WO2025222005A1 - Methods of treating cancer through regulating gene expression - Google Patents

Abstract

Methods of treating cancer in a patient in need thereof are described herein. In some embodiments, the method comprises regulating the expression of at least one gene in a CD8⁺T cell.

Description

METHODS OF TREATING CANCER THROUGH REGULATING GENE EXPRESSION

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority under 35 U.S.C. § 119 to U.S. Provisional Patent Application No. 63/635,044, filed April 17, 2024, which is hereby incorporated by reference in its entirety.

FIELD

[0002] The present application relates to treating cancer, specifically by regulating the expression of genes in a CD8⁺ T cell.

BACKGROUND

[0003] CD8⁺ T cells are often used as surrogate markers in immunotherapy clinical trials for cancer. Despite their crucial role in combating cancerous cells, many clinical trials have failed to achieve the desired outcomes even though CD8⁺ T cells were stimulated by the treatment. This failure is frequently attributed to the exhaustion status of CD8⁺T cells. However, studies have shown that CD8⁺ T cells expressing substantial levels of cytotoxic molecules still could fail to protect the host. This suggests that there are additional factors or mechanisms influencing the efficacy of these cells beyond just the presence of traditional markers. Further, although immunization with some antigens results in the stimulation and proliferation of antigen-specific CD8⁺ T cells with effector function markers, they are unable to effectively kill the target cells in vivo. There is generally a lack of correlation between in vitro measurable CD8⁺ T cell response and tumor rejection; it has been shown that measurable CD8⁺ T cells inconsistently correlate with tumor rejection.

[0004] CD8⁺ T cells play a critical role in fighting cancer cells presenting mutant peptides. Despite their importance, CD8⁺ T cells often struggle to combat abnormal cells, especially when the body experiences immunological disturbances. Numerous studies have explored why CD8⁺ T cells face challenges in cancer, often attributing this to exhaustion — a valid observation confirmed by many studies. However, some CD8⁺ T cells can effectively combat target cells even under chronic antigen stimulation. This suggests that certain characteristics of CD8⁺ T cells enable them to resist chronic stimulation and maintain efficacy. Conversely, some CD8⁺ T cells, despite displaying cytotoxic and effector function markers comparable to efficacious ones, fail to protect the host.

[0005] What is needed is improved predictive models of T cell functionality in cancer therapy, methods for which are disclosed herein. In addition, disclosed herein are identification of genes regulated in effective CD8⁺ T cells, providing new targets for enhancing T cell- mediated tumor rejection.

SUMMARY

[0006] The present application is generally directed to methods of treating cancer in a patient in need thereof.

[0007] In some instances, provided herein is a method of treating cancer in a patient in need thereof, the method comprising: regulating the expression of at least one gene in a CD8⁺ T cell, wherein the at least one regulated gene has a |log2FoldChange| > 1 and an adjusted p-value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor non-rejecting CD8⁺ T cells. In one variation, the regulated gene is not Gzma or Lrigl. In some embodiments, the tumor-rejecting CD8 T cells are Gtf2b specific CD8⁺ T cells and the tumor-non-rejecting CD8⁺ T cells are Trib3 specific CD8⁺ T cells. In some instances, the regulated gene has an Effectiveness Score of 2 or higher. In other instances, the regulated gene has an Effectiveness Score of 3 or higher, or even 4.

[0008] In some instances, provided herein is a method of treating cancer in a patient in need thereof, the method comprising: regulating the expression of at least one gene in a CD8⁺ T cell, wherein gene is selected from the group of 2900027M19Rik, Actb, Adgre4, Atg4d, AU020206, Cd55b, Clec4a3, Commdlb, Dnajc4, Elfn2, Fam3a, Fcgrt, Fez2, Fhl2, imap3, Gml0052, Gml0163, Gml2715, Gml7344, Gml9220, Gm21833, Gm4294, Gm43302, Gm47438, Gm47457, Gm50163, Gm50241, Gpx4-ps2, Hck, Id2, Igkv4-57, Kcncl, Klhl7, Lpar5, Mafb, Mai, Malatl, Mapla, Mertk, Mrcl, Ms4a4b, mt-Rnr2, Myo7a, Plin3, Rab3il 1, Rasgeflb, Rpll5- ps3, Rpsl3-ps4, S100a6, Sdc3, Slc24a3, Slc39al 1, Stk32c, Tafa3, Tgfbi, Trav5-1, Trav8d-1, Xrcc3, and Zfyvel. In some embodiments, the at least one regulated gene is selected from the group of AU020206, Fcgrt, Fez2, Fhl2, Gml0052, Id2, Mafb, Mertk, Ms4a4b, Rab3ill, Stk32c, Tgfbi, and Xrcc3. In other embodiments, the at least one regulated gene is upregulated and is selected from the group of Fhl2, Stk32c, Commdlb, Gm43302, Mai, Atg4d, Dnajc4, Fam3a, Lpar5, Plin3, S100a6 and Zfyvel. In other embodiments, the gene is Fhl2 and/or Stk32c. In still other instances, the regulated gene is down-regulated or knocked out and is selected from the group of Cd55b, Elfn2, Gm47438, Mafb, Rab3ill, Slc24a3, Trav5-1, and Trav8d-1. Alternately, the regulated gene is down-regulated or knocked out and is Cd55b, Mafb, and/or Rab3il 1.

[0009] In one variation of any aspect or embodiment, the CD8⁺ T cell is a cell of the patient. Alternately, the CD8⁺ T cell comes from a healthy donor. In another variation, the CD8⁺ T cell comes from a tumor microenvironment; optionally from the patient, alternately from a donor.

[0010] In some instances, regulating the expression of at least one gene in a CD8⁺ T cell comprises administering to the CD8⁺ T cell a therapy capable of regulating the gene. In some embodiments, the therapy one or more of a small molecule, a monoclonal antibody, a retrovirus, electroporation, CRISPR, RNAi, and shRNA. In some variations, the administration is done ex vivo; in other variations the administration is done in vivo. Examples of CRISPR include but are not limited to CRISPR CAS9, CRISPR CAS 12 or CRISPRi. In some instances, regulating the expression of at least one gene in a CD8⁺ T cell comprises administering to a patient a therapy capable of regulating the gene. In some variations, the therapy is a small molecule, a monoclonal antibody, or CRISPR.

[0011] In some instances, the genes of the present application and modulation thereof are employed in an adoptive T cell therapy, such as CAR T cell therapy or tumor-infiltrating lymphocyte (TIL) therapy.

[0012] In some instances, regulating the expression of at least one gene in a CD8⁺ T cell comprises overexpressing the gene by administering to the T cell (a) a retrovirus or

(b) electroporation, optionally in combination with a plasmid or mRNA. In other instances, regulating the expression of at least one gene in a CD8⁺ T cell comprises down regulating or knocking out the gene by administering (a) a retrovirus; (b) CRISPR; (c) RNAi, and/or (d) shRNA.

[0013] In some instances disclosed herein is a method of treating cancer comprising administering one or more CD8⁺ T cells with a regulated gene to the patient. In some variations, a method of treating cancer further comprises expanding the CD8⁺ T cell having the regulated gene and administering the population of expanded CD8⁺ T cells to the patient. [0014] In some instances, the cancer is susceptible to CD8⁺ T cells. Such cancers include, but are not limited to a head or neck cancer, sarcoma, lung cancer, non-small cell lung cancer, small cell lung cancer, breast cancer, colorectal cancer, pancreatic cancer, bladder cancer, renal cell carcinoma, stomach or gastric cancer, ovarian cancer, thyroid cancer, skin cancer, and melanoma. [0015] In some instances, provided herein is a pharmaceutical composition for the treatment of cancer. In some instances, the composition comprises a CD8⁺ T cell having at least one regulated gene, wherein the at least one regulated gene has a |log2FoldChange| > 1 and an adjusted p-value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor non-rejecting CD8⁺ T cells, wherein the regulated gene is not Gzma or Lrigl . In some variations the composition comprises a population of CD8⁺ T cells having at least one such regulated gene. In some variations, the tumor-rejecting CD8⁺ T cells are Gtf2b specific CD8⁺ T cells and the tumor-non-rejecting CD8⁺ T cells are Trib3 specific CD8⁺ T cells. In some instances, the regulated gene has an Effectiveness Score of 2 or higher. In other instances, the regulated gene has an Effectiveness Score of 3 or higher or even 4.

[0016] In some instances, provided herein is a pharmaceutical composition for the treatment of cancer comprising a population of CD8⁺ T cells having at least one regulated gene selected from the group of 2900027M19Rik, Actb, Adgre4, Atg4d, AU020206, Cd55b, Clec4a3, Commdlb, Dnajc4, Elfn2, Fam3a, Fcgrt, Fez2, Fhl2, imap3, Gml0052, Gml0163, Gml2715, Gml7344, Gml9220, Gm21833, Gm4294, Gm43302, Gm47438, Gm47457, Gm50163, Gm50241, Gpx4-ps2, Hck, Id2, Igkv4-57, Kcncl, Klhl7, Lpar5, Mafb, Mai, Malatl, Mapla, Mertk, Mrcl, Ms4a4b, mt-Rnr2, Myo7a, Plin3, Rab3il 1, Rasgeflb, Rpll5-ps3, Rpsl3-ps4, S100a6, Sdc3, Slc24a3, Slc39all, Stk32c, Tafa3, Tgfbi, Trav5-1, Trav8d-1, Xrcc3, and Zfyvel. In some embodiments, the at least one regulated gene is selected from the group of AU020206, Fcgrt, Fez2, Fhl2 ,Gml0052, Id2, Mafb, Mertk, Ms4a4b, Rab3il 1, Stk32c, Tgfbi, and Xrcc3. In some variations, the at least one regulated gene is upregulated and is selected from the group of Fhl2, Stk32c, Commdlb, Gm43302, Mai, Atg4d, Dnajc4, Fam3a, Lpar5, Plin3, S100a6 and Zfyvel. In other variations, the at least one regulated gene is upregulated and is selected from the group of Fhl2 and/or Stk32c. In still other variations, the at least one regulated gene is down- regulated or knocked out and is selected from the group of Cd55b, Elfn2, Gm47438, Mafb, Rab3il 1 , Slc24a3, Trav5-1, and Trav8d-1. Alternately, the at least one regulated gene is down- regulated or knocked out and is Cd55b, Mafb, and/or Rab3il 1. [0017] In one variation, the CD8⁺ T cell having at least one regulated gene, is a CD8⁺ T cell from the patient. Alternately, the CD8⁺ T cell comes from a healthy donor. In another embodiment, the CD8⁺ T cell comes from a tumor microenvironment.

[0018] In one variation, the population of CD8⁺ T cells has at least gene that has been regulated using one or more of a small molecule, a monoclonal antibody, a retrovirus, electroporation, CRISPR, RNAi, and shRNA. In some instances, CRISPR is CRISPR CAS9, CRISPR CAS 12 or CRISPRi. In other embodiments, the composition comprises an overexpressed gene in a CD8⁺ T cell, wherein the gene has been overexpressed by way of (a) a retrovirus or (b) electroporation optionally in combination with a plasmid or mRNA. In still other embodiments, the composition comprises a down regulated or knocked out gene in a CD8⁺ T cell, which has been down regulated or knocked out using (a) a retrovirus; (b) CRISPR;

(c) RNAi, and/or (d) shRNA.

