienceTMIC Fixation Buffer (eBioscience; cat: 00-8222-49) per the manufacturer’s recommendation and prepared samples for analysis on the BD LSR Fortessa.

[0166] When performing analysis on intracellular antigens, intracellular antigens fixed and stained (1:50 dilution factor) using the Foxp3 / Transcription Factor Staining Buffer Kit (eBioscience; cat: 00-5523-00) after surface staining per the manufacturer’s recommendations. The samples were then prepared for analysis on the BD LSR Fortessa. l. Western blot protocol

[0167] Cells were collected by centrifugation and snap-frozen. Cell pellets were lysed in protein lysis buffer with protease inhibitors (Roche). Whole-cell protein extracts were separated via SDS-PAGE and transferred to a 0.45 μM nitrocellulose membrane. All experimental samples and controls used for one comparative analysis were run on the same blot / gel. m. Block / Probe Conditions

[0168] Antibody staining of Ship1 and Gapdh was performed. Primary antibody binding was detected with IRDye-700- or IRDye-800-conjugated secondary antibodies (LI-COR, Lincoln, NE, USA) using a LI-COR Odyssey Infrared Flatbed Scanner. iii. QUANTIFICATION AND STATISTICAL ANALYSIS a. Statistics

[0169] For the analysis of the custom in vivo miRNA library screen, log2fc was calculated based on read counts from Amplicon sequencing of PCR amplicons of the sgRNA barcode regions compared to the input sgRNA read counts of the CD8 T cells adoptively transferred into MC-38OVA tumor-bearing mice recipients. With an n=4 for log2fc for each gene, the One sample t and Wilcoxon test with a hypothetical mean of 0 was performed, given that an unchanged barcode representation is a log2fc = 0. The Two-way ANOVA test with multiple comparisons was used to analyze tumor growth curves. The t-test with Welsh’s Correction was used for comparisons between the two groups. The One-way ordinary ANOVA test withATTORNEY DOCKET NO.21101.0481P1 multiple comparisons of the means of each group and Brown-Forsythe and Welch ANOVA tests with multiple comparisons of means of each group were used to analyze comparisons of three or groups. Wilcoxon rank sum test with Bonferroni correction was used to calculate the statistical significance of log2fc for gene expression from sequencing data. Computed Pearson correlation coefficients were performed and shown for correlations between two genes. T-tests with pairwise comparisons were performed for gene expression within clinical stratifiers. The Grubbs outlier test with Alpha=0.05 or ROUT outlier test Q=0.01% were performed to exclude outliers in the study, noted the respective FIG.ure legends. With all statistical tests * = p>0.05, ** = p>0.01, *** = p>0.001, and **** = p>0.0001. b. Collection of publicly available transcriptome expression dataset and gene signature profiles related to immunotherapy response.

[0170] Transcriptome expression datasets related to immunotherapy response across 36 public datasets were collected (Table X). To determine if patients responded to immunotherapy treatment, patients were classified as responders or non-responders based on Response Evaluation Criteria in Solid Tumours (RECIST) criteria. Patients recorded as complete response or partial response were classified as responders while patients recorded as stable disease or progressive disease were classified as non-responders. Gene signatures related to immunotherapy response were mainly extracted from previous work. c. Calculation of gene signature performance in predicting immunotherapy response

[0171] Methods to calculate the performance accuracy in predicting immunotherapy response is based on the Area Under the Receiver Operating Curve (AUROC) as previously described. In brief, to calculate the prediction accuracy of individual gene signatures across datasets, the gene expression signatures across samples within a cohort to a Z-score was standardized. Next, the mean Z-score of a gene signature in individual patients was calculated and stratified the patients into “high” (above the mean) or “low” (below the mean) groups based on the mean of the Z-score. Finally, the performance of the gene signature in predicting immunotherapy response was determined by utilizing the Z-score group based on AUROC values. Gene signatures perfectly predicting immunotherapy response will have an AUCROC value of 1, whereas an AUCROC score of 0.5 indicates a random classifier. d. The Cancer Genome Atlas (TCGA) patient data analysis

[0172] TCGA -COAD RNAseq and miRNAseq data as well as the patient-level clinical metadata were downloaded using TCGAbiolinks R package (v2.30). Data were normalized using counts-per-million function of edgeR (v4.0.16) package and log-transformed forATTORNEY DOCKET NO.21101.0481P1 generating visualizations. Differential expression analyses were conducted with raw counts using DESeq2 R package. After differential expression analysis, cluster-wise Gene Set Enrichment Analysis (GSEA) was performed between select pairs of samples. Genome-wide log fold-change values were supplied as pre-ranked input to the fgsea algorithm (v1.28.0).

[0173] In these analyses, 50 Hallmark genesets curated in the Molecular Signatures Data Base (MSigDB) were used. Correlations between CD8A and select genes were examined in the cpm-normalized log-transformed RNAseq and miRNAseq data. Correlation analyses were performed in the whole dataset or in the data subsets with different microsatellite instability levels.

[0174] For miRNA association with CD8A, TCGA-COAD tumor samples were categorized into CD8A-high and CD8A-low subgroups at the median expression value of CD8A within the voom-normalized RNAseq data. Differential expression analysis was performed between these two subgroups using raw miRNAseq data and DESeq2 R package after removing miRNAs that were expressed in less than 10 samples. Log fold-change was shrunk using the apeglm method, and volcano plots were generated to highlight differentially expressed miRNAs.

