A method and system for screening compounds that enhance the efficacy of immunotherapy based on transcriptome

Abstract

The application develops a compound screening calculation framework for improving the efficacy of immunotherapy, and discovers new compounds that can overcome the immunotherapy resistance of liver cancer based on the framework. Specifically, the application first systematically characterizes the differences in tumor microenvironment between immunotherapy response objects and non-response objects, and constructs a weighted model of a 33-immunotherapy-related gene expression signature (IGeS) that can predict immunotherapy response. Then, a novel computational strategy is developed to quantify the changes in tumor microenvironment after intervention by a compound, and to screen compounds with potential to enhance the efficacy of immunotherapy. Finally, the effects of candidate compounds in overcoming the immunotherapy resistance of liver cancer are verified through in vitro and in vivo experiments.

Description

Technical Field

[0001] This invention relates to the fields of bioinformatics and tumor immunotherapy, specifically to a method and system for screening compounds that enhance the efficacy of immunotherapy based on transcriptome analysis, thereby screening for candidate drugs or pharmaceutical compositions that can enhance the efficacy of tumor immunotherapy. Background Technology

[0002] Immunotherapy, especially immune checkpoint inhibitor (ICB) therapy, is an effective cancer treatment strategy superior to traditional methods. Although ICB therapy has significantly improved cancer treatment outcomes clinically, some patients develop primary or acquired resistance due to the high heterogeneity of cancer and the complexity of the immune microenvironment. Therefore, combination therapy with other anticancer drugs using ICBs has received increasing attention and has become an important research direction for overcoming immunotherapy resistance. Many clinical trials focus on combinations of ICB therapy with chemotherapy or targeted therapy. For example, the combination of the angiogenesis inhibitor lenvatinib with anti-PD1 / PDL1 drugs has shown superior synergistic effects in liver cancer. However, most candidate drugs in clinical trials rely on expert experience and prior knowledge.

[0003] Currently, comparative studies of immunotherapy responders and non-responders have identified several molecular markers and models for predicting the effectiveness of immunotherapy. These include molecular markers such as the expression level of PDL1 protein; and models for predicting the effectiveness of immunotherapy such as the Immunophenoscore model.

[0004] Compared to single immunotherapy, computational methods for immunotherapy combination strategies are still in their early stages. Currently, two commonly used strategies exist: one is to build models based on the number of cancer cells and immune cells, as well as the inhibitory concentration of drugs, to estimate the efficacy of ICBs combined with other drugs; the other is to construct sensitive and resistant mouse models, compare the differences between them to identify drug targets, and evaluate the efficacy of drug combinations through in vivo experiments. However, these strategies cannot be applied on a large scale to screen multiple cancer types and multiple drugs. Bottlenecks in predicting ICB combinations with other drugs include: the complex interactions between the immune microenvironment and tumor cells involve multiple genes and pathways; a lack of response data for drug combination therapies in patients and animal models; and the absence of mature methods to measure the impact of other drugs on immunotherapy.

[0005] To address the limitations of the aforementioned strategies, some studies have emerged that infer and validate drugs that may alleviate immunosuppression from the perspective of the tumor microenvironment (TME), indicating the feasibility of predicting combinations of immunotherapy with other drugs by regulating the TME.

[0006] In summary, there is an urgent need in the field to systematically and comprehensively understand the molecular characteristics related to tumor immunity, develop methods to identify TME characteristics related to immunotherapy, and quantify the impact of chemotherapy or targeted drugs on immunotherapy through the regulation of TME using the methods, thereby providing preferred immunotherapy combination strategies. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for screening compounds that enhance the efficacy of immunotherapy based on transcriptome analysis, as well as compounds that enhance the efficacy of tumor immunotherapy discovered based on this method and system and their uses.

[0008] In a first aspect, the present invention provides a method for screening compounds that enhance the efficacy of tumor immunotherapy based on transcriptome analysis, comprising the steps of:

[0009] (1) Collect published tumor microenvironment-related gene sets (TME_GS), and calculate the original scores of the TME_GS using tumor immune infiltration analysis tools; in the discovery set, standardize the original scores of the TME_GS through mathematical transformation, establish a logistic regression model, estimate the regression coefficients and residuals of each TME_GS, then integrate the regression coefficients using meta-analysis to obtain a threshold, and classify the expression features of immunotherapy-related genes (IGeS) according to the threshold; then, based on the dataset, use an expansion strategy to obtain IGeS for specific cancer types, and use machine learning algorithms to construct an immunotherapy response prediction model, which generates the weights of the IGeS;

[0010] (2) In the compound-induced gene expression dataset, the compound-induced gene expression profile is compared with the expression profile of the control treatment to obtain the differential expression between the compound-induced gene expression profile and the control treatment under multiple interference conditions. The genes are ranked according to the differential expression. Through pre-ranked gene set enrichment analysis, the normalized enrichment score (NES) and p-value of the compound's effect on the specific IGeS under the specific interference conditions are calculated. Then, multiple p-values ​​are integrated using a combined Cauchy association test. Data with insignificant p-values ​​after integration are not included in subsequent calculations. The multiple NES values ​​are weighted and integrated to obtain the gene expression perturbation score (PGES) of the compound in the specific cancer type.

[0011] (3) Based on the weight of the IGeS, the PGES of multiple IGeS of the compound in the specific cancer type are weighted and summed using the Enhanced Scoring (BS) algorithm, and the compound in the specific cancer type is sorted according to the BS to obtain the compound that enhances the efficacy of tumor immunotherapy.

[0012] In another preferred embodiment, step (1) further includes the step:

[0013] (1.1) Evaluate the immunotherapy response prediction model based on the validation set; and

[0014] (1.2) Compare the immunotherapy response prediction model with published immunotherapy prediction methods.

[0015] In another preferred embodiment, the discovery set is a tumor transcriptome dataset.

[0016] In another preferred embodiment, the discovery set includes: 5 melanoma datasets, 2 clear cell renal cell carcinoma datasets, 2 lung cancer datasets, 1 gastric cancer dataset, 1 glioblastoma dataset, and 1 bladder cancer dataset.

[0017] In another preferred embodiment, the mathematical transformation is the Z-transform.

[0018] In another preferred embodiment, the tumor microenvironment-related gene set includes: a gene set related to predicting immune checkpoint inhibition response, an anti-tumor or pro-tumor gene set, and a gene set for estimating the characteristics of tumor-infiltrating cells.

