Novel personalized neo-antigen vaccines and markers

Abstract

The present invention provides a personalized neo-antigen vaccine, and uses thereof.The present invention also provides the markers MX1 and PPP1R15A, and uses thereof.The present invention also provides a set of biomarkers, and uses thereof.

Description

[Technical field]

[0001]

[0001] The present disclosure relates generally to novel personalized neo-antigen vaccines, and uses thereof. The present disclosure also relates generally to novel markers MX1 and PPP1R15A, and uses thereof. [Background technology]

[0002]

[0002] Neo-antigen vaccines, synthetic antigens, have been designed to provide sufficient tumor-specific antigens to stimulate T cell-mediated immunity and eliminate tumor cells. Proof of concept for the efficacy of neo-antigen vaccines or combined immune checkpoint inhibition (ICI) has been established in a limited number of patients with melanoma, non-small cell lung cancer, and urothelial carcinoma. 1~4 The clinical use of neoantigen vaccines is expected to be widely launched and approved by FDA. Pancreatic ductal adenocarcinoma (PDAC) is an intractable malignant tumor with the worst prognosis. Even patients who have undergone resection tend to recur and show poor prognosis at the end stage. In resectable pancreatic cancer with low tumor burden and sufficient immune status, adjuvant neoantigen vaccine therapy may achieve good results. Furthermore, in the total resection-recurrence / recurrence-free setting, it is rational to evaluate the effect of neoantigen vaccine or combination of vaccine and ICI, as well as the response mechanism to prevent tumor recurrence and improve prognosis.

[0003]

[0003] A difficulty faced by conventional tumor vaccine therapy is the lack of markers that can detect specific changes in immune cells and correlate with the administration effect of the vaccine. Therefore, there is a high need for treatments and markers for tumor vaccine therapy. Summary of the Invention [Problem to be solved by the invention]

[0004]

[0010] Throughout this disclosure, the articles "a," "an," and "the" are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, "a method" means one method or more than one method.

[0005]

[0011] The present disclosure provides novel methods and compositions for enhancing cell-mediated immunity, stimulating and / or expanding T cells, increasing immunogenicity, treating conditions that would benefit from upregulation of immune responses, and promoting clonal expansion of T cells. The present disclosure also provides methods of using the novel biomarkers MX1 and PPP1R15A. [Means for solving the problem]

[0006]

[0012] In one aspect, the disclosure provides a method of enhancing cell-mediated immunity in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 agonist, thereby enhancing cell-mediated immunity in the subject.

[0007]

[0013] In certain embodiments, the cell-mediated immunity is T cell-mediated immunity.

[0008]

[0014] In one aspect, the disclosure provides a method for stimulating and / or expanding T cells in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 agonist, thereby stimulating and / or expanding T cells in the subject.

[0009]

[0015] In one aspect, the disclosure provides a method of increasing the immunogenicity of an immunogenic composition in a subject, comprising administering to the subject an effective amount of an MX1 agonist in combination with the immunogenic composition.

[0010]

[0016] In certain embodiments, the immunogenic composition is a composition for a vaccine or CAR-T treatment.

[0011]

[0017] In certain embodiments, the vaccine is a tumor vaccine.

[0012]

[0018] In certain embodiments, the subject suffers from a condition that would be expected to benefit from upregulation of the immune response.

[0013]

[0019] In certain embodiments, the condition is a tumor, an infectious disease, a cardiovascular disease, or an inflammatory disease.

[0014]

[0020] In certain embodiments, the condition is a tumor or an infectious disease.

[0015]

[0021] In certain embodiments, the subject receives a treatment whose effectiveness can be increased by enhancing cell-mediated immunity.

[0016]

[0022] In certain embodiments, the treatment is an anti-tumor treatment or an anti-infective treatment.

[0017]

[0023] In certain embodiments, the anti-tumor treatment is selected from the group consisting of chemotherapy, targeted therapy, immunotherapy, cellular therapy (e.g., CAR-T therapy), and tumor vaccine.

[0018]

[0024] In one aspect, the disclosure provides a method of treating a condition that would benefit from upregulation of the immune response in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 agonist.

[0019]

[0025] In certain embodiments, the condition is a tumor, an infectious disease, a cardiovascular disease, or an inflammatory disease.

[0020]

[0026] In certain embodiments, the condition is a tumor or an infectious disease.

[0021]

[0027] In certain embodiments, the MX1 agonist is administered in combination with a therapy to treat the condition.

[0022]

[0028] In certain embodiments, the treatment is an anti-tumor treatment or an anti-infective treatment.

[0023]

[0029] In certain embodiments, the anti-tumor treatment is selected from the group consisting of chemotherapy, targeted therapy, immunotherapy, cell therapy (e.g., CAR-T therapy), tumor vaccine, and CAR-T therapy.

[0024]

[0030] In certain embodiments, the MX1 agonist is selected from the group consisting of full length mRNA of MX1, and small molecule compounds of DMXAA, ADU-S100, 2',3'-cGAMP sodium, and cGAMP.

[0025]

[0031] In one aspect, the disclosure provides a method of promoting clonal expansion of T cells comprising treating T cells with an effective amount of an MX1 agonist under suitable conditions, thereby allowing clonal expansion of the T cells.

[0026]

[0032] In certain embodiments, the T cells are memory T cells.

[0027]

[0033] In one aspect, the disclosure provides a method of promoting T cell activation or promoting T cell cytotoxicity comprising treating T cells with an effective amount of an MX1 agonist under suitable conditions.

[0028]

[0034] In certain embodiments, the T cells are cultured with an MX1 agonist in combination with a second agent to stimulate the T cells.

[0029]

[0035] In certain embodiments, the MX1 agonist is selected from the group consisting of full length mRNA of MX1, and small molecule compounds of DMXAA, ADU-S100, 2',3'-cGAMP sodium, and cGAMP.

[0030]

[0036] In one aspect, the disclosure provides a composition comprising a T cell prepared using the methods provided herein.

[0031]

[0037] In one aspect, the present disclosure provides a method of treating a condition that would benefit from upregulation of an immune response in a subject in need thereof, comprising administering to the subject a composition provided herein.

[0032]

[0038] In one aspect, the disclosure provides a method for evaluating the activation state, or activity or cytotoxicity of a T cell, comprising detecting the expression level of MX1 in a T cell, wherein an increase in the expression level of MX1 compared to a control T cell is an indication of the cytotoxicity of the T cell.

[0033]

[0039] In certain embodiments, the T cell is a CAR-T cell or a TCR-T cell.

[0034]

[0040] In one aspect, the present disclosure provides a method of preparing a population of T cells for cell therapy, comprising: a) identifying a population of T cells that have increased levels of MX1 expression relative to control T cells; and b) Selectively Enriching Identified T Cells for Cellular Therapy The present invention provides a method comprising:

[0035]

[0041] In one aspect, the disclosure provides a method of converting a first population of inactive T cells into a second population of active T cells, comprising: a) providing a first population of inactive T cells, wherein the inactive T cells do not exhibit an increased level of expression of MX1 compared to a control T cell; b) treating the first population of inactive T cells with an effective amount of an MX1 agonist under suitable conditions; and c) detecting the expression level of MX1 in the population of T cells obtained in step b), where an increase in the expression level of MX1 compared to control T cells is indicative of successful conversion of activated T cells into a second population; The present invention provides a method comprising:

[0036]

[0042] In one aspect, the present disclosure provides a method of preparing a population of T cells for cell therapy, comprising: a) identifying a population of T cells as inactive if they do not exhibit an increased level of expression of MX1 compared to control T cells; and b) treating the identified population of inactive T cells with an effective amount of an MX1 agonist under suitable conditions, thereby obtaining a population of activated T cells; The present invention provides a method comprising:

[0037]

[0043] In certain embodiments, the MX1 agonist is selected from the group consisting of full length mRNA of MX1, and small molecule compounds of DMXAA, ADU-S100, 2',3'-cGAMP sodium, and cGAMP.

[0038]

[0044] In one aspect, the disclosure provides a composition comprising a population of T cells prepared or converted by the methods provided herein.

[0039]

[0045] In one aspect, the disclosure provides a method of treating a condition that would benefit from upregulation of an immune response in a subject in need thereof, comprising administering to the subject a population of T cells prepared or converted by the methods provided herein.

[0040]

[0046] In one aspect, the disclosure provides a method of reducing cell-mediated immunity in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 antagonist, thereby reducing cell-mediated immunity in the subject.

[0041]

[0047] In certain embodiments, the cell-mediated immunity is T cell-mediated immunity.

[0042]

[0048] In one aspect, the disclosure provides a method of inactivating T cells in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 antagonist, thereby inactivating T cells in the subject.

[0043]

[0049] In certain embodiments, the subject has been determined to have T cells that express increased levels of MX1 as compared to control T cells.

[0044]

[0050] In certain embodiments, the subject is suffering from a condition characterized by excessive cell-mediated immunity.

[0045]

[0051] In one aspect, the disclosure provides a method of treating a condition that would benefit from downregulation of the immune response in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 antagonist.

[0046]

[0052] In certain embodiments, the condition is an inflammatory disease, an autoimmune disease, an allergic disease, or a T-cell cancer.

[0047]

[0053] In certain embodiments, the condition is one in which the subject has received or is believed to have received an allogeneic or xenogeneic transplant.

[0048]

[0054] In certain embodiments, the MX1 antagonist is selected from the group consisting of CCCP and H-151.

[0049]

[0055] In certain embodiments, the control T cells are CD8+ T cells.

[0050]

[0056] In one aspect, the present disclosure provides a method for assessing a subject's responsiveness to a tumor neo-antigen vaccine, comprising: a) determining an expression level of one or more genes in one or more immune cells from a sample obtained from the subject, wherein the one or more genes are selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATF4, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, DDOST, DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, GLA, LMAN2, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PDE4D, PIM1, PRKACA, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TMEM258, TRDV1, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof; b) comparing the expression level of the one or more genes determined in step a) to a reference level to determine the difference from the reference level; and c) assessing the responsiveness of the subject to the tumor neo-antigen vaccine based on the difference determined in step b) The present invention provides a method comprising:

[0051]

[0057] In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

[0052]

[0058] In certain embodiments, the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of AC011815.2, EGF, GCH1, NUAK1 and SERTAD2, or any combination thereof.

[0053]

[0059] In certain embodiments, the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of ANKRD29, C15orf54, FOSL2, GCH1, PRKACA, SERTAD2, SLC16A6, TRDV1, WDFY3, and ZBTB43, or any combination thereof.

[0054]

[0060] In certain embodiments, the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of AP3M2, ARL5B, ATP5F1B, CHCHD2, COPE, GLA, MN1 and MTDH, or any combination thereof.

[0055]

[0061] In certain embodiments, the one or more immune cells are B cells and the one or more genes are selected from the group consisting of ATF4, ATP5F1B, DDOST, DERL1, DNPH1, LMAN2, MAP9, PDE4D, PIM1, TMEM258 and ZDHHC7, or any combination thereof.

[0056]

[0062] In certain embodiments, the one or more immune cells are NK cells and the one or more genes are selected from the group consisting of COL4A3BP, FERMT3, and NFIL3, or any combination thereof.

[0057]

[0063] In certain embodiments, the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of COPE, CTTNBP2, GK, LRRK1, MTDH, PRMT1, SYT17 and TMED10, or any combination thereof.

[0058]

[0064] In one aspect, the present disclosure provides a method for assessing a subject's responsiveness to a tumor neo-antigen vaccine during a priming phase, comprising: a) determining the expression level of one or more genes in one or more immune cells from a sample obtained from the subject after at least one priming administration of a tumor neo-antigen vaccine, wherein the one or more genes are selected from the group consisting of AC011815.2, ATF4, C15orf54, DDOST, DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, LMAN2, MAP9, NUAK1, PDE4D, PRKACA, SERTAD2, SLC16A6, TMED10, TMEM258, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof; b) comparing the expression level of the one or more genes determined in step a) to a reference level to determine the difference from the reference level; and c) assessing the responsiveness of the subject to at least one priming dose of the tumor neo-antigen vaccine based on the difference determined in step b). The present invention provides a method comprising:

[0059]

[0065] In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages. In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, B cells, NK cells, and macrophages.

[0060]

[0066] In certain embodiments, the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of AC011815.2, EGF, GCH1, NUAK1 and SERTAD2, or any combination thereof.

[0061]

[0067] In certain embodiments, the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of C15orf54, FOSL2, GCH1, PRKACA, SERTAD2, SLC16A6, WDFY3, and ZBTB43, or any combination thereof.

[0062]

[0068] In certain embodiments, the one or more immune cells are B cells and the one or more genes are selected from the group consisting of ATF4, DDOST, DERL1, DNPH1, LMAN2, MAP9, PDE4D, TMEM258 and ZDHHC7, or any combination thereof.

[0063]

[0069] In certain embodiments, the one or more immune cells are NK cells and the gene is FERMT3.

[0064]

[0070] In certain embodiments, the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of GK and TMED10, or any combination thereof.

[0065]

[0071] In one aspect, the present disclosure provides a method for assessing a subject's responsiveness to a tumor neo-antigen vaccine during a boosting phase, comprising: a) determining the expression level of one or more genes in one or more immune cells from a sample obtained from the subject during a boosting phase, wherein the one or more genes are selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, EGF, FERMT3, FOSL2, GK, GLA, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PIM1, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TRDV1 and ZDHHC7, or any combination thereof; b) comparing the expression level of one or more genes determined in step a) to a reference expression level to determine the difference from the reference level; and c) assessing the responsiveness of the subject to at least one boosting administration of the tumor neo-antigen vaccine based on the difference determined in step b). The present invention provides a method comprising:

[0066]

[0072] In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

[0067]

[0073] In certain embodiments, the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of AC011815.2, EGF, NUAK1 and SERTAD2, or any combination thereof.

[0068]

[0074] In certain embodiments, the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of ANKRD29, C15orf54, FOSL2, SLC16A6, and TRDV1, or any combination thereof.

[0069]

[0075] In certain embodiments, the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of AP3M2, ARL5B, ATP5F1B, CHCHD2, COPE, GLA, MN1 and MTDH, or any combination thereof.

[0070]

[0076] In certain embodiments, the one or more immune cells are B cells and the one or more genes are selected from the group consisting of ATP5F1B, MAP9, PIM1, and ZDHHC7, or any combination thereof.

[0071]

[0077] In certain embodiments, the one or more immune cells are NK cells and the one or more genes are selected from the group consisting of COL4A3BP, FERMT3, and NFIL3, or any combination thereof.

[0072]

[0078] In certain embodiments, the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of COPE, CTTNBP2, GK, LRRK1, MTDH, PRMT1, SYT17 and TMED10, or any combination thereof.

[0073]

[0079] In certain embodiments, the expression level of a given gene is represented by the percentage of immune cells of a given type that express the given gene.

[0074]

[0080] In certain embodiments, the reference expression level is the expression level of one or more genes in one or more immune cells from a sample obtained from the subject before receiving the first dose of the tumor neo-antigen vaccine.

[0075]

[0081] In certain embodiments, a subject is determined to have a good response to the tumor neo-antigen vaccine if the difference meets or exceeds a defined threshold, or a subject is determined to have a poor response to the tumor neo-antigen vaccine if the difference is below a defined threshold.

[0076]

[0082] In one aspect, the present disclosure provides a method for predicting risk of tumor recurrence in a subject before or after receiving at least one administration of a tumor neo-antigen vaccine, comprising: a) determining the expression level of one or more genes in one or more immune cells from a sample obtained from the subject, the one or more genes being ABCA13, AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, AL662907.3, APOBEC3C, ATP1B3, ATP6V1B2, C1QBP, CBX5, CCNE2, CD63, CEBPA, CHP1, CLN8, CMBL, CNIH4, COL4A3BP, CPNE3, DACH1, DNAJC12, DNAJC3, DOC2B, EEA1, ENAM, ERG, FAM43A, FBXO43, FIGN, GCA, GNG4, GNLY, GOLIM4, GPM6B, GPR171, GPRC5D, HHEX, HID 1, ILF2, IRF2BP2, KIF22, LACC1, LIPA, MANEA, MECOM, MID1IP1, MX2, MYOM2, NABP1, NEFH, NFE4, NLN, OXCT2, P3H2, PI3, PIM1, PLAU, PRSS3, PSMA3, RAB10, RAB20, RASGRP2, RBMS3, RPL23, RPS6KA5, RPS8, RUNX1, SAMD3, 11-Sep, SERTAD2, SIGLEC6, SIT1, SOCS1, SSR1, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC15, TXNDC17, UAP1, UFM1, UGT2B17, XPNPEP1, ZBTB16, ZNF608 and STAT1, or any combination thereof; b) comparing the expression level of the one or more genes determined in step a) to a reference level to determine the difference from the reference level; and c) assessing the risk of tumor recurrence in the subject based on the difference determined in step b) The present invention provides a method comprising:

[0077]

[0083] In certain embodiments, the one or more genes are selected from the group consisting of AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, ATP1B3, CMBL, DACH1, DNAJC12, DOC2B, EEA1, ERG, FBXO43, FIGN, GPRC5D, HID1, NFE4, OXCT2, P3H2, PI3, PLAU, PRSS3, RASGRP2, RPL23, RPS8, ST6GALNAC1, SYNE2, TRAV16, TREM2, and ZNF608, or any combination thereof.

[0078]

[0084] In certain embodiments, the expression level of one or more genes is determined in one or more immune cells from a sample obtained from the subject after receiving at least one administration of a tumor neo-antigen vaccine, and the one or more genes are selected from the group consisting of AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, ATP1B3, ATP6V1B2, CHP1, CLN8, CMBL, CNIH4, CPNE3, DACH1, DNAJC12, DOC2B, EEA1, ERG, FAM43A, FBXO43, FIGN, GNG4, GPM6 B, GPRC5D, HHEX, HID1, IRF2BP2, KIF22, LACC1, MECOM, MID1IP1, MYOM2, NABP1, NFE4, OXCT2, P3H2, PI3, PLAU, PRSS3, RAB10, RASGRP2, RBMS3, RPL23, RPS8, RUNX1, SAMD3, SERTAD2, SIGLEC6, SIT1, SOCS1, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC17, UGT2B17, XPNPEP1 and ZNF608, or any combination thereof.

[0079]

[0085] In certain embodiments, the expression level of one or more genes is determined in one or more immune cells from a sample obtained from the subject prior to receiving at least one dose of the tumor neo-antigen vaccine, and the one or more genes are selected from the group consisting of ABCA13, AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, AL662907.3, ATP1B3, CBX5, CMBL, DACH1, DNAJC12, DNAJC3, DOC2B, ENAM, ERG, FBXO43, FIGN, GOLIM4, GPRC5D, HID1, ILF2, NEFH, NFE4, NLN, OXCT2, P3H2, PI3, PLAU, PRSS3, PSMA3, RASGRP2, RPL23, RPS6KA5, RPS8, 11-Sep, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC15, UAP1, UFM1, ZBTB16 and ZNF608, or any combination thereof.

[0080]

[0086] In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

[0081]

[0087] In certain embodiments, the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of CHP1, EEA1, RPS8, RUNX1, UGT2B17, XPNPEP1, ZNF608 and STAT1, or any combination thereof.

[0082]

[0088] 77. The method of any one of claims 72 to 76, wherein the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of AL662907.3, CD63, COL4A3BP, CPNE3, EEA1, GPM6B, LIPA, PSMA3, RAB10, RAB20, RBMS3, RPS8, SAMD3, SERTAD2, SOCS1, TXNDC15 and STAT1, or any combination thereof.

[0083]

[0089] In certain embodiments, the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of ABCA13, ATP1B3, DNAJC3, ENAM, FIGN, GNG4, ILF2, KIF22, MANEA, NEFH, NLN, P3H2, PLAU, SIGLEC6, SSR1, TRAV16, UFM1 and ZBTB16, or any combination thereof.

[0084]

[0090] In certain embodiments, the one or more immune cells are B cells and the one or more genes are selected from the group consisting of AC105094.2, AC243829.2, AL021807.1, AL391807.1, ATP6V1B2, C1QBP, CMBL, CNIH4, DOC2B, FAM43A, GCA, GNLY, HID1, LACC1, MID1IP1, MX2, PI3, PIM1, 11-Sep, SIT1 and SYNE2, or any combination thereof.

[0085]

[0091] In certain embodiments, the one or more immune cells are NK cells and the one or more genes are selected from the group consisting of AC022726.2, APOBEC3C, DACH1, ERG, IRF2BP2, MYOM2, NFE4, PRSS3, RPL23, RPS6KA5 and TREM2, or any combination thereof.

[0086]

[0092] In certain embodiments, the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of CBX5, CCNE2, CEBPA, CLN8, DNAJC12, FBXO43, FIGN, GOLIM4, GPR171, GPRC5D, HHEX, MECOM, NABP1, OXCT2, RASGRP2, ST6GALNAC1, TXNDC17 and UAP1, or any combination thereof.

[0087]

[0093] In certain embodiments, the expression level of a given gene is represented by the percentage of immune cells of a given type that express the given gene.

[0088]

[0094] In certain embodiments, the reference expression level is the expression level of the corresponding gene in corresponding immune cells representative of the relapsed subject.

[0089]

[0095] In certain embodiments, the reference expression level is a standard or mean expression level determined from a representative population of recurrent subjects.

[0090]

[0096] In certain embodiments, the subject is determined to be at low risk of tumor recurrence if the difference meets or exceeds a defined threshold, or the subject is determined to be at high risk of tumor recurrence if the difference is below a defined threshold.

[0091]

[0097] In certain embodiments, the reference expression level is the expression level of the corresponding gene in corresponding immune cells that are representative of relapse-free subjects.

[0092]

[0098] In certain embodiments, the reference expression level is a standard or mean expression level determined from a representative population of relapse-free subjects.

[0093]

[0099] In certain embodiments, a subject is determined to be at high risk of tumor recurrence if the difference meets or exceeds a defined threshold, or a subject is determined to be at low risk of tumor recurrence if the difference is below a defined threshold.

[0094] [000100] In one aspect, the present disclosure provides a method for evaluating therapeutic efficacy in a subject treated with an anti-tumor therapy in combination with at least one administration of a tumor neo-antigen vaccine, comprising: a) determining a level of one or more genes in one or more immune cells from a sample obtained from the subject after the treatment, wherein the one or more genes are selected from the group consisting of AC092490.1, ALDH1L2, LILRB5, PALD1, PKP2 and TRAV35, or any combination thereof; b) comparing the level of the one or more genes determined in step a) to a reference level to determine the difference from the reference level; and c) evaluating the efficacy of treatment in the subject based on the difference determined in step b). The present invention provides a method comprising:

[0095] [000101] In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

[0096] [000102] In certain embodiments, the one or more immune cells are CD4+ T cells and the one or more genes are LILRB5.

[0097] [000103] In certain embodiments, the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of ALDH1L2 and PKP2, or any combination thereof.

[0098] [000104] In certain embodiments, the one or more immune cells are B cells and the one or more genes are selected from the group consisting of AC092490.1, PALD1 and TRAV35, or any combination thereof.

[0099] [000105] In certain embodiments, the expression level of a given gene is represented by the percentage of immune cells of a given type that express the given gene.

[0100] [000106] In certain embodiments, the reference expression level is the expression level of the corresponding gene in a corresponding immune cell from a sample obtained from the subject before receiving an anti-tumor treatment.

[0101] [000107] In certain embodiments, the anti-tumor treatment comprises a PD-1 antagonist.

[0102] [000108] In certain embodiments, the subject exhibits tumor recurrence following tumor neo-antigen vaccination.

[0103] [000109] In certain embodiments, the subject undergoes tumor resection surgery prior to receiving the first dose of the tumor neo-antigen vaccine, and optionally, the subject does not receive chemotherapy prior to the resection surgery.

[0104] [000110] In certain embodiments, a subject's tumor tissue, adjacent tissue and / or peripheral blood sample is analyzed to identify one or more tumor-specific mutations in the subject.

[0105] [000111] In certain embodiments, tumor neo-antigen vaccines are prepared based on the identified tumor-specific mutations.

[0106] [000112] In certain embodiments, the subject is diagnosed with pancreatic cancer, optionally pancreatic ductal adenocarcinoma.

[0107] [000113] In certain embodiments, the sample comprises or is derived from peripheral blood mononuclear cells (PBMCs), a blood sample, or tumor-infiltrating immune cells.

[0108] [000114] In certain embodiments, the level of one or more genes is measured by an amplification assay, a hybridization assay, a sequencing method (e.g., single cell sequencing), or an immunoassay (e.g., flow cytometry or immunohistochemistry).

[0109] [000115] In one aspect, the present disclosure provides a kit for assessing a subject's responsiveness to a tumor neo-antigen vaccine, comprising one or more reagents for detecting an expression level of one or more genes in one or more immune cells from a sample obtained from the subject, the one or more genes being selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATF4, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP, and / or IL-16. 2, DDOST, DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, GLA, LMAN2, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PDE4D, PIM1, PRKACA, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TMEM258, TRDV1, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof.

[0110] [000116] In one aspect, the disclosure provides a kit for assessing the responsiveness of a subject to at least one priming administration of a tumor neo-antigen vaccine, comprising one or more reagents for detecting expression levels of one or more genes in one or more immune cells from a sample obtained from the subject after at least one priming administration of the tumor neo-antigen vaccine, wherein the one or more genes are selected from the group consisting of AC011815.2, ATF4, C15orf54, DDOST, DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, LMAN2, MAP9, NUAK1, PDE4D, PRKACA, SERTAD2, SLC16A6, TMED10, TMEM258, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof.

[0111] [000117] In one aspect, the disclosure provides a kit for assessing the responsiveness of a subject to at least one boosting administration of a tumor neo-antigen vaccine, comprising one or more reagents for detecting an expression level of one or more genes in one or more immune cells from a sample obtained from the subject during a boosting phase, wherein the one or more genes are selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, EGF, FERMT3, FOSL2, GK, GLA, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PIM1, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TRDV1, and ZDHHC7, or any combination thereof.

[0112] [000118] In one aspect, the present disclosure provides a kit for predicting risk of tumor recurrence in a subject before or after receiving at least one administration of a tumor neo-antigen vaccine, the kit comprising one or more reagents for detecting expression levels of one or more genes in one or more immune cells from a sample obtained from the subject, the one or more genes being selected from the group consisting of ABCA13, AC022726.2, AC105094.2, AC24382, and the like. 9.2, AL021807.1, AL391807.1, AL662907.3, APOBEC3C, ATP1B3, ATP6V1B2, C1QBP, CBX5, CCNE2, CD63, CEBPA, CHP1, CL N8, CMBL, CNIH4, COL4A3BP, CPNE3, DACH1, DNAJC12, DNAJC3, DOC2B, EEA1, ENAM, ERG, FAM43A, FBXO43, FIGN, GCA, GNG4 , GNLY, GOLIM4, GPM6B, GPR171, GPRC5D, HHEX, HID1, ILF2, IRF2BP2, KIF22, LACC1, LIPA, MANEA, MECOM, MID1IP1, MX2, MYOM2, NABP1, NEFH, NFE4, NLN, OXCT2, P3H2, PI3, PIM1, PLAU, PRSS3, PSMA3, RAB10, RAB20, RASGRP2, RBMS3, RPL23, RP The kit further comprises a gene encoding a marker selected from the group consisting of S6KA5, RPS8, RUNX1, SAMD3, 11-Sep, SERTAD2, SIGLEC6, SIT1, SOCS1, SSR1, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC15, TXNDC17, UAP1, UFM1, UGT2B17, XPNPEP1, ZBTB16, ZNF608 and STAT1, or any combination thereof.

[0113] [000119] In one aspect, the present disclosure provides a kit for evaluating therapeutic efficacy in a subject treated with an anti-tumor therapy in combination with at least one administration of a tumor neo-antigen vaccine, the kit comprising one or more reagents for detecting levels of one or more genes in one or more immune cells from a sample obtained from the subject after treatment, wherein the one or more genes are selected from the group consisting of AC092490.1, ALDH1L2, LILRB5, PALD1, PKP2 and TRAV35, or any combination thereof.

[0114] [000120] In one aspect, the disclosure provides a method of enhancing cell-mediated immunity in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A agonist, thereby enhancing cell-mediated immunity in the subject.

[0115] [000121] In certain embodiments, the cell-mediated immunity is T cell-mediated immunity.

[0116] [000122] In one aspect, the disclosure provides a method of stimulating and / or expanding T cells in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A agonist, thereby stimulating and / or expanding T cells in the subject.

[0117] [000123] In one aspect, the disclosure provides a method of increasing the immunogenicity of an immunogenic composition in a subject, comprising administering to the subject an effective amount of a PPP1R15A agonist in combination with the immunogenic composition.

[0118] [000124] In certain embodiments, the immunogenic composition is a composition for a vaccine or CAR-T treatment.

[0119] [000125] In certain embodiments, the vaccine is a tumor vaccine.

[0120] [000126] In certain embodiments, the subject is suffering from a condition that would be expected to benefit from upregulation of the immune response.

[0121] [000127] In certain embodiments, the subject is determined to exhibit a decreased expression level of PPP1R15A.

[0122] [000128] In certain embodiments, the condition is a tumor, an infectious disease, a cardiovascular disease, or an inflammatory disease.

[0123] [000129] In certain embodiments, the condition is a tumor or an infectious disease.

[0124] [000130] In certain embodiments, the tumor is selected from the group consisting of brain cancer, renal cell carcinoma, ovarian cancer, gastric cancer, bladder cancer, breast cancer, ovarian cancer, prostate cancer, colon cancer, lung cancer, squamous cell carcinoma of the head and neck, colorectal cancer, melanoma, and myeloma.

[0125] [000131] In certain embodiments, a subject receives a treatment whose effectiveness can be increased by enhancing cell-mediated immunity.

[0126] [000132] In certain embodiments, the treatment is an anti-tumor treatment or an anti-infective treatment.

[0127] [000133] In certain embodiments, the anti-tumor treatment is selected from the group consisting of chemotherapy, targeted therapy, immunotherapy, cellular therapy (e.g., CAR-T therapy), and tumor vaccine.

[0128] [000134] In one aspect, the disclosure provides a method of treating a condition expected to benefit from upregulation of the immune response in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A agonist.

[0129] [000135] In certain embodiments, the condition is a tumor, an infectious disease, a cardiovascular disease, or an inflammatory disease.

[0130] [000136] In certain embodiments, the condition is a tumor or an infectious disease.

[0131] [000137] In certain embodiments, the tumor is selected from the group consisting of brain cancer, renal cell carcinoma, ovarian cancer, gastric cancer, bladder cancer, breast cancer, ovarian cancer, prostate cancer, colon cancer, lung cancer, squamous cell carcinoma of the head and neck, colorectal cancer, melanoma, and myeloma.

[0132] [000138] In certain embodiments, the subject is diagnosed with a decreased expression level of PPP1R15A.

[0133] [000139] In certain embodiments, the PPP1R15A agonist is administered in combination with a therapy to treat the condition.

[0134] [000140] In certain embodiments, the treatment is an anti-tumor treatment or an anti-infective treatment.

[0135] [000141] In certain embodiments, the anti-tumor treatment is selected from the group consisting of chemotherapy, targeted therapy, immunotherapy, cell therapy (e.g., CAR-T therapy), tumor vaccine, and CAR-T therapy.

[0136] [000142] In certain embodiments, the PPP1R15A agonist is selected from the group consisting of full-length PPP1R15A mRNA and a synthetic cannabinoid (WIN55,212-2 mesylate [WIN]).

[0137] [000143] In one aspect, the disclosure provides a method of promoting clonal expansion of T cells, comprising treating T cells with an effective amount of a PPP1R15A agonist under suitable conditions, thereby allowing clonal expansion of the T cells.

[0138] [000144] In certain embodiments, the T cells are memory T cells.

[0139] [000145] In one aspect, the disclosure provides a method of promoting T cell activation or promoting T cell cytotoxicity comprising treating T cells with an effective amount of a PPP1R15A agonist under suitable conditions.

[0140] [000146] In certain embodiments, T cells are cultured with a PPP1R15A agonist in combination with a second agent to stimulate the T cells.

[0141] [000147] In certain embodiments, the PPP1R15A agonist is selected from the group consisting of full-length PPP1R15A mRNA and a synthetic cannabinoid (WIN55,212-2 mesylate [WIN]).

[0142] [000148] In one aspect, the disclosure provides a composition comprising T cells prepared using the methods provided herein.

[0143] [000149] In one aspect, the present disclosure provides a method of treating a condition that would benefit from upregulation of an immune response in a subject in need thereof, comprising administering to the subject a composition provided herein.

[0144] [000150] In one aspect, the disclosure provides a method for assessing the activation state, or activity or cytotoxicity of a T cell comprising detecting the expression level of PPP1R15A in a T cell, wherein an increase in the expression level of PPP1R15A compared to a control T cell is an indication of the cytotoxicity of the T cell.

[0145] [000151] In certain embodiments, the T cells are CAR-T cells or TCR-T cells.

[0146] [000152] In one aspect, the disclosure provides a method of preparing a population of T cells for cell therapy, comprising: a) identifying a population of T cells that have increased levels of PPP1R15A expression relative to control T cells; and b) Selectively Enriching Identified T Cells for Cellular Therapy The present invention provides a method comprising:

[0147] [000153] In one aspect, the disclosure provides a method of converting a first population of inactive T cells into a second population of active T cells, comprising: a) providing a first population of inactive T cells, wherein the inactive T cells do not exhibit an increased expression level of PPP1R15A compared to a control T cell; b) treating the first population of inactive T cells with an effective amount of a PPP1R15A agonist under suitable conditions; and c) detecting the expression level of PPP1R15A in the population of T cells obtained in step b), wherein an increase in the expression level of PPP1R15A compared to control T cells is indicative of successful conversion of activated T cells into a second population; The present invention provides a method comprising:

[0148] [000154] In one aspect, the disclosure provides a method of preparing a population of T cells for cell therapy, comprising: a) identifying a population of T cells as inactive if they do not exhibit an increased level of expression of PPP1R15A compared to control T cells; and b) treating the identified population of inactive T cells with an effective amount of a PPP1R15A agonist under suitable conditions, thereby obtaining a population of activated T cells; The present invention provides a method comprising:

[0149] [000155] In certain embodiments, the PPP1R15A agonist is selected from the group consisting of full-length PPP1R15A mRNA, and a synthetic cannabinoid (WIN55,212-2 mesylate [WIN]).

[0150] [000156] In one aspect, the disclosure provides a composition comprising a population of T cells prepared or converted by the methods provided herein.