[0019] The foregoing embodiments and other embodiments are further described in the detailed description which follows.

BRIEF DESCRIPTION OF THE FIGURES

[0020] FIG. l is a graphical representation of differential gene expression analysis data between tumor-rejecting and non-rejecting CD8⁺ T cells.

[0021] FIG. 2Ais a graphical representation of the expression of Stk32c in different subsets of CD8⁺ T cells that represent lowly and highly differentiated CD8⁺ T cells in the tumor microenvironment.

[0022] FIG. 2B is a graphical representation of the expression of Stk32c in Slamf6 (progenitor) and TIM3 (terminally exhausted) CD8⁺ T cells.

[0023] FIG. 3 is a graphical representation of principal component analysis of TOX expression among three cell types: tumor-rejecting (GT ), non-rejecting (T3 ) and control (GT-) CD8⁺ T cells.

[0024] FIG. 4Ais a graphical representation of Gene Set Enrichment Analysis (GSEA) data for tumor-rejecting CD8⁺ T cells compared to non-rejecting CD8⁺ T cells for oxidation phosphorylation genes. [0025] FIG. 4B is a graphical representation of (GSEA) data for tumor-rejecting CD8⁺ T cells compared to non-rejecting CD8⁺ T cells for mTOR signaling pathway genes.

[0026] FIG. 5 is a graphical representation of differential gene expression analysis of efficacious and non-efficacious CD8⁺ T cells from DESeq2 are displayed using a volcano plot, created with Enhance Volcano. The dashed lines indicate the selected thresholds for both values. A |log2 fold change] of 1 (indicating gene expression is twice as high in one group compared to the other) was used as the threshold for identifying differentially expressed genes. This shows higher expression of Fhl2 in efficacious CD8⁺ T cells and higher expression of Lrigl in non- efficacious CD8⁺ T cells. Each dot in the graph represents a gene analyzed, most of which do not meet the parameters defined herein (|log2 fold change] > 1 and adjusted p-value <0.05. (In FIG 5. the adjusted p-value is represented by a horizontal dashed line at -LogioP = 1.3)

[0027] FIG. 6A is a graphical representation of Fhl2 gene expression quantified by qPCR, with the Y-axis showing the fold change of Fhl2 relative to GAPDH, normalized to the naive control.

[0028] FIG. 6B is a graphical representation of flow cytometry analysis of FHL2 expression in naive and activated CD8⁺ T Cells.

[0029] FIG. 7A-7C is a graphical representation of flow cytometric data of in vitro-activated CD8⁺ T cells following stimulation with PMA/Ionomycin. Gating strategy included singlets and live CD8⁺ T cells.

[0030] FIG. 8 is a graphical representation of the change in raw gene expression in bulk RNA-seq of human naive as well as CD3/CD28 stimulated CD8⁺ T cells across different time points, created using ggplot2. The y-axis represents the mean gene expression values, while black dots indicate the expression levels of individual samples at different time points. The dashed line represents a fitted smoothing curve.

[0031] FIG. 9 is a graphical representation of tumor growth in Fhl2 knockout (KO) and wildtype (WT) mice. The graph indicates Bl 6-OVA tumor growth (a preclinical melanoma cell line expressing the model antigen ovalbumin) in mice.

DETAILED DESCRIPTION

[0032] Embodiments described herein can be understood more readily by reference to the following detailed description and examples and their previous and following descriptions. Elements and methods described herein, however, are not limited to the specific embodiments presented in the detailed description and examples. It should be recognized that these embodiments are merely illustrative of the principles of the present invention. Numerous modifications and adaptations will be readily apparent to those of skill in the art without departing from the spirit and scope of the invention.

[0033] All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting.

[0034] In addition, all ranges disclosed herein are to be understood to encompass any and all subranges subsumed therein. For example, a stated range of “1.0 to 10.0” should be considered to include any and all subranges beginning with a minimum value of 1.0 or more and ending with a maximum value of 10.0 or less, e.g., 1.0 to 5.3, or 4.7 to 10.0, or 3.6 to 7.9.

[0035] When a range of integers is given, the range includes any number falling within the range and the numbers defining ends of the range. For example, when the terms “integer from 1 to 20” is used, the integers included in the range are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc., up to and including 20. All ranges disclosed herein are also to be considered to include the end points of the range, unless expressly stated otherwise. For example, a range of “between 5 and 10” should generally be considered to include the end points 5 and 10.

[0036] Further, when the phrase “up to” is used in connection with an amount or quantity, it is to be understood that the amount is at least a detectable amount or quantity. For example, a material present in an amount “up to” a specified amount can be present from a detectable amount and up to and including the specified amount.

[0037] Furthermore, the terms “substantially,” “approximately,” and “about,” as used herein when referring to a measurable value such as an amount of a compound or agent of this invention, dose, time, temperature, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, ±0.5%, or even ±0.1% of the specified amount. The term “consists essentially of’ (and grammatical variants) shall be given its ordinary meaning and shall also mean that the composition or method referred to can contain additional components as long as the additional components do not materially alter the composition or method. The term “consists of’ (and grammatical variants) shall be given its ordinary meaning and shall also mean that the composition or method referred to is closed to additional components. The term “comprising” (and grammatical variants) shall be given its ordinary meaning and shall also mean that the composition or method referred to is open to contain additional components.

[0038] It is also to be understood that the article “a” or “an” refers to “at least one,” unless the context of a particular use requires otherwise.

[0039] Also as used herein, “and/or” refers broadly to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (“or”).

[0040] Compounds, pharmaceutical compositions including the compounds, and methods of preparation and uses thereof are disclosed. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this subject matter belongs. The terminology used in the description of the subject matter herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the subject matter. The present disclosure will be better understood with reference to the following definitions.

Definitions

[0041] The term “effective amount,” as used herein, refers broadly to that amount of a recited compound effective to treat, prevent, or reduce the severity or progression of a disorder in a subject, such as a human subject. This includes improving the subject’s condition (e.g., in one or more symptoms), delaying or reducing the progression of the disease and/or disorder, preventing or delaying the onset of the disorder, and/or changing clinical parameters, disease or illness, etc., as would be well known in the art.

[0042] For example, an effective amount can refer to the amount of a composition, compound, or agent that improves a condition in a subject by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100%. [0043] As used herein, the terms “treating,” “treatment,” and the like are used to mean obtaining a desired pharmacologic and/or physiologic effect. The effect may be prophylactic in terms of completely or partially preventing a disorder or sign or symptom thereof, and/or may be therapeutic in terms of a partial or complete cure for a disorder and/or adverse effect attributable to the disorder, or the relief or elimination of a symptom thereof. Thus, treatment includes preventing or protecting against the disease or disorder, that is, causing the clinical symptoms not to develop; and/or inhibiting the disease or disorder, that is, arresting or suppressing the development of clinical symptoms; and/or relieving the disease or disorder that is, causing the regression of clinical symptoms; and/or reducing the metastasis of the primary tumor or cancer. [0044] Terms such as “administering” or “administration” include acts such as prescribing, dispensing, giving, or taking a substance such that what is prescribed, dispensed, given, or taken is actually contacts the patient’s body externally or internally (or both). Administering when used in reference to a cell includes exposing the cell to a therapy or agent and/or incorporating the therapy or agent into the cell.

[0045] The term “therapeutic dosage” refers to a commonly used dose in clinical practice for the treatment of a disease or condition.

[0046] The “patient” or “subject” treated as disclosed herein is, in some embodiments, a human patient, although it is to be understood that the principles of the presently disclosed subject matter indicate that the presently disclosed subject matter is effective with respect to all vertebrate species, including mammals, which are intended to be included in the terms “subject” and “patient.” Suitable subjects are generally mammalian subjects. The subject matter described herein finds use in research as well as veterinary and medical applications. The term “mammal” as used herein includes, but is not limited to, humans, non-human primates, cattle, sheep, goats, pigs, horses, cats, dog, rabbits, rodents (e.g., rats or mice), monkeys, etc. Human subjects include neonates, infants, juveniles, adults and geriatric subjects. The subject “in need of’ the methods disclosed herein can be a subject that is experiencing a disease state and/or is anticipated to experience a disease state, and the methods and compositions of the invention are used for therapeutic and/or prophylactic treatment.

[0047] In another aspect, methods of treating and/or diagnosing a condition or disease in a human patient and/or subject in need thereof are described herein. In some embodiments, such a method comprises disposing a composition described herein within a biological compartment of the patient and/or subject. The biological compartment can be any suitable biological compartment of the patient and/or subject. In some instances, a biological compartment comprises an internal organ of a patient and/or subject. In some instances, a biological compartment is a cell of a patient and/or subject. In some embodiments, a composition described herein may also be disposed in or delivered to the bloodstream of the patient or in or to a blood vessel of the patient. Disposing or delivering the composition can be carried out in any manner not inconsistent with the objectives of the present disclosure. In some cases, for example, the composition is injected into the biological compartment.

[0048] In some cases, a method described herein comprises releasing at least a portion of the therapy within a cytosol of the cell or population of cells after penetrating the membrane of the cell or population of cells. Further, in some cases, a method described herein also comprises biologically delivering the therapy after penetrating the membrane of the cell or population of cells.

[0049] The Fhl2 (or FHL2) gene encodes "Four and a Half LIM Domains Protein 2" and Fhl2 has been implicated in cancer cell survival and actin rearrangement. Fhl2 has been identified as a co-regulator of Nur77, a transcription factor well-established for its role in modulating the inflammatory response in tumor-infiltrating T cells. While Nur77's functions are well-characterized, the specific contribution of Fhl2 to CD8⁺ T cell function, metabolism, and survival has been unclear. As shown herein, Fhl2 expression increases upon activation in human CD8⁺ T cells, shares common transcriptional regulators with Gzma, and correlates with Gzma levels in both cytometry studies and computational analyses in human samples. Fhl2 was significantly upregulated in terminally differentiated CD8⁺ T cells which had significantly higher Gzmb, Gzma, Ifng, and Prfl compared to the progenitor CD8⁺ T cells. As further shown, Fhl2 plays a crucial role in CD8⁺ T cell-mediated tumor rejection by enhancing proliferation, survival, and actin remodeling, ultimately promoting more effective anti-tumor immunity. In particular, Fhl2 was highly expressed in tumor rejecting CD8⁺ T cells and significantly upregulated in CD8⁺ T cells isolated from LCMV-infected mice treated with aPD-1 and IL-2, compared to untreated animals (2.08 fold change, p adjusted value: 6.64E-06).

[0050] The Lrigl gene encodes “Leucine-Rich Repeats and Immunoglobulin-Like Domains 1.” [0051] The gene expression patterns for cytotoxic proteins in CD8⁺ T cells , include, but are not limited to one or more of perforin- 1 (PRF1 or Prf), granulysin (GNLY), granzyme B (GZMB or Gzmb), granzyme A (GZMA or Gzmb), granzyme K (GZMK)), cytokine interferon-y (IFN-y or Ifng), Tumor Necrosis Factor-a (TNFa or Tnfa) and apoptotic protein Fas ligand (FASL), each of which can be measured and evaluated.