[0019] In another preferred embodiment, the tumor immune infiltration analysis tools include: ConsensusTME-gsva, xcell, MCP-Counter, CIBERSORTX, ImmunCellAI, TIMER, EPIC, Quantiseq, and ssGSEA.

[0020] In another preferred embodiment, the logistic regression model distinguishes between response objects and non-response objects.

[0021] In another preferred embodiment, the threshold includes the odds ratio (OR) and the p-value.

[0022] In another preferred embodiment, the odds ratio of the expression characteristics of the immunotherapy-related genes is >0, and the p-value is <0.05.

[0023] In another preferred embodiment, the immunotherapy-related gene expression features include: immunotherapy sensitivity-related features (sIGeS) and immunotherapy resistance-related features (rIGeS).

[0024] In another preferred embodiment, the odds ratio of the immunotherapy sensitivity-related features is >1, and the p-value is <0.05.

[0025] In another preferred embodiment, the odds ratio of the immunotherapy resistance-related features is <1 and >0, and the p-value is <0.05.

[0026] In another preferred embodiment, the immunotherapy sensitivity-related features include 117.

[0027] In another preferred embodiment, the immunotherapy resistance-related features include 13 characteristics.

[0028] In another preferred embodiment, the dataset includes: tumor clinical information data and tumor cell line data.

[0029] In another preferred embodiment, the tumor clinical information data includes cancer type, genes, and transcriptome.

[0030] In another preferred embodiment, the tumor cell line data includes cancer type and cancer cell line.

[0031] In another preferred embodiment, the expansion strategy includes:

[0032] (i) Calculate the partial Spearman correlation coefficient (PSCC) between the expression characteristics of the immunotherapy-related genes and the genes; and

[0033] (ii) Calculate the percentage (DP) of genes detected in the cancer cell line.

[0034] In another preferred embodiment, the PSCC of the gene in the IGeS of the specific cancer type is >0.4, and the DP is >0.8.

[0035] In another preferred embodiment, the number of genes in the IGeS of the specific cancer type is 200.

[0036] In another preferred embodiment, the machine learning algorithm includes: Elastic Network Regression (EN), Ridge Regression (RR), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Tree (XGBoost).

[0037] In another preferred embodiment, the immunotherapy response prediction model includes: IGeS-EN, IGeS-RR, IGeS-RF, IGeS-SVM, and IGeS-XGBoost.

[0038] In another preferred embodiment, the immunotherapy response prediction model is IGeS-EN.

[0039] In another preferred embodiment, in the verification set described in (1.1), the IGeS-EN selected 33 IGeS.

[0040] In another preferred embodiment, the validation set includes: four melanoma datasets and one bladder cancer dataset.

[0041] In another preferred embodiment, the published immunotherapy prediction method includes: average expression of CD8 characteristic genes, average expression of IFNG characteristic genes, TIDE model, 18-gene score, TIP score, immunophenotypic score (IPS), and T-cell exclusion procedure (T-exclu).

[0042] In another preferred embodiment, the compound-induced gene expression dataset includes: clinical stage, cell line, cancer type, compound annotation information, gene, mechanism of action, expression profile of control treatment, and compound-induced gene expression profile.

[0043] In another preferred embodiment, the expression profile of the control treatment is the expression profile of the dimethyl sulfoxide (DMSO) treatment.

[0044] In another preferred embodiment, the number of cancer types is 13.

[0045] In another preferred embodiment, the compound-induced gene expression dataset comprises two datasets:

[0046] (a) The first dataset includes 1,768 compounds, 12 cell lines from 8 cancer types, 202,133 compound-induced gene expression profiles and 14,304 DMSO-treated expression profiles, wherein the cancer types are: breast cancer, liver cancer, lung adenocarcinoma, pancreatic cancer, prostate cancer, colon cancer, rectal cancer and skin melanoma;

[0047] (b) A second dataset comprising 18,486 compounds from 38 cell lines of 11 cancer types, 443,505 compound-induced gene expression profiles, and 23,252 DMSO-treated expression profiles, wherein the cancer types are: breast cancer, colon cancer, liver cancer, lung adenocarcinoma, lung squamous cell carcinoma, ovarian cancer, prostate cancer, rectal cancer, skin melanoma, gastric cancer, and endometrial cancer.

[0048] In another preferred embodiment, the interference conditions include: cell line, compound dosage, and treatment time.

[0049] In another preferred embodiment, "p-value not significant" means p-value > 0.05.

[0050] In another preferred embodiment, the gene expression perturbation score (PGES) is calculated as follows:

[0051]

[0052] Wherein, n is the number of interference conditions under the specific compound and the specific cancer type;

[0053] cell p ,dosep time p These are the cell line, compound dosage, and treatment time under the interference conditions, respectively;

[0054] Within the same type of cancer, the cell line p The mean correlation coefficient with the corresponding patient sample in the tumor clinical information data, divided by its maximum value; or the mean correlation coefficient of all the cell lines in the cancer type.

[0055] (reference p The following are reference conditions for the compound dosage and the treatment time, based on 10 μM and 24 hours;

[0056] Where m is the quantity corresponding to the compound dosage and the treatment time; This refers to the NES score of the compound under 10 μm and 24-hour treatment conditions; NES condition This refers to the NES score under other processing conditions.

[0057] In another preferred embodiment, the enhanced score is calculated as follows:

[0058]

[0059] Where i is the index of the IGeS;

[0060] N is a subset of the IGeS that exhibit significant expression changes under the interference of the compound;

[0061] Coe i It is the weight of the IGeS, that is, the regression coefficient of the i-th IGeS extracted from the IGeS-EN;

[0062] PGES i It is the perturbation score of the compound on the gene expression of the i-th IGeS in the cancer;

[0063] In another preferred embodiment, significant expression means FDR < 0.05.

[0064] A second aspect of the present invention provides a framework or system for screening compounds that enhance the efficacy of tumor immunotherapy, comprising:

[0065] An input unit is configured to input a compound-induced gene expression dataset, the compound-induced gene expression dataset including a compound-induced gene expression profile and a control treatment expression profile;

[0066] A processing unit, configured to process the gene expression dataset induced by the compound, includes the following steps:

[0067] (I) The gene expression profile induced by the compound is compared with the expression profile of the control treatment to obtain the differential expression between the gene expression profile induced by the compound and the expression profile of the control treatment under multiple interference conditions. The genes are ranked according to the differential expression. Through pre-ranked gene set enrichment analysis, the standardized enrichment score (NES) and p-value of the effect of the compound on the expression characteristics (IGeS) of specific immunotherapy-related genes (IGeS) under specific interference conditions are calculated. Then, multiple p-values ​​are integrated using a polymerized Cauchy association test. Data with insignificant p-values ​​after integration are not included in subsequent calculations. The multiple NES values ​​are weighted and integrated to obtain the gene expression perturbation score (PGES) of the compound in a specific cancer type.