[0151] [000157] In one aspect, the disclosure provides a method of treating a condition that would benefit from upregulation of an immune response in a subject in need thereof, comprising administering to the subject a population of T cells prepared or converted by the methods provided herein.

[0152] [000158] In one aspect, the disclosure provides a method of reducing cell-mediated immunity in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A antagonist, thereby reducing cell-mediated immunity in the subject.

[0153] [000159] In certain embodiments, the cell-mediated immunity is T cell-mediated immunity.

[0154] [000160] In one aspect, the disclosure provides a method of inactivating T cells in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A antagonist, thereby inactivating T cells in the subject.

[0155] [000161] In certain embodiments, the subject has been determined to have T cells that have increased expression levels of PPP1R15A compared to control T cells.

[0156] [000162] In certain embodiments, the subject is suffering from a condition characterized by excessive cell-mediated immunity.

[0157] [000163] In one aspect, the disclosure provides a method of treating a condition that would benefit from downregulation of the immune response in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A antagonist.

[0158] [000164] In certain embodiments, the condition is an autoimmune disease, organ or tissue transplant rejection, graft-versus-host disease, an inflammatory disease, an infectious disease, or cancer.

[0159] [000165] In certain embodiments, the condition is one in which the subject is diagnosed as exhibiting an increased expression level of PPP1R15A.

[0160] [000166] In certain embodiments, the PPP1R15A antagonist is selected from the group consisting of guanabenz and Sephin1.

[0161] [000167] In certain embodiments, the control T cells are CD8+ T cells.

[0162] [000168] In one aspect, the present disclosure provides a method of predicting a risk of developing a disease or condition associated with downregulation of an immune response in a subject, comprising: a) determining the expression level of PPP1R15A from a sample obtained from the subject; b) comparing the level determined in step a) with a reference level to determine the difference from the reference level; and c) predicting the risk of developing a disease or condition associated with downregulation of the immune response based on the difference determined in step b). The present invention provides a method comprising:

[0163] [000169] In certain embodiments, if the difference indicates a decrease in the expression level of PPP1R15A compared to the reference level, the subject is predicted to be at risk of developing a disease or condition associated with downregulation of the immune response.

[0164] [000170] In certain embodiments, the disease or condition is a tumor, an infectious disease, a cardiovascular disease, or an inflammatory disease.

[0165] [000171] In certain embodiments, the tumor is selected from the group consisting of brain cancer, renal cell carcinoma, ovarian cancer, gastric cancer, bladder cancer, breast cancer, ovarian cancer, prostate cancer, colon cancer, lung cancer, squamous cell carcinoma of the head and neck, colorectal cancer, melanoma, and myeloma.

[0166] [000172] In one aspect, the disclosure provides a method of predicting risk of developing a disease or condition associated with upregulation of an immune response in a subject, comprising: a) determining the level of PPP1R15A in T cells from a sample obtained from the subject; b) comparing the level of PPP1R15A determined in step a) with a reference level to determine the difference from the reference level; and c) determining a risk of developing a disease or condition in the subject based on the difference determined in step b). The present invention provides a method comprising:

[0167] [000173] In certain embodiments, the subject is determined to be at risk for developing a disease or condition associated with upregulation of the immune response if the difference meets or exceeds a first defined threshold.

[0168] [000174] In certain embodiments, the disease or condition is an autoimmune disease, organ or tissue transplant rejection, graft-versus-host disease, an inflammatory disease, an infectious disease, or cancer.

[0169] [000175] In one aspect, the disclosure provides a method of predicting the likelihood of a subject in need thereof to respond to treatment with a PPP1R15A agonist, comprising: a) determining the level of PPP1R15A in T cells from a sample obtained from the subject; b) comparing the level of PPP1R15A determined in step a) with a reference level to determine the difference from the reference level; and c) determining the susceptibility of the subject to a response based on the difference determined in step b). The present invention provides a method comprising:

[0170] [000176] In certain embodiments, the subject is determined to be likely to be responsive to treatment with a PPP1R15A agonist if the difference meets or exceeds a first defined threshold. [Brief description of the drawings]

[0171] [Figure 1-1] [000177] Figure 1A shows the timeline of clinical procedures and events for 12 patients who received at least 7 doses of vaccine from surgery until death or end of follow-up. [Figure 1-2] [000178] Figure 1B shows Kaplan-Meier curves showing recurrence-free survival of 12 patients during neoantigen vaccine treatment and those treated with chemotherapy after surgery in ICGC PACA-AU Project, PACA-CA Project and Shanghai Hospital (historical control). [Figure 1-3] [000179] Figure 1C shows Kaplan-Meier curves showing the overall survival rate of 12 patients during neoantigen vaccine treatment and patients treated with chemotherapy after surgery in ICGC PACA-AU Project, PACA-CA Project and Shanghai Hospital (historical control). [Figure 1-4] [000180] Figure ID shows serum CA19-9 levels examined before surgery, before vaccination, during vaccination and follow-up. CA19-9 levels were reported as U / mL. The y-axis is log2 transformed values. The black dotted horizontal line indicates the upper limit of normal reference (37 U / mL) and the red dotted horizontal line indicates a level of 90.65 U / mL (2.45 times 37 U / mL [2.45 times elevated CA19-9 values ​​indicate recurrence with 90% sensitivity and 83.33% specificity]). [Figure 1-5] [000181] Figure IE shows serum CA72-4 levels examined before surgery, before vaccination, during vaccination and follow-up. CA72-4 levels were reported as U / mL. The y-axis is the log2 transformed value. The black horizontal dotted line indicates the upper limit of normal reference (9.8 U / mL) and the red horizontal dotted line indicates a level of 14.7 U / mL (1.5 times 9.8 U / mL). [Figure 2-1][000182] Figure 2A shows the expression diversity of TCR genes in single cell 3' library transcriptome sequencing. Diversity changes during treatment in CD4+, CD8+ and other T cells, respectively. Higher Shannon index indicates higher expression diversity. [000183] Figure 2B shows the comparison of TCR clone diversity in single cell TCR sequencing data. [Figure 2-2] [000184] Figure 2C shows a comparison of cell populations of T cell subtypes on different days during vaccine treatment in single cell RNA sequencing data. The top panel shows the changes in cell populations of CD8+, CD4+ and CD4 / CD8low T cells during treatment. The bottom panel shows the changes in cell populations of effector T (Teff), exhausted T (Tex), T helper 1 (Th1), T helper 9 (Th9), memory T (Tmem) and regulatory T (Treg) cells during treatment. Percentage values ​​were transformed by hyperbolic arcsine function. Values ​​on y-axis show the relative change compared to pre-vaccination. All cell ratios pre-vaccination for all patients were normalized to 0 (horizontal dotted line). Round dots represent patients with tumor recurrence and triangles represent recurrence-free patients. Boxes with a red background indicate that the relative cell proportions of patients on that day were significantly higher than before the vaccine, and boxes with a blue background indicate that the cell proportions of relapsed and non-relapsed patients were significantly different. All p-values ​​were calculated using LMM (see Methods) and corrected for multiple comparisons using the Benjamini-Hochberg adjustment. A P-value <0.05 was considered significant for all tests. [Figure 2-3] [000185] Figure 2D shows the type and percentage of change in the number of TCR clones with the same sequence as the TCR identified in the tumor of the patient during treatment. [000186] Figure 2E shows the difference in the percentage of 4-1BB+ and CD69+ cell populations in CD8+ (top) and CD4+ (bottom) T cells using flow cytometry. * indicates significant difference using the LMM method. [Figure 2-4][000187] Figure 2F shows functional enrichment analysis of genes significantly changed in CD4+, CD8+ and CD4 / CD8low T cells comparing gene expression in pre-vaccine, priming and booster phases. [000188] Figure 2G shows functional enrichment analysis of genes significantly differentially expressed between patients with and without tumor recurrence. All enrichment analyses were performed using annotated genes from hallmark and ontology gene sets in the MSigDB database. [Figure 2-5] [000189] Figure 2H shows the change in the average expression levels of modules for IFN-γ response pathway genes and G2M checkpoint genes in CD4+, CD8+ and CD4 / CD8low T cells. [000190] Figure 2I shows the significant difference in the percentage of cells positively expressing STAT1 between patients with and without tumor recurrence after neoantigen vaccination. All pre-vaccination cell ratios of all patients were normalized to 0 (dotted horizontal lines). Lines indicate the mean values, vertical lines are error bars, SEM. [Figure 3-1] [000191] Figure 3A shows IFN-γ secretion induced by the four neo-antigenic peptides of patient no. 4 using an ELISpot assay. [Figure 3-2] [000192] Figure 3B shows the effect of tumor ablation by four neo-antigen peptides of patient P4 against autologous tumor and blank control. [000193] Figure 3C shows the Uniform Manifold Approximation and Projection (UMAP) plot showing different groups of cell populations and cells under stimulation of different neo-antigen peptides in CD8+ T cells using scRNA-seq data. [Figure 3-3][000194] Figure 3D shows the markers used to annotate the cell types of central memory cells (CM) and tumor-reactive cells (T-reactive), as well as the expression levels of marker genes (MX1 and STAT1) in these two subpopulations. [000195] Figure 3E shows the percentage of cell populations in CD8+ T cells after in vitro stimulation of these four neo-antigenic peptides using single-cell transcriptome sequencing. Red stars indicate the major subtypes (CM and T-reactive) upon stimulation of PCNAT-4-2 and PCNAT-4-3 peptides. [Diagram 3-4] [000196] Figure 3F shows the comparison of TCR clones of PCNAT-4-2 and PCNAT-4-3 stimulation to blank control using single cell TCR sequencing. If a TCR clone was not found in the blank control, the cell with that TCR was defined as "different", otherwise the cell was classified into "attenuation" or "expansion" according to the expression frequency of that TCR compared to the blank. The Y axis shows the number of cells containing the above type of TCR clone. [000197] Figure 3G shows the expression level of MX1 between pre-vaccination and post-vaccination in CD8+ T cells of patients P5 and P9. [Figure 3-5] [000198] Figure 3H shows the upregulation of the mean proportion of MX1+ cells among CD8+ T cells in the blood of patients after neoantigen vaccine treatment (P<0.05, LMM study, see methods). All pre-vaccination cell proportions of all patients were normalized to 0 (horizontal dotted line). Round dots represent patients showing tumor recurrence, and triangles represent recurrence-free patients. [000199] Figure 3I shows the correlation between MX1 expression and gene expression related to cytotoxicity and IFN-γ in relapsed and relapse-free patients. [Diagram 3-6][000200] Figure 3J shows qPCR assay to detect expression levels of MX1 after knockdown with siRNA. NC represents negative control oligonucleotide. Student's t-test was used. Bars, mean; error bars, SEM; ** indicates p<0.01. ns not significant. [000201] Figure 3K shows the percentage of tumor elimination over time for inhibition of MX1 by siRNA and negative control oligonucleotide in PBMC cells. ** indicates P<0.01. [Figure 4-1] [000202] Figure 4A shows significant differences in genes involved in CD4+, CD8+ and CD4 / CD8low T cell activation comparing boosting and priming phases in patients treated with adjuvant anti-PD1 and neo-antigen vaccines only. Red-bordered circles indicate genes significantly altered only in patients with adjuvant anti-PD1. [Figure 4-2] [000203] Figure 4B shows functional enrichment analysis of significantly changed genes in CD8+ T cells comparing gene expression during boosting and priming phases in patients treated with adjuvant anti-PD1 and neo-antigen vaccine alone. [Figure 4-3] [000204] Figure 4C shows the changes in the average expression levels of modules for response to molecules of bacterial origin functional genes and cellular response to biostimulatory functional genes in CD8+ T cells for patients with combined anti-PD1 and neo-antigen vaccine only. [000205] Figure 4D shows survival analysis of genes associated with the effect of neo-antigen vaccine combined with anti-PD1. Gene hazard ratios of genes in each gene set (columns) are shown by treatment arm (rows) of the clinical trial. Vertical lines mark hazard ratios = 1. Vertical jitter distinguishes overlapping points. Hazard ratios < 1 (left of vertical line) indicate greater PFS. Mean hazard ratios and one-sided p-values ​​are shown from one-sample z-tests with hazard ratios of genes in signatures highlighted by associated data in red when P<0.01. [Diagram 5] [000206] Figure 5A shows stacked plots showing the percentages of different immune cells at each stage of vaccination in each patient. The percentages were calculated using single cell transcriptome sequencing, and immune cell types were defined according to gene expression of known markers. All percentages were normalized to 1 for each sample. [000207] Figure 5B shows the change in relative percentages of megakaryocytes, B cells, monocytes, and NK cells after the first neoantigen vaccination. All pre-vaccination cell ratios for all patients were normalized to 0 (horizontal dotted lines). Patients with an "up" pattern were defined as having at least 3 time points showing a ratio of cell populations greater than 20% of the maximum; patients with a "down" pattern were defined as having at least 3 time points showing a ratio of cell populations less than 20% of the maximum; others were defined as "flat". * indicates significant differences (P<0.05) between priming, booster phase and pre-vaccination using the LMM method (see Methods in Supplementary Appendix) [Figure 6] [000208] Figure 6A shows the percentage of TCR clones in peripheral blood of 12 patients that are also identified in tumors. TCR clones in tumors were identified by MiXCR using tumor RNA bulk sequencing data. TCR clones in PBMCs were identified by single cell TCR sequencing. [000209] Figure 6B shows a heat map of ssGSEA scores of immune cells in tumors of 12 patients. Patients were divided into three groups (high, medium and low infiltration) based on the average score of immune cells. [Figure 7] [000210] Figure 7 shows the identification of differences in immune cell populations between pre- and post-vaccination using flow cytometry analysis. Red is non-relapse patients and blue is relapse patients. * indicates significant difference (p<0.05). [Figure 8][000211] Figure 8 shows differential expression of genes associated with T cell activation in patients' blood for priming phase vs. pre-vaccine phase (top) and boosting phase vs. pre-vaccine phase (bottom). Differential expression was performed in CD4+, CD8+ and CD4 / CD8low T cells, respectively. Significantly differentially expressed genes are shown as dots in the plot. For each gene, the mean log fold change and the percentage of cells expressing the gene above background were compared between the two phases. For example, a delta percent of +0.1 indicates that 10% more cells in the priming phase than pre-vaccine express the gene above background. The top 20 altered genes for each function (cytokine, cytotoxicity, IFN response and proliferation) are labeled by their gene name. [Figure 9] [000212] Figure 9A shows gene set enrichment analysis (GSEA) of the interferon gamma response pathway of significantly altered genes in CD8+ T cells. [000213] Figure 9B shows changes in the mean expression levels of modules of IFN-gamma response pathway genes and G2M checkpoint genes in T cells for relapsed and non-relapsed patients. [Figure 10][000214] Figure 10A shows differential expression of genes associated with T cell (top) and antigen presenting cell (bottom) activity in non-relapse vs. relapse patients. Differential expression was performed in pre-vaccine, priming and boosting phases, and in CD4+, CD8+ and CD4 / CD8low T cells, respectively. Significantly differentially expressed genes are shown as dots in the plot. For each gene, the mean log fold change and the percentage of cells expressing the gene above background were compared between the two phases. For example, a delta percent of +0.1 indicates that 10% more cells in the priming phase express the gene above background than in pre-vaccine. The top 20 changed genes in each T cell subpopulation are labeled by their gene name. [000215] Figure 10B shows differential expression of GSVA score of STAT1+ T cells between relapse and non-relapse patients. [Figure 11] [000216] Figure 11 shows ex vivo IFN-γ ELISPOT of PBMCs for each single nano-antigen peptide used in patient vaccinations with three wells per time point, normalized spot counts were calculated using the number of spots formed upon peptide stimulation minus the number of corresponding blank controls. [Figure 12] [000217] Figure 12A shows the intersection of TReactive and CM marker genes. [000218] Figure 12B shows the heatmap of the number of significantly changed genes overlapping with TReactive and CM marker genes in priming and booster phases, respectively, compared to pre-vaccination. [000219] Figure 12C shows the change in the average expression levels of modules of TReactive and CM gene signatures in CD4+, CD8+ and CD4 / CD8low T cells during vaccination. [000220] Figure 12D shows the comparison of the average expression levels of modules of TReactive and CM gene signatures between relapse and non-relapse patients during vaccination. [Figure 13] [000221] Figure 13A shows UMAP plots showing cell populations and expression levels of IFN-γ response pathway-related genes under stimulation of neoantigen peptides of patient P4 in CD8+ T cells using scRNA-seq data. [000222] Figure 13B shows upregulation of the average ratio of corresponding genes in CD8+ T cells in patients after neoantigen vaccine treatment (P<0.05, LMM study, see Methods). All cell ratios before vaccination of all patients were normalized to 0 (horizontal dotted line). Round dots represent patients with tumor recurrence and triangles represent patients without recurrence. [Figure 14] [000223] Figure 14A shows the percentage of cell populations in CD4+ (left) and CD4 / CD8low T cells (right) after in vitro stimulation with neo-antigen peptides. [000224] Figure 14B shows UMAP plots showing expression levels of marker genes of cell populations, different groups of cells, and expanded cell populations under stimulation with PCNAT-4-2 and PCNAT-4-3 in CD4+ (B) using scRNA-seq data. Figure 14C shows UMAP plots showing expression levels of marker genes of cell populations, different groups of cells, and expanded cell populations under stimulation with PCNAT-4-2 and PCNAT-4-3 in CD4 / CD8low T cells (C) using scRNA-seq data. [Figure 15] [000225] Figure 15A shows the average ratio of marker genes identified from in vitro stimulation of neo-antigen peptides in CD4+ (A) in blood of patients during neo-antigen vaccine treatment. All pre-vaccination cell ratios of all patients were normalized to 0 (horizontal dotted line). Round dots represent patients with tumor recurrence and triangles represent patients without recurrence. Figure 15B shows the average ratio of marker genes identified from in vitro stimulation of neo-antigen peptides in CD4 / CD8low T cells (B) in blood of patients during neo-antigen vaccine treatment. All pre-vaccination cell ratios of all patients were normalized to 0 (horizontal dotted line). Round dots represent patients with tumor recurrence and triangles represent patients without recurrence. [Figure 16] [000226] Figure 16A is a heatmap showing genes with top 50 correlation coefficients with MX1 in immune cells. The annotations on the left are the cell type of immune cells and the gene functions involved, including antigen presentation, immune checkpoint, ligand / receptor, molecular function of MHC class I / II protein complex, T cell activation and molecular function of Toll-like receptor, respectively. [000227] Figure 16B is a diagram showing the correlation of CD40 and CD226 with MX1 in CD8+ T cells. [000228] Figure 16C is a diagram showing the change in the ratio of CD40+ and CD226+ cells in CD8+ T cells during vaccination. [000229] Figure 16D is a diagram showing the gene interaction network in different types of immune cells for genes correlated with MX1 in CD8+ T cells. [Figure 17] [000230] Figure 17 shows the sequences of MX1 and PPP1R15A. [Figure 18-1][000231] Figure 18A shows the overall experimental design. Mice were first divided into normal and Sephin1 groups and injected with vehicle or Sephin1 for 2 weeks. Then, B16F1 cells were injected. PBMCs were collected on days 0 and 15 after tumor injection, and immune cells were isolated from tumor tissues on day 15 and subjected to single cell sequencing. [000232] Figure 18B shows the tumor growth curves of normal and Sephin1 groups (n=8). The tumor volume of the Sephin1 group was significantly higher than that of the normal group. Each row was analyzed individually using an unadjusted multiple t-test. Bars: mean; error bars: SEM; *: p<0.05. [000233] Figure 18C shows the tumor weights of the two groups (n=8) on day 15. The tumor weight of the Sephin1 group was significantly higher. An unpaired, two-tailed t-test was used. Bars: mean; Error bars: SEM; **: p<0.01. [000234] Figure 18D shows SCENIC analysis based on single cell sequencing data of different samples. Atf3 regulon was highly active in Sephin1 group in all three sample types. Number of genes in each regulon is shown in brackets. [000235] Figure 18E shows tumor images from normal (top) and Sephin1 (bottom) groups. [Figure 18-2] [000236] Figure 18F shows cell type annotation for all 12 samples. 16 cell types were identified. [000237] Figure 18G shows cell distribution for all 6 sample types (2 samples of each type). [Figure 18-3] [000238] Figure 18H shows the distribution of major cell types in different sample types. Day 0_Blood - PBMCs in blood samples collected on day 0. Day 15_Blood - PBMCs in blood samples collected on day 15. Day 15_Tumor - Cd45+ immune cells in tumor tissue collected on day 15. Chi-square test was used to evaluate the distribution of normal / Sephin1 groups. *: p<0.05; **: p<0.01; ***: p<0.001; ****: p<0.0001. [Figure 19-1][000239] Figure 19A is a UMAP plot showing detailed cell type annotation of lymphocytes. NK - NK cells. NKT - NKT cells. Exhausted_Cd8 - exhausted Cd8+ T cells. Cyt_Cd8 - cytotoxic Cd8+ T cells. Naive_Cd8 - naive Cd8+ T cells. Effector_Cd4 - effector Cd4+ T cells. Treg_Cd4 - regulatory T cells. Naive_Cd4 - naive Cd4+ T cells. [000240] Figure 19B is a diagram showing genetic markers of different types of lymphocytes. Klrb1C - NK cells. Cd3d - T cells. Cd4 - Cd4+ T cells. Cd8a - Cd8+ T cells. Gzmb - cytotoxic T cells. Foxp3 - regulatory T cells. Pdcd1, Havcr2, Lag3-depleted T cells. Cd44-Sell(Cd62L)+-naive T cells. Cd44+Sell(Cd62L)-effector T cells. [Figure 19-2] [000241] Figure 19C shows the distribution of lymphocyte types. The percentages of NK cells and exhausted Cd8+ T cells in the Sephin1 group were significantly decreased compared with those in the normal group. The percentages of NKT cells and cytotoxic Cd8+ T cells were significantly decreased in blood and tumor tissues on day 15, but not in blood on day 0. The distribution of normal / Sephin1 groups was evaluated using chi-square test. *: p<0.05; **: p<0.01; ***: p<0.001; ****: p<0.0001. [Figure 19-3] [000242] Figure 19D shows the percentage changes of Cd4+ T cells, Cd8+ T cells, NK cells, regulatory T cells, exhausted Cd8+ T cells, and activated Cd8+ T cells in all immune cells determined by FACS (n=8). The p-values ​​are marked on the graph using Wilcoxon test without adjustment for each cell type. [Figure 20-1] [000243] Figure 20A shows GSEA of Cd8+ T cells in tumor tissue. Right: pathways upregulated in the Sephin1 group. Left: pathways downregulated in the Sephin1 group. Pink / Blue: pathways significantly up / downregulated in the Sephin1 group. Grey: pathways not significant in the Sephin1 group. [Figure 20-2] [000244] Figure 20B shows the expression scores of cytotoxicity-related genes in Cd8+ T cells. The expression scores were significantly lower in tumor tissues of the Sephin1 group. p-values ​​were calculated by Wilcoxon test and adjusted by Holm-Bonferroni method. [000245] Figure 20C shows the expression scores of genes related to the positive regulation of Cd8+ cell cytotoxicity. The expression scores are significantly downregulated in the Sephin1 group in blood samples and tumor tissues collected on day 0 and day 15. p-values ​​were calculated by Wilcoxon test and adjusted by Holm-Bonferroni method. [Figure 20-3] [000246] Figure 20D shows the expression scores of genes related to the positive regulation of NK cell activity. For blood and tumor tissue samples collected on day 15, all expression scores in the Sephin1 group were significantly lower than those in the normal group. p values ​​were calculated by Wilcoxon test and adjusted by Holm-Bonferroni method. [000247] Figure 20E shows the expression scores of genes related to NK cell activity. For blood and tumor tissue samples collected on day 15, the scores were significantly lower in the Sephin1 group. p values ​​were calculated by Wilcoxon test and adjusted by Holm-Bonferroni method. [Figure 20-4] [000248] Figure 20F shows the SCENIC analysis of different lymphocyte subtypes between normal and Sephin1 groups. The activity of Atf3 regulon was upregulated in exhausted Cd8+ T cells, cytotoxic Cd8+ T cells, NK cells and NKT cells, but downregulated in regulatory T cells. [Figure 21-1][000249] Figure 21A shows the distribution of different TCR clonotypes based on clonotype frequency: special expanded, large, medium and small (high to low). Clonotypes in the Sephin1 group tend to show low frequency. [000250] Figure 21B shows the distribution of cells belonging to different TCR types. The percentage of cells with high TCR frequency, including special expanded and large TCR types, was significantly lower in the Sephin1 group for all three sample types. The distribution of the normal / Sephin1 group was evaluated using chi-square test. *: p<0.05; **: p<0.01; ***: p<0.001; ****: p<0.0001. [000251] Figure 21C shows the UMAP plot of the distribution of all four TCR types. [Figure 21-2] [000252] Figure 21D shows the UMAP plot of TCR types divided by groups. All cells belonging to the special expansion TCR type were in the normal group. [000253] Figure 21E shows the distribution of the four TCR types in different cell types. In addition to T cells, macrophages also showed special expansion, large and small populations. [Figure 21-3] [000254] Figure 21F shows genes that were differentially expressed in different TCR types. The special expanded type had high expression activity and also had significantly higher expression levels of cytotoxicity-related genes such as Gzmb and Gzmk. [Figure 21-4] [000255] Figure 21G shows GSVA of all four TCR types. Enrichment analysis was performed based on GO biological process database. The top 5 most enriched pathways for each cell type are shown. [000256] Figure 21H shows GSEA of specially expanded types. NES-normalized enrichment scores. Significance was calculated as -log10(P). [Figure 22-1] [000257] Figure 22A shows macrophage subtypes named by significantly over-expressed genes in each cluster. Nine subtypes were identified. [000258] Figure 22B shows gene markers for each macrophage subtype. [Figure 22-2][000259] Figure 22C shows the distribution of macrophage subtypes. Chil3+ and Hcar2+ macrophages were mainly present in the Sephin1 group. Chi-square test was used to evaluate the distribution of normal / Sephin1 groups. *: p<0.05; **: p<0.01; ***: p<0.001; ****: p<0.0001. [000260] Figure 22D shows the distribution of macrophage subtypes in different tissues. Chil3+, Fn1+ and Ace+ macrophages were mainly present in blood, and other subtypes were mainly present in tumor tissues. [Figure 22-3] [000261] Figure 22E shows the characteristics of M1-M2 polarization pattern of all macrophages. M1_to_M2 score was calculated by subtracting M2 expression score from M1 expression score. High M1_to_M2 score indicated that cells tended to show M1 polarization. In Sephin1 group, macrophages in blood and tumor tissues collected on day 15 tended to show M2 polarization over M1 polarization. p value was calculated by Wilcoxon test and adjusted by Holm-Bonferroni method. [000262] Figure 22F shows the M1_to_M2 score of macrophage subtypes. Subtypes mainly present in blood, including Ace+, Chil3+ and Fn1+ macrophages, were not significantly different between normal and Sephin1 groups. Subtypes within the tumor tissue tended to show M2 polarization in the Sephin1 group, except for Retnla+ and Hcar2+ macrophages, each of which contained a small number of cells. p values ​​were calculated by Wilcoxon test and adjusted by the Holm-Bonferroni method. [Figure 23-1] [000263] Figure 23A shows the distribution of TCR+ macrophages (TCR_Mac) and other conventional macrophages (Conventional_Mac). [000264] Figure 23B shows the expression of T cell and macrophage gene markers within TCR+ macrophages. [Figure 23-2][000265] Figure 23C shows GSEA of differentially expressed genes between TCR+ and conventional macrophages in tumors. Upregulated genes were enriched in pathways related to T cell activity. [000266] Figure 23D shows the distribution of different TCR types in macrophage subtypes. TCR+ macrophages were most enriched in Fscn1+ macrophages. [Figure 23-3] [000267] Figure 23E shows the M1_to_M2 scores of conventional and TCR+ macrophages in tumor samples. TCR+ macrophages tend to be more M1 polarized than conventional macrophages in normal group and are significantly affected by Sephin1. p-values ​​were calculated by Wilcoxon test and adjusted by Holm-Bonferroni method. [000268] Figure 23F shows the distribution of different TCR types in macrophages. The percentage of highly expanded TCR types, i.e., special expanded and large TCR types, was downregulated in Sephin1 group. The distribution of normal / Sephin1 group was evaluated using chi-square test. *: p<0.05; **: p<0.01; ***: p<0.001; ****: p<0.0001. [Figure 23-4] [000269] Figures 23G-23H show the distribution of macrophages that share TCR with T cells in the tumor microenvironment. Macrophages in the Sephin1 group had a lower percentage of TCR shared with Cd8+ T cells. Figures 23G-23H show the distribution of macrophages that share TCR with T cells in the tumor microenvironment. Macrophages in the Sephin1 group had a lower percentage of TCR shared with Cd8+ T cells. [Figure 24-1] [000270] Figure 24A shows the differentiated communication strength between normal and Sephin1 groups in different tissues. Red: upregulated in Sephin1 group. Blue: downregulated in Sephin1 group. All three tissue types tended to be downregulated in Sephin1 group, especially in tumors. [Figure 24-2][000271] Figure 24B shows the mostly differentiated communication pathways between Cd8+ T cells and NK cells that were downregulated in the Sephin1 group in all three tissue types, including NK-NK, Cd8-Cd8, NK-Cd8 and Cd8-NK communication. [000272] Figure 24C shows the ligand-receptor pairs of mostly downregulated pathways in tumor tissues, including MHC-I, LCK and SELPLG pathways. [Figure 24-3] [000273] Figure 24D shows the most differentiated communication pathways between CD4+ T cells and macrophages that were upregulated in the Sephin1 group in all three tissue types. [000274] Figure 24E shows the ligand-receptor pairs of the most upregulated pathways in tumor tissues, including FN1, GALECTIN, SPP1, MHC-I, THBS, TGFb, APP, THY1, TNF and CSF pathways. [Figure 25-1] [000275] Figure 25A shows the original Seurat clusters of all samples. [000276] Figure 25B shows the cell type specific regulators calculated based on the regulon specificity score (RSS). [Figure 25-2] [000277] FIG. 25C shows the UMAP plot of all samples separated by sample type. [Figure 25-3] [000278] Figure 25D shows GSVA of differentially expressed genes between samples. [000279] Figure 25E shows enriched genes from the Atf3 regulon of the Sephin1 group in blood on days 0 and 15 or in tumor tissue on day 15. [Figure 26-1] [000280] FIG. 26A shows the percentage of different cell types in different sample types. [Figure 26-2][000281] Figure 26B shows gene markers used for cell type annotation: Ptprc-immune cells; Cd3d, Cd4, Cd8a-T cells; Cd79a-B cells; Klrb1c-NK cells; S100a8-granulocytes; Kit-mast cells; Cd200r3-basophils; Cd68, Csflr, Adgre1-macrophages; H2-DMb1, Cd83, Siglech-DCs. [Figure 27-1] [000282] Figure 27A shows a heatmap of gene expression generated using AddModuleScore. [Figure 27-2] [000283] Figure 27B shows the expression patterns of two cell cycle related genes of lymphocyte subtypes. [000284] Figure 27C shows the expression scores of regulatory differentiation related genes in Cd4+ T cells calculated by AddModuleScore. p-values ​​were calculated by Wilcoxon test and adjusted by Holm-Bonferroni method. [Figure 27-3] [000285] Figure 27D shows the GSEA of the most differentially expressed genes between normal and Sephin1 groups in NK cells. Genes in pathways related to cytokine signaling and antigen processing were downregulated. Genes related to cellular response to stress were upregulated, indicating activation of the ISR process. [000286] Figure 27E shows the expression levels of regulatory differentiation-related genes mentioned in Figure 27C. [Figure 28] [000287] Figures 28A-28G show the FACS results of anti-tumor associated lymphocytes in mouse tumor tissues, including Cd4+ and Cd8+ T cells (A&B), NK cells (C), Cd4+ regular T cells (E), exhausted Cd8+ T cells (F) and activated Cd8+ T cells (G). [Figure 29][000288] Figure 29A shows the FACS results of IFNG and CFSE. Top: percentage of IFNG+ cells in Cd8+ T cells. Bottom: percentage of proliferating cells in Cd8+ T cells. Sephin1: Cd8+ T cells supplemented with 20μg / mL Sephin1 and 0.2% DMSO. Control-DMSO: Cd8+ T cells treated with 0.2% DMSO. Control: Cd8+ T cells without Sephin1 or DMSO treatment. [000289] Figure 29B shows the statistical analysis of the results of Sephin1, Control-DMSO, and Control groups. Cd8+ T cells treated with Sephin1 showed significantly lower expression of IFNG and lower proliferation capacity (n=3). Multiple t-test was used unadjusted. Bars: mean; Error bars: SEM; **: p<0.01; ***: p<0.001. [Figure 30-1] [000290] Figure 30A shows the distribution of unique clonotypes. The percentage of non-unique clonotypes in the tumor microenvironment was lower than in blood, and tumors in the Sephin1 group had more unique TCR clonotypes than normals. [000291] Figure 30B shows the distribution of clonal ratios in different sample types. Clonotypes in different samples were ranked by clone number and placed in different containers. Tissues in the Sephin1 group were more enriched in low ranking clonotypes. [Figure 30-2] [000292] Figure 30C shows the diversity analysis of TCR clonotypes in different sample types. Shannon index, inverse Simpson index, Chao1 estimator and species number estimator (ACE (abundance-based coverage estimator)) were used for the analysis. Samples in the Sephin1 group had high TCR richness, and PBMCs had higher TCR richness than immune cells in tumors. [Figure 30-3] [000293] Figure 30D shows the distribution of different TCR clonotypes among samples and groups. Different samples had various TCR clonotype distribution patterns. [Figure 31-1][000294] Figure 31A shows Seurat cluster distribution of macrophages. [000295] Figure 31B shows expression patterns of M1- and M2-related genes in different sample types. Macrophages in immune cells within tumors of Sephin1 group were more likely to express M2-related genes. [000296] Figure 31C shows GSVA of differentially expressed genes in macrophage subtypes. [Figure 31-2] [000297] Figure 31D shows GSEA of macrophages among immune cells in tumors between normal and Sephin1 groups. Genes in pathways related to T cell activation were downregulated in the Sephin1 group. [Figure 32-1] [000298] Figure 32A shows the most highly expressed genes in macrophage subtypes. The top 10 genes for each subgroup are shown. [000299] Figure 32B shows the expression levels of M1 and M2-related genes in TCR+ and conventional macrophages of tumor tissue. [Figure 32-2] [000300] Figure 32C shows SCENIC analysis of macrophages in sample types. Atf3 regulon was upregulated in Sephin1 group in day 0_blood and day 15_tumor samples. [000301] Figure 32D shows GSVA of different TCR types in TCR+ macrophages. Specially expanded clusters were more enriched in pathways related to positive regulation of T cells, mitotic chromosome condensation, cholesterol biosynthesis process, microtubule-based movement, and double-strand break repair by homologous recombination. [Figure 32-3] [000302] Figure 32E shows the distribution of TCR+ and conventional macrophages in normal and Sephin1 groups. [Diagram 33][000303] Figure 33A shows the percentage of macrophages in all myeloid cells and TCR+ macrophages in all macrophages in the tumor microenvironment determined by FACS (n=8). p-values ​​were marked on the graph using Wilcoxon test without adjustment for each cell type. [000304] Figures 33B-33C show FACS results of macrophages and TCR+ macrophages. [000305] Figures 33D-33E show FACS results of TCR+ macrophages in spleen tissue of normal mice. [000306] Figure 33F shows the distribution of macrophages sharing TCR with T cells in different samples. [Diagram 34] [000307] Figure 34A shows the ligand-receptor pairs of the pathways that were mostly downregulated in the blood Sephin1 group on days 0 and 15 between Cd8+ T cells and NK cells. [000308] Figure 34B shows the ligand-receptor pairs of the pathways that were mostly upregulated in the blood Sephin1 group on days 0 and 15 between Cd4+ T cells and macrophages. [000309] Figure 34C shows the tumor growth of 4T1 cells in female BALB / c mice (n=8). Each row was analyzed individually using an unadjusted multiple t-test. Bars: mean; error bars: SEM; *: p<0.05. The tumor growth rate of the Sephin1 group was higher than that of the normal group. However, the results were not as significant as B16F1 cells in male C57BL / 6 mice. [Diagram 35] [000310] Figures 35A-35F show the results of immunofluorescence of ligand-receptor expression in macrophages of tumor tissues at day 15. (A) Merged results of DAPI, F4 / 80, CD44, FN1 and SPP1, 50 μm. (B) Merged and separated results of each molecule, 20 μm. [Figure 36-1] [000311] Figure 36 shows genes associated with significant differential changes in immune responses in peripheral blood of pancreatic cancer patients treated with personalized neoantigen vaccines. [Figure 36-2] Same as above. [Figure 36-3]Same as above. [Figure 37] [000312] Figure 37 shows the percentage change of CD69+ T cells, CD28+ T cells, B cells and NK cells in the blood of patients during vaccination compared to baseline using flow cytometry analysis. P6, P9, P11, P12 and P10 used the flow cytometry analysis results of the blood sample before vaccination (B1) as the baseline, while P1, P2, P4, P7 and P8 used the flow analysis results of the earliest blood sample, respectively, as the baseline due to missing results of the B1 sample. P5 was excluded from this analysis because it only had one flow cytometry result from a blood sample. CD69 and CD28 are markers of T cell activation. CD19 is a marker of B cells. Lymphocytes were sorted by low side scatter (SSC) and CD45+ in flow cytometry analysis. NK cells were sorted by lymphocyte CD19- and CD3-. The mean ± SE % of CD69+CD8+ T cells increased from 20.4 ± 1.4 to 34.6 ± 6.3; CD69+CD4+ T cells increased from 18.3 ± 3.4 to 34 ± 3.5; CD28+CD8+ T cells increased from 39.9 ± 8.6 to 56.5 ± 10.7; CD28+CD4+ T cells increased from 86.7 ± 4.7 to 96.5 ± 1.2; B cells increased from 3.8 ± 1 to 9.6 ± 2.2; and NK cells increased from 20.7 ± 2.7 to 25.9 ± 3. [Figure 38][000313] Figure 38A shows the dynamics of immune cell ratios in peripheral blood during neo-antigen peptide vaccine treatment. A, Time points of blood collection for neo-antigen vaccination and single cell sequencing. Blood samples were obtained a few minutes before administration of the respective vaccine. Figure 38B shows the dynamics of immune cell ratios in peripheral blood during neo-antigen peptide vaccine treatment. B, Dot plots showing the level of significance and directionality of the difference comparing pre-vaccine (as reference) with each time point (columns) in labeled immune cells (rows): monocytes, macrophages, B cells, NK, T cells (rows 1-5) and their subtypes (rows 6-34). Row labels indicate positively expressed gene markers for each subtype. Red / blue dots indicate high / low levels of cell percent change compared to B1 (pre-vaccine); darker intensity reflects larger change; dot size reflects strength of change; white background indicates p<0.05. With single-cell transcriptome data, we classified PBMCs into several immune cell types and their subtypes according to differentially expressed genes and calculated their respective proportions. [Figure 39] [000314] Figure 39 shows a bar graph showing the percent of clonally expanded T cells compared to pre-vaccine and the percent of VRD- and GD-T cells in each T cell subtype. The bottom bar graph gives the average expression levels of CD8 and CD4 genes for each subtype. [Diagram 40][000315] Figure 40 shows the cell abundance (%) of T cell clonotypes in blood samples of patient P6 before vaccination (circle shape), priming vaccination (square shape) and boosting vaccination (triangle shape) with and without post-vaccination clonal expansion (upper and lower panels). Coloring indicates that neo-antigen peptides are specifically recognized by T cell clonotypes. We constructed a total of five peptide-MHC (HLA-A*11:01) tetramers corresponding to two TCR recognition epitopes of neo-antigen peptide-85 (YVECGKAFK and KYVECGKAFK) and three recognition epitopes of neo-antigen peptide-89 (TTSCPECDK, TSCPECDKTSLK and GTTSCPECDK) in patient P6. No tetramer-positive T cell clones targeting neo-antigen peptide-85-epitope 1 were detected. [Diagram 41] [000316] Figure 41 shows bar graphs showing the percent of clonally expanded B cells compared to pre-vaccine and the percent of clonal (>1 cell) B cells in each B cell subtype. The bottom bar graph shows the mean expression levels of TCL1A, AIM2 and IGHA1 genes for each subtype. Clonal expansions were classified according to time of occurrence as: 1) transient expansions, where the percentage of cells at prime was higher than pre-vaccine but lower than pre-vaccine at boosting; 2) priming expansions, where the percentage of cells at both prime and boosting was higher than pre-vaccine; and 3) boosting expansions, where the percentage of cells at prime was lower than pre-vaccine but higher than pre-vaccine at boosting. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0172] [000317] The following description of the present disclosure is intended to merely illustrate various embodiments of the present disclosure. Therefore, the specific modifications discussed are not to be construed as limitations on the scope of the present disclosure. It is clear to those skilled in the art that various equivalents, modifications, and alterations can be made without departing from the scope of the present disclosure, and such equivalent embodiments are understood to be included herein. All references cited herein, including papers, patents, and patent specifications, are incorporated herein by reference in their entirety.