[0052] C0MMD1, or the copper metabolism domain containing 1 gene is also known as MURR1 or C2orf5. Among other functions, it enables phosphatidylinositol-3,4-bisphosphate binding activity; phospholipid binding activity; and protein homodimerization activity. It has been identified in Golgi to plasma membrane transport; negative regulation of protein localization to cell surface; and regulation of protein metabolic process. It has been noted to act upstream of or within negative regulation of NF-kappaB transcription factor activity. The gene is generally located in in cytosol; endosome; and nucleoplasm.

[0053] The MAL gene encodes the myelin and lymphocyte protein (MAL). MAL is expressed in T-cells, polarized epithelial cells, and myelin-forming cells.

[0054] The Atg4d gene (autophagy related 4D cysteine peptidase) is also known as APG4D, AUTL4, APG4-D, or HsAPG4D. This gene belongs to the autophagy-related protein 4 (Atg4) family of C54 endopeptidases. Reduced levels of autophagy have been described in some malignant tumors, and autophagy may control unregulated cell growth linked to cancer.

[0055] Small hairpin RNAs (shRNA) are small molecules of RNA with tight hairpins that are used in the art to silence gene expression through ligand control of RNA interference (RNAi). Expression of shRNA in cells is typically accomplished by delivery of plasmids or through viral or bacterial vectors.

[0056] RNA interference (RNAi) is a biological process where double- stranded RNA (dsRNA) triggers the destruction of messenger RNA (mRNA), preventing protein production. This process effectively silences gene expression by targeting specific mRNA sequences.

[0057] CRISPRi (CRISPR interference) is a method known in the art for selectively repressing gene expression using a catalytically inactive Cas9 (dCas9) protein and a guide RNA (sgRNA). The dCas9 protein, which is modified to lack endonuclease activity, is used to target a specific gene in the genome. The sgRNA guides the dCas9 to the target gene's promoter, where it physically blocks transcription, effectively repressing the gene's expression. [0058] Random somatic mutations in the tumor cell genome, which can immunologically differentiate tumors from normal cells, result in the formation of neoepitopes. Some neoepitopes have the potential to be presented on the major histocompatibility complex I (MHCI) of the tumor cells and stimulate a neoepitope-specific CD8⁺ T cell response that can mediate tumor rejection. Neoepitope cancer immunotherapy research focuses on leveraging such neoepitopes for training the immune system against the tumor to promote personalized cancer vaccines. Doing so requires the selection of the right set of neoepitopes out of many that arise from random mutations.

[0059] Without intending to be bound by theory, it is believed, based on animal and human studies, that the current neoepitope selection methods are far from the ability to precisely predict tumor-rejecting neoepitopes. There are shortcomings in answering key questions about the qualities of the CD8⁺ T cell response that are specifically needed for an effective tumor rejection. It has been previously unknown why some CD8⁺ T cells that are stimulated by the presentation of neoepitopes on MHCIs fail to reject tumors.

[0060] As generally disclosed herein, in one aspect, use of the genes disclosed herein improve selection criteria for tumor-reactive CD8⁺ T cells, enhance persistence and infiltration in solid tumors, and provide biomarkers for predicting immunotherapy response across diverse cancer types, ultimately informing more precise and effective immunotherapeutic strategies.

[0061] In one aspect, disclosed herein is a method for treating cancer comprising disposing a therapy within a biological compartment of the patient, wherein the therapy enhances the expression of the FHL2 gene, the Stk32c gene, the Commdlb gene, the Gm43302 gene, the Mai gene, the Atg4d gene, or a combination of two or more of the foregoing. In some implementations, the therapy comprises a CAR T cell therapy, an adoptive T cell therapy, an antibody, or a small molecule. Moreover, in some cases, the biological compartment of the patient is CD8⁺ T cells.

[0062] As used herein, “efficacious” tumor rejecting CD8⁺ T cells lead to at least a 50% reduction in the volume of a tumor remains after immunotherapy, such as for example, neoepitope-based immunotherapy. Efficacious CD8⁺ T cells are capable of mediating complete tumor rejection following antigen-specific activation. As used herein “non-efficacious” tumorrejecting CD8⁺ T cells lead to little or no reduction in the volume of a tumor, for example at least 95% of the tumor remains after immunotherapy, such as for example, neoepitope-based immunotherapy; in other embodiments, 100% of the tumor remains after immunotherapy. Non- efficacious CD8⁺ T cells are antigen-specific T cells that expand upon immunization but fail to control or eliminate tumors. As disclosed herein, Gtf2b-CD8⁺ T cells were efficacious in killing Meth A Fibrosarcoma cells in vivo, and Trib3-CD8⁺ T cells were non-efficacious. To generate and detect Gtf2b+ CD8 T cells and Trib3+ CD8 T cells the method disclosed in Ebrahimi-Nik H, et al., JCI Insight. 2019 Jun 20;5(14):el29152. doi: 10.1172/j ci. insight.129152. PMID: 31219806; PMCID: PMC6675551 was followed.

[0063] A variety of genome manipulation techniques are known to those of skill in the art. Such technique include, but are not limited to exposure of the gene to a small molecule, a monoclonal antibody, a retrovirus, electroporation, CRISPR, RNAi, and/or shRNA. In some examples, CRISPR can be any of CRISPR CAS9, CRISPR CAS 12 or CRISPRi. In some examples, one or more of CRISPR-Cas9, siRNA, and shRNA can be used to down-regulate or knock out target genes.

[0064] As disclosed herein an “Effectiveness Score,” providing a rating of 0 to 4, refers to the effectiveness of the gene in meeting certain biological criteria, and arises from a balanced evaluation of the engagement of the gene in measured activities and the magnitude of the engagement. As used in the Effectiveness Score, ‘moderate engagement’ refers to an experimentally significant difference (p value less than 0.05) comparing the activity of a CD8⁺ T cell having the referenced gene to a control cell (Wild Type CD8⁺ T cell). Generally, genes having an Effectiveness Score of 0 do not show any significant engagement in the measured activities. Genes having an Effectiveness Score of 1 show at least moderate engagement in 2 or more measured activities. Genes having an Effectiveness Score of 2 show at least moderate engagement in 4 or more measured activities. Genes having an Effectiveness Score of 3 show at least moderate engagement in 5 or more measured activities. Genes having an Effectiveness Score of 4 show at least moderate engagement in 6 or more measured activities. In such measurements, the evaluation probes the correlation of a gene with activation, cytotoxity, and/or function of CD8⁺ T cells. Particular measured activities used in the evaluation of the genes include at least: (1) increased expression of the gene upon CD8⁺ T cell activation; (2) correlation of the gene’s expression with classical cytotoxic and functional markers of CD8⁺ T cells; (3) enhanced migration of CD8⁺ T cells toward the tumor microenvironment via modulation of the gene; (4) increased actin polymerization and F-actin production upon overexpression or downregulation of the gene; (5) reduced apoptosis of activated CD8⁺ T cells through modulation of the gene’s expression; (6) upregulation of cytotoxic markers following modulation of the gene; (7) improved stability of the immunological synapse between CD8⁺ T cells and target cells via gene modulation; (8) decreased exhaustion of CD8⁺ T cells upon modulation of the gene and (9) increased in capacity of CD8⁺ T cells upon modulation of the gene to target and kill tumor cells in vivo. Another measure of tumor rejection/regression or stability compared to control relies on the Tumor Control Index as described in Corwin, W. L., Ebrahimi-Nik, H., Floyd, S. M., Tavousi, P, Mandoiu, 1. 1., & Srivastava, P. K. (2017). Tumor Control Index as a new tool to assess tumor growth in experimental animals. Journal of immunological methods, 445, 71-76. [0065] In one aspect, methods of identifying an effective therapy for treating a cancer are provided. In some embodiments, the method comprises stratification of patient T cell populations based on expression of the identified gene signatures. In some embodiments, provided herein is a method of identifying an effective therapy for treating a tumor cancer in a subject having a tumor cancer, comprising: (a) contacting a first cell from a subject with a therapy; (b) obtaining a first sample from the first cell of step (a); (c) determining the genetic expression of CD8⁺ T cells in the first sample; (d) comparing the genetic expression of CD8⁺ T cells from step (c) to the genetic expression of CD8⁺ T cells in a reference sample prepared by using a second sample obtained from the subject prior to contacting with the therapy, wherein: when the sample from step (c) compared to the reference sample shows: upregulation of at least one gene, wherein the at least one upregulated gene has a log2FoldChange > 1 and an adjusted p-value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor nonrejecting CD8⁺ T cells; and/or down-regulation of at least one gene, wherein the at least one downregulated gene has a log FoldChange < 1 and an adjusted p-value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor non-rejecting CD8⁺ T cells; indicates the therapy is likely to be efficacious for treating a tumor cancer; and when the sample from step (c) compared to the reference sample shows: down-regulation of at least one gene, wherein the at least one down-regulated gene has a log2FoldChange > 1 and an adjusted p- value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor non-rejecting CD8⁺ T cells; and/or upregulation of at least one gene, wherein the at least one upregulated gene has a log2FoldChange < 1 and an adjusted p-value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor non- rejecting CD8⁺ T cells; indicates the therapeutic is unlikely to be efficacious for treating a tumor cancer. In some embodiments, the method further comprises (e) administering to the subject a therapeutically effective amount of the therapy when the therapy is indicated as likely to be efficacious for treating a tumor cancer, or not administering to the subject the therapy when the therapy is indicated as unlikely to be efficacious for treating a tumor cancer. In one variation of any aspect or embodiment, the cancer is susceptible to CD8⁺ T cells.

[0066] In one aspect, methods of monitoring or predicting the responsiveness of subject having a cancer to a therapy are disclosed. In some variations, the method comprises stratification of patient T cell populations based on expression of the identified gene signatures. In some embodiments, the method provided herein comprises: (a) administering the therapy to a subject having a cancer; (b) obtaining a first sample from the subject; (c) determining the genetic expression of CD8⁺ T cells in the first sample; (d) comparing the genetic expression of CD8⁺ T cells from step (c) to the genetic expression of CD8⁺ T cells in a reference sample prepared by using a second sample obtained from the subject prior to contacting with the therapy, when the sample from step (c) compared to the reference sample shows: upregulation of at least one gene, wherein the at least one upregulated gene has a log2FoldChange > 1 and an adjusted p-value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor non-rejecting CD8⁺ T cells; and/or down-regulation of at least one gene, wherein the at least one downregulated gene has a log2FoldChange < 1 and an adjusted p-value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor nonrejecting CD8⁺ T cells; indicates the therapy is likely to be efficacious for treating a tumor cancer; and when the sample from step (c) compared to the reference sample shows: downregulation of at least one gene, wherein the at least one down-regulated gene has a log2FoldChange > 1 and an adjusted p-value < 0.05 a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor non-rejecting CD8⁺ T cells; and/or upregulation of at least one gene, wherein the at least one upregulated gene has a log2FoldChange < 1 and an adjusted p-value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor non-rejecting CD8⁺ T cells; indicates the therapeutic is unlikely to be efficacious for treating a tumor cancer; (e) administering to the subject a therapeutically effective amount of the therapy, when the subject is indicated as likely to be responsive to the therapy, or not administering to the subject the therapy, when the subject is indicated as unlikely to be responsive to the therapy.