[0068] (II) Based on the weights of IGeS generated by the immunotherapy response prediction model, the PGES of multiple IGeS of the compound in the specific cancer type are weighted and summed using the Enhanced Scoring (BS) algorithm, and the compounds in the specific cancer type are sorted according to the BS to obtain the compound that enhances the efficacy of tumor immunotherapy; wherein, the immunotherapy response prediction model is IGeS-EN;

[0069] An output unit, configured to output the result of the processing unit.

[0070] In another preferred embodiment, the compound-induced gene expression dataset includes: clinical stage, cell line, cancer type, compound annotation information, gene, mechanism of action, expression profile of control treatment, and compound-induced gene expression profile.

[0071] In another preferred embodiment, the gene expression profile induced by the compound is the expression profile after treatment with an immune checkpoint inhibitor.

[0072] In another preferred embodiment, the expression profile of the control treatment is the expression profile before immune checkpoint inhibitor treatment.

[0073] In another preferred embodiment, "p-value not significant" means p-value > 0.05.

[0074] In another preferred embodiment, step (II) further includes the step of selecting the compound that enhances the efficacy of tumor immunotherapy based on a screening strategy.

[0075] In another preferred embodiment, the screening strategy includes:

[0076] (a) BS is positive and BS is ranked in the top 20; and / or

[0077] (b) The mechanism of action (MoA) of the compound is known; and / or

[0078] (c) The compound has the same MoA as known immune enhancers.

[0079] In another preferred embodiment, the specific cancer types include: liver cancer, breast cancer, colon adenocarcinoma, lung adenocarcinoma, lung squamous cell carcinoma, pancreatic cancer, ovarian cancer, prostate cancer, rectal adenocarcinoma, skin melanoma, stomach cancer, and endometrial cancer.

[0080] In another preferred embodiment, the cutaneous melanoma includes: primary cutaneous melanoma and metastatic cutaneous melanoma.

[0081] In another preferred embodiment, the specific type of cancer is liver cancer.

[0082] In another preferred embodiment, eight compounds that enhance the efficacy of immunotherapy for liver cancer are obtained through the screening strategy.

[0083] In another preferred embodiment, the compounds that enhance the efficacy of liver cancer immunotherapy include: 3 known compounds and 5 new compounds.

[0084] In another preferred embodiment, the known compounds are phenothiazine, docetaxel, and trametinib.

[0085] In another preferred embodiment, the known compound has the following structure:

[0086]

[0087] In another preferred embodiment, the novel compound is CGP-60474, PD-0325901, Tubacin, Tyrphostin-AG-494, and SB-366791.

[0088] In another preferred embodiment, the new compound has the following structure:

[0089]

[0090]

[0091] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for liver cancer further include: BRD-K18059480, BRD-K10405667, BRD-K42681396, CO-102862, BRD-K69347381, BRD-K94987138, BRD-K46692335, cyclopamine, RDR-01440SC, palmastat, oxyscopolamine, Proxyfan, BRD-K89558221, and BRD-K50746. 423, BRD-K03358980, Core proteoglycan, AZD-7762, ERK inhibitor 11E, GW-0742, WZ-3105, OTS-167, PD-184352, AS-605240, PHA-767491, JW-7-24-1, A-66, Gerdemycin, SB-590885, TPCA-1, Gefitinib, Mitoxantrone, CP-724714, Torin-1, Sorafenib, Rhizobacterium tumefaciensin, GSK-J2.

[0092] In another preferred embodiment, the compounds that enhance the efficacy of breast cancer immunotherapy include: retinoic acid, asteroidin, LY-294002, crotonin, vu0155056, homoharringtonine, allopurinol, cescatinib, bimetinib, 4,5,6,7-tetrabromo-1H-benzotriazole, BRD-K84109969, BRD-K05832178, BRD-K90045406, BRD-K85126547, BRD-K92334440, BRD-K85118298, BRD-K78206606, BRD-K81476029, BRD-K43212439, and BRD-K22177698.

[0093] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for colon adenocarcinoma include: SN-38, epirubicin hydrochloride, mitoxantrone, aphidicolin, davallin, doxorubicin hydrochloride, camptothecin, FdCyd, daunorubicin, propafenone hydrochloride, GR-113808, griseofulvin, BRD-K45734366, BRD-K35046132, 17β estradiol, BRD-K54877231, BRD-K16561280, BRD-K56076713, BRD-K03652504, and EMF-sumo1-7.

[0094] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for lung adenocarcinoma include: Tuzarseti, GSK-1070916, MLN-8054, Lincitinib, Mitoxantrone, JWE-035, TWS-119, KI-20227, AZ-20, Pelitinib, BRD-K59777289, BRD-K72095065, BRD-K95570609, BRD-K02041150, BRD-K85426712, BRD-K31815095, BRD-K90897407, BRD-K35945912, BRD-K16910145, and BRD-K28362289.

[0095] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for lung squamous cell carcinoma include: 7b-cis, Importazole, Palmadustat, Tosedostat, GW-405833, NSC-23766, Calcitriol, Ursolic acid, Nimodipine, BRD-K21614065, SID-26681509, Darinaparsin, LDN-193189, Dascetrapip, BRD-K61053657, FPL-64176, BMS-299897, Clonidamine, C-55649, and AM-580.

[0096] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for pancreatic cancer include: SDZ-NKT-343, Balapiravir, pyrcotahedramide, semasanitary, Sarsagenin, ilomastastat, NMS-1286937, bexarotine, dexamethasone, BMS-707035, ozagrel, PD-153035, tazarotene, dipriglurant, aspirin, tirasemtiv, danazol, mycophenolate mofetil, and ifosfamide.

[0097] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for ovarian cancer include: olaparib, importazole, Nutlin-3, BX-795, teniposide, narciclasine, LDN-193189, idarubicin hydrochloride, SN-38, parbendazole, BRD-K92980438, pipexiline maleate, BRD-K81647657, Dorsomorphin, 2-fluoropalmitic-acid, ixazomib citrate, PI-103, PX-12, phorbolol, 2',5'-dideoxyadenosine, and DL-PDMP.