[0173] [000318]Definition [000319] As used herein, the term "neoantigens" or "neoantigenic" refers to a class of tumor antigens that arise from tumor-specific mutations that alter the amino acid sequence of the genome that codes for the protein.

[0174] [000320] As used herein, the terms "prevent," "preventing," "prevention," "prophylactic treatment," and the like refer to reducing the likelihood of onset of a disease or condition in a subject who does not have the disease or condition but is at risk of or susceptible to developing the disease or condition.

[0175] [000321] As used herein, "treating" or "treatment" of a condition includes alleviating the condition, slowing the onset or rate of occurrence of the condition, reducing the risk of developing the condition, preventing or slowing the onset of symptoms associated with the condition, reducing or terminating symptoms associated with the condition, producing complete or partial regression of the condition, curing the condition, or some combination thereof.

[0176] [000322] As used herein, the term "subject" refers to a human or any non-human animal or mammal (e.g., a mouse, rat, rabbit, dog, cat, cow, pig, sheep, horse, or primate). In many embodiments, the subject is a human. A subject may be a patient, and refers to a human who visits a health care provider for diagnosis or treatment of a disease. The term "subject" is used interchangeably herein with "individual" or "patient." A subject may be afflicted by or susceptible to a disease or disorder, but may or may not exhibit symptoms of the disease or disorder.

[0177] [000323] The terms "administer", "administering" or "administration" include any method of delivery of a pharmaceutical composition or agent to a system of a subject or to a particular area in or on a subject. In certain embodiments, the agent is delivered orally or parenterally. In certain embodiments, the agent is delivered by injection or infusion or delivered locally, including transmucosally. In certain embodiments, the agent is delivered by inhalation. In certain embodiments of the invention, the agent is administered by parenteral delivery, including intravenous, intramuscular, subcutaneous, intramedullary, as well as intrathecal, direct intraventricular, intraperitoneal, intranasal, or intraocular injections. In one embodiment, the agent may be administered by direct injection into a tumor. In some embodiments, the agent may be administered by intravenous injection or intravenous infusion. In certain embodiments, the agent may be administered by continuous infusion. In certain embodiments, the administration is not oral. In certain embodiments, the administration is systemic. In certain embodiments, the administration is local. In some embodiments, one or more routes of administration may be combined, such as intravenous and intratumoral, or intravenous and peroral, or intravenous and oral, or intravenous and topical, or intravenous and transdermal or transmucosal. Administering an agent may be performed by many people working together. Administering an agent may include, for example, prescribing an agent to be administered to a subject and / or providing instructions for taking a particular agent, either directly or through another person, for self-delivery, e.g., oral delivery, subcutaneous delivery, intravenous delivery via a central line, etc., or for delivery by a trained professional, e.g., intravenous delivery, intramuscular delivery, intratumoral delivery, continuous infusion, etc.

[0178] [000324] The term "therapeutically effective amount" or "effective amount" refers to that amount of a pharmaceutical agent that produces some desired local or systemic therapeutic effect at a reasonable benefit / risk ratio applicable to any treatment. When administered to prevent a disease, the amount is sufficient to avoid or delay the onset of the disease. A therapeutically effective amount or effective amount need not cure or prevent a disease or condition from all occurrences. In certain embodiments, the therapeutically effective amount of a pharmaceutical agent is expected depending on its therapeutic index, solubility, and the like.

[0179] [000325] With respect to biomarkers (e.g., MX1 and PPP1R15A, as well as other biomarkers provided herein), the term "level" refers to the amount or quantity of the biomarker of interest present in a sample. Such amount or quantity can be expressed in absolute terms, i.e., the total quantity of the biomarker in the sample, or in relative terms, i.e., the concentration or percentage of the biomarker in the sample. The level of a biomarker can be measured at the DNA level (e.g., expressed by the amount or quantity or copy number of a gene in a chromosomal region), the RNA level (e.g., mRNA amount or quantity), or the protein level (e.g., protein or protein complex amount or quantity).

[0180] [000326] With respect to a biomarker (eg, MX1 and PPP1R15A, as well as other biomarkers provided herein), the term "expression level" refers to the amount or quantity of the biomarker expressed, such as the mRNA level or protein level.

[0181] [000327] The terms "determine", "measure" and "detect" are used interchangeably and can refer to both quantitative and semi-quantitative measurements. The level (e.g., expression level) of a biomarker (e.g., MX1 and PPP1R15A and other biomarkers provided herein) at the DNA or RNA level can be measured by any method known in the art, including but not limited to, amplification assay, hybridization assay, or sequencing assay. The expression level of a biomarker at the protein level can be measured by any method known in the art, including but not limited to, immunoassay.

[0182] [000328] Nucleic acid amplification assays involve copying a target nucleic acid (e.g., DNA or RNA), thereby increasing the number of copies of the amplified nucleic acid sequence. Amplification can be exponential or linear. Exemplary nucleic acid amplification methods include, but are not limited to, amplification using polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), quantitative real-time PCR (qRT-PCR), quantitative PCR, e.g., TaqMan®, nested PCR, and the like.

[0183] [000329] Nucleic acid hybridization assays use probes that hybridize to a target nucleic acid, thereby allowing detection of the target nucleic acid. Non-limiting examples of hybridization assays include Northern blotting, Southern blotting, in situ hybridization, microarray analysis, and multiplex hybridization assays.

[0184] [000330] Sequencing method allows the determination of the nucleic acid sequence of target nucleic acid, and also allows the enumeration of sequenced target nucleic acid, thereby measuring the level of target nucleic acid.Examples of sequencing method include, but are not limited to, RNA sequencing, pyrosequencing, high-throughput sequencing, and single-cell sequencing.

[0185] [000331] Immunoassays typically involve detecting or measuring the presence or level of a target polypeptide or protein using an antibody that specifically binds to a biomarker polypeptide or protein (e.g., MX1 and PPP1R15A and other biomarkers provided herein). Such antibodies can be obtained using methods known in the art or can be obtained commercially. Examples of immunoassays include, but are not limited to, Western blotting, enzyme-linked immunosorbent assay (ELISA), enzyme immunoassay (EIA), radioimmunoassay (RIA), sandwich assay, competitive assay, immunofluorescence staining and imaging, immunohistochemistry (IHC), and fluorescence-activated cell sorting (FACS).

[0186] [000332] In all instances herein where there is a series of recited numerical values, it is understood that any recited numerical value may be the upper or lower limit of a numerical range. It is further understood that the present invention encompasses all such numerical ranges, i.e., ranges having a combination of upper and lower numerical limits, each of the upper and lower numerical limits may be any numerical value recited herein. Ranges provided herein are understood to include all values ​​within the range. For example, 1-10 is understood to include all values ​​1, 2, 3, 4, 5, 6, 7, 8, 9, and 10, as well as fractional values, where appropriate. Similarly, a range delimited by "at least" is understood to include the lower value provided and all higher numerical values.

[0187] [000333] As used herein, "about" is understood to include within 3 standard deviations of the mean, or within the standard range of tolerance in a particular technical field. In certain embodiments, about is understood to vary by no more than 0.5.

[0188] [000334] The articles "a" and "an" are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, "an element" means one element or more than one element.

[0189] [000335] The term "including" is used herein to mean the phrase "including but not limited to," and is used interchangeably with the phrase "including but not limited to." Similarly, "such as" is used herein to mean the phrase "such as but not limited to," and is used interchangeably with the phrase "such as but not limited to."

[0190] [000336] The term "or" is used inclusively herein to mean, and is used interchangeably with, the term "and / or," unless context clearly indicates otherwise.

[0191] [000337] Part I: MX1 and PPP1R15A [000338] I. Overview [000339] The present invention is based at least in part on the discovery of the role of MX1 and PPP1R15A in the immune system and cell-based immunity. Thus, methods of use are provided herein that include regulating MX1 or PPP1R15A.

[0192] [000340] MX1 and PPP1R15A are identified using single cell sequencing (sc-Seq) to improve immune response after administration of neo-antigen tumor vaccine.The advantage of using single cell sequencing (sc-Seq) as efficacy monitor is that it is not limited to known immune cell markers, but can help obtain new markers that are specific and directly related to vaccines.

[0193] [000341] MX1 is MX dynamin-like GTPase 1. The term "MX1" as used herein refers to the MX1 gene and MX1 gene products, such as the mRNA of the MX1 gene and the protein encoded by the MX1 gene. It is intended to include fragments, variants and derivatives thereof. The human MX1 gene is located on chromosome 21 (21:41,420,329-41,459,214, 21q22.3 by Genome Reference Consortium Human Build38 patch release 13). It has GeneID 4599 in the NCBI database (sequences are incorporated herein as SEQ ID NOs: 1-4).

[0194] [000342] MX1 encodes a guanosine triphosphate (GTP)-metabolizing protein that participates in the cellular antiviral response. The encoded protein is induced by type I and type II interferons and antagonizes the replication process of several different RNA and DNA viruses. There is a related gene located adjacent to this gene on chromosome 21, and multiple pseudogenes located in a cluster on chromosome 4. Alternative splicing results in multiple transcript variants.

[0195] [000343] PPP1R15A is protein phosphatase 1 regulatory subunit 15A, and as used herein, refers to the PPP1R15A gene and PPP1R15A gene products, such as the mRNA of the PPP1R15A gene and the protein encoded by the PPP1R15A gene. It is intended to include fragments, variants and derivatives thereof. The human PPP1R15A gene is located on chromosome 19 (19:48872421-48876058, 19q13.33 by Genome Reference Consortium Human Build38 patch release 13). It has GeneID23645 in the NCBI database (the sequence is incorporated herein as SEQ ID NO:5).

[0196] [000344] PPP1R15A is a member of a group of genes whose transcription levels are increased under stressful growth arrest conditions and after treatment with DNA damaging agents. Induction of PPP1R15A by ionizing radiation occurs in certain cell lines regardless of p53 status, and its protein response correlates with apoptosis after ionizing radiation.

[0197] [000345] In one embodiment, a method of use is provided that comprises administering an MX1 agonist or a PPP1R15A agonist.

[0198] [000346] As used herein, the term "agonist" refers to an agent that increases (e.g., agonizes, increases, elevates, improves, or enhances) the biological effect of a target molecule (e.g., MX1 or PPP1R15A). The activating effect can be exerted, for example, by activating or increasing other molecules in a signal transduction pathway (e.g., upstream signal transduction molecules or downstream signal transduction molecules), for example, by increasing the amount of the target molecule, or by enhancing the activity of the target molecule, or by enhancing the activity of the signal transduction pathway of the target molecule. Such agonists can include any compound, protein, or nucleic acid, or any derivative thereof, that provides agonistic effect.

[0199] [000347] MX1 agonists can increase the expression and / or function of MX1, which function includes the ability of MX1 to bind to its target. The types of agents that can act as MX1 agonists are known to those skilled in the art. For example, the agonist can increase MX1 expression at the nucleic acid (e.g., mRNA) level or protein level. In another example, the MX1 agonist can be an agent that activates MX1 or an MX1 target. In some embodiments, the MX1 agonist is a nucleic acid molecule, a protein molecule, a compound, etc. The nucleic acid molecule can be selected from an mRNA that codes for MX1, an activating oligonucleotide that targets MX1, an agent that increases the expression of MX1, or an agent that activates the signaling pathway of MX1. Specifically, the MX1 agonist can be an mRNA that codes for MX1, a gene expression vector that can express MX1, or an MX1 protein or agonist fragment, etc.

[0200] [000348] In certain embodiments, the MX1 agonist is selected from the group consisting of full-length mRNA of MX1 and small molecule compounds including DMXAA, ADU-S100, 2',3'-cGAMP sodium and cGAMP. The chemical structure of the small molecule compound is shown below.

[0201] DMXAA:C 17 H 14 O4

[0202] [ka]

[0203] ADU-S100:C 20 H 22 N 10 O 10 P2S2.2Na

[0204] [ka]

[0205] 2',3'-cGAMP sodium: C 20 H 22 N 10 NaO 13 P2

[0206] [ka]

[0207] cGAMP:C 20 H 24 N 10 O 13 P2

[0208] [ka]

[0209] [000349] A PPP1R15A agonist can increase the expression and / or function of PPP1R15A, including the ability of PPP1R15A to enhance the dephosphorylation of downstream eIF2α. The types of agents that can act as PPP1R15A agonists are known to those skilled in the art. For example, an agonist can increase PPP1R15A expression at the nucleic acid (e.g., mRNA) level or protein level. In another example, a PPP1R15A agonist can be an agent that activates PPP1R15A or its target eIF2α. In some embodiments, a PPP1R15A agonist is a nucleic acid molecule, a protein molecule, a compound, etc. The nucleic acid molecule can be selected from an mRNA encoding PPP1R15A, an activating oligonucleotide that targets PPP1R15A, an agent that increases the expression of PPP1R15A, or an agent that activates the signaling pathway of PPP1R15A. Specifically, the PPP1R15A agonist may be an mRNA encoding PPP1R15A, a gene expression vector capable of expressing PPP1R15A, or a PPP1R15A protein or agonist fragment, and the like.

[0210] [000350] In another embodiment, methods of use are also provided that include administering an MX1 antagonist or a PPP1R15A antagonist.

[0211] [000351] As used herein, the term "antagonist" refers to an agent that inhibits (e.g., antagonizes, reduces, decreases, blocks, reverses, or alters) the biological effect of a target molecule (e.g., MX1 or PPP1R15A). Inhibitory effects can be exerted, for example, by interfering with or reducing other molecules in a signal transduction pathway (e.g., upstream signal transduction molecules or downstream signal transduction molecules), for example, by reducing the amount of the target molecule, or by suppressing the activity of the target molecule, or by suppressing the activity of the signal transduction pathway of the target molecule. Such antagonists can include any compound, protein, or nucleic acid, or any derivative thereof, that provides antagonistic effects.

[0212] [000352] MX1 antagonists can partially inhibit, i.e., reduce, or completely inhibit, i.e., eliminate, the expression and / or function of MX1, including the ability of MX1 to bind to its target. The types of agents that can act as MX1 antagonists are known to those skilled in the art. For example, the antagonist can be an inhibitor, a blocker, and the like. In another example, the antagonist can inhibit MX1 expression at the nucleic acid (e.g., mRNA) level or protein level. In a further example, the antagonist can be an agent that competes with MX1 for binding to its target. In some embodiments, the MX1 antagonist is a nucleic acid molecule, a protein molecule, a compound, and the like. The nucleic acid molecule can be selected from an interfering RNA against MX1, an antisense oligonucleotide against MX1, an agent that knocks out or knocks down the expression of MX1. In particular, the MX1 antagonist can be a gene knockout vector or a gene expression vector that can express siRNA, miRNA, shRNA, siRNA, shRNA, interfering RNA, and the like. The protein molecule may be selected from anti-MX1 antibodies, which may be monoclonal or polyclonal antibodies. As an example, the agent capable of competing with MX1 for binding to its target may be CCCP or H-151.

[0213] [000353] In certain embodiments, the MX1 antagonist is selected from the group consisting of CCCP and H-151. The chemical structures of the compounds are provided below.

[0214] CCCP:C9H5CIN4

[0215] [ka]

[0216] H-151:C 12 H 17 N3O

[0217] [ka]

[0218] [000354] A PPP1R15A antagonist can partially inhibit, i.e., reduce, or completely inhibit, i.e., eliminate, the expression and / or function of PPP1R15A, including the ability of PPP1R15A to enhance the dephosphorylation of downstream eIF2α. The types of agents that can act as PPP1R15A antagonists are known to those skilled in the art. For example, the antagonist can be an inhibitor, a blocker, etc. In another example, the antagonist can inhibit PPP1R15A expression at the nucleic acid (e.g., mRNA) level or protein level. In a further example, the antagonist can be an agent that competes with PPP1R15A for binding to eIF2α. In some embodiments, the PPP1R15A antagonist is a nucleic acid molecule, a protein molecule, a compound, etc. The nucleic acid molecule can be selected from interfering RNA against PPP1R15A, antisense oligonucleotide against PPP1R15A, and an agent that knocks out or knocks down the expression of PPP1R15A. Specifically, the PPP1R15A antagonist can be a gene knockout vector or a gene expression vector that can express siRNA, miRNA, shRNA, siRNA, shRNA, interfering RNA, etc. The protein molecule can be selected from an anti-PPP1R15A antibody that can be a monoclonal antibody or a polyclonal antibody. As an example, the agent that can compete with PPP1R15A for binding to eIF2α can be guanabenz or Sephin1.

[0219] [000355] Guanabenz is an alpha agonist of the alpha-2 adrenergic receptor and has the chemical structure shown below:

[0220] [ka]

[0221] [000356] Sephin1 is an inhibitor of the regulatory subunit of protein phosphatase 1, PPP1R15A, and has the chemical structure shown below:

[0222] [ka]

[0223] [000357] I. Methods for in vivo use to increase immune responses [000358] The present disclosure provides methods and compositions for enhancing cell-mediated immunity, stimulating and / or expanding T cells, increasing immunogenicity, and treating conditions that are expected to benefit from upregulation of immune response in subjects.In certain embodiments, the method comprises administering an effective amount of MX1 agonist to subjects.In certain embodiments, the method comprises administering an effective amount of PPP1R15A agonist to subjects.

[0224] [000359] It has been unexpectedly discovered that MX1 agonists or PPP1R15A agonists can promote immune responses in vivo and are therefore useful for enhancing or improving immunity (e.g., cell-based immunity) in subjects in need thereof.

[0225] [000360] In certain embodiments, a subject in need thereof may be a subject suffering from a condition that would be expected to benefit from upregulation of an immune response, e.g., from the induction of a sustained immune response, or from the stimulation of anti-tumor immunity, or from the inhibition of immune inhibitory receptor signaling.

[0226] [000361] In certain embodiments, the condition is a tumor, an infectious disease, a cardiovascular disease, or an inflammatory disease. In certain embodiments, the condition is a tumor, or an infectious disease.

[0227] [000362] In certain embodiments, the subject receives a treatment whose effectiveness can be increased by enhancing cell-mediated immunity.In certain embodiments, the treatment is an anti-tumor treatment or an anti-infectious disease treatment.In certain embodiments, the anti-tumor treatment is selected from the group consisting of chemotherapy, targeted therapy, immunotherapy, cell therapy (e.g., CAR-T therapy), and tumor vaccine.

[0228] [000363] A) Methods for enhancing cell-mediated immunity [000364] In one aspect, the disclosure provides a method of enhancing cell-mediated immunity in a subject in need thereof.

[0229] [000365] As used herein, the term "cell-mediated immunity" can be immunity mediated by any immune cell, such as T cells, natural killer (NK) cells, macrophages, etc. In certain embodiments, the cell-mediated immunity is T cell-mediated immunity.

[0230] [000366] T cell mediated immunity may be determined using any suitable method known in the art, including, but not limited to, T cell mediated cytotoxicity against target cells (e.g., cancer cells), T cell mediated induction of a local inflammatory response, or T cell proliferation.

[0231] [000367] In certain embodiments, the methods include administering to the subject an effective amount of an MX1 agonist, thereby enhancing cell-mediated immunity in the subject.

[0232] [000368] In certain embodiments, the methods include administering to the subject an effective amount of a PPP1R15A agonist, thereby enhancing cell-mediated immunity in the subject.

[0233] [000369] B) Methods for stimulating and / or expanding T cells [000370] In one aspect, the disclosure provides a method of stimulating and / or expanding T cells in a subject in need thereof.

[0234] [000371] With respect to T cells, the terms "stimulation" or "stimulating" refer to a primary response induced by binding of a stimulatory domain or molecule (e.g., the TCR / CD3 complex) with its cognate ligand (e.g., a peptide-tagged MHC molecule), thereby mediating a signaling event such as a T cell response via the TCR / CD3 complex. T cell stimulation can mediate T cell proliferation, activation, differentiation, and the like.

[0235] [000372] With respect to T cells, the term "expansion" or "expanding" refers to increasing the number of T cells or promoting T cell proliferation. Typically, T cells can be expanded by contacting them with an agent that stimulates a CD3 / TCR complex-associated signal (e.g., an anti-CD3 antibody) and a ligand that stimulates a costimulatory molecule on the surface of the T cell (e.g., an anti-CD28 antibody).

[0236] [000373] In certain embodiments, the methods include administering to the subject an effective amount of an MX1 agonist, thereby stimulating and / or expanding T cells in the subject.

[0237] [000374] In certain embodiments, the methods include administering to the subject an effective amount of a PPP1R15A agonist, thereby stimulating and / or expanding T cells in the subject.

[0238] [000375] C) Methods for increasing the immunogenicity of an immunogenic composition [000376] In one aspect, the disclosure provides a method of increasing the immunogenicity of an immunogenic composition in a subject in need thereof.

[0239] [000377] The term "immunogenic composition" as used herein includes any suitable composition intended to induce an immune response in a subject. The intended immune response can be either prophylactic or therapeutic. Examples of immunogenic compositions include, but are not limited to, vaccines and cell therapies such as chimeric antigen receptor (CAR)-T treatments. In certain embodiments, the vaccine is a tumor vaccine. In certain embodiments, the tumor vaccine comprises a neoantigen.

[0240] [000378] The term "increasing immunogenicity," as used herein, means enhancing the intended immune response of an immunogenic composition.

[0241] [000379] In certain embodiments, the methods include administering to the subject an effective amount of an MX1 agonist, thereby increasing the immunogenicity of the immunogenic composition in the subject.

[0242] [000380] In certain embodiments, the methods include administering to the subject an effective amount of a PPP1R15A agonist, thereby increasing the immunogenicity of the immunogenic composition in the subject.

[0243] [000381] D) Methods of Treating Cells [000382] In another aspect, the disclosure provides a method of promoting clonal expansion of T cells, or promoting T cell activation, or promoting T cell cytotoxicity.

[0244] [000383] In certain embodiments, the methods include administering to the subject an effective amount of an MX1 agonist, thereby treating the condition in the subject.

[0245] [000384] In certain embodiments, the methods include administering to the subject an effective amount of a PPP1R15A agonist, thereby treating the condition in the subject.

[0246] [000385] In certain embodiments, the condition is a tumor, an infectious disease, a cardiovascular disease, or an inflammatory disease. In certain embodiments, the condition is a tumor, or an infectious disease.

[0247] [000386] In certain embodiments, the method further comprises administering in combination with a therapy to treat the condition. In certain embodiments, the combined therapy is an anti-tumor therapy or an anti-infective therapy.

[0248] [000387] A treatment administered before or after another drug (e.g., MX1 agonist or PPP1R15A agonist provided herein) is considered to be administered "in combination" with that drug, as that term is used herein, even if the treatment and the other drug are administered via different routes. Where possible, a treatment administered in combination with a drug disclosed herein (e.g., MX1 agonist or PPP1R15A agonist) is administered according to the schedule listed in the product information sheet of the treatment, or according to the 2003 Physicians' Desk Reference (57th edition; Medical Economics Company; ISBN: 1563634457; 57th edition (November 2002)) or a protocol known in the art.

[0249] [000388] II. METHODS FOR IN VITRO USE [000389] The present disclosure provides methods and compositions for promoting clonal expansion of T cells, or promoting T cell activation, or promoting the cytotoxicity of T cells.In certain embodiments, the method comprises treating T cells with an effective amount of MX1 agonist under suitable conditions.In certain embodiments, the method comprises treating T cells with an effective amount of PPP1R15A agonist under suitable conditions.

[0250] [000390] It has been unexpectedly discovered that the compositions provided herein (i.e., MX1 agonists or PPP1R15A agonists) can promote T cell activation and / or T cell proliferation in vitro and are therefore useful for treating and / or preparing T cells useful for cell therapy.

[0251] [000391] A) Methods for promoting clonal expansion of T cells [000392] In one aspect, the disclosure provides a method of promoting clonal expansion of a cell, e.g., an immune cell, specifically a T cell.

[0252] [000393] The term "clonal expansion" as used herein refers to the proliferation of cells with a particular combinatorial antigen receptor sequence, which may be productively rearranged and expressed, e.g., proliferation in response to antigenic stimulation. The expanded cell clones may have a shared combination of germline V, D, and J regions, and junctional nucleotides. In certain embodiments, the expanded cell clones may have combinatorial antigen receptors with identical germline regions and substantially identical junctional nucleotides, e.g., less than one nucleotide, less than two nucleotides, less than three nucleotides, that differ.

[0253] [000394] In certain embodiments, the method includes treating T cells with an effective amount of an MX1 agonist under suitable conditions, thereby promoting clonal expansion of the cells, e.g., T cells. In certain embodiments, the method includes treating T cells with an effective amount of a PPP1R15A agonist under suitable conditions, thereby promoting clonal expansion of the cells, e.g., T cells.

[0254] [000395] In certain embodiments, the cells are lymphocytes expressing immunoglobulins, including pre-B cells, B cells, e.g., memory B cells, and plasma cells. In certain embodiments, the cells are lymphocytes expressing T cell receptors, including thymocytes, NK cells, pre-T cells, and T cells, and many subsets of T cells, e.g., Th1, Th2, Th17, CTL, Treg, etc., are known in the art.

[0255] [000396] In certain embodiments, the cell is a T cell. In certain embodiments, the T cell is a memory T cell. Memory T cells express specific T cell receptors and are antigen-specific.

[0256] [000397] In certain embodiments, the T cells are CAR-T cells or TCR-T cells.

[0257] [000398] B) Methods of Promoting T Cell Activation or Promoting T Cell Cytotoxicity [000399] In another aspect, the disclosure provides a method of promoting T cell activation or promoting T cell cytotoxicity.

[0258] [000400] In certain embodiments, the method comprises treating T cells with an effective amount of MX1 agonist under suitable conditions, thereby promoting T cell activation or promoting T cell cytotoxicity.In certain embodiments, T cells are cultured with MX1 agonist in combination with a second agent for stimulating T cells.

[0259] [000401] In certain embodiments, the method comprises treating T cells with an effective amount of a PPPP1R15A agonist under suitable conditions, thereby promoting T cell activation or promoting T cell cytotoxicity. In certain embodiments, the T cells are cultured with the PPP1R15A agonist in combination with a second agent for stimulating the T cells.

[0260] [000402] C) Compositions Comprising Prepared T Cells [000403] In another aspect, the disclosure provides a composition comprising T cells prepared using any embodiment of the methods provided herein.

[0261] [000404] In another aspect, the disclosure provides a method of treating a condition that would benefit from upregulation of an immune response in a subject in need thereof, comprising administering to the subject a composition provided herein comprising T cells prepared using the methods described above.

[0262] [000405] III. Methods of Detection [000406] The present disclosure provides a method and composition for detecting the expression level of MX1 in T cell.It is unexpectedly found that the level of MX1 is related to the activation state, or activity or cytotoxicity of T cell.Therefore, MX1 is useful as a biomarker for the evaluation of T cell status.

[0263] [000407] The present disclosure provides a method and composition for detecting the expression level of PPP1R15A in T cell.It has been unexpectedly found that the level of PPP1R15A is related to the activation state, or activity or cytotoxicity of T cell.Therefore, PPP1R15A is useful as a biomarker for the evaluation of T cell status.

[0264] [000408] In certain embodiments, the level of the detected biomarker is compared to the level of the biomarker in a control cell or to a control level. In certain embodiments, the control cell is a control T cell.

[0265] [000409] In certain embodiments, the term "control T cells" refers to T cells that express normal or baseline levels of a biomarker (i.e., MX1 or PPP1R15A), e.g., CD8+ T cells from a healthy cell or tissue sample.

[0266] [000410] In certain embodiments, the term "control level" of a biomarker described herein (i.e., MX1 or PPP1R15A) can be the normal or baseline level of the biomarker, e.g., the level of the biomarker in a healthy cell or tissue sample, or the average level of the biomarker in a control cell population.