EXAMPLES

[0067] The Examples further describe various aspects of embodiments of the present disclosure. These Examples are not meant to limit embodiments solely to such Examples herein but rather to illustrate some possible implementations.

EXAMPLE 1

[0068] To generate and detect Gtf2b+ CD8 T cells and Trib3+ CD8 T cells the method disclosed in Ebrahimi-Nik H, et al., JCI Insight. 2019 Jun 20;5(14):el29152. doi: 10.1172/j ci. insight.129152. PMID: 31219806; PMCID: PMC6675551 was followed.

[0069] As disclosed herein, in a study of tumor-rejecting CD8⁺ T cells, the RNA-Seq data of two sets of neoepitope-specific CD8⁺ T cells, one set being tumor-rejecting and one set being tumor non-rejecting, were compared (FIG. 1). The volcano plot was generated using Partek software, which applied ANOVA and non-adjusted p-values, resulting in a large number of hits. As disclosed herein, later analyses use adjusted p-values to correct for multiple comparisons, providing a focused set of differentially expressed genes.

[0070] Briefly, mice were immunized with bone marrow-derived dendritic cells (BMDCs) pulsed with 40 pg of the rejecting neoepitope Gtf2b or the non-rejecting neoepitope Trib3, administered twice with a one-week interval. Seven days after the second immunization, spleens were harvested, and CD8⁺ T cells were enriched using magnetic beads and stained with CD8, CD44, and tetramer. Spleens from naive mice or mice immunized with BMDCs pulsed with vehicle served as controls for setting the gating parameters for tetramer+ CD8⁺ T cells. The tetramer+ CD8 T cells were sorted, and its total RNA was isolated was extracted. RNA-seq libraries were prepared using the NEBNext® Multiplex Oligos for Illumina® (Dual Index Primers Set 1; NEB #E7600, Illumina San Diego, CA) following the manufacturer’s protocol. Barcoded libraries were pooled and sequenced on a NovaSeq 6000 (Illumina, San Diego) platform using paired-end 150 bp reads. This comparative analysis revealed that tumor-rejecting CD8⁺ T cells highly and differentially expressed the Stk32c gene, which encodes a serine tyrosine kinase. The expression of the Stk32c gene in two subsets of CD8⁺ T cells that represent lowly and highly differentiated CD8⁺ T cells in the tumor microenvironment was also assessed, and it was found that there was a significantly higher expression of the Stk32c gene in terminally exhausted CD8⁺ T cells compared to progenitor exhausted CD8⁺ T cells (FIG. 2A-B). These data are normalized individually within each sample, but not across samples.

[0071] A principal component analysis (PCA) of TOX expression among tumor-rejecting Gtf2b-specific CD8 T cells (GT ), non-rejecting Trib3-specific Cd8 T cells (T3 ), and control (GT-) CD8 T cells is shown in FIG. 3. Transcriptomic data were analyzed using Partek Flow software (Illumina San Diego, CA). TPM-normalized TOX expression values were plotted across cell types using dot plots. The y-axis (logio scale) illustrates gene expression differences across multiple orders of magnitude. TOX is an indication of cells being exhausted and non-functional. As shown, the TOX expression of tumor-rejecting CD8⁺ T cells is very low compared to the nonrejecting or control CD8⁺ T cells. This suggests that Stk32c has opposite correlation to TOX expression. Not intending to be bound by theory, it is believed that the Stk32c gene has a role in regulating the TOX expression in tumor-rejecting CD8⁺ T cells.

[0072] FIG. 4A-B illustrate a gene set enrichment analysis (GSEA) of tumor-rejecting CD8⁺ T cells compared to non-rejecting CD8⁺ T cells. The analysis was performed using Partek Flow, where transcriptomic data from sorted CD8⁺ T cells were ranked based on differential expression. Gene sets associated with mTOR signaling and oxidative phosphorylation were significantly enriched in tumor-rejecting CD8⁺ T cells, as shown by a positive enrichment score. These data indicate that CD8⁺ T cells capable of tumor rejection exhibit enhanced expression of genes involved in metabolic fitness and mitochondrial function, consistent with a less exhausted and more bioenergetically competent phenotype. Not intending to be bound by theory, it is believed that the Stk32c gene has a role in the metabolism and fitness of tumor-rejecting CD8⁺ T cells in the tumor microenvironment. Again not intending to be bound by theory, it is believed that the Stk32c gene and other genes that are differentially expressed in tumor-rejecting CD8⁺ T cells are a therapeutic target for treating cancer.

EXAMPLE 2

[0073] Comparison of gene expression levels between terminal and progenitor CD8⁺ T cells. Data Processing used fastq-dump to download high-quality FASTQ files from the following datasets: (a) GSE123738 (Chen et al., Nature, 2019 Mar;567(7749):530-534): 12 melanoma samples; (b) GSE83978 (Utzschneider et al., Immunity, 2016 Aug 16;45(2):415-27): 6 LCMV-infected mouse samples; (c) GSE123235 (Miller et al., Nature Immunology, 2019 20 pp. 326-336): 4 LCMV samples and 8 melanoma samples. Quality control was performed using FastQC. Sequencing reads were aligned to the mouse mmlO reference genome using HISAT2. The resulting SAM files were compressed, sorted, and indexed with Samtools. Read quantification was performed with featureCounts, using the GENCODE M14 gene annotation. DESeq2 was used to conduct differential expression analysis. Boxplots were used to compare gene expression levels between terminal and progenitor CD8 T cells. Statistical significance was annotated using adjusted p-values from DESeq2. Stk32c and Fhl2, which were upregulated in the efficacious CD8⁺ T cells as disclosed herein, were also shown to be significantly upregulated in terminally differentiated CD8⁺ T cells. These terminal cells exhibited markedly higher expression of cytotoxic effector genes, including Gzmb, Gzma, Ifng, and Prfl, compared to progenitor CD8⁺ T cells. This is consistent with the association of each of Stk32c and Fhl2 with enhanced cytotoxicity and effector function in CD8⁺ T cells.

EXAMPLE 3

[0074] Comparison of two groups of antigen-specific CD8⁺ T cells: Gtf2b-CD8⁺ T cells, which were efficacious in killing Meth A Fibrosarcoma cells in vivo, and Trib3-CD8⁺ T cells, which were non-efficacious were investigated. Both groups target their respective cognate antigens, Gtf2b (TGAARFDEF) and Trib3 (VGPEILSSL), presented on the Meth A Fibrosarcoma cell membrane. Similar levels of antigen presentation on the cell membrane were observed via mass spectrometry and both antigens were able to stimulate and significantly expand their corresponding CD8⁺ T cells upon immunization. However, their functional outcomes were strikingly different: Gtf2b-specific CD8⁺ T cells completely rejected tumors (100% tumor rejection), while Trib3 -specific CD8⁺ T cells failed to induce any rejection (0% tumor rejection). One variable that differed between the efficacious and non-efficacious CD8⁺ T cell groups was the antigen specificity (the rest of the variables were the same: tumor model, way of immunization, the amount of the expression of the antigen and etc.); this is consistent with differences in the gene expression being driven by TCR stimulation. The FASTQ files were obtained directly from sequencing (NovaSeq 6000, Illumina, San Diego, CA) the transcriptome of Gtf2b-specific (efficacious) and Trib3 -specific (non-efficacious) CD8⁺ T cells. The antigen- specific CD8⁺ T cells were sorted with tetramers and the transcriptomics were compared. Sequencing the transcriptome, or RNA sequencing (RNA-seq), is a process that analyzes the complete set of RNA transcripts in a sample to understand gene expression and regulation. Based on the analysis, several genes were significantly differentially expressed between the two groups, including for example, Fhl2 and Lrigl . (FIG. 5) Genes typically associated with enhanced CD8⁺ T cell cytotoxicity and function, such as Ifng, Tnfa, Prf, and Gzmb, showed similar expression in both the efficacious (Gtf2b-specific) and non-efficacious (Trib3 -specific) CD8⁺ T cells, except for Gzma, consistent with flow cytometry characterization of these CD8⁺ T cells. This differential regulation of Gzma versus Gzmb has gained attention in recent studies. Despite comparable expression of the key functional genes, one group failed to effectively kill target cells in vivo. Lrigl, which was significantly expressed in non-efficacious CD8⁺ T cells, was recently discovered to be a new suppressive ligand of VISTA and correlates with tumor rejection failure in CD8⁺ T cells. Lrigl, as shown herein is an example of a gene which should be down- regulated to achieve therapeutic efficacy as disclosed herein.

Method of Genetic analysis

[0075] FASTQ files can be obtained directly from sequencing the transcriptome of selected CD8⁺ T cells (either efficacious or non-efficacious as described above), as disclosed herein. Raw FASTQ files are also available as open source data from independent studies and can be used in the pipeline analyses consistent with the methods disclosed herein.

[0076] There are a variety of tools, or pipelines, that were used in the analyses disclosed herein. Each of the pipelines align raw sequencing reads to the genome (STAR_featureCounts_ DESeq2 and Hisat2_featureCounts_DESeq2) or to the transcriptome (kallisto_tximport_ DESeq2, kallisto tximport edgeR, and kallisto tximport Limma). In each case, p-values were adjusted for multiple comparisons across pipelines using the Benjamini -Hochberg (BH) method. [0077] Other tools, such as ANOVA-based differential gene expression analysis, can be employed according to the methods disclosed herein, with the understanding that differences to the genome/transcriptome alignment, the alignment tool, alignment sensitivity and mapping quality can yield slightly different calculations of ‘significance’ for the genes in question.

[0078] As demonstrated with Fhl2, overall qualities, including the biological relevance derived from experimental validation, can be taken into account to assign each gene an Effectiveness Score. In a mouse model of cancer, genes of the present application, when regulated (modulated) in CD8 T cells and then adoptively transferred into tumor bearing mice will lead to a reduction in tumor size compared to the control group of mice not treated with CD8⁺ T cells having expression of the gene of the present application enhanced.

[0079] The results of the differential expression analysis conducted using DESeq2 were visualized with a volcano plot, generated using the EnhanceVolcano R package (FIG. 5). This plot provides a graphical summary of both the magnitude and significance of gene expression changes between two conditions.

[0080] The x-axis represents the log2 fold change (Tog2FoldChange’ or Tog2FC’), indicating the direction and magnitude of differential expression. Genes with a positive log2FC are upregulated in the efficacious CD8⁺ T cells, and those with a negative log2FC are downregulated in these cells compared to non-efficacious CD8⁺ T cells. Genes with a negative log2FC had higher expression in non-efficacious CD8⁺ T cells.

[0081] The y-axis displays the -logio of the adjusted p-value, quantifying statistical significance. Genes plotted higher on the y-axis have lower adjusted p-values and are therefore more significantly differentially expressed.

[0082] Threshold lines are included to highlight biologically relevant genes. A vertical dashed line at log2FC = ±1 represents the cutoff for fold-change significance. Within the nonsignificant cutoff are thousands of genes, which were not deemed impactful. In FIG 5. the adjusted p-value is represented by a horizontal dashed line at -LogioP = 1.3. As shown, even amongst those genes found to have a significant fold change, there are still thousands of genes not deemed to meet the significance threshold. Genes that pass both thresholds (i.e., |log2FC| > 1 and adjusted p-value < 0.05) are highlighted to emphasize those with both strong effect sizes and high statistical significance.