[0098] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for prostate cancer include: Phorbol-myristate-acetate, SB-939, naphazoline hydrochloride, JNJ-26481585, ixazomib citrate, clofarapine, AV-608, chlorsenoyl, nicotinamide, mirtazapine hydrochloride, BRD-K92476250, BRD-K58941403, BRD-K59995645, BRD-K32301393, BRD-K76437403, BRD-K05814022, BRD-K78826848, BRD-K70861166, BRD-K74227932, and BRD-K06550805.

[0099] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for rectal adenocarcinoma include: epirubicin hydrochloride, SN-38, JNJ-26481585, tazarotene, pevonedistat, naphazoline hydrochloride, L-NAME, doxorubicin hydrochloride, FdCyd, PF-04620110, BRD-K54877231, BRD-K28040935, anandamide, BRD-A38793261, EMF-sumo1-7, ropinirole, BRD-K30456673, BRD-K56076713, BRD-K30645599, BRD-K42687792, and BRD-K30007764.

[0100] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for cutaneous melanoma include: dabrafenib, brenalapa, cobifenib, vemurafenib, and lenvatinib.

[0101] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for metastatic cutaneous melanoma include: NVP-AUY922, LCL-161, mycophenolate mofetil, LGX-818, Ro-4987655, NVP-TAE684, bemettinib, dabrafenib mesylate, retinoic acid, genz-644282, BRD-K85388022, BRD-K15104539, BRD-K48752687, procaterol, WH-4025, WH-4025, BRD-K06892639, hydralazine hydrochloride, BRD-K16383683, and LY-255283.

[0102] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for primary cutaneous melanoma include: NVP-TAE684, Refametinib, PHA-848125, dabrafenib mesylate, mycophenolate mofetil, Aphidicolin, LCL-161, Ro-4987655, LGX-818, NVP-TAE226, BRD-K68287477, BRD-K16383683, BRD-K98192020, quercetin, BRD-K36853464, BRD-K67503471, BRD-K98168260, BRD-K70165037, BRD-K21787157, and mefenamic acid.

[0103] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for gastric cancer include: tamoxifen citrate, phenoxazine, gossypol, bladixin sodium, quinlavaben, pemetrexed disodium, M2-PK activator, temozolomide, AKT inhibitor IV, mesoridazine besylate, Ro-28-1675, RHO kinase inhibitor III, IKK inhibitor X, GSK-2334470, UNC-0321, AC-55649, pipexciline maleate, solidegan phosphate, BRD-K41087962, and BX-795.

[0104] In another preferred embodiment, the compounds that enhance the efficacy of immunotherapy for endometrial cancer include: FK-866, AKT inhibitor IV, phenoxazine, COT-10b, PAC-1, Serdemetan, QS-11, CHEMBL-399379, BRD-K89692698, flusperidin, menadione, palmastat, RLM-2-12, PIK-90, tamoxifen citrate, tesimolimus, NSC-23766, minoxidil, MK-1775, and velipanib.

[0105] In another preferred embodiment, the framework or system is also used to screen compounds that have no effect on the efficacy of tumor immunotherapy and / or reduce the efficacy of tumor immunotherapy.

[0106] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for liver cancer and / or reduce the efficacy of immunotherapy for liver cancer include: Y-27632, WYE-125132, JQ1-(+), Tie2 kinase inhibitors, CGK-733, TAK-715, PLX-4720, PFI-1, tevozaniol, astaxanthin, quezartinib, enzatolin, GSK-1904529A, and PD-1. 73074, SB-525334, GSK-J1, Imatinib, GDC-0980, GDC-0980, BMS-777607, TG-101348, VU-0420364-1, BRD-K52678950, Lichenoxane, Paclitaxel, PLX-4720, Piroxicam, BRD-K01089529, VU-0365117-1.

[0107] In another preferred embodiment, compounds that have no effect on the efficacy of breast cancer immunotherapy and / or reduce the efficacy of breast cancer immunotherapy include: L-690330, thioguanine, ergoline, aramid, saquinavir mesylate, valsartan, CI-976, EPZ-005687, guanabenyl acetate, diphenhydramine hydrochloride, BRD-K07951922, BRD-K73674426, BRD-K19549666, BRD-K40963620, BRD-K93186542, BRD-K24351638, BRD-K62999057, BRD-K34106732, BRD-K51218228, and BRD-K73919378.

[0108] In another preferred embodiment, compounds that have no effect on the efficacy of colonic gland immunotherapy and / or reduce the efficacy of colonic gland immunotherapy include: phenylephrine hydrochloride, fluocinolone acetonide, FK-866, fenodopam mesylate, 10-DEBC, isoniazid, norethindrone, miglustat, alprostadil, radiocicol, BRD-K04985042, BRD-K37933621, ST-014147, BRD-K89148860, BRD-K32857328, BRD-K71673735, ST-013960, BRD-A12643502, BRD-K63425657, and zipretem.

[0109] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for lung adenocarcinoma and / or reduce the efficacy of immunotherapy for lung adenocarcinoma include: curcumin, dexamethasone, Ginkgetin, sotrastuxin, Gossypetin, EPZ-005687, AGK-2, Exifone, LY-294002, Rolipram, BRD-K17466145, BRD-K43108514, BRD-K97961692, BRD-K75981692, BRD-K05543055, BRD-K23488072, BRD-K63712590, III606050, BRD-K33159591, and BRD-K12695722.

[0110] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for lung squamous cell carcinoma and / or reduce the efficacy of immunotherapy for lung squamous cell carcinoma include: tepifenabil, lovastatin, BRD-K56477805, L-690330, BI-2536, oligomycin A, quinlavaben, vorinostat, PF-750, CYT-997, SB-216763, BRD-K28112517, phosphosaspartic acid, valproic acid, sulfonated dextran, TW-37, ISOX, capsaicin, ABT-751, Chaetocin, KU-0060648, and zablaline.

[0111] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for pancreatic cancer and / or reduce the efficacy of immunotherapy for pancreatic cancer include: LY-2090314, XL-888, sumatriptan succinate, JNJ-26481585, BMS-299897, cabazitaxel, RG-2833, IKK2 inhibitor V, ciocalcitriol, flortinib, perampanel, compustin, ixaprone, oxamite, caffeine, docetaxel, clotrimazole, UK-383367, 3,3'-Diindolylmethane, and cyclothionol.