[0267] [000411] In certain embodiments, the control level can be the typical level, measured level, or range of the level of the corresponding biomarker that is usually observed in one or more healthy cell or tissue samples, or one or more control cell or tissue samples. In certain embodiments, the reference level can be the average level of the corresponding biomarker in a control cell population. For example, it can be the empirical level of the biomarker that is considered to be representative of the control sample. In certain embodiments, the reference level of the biomarker described herein is obtained using the same or comparable measurement method or assay used in measuring the level of the biomarker provided herein.

[0268] [000412] A). Methods for assessing the activation state, activity or cytotoxicity of T cells [000413] In another aspect, the disclosure provides methods for assessing the activation state, or activity or cytotoxicity of a T cell.

[0269] [000414] In certain embodiments, the method includes detecting the expression level of MX1 in T cells, and an increase in the expression level of MX1 compared to a control T cell or compared to a control level is an indication of the cytotoxicity of the T cells.

[0270] [000415] In certain embodiments, the method comprises detecting the expression level of PPP1R15A in T cells, and an increase in the expression level of PPP1R15A compared to a control T cell or compared to a control level is an indication of the cytotoxicity of the T cells. In certain embodiments, the control T cells are CD8+ T cells.

[0271] [000416] B). Methods for preparing populations of T cells for cell therapy [000417] In another aspect, the disclosure provides methods for assessing the activation state, or activity or cytotoxicity of a T cell.

[0272] [000418] In certain embodiments, the method includes the steps of: a) identifying a population of T cells that have increased expression levels of MX1 compared to control T cells; and b) selectively enriching the identified T cells for cell therapy.

[0273] [000419] In certain embodiments, the method includes the steps of: a) identifying a population of T cells that have increased expression levels of PPP1R15A compared to control T cells; and b) selectively enriching the identified T cells for cell therapy.

[0274] [000420] C) Methods for converting inactive T cells into active T cells, compositions comprising converted T cells, and methods of use [000421] In another aspect, the disclosure provides a method of converting a first population of inactive T cells into a second population of active T cells.

[0275] [000422] In certain embodiments, the method includes: a) providing a first population of inactive T cells, in which the inactive T cells do not exhibit an increased expression level of MX1 compared to control T cells; b) treating the first population of inactive T cells with an effective amount of an MX1 agonist under suitable conditions; and c) detecting the expression level of MX1 in the population of T cells obtained in step b), where an increased expression level of MX1 compared to control T cells is indicative of successful conversion to a second population of active T cells.

[0276] [000423] In certain embodiments, the method comprises: a) providing a first population of inactive T cells, wherein the inactive T cells do not exhibit an increased expression level of PPP1R15A compared to control T cells; b) treating the first population of inactive T cells with an effective amount of a PPP1R15A agonist under suitable conditions; and c) detecting the expression level of PPP1R15A in the population of T cells obtained in step b), wherein an increased expression level of PPP1R15A compared to control T cells is indicative of successful conversion to a second population of active T cells.

[0277] [000424] In another aspect, the disclosure provides a composition comprising a population of T cells prepared or converted using any embodiment of the method of converting a first population of inactive T cells to a second population of active T cells as provided herein.

[0278] [000425] In another aspect, the disclosure provides a method of treating a condition that would benefit from upregulation of an immune response in a subject in need thereof, comprising administering to the subject a population of T cells prepared or converted by the methods provided herein.

[0279] [000426] D) Methods for preparing populations of T cells for cell therapy and compositions comprising prepared populations of T cells [000427] In another aspect, the disclosure provides a method of preparing a population of T cells for cell therapy.

[0280] [000428] In certain embodiments, the method includes: a) identifying a population of T cells as inactive if they do not exhibit an increased level of expression of MX1 compared to control T cells; and b) treating the identified population of inactive T cells with an effective amount of an MX1 agonist under suitable conditions, thereby obtaining a population of activated T cells.

[0281] [000429] In certain embodiments, the method comprises: a) identifying a population of T cells as inactive if they do not exhibit an increased expression level of PPP1R15A compared to control T cells; and b) treating the identified population of inactive T cells with an effective amount of a PPP1R15A agonist under suitable conditions, thereby obtaining a population of activated T cells.

[0282] [000430] In another aspect, the disclosure provides compositions comprising a population of T cells activated or prepared using any embodiment of the methods of preparing a population of T cells for cell therapy provided herein.

[0283] [000431] In another aspect, the disclosure provides a method of treating a condition that would benefit from upregulation of an immune response in a subject in need thereof, comprising administering to the subject a population of T cells prepared or activated by the methods provided herein.

[0284] [000432] IV. Methods for Reducing Unwanted Immune Conditions [000433] In another aspect, the present disclosure further provides the method and composition for reducing cell-mediated immunity, inactivating T cell, and treating the condition that is expected to benefit from the downregulation of immune response in a subject.In certain embodiments, the method comprises administering an effective amount of MX1 antagonist to a subject.In certain embodiments, the method comprises administering an effective amount of PPP1R15A antagonist to a subject.

[0285] [000434] It has been unexpectedly found that MX1 or PPP1R15A is elevated in activated immune cells, specifically activated T cells, and therefore it is expected that reducing or antagonizing MX1 or PPP1R15A may be useful for reducing unwanted or undesirable immunity (e.g., cell-based immunity) and treating conditions or diseases associated with such unwanted or undesirable immune / inflammatory states in subjects in need thereof.

[0286] [000435] In another embodiment, the present disclosure provides a composition for reducing cell-mediated immunity in a subject in need thereof, comprising an MX1 antagonist. Examples of MX1 antagonists include CCCP and H-151.

[0287] [000436] In certain embodiments, the subject is determined to have T cells that have increased levels of MX1 expression compared to control T cells. The expression level of MX1 can be determined using any suitable method known in the art as well as those described in this disclosure.

[0288] [000437] In another embodiment, the present disclosure provides a composition for reducing cell-mediated immunity in a subject in need thereof, comprising a PPP1R15A antagonist. Examples of PPP1R15A antagonists include guanabenz and Sephin1.

[0289] [000438] In certain embodiments, the subject is determined to have T cells that have increased expression levels of PPP1R15A compared to control T cells. The expression level of PPP1R15A can be determined using any suitable method known in the art as well as those described in this disclosure.

[0290] [000439] In certain embodiments, the subject is suffering from a condition characterized by excessive cell-mediated immunity.

[0291] [000440] In certain embodiments, the condition is an inflammatory disease, an autoimmune disease, an allergic disease, or a T-cell cancer. In certain embodiments, the condition is lupus erythematosus, rheumatoid arthritis, inflammatory bowel disease (IBD), or GVHD.

[0292] [000441] In certain embodiments, the condition is one in which the subject has received or is believed to have received an allogeneic or xenogeneic transplant.

[0293] [000442] A) Methods of reducing cell-mediated immunity [000443] In another aspect, the disclosure provides a method of reducing cell-mediated immunity in a subject in need thereof.

[0294] [000444] In certain embodiments, the methods include administering to the subject an effective amount of an MX1 antagonist, thereby reducing cell-mediated immunity in the subject.

[0295] [000445] In certain embodiments, the methods include administering to the subject an effective amount of a PPP1R15A antagonist, thereby reducing cell-mediated immunity in the subject.

[0296] [000446] In certain embodiments, the cell-mediated immunity is T cell-mediated immunity.

[0297] [000447] B) Methods for inactivating T cells [000448] In another aspect, the disclosure provides a method of inactivating T cells in a subject in need thereof.

[0298] [000449] In certain embodiments, the methods include administering to the subject an effective amount of an MX1 antagonist, thereby inactivating T cells in the subject.

[0299] [000450] In certain embodiments, the methods include administering to the subject an effective amount of a PPP1R15A antagonist, thereby inactivating T cells in the subject.

[0300] [000451] C) Methods of Treating Conditions [000452] In another aspect, the disclosure provides a method of treating a condition that would benefit from downregulation of the immune response in a subject in need thereof.

[0301] [000453] In certain embodiments, the methods comprise administering to the subject an effective amount of an MX1 antagonist.

[0302] [000454] In certain embodiments, the methods include administering to the subject an effective amount of a PPP1R15A antagonist.

[0303] [000455] In certain embodiments, the condition expected to benefit from downregulation of the immune response is an autoimmune disease, transplant rejection, or an inflammatory condition. In certain embodiments, the autoimmune disease is selected from the group consisting of lupus erythematosus, rheumatoid arthritis, inflammatory bowel disease (IBD), and GVHD.

[0304] [000456] Part II: Biomarkers of tumor neoantigen vaccine efficacy [000457] In another aspect, the present disclosure provides a method of assessing a subject's responsiveness to a tumor neo-antigen vaccine, specifically assessing a subject's responsiveness to at least one priming administration of a tumor neo-antigen vaccine or assessing a subject's responsiveness to at least one boosting administration of a tumor neo-antigen vaccine.

[0305] [000458] As used in this disclosure, the term "responsiveness" to a tumor neo-antigen vaccine refers to the immune response generated after administration of the tumor neo-antigen vaccine. The tumor neo-antigen vaccine is believed to activate the immune system, specifically, to induce an anti-tumor immune response. Such an immune response may involve changes in the expression levels of certain genes in different immune cells. Characterization of the differential expression of these markers may provide an indication of the level of immune response induced by the tumor neo-antigen vaccine.

[0306] [000459] In another aspect, the disclosure provides a method for predicting the risk of tumor recurrence in a subject before or after receiving at least one administration of a tumor neo-antigen vaccine.

[0307] [000460] In yet another aspect, the disclosure provides a method of evaluating therapeutic efficacy in a subject treated with an anti-tumor therapy in combination with at least one administration of a tumor neo-antigen vaccine.

[0308] [000461] Tumor neoantigen vaccine [000462] Tumor neo-antigen vaccines can be synthetic antigens (e.g., peptide antigens or polynucleotides encoding such peptide antigens) designed to induce anti-tumor immune responses in subjects. Tumor neo-antigen vaccines can be individualized and prepared based on tumor neo-antigens identified in the subject.

[0309] [000463] In certain embodiments, the subject has been diagnosed with cancer. In certain embodiments, the cancer is resectable. In certain embodiments, the subject has undergone tumor resection surgery. In certain embodiments, the subject has not undergone chemotherapy prior to the resection surgery.

[0310] [000464] In certain embodiments, a subject's tumor tissue, adjacent tissue and / or peripheral blood sample is analyzed to identify one or more tumor-specific mutations in the subject.

[0311] [000465] In certain embodiments, tumor neo-antigen vaccines are prepared based on the identified tumor-specific mutations.

[0312] [000466] In certain embodiments, the subject is diagnosed with pancreatic cancer, optionally pancreatic ductal adenocarcinoma.

[0313] [000467] 1. Biomarkers for assessing responsiveness to neoantigen vaccines [000468] Specifically, the inventors have unexpectedly identified several genes whose changes in certain immune cells correlate with the efficacy of tumor neo-antigen vaccines and are therefore useful as biomarkers for assessing a subject's responsiveness to tumor neo-antigen vaccines.

[0314] [000469] In certain embodiments, the present disclosure provides a method for evaluating the responsiveness of a subject to a tumor neo-antigen vaccine. In certain embodiments, such a method includes: a) determining the expression level of one or more genes in one or more immune cells from a sample obtained from a subject receiving a tumor neo-antigen vaccine.

[0315] [000470] In certain embodiments, the one or more genes are selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATF4, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, DDOST, DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, GLA, LMAN2, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PDE4D, PIM1, PRKACA, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TMEM258, TRDV1, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof. Biomarkers are provided in Figure 36.

[0316] [000471] In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

[0317] [000472] In certain embodiments, the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of AC011815.2, EGF, GCH1, NUAK1 and SERTAD2, or any combination thereof.

[0318] [000473] In certain embodiments, the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of ANKRD29, C15orf54, FOSL2, GCH1, PRKACA, SERTAD2, SLC16A6, TRDV1, WDFY3 and ZBTB43, or any combination thereof.

[0319] [000474] In certain embodiments, the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of AP3M2, ARL5B, ATP5F1B, CHCHD2, COPE, GLA, MN1 and MTDH, or any combination thereof.

[0320] [000475] In certain embodiments, the one or more immune cells are B cells and the one or more genes are selected from the group consisting of ATF4, ATP5F1B, DDOST, DERL1, DNPH1, LMAN2, MAP9, PDE4D, PIM1, TMEM258 and ZDHHC7, or any combination thereof.

[0321] [000476] In certain embodiments, the one or more immune cells are NK cells and the one or more genes are selected from the group consisting of COL4A3BP, FERMT3 and NFIL3, or any combination thereof.

[0322] [000477] In certain embodiments, the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of COPE, CTTNBP2, GK, LRRK1, MTDH, PRMT1, SYT17 and TMED10, or any combination thereof.

[0323] [000478] Specifically, the inventors have unexpectedly found that biomarkers can be different at different vaccination stages. In general, repeated administration of vaccine is believed to induce strong and long-lasting protective immunity. The first vaccine administration is believed to prime the immune system, for example, by activating naive T cells, which then undergo proliferation, contraction and differentiation into primary memory T cells. Subsequent vaccine administration is believed to boost the immune system, for example, by restimulating primary memory T cells.

[0324] [000479] In certain embodiments, the subject receives multiple doses of the tumor neo-antigen vaccine. As used herein, the first few doses of the tumor neo-antigen vaccine are called priming doses and are administered close to each other in time. In certain embodiments, the subject receives one, two, three, four, or five or more priming doses of the tumor neo-antigen vaccine. In certain embodiments, the priming doses are administered within 20 days, within 22 days, within 25 days, within 30 days, within 40 days, or within 45 days. In certain embodiments, the priming doses are administered on the 1st, 4th, 8th, 15th, and / or 22nd day. The period during which the priming doses are administered is the priming phase of vaccination. In certain embodiments, the priming phase is at most 20 days, 22 days, 25 days, 30 days, 40 days, or 45 days.

[0325] [000480] After the priming phase, the subject can receive further doses of tumor neo-antigen vaccine, which are called boosting doses. In certain embodiments, the subject receives one, two or more boosting doses of tumor neo-antigen vaccine. In certain embodiments, the boosting doses are administered at week 12 and / or week 20. The period after the priming phase is the boosting phase of vaccination, during which the boosting doses are administered. In certain embodiments, the boosting phase begins 28 days, 30 days, 35 days, 40 days, 45 days, 50 days, 55 days or 60 days after the last priming dose.

[0326] [000481] A) Priming phase biomarkers [000482] In certain embodiments, the present disclosure provides a method for assessing a subject's responsiveness to a tumor neo-antigen vaccine during a priming phase, comprising: a) determining the expression level of one or more genes in one or more immune cells from a sample obtained from the subject after at least one priming administration of the tumor neo-antigen vaccine.

[0327] [000483] Specifically, the inventors have unexpectedly identified several genes whose changes in certain immune cells correlate with the efficacy of a tumor neo-antigen vaccine during the priming phase and are therefore useful as biomarkers for assessing a subject's responsiveness to a tumor neo-antigen vaccine during the priming phase.

[0328] [000484] In certain embodiments, the one or more genes are selected from the group consisting of AC011815.2, ATF4, C15orf54, DDOST, DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, LMAN2, MAP9, NUAK1, PDE4D, PRKACA, SERTAD2, SLC16A6, TMED10, TMEM258, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof.

[0329] [000485] In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages. In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, B cells, NK cells, and macrophages.

[0330] [000486] In certain embodiments, the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of AC011815.2, EGF, GCH1, NUAK1 and SERTAD2, or any combination thereof.

[0331] [000487] In certain embodiments, the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of C15orf54, FOSL2, GCH1, PRKACA, SERTAD2, SLC16A6, WDFY3 and ZBTB43, or any combination thereof.

[0332] [000488] In certain embodiments, the one or more immune cells are B cells and the one or more genes are selected from the group consisting of ATF4, DDOST, DERL1, DNPH1, LMAN2, MAP9, PDE4D, TMEM258 and ZDHHC7, or any combination thereof.

[0333] [000489] In certain embodiments, the one or more immune cells are NK cells and the gene is FERMT3, or any combination thereof.

[0334] [000490] In certain embodiments, the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of GK and TMED10, or any combination thereof.

[0335] [000491] B) Biomarkers in the boosting phase [000492] In certain embodiments, the present disclosure provides a method for evaluating the responsiveness of a subject to a tumor neo-antigen vaccine during a boosting phase, comprising: a) determining the expression level of one or more genes in one or more immune cells from a sample obtained from the subject during the boosting phase. In certain embodiments, the subject has completed all priming doses of the tumor neo-antigen vaccine.

[0336] [000493] Specifically, the inventors have unexpectedly identified several genes whose changes in certain immune cells correlate with the efficacy of a tumor neo-antigen vaccine during the boosting phase and are therefore useful as biomarkers for assessing a subject's responsiveness to a tumor neo-antigen vaccine during the boosting phase.

[0337] [000494] In certain embodiments, the one or more genes are selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, EGF, FERMT3, FOSL2, GK, GLA, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PIM1, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TRDV1 and ZDHHC7, or any combination thereof.

[0338] [000495] In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

[0339] [000496] In certain embodiments, the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of AC011815.2, EGF, NUAK1 and SERTAD2, or any combination thereof.

[0340] [000497] In certain embodiments, the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of ANKRD29, C15orf54, FOSL2, SLC16A6 and TRDV1, or any combination thereof.

[0341] [000498] In certain embodiments, the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of AP3M2, ARL5B, ATP5F1B, CHCHD2, COPE, GLA, MN1 and MTDH, or any combination thereof.

[0342] [000499] In certain embodiments, the one or more immune cells are B cells and the one or more genes are selected from the group consisting of ATP5F1B, MAP9, PIM1 and ZDHHC7, or any combination thereof.

[0343] [000500] In certain embodiments, the one or more immune cells are NK cells and the one or more genes are selected from the group consisting of COL4A3BP, FERMT3 and NFIL3, or any combination thereof.

[0344] [000501] In certain embodiments, the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of COPE, CTTNBP2, GK, LRRK1, MTDH, PRMT1, SYT17 and TMED10, or any combination thereof.

[0345] [000502] C) Expression levels of biomarkers [000503] In certain embodiments, the expression level of a given gene (i.e., a biomarker) provided herein is determined in a given immune cell in a sample. In certain embodiments, the sample comprises or is derived from a peripheral blood mononuclear cell (PBMC), a blood sample, or a tumor-infiltrating immune cell.

[0346] [000504] In certain embodiments, the expression level of a given gene is represented by the percentage of immune cells of a given type that express the given gene.

[0347] [000505] In certain embodiments, the expression level of a given gene is represented by the average level of the given gene expressed in a given cell type.

[0348] [000506] Any suitable method may be used for such determination, such as those described in section 1.5.12 of Example 1. In certain embodiments, expression levels are determined by sequencing, such as single-cell RNA sequencing.

[0349] [000507] In certain embodiments, the reference expression level is the expression level of one or more genes in one or more immune cells from a sample obtained from the subject before receiving the first dose of the tumor neo-antigen vaccine.

[0350] [000508] In certain embodiments, the method for assessing responsiveness to a neo-antigen vaccine further comprises step b): comparing the expression levels of one or more genes determined in step a) to a reference expression level and determining a difference from the reference level.

[0351] [000509] In certain embodiments, the difference is determined as the change in the percentage of immune cells of a given type that express a given gene in each sample obtained from a subject at each time point, e.g., before and after vaccination, before vaccination and during a priming phase, or before vaccination and during a boosting phase.

[0352] [000510] In certain embodiments, the method for assessing responsiveness to a neo-antigen vaccine further comprises step c): assessing the subject's responsiveness to at least one boosting administration of the tumor neo-antigen vaccine based on the difference determined in step b).

[0353] [000511] In certain embodiments, a subject is determined to have a good response to the tumor neo-antigen vaccine if the difference meets or exceeds a defined threshold, or a subject is determined to have a poor response to the tumor neo-antigen vaccine if the difference is below a defined threshold.

[0354] [000512] In certain embodiments, the threshold is 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20%.

[0355] [000513] 2. Biomarkers for predicting the risk of tumor recurrence [000514] In another aspect, the inventors have unexpectedly identified several genes whose changes in certain immune cells correlate with tumor recurrence in tumor neo-antigen vaccines and are therefore useful as biomarkers for predicting the risk of tumor recurrence in subjects receiving tumor neo-antigen vaccines.

[0356] [000515] In certain embodiments, tumor recurrence can be indicated by tumor recurrence in a subject. In certain embodiments, a subject has a complete tumor tissue resection before receiving a tumor neo-antigen vaccine, and tumor recurrence can be an indication of tumor recurrence. In certain embodiments, tumor recurrence can be indicated by an abnormal increase in the level of serum tumor markers, such as CA19-9 or CA72-4.

[0357] [000516] In certain embodiments, the present disclosure provides a method for predicting the risk of tumor recurrence in a subject before or after receiving at least one dose of a tumor neo-antigen vaccine. In certain embodiments, such a method includes: a) determining the expression level of one or more genes in one or more immune cells from a sample obtained from a subject receiving a tumor neo-antigen vaccine.

[0358] [000517] A) Biomarkers of Tumor Recurrence [000518] In certain embodiments, the one or more genes are selected from the group consisting of ABCA13, AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, AL662907.3, APOBEC3C, ATP1B3, ATP6V1B2, C1QBP, CBX5, CCNE2, CD63, CEBPA, CHP1, CLN8, CMBL, CNIH4, COL4A3BP, CPNE3, DACH1, DNAJC12, DNAJC3, DOC2B, EEA1, ENAM, ERG, FAM43A, FBXO43, FIGN, GCA, GNG4, GNLY, GOLIM4, GPM6B, GPR171, GPRC5D, HHEX, HID1, ILF2, IRF2BP2, KI The biomarkers are selected from the group consisting of F22, LACC1, LIPA, MANEA, MECOM, MID1IP1, MX2, MYOM2, NABP1, NEFH, NFE4, NLN, OXCT2, P3H2, PI3, PIM1, PLAU, PRSS3, PSMA3, RAB10, RAB20, RASGRP2, RBMS3, RPL23, RPS6KA5, RPS8, RUNX1, SAMD3, 11-Sep, SERTAD2, SIGLEC6, SIT1, SOCS1, SSR1, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC15, TXNDC17, UAP1, UFM1, UGT2B17, XPNPEP1, ZBTB16, ZNF608 and STAT1, or any combination thereof. The biomarkers are provided in FIG.

[0359] [000519] In certain embodiments, the one or more genes are selected from the group consisting of AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, ATP1B3, CMBL, DACH1, DNAJC12, DOC2B, EEA1, ERG, FBXO43, FIGN, GPRC5D, HID1, NFE4, OXCT2, P3H2, PI3, PLAU, PRSS3, RASGRP2, RPL23, RPS8, ST6GALNAC1, SYNE2, TRAV16, TREM2 and ZNF608, or any combination thereof.

[0360] [000520] In certain embodiments, the expression level of one or more genes is determined in one or more immune cells from a sample obtained from the subject after receiving at least one dose of a tumor neo-antigen vaccine, and the one or more genes are selected from the group consisting of AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, ATP1B3, ATP6V1B2, CHP1, CLN8, CMBL, CNIH4, CPNE3, DACH1, DNAJC12, DOC2B, EEA1, ERG, FAM43A, FBXO43, FIGN, GNG4, GNG5, GNG6, GNG7, GNG8, GNG9, GNG10, GNG11, GNG12, GNG13, GNG14, GNG15, GNG16, GNG17, GNG18, GNG19, GNG20, GNG21, GNG22, GNG23, GNG24, GNG25, GNG26, GNG27, GNG28, GNG29, GNG30, GNG31, GNG32, GNG33, GNG41, GNG42, GNG43, GNG5, GNG5, GNG6, GNG7, GNG8, GNG9, GNG10, GNG11, GNG12, GNG13, GNG14, GNG15, GNG16, GNG17, GNG18, GNG19, GNG19, GNG20, GNG19, GNG10, GNG11, GNG12, GNG13, GNG14, GNG15, GNG16, GNG17, GNG18, GNG19, GNG20, GNG19, GNG19, GNG21, GNG PM6B, GPRC5D, HHEX, HID1, IRF2BP2, KIF22, LACC1, MECOM, MID1IP1, MYOM2, NABP1, NFE4, OXCT2, P3H2, PI3, PLAU, PRSS3, RAB10, RASGRP2, RBMS3, RPL23, RPS8, RUNX1, SAMD3, SERTAD2, SIGLEC6, SIT1, SOCS1, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC17, UGT2B17, XPNPEP1 and ZNF608, or any combination thereof.

[0361] [000521] In certain embodiments, the expression level of one or more genes is determined in one or more immune cells from a sample obtained from the subject prior to receiving at least one dose of a tumor neo-antigen vaccine, and the one or more genes are selected from the group consisting of ABCA13, AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, AL662907.3, ATP1B3, CBX5, CMBL, DACH1, DNAJC12, DNAJC3, DO C2B, ENAM, ERG, FBXO43, FIGN, GOLIM4, GPRC5D, HID1, ILF2, NEFH, NFE4, NLN, OXCT2, P3H2, PI3, PLAU, PRSS3, PSMA3, RASGRP2, RPL23, RPS6KA5, RPS8, 11-Sep, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC15, UAP1, UFM1, ZBTB16 and ZNF608, or any combination thereof.

[0362] [000522] In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

[0363] [000523] In certain embodiments, the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of CHP1, EEA1, RPS8, RUNX1, UGT2B17, XPNPEP1, ZNF608 and STAT1, or any combination thereof.

[0364] [000524] In certain embodiments, the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of AL662907.3, CD63, COL4A3BP, CPNE3, EEA1, GPM6B, LIPA, PSMA3, RAB10, RAB20, RBMS3, RPS8, SAMD3, SERTAD2, SOCS1, TXNDC15 and STAT1, or any combination thereof.

[0365] [000525] In certain embodiments, the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of ABCA13, ATP1B3, DNAJC3, ENAM, FIGN, GNG4, ILF2, KIF22, MANEA, NEFH, NLN, P3H2, PLAU, SIGLEC6, SSR1, TRAV16, UFM1 and ZBTB16, or any combination thereof.

[0366] [000526] In certain embodiments, the one or more immune cells are B cells and the one or more genes are selected from the group consisting of AC105094.2, AC243829.2, AL021807.1, AL391807.1, ATP6V1B2, C1QBP, CMBL, CNIH4, DOC2B, FAM43A, GCA, GNLY, HID1, LACC1, MID1IP1, MX2, PI3, PIM1, 11-Sep, SIT1 and SYNE2, or any combination thereof.

[0367] [000527] In certain embodiments, the one or more immune cells are NK cells and the one or more genes are selected from the group consisting of AC022726.2, APOBEC3C, DACH1, ERG, IRF2BP2, MYOM2, NFE4, PRSS3, RPL23, RPS6KA5 and TREM2, or any combination thereof.

[0368] [000528] In certain embodiments, the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of CBX5, CCNE2, CEBPA, CLN8, DNAJC12, FBXO43, FIGN, GOLIM4, GPR171, GPRC5D, HHEX, MECOM, NABP1, OXCT2, RASGRP2, ST6GALNAC1, TXNDC17 and UAP1, or any combination thereof.

[0369] [000529] B) Expression levels of biomarkers [000530] In certain embodiments, the expression level of a given gene (i.e., a biomarker) provided herein is determined in a given immune cell in a sample. In certain embodiments, the sample comprises or is derived from a peripheral blood mononuclear cell (PBMC), a blood sample, or a tumor-infiltrating immune cell.

[0370] [000531] In certain embodiments, the expression level of a given gene is represented by the percentage of immune cells of a given type that express the given gene.

[0371] [000532] In certain embodiments, the expression level of a given gene is represented by the average level of the given gene expressed in a given cell type.

[0372] [000533] Any suitable method may be used for such determination, such as those described in section 1.5.12 of Example 1. In certain embodiments, expression levels are determined by sequencing, such as single-cell RNA sequencing.

[0373] [000534] In certain embodiments, the method for predicting the risk of tumor recurrence in a subject after receiving a neo-antigen vaccine further comprises step b): comparing the expression levels of one or more genes determined in step a) to a reference expression level and determining a difference from the reference level.

[0374] [000535] In certain embodiments, the difference is determined as the difference in the percentage of immune cells of a given type that express a given gene in each sample obtained from the subject at each time point compared to a reference level.

[0375] [000536] In certain embodiments, the method for predicting the risk of tumor recurrence in a subject after receiving a neo-antigen vaccine further comprises step c): evaluating the responsiveness of the subject to at least one boosting administration of the tumor neo-antigen vaccine based on the difference determined in step b).

[0376] [000537] In certain embodiments, the reference expression level is the standard or average expression level determined from a representative population of recurrent subjects.In certain embodiments, the reference expression level is the expression level of the corresponding gene in the corresponding immune cells that are representative of recurrent subjects.In such embodiments, the subject is determined as having a low risk of tumor recurrence when the difference reaches or exceeds a specified threshold, or the subject is determined as having a high risk of tumor recurrence when the difference is lower than a specified threshold.

[0377] [000538] In certain embodiments, the reference expression level is the expression level of the corresponding gene in the corresponding immune cell that is representative of relapse-free subjects.In certain embodiments, the reference expression level is the standard or average expression level determined from a representative population of relapse-free subjects.In such embodiments, the subject is determined as having high risk of tumor recurrence when the difference reaches or exceeds a specified threshold, or the subject is determined as having low risk of tumor recurrence when the difference is lower than a specified threshold.

[0378] [000539] In certain embodiments, the threshold is 20%, 30%, 40%, 50%, 60%, 70%, or 80%.

[0379] [000540] 3. Biomarkers of efficacy of combination of antitumor treatment and tumor neoantigen vaccine [000541] In another aspect, the inventors have unexpectedly identified several genes whose changes in certain immune cells correlate with therapeutic efficacy in subjects treated with anti-tumor therapy in combination with at least one administration of a tumor neo-antigen vaccine, and are therefore useful as biomarkers to assess such therapeutic efficacy.

[0380] [000542] In certain embodiments, the subject exhibits tumor recurrence after tumor neoantigen vaccination. In certain embodiments, the relapsed subject receives an anti-tumor treatment. In certain embodiments, the anti-tumor treatment is an immunotherapy (e.g., an anti-PD-1 treatment). In certain embodiments, the anti-tumor treatment includes a PD-1 antagonist. In certain embodiments, the PD-1 antagonist is an anti-PD-1 antibody or an anti-PD-L1 antibody.

[0381] [000543] In certain embodiments, the present disclosure provides a method for evaluating the therapeutic effect in a subject treated with an anti-tumor therapy in combination with at least one administration of a tumor neo-antigen vaccine. In certain embodiments, such a method comprises determining the level of one or more genes in one or more immune cells from a sample obtained from the subject after the anti-tumor treatment.

[0382] [000544] A) Biomarkers of efficacy of antitumor treatment combined with tumor neo-antigen vaccine [000545] In certain embodiments, the one or more genes are selected from the group consisting of AC092490.1, ALDH1L2, LILRB5, PALD1, PKP2 and TRAV35, or any combination thereof. The biomarkers are provided in Figure 36.

[0383] [000546] In certain embodiments, the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages. In certain embodiments, the one or more immune cells are selected from the group consisting of CD4+ T cells, monocytes, and B cells.

[0384] [000547] In certain embodiments, the one or more immune cells are CD4+ T cells and the one or more genes are LILRB5.

[0385] [000548] In certain embodiments, the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of ALDH1L2 and PKP2, or any combination thereof.

[0386] [000549] In certain embodiments, the one or more immune cells are B cells and the one or more genes are selected from the group consisting of AC092490.1, PALD1 and TRAV35, or any combination thereof.

[0387] [000550] B) Expression levels of biomarkers [000551] In certain embodiments, the expression level of a given gene (i.e., a biomarker) provided herein is determined in a given immune cell in a sample. In certain embodiments, the sample comprises or is derived from a peripheral blood mononuclear cell (PBMC), a blood sample, or a tumor-infiltrating immune cell.

[0388] [000552] In certain embodiments, the expression level of a given gene is represented by the percentage of immune cells of a given type that express the given gene.

[0389] [000553] In certain embodiments, the expression level of a given gene is represented by the average level of the given gene expressed in a given cell type.

[0390] [000554] Any suitable method may be used for such determination, such as those described in section 1.5.12 of Example 1. In certain embodiments, expression levels are determined by sequencing, such as single-cell RNA sequencing.

[0391] [000555] In certain embodiments, the method for evaluating the therapeutic effect in a subject treated with an antitumor therapy in combination with at least one administration of a tumor neo-antigen vaccine further comprises step b): comparing the level of one or more genes determined in step a) with a reference level and determining the difference from the reference level. In certain embodiments, the method further comprises evaluating the therapeutic effect in the subject based on the difference determined in step b).

[0392] [000556] In certain embodiments, the reference expression level is the expression level of the corresponding gene in a corresponding immune cell from a sample obtained from the subject before receiving the anti-tumor treatment.

[0393] [000557] In certain embodiments, the threshold is 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20%.

[0394] [000558] 4. Kit [000559] In another aspect, the present disclosure provides a kit for assessing a subject's responsiveness to a tumor neo-antigen vaccine, comprising one or more reagents for detecting an expression level of one or more genes in one or more immune cells from a sample obtained from the subject, the one or more genes being selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATF4, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, and / or IL-16. , DDOST, DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, GLA, LMAN2, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PDE4D, PIM1, PRKACA, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TMEM258, TRDV1, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof.

[0395] [000560] In another aspect, the disclosure further provides a kit for assessing a subject's responsiveness to a tumor neo-antigen vaccine during a priming phase, comprising one or more reagents for detecting expression levels of one or more genes in one or more immune cells from a sample obtained from the subject after at least one priming administration of the tumor neo-antigen vaccine, wherein the one or more genes are selected from the group consisting of AC011815.2, ATF4, C15orf54, DDOST, DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, LMAN2, MAP9, NUAK1, PDE4D, PRKACA, SERTAD2, SLC16A6, TMED10, TMEM258, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof.

[0396] [000561] In another aspect, the disclosure further provides a kit for assessing the responsiveness of a subject to a tumor neo-antigen vaccine during a boosting phase, comprising one or more reagents for detecting an expression level of one or more genes in one or more immune cells from a sample obtained from the subject during the boosting phase, wherein the one or more genes are selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, EGF, FERMT3, FOSL2, GK, GLA, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PIM1, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TRDV1 and ZDHHC7, or any combination thereof.