[0083] As used in Tables 1-5, “baseMean” refers to Average normalized expression of a gene across all samples and represents the general abundance of the gene; higher values indicate stronger expression. “log2FoldChange” and “Log FC” each refer to Log2-transformed fold change between two conditions (e.g., disease vs. control) and generally indicates the direction and magnitude of differential expression. “IfcSE” refers to the standard error of the log2 fold change and represents uncertainty in the estimated fold change; lower values suggest more reliable results. “Stat” refers to the Wald test statistic and is used to derive the p-value; higher absolute values indicate stronger evidence for differential expression. “Pvalue” is the raw p-value from the statistical test and reflects the probability that the observed expression difference is due to chance. “Padj” refers to the adjusted p-value (using Benjamini -Hochberg FDR), which controls for multiple testing; genes with low padj are considered significantly differentially expressed. “logCPM” refers to Log2 counts per million (average expression level) and generally represents gene abundance normalized by sequencing depth; helps interpret fold changes in context. “LR” refers to the Likelihood Ratio statistic from the model and quantifies how well the model with group differences fits better than the null model. “FDR” refers to the False Discovery Rate (Benjamini -Hochberg adjusted p-value), which is generally used to identify significantly differentially expressed genes with control for multiple comparisons. “AveExpr” refers to the Average Log2 expression level across all samples and generally shows overall gene expression, helping to contextualize the fold change. As used “T” refers to the moderated t-statistic using empirical Bayes shrinkage, combining information across genes to stabilize variance estimation, improving statistical power. As used, “B” refers to Log-odds of differential expression (log-odds that the gene is truly differentially expressed); higher B values indicate stronger evidence for a gene being differentially expressed.

[0084] Hi sat2_featureC ounts DES eq2 : Raw sequencing reads were preprocessed using fastp

(vO.23.2) for quality control, trimming, and filtering. The cleaned reads were aligned to the reference genome using HISAT2 (v2.1.0), and gene-level quantification was performed with featureCounts (v2.0.1) using the GENCODE M25 genome annotation. The resulting count matrix was analyzed using DESeq2 (vl.42.1). Differential expression analysis was conducted using the Wald test, with significantly differentially expressed genes defined as |log2FoldChange| > 1 and adjusted p-value < 0.05. Expression values were normalized and log-transformed for downstream analyses. Results are shown in Table 1 for the eight genes meeting the necessary parameters from amongst the -13,000 tested. Gzma is a known cytotoxic marker.

Table 1. Genes found to be significant by Hisat2_featureCounts_DESeq2 analysis of -13,000 genes analyzed

[0085] kallisto_tximport_DESeq2: Raw reads were first processed using fastp (vO.23.2) for quality control. Transcript-level quantification was then performed using kallisto (vO.46.2) with GENCODE M25 transcriptome reference. Transcript abundances were imported and summarized to the gene level using the tximport package (vl.30.0). Gene-level counts were analyzed with DESeq2 (vl.42.1) using the default Wald test. Differentially expressed genes were defined by |log2FoldChange| > 1 and adjusted p-value < 0.05. Expression data were log-transformed and normalized prior to downstream analysis. Results are shown in Table 2 for the 17 genes meeting the necessary parameters from amongst the -14,000 tested. Gzma is a known cytotoxic marker identified by this analysis.

Table 2. Genes found to be significant by kallisto_tximport_DESeq2 analysis of -14,000 genes analyzed

[0086] STAR featureCounts DESeq2: raw sequencing reads were preprocessed using fastp (vO.23.2) for quality control, trimming, and filtering. The processed reads were then aligned to the reference genome using STAR (v2.5.2a), followed by gene-level quantification with featureCounts (v2.0.1) based on genome annotation from GENCODE M25. Results are shown in Table 3 for the 20 genes meeting the necessary parameters from amongst the -16,000 tested. Gzma is a known cytotoxic marker and its presence in this list reinforces the methods of this analysis.

Table 3. Genes found to be significant by STAR_featureCounts_DESeq2 analysis of -16,000 genes analyzed [0087] kallisto tximport edgeR: After fastp (vO.23.2) preprocessing, transcript-level quantification was carried out using kallisto (vO.46.2). Abundances were summarized to the gene level with tximport (vl.30.0). The gene-level count matrix was analyzed using edgeR (v4.0.16), employing trimmed mean of M-values (TMM) normalization and quasi-likelihood F-tests to identify differentially expressed genes. Genes with |log2FoldChange| > 1 and FDR < 0.05 were considered significant. Results are shown in Table 4 for the thirteen genes meeting the necessary parameters from amongst the -11,000 tested. As noted earlier, Gzma is a known cytotoxic marker.

Table 4. Genes found to be significant by kallisto tximport edgeR analysis of -11,000 genes analyzed

[0088] kallisto_tximport_Limma: Reads were quality-checked and filtered using fastp (vO.23.2), followed by transcript quantification via kallisto (vO.46.2). Gene-level counts were obtained using tximport (vl.30.0). Differential expression analysis was performed using the limma-voom pipeline (limma v3.58.1), with voom transformation applied to model meanvariance relationships. Genes with |log2FoldChange| > 1 and adjusted p-value < 0.05 were defined as significantly differentially expressed. Results are shown in Table 5 for the 45 genes meeting the necessary parameters from amongst the -23,000 genes tested. In pipeline analysis the raw p value for Gzma is significant, but the adjusted p value is not. Table 5. Genes found to be significant by kallisto_tximport_Limma analysis of -23,000 genes analyzed

[0089] Computational analysis of the efficacious CD8⁺ T cells revealed Fhl2, was significantly upregulated in CD8⁺ T cells linked to improved immunotherapy responses: its expression increases upon CD8⁺ T cell activation; it correlates with enhanced cytotoxicity; and it is linked to improved migratory capacity in CD8⁺ T cells. Such experimental findings were used to validate Fhl2's Effectiveness Score, in concert with the differential gene expression analysis. This is relevant when the adjusted p-value for a gene may be only significant in one gene expression analytical tool, as disclosed herein. When a gene is considered “significant” as disclosed herein, if it meets the thresholds of |log2FoldChange| > 1 and adjusted p-value < 0.05 in any pipeline analysis, its Effectiveness Score can be determined as disclosed herein. Each pipeline method has its strengths under different data conditions; sensitivity and specificity should be balanced, reducing the risk of overlooking potentially meaningful genes that only appear in a single pipeline. Genes that meet the threshold can be further evaluated as the model gene, Fhl2 was, by looking at, amongst other variables disclosed within the Effectiveness Score. [0090] qPCR analysis of Fhl2. qPCR analysis of Fhl2 expression in CD3/CD28-stimulated mouse CD8⁺ T cells showed a significant upregulation of Fhl2 at 48 hours post-stimulation, with elevated levels persisting for up to 5 days (FIG.6A). To perform this analysis, mouse CD8⁺ T cells were activated in vitro with plate-bound anti-CD3 (2 pg/mL) and soluble anti-CD28 (2 pg/mL) antibodies. Total RNA was extracted at defined time points post-stimulation, and cDNA was synthesized using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Waltham, MA). Quantitative PCR was performed using TaqMan® Gene Expression Assays and TaqMan® Gene Expression Master Mix according to the manufacturer's protocol (PN 4333458N, Thermo Fisher Scientific, Waltham, MA). Reactions were run in 20 pL volumes on an Applied Biosystems real-time PCR system under standard cycling conditions. Fhl2 expression was normalized to an endogenous control (e.g., Gapdh), and relative expression levels were calculated using the AACt method. A similar upregulation pattern was observed in several other novel genes associated with enhanced function in CD8⁺ T cells. CD8⁺ T cells were isolated from the spleens of naive C57BL/6 mice using the Miltenyi Negative Selection CD8⁺ T Cell Isolation Kit (Miltenyi Biotec, Inc. Auburn, CA), following the manufacturer’s instructions. For the naive group, freshly isolated CD8⁺ T cells were used immediately. For the activated group, CD8⁺ T cells were stimulated with plate-bound anti-CD3 (2 pg/mL), soluble anti-CD28 (2 pg/mL), and IL-2 (10 ng/mL) for 48 hours. Following stimulation, cells were stained with a viability dye, anti-CD8, anti-CD44, and a primary antibody against FHL2, detected using an Alexa Fluor 700-conjugated anti-IgGl secondary antibody. Flow cytometry analysis was performed using FlowJo software (FlowJo, Ashland, OR). CD8⁺CD44^Ahi T cells were gated. FIG. 6B displays Alexa Fluor 700 signal intensity on the x-axis, with the y-axis normalized to mode for naive and activated CD8⁺ T cells.

[0091] To further investigate the functional role of Fhl2 in CD8⁺ T cell activity, the activated CD8⁺ T cells were stimulated with PMA/Ionomycin for 4 hours, allowing assessment of cytokine production and exploration of potential connections between the expression of the gene and key markers of cytokine production and cytotoxicity, such as granzyme A and TNF. Notably, CD8⁺ T cells with higher Fhl2 expression also exhibited increased levels of granzyme A (Gzma) (FIG.7A-7C), and TNF, consistent with a functional link between Fhl2 expression and enhanced cytotoxic potential.

[0092] To determine whether the association between Gzma and Fhl2 expression is driven by shared regulatory mechanisms, a comprehensive computational analysis used the unified atlas of CD8⁺ T cells, which encompasses 166 combined bulk ATAC-seq datasets and 136 RNA-seq datasets from mouse models of LCMV infection and cancer. Differentially expressed genes in terminally differentiated CD8⁺ T cells (PD-1", TIM-3⁺, CD39⁺) were identified, indicative of chronic antigen stimulation, compared to progenitor CD8⁺ T cells (PD-1⁺, TCF1⁺). Notably, both Fhl2 and Gzma were significantly upregulated in the terminally differentiated CD8⁺ T cells. The corresponding regions of differential open chromatin were then mapped using ATAC-seq data to investigate changes in chromatin accessibility associated with the gene expression differences. Using FIMO, the sequences were scanned to find matched motifs for transcription factors and subsequently transcription factors capable of recognizing these motifs were identified. Four transcription factors were found to bind to both Gzma and Fhl2, and they were significantly upregulated in more differentiated CD8⁺ T cells (Table 6). In Table 6, “Summit #” refers to the open chromatin region identified by ATAC-seq. “TF” denotes the transcription factor, and “Motif ID” is the sequence recognized by the transcription factor. “Score” (out of 10) represents the strength of the motif match. “TF.baseMean” is the average expression/reads for each transcription factor, while “TF.logFC” denotes the log fold change of each transcription factor in terminally differentiated CD8⁺ T cells (PD-U, TIM-3⁺, CD39⁺) compared to progenitor CD8⁺ T cells (PD-U, TCF ) as described in (21). “TF.padj” represents the adjusted p-value for the differential expression of transcription factors.

Table 6. Common transcription factors between Fhl2 and Gzma genes; the corresponding motifs and statistical analyses.