[0112] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for ovarian cancer and / or reduce the efficacy of immunotherapy for ovarian cancer include: RHO kinase inhibitor III, maraviro, SJ-172550, BRD-K59470558, L-690488, nifedipine, Triacsin-c, 10-DEBC, 4-iodo-6-phenylpyrimidine, PKCβ inhibitor, BRD-K64970062, temozolomide, lovastatin, KU-0060648, PRIMA-1-MET, m-3M3FBS, geldmycin, BRD-K28100108, fatostatin, BRD-K08451418, IKK3 inhibitor IX, DG-041, TG-101348, and PT-630.

[0113] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for prostate cancer and / or reduce the efficacy of immunotherapy for prostate cancer include: 17-allylaminogeldemycin, clobetasol, troglitazone, felastasil, beclomethasone dipropionate, tetrindole, rimexone, mibampator, bupropion hydrochloride, indobufen, BRD-K32854531, BRD-K93845141, BRD-K71101608, BRD-K72801689, BRD-K87010407, BRD-K95323755, BRD-K49594309, BRD-K20609812, BRD-K02291788, and BRD-K21626096.

[0114] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for rectal adenocarcinoma and / or reduce the efficacy of immunotherapy for rectal adenocarcinoma include: Siramesine, Tanaproget, isoniazid, norethindrone, phenylephrine hydrochloride, OSI-027, OSI-027, riluzole, miglustat, Merbarone, BRD-K43212439, BRD-K16673836, BAS-00535043, BRD-K44890999, Totaralolal, BRD-K32857328, BRD-K89148860, alprazolam, BRD-K34545522, and BRD-K72505034.

[0115] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for metastatic cutaneous melanoma and / or reduce the efficacy of immunotherapy for metastatic cutaneous melanoma include: estriol, lopinavir, VE-821, hydroxypurine, bevirimilamide, ressatopicil, ixazomib citrate, felbamate, epigallocatechin gallate, nimodipine, duloxetine hydrochloride, tubaic acid, BRD-K15042749, cyproterone acetate, BRD-K67399667, BRD-K15955205, BRD-K43256821, BRD-K51896917, and fenfenidil tartrate.

[0116] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for primary cutaneous melanoma and / or reduce the efficacy of immunotherapy for primary cutaneous melanoma include: I-BET-151, Combustin, Sea haretoxin 10, Clarithromycin, Bevirimilamide, Hydroxypurinol, ICI-192605, Lidocaine, Ixazomib citrate, Ophenazine, Ephedraine, Tubaic-acid, BRD-K81812196, BRD-K90748376, BRD-K51896917, WT-171, BRD-K25031586, BRD-K59194364, Mesna, and BRD-K85932041.

[0117] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for gastric cancer and / or reduce the efficacy of immunotherapy for gastric cancer include: NVP-BEZ235, Rolipram, Gemcitabine, BRD-K40758391, Quinoclamine, Cerulenin, 10-DEBC, Limenidine phosphate, BRD-K78716413, Cytochalasin B, BRD-A24054354, Pyrantel thiamethoxam, BRD-K92317137, TW-37, BMS-536924, RS-23597-190, Rucapaparib, BRD-K56477805, TGX-115, NSC-95397, 15-delta prostaglandin J2, DC-45-A2, Elastin, Darinaparsin, and CI-976.

[0118] In another preferred embodiment, compounds that have no effect on the efficacy of immunotherapy for endometrial cancer and / or reduce the efficacy of immunotherapy for endometrial cancer include: ISOX, phorbolol, BAY-K-8644-(S)-(-), alastine, Pifithrin-alpha, vorinostat, PD-98059, rosiglitazone maleate, BRD-K92980438, simvastatin, nystatin a, T-5345967, oligomycin A, BRD-K61053657, YM-155, sulfamethoxazole, BRD-A24054354, BRD-K52904470, BRD-K40758391, APO-866, nicardipine, BMS-641988, valproic acid, Ro-28-1675, and NF-449.

[0119] In a third aspect, the present invention provides a compound that enhances the efficacy of tumor immunotherapy, said compound being obtained by screening using the method described in the first aspect of the present invention;

[0120] The compounds that enhance the efficacy of tumor immunotherapy include compounds having the following structures:

[0121]

[0122] In a fourth aspect, the present invention provides a medicament or pharmaceutical composition comprising the compound described in the third aspect of the present invention.

[0123] In another preferred embodiment, the drug or drug composition further includes other compounds that enhance the efficacy of tumor immunotherapy.

[0124] In another preferred embodiment, the other compounds that enhance the efficacy of tumor immunotherapy include phenothiazines, docetaxel, and trametinib.

[0125] In another preferred embodiment, the drug or drug composition further includes drugs selected from the group consisting of immunotherapeutic drugs, non-immunotherapy drugs, or combinations thereof.

[0126] In another preferred embodiment, the immunotherapy drug includes: PD-1 and anti-PD-1.

[0127] In another preferred embodiment, the drug or drug composition further includes a pharmaceutically acceptable carrier, diluent, and excipient.

[0128] A fifth aspect of the present invention provides a medicine box, comprising:

[0129] (A) A first container, and a compound located in the first container that enhances the efficacy of tumor immunotherapy; and

[0130] (B) A second container, and other drugs for treating tumors located in the second container, or drugs containing other drugs for treating tumors.

[0131] In another preferred embodiment, the first container and the second container may be the same or different containers.

[0132] In another preferred embodiment, the drug in the first container is a single-ingredient preparation containing compounds that enhance the efficacy of tumor immunotherapy.

[0133] In another preferred embodiment, the drug in the second container is a single-ingredient preparation containing other drugs for treating tumors, or containing other drugs for treating tumors.

[0134] In another preferred embodiment, the dosage form of the drug includes: oral dosage form and injectable dosage form.

[0135] A sixth aspect of the present invention provides the use of the compound described in the third aspect of the present invention, the drug or pharmaceutical composition described in the fourth aspect of the present invention, or the medicament described in the fifth aspect of the present invention for the preparation of an immunotherapy enhancer for treating tumors.

[0136] In another preferred embodiment, the tumor includes: liver cancer, breast cancer, colon adenocarcinoma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian cancer, prostate cancer, rectal adenocarcinoma, skin melanoma, gastric cancer, and endometrial cancer.

[0137] In another preferred embodiment, the tumor is liver cancer.

[0138] In another preferred embodiment, the drug or drug composition is selected from the group consisting of compounds that enhance the efficacy of tumor immunotherapy, immunotherapeutic drugs, or combinations thereof.

[0139] In another preferred embodiment, the compounds that enhance the efficacy of tumor immunotherapy include: phenothiazines, docetaxel, trametinib, CGP-60474, PD-0325901, tubacin, Tyrphostin-AG-494, and SB-366791.