[0397] [000562] In another aspect, the present disclosure provides a kit for predicting risk of tumor recurrence in a subject before or after receiving at least one administration of a tumor neo-antigen vaccine, the kit comprising one or more reagents for detecting expression levels of one or more genes in one or more immune cells from a sample obtained from the subject, the one or more genes being selected from the group consisting of ABCA13, AC022726.2, AC105094.2, AC2438, AC311062, AC311064, AC311066, AC311068, AC311068, AC311069 ... 29.2, AL021807.1, AL391807.1, AL662907.3, APOBEC3C, ATP1B3, ATP6V1B2, C1QBP, CBX5, CCNE2, CD63, CEBPA, CHP1, CL N8, CMBL, CNIH4, COL4A3BP, CPNE3, DACH1, DNAJC12, DNAJC3, DOC2B, EEA1, ENAM, ERG, FAM43A, FBXO43, FIGN, GCA, GNG4, GNLY, GOLIM4, GPM6B, GPR171, GPRC5D, HHEX, HID1, ILF2, IRF2BP2, KIF22, LACC1, LIPA, MANEA, MECOM, MID1IP1, MX2, M YOM2, NABP1, NEFH, NFE4, NLN, OXCT2, P3H2, PI3, PIM1, PLAU, PRSS3, PSMA3, RAB10, RAB20, RASGRP2, RBMS3, RPL23, RPS6 The kit further provides a gene encoding a marker selected from the group consisting of KA5, RPS8, RUNX1, SAMD3, 11-Sep, SERTAD2, SIGLEC6, SIT1, SOCS1, SSR1, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC15, TXNDC17, UAP1, UFM1, UGT2B17, XPNPEP1, ZBTB16, ZNF608 and STAT1, or any combination thereof.

[0398] [000563] In another aspect, the present disclosure further provides a kit for evaluating therapeutic efficacy in a subject treated with an anti-tumor therapy in combination with at least one administration of a tumor neo-antigen vaccine, comprising one or more reagents for detecting levels of one or more genes in one or more immune cells from a sample obtained from the subject after treatment, wherein the one or more genes are selected from the group consisting of AC092490.1, ALDH1L2, LILRB5, PALD1, PKP2 and TRAV35, or any combination thereof.

[0399] [000564] Measuring or detecting can be at the RNA level, DNA level and / or protein level. Suitable reagents for detecting target RNA, target DNA or target protein can be used.

[0400] [000565] In certain embodiments, the detection reagent comprises a primer or probe capable of hybridizing to a polynucleotide of a gene of interest (e.g., a biomarker for assessing responsiveness to a neo-antigen vaccine as disclosed herein, a biomarker for the priming phase as disclosed herein, a biomarker for the boosting phase as disclosed herein, a biomarker for tumor recurrence as disclosed herein). In some embodiments, the primer and / or probe may or may not be detectably labeled. In certain embodiments, the kit may further comprise other reagents for carrying out the methods described herein. In such applications, the kit may comprise any or all of the following: suitable buffers, reagents for isolating nucleic acids, reagents for amplifying nucleic acids (e.g., polymerase, dNTP mix), reagents for hybridizing nucleic acids, reagents for sequencing nucleic acids, reagents for quantifying nucleic acids (e.g., intercalating agents, detection probes), reagents for isolating proteins, and reagents for detecting proteins (e.g., secondary antibodies). Typically, the reagents useful in any of the methods provided herein are contained in a carrier or multi-chambered container. The carrier may be, for example, a container or support in the form of a bag, box, tube, rack, optionally compartmentalized.

[0401] [000566] The term "primer" as used herein refers to an oligonucleotide that can specifically hybridize to a target polynucleotide sequence due to sequence complementarity of at least a portion of the primer within the sequence of the target polynucleotide sequence. A primer can have a length of at least 8 nucleotides, typically 8-70 nucleotides, usually 18-26 nucleotides. For proper hybridization to a target sequence, a primer can have at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% sequence complementarity to the hybridizing portion of the target polynucleotide sequence. Primers are useful in nucleic acid amplification reactions in which the primer is extended to produce a new strand of polynucleotide. Primers can be readily designed by one of skill in the art using common knowledge known in the art such that they can specifically anneal to a nucleotide sequence of a target nucleotide sequence of at least one biomarker provided herein. Typically, the 3' nucleotide of the primer is designed to be complementary to the target sequence at the corresponding nucleotide position to provide optimal primer extension by a polymerase.

[0402] [000567] The term "probe" as used herein refers to an oligonucleotide or analog thereof that can specifically hybridize to a target polynucleotide sequence due to sequence complementarity of at least a portion of the probe within the sequence of the target polynucleotide sequence. Exemplary probes can be, for example, DNA probes, RNA probes, or protein nucleic acid (PNA) probes. A probe can have a length of at least 8 nucleotides, typically 8-70 nucleotides, usually 18-26 nucleotides. For proper hybridization to a target sequence, a probe can have at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% sequence complementarity to the hybridizing portion of the target polynucleotide sequence.

[0403] [000568] In certain embodiments, the primers or probes provided herein comprise polynucleotide sequences hybridizable to the polynucleotides of the gene of interest (e.g., biomarkers for assessing responsiveness to a neo-antigen vaccine disclosed herein, biomarkers for the priming phase disclosed herein, biomarkers for the boosting phase disclosed herein, biomarkers for tumor recurrence disclosed herein). In certain embodiments, the primers or probes provided herein comprise polynucleotide sequences having at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or 100% complementarity to a portion within the polynucleotide of the gene of interest (e.g., biomarkers for assessing responsiveness to a neo-antigen vaccine disclosed herein, biomarkers for the priming phase disclosed herein, biomarkers for the boosting phase disclosed herein, biomarkers for tumor recurrence disclosed herein).

[0404] [000569] Additionally, the kits can include instructions containing directions (i.e., protocols) for the practice of the methods provided herein. Instructions typically include, but are not limited to, written materials or printed materials.

[0405] [000570] In certain embodiments, the kit may further comprise a computer program product stored in a computer readable medium. When the computer program product is executed by a computer, it performs the steps of predicting the risk of tumor recurrence in a subject before or after receiving at least one dose of the tumor neo-antigen vaccine, evaluating the therapeutic effect in a subject treated with an anti-tumor therapy in combination with at least one dose of the tumor neo-antigen vaccine according to the method disclosed herein, and evaluating the responsiveness of the subject to the tumor neo-antigen vaccine. Any medium capable of storing such computer executable instructions and transmitting them to an end user is contemplated by the present invention. Such medium includes, but is not limited to, electronic storage media (e.g., magnetic disks, tapes, cartridges, chips), optical media (e.g., CD ROM), and the like. Such medium may include an address to an internet site providing such instructions.

[0406] [000571] The computer program may be coded and communicated using a carrier wave adapted for communication over wired, optical, and / or wireless networks compatible with various protocols, including the Internet. As such, a computer readable medium according to an embodiment of the present invention may be created using a data signal coded by such a program. A computer readable medium coded by a program code may be packaged with a compatible device or provided separately from other devices (e.g., by Internet download). Any such computer readable medium may be present on or within a single computer product (e.g., a hard drive, CD, or entire computer system) or may be present on or within different computer products within a system or network.

[0407] [000572] In some embodiments, the present disclosure provides oligonucleotide probes attached to a solid support, such as an array slide or chip, as described, for example, in DNA Microarrays: A Molecular Cloning Manual (2003), edited by Bowtell and Sambrook, Cold Spring Harbor Laboratory Press. The construction of such devices is well known in the art. EXAMPLES

[0408] Example 1 [000573]1.1 Method [000574] 1.1.1 Study Design [000575] We conducted a prospective, open-label, single-arm Phase Ib study in one medical center in China. The authors designed the study. The personalized neo-antigen vaccine was supplied to Shanghai Hospital by ANDA Biopharmaceutical Development. An independent Data Safety Monitoring Committee was established to review all study data and ensure the ethical conduct of the study. The study was approved by the Institutional Review Board at Shanghai Hospital, Shanghai, China. All participants provided written informed consent.

[0409] [000576] Patients and Procedures [000577] Eligible patients were aged 20-75 years, had pathologically confirmed pancreatic ductal adenocarcinoma, had not received chemotherapy prior to resection, and had undergone complete microscopic (R0 [no cancer cells within 1 mm of all resection margins]) resection (Table 1). Key exclusion criteria were radiographically confirmed recurrence or metastasis within 180 days after surgery, poor postoperative recovery, clinically significant organ failure, unstable angina, symptomatic congestive heart failure, significant arrhythmias, myocardial infarction within the past 6 months, prolonged QT interval (>450 ms), and history of malignancy other than pancreatic cancer.

[0410] [000578]

[0411] [Table 1]

[0412] [000579] After enrollment, all patients underwent tumor resection surgery, and a portion of tumor tissue, adjacent tissue and peripheral blood samples were sent to the pathology department for pathological examination and to a third-party company for DNA and RNA sequencing to identify tumor-specific mutations. Personalized neo-antigen peptides were designed according to those mutations and the patient's HLA type, and then synthesized. Clinical samples are described in the Methods section of the supplement. Before the personalized vaccine was prepared, all patients received gemcitabine, Abraxane or S-1 adjuvant chemotherapy. After chemotherapy, patients were evaluated for continued participation according to the inclusion / exclusion criteria. Then, all eligible patients were administered five doses of priming vaccination and two doses of boosting vaccination within one month. If tumor recurrence or abnormal elevation of serum CA19-9 or CA72-4 levels was found during treatment, patients were also treated with conventional chemotherapy or PD-1 / PD-L1 antibodies.

[0413] [000580] The personalized vaccine consisted of 8-25 different peptides (by 27 amino acids) grouped into 2-4 pools and 0.5 mg of poly-ICLC as adjuvant for each pool. The vaccine was administered subcutaneously on days 1, 4, 8, 15, and 22 (priming phase) and at weeks 12 and 20 (boosting phase). Dosing was 0.3 mg / peptide for each patient (Table 2) and the injection site was the non-rotating limb. Details of the manufacturing and procedures are described in the Methods section of the supplement.

[0414] [000581]

[0415] [Table 2]

[0416] [000582] 1.1.2 Evaluation items and evaluation [000583] The primary endpoint is safety, assessed by the rate of grade 3 or higher adverse events (graded according to the Common Terminology Criteria for Adverse Events, 5th Edition, established by the National Cancer Institute, USA). Adverse events were assessed throughout vaccination for their incidence, grade and association with the vaccine up to 2 years after surgery. Safety assessments also included clinical examinations, electrocardiograms, abdominal echocardiograms, body temperature, heart rate, blood pressure, respiratory rate, and physical appearance of the skin and the five senses.

[0417] [000584] Key secondary endpoints were post-treatment serum CA19-9 or CA72-4 levels, overall survival (OS) and recurrence-free survival (RFS). We assessed the proportion of patients without abnormal elevation of serum CA19-9 or CA72-4 levels during vaccination and follow-up after treatment (abnormality defined as CA19-9 ≥ 90.65 U / mL or CA27-4 ≥ 14.7 U / mL). OS was calculated from the date of surgery to the date of death (any cause). RFS was calculated from the date of surgery to the date of first tumor recurrence (confirmed by imaging). Data of patients without events at the time of analysis were censored at the date of last follow-up.

[0418] [000585] Exploratory endpoints were immunological correlates of responses in peripheral blood after and during vaccination. Ex vivo ELISpot was performed to detect peptide IFN-γ responses upon stimulation of PBMCs. Ten-color flow cytometry and single-cell transcriptome sequencing were performed in all 12 patients to evaluate immunological correlates. Single-cell T / B cell repertoire (TCR / BCR) sequencing in 10 patients was performed to profile the expansion of T / B cell clonotypes. Vaccine-associated immunological responses were evaluated by comparison with pre-vaccination (baseline). Details of sequencing and analysis are described in the Methods section of the supplement.

[0419] [000586]1.1.3 Statistical analysis [000587] All analyses were performed in patients who completed 7 doses of the vaccine. Adverse events and side effects were summarized descriptively. We assumed that a treatment was not safe if the probability of a grade 3 or higher adverse effect was 50% or higher than 25%. The probability of risk of grade 3 or higher AEs was calculated by exact binomial test (one-sided). Increases in serum CA19-9 or CA72-4 levels were summarized descriptively. Kaplan-Meier methods were used to analyze recurrence-free survival and overall survival (further details were provided in the Methods section of the Supplement). The extent of follow-up included the entire follow-up time until July 2022. We performed immunological analyses on available biological samples and correlation data were analyzed as described in the Methods section of the Supplement. P values ​​reported were two-sided and the significance level was set at 0.05 for all analyses unless otherwise stated. R software (version 3.6.1) was used for all statistical analyses and plotting.

[0420] [000588]1.2 Results [000589] 1.2.1 Extending survival of pancreatic cancer patients receiving personalized neoantigen immunotherapy after surgery [000590] From February 9, 2018 to April 4, 2020, we enrolled 14 patients, all of whom received at least one dose of the vaccine at the Department of Hepatobiliary and Pancreatic Surgery of Shanghai Hospital. Of these patients, one withdrew from informed consent, one refused to undergo evaluations specified during the protocol, and finally 12 were eligible for inclusion in the study (Table 3). They were Han Chinese with a mean age of 59.2 years. Among them, 11 and 10 patients had somatic mutations in TP53 and KRAS, respectively, which frequently occur in PDAC.

[0421] [000591]

[0422] [Table 3-1]

[0423] [Table 3-2]

[0424] [000592] [000593] [000594] [000595] [000596] At a median postoperative follow-up of 44.25 months (range 31-53.5), 3 of the 12 patients died after tumor recurrence, but their deaths were unrelated to the vaccine. The 2- and 3-year OS rates were 100% (95% CI, 74-100) and 83% (95% CI, 52-98), respectively. The 2- and 3-year RFS rates were 83% (95% CI, 52-98) and 67% (95% CI, 35-90), respectively (Figure 1A). The mean OS of the 12 patients was 44.6 months (range, 31.4-54.6), and the mean RFS was 38.3 months (range, 8.6-54.6). The mean OS and RFS of enrolled patients were numerically greater than those of historical controls from the same hospital or patients treated with adjuvant chemotherapy in other published data for PDAC (Figure 1BC).

[0425] [000597] At a median postoperative follow-up of 31 months (range 18.5-58.5), serum CA19-9 levels in nine patients (75%, 95% CI, 42.8-94.5) (two [P3 and P6] had tumor recurrence confirmed by imaging at the end) were stable and mostly below the upper limit of normal reference (37 U / mL). Of these nine patients, P6 showed two abnormal increases in CA72-4 levels after priming and boosting vaccinations, respectively. Two patients (16.7%, 95% CI, 2.1-48.4) (two patients with tumor recurrence confirmed by imaging) experienced an increase in serum CA19-9 levels and a decline to normal after boosting vaccinations, but ultimately had extremely high levels (Figures 1A and 1B).

[0426] [000598]

[0427] [Table 4]

[0428] [000599] 1.2.2 T cell activation and upregulation of IFN-γ response pathways in activated T cells during neoantigen vaccination using single cell sequencing [000600] Patients' PBMCs were collected on the day of vaccination, and at weeks 7, 15, and 23, and single-cell RNA sequencing (scRNA-seq) was performed. Among immune cells, T cells accounted for the highest proportion, followed by NK cells and monocytic cells (Figure 5A). During vaccination, B cells, megakaryocytes, and monocytic cells significantly increased, while NK cells significantly decreased (Figure 5B).

[0429] [000601] Diverse TCR gene expression was significantly increased after vaccination, especially during the booster phase (Figure 2A). Single cell TCR sequencing (scTCR-seq) simultaneously confirmed a significant increase in TCR clonal diversity in CD8+ T cells (Figure 2B). CD8+ T cells were slightly increased on day 22 (i.e., at the end of the priming phase). Effector T, Th1, Th9 and Treg cells were significantly increased in the boosting phase, and relapsed patients showed more exhausted T cells (Figure 2C).

[0430] [000602] Tumor-infiltrating T cells were identified by comparing TCRs in the tumor with TCR clones in the blood from scTCR-seq. Infiltrating T cells were enriched and amplified in the blood of relapse-free patients (Figures 6A and 6B and Figure 2D). scRNA-seq showed that T cell activation was associated with genes including proliferation, cytotoxicity, interferon response, and cytokines in both priming and boosting phases in CD4+, CD8+, and CD4 / CD8low T cells (Figure 8). The above results indicate active responses of T cells during neo-antigen immunotherapy. Flow cytometry analysis further confirmed T cell activation by an increase in 4-1BB+ and CD69+ cells in T cells in the blood of relapse-free patients (Figure 2E and Figure 7).

[0431] [000603] Transcriptional profiling of changes in T cells during vaccination showed that IFN-γ response and proliferation-related G2M gene signatures were enriched in CD4+, CD8+ or other T cells (more than CD8+) (Figure 2F and Figure 9A). By examining non-relapse vs. relapse, activation of the IFN-γ response pathway was also more prominent in the non-relapse group (Figure 2G), with genes involved in T cell activation and antigen presentation prominent (Figure 10A). Similarly, the average expression of IFN-γ and G2M gene modules both increased significantly in T cells during vaccination (Figure 2H and Figure 9B). Furthermore, a key downstream gene for IFN-γ response, STAT1, showed significantly higher expression in T cells in the non-relapse group than in the relapse group (Figure 2I). This difference in STAT1 remains in T cells infiltrating the patient's tumor (Figure 10B). The above results indicate a critical role of the IFN-γ response pathway in T cell activation during neoantigen vaccination.

[0432] [000604] 1.2.3 Enhanced antitumor activity of central memory and tumor-reactive T cells by upregulation of IFN-γ response pathway genes [000605] Elispot assay showed that most of the neoantigen peptides, including the four neoantigen peptides (PCNAT-4-1 to 4) designed for patient P4 (Figure 3A), could efficiently stimulate the secretion of IFN-γ from the patient's PBMCs (Figure 11). However, only PCNAT-4-2 and PCNAT-4-3 exerted their tumor-killing function (Figure 3A). In those situations, in CD8+ T cells, there were two predominant subtypes of cells, mainly central memory (CM) cells and tumor-reactive (TReactive) cells. 7 (Fig. 3C-3E). The difference in TCR clones between these two subtypes was significant compared to the blank (Fig. 3F), indicating the expansion of peptide-specific T cells after stimulation. These two subtypes specifically expressed MX1 and STAT1 (Fig. 3D). Although these subtypes co-expressed many shared IFN-γ response genes (Fig. 12A and Fig. 13A), they seem to be activated in different phases. The TReactive subtype was likely to be activated upon priming and maintained during vaccination, whereas the CM subtype was not elevated upon priming in CD8+ T cells (Fig. 12B-12D). Those marker genes including MX1 and other IFN-γ response genes were also significantly upregulated in the blood of patients after vaccination (Fig. 3G and 3H and Fig. 13B). The association between IFN-γ response and tumor killing may also support that patients gained antitumor capabilities after neoantigen vaccination. However, the IFN-γ response pathway was not stimulated in the predominant subpopulations of CD4+ and CD4 / CD8low T cells (Figure 14). In CD4+ T cells, the CXCL13+ subpopulation was the predominant cluster in the stimulation of PCNAT-4-2-4 (Figures 14A and 14B). The CXCL13+ subpopulation of CD4+ T cells has been reported to be responsible for antitumor cytotoxicity in tumor-infiltrating T cells. 7In CD4 / CD8low T cells, a high proportion of the subpopulations were effector T cells (CXCR6+, KLRB1+ and CTSH+) (Figures 14A and 15C). These markers were also significantly upregulated in the blood of patients after neoantigen vaccination (Figures 15A and 15B).

[0433] [000606] MX1, a marker of CM and TReactive cells, positively correlated with genes involved in cytotoxic function during vaccine treatment (Figure 3I). Most of the genes that were strongly positively correlated with MX1 were related to T cell activation (Figure 16). MX1 depletion clearly impaired the antitumor ability of immune cells in PBMCs (Figures 3J and 3K).

[0434] [000607] 1.2.4 Benefit from the combination of neoantigen vaccination and anti-PD-1 treatment for patients with tumor relapse by activating bacterial stimulatory pathways in CD8+ T cells [000608] Three patients who had tumor recurrence before / during vaccination were treated with adjuvant anti-PD1 in the boosting phase (). Combination treatment significantly increased the expression of genes associated with cytotoxicity, which were enriched in CD8+ T cells (Figure 4A). Enriched functions of these genes were response to molecules of bacterial origin and cellular response to biostimulatory pathways (Figure 4B), and the average expression of these two modules was significantly increased only in relapsed patients after combination treatment (Figure 4C). Patients who responded to anti-PD1 had higher CD4+ T cells in peripheral blood than those who did not (Figure 4C). 8 and tumor-infiltrating T cells 7 Although it has been reported that patients benefit from this, our results showed that when using a combination of neoantigen vaccine and anti-PD1, the primary response in T cells was enriched in CD8+ T cells. Expression of these genes was positively associated with progression-free survival in the atezolizumab-containing arm from three clinical trials. 9~11 (FIG. 4D). These results are expected to partly explain why disease in recurrent patients can be controlled in our study.

[0435] [000609] 1.2.5 Dynamics of immunological responses in peripheral blood [000610] As immunological parameters are important determinants of efficacy, we also investigated immunological correlates in peripheral blood. IFN-γ responses of 96% (177 / 185) of individualized neoantigen peptides or pools in stimulating PBMCs were detectable by ex vivo ELISpot in all 12 patients. Ten-color flow cytometry analysis of blood samples showed an increase in the proportion of CD69+ or CD28+ T cells and B or NK cells during vaccination (Figure 37). Using single-cell transcriptome sequencing, we investigated the systemic changes in the percentage of immune cell types and their subtypes (data below are shown as pre-vaccine mean ± SE [%] against post-vaccine maximum mean ± SE). Compared to pre-vaccination (Figure 38), the inventors found that inflammation-related macrophage subtypes (CD63+NFKBIA+APOBEC3A+20,21; 12.9±18.3 to 53.4±15.5), macrophage_1 (CDKN1C+CKB+; 23.7±10.1 to 43.9±7.2), and T cell_6 (CDKN1C+CKB+; 23.7±10.1 to 43.9±7.2) in non-relapse patients. We observed a significant increase in cytotoxic T cells (PTGDS+GNLY+GZMB+PRF1+; 2.8±0.3-4.8±1.2), central memory T cells (CCR7+SELL+LEF1+; 22.1±3.2-28.3±4.3), and B cells_0 (TCL1A+IL4R+CXCR4+, which could be early B cells 22; 34.4±6.2-61.9±7.7), as well as a significant increase in HLA gene+ monocytes (47.3±2.9-73.1±6.9), proliferation-associated T cells (STMN1+HMGB2+MKI67+; 1.1±0.3-11±10.3), and immunoglobulin gene-positive B cells (4.9±1.9-35±31.6) in relapsed patients.Both relapsed and non-relapsed patients showed a significant increase in regulatory T cells (RTKN2+IL2RA+FOXP3+; 2.7±0.3 to 5.7±0.5 in non-relapsed patients; 3.1±0.4 to 5.1±0.3 in relapsed patients), as well as γδ-T cells (TRDC+TRGC1+KLRB1+; 6.5±1.5 to 3.2±0.4 in non-relapsed patients; 9±2.5 to 7.8±4 in relapsed patients). 9), and showed a significant decrease in antigen-specific memory B cells (FCRL5+ZEB2+23; 11.9±2.8-8.6±2.6 in relapse-free patients; 8.1±0.3-3.4±3.4 in relapsed patients) and germinal center B cells (AIM2+TNFRSF13B+24,25; 36.2±5-24.8±6.2 in relapse-free patients; 35.5±6.8-8.7±8.7 in relapsed patients).

[0436] [000611] 1.2.6 Identification of genes associated with significant differential changes in immune responses in peripheral blood of pancreatic cancer patients treated with personalized neoantigen vaccines [000612] 1.2.6.1 Genes whose expression levels changed significantly during the priming or boosting phase of vaccination compared to pre-vaccination (Table 5) [000613] Genes were selected with adjusted P-value <0.05, percent change (%, compared to baseline) >10 and AUC >0.9.

[0437] [000614]

[0438] [Table 5-1]

[0439] [Table 5-2]

[0440] [Table 5-3]

[0441] [Table 5-4]

[0442] [000615] 1.2.6.2 Genes significantly differently expressed in patients with and without tumor recurrence (Table 6) [000616] Genes were selected with adjusted P-value <0.05, percent change (%, recurrence-free vs. recurrence) >40 and AUC >0.9.

[0443] [000617]

[0444] [Table 6-1]

[0445] [Table 6-2]

[0446] [Table 6-3]

[0447] [Table 6-4]

[0448] [Table 6-5]

[0449] [Table 6-6]

[0450] [Table 6-7]

[0451] [Table 6-8]

[0452] [Table 6-9]

[0453] [Table 6-10]

[0454] [Table 6-11]

[0455] [Table 6-12]

[0456] [000618] 1.2.6.3 Genes whose expression was significantly altered following vaccine in combination with PD1 treatment (Table 7) [000619] Genes were selected with adjusted P value <0.05 and percent change (%, compared to pre-anti-PD1) >10.

[0457] [000620]

[0458] [Table 7]

[0459] [000621] 1.2.7 Expansion of T cell clonotypes during vaccination [000622] To test the hypothesis that vaccination increased the frequency of specific T cell clones in blood, we first performed single cell TCR sequencing on blood samples from 10 patients (P3-12; 3 with relapse and 7 without relapse) before vaccination and at the end of the priming and boosting phases. After vaccination, a mean of 67.3% (range, 38.3-84.6) of the detected T cell clonotypes per patient were abundantly expanded, whereas a minority (mean 2.3%, range 0.7-6.1) were hyperclonal. Among the expanded hyperclonal clonotypes, 46.4% (range, 16.7-80) per patient were pre-existing hyperclonals before vaccination (Guarding T cell clonotypes [GD-T]) and the remainder were expanded from nonclonals (vaccine-associated de novo T cell clonotypes [VRD-T]). We further classified the expanded clones by subtype and phase (Figure 39). We observed that CD8+ T cells had a high average percentage of priming phase expanded cells, while CD8+ memory T cells and CD4+ T cells had a high average percentage of boosting phase expanded cells. Both GD-T and VRD-T cells had a high average percentage of priming phase expanded cells and were enriched for CD8 / 4+ cytotoxic T cells. Three patients who relapsed (P4, P6, P9) had a low percentage of GD-T in CD8+ cytotoxic T cells, and P4 also had less GD-T in CD4+ cytotoxic T cells. P9 had less VRD-T in CD8 / 4+ cytotoxic T cells.

[0460] [000623] To test whether these expanded T cell clonotypes after vaccination were directed against neoantigens, we identified neoantigen-specific T cell clones in the blood of patient P6 using DNA-barcoded neoantigen peptide-MHC tetramers by single-cell TCR sequencing. Among hyperclonal T cell clonotypes, those expanded after vaccination had a significantly higher proportion (29.8%, 95% CI, 17.3-44.9) of neoantigen 85-specific T cells (Figure 40) than those of non-expanded clones (P=0.005, Pearson's chi-square test) (7.0%, 95% CI, 1.9-17.0).

[0461] [000624] 1.2.8 Expansion of B cell clonotypes during vaccination [000625] To test whether vaccination induces specific B cell clones in the blood, we also performed single cell BCR sequencing on blood samples from 10 patients (P3-12). An average of 70.3% (range, 55.3-88.4) of all B cell clonotypes were heavily expanded after vaccination, but only 0.35% (range, 0-0.83) were clonal. Two relapse patients (P4 and P9) and one non-relapse patient (P11) did not expand clonal B cells. These expanded clonal B cells were enriched in AIM2+CRIP1+ and TCL1A+IL4R+FCER2+ early B cells (Figure 41), consistent with the increase in the percentage of TCL1A+ B cells in non-relapse patients seen above.

[0462] [000626]1.3 Discussion [000627] This phase Ib study demonstrated that treatment with a personalized neoantigen peptide-based vaccine after postoperative chemotherapy was safe in 12 patients with resected PDAC. All enrolled patients were well tolerated with only mild adverse events. A large percentage of patients were alive 3 years after surgery, and more than half of the patients achieved beneficial control of tumor recurrence. Inflammatory macrophages, cytotoxic T cells, central memory T cells, and TCL1A+B cells could be systemically increased or clonally expanded by vaccination.

[0463] [000628] Due to the progression of PDAC with continued treatment resistance and aggressive cell biology, available treatment options are insufficient to achieve a curative outcome. 12、13 As a result, clinical trials continued to be largely based on empirical drug combinations. Surgery was the only available option for long-term treatment and could extend overall survival by a mean of 10 months. 14 Empirical drug combinations are currently the primary treatment strategy in clinical trials. 15 However, patients have poor long-term survival due to a high frequency of tumor recurrence. Here, our study demonstrates that an adjuvant personalized neoantigen vaccine is feasible and can immunize patients and enhance the control of PDAC recurrence after surgical resection, thus resulting in beneficial clinical outcomes. Indeed, long-term pancreatic cancer survivors appear to harbor highly immunogenic naturally occurring tumor neoantigens, indicating that neoantigen-based immunotherapy may be beneficial for the survival of pancreatic cancer patients. 16 .

[0464] [000629] Using time-course single-cell sequencing at different time points, our study first aimed to determine how the functional state of circulating vaccine-induced immune cells dynamically evolved during vaccination. The results showed that after vaccination, especially in the boosting phase, patients could elicit neo-antigen-specific T cell immunity, including IFN-γ-responsive genes (e.g., STAT1 and MX1). Although the primary role of IFN is involved in antiviral immune responses, our data demonstrated the relationship of these molecules to tumor killing and patient prognosis. Indeed, intratumoral stimulation of IFN or downstream genes correlated with the control of virus-independent malignancies. 17 , IFN-stimulated gene agonists exerted antitumor functions in PDAC-implanted mice 18 The relationship between TCR diversity and ICI therapeutic response is controversial. 19、20 and anti-PD1 treatment 21、22 Although anti-CTLA4 treatment is associated with improved clinical outcomes in patients with BRCA1-associated ... 23 TCR diversity in peripheral CD8+ T cells may serve as a predictor of patient prognosis before ICI treatment in non-small cell lung cancer 24 In contrast, neoantigen vaccines, with subdominant affinity recognized by many other T cells, may expand the breadth of the TCR repertoire and clonal diversity. 25、26 In our study, TCR diversity was significantly increased after neo-antigen vaccination, especially in the booster phase. The relevant genes and amplified TCRs identified in this study may be used in the future as detection targets to stratify patients for neo-antigen vaccine sensitivity, but a large patient population is needed to evaluate the results.

[0465] [000630] Although the response rate to anti-PD1 therapy is low 27In our study, although with limited sample size, two patients showed a reduction in tumor index after combination of neo-antigen and anti-PD1 treatment. As mentioned above, relapsed patients lacked strong T cell responses before and / or after vaccination, indicating that differences in T cell status may explain the weak immune status of these patients during vaccination. After combination with anti-PD1 treatment, relapsed patients elicited activation of cytotoxic genes in CD8+ T cells in addition to those activated by neo-antigen vaccine alone in non-relapsed patients. During neo-antigen vaccination, combined anti-PD1 treatment indicates that it can alleviate the patient's immune suppression, but the patient's immune cells may not express PD1 or the tumor may not express PDL1. For these patients, after administration of anti-PD1, neo-antigen vaccine may help activate bacterial stimulatory pathways in CD8+ T cells that are different from those seen by neo-antigen vaccine alone in non-relapsed patients and different from those seen by anti-PD1 alone in other studies. These data provide a rationale and highlight the possibilities for further development of neo-antigen vaccine alone and in combination with ICI treatment.

[0466] [000631] 1.4 Reference

[0467] [Table 8-1]

[0468] [Table 8-2]

[0469] [Table 8-3]

[0470] [Table 8-4]

[0471] [000632] 1.5 Addendum Method [000633] 1.5.1 Clinical Data and Biological Specimens [000634] During the vaccine treatment period, adverse events, laboratory values, 12-lead electrocardiograms (ECGs), vital signs, and physical examinations were evaluated periodically and graded according to the Common Terminology Criteria for Adverse Events, 5th edition, established by the National Cancer Institute, United States, on the date of first vaccination and 1 week after each vaccination. During the follow-up period, the above safety assessments were performed every 3 months, and radiological examinations were performed every 6 months. Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1) and immune-related response criteria (irRC) guidelines were used for clinical evaluation of disease progression. Data cut-off was October 2021. Blood and serum samples were obtained from study participants throughout treatment. Sample storage and culture are described in the Methods section of the supplement.

[0472] [000635] 1.5.2 Production of personalized neo-antigen vaccines [000636] Next generation sequencing [000637] The personalized neo-antigen vaccine was prepared based on the analysis of whole exome sequencing (WES) and RNA-seq data generated from freshly frozen tumors and whole blood of patients obtained at the time of diagnostic resection. Whole blood and tumor tissue samples' whole exome sequencing (WES), and tumor tissue samples' RNA sequencing were performed by Shanghai Biotecan Laboratory. DNA of tumor tissue samples was extracted using QIAamp DNA Mini Kit (QIAGEN), whole blood's DNA was extracted using QIAamp DNA Blood Mini Kit (QIAGEN), and total RNA of tumor tissue samples was extracted using RNAiso Plus (TAKARA). DNA libraries were constructed using the SureSelectXT Target Enrichment System for Illumina Paired-End Multiplexed Sequencing Library (Agilent Technologies), and RNA libraries were constructed using the NEBNext rRNA Depletion Kit (Human / Mouse / Rat) (E6310) and NEBNext Ultra Directional RNA Library Prep Kit for Illumina (E7420) (NEW ENGLAND BioLabs). WES and RNA-sequencing raw data were generated using the NextseqTM CN 500 System (Illumina).

[0473] [000638] Somatic mutation calling.

[0474] [000639] For somatic mutation detection, tumors and matched blood samples from patients were analyzed for single nucleotide variants. We used the BWA-MEM algorithm, which is usually recommended for high-quality queries, to map WGS and WES data to the human reference genome hg19 with default parameters. We used fastp (version 0.20.0) to create a quality control for the sequencing data. Clean data was aligned to NCBI Human Reference Genome Build hg19 using Burrows-Wheeler Aligner software (BWA, version 0.7.17). Somatic single nucleotide variations (sSNVs) were detected using Genome Analysis Toolkit (GATK, version 4.1.1.0) and VarScan2 (version). All somatic mutations were annotated using Ensembl Variant Effect Predictor (VEP, version 3.9) to associate variants with genes, transcripts, and possible amino acid sequence changes.