[0175] Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the method and compositions described herein. Such equivalents are intended to be encompassed by the following claims.

Claims

ATTORNEY DOCKET NO.21101.0481P1 CLAIMS We claim:

1. A method of predicting a subject’s responsiveness to an immunotherapy comprising: a. detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; and b. comparing the presence and / or amount of the miR155 gene signature to a control sample or threshold, wherein the presence and / or an altered amount of the miR155 gene signature relative to the presence or amount in the control sample or threshold indicates the subject will be responsive or is responding to an immunotherapy.

2. A method for selecting a subject having cancer to treat with an immunotherapy comprising: a) detecting the presence and / or amount of a miR155 gene signature in the patient or a sample from the patient, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; b) comparing the presence or amount of the miR155 gene signature to a control sample or threshold, and c) selecting the patient having the presence and / or an altered amount of the miR155 gene signature relative to the presence or amount in the control sample or threshold for treatment with an immunotherapy.

3. A method for identifying a subject currently responding to an immunotherapy comprising: a) detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject prior to treatment with the immunotherapy;ATTORNEY DOCKET NO.21101.0481P1 b) detecting the presence and / or amount of a miR155 gene signature in the subject or a sample from the subject during or after treatment with the immunotherapy; and c) identifying the subject as responding to the immunotherapy when the presence and / or amount of the miR155 gene signature has changed in the subject or a sample from the subject during or after treatment with the immunotherapy relative to the presence and / or amount of the miR155 gene signature in the subject or the sample from the subject prior to treatment with the immunotherapy; wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

4. The method of any one of claims 1-3, further comprising administering an immunotherapy to the subject.

5. The method of any one of claims 1-4, wherein the immunotherapy is an immune checkpoint inhibitor (ICI).

6. A method of treating a subject having cancer comprising a) detecting the presence or altered amount of a miR155 gene signature in the subject or a sample obtained from the subject, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; and b) administering an immune checkpoint inhibitor (ICI) to the subject.

7. The method of any one of claims 1-6, wherein the miR155 gene signature is detected in CD8+ T cells.

8. The method of any one of claims 1-7, wherein the altered amount of the miR155 gene signature is an increase of the miR155 gene signature over a control sample or threshold.

9. The method of any one of claims 1-8, wherein the cancer is colon cancer, kidney cancer, malignant pleural mesothelioma, non-small cell lung cancer, gastric cancer, esophagealATTORNEY DOCKET NO.21101.0481P1 cancer, melanoma, breast cancer, bladder cancer, colorectal cancer, pancreatic adenocarcinoma, head and neck squamous cell carcinoma, or thymic carcinoma.

10. The method of any one of claims 1-7, wherein the ICI is anti-PD-1, anti-PD-L1, anti- Lag3, or anti-CTLA4 therapy.

11. The method of any one of claims 1-10, wherein the presence and / or amount of the miR155 gene signature is detected at the DNA or RNA level.

12. The method of claim 7, wherein detecting the presence of the miR155 gene signature in CD8+ T cells comprises obtaining a sample from the subject, wherein the sample comprises CD8+ T cells.

13. The method of any one of claims 1-12, wherein the sample is blood, plasma, spinal fluid, saliva, or urine.

14. The method of claim 13, wherein the sample is from a tumor biopsy.

15. The method of any one of claims 1-14, further comprising administering a second cancer therapeutic to the subject.

16. A recombinant cell comprising altered expression of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

17. A recombinant cell comprising an agent capable of altering the expression of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

18. The recombinant cell of claims 16-17, wherein the recombinant cell is a T cell.

19. The recombinant cell of claim 18, wherein the T cell is a CD8+ T cell.

20. A method of increasing T effector cells in a subject comprising administering, to the subject, an agent that increases the levels of miR-155.

21. The method of claim 20, wherein the agent is a composition comprising miR-155.ATTORNEY DOCKET NO.21101.0481P1 22. The method of claim 20, wherein the agent is a nanoparticle comprising miR-155, an exosome comprising miR155, a miR155 mimic, a CAR T cell capable of overxpressing miR-155, a viral vector capable of overxpressing miR-155, or a secondary immunotherapy agent.

23. The method of any one of claims 20-22, wherein the agent causes an increase in differentiation of T stem-cell-like memory (Tscm) or T central memory-like (Tcm) cells to T effector (Teff) cells.

24. A method of identifying a cancer that can be effectively treated with an immune checkpoint inhibitor (ICI) comprising: detecting the presence and / or amount of a miR155 gene signature in a sample, obtained from a subject having cancer, comprising cancer cells or nucleic acids derived from cancer cells, wherein the cancer cells comprise CD8+ cells, wherein the miR155 gene signature comprises one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT; wherein the presence and / or amount of the miR155 gene signature is used to identify the sample as being from a subject having a cancer that can be effectively treated with an ICI.

25. A nucleic acid array comprising at least fifteen probes immobilized on a solid support, each of said probes being between about 15 and about 500 nucleotides in length, each of said probes being derived from a sequence corresponding to, or complementary to, a transcript of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

26. A kit comprising one or more oligonucleotides, wherein the one or more oligonucleotides specifically bind to a nucleic acid sequence encoding one or more of miR155, CD3E, CD3G, CD8A, CD8B, CXCR6, FXYD5, GZMB, ID2, IFNgamma, LAG3, NKG7, PDCD1, S100A4, and TIGIT.

27. The kit of claim 26, wherein one or more oligonucleotides are primers, probes, or a combination thereof.

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