[0140] In another preferred embodiment, the immunotherapy drug includes a PD-1 inhibitor.

[0141] In another preferred embodiment, the PD-1 inhibitor comprises an anti-PD-1 monoclonal antibody.

[0142] In another preferred embodiment, in a liver cancer cell line, the compound that enhances the efficacy of tumor immunotherapy has a half-maximal inhibitory concentration.

[0143] In another preferred embodiment, the liver cancer cell line includes: human liver cancer cell line and non-human mammalian liver cancer cell line.

[0144] In another preferred embodiment, the human liver cancer cell lines include: Huh7 and Hep3B.

[0145] In another preferred embodiment, in Huh7, the half-maximal inhibitory concentration of the docetaxel is 1.95 ± 0.1 nM.

[0146] In another preferred embodiment, in Huh7, the half-maximal inhibitory concentration of CGP-60474 is 28.59 ± 4.08 nM.

[0147] In another preferred embodiment, in Huh7, the half-maximal inhibitory concentration of the Tubacin is 14.81 ± 0.38 μM.

[0148] In another preferred embodiment, the drug or drug composition is administered to organoid tumor spheroids (PDOTs), resulting in a reduced ratio of live cells to total cells.

[0149] In another preferred embodiment, the drug or drug composition is selected from the group consisting of: PD-1 inhibitors, phenothiazines, combinations of phenothiazines and PD-1 inhibitors, docetaxel, combinations of docetaxel and PD-1 inhibitors, CGP-60474, combinations of CGP-60474 and PD-1 inhibitors, trametinib, combinations of trametinib and PD-1 inhibitors, PD-0325901, combinations of PD-0325901 and PD-1 inhibitors, tubacin, combinations of tubacin and PD-1 inhibitors, Tyrphostin-AG-494, combinations of Tyrphostin-AG-494 and PD-1 inhibitors, SB-366791, combinations of SB-366791 and PD-1 inhibitors, or combinations thereof.

[0150] In another preferred embodiment, the application concentration of the phenothiazine, docetaxel, PD-0325901, Tyrphostin-AG-494, and SB-366791 is 10 μM.

[0151] In another preferred embodiment, the docetaxel is administered at a concentration of 1.95 ± 0.1 nM.

[0152] In another preferred embodiment, the application concentration of CGP-60474 is 28.59 ± 4.08 nM.

[0153] In another preferred embodiment, the application concentration of Tubacin is 14.81 ± 0.38 μM.

[0154] In another preferred embodiment, the combination of the compound that enhances the efficacy of tumor immunotherapy and the immunotherapy drug is administered to organoid tumor spheroids, resulting in a significant reduction in the ratio of live cells to total cells.

[0155] In another preferred embodiment, the drug or drug composition is selected from the group consisting of: a combination of CGP-60474 and a PD-1 inhibitor, or a combination of SB-366791 and a PD-1 inhibitor.

[0156] In another preferred embodiment, when the combination of CGP-60474 and the PD-1 inhibitor is administered to organoid tumor spheroids, the "significantly reduced ratio of live cells to total cells" means that, compared with the ratio D00 of live cells to total cells when the PD-1 inhibitor is used alone, the ratio D1 of live cells to total cells of the combination of CGP-60474 and the PD-1 inhibitor satisfies D1 / D00≤60%, preferably≤50%, more preferably≤40%, such as 35%-55%.

[0157] In another preferred embodiment, when the combination of CGP-60474 and the PD-1 inhibitor is administered to organoid tumor spheroids, the "significantly reduced ratio of live cells to total cells" means that, compared with the ratio D01 of live cells to total cells obtained by using CGP-60474 alone, the ratio D1 of live cells to total cells obtained by the combination of CGP-60474 and the PD-1 inhibitor satisfies D1 / D01≤75%, preferably≤50%, more preferably≤40%, such as 35%-70%.

[0158] In another preferred embodiment, when the combination of SB-366791 and the PD-1 inhibitor is administered to organoid tumor spheroids, the "significantly reduced ratio of live cells to total cells" means that, compared with the ratio E00 of live cells to total cells when the PD-1 inhibitor is used alone, the ratio E1 of live cells to total cells of the combination of SB-366791 and the PD-1 inhibitor satisfies E1 / E00≤50%, preferably≤45%, more preferably≤40%, such as 38%-50%.

[0159] In another preferred embodiment, when the combination of SB-366791 and the PD-1 inhibitor is administered to organoid tumor spheroids, the "significantly reduced ratio of live cells to total cells" means that, compared with the ratio E01 of live cells to total cells when SB-366791 is used alone, the ratio E1 of live cells to total cells when the combination of SB-366791 and the PD-1 inhibitor is E1 / E01≤60%, preferably≤45%, more preferably≤40%, such as 38%-57%.

[0160] In another preferred embodiment, the drug or drug composition was administered to a spontaneous hepatocellular carcinoma tumor animal model, resulting in a reduction in tumor weight.

[0161] In another preferred embodiment, the spontaneous hepatocellular carcinoma tumor animal model includes: C57BL / 6J tumor mice.

[0162] In another preferred embodiment, the C57BL / 6J tumor mice are constructed by injecting C57BL / 6J mice with pT3-EF1a-c-myc, pX330 p53 and Sleeping Beauty SB100x transposase-encoding plasmids.

[0163] In another preferred embodiment, the injection dose of pT3-EF1a-c-myc is 20±2μg; the injection dose of pX330 p53 is 20±2μg; and the injection dose of Sleeping Beauty SB100x is 10±1μg.

[0164] In another preferred embodiment, the injection method includes tail vein injection.

[0165] In another preferred embodiment, the drug or drug composition is selected from the group consisting of: CGP-60474, a combination of CGP-60474 and an anti-PD-1 monoclonal antibody, SB-366791, a combination of SB-366791 and an anti-PD-1 monoclonal antibody, and an anti-PD-1 monoclonal antibody.

[0166] In another preferred embodiment, the dosage of CGP-60474 is 10 ± 1 mg / kg.

[0167] In another preferred embodiment, the dosage of SB-366791 is 20±2 mg / kg.

[0168] In another preferred embodiment, the dose of the anti-PD-1 monoclonal antibody is 10 ± 1 mg / kg.

[0169] In another preferred embodiment, administration of the combination of the compound that enhances the efficacy of tumor immunotherapy and the immunotherapy drug to a spontaneous hepatocellular carcinoma tumor animal model resulted in a significant reduction in tumor weight.