[0475] [000640] Transcript abundance and HLA calling.

[0476] [000641] For transcript abundance estimation, RNA-seq data were processed using Aligner HISAT2 (version 2.1.0) for RNA-seq mapping to the hg19 reference genome, and transcriptome models were assembled and transcript abundance estimated using StringTie (version 1.3.6).

[0477] [000642] For HLA calling, 4-digit HLA class I alleles (HLA-A, HLA-B, and HLA-C) and class II alleles (HLA-DRB1, HLA-DQB1, HLA-DPB1) were identified from the RNA-seq data using Seq2HLA software.

[0478] [000643] Identification of neoepitopes for peptide design.

[0479] [000644] For each nonsynonymous mutation identified by targeted NGS from the patient, a long peptide with 27 amino acids containing the mutated amino acid at position 14 was designed. The peptide binding prediction tool ANN method (version 2.19.2) from the Immune Epitope Database (IEDB) was used to predict the MHC class I binding of the 8-11mer mutant peptides to the patient's HLA-A, HLA-B, and HLA-C alleles. NetMHCII (version 2.17.6) was used to predict the MHC class II binding of the 15mer mutant peptides to the patient's HLA-DR, HLA-DQ, and HLA-DP. The HLA binding affinity scores of each variant were predicted and the best consensus was screened.

[0480] [000645] Neoepitope prioritization and selection.

[0481] [000646] Mutated target neoepitopes per patient are required to be selected and prioritized for peptide preparation. Main principles were applied to rank the neoepitope scores: (1) neo-ORFs containing the predicted binding epitope; (2) high affinity binding scores (<500 nM) combined with RNA encoding the mutation at high expression levels; (3) high variant allele frequency.

[0482] [000647] Long peptide production, pooling and neo-antigen vaccine preparation.

[0483] [000648] 27 amino acid long neoantigen-derived peptides were synthesized (Sangon Biotech, Shanghai, China) and purified (Qiaoyuan Biotech, Shanghai, China) under Good Manufacturing Practice (GMP) methods. Bottles of 300 μg of each peptide were produced and stored frozen at -80°C. Each peptide was tested for identity, sterility and endotoxin prior to clinical use. Each patient's vaccine has four pools (A, B, C, D), with each pool containing 4-6 different peptides. On the day of vaccination, each pool was added with 2 ml of 5% glucose injection and mixed with 0.25 ml of poly-ICLC for the final dose of vaccine administered subcutaneously (sc) on days 1, 4, 8, 15, 22 (priming phase) and at weeks 12 and 20 (booster phase). Each of the four vaccine pools was injected into two arms and inner thighs of the patient.

[0484] [000649] 1.5.3 Vaccine Administration [000650] The detailed vaccine administration plan is as follows: the right arm, left arm, right thigh and left thigh of the patient are selected as four injection sites, and multiple neo-antigen peptides designed for each patient are randomly and evenly distributed to the above four injection sites. If the number of peptides designed for a patient is less than 15, the vaccine is divided into two groups for injection on average to avoid the situation of too little vaccine in each group. The vaccine is transported to the hospital with dry ice on the day of treatment to ensure the stability of the peptide vaccine.

[0485] [000651] In this treatment protocol, the vaccine dose and vaccination interval refer to the article by Catherine J Wu in Nature, who demonstrated the possibility of neoantigen vaccine therapy and designed a treatment protocol for patients with melanoma. In this clinical trial, we used different doses for different patients, with a minimum of 2.4 mg and a maximum of 7.5 mg. Many clinical trials have shown that adding poly-ICLC as an adjuvant can further accelerate the induction of specific immune responses to neoantigen vaccines (Sabbatini P et al. Clin Cancer Res. 2012). Adjuvant doses (1-1.6 mg) are commonly used in clinical treatments (Okada H et al. J Clin Oncol. 2011; Rosenfeld MR et al. Neuro Oncol. 2010). This dose can ensure effective stimulation with few side effects. This project used 0.5 mg poly-ICLC for each vaccine group.

[0486] [000652] 1.5.4 Biological specimen collection, storage, and culture [000653] Blood and serum samples were obtained from study participants throughout treatment. Patient PBMCs were isolated by Ficoll density gradient centrifugation (GE Healthcare) and cryopreserved with 10% DMSO in FBS (Gemini). Cells and serum from patients were first gradually cooled to -80°C in a cryopreservation box and then stored in liquid nitrogen until the time of analysis.

[0487] [000654] Fresh tumor samples were obtained immediately after surgery. A portion of the sample was set aside for formalin fixation and paraffin embedding (FFPE). For construction of patient-derived tumor cell lines, the samples were minced and digested using the gentleMACS Octo system. After dissociation, the cell suspension was filtered through a 70 μm filter, washed in DMEM medium with FBS, and pelleted by centrifugation at 400 g for 10 minutes at 18° C. The cells were then resuspended in DMEM medium with 20% FBS and cultured in 6-well plates. The remainder of the sample was used for the generation of personalized neo-antigen vaccines, and DNA and RNA sequencing was performed if pathology reexamination confirmed sufficient tumor cellularity.

[0488] [000655] 1.5.5 Follow-up and recurrence patterns [000656] Institutional follow-up was completed in collaboration with a follow-up specialist, and third-party professional data was provided by LinkDoc Technology co.Ltd. (Beijing, China). The strategy and definition of recurrence were described below. If an increase in preoperative levels of CA19-9 was observed, these levels were evaluated every 3 months thereafter. Computed tomography (CT) or magnetic resonance imaging (MRI) was performed every 3 months for the first 2 years and every 6 months for the next 3 years to screen for recurrence. Biopsies were only rarely performed when imaging findings were consistent with recurrence, but MRI and / or fluorodeoxyglucose positron emission tomography (FDG-PET) were performed if necessary to clarify equivocal CT findings. Local recurrence was defined as recurrence in the remnant pancreas and surgical bed, including the celiac or superior mesenteric artery, soft tissue along the aorta, or around the pancreaticojejunostomy. Distal recurrences were classified into three different categories: “liver only” and “lung only” for isolated liver and lung recurrences, respectively, and “other” for recurrences occurring in other less frequent locations.

[0489] [000657] 1.5.6 Definitions of survival and statistical analyses [000658] Recurrence-free survival (RFS) was calculated from the date of pancreatic resection to the date of recurrence or to the date of last follow-up if no recurrence occurred. Overall survival (OS) was defined as the time from pancreatic resection to either death or last follow-up. The log-rank test was used to test for statistically significant differences in the curves of the three groups and to obtain the corresponding P values. A two-sided P value of <0.05 was considered statistically significant. Statistical analysis was performed using SPSS23.0 software (IBM, Armonk, NY, USA).

[0490] [000659] To address potential confounding factors, we used logistic regression analysis to model the likelihood of treatment and used the estimated likelihood as a propensity score. We included relevant baseline variables expected to affect treatment decisions, including sex, T stage, N stage, differentiation grade, and tumor site. Variables were selected based on clinical experience, successful balancing of distribution between groups. For propensity score matching analysis, we used "best pair" matched without replacement, and vaccinated and non-vaccinated patients matched in a 1:1 ratio. For propensity score matching analysis, we used the "MatchIt" package from R software, version 4.1.0.

[0491] [000660] 1.5.7 IFN-γ ELISPOT assay [000661] Fresh or thawed cryopreserved PBMCs were cultured in X-VIVO15 medium (Lonza) supplemented with 10% heat-inactivated FBS and penicillin-streptomycin (100 U / ml, Gibco). For in vitro stimulation and expansion of antigen-specific T cells, PBMCs were cultured at 2 × 10 per well in 96-well round-bottom cell culture plates (Corning). 5Cells were stimulated with individual peptides (10 μg / ml) or pooled peptides (2 μg / ml each) in the presence of IL-7 (20 ng / ml, R&D Systems). In vitro stimulation was performed in the presence or absence of anti-HLA-DR (10 μg / ml, clone L243, Biolegend) and anti-HLA-A, B, C (10 μg / ml, clone W6 / 32, Biolegend), which were added 1 h before the addition of peptides. On day 4, IL-2 (20 U / ml, R&D Systems) was added. On days 4, 6, and 10, half the medium supplemented with cytokines, peptides, and blocking antibodies was replaced. On day 13, plates were centrifuged and the supernatant was removed. Cells were resuspended in 200 μl of medium and counted. IFNγ ELISPOT assays were performed using 96-well MultiScreen Filter Plates (Millipore) coated overnight with 15 μg / ml anti-human IFNγ mAb (1-D1K, Mebtech). Plates were washed with PBS and blocked with X-VIVO medium before the addition of pre-stimulated PBMCs. Concanavalin A (5 μg / ml, Sigma Aldrich) was added as a positive control. Plates were rinsed with PBS and then 1 μg / ml anti-human IFNγ mAb (7-B6-1 Biotin, Mabtech) was added, followed by streptavidin-ALP (Mabtech). After rinsing, Immunospots were developed using BCIP / NBT-Plus Substrate for ELISpot (Mabtech) and spots were imaged and enumerated using an Immunospot Analyzer (Cellular Technology Limited). Responses were scored as positive if there were more spot-forming cells than in the blank control.

[0492] [000662] 1.5.8 Neoantigenic peptide cytotoxicity assay.

[0493] [000663] To analyze the ability of peptide-stimulated immune cells to kill tumor cells, PBMCs from patients were plated at 2 x 10 per well in 96-well round-bottom plates. 5 The cells were plated at a density of 10x10 cells and incubated for 7 days in X-Vivo medium containing 10% FBS, penicillin-streptomycin (same as for IFN-γ ELISPOT assay). Individual peptides (10 μg / ml) or pooled peptides (2 μg / ml each) were added in the presence of 20 ng / ml IL-7 (20 ng / ml, R&D Systems). Half the amount of fresh medium supplemented with 20 U / ml IL-2 and peptides was added to the cultures every 3 days. Target tumor cells were cultured in 6 cm dishes and incubated in DMEM medium (Gibco) containing 10% FBS, penicillin and streptomycin. After PBMC stimulation with peptides, tumor cells were labeled by fluorescence. Target tumor cells were incubated with CMFDA (Nexcelom, stains viable cells) for 30 min in a culture environment of 37°C and 5% CO2. Then, the labeled tumor cells were cultured in 96-well plates pre-incubated with collagen I (Corning) overnight. Stimulated PBMCs and labeled tumor cells were co-cultured at a ratio of 10:1 in DMEM containing 10% FBS and 125X PI (to stain dead cells). Celigo Image Cytometer (Nexcelom) was used to observe the fluorescence intensity of stained live tumor cells and dead cells, which can calculate the killing ratio of PBMCs. We also used another device, xCELLigence (Agilent), to observe the real-time resistance change of tumor cells during the co-culture of PBMCs and tumor cells, which also reflects the killing ratio of PBMCs.

[0494] [000664] 1.5.9 Single-cell RNA sequencing and T cell repertoire profiling.

[0495] [000665] We performed 3' gene expression profiling on single cell suspensions using Chromium Single Cell Gene Expression Solution from 10x Genomics according to the manufacturer's instructions. Up to 9000 cells were loaded onto the 10xGenomics cartridge for each sample. Single cell TCR-seq enrichment libraries were prepared using the 10X Single Cell Immune Profiling Solution Kit according to the manufacturer's instructions. Cell barcoded 3' gene expression libraries and scTCR sequencing libraries were sequenced on an Illumina Nova-PE150 system.

[0496] [000666] 1.5.10 Single-cell transcriptome data generation and quality control [000667] Single-cell libraries were prepared by Illumina HiSeqXTen instrument using 150 nucleotide pair end sequencing. FASTQ files generated from sequencing were processed using Cell Ranger 3.1.0 pipeline (10X Genomics) with default parameters. Human genome, GRCh38, was used as reference for read mapping. Cell Ranger pipeline finally generated Gene-Barcode matrix containing filtered cell barcodes and molecular barcodes (UMI). For each sample, we individually imported gene-barcode matrix into Seurat (v3.1.1) R toolkit1 for quality control and normalization. Cells (>200 genes per cell, <4000 genes per cell and <20% mitochondrial genes per cell) were selected for downstream analysis of our single-cell RNAseq data. Each sample from each stage for one patient is examined as a Seurat object and integrated by patient using the FindIntegrationAnchors and IntegrateData functions of the Seurat package, designed for comparative analysis across batches or databases using anchoring algorithms.

[0497] [000668] 1.5.11 Identification of major cell types and expression of cellular markers by Shared Nearest Neighbor (SNN) [000669] Seurat objects aggregated by patient were imported for clustering individually using FindNeighbor and FindCluster functions (with parameter decomposition=1) in Seurat package. Expression of known immune cell markers was then used to characterize the identity of cell type for each cluster. Specifically, CD68, CSF1R were used for macrophages, CD14, S100A12 for monocytes, ITGAM, CD33 for MDSCs, CD19, MS4A1, CD79A for B cells, ITGAX, CD83 for DCs, PF4, PPBP for megakaryocytes, CD247, KLRB1 for NK, TIMP2, ITGA2B for platelets, and CD3D for T cells. Scaled data of these gene expressions obtained from Seurat objects were used to infer cell type. Based on these known genes, we scored gene expression of a given gene (g) for a given cell type (i) in each cluster:

[0498]

number

[0499] [000670] In cells of this cluster, R denotes the ratio of scaled expression of marker g(cell type i)>0. n denotes the total number of genes in cell type i.

[0500] [000671] Furthermore, the type of each cluster was labeled by the cell type with the maximum score compared across all cell type scores. If the maximum score of one cluster was less than 0.1, the cluster was defined as unknown cells. Finally, the same labeled clusters were merged. The subsets of T cells were further carried out by the same approach and grouped into CD4+, CD8+, CD4+CD8+ and CD4 / CD8low T cells based on the expression of genes CD4 and CD8A.

[0501] [000672] 1.5.12 Comparison of cell type percentages and cell subtype gene expression across treatments and between patients [000673] The percentage of cell types was calculated, and normalized gene expression values ​​were obtained for each cell type in patients before and after treatment and at different stages of treatment. The percentage of cell types is the percentage of each of the above-defined cell types, including B cells, T cells, monocytes, macrophages, etc., in the total cells of one sample. This value was used to estimate the increase or decrease in the relative cell population amount of the main cell types during immunotherapy. Furthermore, gene expression in each cell type was converted into two types of values: 1) the proportion of cells that positively express a gene in a given cell type; 2) the average value of gene expression in a given cell type. The two types of values ​​were further used to estimate the change in the relative amount of cell subpopulations that express a particular gene, as well as the change in the expression level of each gene in a given cell type. Positive expression was defined by the distribution of normalized expression values ​​of a given gene in each patient. The threshold for splitting into positive and negative was chosen at the first pit from low to high, but in the absence of pits for genes in a patient, the geometric mean of their expression was defined as the threshold. All pit points in the distribution were determined by the first derivative equal to 0 and the second derivative greater than 0, using the R function "diff".

[0502] [000674] On the one hand, each patient had time series data of different stages. On the other hand, patients could be divided into tumor recurrence group (P2, P4, P6 and P9) and recurrence-free group (P1, P3, P5, P7, P8, P10, P11 and P12), or into anti-PD1 treated group (P4, P6 and P9) and non-anti-PD1 treated group (same as recurrence-free group). Of note, when we performed statistics on single cell data and compared immune status between patients, P3 showed no evidence of recurrence (including elevated tumor index or tumor recurrence on images) for more than one year after the last vaccination, and therefore, the single cell data of P3 during the vaccination phase was used in the recurrence-free group for statistical difference. A linear mixed model (LMM) considering multiple variables simultaneously was applied to investigate the association between the proportion or gene expression changes of these immune cells and treatment stage or patient group. LMM was performed using R package lme42. In the model, the response variable (y) could be the percentage of cell types, the percentage of gene-based positively expressed cell subpopulations, or the expression value of a gene. Explanatory variables included patient group and treatment stage as two fixed effects. Individual patient was a random effect to account for the same patient being measured more than once. To obtain power, there were 7-10 detection time points per patient, but we only compared differences between three phases: months or 1 day before the first vaccination (pre-vaccination), 1-22 days after the first vaccination (priming), and 50-162 days after the first vaccination (boosting). To test for differences in cell proportions and gene expression within treatment stages and patient groups, respectively, we used the generalized linear hypothesis (glht) function in the R package multcomp3 to generate the following contrasts: 1) differences between pre-vaccination and priming phases, 2) differences between pre-vaccination and boosting phases, 3) differences between priming and boosting phases, 3) differences between patient groups in the pre-vaccination phase, 4) priming phase, and 5) boosting phase. The resulting p-values ​​were adjusted using the Benjamini-Hochberg procedure. These analyses, tests, and visualizations were developed in the R environment and scripted with in-house R code.A P value <0.05 was considered significant for all tests. ROC (Receiver Operating Characteristic) curves were used to assess differences between priming and pre-vaccination, or between boosting and pre-vaccination, or between relapse-free and relapse patients. AUC (area under the ROC) was used as a metric of difference. These analyses, tests and visualizations were developed in the R environment and scripted with in-house R code. An adjusted P value <0.05 was considered significant for all tests.

[0503] [000675] 1.5.13 Gene module average expression analysis for single-cell RNA-seq data [000676] Gene module scores were calculated based on the expression of genes in a given pathway module using the "AddModuleScore" function provided by the Seurat package. This function assigned a score (i.e., average expression) to each cell. Scores were contrasted between different phases by using the LMM method.

[0504] [000677] 1.5.14 Single-cell RNA-seq data analysis in cytotoxicity assays [000678] Raw sequencing data was also processed individually using the Cell Ranger 3.1.0 pipeline with default parameters, and the output was imported into Seurat for filtering low quality cells (>200 genes per cell, <5000 genes per cell, and <15% mitochondrial genes per cell) and normalization. Every single neo-epitope-stimulated sample and one blank control sample were merged using the function IntegrateData. Cytotoxicity markers were identified by comparing neo-epitope stimulated samples to blank controls using the function FindMarker with options min.pct=0.01, logfc.threshold=0.2, test.use="wilcox", respectively.

[0505] [000679] 1.5.15 Single Cell T Cell Receptor V(D)J Clonotype (TCR) Data Generation and Analysis [000680] The TCR data for each sample was processed using the Cell Ranger 3.1.0 pipeline ("cellranger vdj" command) with default parameters using the human reference genome GRCh38. For each sample, Cell Ranger generated an output file, filtered_contig_annotations.csv, containing the TCR α- and β-chain CDR3 nucleotide sequences of single cells identified by barcodes. To compare TCR sequences across patients and treatment stages, we merged the separate outputs of samples using the procedure and code script from Thomas D.Wu et al.4. Cell Ranger also annotated the TCR V(D)J genes of each clonotype, and TCR gene utilization was also counted according to the merged results. Since single cell TCR-seq was performed using the Chromium Single Cell5' library, their gene expression counterparts were also sequenced and cells were labeled with the same barcode. Cells that simultaneously contained a TCR clonotype and the expressed gene CD3D were considered T cells and were classified into CD4+, CD8+ and CD4lowCD8low T cells based on CD4 and CD8A expression using the same approach described for single-cell transcriptome data.

[0506] [000681] 1.5.16 TCR clone prediction in patient tumors [000682] The sequences of tumor-infiltrating TCR clones were predicted using MiXCR software 5. A typical analysis workflow for processing RNA sequencing data was applied to the patient's tumor. The alpha and beta chain CDR3 nucleotide sequences from each patient's tumor sequence were obtained, and the sequences were matched with the TCR clones of T cells in blood by the software blastn (options -evalue 0.01-num_alignments1). The TCR clones that could be matched were considered to occur together in blood and tumor tissues.

[0507] [000683] 1.5.17 Assessment of diversity of TCR gene usage [000684] TCR diversity was assessed using the Shannon diversity index. The Shannon index value for each cell was calculated by the following equation:

[0508]

number

[0509] [000685] Where p represents the relative value of TCR gene i divided by the sum of all V(D)J genes.

[0510] [000686] For the 3' single-cell transcriptome data, Seurat-normalized TCR V(D)J gene expression values ​​were used. For the 5' single-cell TCR-seq data, the number of V(D)J genes received by all TCR clones in each cell was used.

[0511] [000687] 1.5.18 Knockdown of MX1 in PBMCs and qPCR Assay [000688] PBMCs were cultured for 1 week in X-VIVO15 medium (Lonza) supplemented with 10% fetal bovine serum (Gemini), penicillin-streptomycin (Gibco), antibiotic-antimycotic (Gibco), 20ng / ml recombinant human IL-7 (R&D), 50U / ml recombinant human IL-2 (R&D). MX1 siRNA was then transiently transfected into PBMCs using TransIT-TKO kit (Mirus). After 3 days of culture at 37°C, PBMCs were harvested. To test the killing effect of PBMCs after siRNA interference, a portion of PBMCs was co-cultured with tumor cells. The other part of PBMCs was subjected to RNA extraction by AIIPrep DNA / RNA kit (QIAGEN), reverse transcribed into cDNA by PrimeScript RT reagent kit (Takara), and then qPCR was performed using NovoStart SYBR qPCR SuperMix Plus (Novoprotein) to confirm the effectiveness of siRNA interference. The relative expression of MX1 gene at the mRNA level was calculated by the 2-ΔΔCt method. GAPDH was used as a housekeeping gene. The siRNA and primer sequences were as follows:

[0512] [000689]siRNA, 5'-GCTTTGTGAATTACAGGACAT-3'; [000690] MX1, 5′-GTTTCCGAAGTGGACATCGCA-3′ (forward); [000691] 5'-CTGCACAGGTTGTTCTCAGC-3' (reverse); [000692] GAPDH, 5'-GGAGCGAGATCCCTCCAAAAT-3' (forward); [000693] 5'-GGCTGTTGTCATACTTCTCATGG-3' (reverse) [000694]1.6 Supplementary reference

[0513] [Table 9]

[0514] Example 2 [000695] In recent years, cancer treatment and cancer therapy have been more focused on precisely targeting tumors without damaging normal tissues. In this process, immunotherapy is more widely used, and the tumor immune microenvironment (TIME) is very important for this kind of cancer treatment [5]. Several kinds of lymphocytes are important for antitumor immune activity, including cytotoxic T cells [6,7], which can identify tumor cells and precisely perform cytotoxic functions, and natural killer cells (NK cells) [8-10], which can kill tumor cells even in the absence of antigen presentation. Some myeloid cells are also important in the tumor immune environment, including dendritic cells (DCs) and tumor-associated macrophages (TAMs) [11,12]. Therefore, understanding the role of different cells in the TIME as well as genes and proteins that can affect their functions is very important for immunotherapy in cancer treatment.

[0515] [000696] Protein phosphatase 1 regulatory subunit 15A (PPP1R15A) is an important gene in mammalian cells, including human and mouse cells. PPP1R15A, also known as Growth Arrest And DNA Damage-Inducible Protein (GADD34), plays an important role in apoptosis and can be induced when DNA damage occurs

[13] . Further studies showed that PPP1R15A can bind to the catalytic subunit protein phosphatase 1 (PP1c) and promote the dephosphorylation of eukaryotic translation initiation factor 2α (eIF2α) [14,15]. eIF-2α plays a very important role in the integrated stress response (ISR) in cells

[16] , therefore the phosphorylation and dephosphorylation process of eIF-2α may be important for the ISR process. As an evolutionarily conserved process, the ISR is associated with both UPR and HSR activation [17,18] and is activated by both the ER and the cytosolic lumen

[19] , and this process is important for allowing cells, tissues, and organs to adapt to diverse environments and maintain homeostasis

[20] .

[0516] [000697] The ISR functions through control over the ternary complex (TC)

[20] , and eIF2 is a key compartment of the TC. eIF2 is composed of three subunits, eIF2α, eIF2β, and eIF2γ. In the four units, phosphorylation of serine 51 of eIF2α is the most important process

[21] . When eIF2α is phosphorylated, global protein synthesis is reduced and translation of activating transcription factor 4 (ATF4) is activated

[22] , which is beneficial for cell survival and recovery. Conversely, dephosphorylation of eIF2α restores normal protein synthesis processes in cells [23,24]. ISR outcomes and levels are determined by the level of phosphorylation of eIF2α and the activity of ATF4 [25,26]. ATF4 can function as a complex with bZIP transcription factors of other species

[27] , including C / EBP homologous protein (CHOP)

[28] , which is more commonly found in cell biological processes. The ATF4-CHOP complex plays an important role in the mammalian autophagy process, including the induction of autophagy and the activation of autophagy-related genes [28, 29]. Target genes of the ATF4-CHOP complex include ATF3, PPP1R15A, TRIB3, etc.

[30] . Many autophagy genes can also be upregulated, including ATG3, ATG5, ATG7, ATG10, ATG12, ATG16, BECN1, GABARAP, GABARAPL2, MAP1LC3B, and SQSTM1

[29] .

[0517] [000698] Based on the fact that eIF2, especially eIF2α, is important for the ISR process, controlling the phosphorylation level of eIF2α may be important. Dephosphorylation of eIF2α is carried out by two phosphatase complexes, the PPP1R15A-PP1c complex and the PPP1R15B-PP1c complex. PPP1R15B, also known as a constitutive repressor of eIF2α phosphorylation (CReP), can constitutively suppress phosphatase activity directed towards eIF2α and thus activate the ISR process in a defined manner

[31] . Meanwhile, PPP1R15A is used as a feedback method to antagonize the relative strength of ISR activation

[23] . Due to the different functional forms of PPP1R15A and PPP1R15B, selective inhibition of the function of PPP1R15A is safe, whereas inhibition of the function of PPP1R15B may be lethal

[26] . Therefore, inhibitors of PPP1R15A can be used as regulators of the ISR process in cells.

[0518] [000699] The ISR has diverse effects on cell biological processes and can affect the function and homeostasis of the mammalian body. Several studies have revealed that the ISR functions in cognitive and neurodegenerative disorders [32-34], metabolic disorders [35,36], and cancer [37,38], and can also have important effects on mammalian immunity. Existing studies have shown that the ISR can also affect the innate immune response [39,40], as well as the secretion of cytokines in several species, including IL1β and IL6 [41,42]. These effects may be highly dependent on the eIF2 complex, especially the phosphorylation and dephosphorylation of the eIF2α subunit

[43] , while the activation of ATF4 is also important in related processes

[44] .

[0519] [000700] Sephin1 is a selective inhibitor of holophosphatase. It can selectively bind to PPP1R15A and thus inhibit the formation of PPP1R15A-PP1c complex. Due to its high selectivity, it does not bind to PPP1R15B and is safer for animals

[45] . Sephin1 has been reported to have the ability to restore motor function and rescue myelin deficiency in mouse models

[45] . However, in our study, we found that injection of Sephin1 could inhibit immune system function in C57BL / 6 mice, and the tumor growth rate in the group of mice injected with Sephin1 was much higher than that in the control group. This result indicates that the function of PPP1R15A may be important for anti-tumor immune activity. By single-cell sequencing, we found that the impact of Sephin1 on the mouse tumor microenvironment is complex and the immunosuppressive effect may exist in multiple immune cell types.

[0520] [000701] 2.1 Materials and Methods [000702] 2.1.1 Reagent preparation [000703] Sephin1 solution was prepared before injection. 50mg Sephin1 (APExBIO, A8708-50) was first dissolved in 625μl DMSO and then dissolved in 12.5ml PBS. The final solution contained 4mg / ml Sephin1 and 5% DMSO. This solution was used for mouse injection. The same volume of DMSO solution with the same percentage (5% DMSO in PBS) was used in the control group.

[0521] [000704] 2.1.2 Mouse injections [000705] Six to eight week old C57BL / 6 mice were used in this experiment. The mice were first divided into two groups, a control group and a Sephin1 group. The Sephin1 group was injected intraperitoneally with 100 μl of the Sephin1 solution prepared in the first step, and the control group was injected with an equal volume of 5% DMSO-PBS solution. Both groups were injected three times a week, and the injections continued for two weeks. All mice were then injected with 3 × 10 5 Each mouse was subcutaneously inoculated with 10 B16F1 cells. Approximately one week after injection, the tumor volume was measured and analyzed. The tumor tissues were harvested after two weeks of development for further experiments. In addition, the proliferation rate of a mouse triple-negative breast cancer cell line, 4T1, was measured in 8-week-old female BALB / c mice (8 mice per group). Two weeks after injection with DMSO or Sephin1, each BALB / c mouse was inoculated with 10 6 4T1 cells were inoculated subcutaneously, and the tumor volume was then measured every 2 to 3 days.

[0522] [000706] 2.1.3 Generation of Mouse PBMCs [000707] Peripheral blood samples were collected from mouse eyes. Each sample was first mixed with 200 μl of EDTA and then mixed with an equal volume of PBS. Equal volumes of Ficoll-Paque PREMIUM (Amersham / GE, 17544602) and blood-EDTA-PBS solution were added to a 15 ml centrifuge tube and centrifuged at 400 g for 20 min. Peripheral blood mononuclear cells (PBMCs) were collected and red blood cells were removed by ACK lysis buffer (ThermoFisher, A1049201). Lysed cells were filtered through a 30 μm MACS SmarterStrainer (Miltenyi / MACS, 130-110-915), washed 1-2 times with PBS, and resuspended with an appropriate volume of PBS. The cells were stained with AO / PI (Nexcelom Bioscience, CS2-0106-5mL) and counted using a Cellometer K2 (Nexcelom Bioscience).

[0523] [000708] 2.1.4 Tumor Tissue Processing [000709] Tumor tissue was collected and cut into small pieces (approximately 1-2 mm). We then lysed the tumor tissue by Mouse Tumor Dissociation Kit (Miltenyi / MACS, 130-096-730) according to standard procedures. The cell suspension was then filtered through a 30 μm MACS SmarterStrainer. We then performed either FACS analysis or FACS sorting procedures (sorting of CD45+ and live cells).

[0524] [000710] 2.1.5 FACS sorting and analysis [000711] A FACS sorting procedure was performed prior to single cell library construction of immune cells in tumor samples. Tumor cell suspensions were first incubated with mouse CD45 antibody (BioLegend, 157607) for 30 min, then incubated with PI (Nexcelom Bioscience, CS1-0109-5mL). Cells were sorted by BD SORP FACSAria machine.

[0525] [000712] FACS analysis was performed on tumor tissues. After obtaining single cell suspensions, each sample was first diluted with approximately 5 x 10 6The cells were stimulated with Cell Activation Cocktail containing Brefeldin A (BioLegend, 423303) at a concentration of 1:500 cells / mL, and the volume ratio of the cocktail to the cell suspension was 1:500. After 4 h of stimulation at 37°C, the cells were centrifuged at 400g for 7 min, the supernatant was discarded, and the FACS antibody was incubated with the sample. Cells were first cloned for CD45 (Thermo Fisher, 12-0451-83; BioLegend, 103105; BioLegend, 157607), CD3E (BD Pharmingen, 553064), CD4 (BioLegend, 100548), CD8A (Thermo Fisher, 25-0081-81), NK1.1 (Thermo Fisher, 48-5941-80), FOXP3 (BioLegend, 126419), PD-1 (Thermo Fisher, 17-9985-82), CD11b (BioLegend, 101215), F4 / 80 (BioLegend, 123125), TCRβ (BioLegend, 109205) and LIVE / DEAD Viability. The cells were incubated with mouse surface antibody containing reagent from the Kit (ThermoFisher, L34994 / L34963) for 30 minutes. After that, the cells were centrifuged at 1500 rpm for 5 minutes and washed once with PBS. Then, Cytofix / Cytoperm Kit (554714, BD Pharmingen) was used for cell fixation and permeabilization. Then, the cells were washed and incubated with mouse IFNG antibody (BioLegend, 505806) for 30 minutes. After that, the cells were washed and resuspended with BD Perm / Wash buffer from the Cytofix / Cytoperm Kit. The prepared cell suspension was analyzed on a CytoFLEX LX machine from Beckman Coulter.

[0526] [000713] 2.1.6 Library construction and sequencing for single-cell RNA-seq [000714] All 12 samples were used for single cell library construction. First, after 2 weeks of DMSO / Sephin1 injection, we randomly selected 2 mice from each group and collected a total of 4 PBMC samples. Second, after 2 weeks of B16F1 cell injection, we also selected 4 mice from the two groups and collected 4 PBMC samples and 4 tumor immune samples (CD45+ cells separated from tumor tissue by FACS).

[0527] [000715] The cell suspension sample we obtained in the last step was used for single cell library construction. We performed single cell immune profiling according to the standard procedure from 10X Genomics. The library construction kits we used include Chromium Next GEM Single Cell 5' Library&Gel Bead Kit V1.1 (16rxns, PN-1000165), Chromium Single Cell 5' Library Construction Kit (16rxns, PN-1000020), Chromium Single Cell V(D)J Enrichment Kit (Mouse T cell, 96rxns, PN-1000071), Chromium Next GEM Chip G Single Cell Kit (48rxns, PN-1000120) and Single Index Kit T Set A (96rxns, PN-1000213). After single-cell library construction, all samples were sequenced by Illumina NovaSeq PE150 platform. We obtained a total of 12 5' gene expression libraries and 12 matched TCR enriched libraries. All gene expression libraries were sequenced with a data size of 80G each, and the TCR enriched library was 10G.

[0528] [000716] 2.1.7 Single Cell Data Processing and Integration with TCR Enrichment Data [000717] First, a total of 12 expression sequencing data and 12 TCR enrichment sequencing data were analyzed using Cell Ranger (version 6.0.0) software from 10X Genomics. Single cell expression data were then imported into R and integrated by Seurat (version 3.2.3). To minimize information loss and simultaneously remove low quality and duplicate cells, genes expressed in at least two cells were kept, and cells with more than 100 and less than 4000 genes were kept. Cells were also filtered by the percentage of mitochondrial genes, and cells that were discrete in the violin diagram were removed. Filtered cells were then integrated, normalized, scaled, and clustered by Seurat. Cell type annotation was performed using classical immune cell markers.

[0529] [000718] The filtered TCR contig matrix was analyzed and integrated using scRepertoire (version 1.2.1)

[62] and then integrated with the gene expression data. The integration process was scripted and performed in python (version 2.7.5) and R (version 3.6.3) platforms. Cells between the TCR enrichment data and the expression data were matched by their specific barcode sequences. Plots of the different TCR types were generated by ggplot2 (version 3.3.5).