[0093] Computational analysis is consistent with the role the genes play critical roles in human CD8⁺ T cells. Their expression rapidly increased shortly after in vitro stimulation and remained elevated for over a week, consistent with findings from the mouse model. (FIG. 8) [0094] C57BL/6 (WT) and FHL2 knockout (FHL27 ) mice were subcutaneously challenged with 1 x 10⁶ B 16-OVA melanoma cells. The B16-OVA model expresses ovalbumin, making it immunogenic and responsive to host immune pressure. Tumor size was measured three times per week. Tumor growth data were analyzed using two-way ANOVA, and results are presented as mean ± SEM (FIG. 9). The analysis revealed a significant difference in tumor growth between FHL2 / and WT groups (p < 0.0001 for interaction, F(10,78) = 24.63). Without being bound by theory, the absence of the Fhl2 gene has a role tumor progression.

[0095] T cell Migration Assay. CD8+ T cells were isolated and activated for five days before use. Activated mouse T cells were diluted to 3 x 10⁶ cells/mL in complete media (RPMI1640 containing 10% fatty acid-free bovine serum albumin, 10 mM nonessential amino acids, 10 mM sodium pyruvate, 10 mM pennstrep, and 10 mM HEPES buffer). 100 pL of T cells were added to the Transwell insert (3-pm pore, Coming, Coming, NY). 600 pL of complete media containing 50 ng/mL of CXCL11 (Thermo Fisher Scientific, Waltham, MA) was added to a 24-well plate. The inserts and well plate were incubated separately for 30 minutes. Transwell inserts were then loaded onto the well plate and further incubated overnight. The number of T cells that remained in the Transwell insert and those that migrated to the well plate were determined separately by flow cytometry. Migration ratios were expressed as a ratio of the number of live migrated cells compared to total live cells. Wildtype T cells (0.45 and 0.32; mean 0.385) show a significantly higher migration ratio than Knockout cells (0.16 and 0.15; mean 0.155), consistent with the essential role FHL2 plays in the chemotaxis of CD8⁺ T cells towards a chemokine attractant and in T cell migration towards a tumor and subsequent invasion.

[0096] Day 3 CFSE Proliferation Assay of WT and FHL2 KO CD8⁺ T Cells. Splenic CD8⁺ T cells were labeled with CFSE (carboxyfluorescein succinimidyl ester) a cell-permeable dye that is progressively diluted as cells divide. The cells were stimulated with anti-CD3/CD28 antibodies. The mean fluorescence intensity (MFI) of CFSE-labeled CD8⁺ T cells from wild-type and FHL2 knockout mice was measured on day 3 post-stimulation via flow cytometry. Three individual biological replicates were measured and group means (for WT = 2,281; for Fhl2 = 4,281) were calculated. The significantly higher CFSE MFI (reflecting reduced proliferation) of the FHL2 KO CD8⁺ T cells that was observed compared to WT cells (p < 0.05, one-tailed Mann- Whitney test) is consistent with FHL2 deficiency impairing T cell proliferation.

EXAMPLE 4

[0097] Role of the genes of the present application (e.g. meeting the thresholds identified herein, including those genes listed in Tables 1-5) in the cytotoxicity, proliferation, survival, and effector function of CD8⁺ T cells: Gene editing techniques are used to manipulate the genes and compare the functional capacities of genome-edited CD8⁺ T cells with control cells. Various assays are employed to assess proliferation, survival, migration, cytotoxicity, and effector function of genome-edited CD8⁺ T cells. The function of the identified genes in a diverse range of CD8⁺ T cell responses are evaluated, including CD8⁺ T cells that recognize endogenously mutated self-peptides in cancer cells. [0098] As discussed herein, the only variable between efficacious and non-efficacious CD8⁺ T cell groups was the antigen specificity (the rest of the variables were the same: tumor model, way of immunization, the amount of the expression of the antigen and etc.), consistent with the observation that differences in the gene expression are driven by TCR stimulation.

[0099] Evaluation of CD8⁺ T cells that fail to kill target cells, despite appearing efficacious based on traditional cytotoxicity: The function of the identified genes, including Fhl2, in the biology, differentiation, activation, and efficacy of antigen specific CD8⁺ T cells is evaluated. Additionally, the signaling pathways of the novel genes and the extent of cross-talk with the TCR (T-cell receptor) signaling pathway are evaluated using techniques such as RNA sequencing, mass spectrometry -based proteomics, and phospho-flow cytometry.

[00100] Compare efficacious and non-efficacious CD8⁺ T cells in both tumor models: Evaluate how identified genes influence T cell function broadly and whether such influence is context dependent or independent of environment. Compare multiple antigen-specific CD8⁺ T cells across two tumor models to further expand the results, and determine if the same set of genes consistently appear among efficacious and non-efficacious CD8⁺ T cells or if CD8⁺ T cell function is context-dependent, requiring different gene sets to be upregulated for efficacy in different models. The model uses the pdpr antigen, which induces an efficacious CD8⁺ T cell response, IGPRALDVL and the prpfl9- 1 antigen, which induces a non-efficacious CD8⁺ T cell response, KYLQVASHVGL from Meth A Fibrosarcoma in BALB/c mice which are derived from a well-characterized CD8⁺ T cell library. Also used is the Faml7 antigen, which induces an efficacious CD8⁺ T cell response, QTLLELSKGKPPHPMAWFVSLDGKPVAQV, and the Trim21 antigen, which induces a non-efficacious CD8⁺ T cell response, ERSGSWNLDTLDIDTPDLTSTCPVPGRKK, derived from the well characterized FABF tumor model CD8⁺ T cell library in BL/6 mice. The model also utilizes GP33-41, which induces an efficacious CD8⁺ T cell response.

[00101] Mice are immunized with dendritic cells loaded with each epitope separately, twice with a one-week interval (according to the method generally disclosed in Ebrahimi-Nik H, et al. JCI Insight. 4(14):el29152 and Ebrahimi-Nik H, et al. Cancer Immunol Immunother. 2018 Sep 1;67(9): 1449-59). Briefly, CD8⁺ T cells are isolated from harvested spleens, stained with specific tetramers, and sorted following the procedure. The transcriptomes of these CD8⁺ T cells are sequenced and compared across each pair using the same methods disclosed herein. Differentially expressed genes are analyzed and new gene candidates are computationally evaluated across other independent CD8⁺ T cell studies. Flow cytometry is used to analyze both traditional markers and novel candidate gene markers at the protein level.

[00102] The cognate antigens are presented on the cell membrane of target cells via targeted mass-spectrometry. Only those antigens and CD8⁺ T cell responses that consistently cause tumor rejection (or non-rejection) in vivo and produce comparable CD8⁺ T cell responses upon immunization were selected, providing a robust model to resolve why some CD8⁺ T cells fail to combat target cells although they have characteristics of efficacious CD8⁺ T cells (i.e. expressing significant amount of Ifng, granzymes and other functional molecules). These genes are tested in different pairs of CD8⁺ T cells in different tumor models.

[00103] To assess the relative contribution of Gzma compared to the genes in CD8⁺ T cell efficacy, the sensitivity of Meth A Fibrosarcoma cells is assessed with respect to Gzma, with Gzmb serving as a control. The tumor cells are treated with recombinant GZMA or GZMB in the presence of perforin to facilitate granzyme entry and measure viability of tumor cells, thereby determining the functional impact of Gzma in tumor cell killing and its role alongside other disclosed genes.

EXAMPLE 5

[00104] To determine the role of the candidate genes in biology and function of CD8⁺ T cells a variety of in vitro and in vivo experiments are used, including genome manipulation techniques such as CRISPR-Cas9, siRNA, and shRNAto knock down or knock out target genes. Using siRNA and shRNA as alternatives minimizes off-target effects of CRISPR. For knockout (KO) or knockdown validation, multiple methods, including flow cytometry and Western blotting are used. All antibodies undergo rigorous testing for specificity and binding efficiency prior to the actual experiments.

[00105] The function of these engineered CD8⁺ T cells are evaluated against control cells. Proliferation is assessed using CFSE, and survival evaluated using caspase assays. The impact of these genes on CD8⁺ T cell function are analyzed by highly dimensional spectral flow cytometry, using markers such as perforin, granzyme A and B, Fas ligand, TNF-a, IFN-y, CD107a, Nur77, CD25, CD69, Ki67, and CD44. [00106] Functional characterization of gene KO CD8⁺ T cells in vitro. To measure the impact of gene deletion, such as Fhl2 deletion, on CD8⁺ T cell proliferation, survival, and activation, spleens from gene KO mice, such as Fhl2 KO mice, are processed into single-cell suspensions, and CD8⁺ T cells are isolated using Miltenyi negative selection. Cells are CFSE- labeled, activated with anti-CD3, anti-CD28, and IL-2, and expanded for 48 hours. Proliferation is assessed at 48, 72, and 96 hours post-activation using CFSE dilution, based on an optimized proliferation assay as disclosed in Baumgartner CK, Ebrahimi-Nik H, et al. Nature. 2023 Oct; 622(7984): 850-62. To assess survival and apoptosis, cells are stained with Annexin V/PI (Thermo Fisher Scientific) at 24 and 48 hours post-activation and analyzed by flow cytometry. Activation markers (CD25, CD69, CD44) are measured at 48 and 72 hours, while effector function and cytotoxic markers (IFN-y, TNF-a, perforin, granzyme A, granzyme B) are assessed at 4-5 days post-activation. Exhaustion markers (PD-1, TOX, Tim-3, CTLA-4, LAG-3) are evaluated at day 8 following chronic CD3 stimulation. Spectral flow cytometry is used to generate a multidimensional analysis of functional, activation, and exhaustion signatures across time points.

[00107] Transcriptomic and proteomic profiling of gene KO CD8⁺ T cells. To define gene- regulated pathways, RNA sequencing (RNA-seq) and mass spectrometry -based proteomics are used to compare knockout versus wild-type CD8⁺ T cells, allowing mapping of the precise signaling pathways influenced by the genes. Testing is performed on WT and gene KO CD8⁺ T cells harvested four days post-activation, based on the activation studies showing gene expression becomes significantly upregulated around day 4-5. Three biological replicates per group are used for RNA isolation and protein lysate preparation. In this way, the signaling pathways regulated by the genes are identified, as are the transcription factors and regulatory elements involved in those processes.

[00108] Evaluating CD8⁺ T cell migration and tumor infiltration in vivo. Using Fhl2 as a representative gene: Given FHL2’s role in actin remodeling, its deficiency impact on CD8⁺ T cell migration is evaluated. OT1/Fhl2 KO mice are generated through crossing OT1 and Fhl2 KO mice. CD8⁺ T cells from these mice are CFSE-labeled, while WT OT1⁺ CD8⁺ T cells are CellTrace Violet (CTV)-labeled, and a 1 : 1 mixture is adoptively transferred into MC38-OVA tumor-bearing mice, a previously validated model. 6-8-week-old WT C57BL/6 mice are challenged with 1 million MC38-OVA cells, followed by adoptive transfer of 3 million OT1/Fhl2 KO ⁺ OT1 WT CD8⁺ T cells at day 10 post-tumor challenge. At 48 hours post-transfer, tumors, spleens, and tumor-draining lymph nodes are analyzed by flow cytometry to compare Fhl2 KO vs. WT CD8⁺ T cell infiltration. Fhl2 KO CD8⁺ T cells can exhibit reduced tumor infiltration, consistent with the role for Fhl2 in T cell migration.