[0170] In another preferred embodiment, C57BL / 6J tumor mice are administered a combination of CGP-60474 and an anti-PD-1 monoclonal antibody. The term "significantly reduced tumor weight" means that, compared to the tumor weight F00 of CGP-60474 alone, the tumor weight F1 of the combination of CGP-60474 and the anti-PD-1 monoclonal antibody satisfies F1 / F00 ≤ 55%, preferably ≤ 50%, more preferably ≤ 45%, such as 40%-50%.

[0171] In another preferred embodiment, C57BL / 6J tumor mice are administered a combination of CGP-60474 and an anti-PD-1 monoclonal antibody. The term "significantly reduced tumor weight" means that, compared to the tumor weight F01 of the combination of CGP-60474 and the anti-PD-1 monoclonal antibody alone, the tumor weight F1 is F1 / F01 ≤ 60%, preferably ≤ 55%, more preferably ≤ 50%, such as 45%-55%.

[0172] In another preferred embodiment, C57BL / 6J tumor mice are administered a combination of SB-366791 and an anti-PD-1 monoclonal antibody. The term "significantly reduced tumor weight" means that, compared to the tumor weight G00 of SB-366791 alone, the tumor weight G1 of the combination of SB-366791 and the anti-PD-1 monoclonal antibody satisfies G1 / G00 ≤ 40%, preferably ≤ 35%, more preferably ≤ 30%, such as 25%-35%.

[0173] In another preferred embodiment, C57BL / 6J tumor mice are administered a combination of SB-366791 and an anti-PD-1 monoclonal antibody. The term "significantly reduced tumor weight" means that, compared to the tumor weight G01 obtained with the anti-PD-1 monoclonal antibody alone, the tumor weight G1 of the combination of SB-366791 and the anti-PD-1 monoclonal antibody satisfies G1 / G01 ≤ 50%, preferably ≤ 45%, more preferably ≤ 40%, such as 35%-45%.

[0174] It should be understood that, within the scope of this invention, the above-described technical features of this invention and the technical features specifically described below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be described in detail here. Attached Figure Description

[0175] Figure 1 The integrated analysis revealed features associated with pan-cancer immunotherapy.

[0176] Figure 1 A shows a flowchart illustrating the process of integrating immunotherapy datasets for multiple cancer types and identifying features related to the effectiveness of immunotherapy.

[0177] Figure 1 B shows the immunotherapy-related gene expression signatures (IGeS) identified in the 12 cohorts of the discovery set (Meta_P<0.05). The color represents the odds ratio (OR) of IGeS scores between responders and non-responders. IGeS with an OR>1 or OR<1 are named sensitive IGeS (red) and resistant IGeS (green), respectively.

[0178] Figure 2This demonstrates that IGeS can predict the efficacy of pan-cancer immunotherapy.

[0179] Figure 2 A represents the ROC curve, where different ROC curves indicate the predictive performance of different models in the discovery set.

[0180] Figure 2 B shows the comparison results of IGeS-EN with other published features in predicting the effectiveness of immunotherapy. The values ​​in the heatmap are the area under the curve (AUC) obtained from the discovery set.

[0181] Figure 2 C and 2D show the results of similar analyses performed on five other queues in the validation set.

[0182] Figure 3 The computational framework IGeS-BS for screening compounds that can enhance the efficacy of immunotherapy is shown.

[0183] Figure 3 A is a flowchart of IGeS-BS.

[0184] Figure 3 B shows the ICB enhancement score of FDA-approved anticancer drugs in pan-cancer studies.

[0185] Figure 4 The results show the validation of the IGeS-BS prediction accuracy.

[0186] Figure 4 A-4C shows the results of comparing the ICB enhancement scores of known immunotherapy enhancers with those of other compounds. Figure 4 A is a known enhancer that has been approved by the FDA or is in clinical trials; Figure 4 B is a known enhancer approved by the FDA or in a phase 3 clinical trial; Figure 4 C is a known enhancer approved by the FDA.

[0187] Figure 4 D shows the ranking of compounds in melanoma (SKCM) based on ICB enhancement scores.

[0188] Figure 4 E is a radar chart of the IGeS score of bis(renapa) in the LINCS cell line data. Red represents sIGeS, and green represents rIGeS.

[0189] Figure 4 F shows the changes in MHC profiles after birenapa treatment in melanoma-affected mice. Gene expression profiles are derived from GSE149825.

[0190] Figure 4 G shows the ranking of compounds in ovarian cancer (OV) based on ICB enhancement scores.

[0191] Figure 4 H is a radar chart of the IGeS score of olaparib in LINCS cell line data. Red represents sIGeS, and green represents rIGeS.

[0192] Figure 4 Figure 1 shows the changes in MHC profiles following olaparib treatment in mice with ovarian cancer. Gene expression profiles were derived from GSE120500.

[0193] Figure 5 This demonstrates the application of IGeS-BS in liver cancer.

[0194] Figure 5 A shows the results of experimental verification of the top-ranked compounds.

[0195] Figure 5 B shows the IC50 values ​​of eight candidate compounds in the Huh7 cell line.

[0196] Figure 5 C shows the selection of three HCC patients resistant to PD-1 inhibitors to establish an organoid model, testing the efficacy of eight candidate compounds alone and in combination with PD-1 inhibitors. P-values ​​were calculated using a t-test. P = *** indicates P < 0.001.

[0197] Figure 5 D shows a heatmap of the results of three PDOTs drug tests, with green and red representing the percentage of surviving and dead cells, respectively.

[0198] Figure 6 The results showed that treating mice with CGP-60474 or SB-366791 overcame resistance to immunotherapy in liver cancer.

[0199] Figure 6 A is a schematic diagram of in vivo treatment in a mouse model of liver cancer.

[0200] Figure 6 B and C show the comparison results of tumor weight before and after treatment. P-values ​​were calculated using the Welch T-test. ** indicates P < 0.01.

[0201] Figure 6 D shows the distribution of "T cell activation" and "cell cycle" scores calculated from the transcriptome across different treatment groups.

[0202] Figure 6 E shows the ratio of M2 to M1 macrophage scores calculated from the transcriptome. * indicates P < 0.05.