[0530] [000719] 2.1.8 Control gene set analysis performed by SCENIC [000720] SCENIC

[63] analysis was also performed to analyze the activity of key transcription factors and their associated genes. First, 5000 cells from a total of 12 samples were randomly selected, and high activity co-expression networks were identified using GENIE3 (version 1.8.0). Then, SCENIC analysis was performed on all cells, and regulons were filtered from the co-expression networks. Then, we calculated the activity of different regulons from different sample types and cell types.

[0531] [000721] 2.1.9 Analysis of differential gene patterns in different clusters [000722] Differentially expressed genes in different clusters or samples were identified by FindMarkers package from Seurat. Then, enrichment analysis, including GSVA and GSEA, was performed using the differentially expressed genes and was completed by R packages GSVA (version 1.30.0) and fgsea (version 1.8.0). In addition, AddModuleScore package from Seurat was also used to analyze the expression activity of genes involved in important pathways related to anti-tumor immunity. All processes were completed in python (version 2.7.5) and R (version 3.6.3) platforms. Plots were completed with ggplot2 (version 3.3.5), ggpubr (version 0.4.0), pheatmap (version 1.0.12) and ComplexHeatmap (2.8.0) packages in R.

[0532] [000723] 2.1.10 Cell-cell communication analysis [000724] Cell-cell communication analysis was mainly completed by the R package CellChat (version 1.0.0)

[64] . Five cell types that play important roles in antitumor immunity were selected for communication analysis. The number and strength of communication between normal and Sephin1 groups in different tissues were calculated and compared.

[0533] [000725] 2.1.11 In vitro analysis of the effects of Sephin1 on mouse CD8+ T cells [000726] The day before CD8+ T cell isolation, round-bottom 96-well plates were first prepared by incubation with 100 μl of PBS supplemented with 1 μg / ml anti-mouse CD3ε (BioXCell, BE0001-1-5MG) and anti-mouse CD28 (BioXCell, BE0015-1-5MG). The supernatant was discarded before use.

[0534] [000727] CD8+ T cells were isolated from the spleen tissue of adult male C57BL / 6 mice by MojoSort Mouse CD8 T Cell Isolation Kit (BioLegend, 480035) according to standard protocols. The isolated CD8+ T cells were first incubated with reagents from the CFSE Cell Division Tracker Kit (BioLegend, 423801) according to standard protocols, and then resuspended in RPMI1640 medium (Gibco, 11875093) supplemented with 10% FBS (Biological Industries, 04-001-1A) and 1% Pen Strep (Gibco, 15140122). Then, 20ng / mL of mouse IL2 (Novoprotein, CK24) and IL7 (Novoprotein, CC73) were added, and the concentration of cells was 10 6 The number of cells was 1 / mL.

[0535] [000728] The isolated CD8+ T cells were then incubated in pre-coated 96-well plates for 72 hours. Afterwards, the cells were harvested and stained with LIVE / DEAD Fixable Violet Dead Cell Stain Kit (Invitrogen, L34963), PerCP / Cyanine 5.5-conjugated anti-mouse CD8a antibody (BioLegend, 100733) and PE-conjugated anti-mouse IFN-γ antibody (BioLegend, 163503) as described above. The prepared cell suspension was analyzed with CytoFLEX LX from Beckman Coulter.

[0536] [000729] 2.1.12 Immunofluorescence Analysis [000730] Immunofluorescence analysis was performed with mouse antibodies to F4 / 80 (Servicebio, GB11027), CD44 (Servicebio, GB112054), FN1 (Servicebio, GB112093) and SPP1 (Servicebio, GB11500). Tumor tissues were first fixed with paraformaldehyde, embedded in paraffin, and then used for immunofluorescence staining.

[0537] [000731]2.1.13 Statistical analysis [000732] All statistical analyses were performed using GraphPad Prism 7 (La Jolla, CA, USA) or R (version 3.6.3). Differences between two groups were calculated by either unpaired two-tailed Student's t-test (figures without statistical method annotation) or other suitable statistical methods (annotated in figures). Statistical parameters were: * :p<0.05, ** :p<0.01, *** :p<0.001, **** : shown as p<0.0001.

[0538] [000733]2.2 Results [000734] 2.2.1 Sephin1 accelerates tumor progression by activating the ISR and suppressing the immune response.

[0539] [000735] Three types of samples, including PBMCs collected on day 0 (the day of tumor cell injection) and day 15 (15 days after tumor cell injection) and immune cells isolated from tumor tissues collected on day 15, were collected for single cell sequencing (Figure 18A). Tumor growth curves showed that the tumor growth rate of the Sephin1 group was significantly higher than that of the normal group (Figure 18B). Tumor tissues collected from both groups on day 15 showed that the tumor volume and weight of the Sephin1 group were also significantly higher than that of the normal group (Figures 18C and 18E). Apart from these findings, the inventors repeated the in vivo Sephin1 experimental procedure with female BALB / c mice implanted with mouse triple-negative breast cancer cell line 4T1. The tumor growth rate of 4T1 cells in the Sephin1 group was also higher than that of the normal group, indicating that the suppressive effect of Sephin1 on anti-tumor immunity may be common among different tumor types. However, the results were not as significant as those of the B16F1 cell line in male C57BL / 6 mice (Figure 34C), indicating that the mechanisms underlying the differences between the two cell lines need to be further investigated.

[0540] [000736] Raw single-cell sequencing data were first analyzed using CellRanger software developed by 10X Genomics, then merged, filtered and clustered by Seurat. After quality control, 68531 cells from 12 samples were included. The 12 samples were analyzed by sample type, and for a total of 6 sample types, the samples included blood normal and Sephin1 samples collected on day 0 and day 15, and tumor immune cell samples collected on day 15. SCENIC analysis was performed and used to compare the samples to analyze regulon activity. Regulons are co-expression modules with significant motif enrichment of certain upstream regulators, and high regulon activity reveals high cis-regulatory activity

[63] . In all 6 sample types, 27 regulons were identified, and the regulons were divided into 10 clusters by K-mean based on their activity profiles (Figure 18D). The Atf3 regulon, which includes genes involved in the ISR, such as Atf4 and Atf3, and related regulatory genes, had high activity levels in blood samples of the Sephin1 group collected on days 0 and 15, as well as in tumor samples collected on day 15. Regulons in the same cluster as Atf3, including Jun, Maf_extended, Mafb_extended, and Fos regulons, had activity profiles relative to that of the Atf3 regulon. However, regulons related to immune cell activity and differentiation, such as Stat1-, Stat3-, and Stat4-related regulons, were downregulated in the Sephin1 group. Furthermore, RSS analysis also showed that the Atf3 regulon was the most specific regulon in tumor samples on day 15 (Figure 25B).

[0541] [000737] 2.2.2 Sephin1 induces suppression of antitumor immunity mediated by diverse immune cell types [000738] To further explore the regulatory effect of Sephin1 on the immune microenvironment, we analyzed the compositional changes of broad categories of immune cells in peripheral blood and tumors. Cells from all 12 samples were first divided into six sample types (Figs. 18G and 25C), then preprocessed, merged and clustered by Seurat, and a total of 58 clusters were identified (Fig. 25A). These clusters were classified as different cell types based on classical cell markers, and all cells were divided into 16 types, including five lymphoid cell types (NK cells, NKT cells, Cd8+ T cells, Cd4+ T cells and B cells) and six myeloid cell types (basophils, granulocytes, DC_macrophages, macrophages, DC and mast cells; Fig. 18F). Marker gene expression profiles of different cell types are also shown (Fig. 26A). In the UMAP graph grouped by sample (Figure 18G), we found that the clustering between samples did not have a significant batch effect, indicating that the sequencing data could be compared between different samples. The percentage of different cell types was calculated and analyzed (Figures 18H and 26B), and different samples showed different cell type distribution profiles. Cell types related to anti-tumor immune activity seemed to be more reduced in the Sephin1 group, especially in the tumor microenvironment at day 15, including Cd8+ T cells, NKT cells, NK cells, and DCs. However, the distribution of macrophages seemed to be more enriched in the Sephin1 group. Subtype analysis was further performed on these important immune cell types.

[0542] [000739] GSVA analysis was also performed comparing different sample types (Figure 25D). In tumor samples, gene expression levels related to the melanin biosynthesis process, cellular response to amino acid stimulation and ATP hydrolysis coupled proton transport were upregulated in the Sephin1 group, showing high ISR levels.

[0543] [000740] 2.2.3 Sephin1 can result in a significant decrease in the percentage of anti-tumor lymphocytes [000741] Considering the functional diversity of immune cells, we wondered which lymphocyte type plays the most important role in Sephin1-induced immune suppression. We focused on several important antitumor immune cell types and analyzed their transcriptional profiles. B cells showed similar distribution and expression patterns between normal and Sephin1 groups and were therefore excluded from further analysis. The remaining four lymphocyte cell types were further annotated as 8 subtypes. In contrast to NK cells and NKT cells, Cd4+ T cells were annotated into three subgroups: effector T cells, regulatory T cells and naive T cells. Furthermore, Cd8+ T cells were annotated into three subgroups: exhausted T cells, cytotoxic T cells and naive T cells (Figure 19A). Different markers were used for cell annotation (Figure 19B). Compared with blood samples collected on day 0, blood and tumor samples collected on day 15 showed more obvious changes in immune cell composition induced by Sephin1 (Figure 19C). The percentages of NK cells, NKT cells, exhausted Cd8+ T cells and cytotoxic Cd8+ T cells were all significantly decreased in blood and tumor samples collected on day 15 from the Sephin1 group. However, the changes were not significant in blood samples on day 0. The percentage of naive_Cd8+ T cells was slightly decreased in the day 15 samples but increased in the day 0 samples. The number of regulatory T cells was slightly increased in the day 15 samples but was not as high as in the day 0 samples. Using FACS, these results were confirmed and the percentage of anti-tumor associated immune cell types in all immune cells was calculated and compared between the normal and Sephin1 groups in the tumor microenvironment. The percentages of CD4+ and CD8+ T cells, NK cells and activated CD8+ T cells were all significantly downregulated in the Sephin1 group, while regular CD4+ T cells and exhausted CD8+ T cells did not change significantly between the two groups (Figure 19D, and Figures 28A-28G). We observed that the percentages of exhausted and activated CD8+ T cells and NK cells were significantly decreased, while the percentage of regulatory T cells was increased, by FACS analysis.

[0544] [000742] Apart from the compositional changes, Sephin1 also affected the expression level in lymphocytes. To determine the effect of Sephin1 on the expression patterns of different lymphocyte subtypes, we analyzed the differential expression levels between normal and Sephin1 groups. In Cd8+ T cells, we found that several important pathways related to antitumor immunity were significantly downregulated in the Sephin1 group (Figure 20A). The significantly downregulated pathways included cytokine-mediated signaling, cell surface receptor signaling, signal transduction, response to peptide hormone stimulation, positive regulation of calcium-mediated signaling, cell proliferation, G protein-coupled receptor signaling and T cell receptor signaling pathways. The downregulation of these pathways indicated that the function of Cd8+ T cells was significantly suppressed. The significantly upregulated pathways in the Sephin1 group included pathways related to translation, translation elongation and cell division, indicating a defect in the regulatory function feedback in the translation process.

[0545] [000743] To identify the specific pathways most affected by Sephin1, we calculated the expression scores of Cd8+ T cells using the Seurat package AddModuleScore. Genes involved in T cell cytotoxicity and positive regulation of T cell cytotoxicity from Gene Ontology were analyzed. Genes involved in T cell cytotoxicity had significantly lower expression scores in the day 15 tumor microenvironment samples, but not in day 0 or day 15 blood samples from the Sephin1 group (Figure 20B). However, the expression levels of genes related to positive regulation of T cell cytotoxicity were significantly downregulated in all three sample types (Figure 20C). These results indicated that the activity of Cd8+ T cells was inhibited by Sephin1, especially in the tumor microenvironment. In vitro analysis of Cd8+ T cells comparing the control and Sephin1 groups also showed that the percentage of cells expressing IFN-γ in the Sephin1 group was significantly lower than that in the control group, indicating that the Sephin1 group had lower Cd8+ T cell activity in vitro (Figures 29A and 29B).

[0546] [000744] Scores of genes related to positive regulation (Figure 20D) and activity (Figure 20E) of NK cells from Gene Ontology were also calculated, and they showed similar patterns. GSEA of NK cells also showed that genes related to NK cell activity, including genes involved in cytokine-mediated signaling pathways and induction of apoptosis, were downregulated in the tumor Sephin1 group (Figure 27D). Treg differentiation scores were also calculated for regulatory T cells, and scores were significantly upregulated in PBMCs of the Sephin1 group, but downregulated in tumor samples (Figure 27C). Gene expression levels of these gene sets were also calculated and compared between different clusters of different sample types (Figure 27A). Expression levels of positive regulation and activity-related genes of NK cells were downregulated in both blood and tumor microenvironment at day 15, but slightly upregulated in blood at day 0. We also performed SCENIC analysis of all lymphocyte subtypes (Figure 20F). The activity of the Atf3 regulon was upregulated in exhausted CD8+ T cells, cytotoxic CD8+ T cells, NK cells and NKT cells, but downregulated in regulatory T cells. These results indicated that the regulatory effect of Sephin1 may vary among different cell types, but have similar immune inhibitory functions. The PI3K-related regulon showed a similar pattern in these cell types, and the activity scores of the PI3K-related regulon were all downregulated in cytotoxic T cells and regulatory T cells.

[0547] [000745] 2.2.4 TCR analysis revealed that specific T cell proliferation can be inhibited by Sephin1 [000746] By analyzing CSFE stained Cd8+ T cells in vitro, we found that the Sephin1 group had significantly lower proliferation capacity than the control group (Figures 29A and 29B). Although the overall expression of proliferation-related genes did not differ between the two groups, the clonal expansion of T cells in the tumor was inhibited in the Sephin1 group based on single cell TCR analysis. TCRs were divided into four types: special expansion, large, medium, and small, based on the method described.

[0548] [000747] First, we calculated the percentage of different categories of clonotypes in one sample (Figure 21A). For all three sample types, we found that the samples in the Sephin1 group had a lower percentage of highly expanded clonotypes. In blood samples collected on day 0 or day 15, the percentage of TCR types in the large and medium categories was significantly lower in the Sephin1 group. In tumor samples on day 15, the special expanded, large, and medium TCR types were also significantly downregulated in the Sephin1 samples. Apart from the clonotype percentages, the percentage of cells belonging to each TCR type showed a similar pattern. The percentage of cells belonging to special expanded and large was downregulated in the Sephin1 samples for all three sample types, while the small percentage was upregulated (Figure 21B). The ranking of the different TCR clonotypes showed similar results. Clonotypes were ranked according to the proportion of their clonal abundance within the complete TCR repertoire of a sample, and the percentage of highly ranked clonotypes was reduced in all three sample types (Figure 30B).

[0549] [000748] Further analysis found that although TCR+ cells were distributed mainly among T cells, many TCR+ cells were also found in the macrophage population, and a relatively high proportion of macrophages had specially expanded or large clonal TCR types (Figures 21C and 21E). Furthermore, the percentage of highly expanded TCR types, i.e., specially expanded or large TCR types, was also higher in the normal group than in the Sephin1 group in both T cells and macrophages (Figures 21B, 21D, and 23F).

[0550] [000749] To determine the expression patterns of different TCR types, especially the highly expanded TCR types, we analyzed the differentially expressed genes in different TCR types. The special expanded type had much higher expression levels of cytotoxicity-related genes, such as Gzmb and Gzmk (Figure 21F). GSVA was performed according to the expression levels of the differentially expressed genes of each TCR type, and the results showed that the highly expanded TCR cell types had high metabolic activity. Pathways, such as cholesterol biosynthesis process and tricarboxylic acid cycle, were found to be highly expressed in the special expanded type. Immune-related pathways, such as the cellular response to hypoxia pathway and the presentation of exogenous peptide antigens via antigen processing and MHC class II pathway, were highly activated (Figure 21G). Immune-related and cell death-related pathways, such as the positive regulation of inflammatory response and natural killer cell chemotaxis pathway, were also upregulated in the special expanded type (Figure 21H).

[0551] [000750] 2.2.5 Sephin1 macrophages are more M2 polarized [000751] In addition to antitumor lymphocytes, macrophages also play an important role in the tumor microenvironment. Therefore, we also analyzed the characteristics and functions of macrophages in different samples. Macrophages in all merged samples were divided into 11 clusters by Seurat (Figure 31A), and we annotated them into 9 subtypes named based on their specific marker genes (Figures 22A and 22B). Chil3+, Fn1+ and Ace+ macrophages are mainly present in blood. Ifitm6+, Hcar2+, Retnla+, Spp1+, C1qb+ and Fscn1+ macrophages are mainly present in tumor tissues (Figure 22D). There were two macrophage subgroups mainly present in the Sephin1 group: Chil3+ macrophages mainly present in blood samples on day 15 and Hcar2+ macrophages mainly present in tumor samples on day 15 (Figure 22C). GSVA of different macrophage subtypes showed that genes highly expressed in the Chil3+ group were enriched in the regulation of G protein-coupled receptor protein signaling and negative regulation of the NF-κB pathway (Figures 31C and 32A).

[0552] [000752] There are two macrophage polarization states, M1 and M2. Typically, M1 macrophages produce type I proinflammatory cytokines and have antitumor functions, whereas M2 macrophages produce type II cytokines and have tumorigenic functions [65,66]. We then analyzed the expression levels of genes associated with M1- and M2-polarized states by calculating the gene expression scores of M1 and M2 polarization by AddModuleScore, and then the M1_to_M2 score, which was determined by subtracting the M2 score from the M1 score

[67] . The higher the M1_to_M2 score, the more cells were polarized toward the M1 state. We found that in blood and tumor tissues collected on day 15, macrophages in the Sephin1 group were significantly polarized toward the M2 state compared to those in the normal group, but the variation in blood samples on day 0 was not significant (Figures 22E and 31B). We then analyzed the M1- and M2-polarized states of different macrophage subtypes. Except for two subtypes with too few cells (Retnla+ and Hcar2+ macrophages), we found that the subtypes predominantly present in tumor tissues tended to be more M2 polarized, including Spp1+, Clqb+, Ifitm6+ and Facn1+ macrophages (Figure 22F).

[0553] [000753] GSEA was also performed on macrophages of tumor tissues to compare the Sephin1 group with the normal group. We selected the 20 most up-regulated and down-regulated pathways between the two groups based on normalized enrichment score (NES) (Figure 31D). We found that pathways related to T cell activation, antigen processing and presentation were down-regulated in the Sephin1 group. In contrast, pathways related to the ISR process, such as the cellular response to hypoxia pathway, were up-regulated in the Sephin1 group. Furthermore, SCENIC analysis of macrophages also showed that the ISR-related regulon, namely the Atf3 regulon, had high activity in the Sephin1 group (Figure 32C).

[0554] [000754] 2.2.6 Macrophage subtypes with TCR expression may have important functions in antitumor immunity [000755] By TCR analysis, we found that TCR was not only present in T cells but also in macrophages, and the percentage of TCR+ macrophages of all three TCR types was high at approximately 0.3 (specially expanded type, FIG. 21E). Furthermore, GSEA comparing macrophages between normal and Sephin1 groups showed that pathways related to T cell activity were affected by Sephin1 (FIG. 31D). These results indicated that macrophages with TCR sequences may have important functions in the immune system and antitumor procedures. Studies on CD3+ macrophages found that this cell type can produce proinflammatory cytokines

[68] , but their role in antitumor immunity remains unknown. In our study, we found that CD3+ macrophages, especially TCR+ macrophages, may have important functions in antitumor immune activity. TCR+ macrophages were present in both blood and tumor tissues, but were more enriched in the tumor microenvironment (Figures 23A and 32E). In tumors, approximately 13.7% of macrophages were TCR+, whereas in blood, only approximately 0.5% of macrophages were TCR+. Both macrophage and T cell marker genes were highly expressed by this cell type, including Cd3d in T cells and Cd68, Csf1r and Adgre1 in macrophages (Figure 23B). GSEA comparing TCR+ and conventional macrophages showed that upregulated genes in TCR+ macrophages were more enriched in pathways related to T cell activation and regulation, while downregulated genes were enriched in pathways related to innate immune responses, cytokine secretion and other related pathways (Figure 23C). Special expanded and large TCR types were mainly present in C1qb+ and especially Fscn1+ macrophages, which were mainly present in tumor tissues (Figure 23D). GSVA of TCR+ macrophages was also performed by TCR type (Figure 32D). In the specially expanded macrophage population, the most upregulated genes were enriched in the positive regulation of T cell-mediated cytotoxicity pathways, resembling cytotoxic T cell function.The results also demonstrated that TCR+ macrophages could carry out antitumor functions in both a macrophage-like and T cell-like manner.

[0555] [000756] We also calculated the M1_to_M2 scores of both conventional and TCR+ macrophages by AddModuleScore (Figure 23E). TCR+ macrophages had significantly higher M1_to_M2 scores in the normal group, indicating that these macrophages may have more anti-tumor functions. The difference between the two macrophage types was not significant in the Sephin1 group, but both cell types were more polarized toward M2 state in the Sephin1 group. These results indicated that TCR+ macrophages appear to have anti-tumor functions mediated by both macrophage- and T cell-related pathways, and these functions can be inhibited by Sephin1.

[0556] [000757] Distribution analysis of different types of TCR+ macrophages in different tissue types showed that the percentage of specially enlarged and large macrophages was significantly downregulated in the Sephin1 group, while the percentage of medium and small macrophages was upregulated, consistent with the overall TCR+ cell pattern (Figure 23F). In addition, we also calculated the percentage of TCR+ macrophages with TCR sequences shared with Cd4+ (Cd4-shared) and Cd8+ T cells (Cd8-shared) in tumor tissues. We found that in the Sephin1 group, Cd8-shared macrophages were significantly less than in the normal group (Figures 23G and 23H), indicating that TCR+ macrophages can obtain TCR sequences by interacting with T cells. The presence of TCR+ macrophages was also revealed by FACS results in the tumor microenvironment, with no significant difference between the normal and Sephin1 groups (Figures 33A-33C). Furthermore, we also found that TCR+ macrophages were present in normal mouse spleen tissue (Figures 33D and 33E).

[0557] [000758] 2.2.7 Sephin1 suppresses antitumor immunity at the cell-cell communication level [000759] To find the differentially expressed genes and communication strength between normal and Sephin1 groups, we calculated the cell-cell communication scores and strengths between different samples by CellChat

[64] . In tumor tissues, most communication strengths were downregulated in the Sephin1 group, except for macrophage-macrophage and macrophage-Cd4+T cell communication. In all three tissue types, the communication between Cd8+T cells and NK cells was all downregulated in the Sephin1 group, indicating that these cell-cell communications may have more important functions (Figure 24A).

[0558] [000760] By analyzing the differentially expressed ligand-receptor pairs between normal and Sephin1 groups, and between Cd8+ T cells and NK cells, we found that these pairs were mostly enriched in MHC-I-related pathways (Figure 24B). Besides, the intensities of ligand-receptor pairs in MHC-I pathways were all downregulated in Sephin1 groups in all three tissue types. This result indicated that the influence of Sephin1 on antitumor immunity was enriched in MHC-I-related pathways. Apart from this, two other pathways with relatively high overall intensity, including LCK pathway and SELPLG pathway, also show a tendency to decrease in Sephin1 group (Figure 24B). In the tumor microenvironment, Selplg-Sell and Lck (Cd8a+Cd8b1) pairs were included in the pairs that were significantly decreased in Sephin1 group compared to normal group (Figure 24C).

[0559] [000761] On the contrary, the communication intensity of macrophage-Cd4+T cells and macrophage-macrophage was upregulated in the Sephin1 group. Therefore, we also analyzed the differentially expressed communication pathways between these two cell types (Figure 24D). Pathways with high expression levels and significantly upregulated in the Sephin1 group included FN1, GALECTIN, SPP1, MHC-I, THBS, TGFb, APP, THY1, TNF and CSF, many of which were related to antitumor immune suppression. We further analyzed the ligand-receptor pairs in these pathways (Figures 24E and 34B). The MHC-I pathway was upregulated in the Sephin1 group of tumor tissues by overall intensity (Figure 24D), but the number of upregulated ligand-receptor pairs was less than that of downregulated ones (Figure 24E). Moreover, the most upregulated pairs were enriched in macrophage-macrophage interactions, which was also assessed by immunofluorescence results (FN1-CD44 & SPP1-CD44 pairs, Figures 35A-35F). Furthermore, in tumor tissues, the upregulated ligand-receptor pairs were mostly present in macrophage-macrophage communication, including Thbs1, Spp1, Lgals9 and Csf-related pairs, which were highly associated with immune suppression.

[0560] [000762]2.3 Discussion [000763] Sephin1 is a selective inhibitor of PPP1R15A and can inhibit the dephosphorylation of eIF2α by inhibiting the formation of PPP1R15A-PP1c complex

[31] . eIF2α is a key component of the integrated stress response process (ISR) and can be induced by both external factors and endogenous cellular stress, including oncogene activation [16,69,70]. The use of Sephin1 in mammals can result in the promotion of ISR activity and thus has been used as a possible treatment in neurological, motor and protein homeostasis-related diseases [45,71,72]. In our study, we found that the use of Sephin1 in mice can result in antitumor immune suppression, which is most likely achieved by the ISR process by single cell expression analysis. In C56BL / 6 mice subcutaneously injected with B16F1 cells, the tumor growth rate of the Sephin1 group was significantly higher than that of the normal group, indicating a possible relationship between the ISR process and antitumor immune activity. SCENIC analysis of single-cell data of all immune cells between normal and Sephin1 groups showed that all three sample types showed high activity of Atf3 regulon, including core genes related to ISR, and other related regulons with Sephin1 group showing high ISR level. However, regulons related to immune cell activity were downregulated in Sephin1 group, indicating induction of immune suppressive effect by Sephin1. To fully understand the suppressive effect on antitumor immune activity mediated by different kinds of immune cell types, we analyzed the expression and distribution patterns of different cell types in different tissues.

[0561] [000764] Lymphocytes, which are important for antitumor immunity, seem to be more affected by Sephin1 injection. NK cells, NKT cells, and Cd8+ cells were all significantly decreased among immune cells in tumor tissues of the Sephin1 group, while regulatory T cells were more enriched. Furthermore, as important antitumor cell types in the innate and adaptive immune system [73, 74], Cd8+ T cells and NK cells also showed lower expression and cell killing activity in the Sephin1 group. Regarding NKT cells, previous studies have shown that depending on the cell type, NKT cells can suppress (type I NKT cells) or promote (type II NKT cells) tumor development [75, 76]; therefore, the effect of reduction in NKT cells is controversial. Furthermore, enrichment of regulatory T cells in the Sephin1 group also showed suppression of antitumor immunity

[77] . SCENIC analysis showed that the Arf3 regulon activity of Sephin1 in tumor tissues was high in antitumor cell types, such as NK cells, NKT cells, and Cd8+ T cells, but low in inhibitory T cell types and regulatory Cd4+ T cells

[78] , also indicating an antitumor suppressive effect of Sephin1.

[0562] [000765] By analyzing the distribution of TCR clonotypes, we found that tumor-specific T cell proliferation was also suppressed by Sephin1 injection. TCR sequencing analysis showed that highly expanded TCR clonotypes were significantly reduced in the Sephin1 group in terms of both clonotype and clone numbers. Highly expanded TCR clonotypes were more enriched in cytotoxic Cd8+ T cells and macrophages, and showed high expression of genes related to cytotoxicity-related pathways, indicating that these cells were important for tumor-specific identification and cell killing. Furthermore, low clonal number clonotypes were more enriched in naive T cells.

[0563] [000766] Macrophages may also exert important antitumor immune activity. Macrophages may have a tendency to polarize towards M1 or M2 state [79,80], but exist along a continuum and cannot be clearly divided into M1 or M2 types [67,81]. Previous studies have shown that M1 macrophages are pro-inflammatory, whereas M2 macrophages are anti-inflammatory

[82] . In the tumor microenvironment, M1-like macrophages seem to have more antitumor function, whereas M2 macrophages have the opposite effect

[83] . In our experiments, by evaluating the polarization tendency through the evaluation of a series of M1 and M2-related genes, we found that in the Sephin1 group, macrophages tended to show M2 polarized state and seemed to be more promoting tumor development. Furthermore, macrophage subtypes in the tumor microenvironment were more profoundly influenced by Sephin1 than those in the blood, indicating that Sephin1 has a strong influence on tumor-associated macrophages.

[0564] [000767] Previous studies have shown that CD3+TCRαβ+ macrophages can produce proinflammatory cytokines and have important functions in infection-related biological processes

[68] , and this type of macrophage can be generated by trogocytosis between macrophages and T cells [84,85]. However, the traditional trogocytosis theory only involves the exchange of membranes and membrane-associated proteins. In our study, single-cell sequencing data showed that at the mRNA level, a large number of macrophages in the tumor microenvironment contained mRNA for TCR and other T cell-related genes. This phenomenon indicated that the interaction between APCs and T cells may not be limited to the cell surface, but also involves a deeper level of material exchange. Furthermore, clonotype analysis of TCR+ macrophages showed that macrophages with higher TCR frequency seemed to be more suppressed in the Sephin1 group. Besides, TCR+ macrophages were more prone to undergo M1 polarization than conventional macrophages and were more enriched in the tumor microenvironment. All these results indicated that TCR+ macrophages may play an important role in both T cell- and macrophage-related pathways. The inhibitory effect of Sephin1 on this cell type was more significant than that on conventional macrophages.

[0565] [000768] Based on these results, we further analyzed the cell-cell communication between CD8+ T cells, CD4+ T cells, NK cells, macrophages and DCs, and analyzed cell pairs with similar patterns among all three tissue types. In the cell-cell communication downregulated in the Sephin1 group, MHC-I, LCK and SELPLG pathways were significantly downregulated and also had relatively high communication strength. SELPLG pathway is known for its cell-cell adhesion function and may have a function in anti-tumor immunity [86, 87]. MHC-I and LCK pathways have important functions in antigen presentation and were also related to each other. LCK is known to induce the first TCR triggering event

[88] . FN1, GALECTIN, SPP1, THBS, TGFb, APP, THY1, TNF and CSF pathways were upregulated in the Sephin1 group. Most of these pathways were anti-tumor suppressive. GALECTIN can result in T cell inhibition through Lgals9-Havcr2 interaction

[89] . SPP1 can promote immune escape in tumor tissues

[90] . THBS1 can limit antitumor immunity through CD47-dependent regulation of innate and adaptive immune cells

[91] . CSF1 / CSF1R pathway can result in inhibition of T cell checkpoint immunity

[92] . TNF is an important pathway of cell apoptosis and is highly associated with the ISR process, which can also trigger death signaling in immune cells

[93] . TGFb is known as an important marker of M2 macrophages and is highly associated with tumor-promoting effects

[94] . APP and THY1 pathways may also have important functions in antitumor immunity, but studies of these two pathways in antitumor immunity are still lacking.

[0566] [000769] In conclusion, injection of Sephin1 can result in the suppression of antitumor immunity during the development of implanted B16F1 tumors. This finding was also evaluated in another model using 4T1 tumor cells. As a selective inhibitor of PPP1R15A, Sephin1 can inhibit the binding of PPP1R15A to the PPP1R15A-PP1c complex and promote an integrated stress response in mice. From our results, we reasoned that PPP1R15A and other ISR-related genes and their protein products may be important potential targets in tumor immunotherapy. ISR is also an important pathway associated with immune response in mammals. A novel macrophage subtype was identified that was highly associated with Sephin1 treatment and plays an important role in antitumor immunity, indicating a possible mechanism by which Sephin1 exerts its tumor-promoting effect. Furthermore, cell-cell communication analysis also revealed that antitumor-associated immune interactions were suppressed by Sephin1 in mouse blood and tumor microenvironment. In other words, PPP1R15A and its associated ISRs play important roles in the immune system, especially in antitumor immunity, and may be used as a new target for tumor immunotherapy.The inhibitor Sephin1 also has potential for immune-related diseases, such as autoimmune diseases [95, 96].

[0567] [000770] 2.4 Reference

[0568] [Table 10-1]

[0569] [Table 10-2]

[0570] [Table 10-3]

[0571] [Table 10-4]

[0572]

Table 10-5

[0573]

Table 10-6

[0574]

Table 10-7

[0575]

Table 10-8

Claims

1. A method of enhancing cell-mediated immunity in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 agonist, thereby enhancing cell-mediated immunity in the subject.

2. The method of claim 1 , wherein the cell-mediated immunity is T cell-mediated immunity.

3. A method of stimulating and / or expanding T cells in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 agonist, thereby stimulating and / or expanding T cells in the subject.

4. A method of increasing the immunogenicity of an immunogenic composition in a subject, comprising administering to the subject an effective amount of an MX1 agonist in combination with the immunogenic composition.

5. The method of claim 4, wherein the immunogenic composition is a vaccine or a composition for CAR-T treatment.

6. The method of claim 5 , wherein the vaccine is a tumor vaccine.

7. The method of any one of claims 1 to 6, wherein the subject is suffering from a condition that is expected to benefit from upregulation of the immune response.

8. 8. The method of claim 7, wherein the condition is a tumor, an infectious disease, a cardiovascular disease or an inflammatory disease.

9. 9. The method of claim 8, wherein the condition is a tumor or an infectious disease.

10. The method of any one of claims 1 to 6, wherein the subject receives a treatment whose effectiveness may be increased by enhancing cell-mediated immunity.

11. 11. The method of claim 10, wherein the treatment is an anti-tumor treatment or an anti-infective treatment.

12. 12. The method of claim 11, wherein the anti-tumor treatment is selected from the group consisting of chemotherapy, targeted therapy, immunotherapy, cell therapy (e.g., CAR-T therapy), and tumor vaccine.

13. A method of treating a condition that would benefit from upregulation of the immune response in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 agonist.

14. 14. The method of claim 13, wherein the condition is a tumor, an infectious disease, a cardiovascular disease or an inflammatory disease.

15. 15. The method of claim 14, wherein the condition is a tumor or an infectious disease.

16. 14. The method of claim 13, wherein the MX1 agonist is administered in combination with a therapy to treat the condition.

17. 17. The method of claim 16, wherein the treatment is an anti-tumor treatment or an anti-infective treatment.

18. 18. The method of claim 17, wherein the anti-tumor treatment is selected from the group consisting of chemotherapy, targeted therapy, immunotherapy, cell therapy (e.g., CAR-T therapy), tumor vaccine, and CAR-T therapy.