[00109] The Role of Gene in Neoepitope-Specific CD8⁺ T Cell Tumor Rejection. Using Fhl2 as a representative gene: To measure Fhl2 KO impact on tumor rejection by antigenspecific CD8⁺ T cells, CRISPR RNP -mediated Fhl2 KO are generated in naive BALB/c CD8⁺ T cells (alternately BALB/c gene KO mice can be used). This method uses high-specificity guide RNAs (>70 score, Broad Institute CRISPR Tool), with KO efficiency validated by flow cytometry using an FHL2-specific antibody. Four million Fhl2 KO or control CD8⁺ T cells are adoptively transferred into WT BALB/c mice, followed by immunization with Trib3 (nonrejecting) or Gtf2b (rejecting) neoepitopes, loaded onto bone marrow-derived dendritic cells (20 nM) as per established protocols. Mice are re-immunized on day 7 and challenged with 95,000 Meth A fibrosarcoma cells on day 14. Tumor growth is monitored to compare tumor rejection efficacy between WT and Fhl2 KO CD8⁺ T cells.

[00110] BL/6 FABF colon carcinoma tumor model (whole-body Fhl2 KO mice provide a 100% KO CD8⁺ T cell population for adoptive transfer). Comparing the tumor rejection efficacy of CD8⁺ T cells isolated from Fhl2 KO mice with control CD8⁺ T cells addresses the influence of Fhl2 on the in vivo cytotoxic function of CD8⁺ T cells. The method comprises challenging C57BL/6 mice with the FABF tumor cell line and subsequently performing adoptive transfers of either Fhl2 KO or wild-type (WT) CD8⁺ T cells. The mice are immunized with the Fam 17 peptide, a known tumor-associated antigen that elicits a robust CD8⁺ T cell response and significant tumor rejection. Tumor growth is monitored over time to assess differences in tumor rejection efficacy between Fhl2 KO and WT CD8⁺ T cells.

[00111] To rule out a single cytotoxic marker as the sole driver of tumor rejection, instead of the identified gene, Meth A Fibrosarcoma cells are treated with recombinant the marker in the presence of perforin, and cell viability is assessed. This confirms that tumor rejection defects in genetic KO CD8⁺ T cells persist independently of the cytotoxic marker, further validating the gene’s role in CD8⁺ T cell function disclosed herein.

[00112] In parallel, chimeric bone marrow mice are used to investigate the functional impact of Fhl2 on CD8⁺ T cells in a mixed immune environment. Wild-type CD45.2 C57BL/6 mice are irradiated and bone marrow transplants performed using a 50:50 mix of Fhl2 KO CD45.1 bone marrow and wild-type CD45.2 bone marrow. After six weeks of reconstitution, the composition of the CD8⁺ T cell populations is assessed by flow cytometry, specifically tracking Fhl2 KO CD45.1 CD8⁺ T cells and wild-type CD45.2 CD8⁺ T cells. The chimeric mice are then challenged with the FABF tumor cell line and immunized with FABS neoepitopes. Two weeks post-tumor challenge, CD8⁺ T cells are harvested from the tumor microenvironment (TME), and their functional characteristics (e.g., cytokine production, exhaustion markers, and cytotoxicity) compared between Fhl2 KO and WT CD8⁺ T cells.

[00113] To specifically explore TCR signaling integration with and influence of the novel candidate genes’ pathways, early and late TCR signaling events (e.g., ZAP-70 phosphorylation, LAT activation, and downstream pathways such as MAPK/ERK and PI3K/AKT) are evaluated in both WT and KO CD8⁺ T cells. Antigen levels are titrated to modulate TCR signal strength and assess the impact on the expression and function of the novel genes, thereby assessing how differences in TCR signaling influence the cytotoxic and proliferative capacity of CD8⁺ T cells, providing insights into the regulatory networks involving the genes.

EXAMPLE 6

[00114] Isolate and activate CD8⁺ T cells from healthy donors and perform CRISPR/Cas9- mediated knockout to assess genetic impact on proliferation, survival, and cytotoxicity. In the following example Fhl2 is used as a model.

[00115] Functional and Molecular Characterization of Fhl2 KO Human CD8⁺ T Cells.

To determine the intrinsic impact of Fhl2 deletion on CD8⁺ T cell proliferation, survival, activation, and molecular pathways, CD8⁺ T cells from healthy donors are isolated and activated via CD3/CD28 as described above. Proliferation assessment, apoptosis staining, and flow cytometry analysis of activation, cytotoxicity, and exhaustion markers are performed using human-specific antibodies, following the approach described above. RNA-seq and RPPA are performed on day 4 activated KO and WT human CD8⁺ T cells to define the transcriptional and post-translational landscape of Fhl2 deletion, identifying key regulatory networks and signaling pathways that drive CD8⁺ T cell function, thereby highlighting pathways regulated by Fhl2. Evaluation of the expression of Fhl2 in patient-derived CD8⁺ T cells confirms correlation with immune responsiveness and cancer patient outcomes, as generally described herein. References:

Ashouri JF, Weiss A. Endogenous Nur77 is a specific indicator of antigen receptor signaling in human T and B cells. J Immunol Baltim Md 1950. 2017 Jan 15; 198(2):657- 68.

Basu H, Pekkumaz G, Falk J, Wei W, Chin M, Steen J, et al. FHL2 anchors mitochondria to actin and adapts mitochondrial dynamics to glucose supply. Journal of Cell Biology. 2021 Aug 3;220(10):e201912077.

Baumgartner CK, Ebrahimi-Nik H, Iracheta-Vellve A, Hamel KM, Olander KE, Davis TGR, et al. The PTPN2/PTPN1 inhibitor ABBV-CLS-484 unleashes potent anti-tumour immunity. Nature. 2023 Oct;622(7984):850-62.

Brennick CA, George MM, Corwin WL, Srivastava PK, Ebrahimi-Nik H. Neoepitopes as Cancer Immunotherapy Targets: Key Challenges and Opportunities. Immunotherapy. 2017 Mar 1;9(4):361-71.

Brennick CA, George MM, Moussa MM, Hagymasi AT, Seesi SA, Shcheglova TV, et al. An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection. J Clin Invest 2021 Feb 1;131(3).

Cao X, Cai SF, Fehniger TA, Song J, Collins LI, Piwnica-Worms DR, et al. Granzyme B and Perforin Are Important for Regulatory T Cell-Mediated Suppression of Tumor Clearance. Immunity. 2007 Oct 26;27(4):635-46.

Carreno BM, Magrini V, Becker-Hapak M, Kaabinejadian S, Hundal J, Petti AA, et al. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science. 2015 May 15;348(6236):803-8.

Corwin, W. L , Ebrahimi-Nik, H., Floyd, S. M., Tavousi, P, Mandoiu, 1. 1., & Srivastava, P K. (2017). Tumor Control Index as a new tool to assess tumor growth in experimental animals. Journal of immunological methods 445, 71-76.

Duan F, Duitama J, Al Seesi S, Ayres CM, Corcelli SA, Pawashe AP, et al. Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J Exp Med. 2014 Oct 20;211(11):2231—48.

Ebrahimi-Nik H, Corwin WL, Shcheglova T, Das Mohapatra A, Mandoiu II, Srivastava PK. CDl lc+ MHCIIlo GM-CSF-bone marrow-derived dendritic cells act as antigen donor cells and as antigen presenting cells in neoepitope-elicited tumor immunity against a mouse fibrosarcoma. Cancer Immunol Immunother. 2018 Sep 1 ;67(9): 1449— 59.

Ebrahimi-Nik H, Michaux J, Corwin WL, Keller GLJ, Shcheglova T, Pak H, et al. Mass spectrometry-driven exploration reveals nuances of neoepitope-driven tumor rejection. JCI Insight. 4(14):el29152.

Ebrahimi-Nik H, Moussa M, Englander RP, Singhaviranon S, Michaux J, Pak H, et al. Reversion analysis reveals the in vivo immunogenicity of a poorly MHC I-binding cancer neoepitope. Nat Commun. 2021 Nov 5; 12(1): 6423.

Fujimoto Y, Nakazawa N. The roles of FHL2 as a mechanotransducer for cellular functions in the mechanical environment. Front Cell Dev Biol [Internet], 2024 Jul 26 [cited 2025 Feb 28]; 12. Available from: https://www.frontiersin.org/joumals/cell-and-developmental- biology/articles/10.3389/fcell.2024.1431690/full

Gallimore A, Glithero A, Godkin A, Tissot AC, Pliickthun A, Elliott T, et al. Induction and Exhaustion of Lymphocytic Choriomeningitis Virus-specific Cytotoxic T Lymphocytes Visualized Using Soluble Tetrameric Major Histocompatibility Complex Class I-Peptide Complexes. J Exp Med. 1998 May 4; 187(9): 1383-93.

Giles JR, Manne S, Freilich E, Oldridge DA, Baxter AE, George S, et al. Human epigenetic and transcriptional T cell differentiation atlas for identifying functional T cell-specific enhancers. Immunity. 2022 Mar 8;55(3): 557-574. e7.

Hashimoto M, Araki K, Cardenas MA, Li P, Jadhav RR, Kissick HT, et al. PD-1 combination therapy with IL-2 modifies CD8+ T cell exhaustion program. Nature. 2022 Oct;610(7930): 173— 81.

Hellstrom KE, Hellstrom I. From the Hellstrom paradox toward cancer cure. Prog Mol Biol Transl Sci. 2019;164: 1-24.

Keskin DB, Anandappa AJ, Sun J, Tirosh I, Mathewson ND, Li S, et al. Neoantigen vaccine generates intratumoral T cell responses in phase lb glioblastoma trial. Nature. 2019 Jan;565(7738):234-9.

Kurakula K, van der Wai E, Geerts D, van Tiel CM, de Vries CJM. FHL2 Protein Is a Novel Co-repressor of Nuclear Receptor Nur77. J Biol Chem. 2011 Dec 30;286(52):44336— 43. Lith SC, van Os BW, Seijkens TTP, de Vries CJM. ‘Nur Turing tumor T cell tolerance and exhaustion: novel function for Nuclear Receptor Nur77 in immunity. Eur J Immunol. 2020 Nov;50(ll): 1643-52.

Liu S, Matsuzaki J, Wei L, Tsuji T, Battaglia S, Hu Q, et al. Efficient identification of neoantigen-specific T-cell responses in advanced human ovarian cancer. J Immunother Cancer. 2019 Jun 20;7(l):156.

Lui Y, Jenkins E, Ng EZQ, Kotowski M, Mullin SJ, Clarke J, et al. Cell state and transcription factor modulation during extended ex vivo CD8+ T-cell expansion bioRxiv; 2024. p. 2024.07.17.603780.

Mohankumar K, Wright G, Kumaravel S, Shrestha R, Zhang L, Abdelrahim M, et al. Bisindole-derived NR4A1 antagonists inhibit colon tumor and splenic growth and T-cell exhaustion. Cancer Immunol Immunother CII. 2023 Dec;72(12):3985-99.

Pardo J, Balkow S, Anel A, Simon MM. The differential contribution of granzyme A and granzyme B in cytotoxic T lymphocyte-mediated apoptosis is determined by the quality of target cells. Eur J Immunol. 2002;32(7): 1980-5.

Pircher H, Btirki K, Lang R, Hengartner H, Zinkernagel RM. Tolerance induction in double specific T-cell receptor transgenic mice varies with antigen. Nature. 1989 Nov 30;342(6249):559-61.

Pritykin Y, van der Veeken J, Pine AR, Zhong Y, Sahin M, Mazutis L, et al. A unified atlas of CD8 T cell dysfunctional states in cancer and infection. Mol Cell. 2021 Jun 3;81(ll):2477-2493.el0.

Sayour EJ, Grippin A, De Leon G, Stover B, Rahman M, Karachi A, et al. Personalized Tumor RNA Loaded Lipid-Nanoparticles Prime the Systemic and Intratumoral Milieu for Response to Cancer Immunotherapy. Nano Lett. 2018 Oct 10; 18(10):6195— 206.

Schietinger A, Philip M, Krisnawan VE, Chiu EY, Delrow JJ, Basom RS, et al. Tumor- Specific T Cell Dysfunction Is a Dynamic Antigen-Driven Differentiation Program Initiated Early during Tumorigenesis. Immunity. 2016 Aug 16;45(2):389— 401.

Singhaviranon S, Dempsey JP, Hagymasi AT, Mandoiu II, Srivastava PK. Low-avidity T cells drive endogenous tumor immunity in mice and humans. Nat Immunol. 2025 Feb;26(2):240-51. Ta HM, Roy D, Zhang K, Alban T, Juric I, Dong J, et al. LRIG1 engages ligand VISTA and impairs tumor-specific CD8+ T cell responses. Sci Immunol. 2024 May 17;9(95):eadi7418.

Tsao HW, Anderson S, Finn KJ, Perera JJ, Pass LF, Schneider EM, et al. Targeting the aminopeptidase ERAP enhances antitumor immunity by disrupting the NKG2A-HLA-E inhibitory checkpoint. Immunity. 2024 Dec 10; 57(12) :2863 -2878.e 12.

Zehn D, Thimme R, Lugli E, de Almeida GP, Oxenius A. ‘Stem-like’ precursors are the fount to sustain persistent CD8+ T cell responses. Nat Immunol. 2022 Jun;23(6):836-47.

Zienert E, Eke I, Aust D, Cordes N. LIM-only protein FHL2 critically determines survival and radioresistance of pancreatic cancer cells. Cancer Lett. 2015 Aug 1 ;364(1): 17-24.

[00116] Various embodiments of the invention have been described in fulfillment of the various objectives of the invention. It should be recognized that these embodiments are merely illustrative of the principles of the present invention. Numerous modifications and adaptations thereof will be readily apparent to those skilled in the art without departing from the spirit and scope of the invention.

Claims

1. A method of treating cancer in a patient in need thereof, the method comprising: regulating the expression of at least one gene in a CD8⁺ T cell, wherein the at least one regulated gene has a |log2FoldChange| > 1 and an adjusted p-value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor non-rejecting CD8⁺ T cells, wherein the regulated gene is not Gzma or Lrigl.

2. The method of claim 1, wherein the tumor-rejecting CD8⁺ T cells are Gtf2b specific CD8⁺ T cells and the tumor-non-rejecting CD8⁺ T cells are Trib3 specific CD8⁺ T cells.

3. The method of claim 1 or claim 2, wherein the regulated gene has an Effectiveness Score of 2 or higher.

4. The method of any one of claims 1-3, wherein the at least one regulated gene is selected from the group of 2900027M19Rik, Actb, Adgre4, Atg4d, AU020206, Cd55b, Clec4a3, Commdlb, Dnajc4, Elfn2, Fam3a, Fcgrt, Fez2, Fhl2, imap3, Gml0052, Gml0163, Gml2715, Gml7344, Gml9220, Gm21833, Gm4294, Gm43302, Gm47438, Gm47457, Gm50163, Gm50241, Gpx4-ps2, Hck, Id2, Igkv4-57, Kcncl, Klhl7, Lpar5, Mafb, Mai, Malatl, Mapla, Mertk, Mrcl, Ms4a4b, mt-Rnr2, Myo7a, Plin3, Rab3il 1, Rasgeflb, Rpll5-ps3, Rpsl3-ps4, S100a6, Sdc3, Slc24a3, Slc39all, Stk32c, Tafa3, Tgfbi, Trav5-1, Trav8d-1, Xrcc3, and Zfyvel.

5. The method of any one of claims 1-4, wherein the at least one regulated gene is selected from the group of AU020206, Fcgrt, Fez2, Fhl2, Gml0052, Id2, Mafb, Mertk, Ms4a4b, Rab3il 1 , Stk32c, Tgfbi, and Xrcc3.

6. The method of any one of claims 1-5, wherein the at least one regulated gene is upregulated and is selected from the group of Fhl2, Stk32c, Commdlb, Gm43302, Mai, Atg4d, Dnajc4, Fam3a, Lpar5, Plin3, S100a6 and Zfyvel.

7. The method of any one of claims 1-6, wherein the at least one regulated gene is upregulated and is Fhl2 and/or Stk32c.

8. The method of any one of claims 1-7, wherein the at least one regulated gene is down- regulated and is selected from the group of Cd55b, Elfn2, Gm47438, Mafb, Rab3il 1 , Slc24a3, Trav5-1, and Trav8d-1.

9. The method of any one of claims 1-8, wherein the at least one regulated gene is down- regulated and is Cd55b, Mafb, and/or Rab 3 ill.

10. The method of any one of claims 1-9, wherein the CD8 T cell is a cell of the patient.

11. The method of any one of claims 1-9, wherein the CD8 T cell comes from a healthy donor.

12. The method of claim 1 or claim 10, wherein the CD8⁺ T cell comes from a tumor microenvironment.

13. The method of any one of claims 1-12, wherein regulating the expression of at least one gene in a CD8⁺ T cell comprises administering to the CD8⁺ T cell one or more of a small molecule, a monoclonal antibody, a retrovirus, electroporation, CRISPR, RNAi, and shRNA.

14. The method of claim 13 wherein CRISPR is CRISPR CAS9, CRISPR CAS12 or CRISPRi.

15. The method of any one of claims 1-12 wherein regulating the expression of at least one gene in a CD8⁺ T cell comprises administering to a patient a small molecule, a monoclonal antibody, or CRISPR.

16. The method of any one of claims 1-13 wherein regulating the expression of at least one gene in a CD8⁺ T cell comprises use of (a) a retrovirus or (b) electroporation optionally in combination with a plasmid or mRNA, wherein the regulated gene is over-expressed.

17. The method of any one of claims 1-15 wherein regulating the expression of at least one gene in a CD8⁺ T cell comprises use of (a) a retrovirus; (b) CRISPR; (c) RNAi, or (d) shRNA, wherein the regulated gene is down regulated or knocked out.

18. The method of any one of claims 1-17 further comprising administering the CD8⁺ T cell with the regulated gene to the patient.

19. The method of any one of claims 1-17 further comprising expanding the CD8⁺ T cell having the regulated gene and administering the population of expanded CD8⁺ T cells to the patient.

20. The method of any one of claims 1-21, wherein the cancer is susceptible to CD8⁺ T cells.

21. The method of any one of claims 1-21, wherein the cancer is a head or neck cancer, sarcoma, lung cancer, non-small cell lung cancer, small cell lung cancer, breast cancer, colorectal cancer, pancreatic cancer, bladder cancer, renal cell carcinoma, stomach or gastric cancer, ovarian cancer, thyroid cancer, skin cancer, or melanoma.

22. A pharmaceutical composition comprising a CD8⁺ T cell having at least one regulated gene, wherein the at least one regulated gene has a |log2FoldChange| > 1 and an adjusted p- value < 0.05 in a differential gene expression analysis of tumor-rejecting CD8⁺ T cells compared to tumor non-rejecting CD8⁺ T cells, wherein the regulated gene is not Gzma or Lrigl.

23. The composition of claim 22, wherein the tumor-rejecting CD8⁺ T cells are Gtf2b specific CD8⁺ T cells and the tumor-non-rejecting CD8⁺ T cells are Trib 3 specific CD8⁺ T cells.

24. The composition of claim 22 or claim 23, wherein the regulated gene has an Effectiveness Score of 2 or higher.

25. The composition of any one of claims 22-24, wherein the at least one regulated gene is selected from the group of 2900027M19Rik, Actb, Adgre4, Atg4d, AU020206, Cd55b, Clec4a3, Commdlb, Dnajc4, Elfn2, Fam3a, Fcgrt, Fez2, Fhl2, imap3, Gml0052, Gml0163, Gml2715, Gml7344, Gml9220, Gm21833, Gm4294, Gm43302, Gm47438, Gm47457, Gm50163, Gm50241, Gpx4-ps2, Hck, Id2, Igkv4-57, Kcncl, Klhl7, Lpar5, Mafb, Mai, Malatl, Mapla, Mertk, Mrcl, Ms4a4b, mt-Rnr2, Myo7a, Plin3, Rab3il 1, Rasgeflb, Rpll5-ps3, Rpsl3-ps4, S100a6, Sdc3, Slc24a3, Slc39al 1, Stk32c, Tafa3, Tgfbi, Trav5-1, Trav8d-1, Xrcc3, and Zfyvel.

26. The composition of any one of claims 22-25, wherein the at least one regulated gene is selected from the group of AU020206, Fcgrt, Fez2, Fhl2 ,Gml0052, Id2, Mafb, Mertk, Ms4a4b, Rab3il 1 , Stk32c, Tgfbi, and Xrcc3.

27. The composition of any one of claims 22-26, wherein the at least one regulated gene is upregulated and is selected from the group of Fhl2, Stk32c, Commdlb, Gm43302, Mai, Atg4d, Dnajc4, Fam3a, Lpar5, Plin3, S100a6 and Zfyvel.

28. The composition of claim 27, wherein the at least one regulated gene is upregulated and is selected from the group of Fhl2 and/or Stk32c.

29. The composition of any one of claims 22-25, wherein the at least one regulated gene is down-regulated and is selected from the group of Cd55b, Elfn2, Gm47438, Mafb, Rab3il 1 , Slc24a3, Trav5-1, and Trav8d-1.

31. The composition of any one of claims 24-30, wherein the at least one regulated gene is down-regulated and is Cd55b, Mafb, and/or Rab3il 1.

32. The composition of any one of claims 22-31, wherein the CD8⁺ T cell is a cell of the patient.

33. The composition of any one of claims 22-31, wherein the CD8⁺ T cell comes from a healthy donor.

34. The composition of claim 22 or claim 32, wherein the CD8⁺ T cell comes from a tumor microenvironment.

35. The composition of any one of claims 22-34, wherein regulating the expression of at least one gene in a CD8⁺ T cell comprises use of one or more of a small molecule, a monoclonal antibody, a retrovirus, electroporation, CRISPR, RNAi, and shRNA.

36. The composition of claim 35, wherein CRISPR is CRISPR CAS9, CRISPR CAS12 or CRISPRi.

37. The composition of any one of claims 22-34, wherein regulating the expression of at least one gene in a CD8⁺ T cell comprises use of (a) a retrovirus or (b) electroporation optionally in combination with a plasmid or mRNA, wherein the regulated gene is over-expressed.

38. The composition of any one of claims 22-34, wherein regulating the expression of at least one gene in a CD8⁺ T cell comprises use of (a) a retrovirus; (b) CRISPR; (c) RNAi, or

(d) shRNA, wherein the regulated gene is down regulated or knocked out.