19. The method of any one of the preceding claims, wherein the MX1 agonist is selected from the group consisting of full length MX1 mRNA, and DMXAA, ADU-S100, 2',3'-cGAMP sodium and small molecule compounds of cGAMP.

20. A method of promoting clonal expansion of T cells comprising treating T cells with an effective amount of an MX1 agonist under suitable conditions, thereby allowing clonal expansion of the T cells.

21. 21. The method of claim 20, wherein the T cell is a memory T cell.

22. A method of promoting T cell activation or promoting T cell cytotoxicity, comprising treating T cells with an effective amount of an MX1 agonist under suitable conditions.

23. 23. The method of claim 22, wherein the T cells are cultured with an MX1 agonist in combination with a second agent to stimulate the T cells.

24. The method of any one of claims 20 to 23, wherein the MX1 agonist is selected from the group consisting of full length MX1 mRNA, and DMXAA, ADU-S100, 2',3'-cGAMP sodium and small molecule compounds of cGAMP.

25. A composition comprising T cells prepared using the method of any one of claims 20 to 23.

26. 26. A method of treating a condition expected to benefit from upregulation of an immune response in a subject in need thereof, comprising administering to the subject the composition of claim 25.

27. A method for evaluating the activation state, or activity or cytotoxicity of T cells, comprising detecting the expression level of MX1 in T cells, wherein an increase in the expression level of MX1 compared to a control T cell is an indication of the cytotoxicity of the T cell.

28. The method of claim 27, wherein the T cell is a CAR-T cell or a TCR-T cell.

29. 1. A method for preparing a population of T cells for cell therapy, comprising: a) identifying a population of T cells that have increased levels of MX1 expression relative to control T cells; and b) selectively enriching the identified T cells for cell therapy The method includes:

30. 1. A method for converting a first population of inactive T cells into a second population of active T cells, comprising: a) providing a first population of inactive T cells, wherein the inactive T cells do not exhibit an increased expression level of MX1 compared to a control T cell; b) treating the first population of inactive T cells with an effective amount of an MX1 agonist under suitable conditions; and c) detecting the expression level of MX1 in the population of T cells obtained in step b), where an increase in the expression level of MX1 compared to control T cells is indicative of successful conversion of activated T cells into a second population; The method includes:

31. 1. A method for preparing a population of T cells for cell therapy, comprising: a) identifying a population of T cells as inactive if they do not exhibit an increased level of expression of MX1 compared to control T cells; and b) treating the identified population of inactive T cells with an effective amount of an MX1 agonist under suitable conditions, thereby obtaining a population of activated T cells; The method includes:

32. The method of any one of claims 27 to 31, wherein the MX1 agonist is selected from the group consisting of full-length MX1 mRNA, and DMXAA, ADU-S100, 2',3'-cGAMP sodium and small molecule compounds of cGAMP.

33. A composition comprising a population of T cells prepared or converted by the method of any one of claims 27 to 31.

34. 32. A method of treating a condition that would benefit from upregulation of an immune response in a subject in need thereof, comprising administering to the subject a population of T cells prepared or converted by the method of any one of claims 27 to 31.

35. A method of reducing cell-mediated immunity in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 antagonist, thereby reducing cell-mediated immunity in the subject.

36. 36. The method of claim 35, wherein the cell-mediated immunity is T cell-mediated immunity.

37. A method of inactivating T cells in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 antagonist, thereby inactivating T cells in the subject.

38. The method of any one of claims 35 to 37, wherein the subject has been determined to have T cells that have increased levels of MX1 expression compared to control T cells.

39. The method of any one of claims 35 to 37, wherein the subject is suffering from a condition characterized by excessive cell-mediated immunity.

40. A method of treating a condition that would benefit from downregulation of the immune response in a subject in need thereof, comprising administering to the subject an effective amount of an MX1 antagonist.

41. 41. The method of claim 39 or 40, wherein the condition is an inflammatory disease, an autoimmune disease, an allergic disease, or a T-cell cancer.

42. 41. The method of claim 39 or 40, wherein the condition is one in which the subject has received or is believed to have received an allogeneic or xenogeneic transplant.

43. The method of any one of claims 35 to 42, wherein the MX1 antagonist is selected from the group consisting of CCCP and H-151.

44. The method of any one of claims 27 and 29 to 31, wherein the control T cells are CD8+ T cells.

45. 1. A method for assessing the responsiveness of a subject to a tumor neo-antigen vaccine, comprising: a) determining an expression level of one or more genes in one or more immune cells from a sample obtained from the subject, the one or more genes being selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATF4, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, DDOST, DERL1, DNPH1, EGF, FERMT 3, FOSL2, GCH1, GK, GLA, LMAN2, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PDE4D, PIM1, PRKACA, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TMEM258, TRDV1, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof; b) comparing the expression level of one or more genes determined in step a) with a reference level to determine the difference from the reference level; and c) assessing the responsiveness of the subject to the tumor neo-antigen vaccine based on the difference determined in step b). The method includes:

46. 46. ​​The method of claim 45, wherein the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

47. 47. The method of claim 45 or 46, wherein the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of AC011815.2, EGF, GCH1, NUAK1 and SERTAD2, or any combination thereof.

48. 47. The method of claim 45 or 46, wherein the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of ANKRD29, C15orf54, FOSL2, GCH1, PRKACA, SERTAD2, SLC16A6, TRDV1, WDFY3 and ZBTB43, or any combination thereof.

49. 47. The method of claim 45 or 46, wherein the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of AP3M2, ARL5B, ATP5F1B, CHCHD2, COPE, GLA, MN1 and MTDH, or any combination thereof.

50. 47. The method of claim 45 or 46, wherein the one or more immune cells are B cells and the one or more genes are selected from the group consisting of ATF4, ATP5F1B, DDOST, DERL1, DNPH1, LMAN2, MAP9, PDE4D, PIM1, TMEM258 and ZDHHC7, or any combination thereof.

51. 47. The method of claim 45 or 46, wherein the one or more immune cells are NK cells and the one or more genes are selected from the group consisting of COL4A3BP, FERMT3 and NFIL3, or any combination thereof.

52. 47. The method of claim 45 or 46, wherein the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of COPE, CTTNBP2, GK, LRRK1, MTDH, PRMT1, SYT17 and TMED10, or any combination thereof.

53. 1. A method for assessing a subject's responsiveness to a tumor neo-antigen vaccine during a priming phase, comprising: a) determining the expression level of one or more genes in one or more immune cells from a sample obtained from the subject after at least one priming administration of a tumor neo-antigen vaccine, wherein the one or more genes are selected from the group consisting of AC011815.2, ATF4, C15orf54, DDOST, DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, LMAN2, MAP9, NUAK1, PDE4D, PRKACA, SERTAD2, SLC16A6, TMED10, TMEM258, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof; b) comparing the expression level of one or more genes determined in step a) with a reference level to determine the difference from the reference level; and c) assessing the responsiveness of the subject to at least one priming administration of the tumor neo-antigen vaccine based on the difference determined in step b). The method includes:

54. 54. The method of claim 53, wherein the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

55. 54. The method of claim 53, wherein the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, B cells, NK cells, and macrophages.

56. 55. The method of claim 53 or 54, wherein the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of AC011815.2, EGF, GCH1, NUAK1 and SERTAD2, or any combination thereof.

57. 55. The method of claim 53 or 54, wherein the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of C15orf54, FOSL2, GCH1, PRKACA, SERTAD2, SLC16A6, WDFY3 and ZBTB43, or any combination thereof.

58. 55. The method of claim 53 or 54, wherein the one or more immune cells are B cells and the one or more genes are selected from the group consisting of ATF4, DDOST, DERL1, DNPH1, LMAN2, MAP9, PDE4D, TMEM258 and ZDHHC7, or any combination thereof.

59. 55. The method of claim 53 or 54, wherein the one or more immune cells are NK cells and the gene is FERMT3.

60. 55. The method of claim 53 or 54, wherein the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of GK and TMED10, or any combination thereof.

61. 1. A method for assessing a subject's responsiveness to a tumor neo-antigen vaccine during a boosting phase, comprising: a) determining the expression level of one or more genes in one or more immune cells from a sample obtained from the subject during a boosting phase, wherein the one or more genes are selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, EGF, FERMT3, FOSL2, GK, GLA, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PIM1, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TRDV1 and ZDHHC7, or any combination thereof; b) comparing the expression level of one or more genes determined in step a) with a reference expression level to determine the difference from the reference level; and c) assessing the responsiveness of the subject to at least one boosting administration of the tumor neo-antigen vaccine based on the difference determined in step b). The method includes:

62. 62. The method of claim 61, wherein the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

63. 63. The method of claim 61 or 62, wherein the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of AC011815.2, EGF, NUAK1 and SERTAD2, or any combination thereof.

64. 63. The method of claim 61 or 62, wherein the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of ANKRD29, C15orf54, FOSL2, SLC16A6 and TRDV1, or any combination thereof.

65. 63. The method of claim 61 or 62, wherein the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of AP3M2, ARL5B, ATP5F1B, CHCHD2, COPE, GLA, MN1 and MTDH, or any combination thereof.

66. 63. The method of claim 61 or 62, wherein the one or more immune cells are B cells and the one or more genes are selected from the group consisting of ATP5F1B, MAP9, PIM1 and ZDHHC7, or any combination thereof.

67. 63. The method of claim 61 or 62, wherein the one or more immune cells are NK cells and the one or more genes are selected from the group consisting of COL4A3BP, FERMT3 and NFIL3, or any combination thereof.

68. 63. The method of claim 61 or 62, wherein the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of COPE, CTTNBP2, GK, LRRK1, MTDH, PRMT1, SYT17 and TMED10, or any combination thereof.

69. The method of any one of claims 45 to 68, wherein the expression level of a given gene is represented by the percentage of immune cells of a given type that express the given gene.

70. The method according to any one of claims 45 to 69, wherein the reference expression level is the expression level of the one or more genes in one or more immune cells from a sample obtained from the subject before receiving a first dose of the tumor neo-antigen vaccine.

71. The method according to any one of claims 45 to 70, wherein the subject is determined as exhibiting a good response to the tumor neo-antigen vaccine if the difference reaches or exceeds a defined threshold, or the subject is determined as exhibiting a poor response to the tumor neo-antigen vaccine if the difference is below a defined threshold.

72. A method for predicting the risk of tumor recurrence in a subject before or after receiving at least one dose of a tumor neo-antigen vaccine, a) determining an expression level of one or more genes in one or more immune cells from a sample obtained from the subject, the one or more genes being selected from the group consisting of ABCA13, AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, AL662907.3, APOBEC3C, ATP1B3, ATP6V1B2, C1 QBP, CBX5, CCNE2, CD63, CEBPA, CHP1, CLN8, CMBL, CNIH4, COL4A3BP, CPNE3, DACH1, DNAJC12, DNAJC3, DO C2B, EEA1, ENAM, ERG, FAM43A, FBXO43, FIN, GCA, GNG4, GNLY, GOLIM4, GPM6B, GPR171, GPRC5D, HHEX, HID 1, ILF2, IRF2BP2, KIF22, LACC1, LIPA, MANEA, MECOM, MID1IP1, MX2, MYOM2, NABP1, NEFH, NFE4, NLN, OXC T2, P3H2, PI3, PIM1, PLAU, PRSS3, PSMA3, RAB10, RAB20, RASGRP2, RBMS3, RPL23, RPS6KA5, RPS8, RUNX1, S AMD3, 11-Sep, SERTAD2, SIGLEC6, SIT1, SOCS1, SSR1, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC15, TXNDC17, UAP1, UFM1, UGT2B17, XPNPEP1, ZBTB16, ZNF608 and STAT1, or any combination thereof; b) comparing the expression level of one or more genes determined in step a) with a reference level to determine the difference from the reference level; and c) assessing the risk of tumor recurrence in the subject based on the difference determined in step b). The method includes:

73. 73. The method of claim 72, wherein the one or more genes are selected from the group consisting of AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, ATP1B3, CMBL, DACH1, DNAJC12, DOC2B, EEA1, ERG, FBXO43, FIGN, GPRC5D, HID1, NFE4, OXCT2, P3H2, PI3, PLAU, PRSS3, RASGRP2, RPL23, RPS8, ST6GALNAC1, SYNE2, TRAV16, TREM2 and ZNF608, or any combination thereof.

74. The expression level of one or more genes is determined in one or more immune cells from a sample obtained from the subject after receiving at least one dose of a tumor neo-antigen vaccine, and the one or more genes are selected from the group consisting of AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, ATP1B3, ATP6V1B2, CHP1, CLN8, CMBL, CNIH4, CPNE3, DACH1, DNAJC12, DOC2B, EEA1, ERG, FAM43A, FBXO43, FIG, GNG4, GPM6B, GPRC5D, HHEX, HHEX1, HHEX2 ...

74. The method of claim 72 or 73, wherein the gene is selected from the group consisting of ID1, IRF2BP2, KIF22, LACC1, MECOM, MID1IP1, MYOM2, NABP1, NFE4, OXCT2, P3H2, PI3, PLAU, PRSS3, RAB10, RASGRP2, RBMS3, RPL23, RPS8, RUNX1, SAMD3, SERTAD2, SIGLEC6, SIT1, SOCS1, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC17, UGT2B17, XPNPEP1 and ZNF608, or any combination thereof.

75. The expression level of one or more genes is determined in one or more immune cells from a sample obtained from the subject prior to receiving at least one dose of the tumor neo-antigen vaccine, the one or more genes being selected from the group consisting of ABCA13, AC022726.2, AC105094.2, AC243829.2, AL021807.1, AL391807.1, AL662907.3, ATP1B3, CBX5, CMBL, DACH1, DNAJC12, DNAJC3, DOC2B, ENAM, ERG, FBXO4, EGFR, EGFR1, EGFR2, EGFR3, EGFR4, EGFR5, EGFR6, EGFR7, EGFR8, EGFR9, EGFR10, EGFR11, EGFR12, EGFR13, EGFR14, EGFR15, EGFR16, EGFR17, EGFR18, EGFR19, EGFR20, EGFR21, EGFR22, EGFR23, EGFR24, EGFR25, EGFR26, EGFR27, EGFR28, EGFR29, EGFR30, EGFR31, EGFR32, EGFR33, EGFR40, EGFR41, EGFR42, EGFR43, EGFR44, EGFR55, EGFR56, EGFR57, EGFR68, EGFR69, EGFR70, EGFR71, EGFR82, EGFR93, EGFR94, EGFR95, EGFR96, EGFR97, EGFR98, EGFR99, EGFR100, EGFR111, EGFR120, EGFR130, 3, FIGN, GOLIM4, GPRC5D, HID1, ILF2, NEFH, NFE4, NLN, OXCT2, P3H2, PI3, PLAU, PRSS3, PSMA3, RASGRP2, RPL23, RPS6KA5, RPS8, 11-Sep, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC15, UAP1, UFM1, ZBTB16 and ZNF608, or any combination thereof.

76. 76. The method of any one of claims 72-75, wherein the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells and macrophages.

77. 77. The method of any one of claims 72-76, wherein the one or more immune cells are CD8+ T cells and the one or more genes are selected from the group consisting of CHP1, EEA1, RPS8, RUNX1, UGT2B17, XPNPEP1, ZNF608 and STAT1, or any combination thereof.

78. 77. The method of any one of claims 72-76, wherein the one or more immune cells are CD4+ T cells and the one or more genes are selected from the group consisting of AL662907.3, CD63, COL4A3BP, CPNE3, EEA1, GPM6B, LIPA, PSMA3, RAB10, RAB20, RBMS3, RPS8, SAMD3, SERTAD2, SOCS1, TXNDC15 and STAT1, or any combination thereof.

79. 77. The method of any one of claims 72-76, wherein the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of ABCA13, ATP1B3, DNAJC3, ENAM, FIGN, GNG4, ILF2, KIF22, MANEA, NEFH, NLN, P3H2, PLAU, SIGLEC6, SSR1, TRAV16, UFM1 and ZBTB16, or any combination thereof.

80. 77. The method of any one of claims 72-76, wherein the one or more immune cells are B cells and the one or more genes are selected from the group consisting of AC105094.2, AC243829.2, AL021807.1, AL391807.1, ATP6V1B2, C1QBP, CMBL, CNIH4, DOC2B, FAM43A, GCA, GNLY, HID1, LACC1, MID1IP1, MX2, PI3, PIM1, 11-Sep, SIT1 and SYNE2, or any combination thereof.

81. 77. The method of any one of claims 72-76, wherein the one or more immune cells are NK cells and the one or more genes are selected from the group consisting of AC022726.2, APOBEC3C, DACH1, ERG, IRF2BP2, MYOM2, NFE4, PRSS3, RPL23, RPS6KA5 and TREM2, or any combination thereof.

82. 77. The method of any one of claims 72-76, wherein the one or more immune cells are macrophages and the one or more genes are selected from the group consisting of CBX5, CCNE2, CEBPA, CLN8, DNAJC12, FBXO43, FIGN, GOLIM4, GPR171, GPRC5D, HHEX, MECOM, NABP1, OXCT2, RASGRP2, ST6GALNAC1, TXNDC17 and UAP1, or any combination thereof.

83. The method of any one of claims 72 to 82, wherein the expression level of a given gene is represented by the percentage of immune cells of a given type that express the given gene.

84. The method according to any one of claims 72 to 83, wherein the reference expression level is the expression level of the corresponding gene in corresponding immune cells representative of relapsed subjects.

85. The method of any one of claims 72 to 83, wherein the reference expression level is a standard or mean expression level determined from a representative population of recurrent subjects.

86. The method of claim 84 or 85, wherein the subject is determined to be at low risk of tumor recurrence if the difference meets or exceeds a predefined threshold, or the subject is determined to be at high risk of tumor recurrence if the difference is below a predefined threshold.

87. The method according to any one of claims 72 to 83, wherein the reference expression level is the expression level of the corresponding gene in corresponding immune cells that are representative of relapse-free subjects.

88. The method of any one of claims 72 to 83, wherein the reference expression level is a standard or mean expression level determined from a representative population of relapse-free subjects.

89. The method of claim 87 or 88, wherein the subject is determined to be at high risk of tumor recurrence if the difference meets or exceeds a specified threshold, or the subject is determined to be at low risk of tumor recurrence if the difference is below a specified threshold.

90. 1. A method for evaluating therapeutic efficacy in a subject treated with an antitumor therapy in combination with at least one administration of a tumor neo-antigen vaccine, comprising: a) determining the level of one or more genes in one or more immune cells from a sample obtained from the subject after treatment, wherein the one or more genes are selected from the group consisting of AC092490.1, ALDH1L2, LILRB5, PALD1, PKP2 and TRAV35, or any combination thereof; b) comparing the level of the one or more genes determined in step a) to a reference level to determine the difference from the reference level; and c) evaluating the efficacy of the treatment in the subject based on the difference determined in step b). The method includes:

91. 91. The method of claim 90, wherein the one or more immune cells are selected from the group consisting of CD8+ T cells, CD4+ T cells, monocytes, B cells, NK cells, and macrophages.

92. 92. The method of claim 90 or 91, wherein the one or more immune cells are CD4+ T cells and the one or more genes is LILRB5.

93. 92. The method of claim 90 or 91, wherein the one or more immune cells are monocytes and the one or more genes are selected from the group consisting of ALDHIL2 and PKP2, or any combination thereof.

94. 92. The method of claim 90 or 91, wherein the one or more immune cells are B cells and the one or more genes are selected from the group consisting of AC092490.1, PALD1 and TRAV35, or any combination thereof.

95. 95. The method of any one of claims 90 to 94, wherein the expression level of a given gene is represented by the percentage of immune cells of a given type that express the given gene.

96. The method according to any one of claims 90 to 95, wherein the reference expression level is the expression level of the corresponding gene in a corresponding immune cell from a sample obtained from the subject before undergoing an anti-tumor treatment.

97. 97. The method of any one of claims 90 to 96, wherein the anti-tumor treatment comprises a PD-1 antagonist.

98. The method of any one of claims 90 to 97, wherein the subject exhibits tumor recurrence following tumor neoantigen vaccination.

99. The method of any one of claims 45 to 98, wherein the subject undergoes tumor resection surgery prior to receiving the first dose of the tumor neo-antigen vaccine, and optionally, the subject does not undergo chemotherapy prior to the resection surgery.

100. The method of any one of claims 45 to 99, wherein tumor tissue, adjacent tissue and / or peripheral blood samples from the subject are analyzed to identify one or more tumor-specific mutations in the subject.

101. The method of claim 100, wherein a tumor neo-antigen vaccine is prepared based on the identified tumor-specific mutations.

102. The method of any one of claims 45 to 101, wherein the subject is diagnosed with pancreatic cancer, optionally pancreatic ductal adenocarcinoma.

103. The method of any one of claims 45 to 102, wherein the sample comprises or is derived from peripheral blood mononuclear cells (PBMCs), a blood sample, or tumor infiltrating immune cells.

104. 104. The method of any one of claims 45 to 103, wherein the level of the one or more genes is measured by an amplification assay, a hybridization assay, a sequencing method (e.g. single cell sequencing), or an immunoassay (e.g. flow cytometry or immunohistochemistry).

105. A kit for assessing the responsiveness of a subject to a tumor neo-antigen vaccine, comprising one or more reagents for detecting the expression level of one or more genes in one or more immune cells from a sample obtained from the subject, the one or more genes being AC011815.2, ANKRD29, AP3M2, ARL5B, ATF4, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, DDOST , DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, GLA, LMAN2, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PDE4D, PIM1, PRKACA, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TMEM258, TRDV1, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof.

106. 1. A kit for assessing the responsiveness of a subject to at least one priming administration of a tumor neo-antigen vaccine, comprising one or more reagents for detecting the expression level of one or more genes in one or more immune cells from a sample obtained from the subject after at least one priming administration of the tumor neo-antigen vaccine, wherein the one or more genes are selected from the group consisting of AC011815.2, ATF4, C15orf54, DDOST, DERL1, DNPH1, EGF, FERMT3, FOSL2, GCH1, GK, LMAN2, MAP9, NUAK1, PDE4D, PRKACA, SERTAD2, SLC16A6, TMED10, TMEM258, WDFY3, ZBTB43 and ZDHHC7, or any combination thereof.

107. 1. A kit for assessing the responsiveness of a subject to at least one boosting administration of a tumor neo-antigen vaccine, comprising one or more reagents for detecting the expression level of one or more genes in one or more immune cells from a sample obtained from the subject during a boosting phase, wherein the one or more genes are selected from the group consisting of AC011815.2, ANKRD29, AP3M2, ARL5B, ATP5F1B, C15orf54, CHCHD2, COL4A3BP, COPE, CTTNBP2, EGF, FERMT3, FOSL2, GK, GLA, LRRK1, MAP9, MN1, MTDH, NFIL3, NUAK1, PIM1, PRMT1, SERTAD2, SLC16A6, SYT17, TMED10, TRDV1 and ZDHHC7, or any combination thereof.

108. A kit for predicting the risk of tumor recurrence in a subject before or after receiving at least one administration of a tumor neo-antigen vaccine, comprising one or more reagents for detecting the expression level of one or more genes in one or more immune cells from a sample obtained from the subject, wherein the one or more genes are selected from the group consisting of ABCA13, AC022726.2, AC105094.2, AC243829.2, AL021807, and / or AL022726.

2. .. 1, AL391807.1, AL662907.3, APOBEC3C, ATP1B3, ATP6V1B2, C1QBP, CBX5, CCNE2, CD63, CEBPA, CHP1, CLN8, CMBL, CN IH4, COL4A3BP, CPNE3, DACH1, DNAJC12, DNAJC3, DOC2B, EEA1, ENAM, ERG, FAM43A, FBXO43, FIGN, GCA, GNG4, GNLY, G OLIM4, GPM6B, GPR171, GPRC5D, HHEX, HID1, ILF2, IRF2BP2, KIF22, LACC1, LIPA, MANEA, MECOM, MID1IP1, MX2, MYOM 2, NABP1, NEFH, NFE4, NLN, OXCT2, P3H2, PI3, PIM1, PLAU, PRSS3, PSMA3, RAB10, RAB20, RASGRP2, RBMS3, RPL23, RPS 6KA5, RPS8, RUNX1, SAMD3, 11-Sep, SERTAD2, SIGLEC6, SIT1, SOCS1, SSR1, ST6GALNAC1, SYNE2, TRAV16, TREM2, TXNDC15, TXNDC17, UAP1, UFM1, UGT2B17, XPNPEP1, ZBTB16, ZNF608 and STAT1, or any combination thereof.

109. 1. A kit for evaluating therapeutic efficacy in a subject treated with an anti-tumor therapy in combination with at least one administration of a tumor neo-antigen vaccine, comprising one or more reagents for detecting the level of one or more genes in one or more immune cells from a sample obtained from the subject after treatment, wherein the one or more genes are selected from the group consisting of AC092490.1, ALDH1L2, LILRB5, PALD1, PKP2 and TRAV35, or any combination thereof.

110. A method of enhancing cell-mediated immunity in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A agonist, thereby enhancing cell-mediated immunity in the subject.

111. The method of claim 110, wherein the cell-mediated immunity is T cell-mediated immunity.

112. A method of stimulating and / or expanding T cells in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A agonist, thereby stimulating and / or expanding T cells in the subject.

113. 20. A method of increasing the immunogenicity of an immunogenic composition in a subject, comprising administering to the subject an effective amount of a PPP1R15A agonist in combination with the immunogenic composition.

114. The method of claim 113, wherein the immunogenic composition is a vaccine or a composition for CAR-T treatment.

115. 115. The method of claim 114, wherein the vaccine is a tumor vaccine.

116. 116. The method of any one of claims 110 to 115, wherein the subject is suffering from a condition that would benefit from upregulation of the immune response.

117. The method of any one of claims 110 to 115, wherein the subject is determined to exhibit a decreased expression level of PPP1R15A.

118. 117. The method of claim 116, wherein the condition is a tumor, an infectious disease, a cardiovascular disease or an inflammatory disease.

119. 119. The method of claim 118, wherein the condition is a tumor or an infectious disease.

120. 120. The method of claim 119, wherein the tumor is selected from the group consisting of brain cancer, renal cell carcinoma, ovarian cancer, gastric cancer, bladder cancer, breast cancer, ovarian cancer, prostate cancer, colon cancer, lung cancer, squamous cell carcinoma of the head and neck, colorectal cancer, melanoma, and myeloma.

121. 116. The method of any one of claims 110 to 115, wherein the subject is receiving a treatment whose effectiveness may be increased by enhancing cell-mediated immunity.

122. 122. The method of claim 121, wherein the treatment is an anti-tumor treatment or an anti-infective treatment.

123. 123. The method of claim 122, wherein the anti-tumor treatment is selected from the group consisting of chemotherapy, targeted therapy, immunotherapy, cell therapy (e.g., CAR-T therapy), and tumor vaccine.

124. A method of treating a condition that would benefit from upregulation of the immune response in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A agonist.

125. 125. The method of claim 124, wherein the condition is a tumor, an infectious disease, a cardiovascular disease or an inflammatory disease.

126. 126. The method of claim 125, wherein the condition is a tumor or an infectious disease.

127. 127. The method of claim 126, wherein the tumor is selected from the group consisting of brain cancer, renal cell carcinoma, ovarian cancer, gastric cancer, bladder cancer, breast cancer, ovarian cancer, prostate cancer, colon cancer, lung cancer, squamous cell carcinoma of the head and neck, colorectal cancer, melanoma, and myeloma.

128. The method of claim 124, wherein the subject is diagnosed with a decreased expression level of PPP1R15A.

129. 125. The method of claim 124, wherein the PPP1R15A agonist is administered in combination with a therapy to treat the condition.

130. 130. The method of claim 129, wherein the treatment is an anti-tumor treatment or an anti-infective treatment.

131. 131. The method of claim 130, wherein the anti-tumor treatment is selected from the group consisting of chemotherapy, targeted therapy, immunotherapy, cell therapy (e.g., CAR-T therapy), tumor vaccine, and CAR-T therapy.

132. The method of any one of claims 110 to 131, wherein the PPP1R15A agonist is selected from the group consisting of full-length PPP1R15A mRNA and a synthetic cannabinoid (WIN55,212-2 mesylate [WIN]).

133. A method of promoting clonal expansion of T cells comprising treating T cells with an effective amount of a PPP1R15A agonist under suitable conditions, thereby allowing clonal expansion of the T cells.

134. The method of claim 133, wherein the T cell is a memory T cell.

135. A method of promoting T cell activation or promoting T cell cytotoxicity comprising treating T cells under suitable conditions with an effective amount of a PPP1R15A agonist.

136. 136. The method of claim 135, wherein the T cells are cultured with a PPP1R15A agonist in combination with a second agent to stimulate the T cells.

137. The method of any one of claims 133 to 136, wherein the PPP1R15A agonist is selected from the group consisting of full-length PPP1R15A mRNA and a synthetic cannabinoid (WIN55,212-2 mesylate [WIN]).

138. A composition comprising T cells prepared using the method of any one of claims 133 to 137.

139. 139. A method of treating a condition expected to benefit from upregulation of an immune response in a subject in need thereof, comprising administering to the subject the composition of claim 138.

140. A method for evaluating the activation state, or activity or cytotoxicity of a T cell, comprising detecting the expression level of PPP1R15A in a T cell, wherein an increase in the expression level of PPP1R15A compared to a control T cell is an indication of the cytotoxicity of the T cell.

141. The method of claim 140, wherein the T cell is a CAR-T cell or a TCR-T cell.

142. 1. A method for preparing a population of T cells for cell therapy, comprising: a) identifying a population of T cells that have increased levels of PPP1R15A expression relative to control T cells; and b) selectively enriching the identified T cells for cell therapy The method includes:

143. 1. A method for converting a first population of inactive T cells into a second population of active T cells, comprising: a) providing a first population of inactive T cells, wherein the inactive T cells do not exhibit an increased expression level of PPP1R15A compared to a control T cell; b) treating the first population of inactive T cells with an effective amount of a PPP1R15A agonist under suitable conditions; and c) detecting the expression level of PPP1R15A in the population of T cells obtained in step b), wherein an increase in the expression level of PPP1R15A compared to control T cells is indicative of successful conversion of activated T cells into a second population; The method includes:

144. 1. A method for preparing a population of T cells for cell therapy, comprising: a) identifying a population of T cells as inactive if they do not exhibit an increased level of expression of PPP1R15A compared to control T cells; and b) treating the identified population of inactive T cells with an effective amount of a PPP1R15A agonist under suitable conditions, thereby obtaining a population of activated T cells; The method includes:

145. The method of any one of claims 140 to 144, wherein the PPP1R15A agonist is selected from the group consisting of full-length PPP1R15A mRNA and a synthetic cannabinoid (WIN55,212-2 mesylate [WIN]).

146. 146. A composition comprising a population of T cells prepared or converted by the method of any one of claims 140 to 145.

147. 146. A method of treating a condition that would benefit from upregulation of an immune response in a subject in need thereof comprising administering to the subject a population of T cells prepared or converted by the method of any one of claims 140-145.

148. A method of reducing cell-mediated immunity in a subject in need thereof, comprising the step of administering to the subject an effective amount of a PPP1R15A antagonist, thereby reducing cell-mediated immunity in the subject.

149. The method of claim 148, wherein the cell-mediated immunity is T cell-mediated immunity.

150. A method of inactivating T cells in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A antagonist, thereby inactivating T cells in the subject.

151. The method of any one of claims 148 to 150, wherein the subject has been determined to have T cells that have increased levels of expression of PPP1R15A compared to control T cells.

152. The method of any one of claims 148-150, wherein the subject is suffering from a condition characterized by excessive cell-mediated immunity.

153. A method of treating a condition that would benefit from downregulation of the immune response in a subject in need thereof, comprising administering to the subject an effective amount of a PPP1R15A antagonist.

154. 154. The method of claim 152 or 153, wherein the condition is an autoimmune disease, organ or tissue transplant rejection, graft-versus-host disease, an inflammatory disease, an infectious disease, or cancer.

155. The method of any one of claims 148 to 154, wherein the subject is diagnosed as exhibiting an increased expression level of PPP1R15A.

156. The method of any one of claims 148 to 155, wherein the PPP1R15A antagonist is selected from the group consisting of guanabenz and Sephin1.

157. The method of any one of claims 140 and 142 to 144, wherein the control T cells are CD8+ T cells.

158. 1. A method for predicting a risk of developing a disease or condition associated with downregulation of an immune response in a subject, comprising: a) determining the expression level of PPP1R15A from a sample obtained from the subject; b) comparing the level determined in step a) with a reference level to determine the difference from the reference level; and c) predicting the risk of developing a disease or condition associated with downregulation of the immune response based on the difference determined in step b). The method includes:

159. The method of claim 158, wherein if the difference indicates a decrease in the expression level of PPP1R15A compared to the reference level, the subject is predicted to be at risk of developing a disease or condition associated with downregulation of the immune response.

160. 159. The method of claim 158, wherein the disease or condition is a tumor, an infectious disease, a cardiovascular disease or an inflammatory disease.

161. 161. The method of claim 160, wherein the tumor is selected from the group consisting of brain cancer, renal cell carcinoma, ovarian cancer, gastric cancer, bladder cancer, breast cancer, ovarian cancer, prostate cancer, colon cancer, lung cancer, squamous cell carcinoma of the head and neck, colorectal cancer, melanoma, and myeloma.

162. 1. A method for predicting a risk of developing a disease or condition associated with upregulation of an immune response in a subject, comprising: a) determining the level of PPP1R15A in T cells from a sample obtained from the subject; b) comparing the level of PPP1R15A determined in step a) with a reference level to determine the difference from the reference level; and c) determining the risk of developing a disease or condition in the subject based on the difference determined in step b). The method includes:

163. The method of claim 162, wherein the subject is determined to be at risk for developing a disease or condition associated with upregulation of the immune response if the difference meets or exceeds a first defined threshold.

164. 164. The method of claim 162 or 163, wherein the disease or condition is an autoimmune disease, organ or tissue transplant rejection, graft-versus-host disease, an inflammatory disease, an infectious disease, or cancer.

165. 1. A method for predicting the likelihood of a subject in need thereof to respond to treatment with a PPP1R15A agonist, comprising: a) determining the level of PPP1R15A in T cells from a sample obtained from the subject; b) comparing the level of PPP1R15A determined in step a) with a reference level to determine the difference from the reference level; and c) determining the susceptibility of the subject to response based on the difference determined in step b). The method includes:

166. 166. The method of claim 165, wherein the subject is determined to be likely to be responsive to treatment with a PPP1R15A agonist if the difference meets or exceeds a first defined threshold.

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