[0203] Figure 6F and 6G show examples of genes that were upregulated or downregulated after combination therapy. Detailed Implementation

[0204] Through thorough and rigorous data analysis and experimental verification, the inventors developed a computational framework for compound screening to enhance the efficacy of immunotherapy, and based on this framework, discovered novel compounds that can overcome immunotherapy resistance in liver cancer. Specifically, this invention first systematically characterized the differences in the tumor microenvironment between immunotherapy responders and non-responders, and constructed a weighted model of 33 immunotherapy-related gene expression signatures (IGeS) capable of predicting immunotherapy response. Subsequently, a novel computational strategy was developed to quantify changes in the tumor microenvironment after compound intervention and to virtually screen compounds with the potential to enhance immunotherapy efficacy. Finally, the effectiveness of candidate compounds in overcoming immunotherapy resistance in liver cancer was verified through in vitro and in vivo experiments. Based on these findings, this invention was completed.

[0205] It should be understood that the specific methods and experimental conditions of the invention described below in varying degrees of detail are intended to provide a substantive understanding of the invention. Definitions of certain terms used in this specification are provided below. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0206] the term

[0207] As used herein, the terms “comprising,” “including,” and “containing” are used interchangeably and include not only closed definitions but also semi-closed and open definitions. In other words, the terms include “consisting of” and “substantially consisting of”.

[0208] As used herein, the terms “compound” and “drug” are used interchangeably and both refer to the small molecule compounds that are potential drugs involved in this invention.

[0209] As used in this article, the terms "responder and non-responder," "responsive and non-responsive," and "responder group and non-responder group" are used interchangeably. Studies using the RECIST evaluation criteria categorize patients with complete or partial remission into the response group and those with stable or progressive disease into the non-responder group; for studies reporting survival, patients are categorized into response and non-responder groups based on the survival thresholds used in the original study.

[0210] As used in this article, the term "PD-1 inhibitor" refers to a common type of tumor immunotherapy drug, and "anti-PD-1 monoclonal antibody" is one of them. Both PD-1 inhibitors and anti-PD-1 monoclonal antibodies exert their good immune efficacy by blocking the PD-1 / PD-L1 signaling pathway and restoring the killing function of T cells.

[0211] Immunotherapy-related gene expression characteristics

[0212] As used herein, the terms "immunotherapy-related gene expression signature" and "IGeS" are used interchangeably and refer to an Immune Gene expression signature, which is a set of genes. This invention collects tumor-related transcriptome datasets, namely the Tumor Microenvironment-Related Gene Set (TME_GS). First, it calculates the raw scores of TME_GS using a tumor immune infiltration analysis tool. Then, it standardizes the raw scores of TME_GS through mathematical transformations, establishes a logistic regression model, estimates the regression coefficients and residuals for each TME_GS set, and then integrates these regression coefficients using meta-analysis to obtain the odds ratio (OR) and p-value. Finally, it classifies the expression signatures of immunotherapy-related genes based on the OR and p-value.

[0213] Based on the odds ratio (OR) and p-value, the expression features of immunotherapy-related genes were further divided into immunotherapy sensitivity-related features (sIGeS) and immunotherapy resistance-related features (rIGeS). Among them, the OR of sIGeS was >1 and the p-value was <0.05, while the OR of rIGeS was <1 and >0 and the p-value was <0.05.

[0214] IGeS-BS

[0215] As used herein, the terms “IGeS-BS,” “IGeS-BS strategy,” and “IGeS-BS framework” are used interchangeably to refer to the Immune Gene expression signature-Boosting Score system designed in this invention for screening compounds that enhance the efficacy of immunotherapy.

[0216] Specifically, the system includes the following steps:

[0217] (I) In the compound-induced gene expression dataset, the compound-induced gene expression profile is compared with the control expression profile to obtain the differential expression between the compound-induced gene expression profile and the control expression profile under multiple interference conditions. Genes are ranked according to the differential expression. Through pre-ranked gene set enrichment analysis, the standardized enrichment score (NES) and p-value of the compound's effect on specific immunotherapy-related gene expression features (IGeS) under specific interference conditions are calculated. Then, multiple p-values ​​are integrated using a combined Cauchy association test. Data with insignificant p-values ​​after integration are not included in subsequent calculations. The multiple NES values ​​are weighted and integrated to obtain the gene expression perturbation score (PGES) of the compound in a specific cancer type.

[0218] (II) Based on the weights of IGeS generated by the immunotherapy response prediction model, the PGES of multiple IGeS of the compound in the specific cancer type are weighted and summed using the Enhanced Scoring (BS) algorithm, and the compounds in the specific cancer type are sorted according to the BS to obtain the compound that enhances the efficacy of tumor immunotherapy.

[0219] Immunotherapy combination strategy

[0220] Compared to single immunotherapy, computational methods for combined immunotherapy strategies are still in their early stages. One intuitive strategy is to determine the number of cancer cells and infiltrating immune cells, as well as their inhibitory concentrations in mouse model drug treatment experiments, and then establish difference equations to estimate the efficacy of ICBs combined with other drugs. Another strategy is to construct sensitive and resistant mouse models, analyze the differential network modules between the two states, identify potential drug-targeting genes from these modules, and finally evaluate the synergistic anti-cancer effect of drug combinations through in vivo experiments. Neither of these strategies can be applied on a large scale to screen multiple cancer types and multiple drugs.

[0221] Molecular markers and models for immunotherapy

[0222] There are currently some commonly used molecular markers and models for immunotherapy.

[0223] The U.S. Food and Drug Administration (FDA) has approved PDL1 protein expression levels, tumor mutational burden, microsatellite instability, and DNA mismatch repair deficiencies as biomarkers for ICB therapy. Furthermore, markers of cytotoxic T lymphocytes (CD8A and CD8B) and the interferon-γ-related gene set (IFNG, STAT1, IDO1, CXCL10, CXCL9, HLA-DRA) are also associated with the efficacy of immunotherapy.

[0224] The Immunophenoscore model predicts response to immunotherapy by weighted integration of a set of 160 immune-related genes (such as antigen presentation, immune checkpoints and regulators, immune effector cells, and suppressor cells). The Tumor Immune Dysfunction and Rejection (TIDE) algorithm constructs features of T cell dysfunction and rejection and integrates them to determine whether a tumor responds to immunotherapy. Another published model defines a 15-gene Tumor Immune Phenotypic (TIP) signature to distinguish between “cold” and “hot” tumors and predicts immunotherapy response based on their expression patterns.

[0225] The compound discovered in this invention enhances the efficacy of tumor immunotherapy.

[0226] Using the method or system provided by this invention, eight compounds that enhance the efficacy of immunotherapy for liver cancer were screened, including three known compounds and five novel compounds. The known compounds are FDA-approved drugs: phenothiazines, docetaxel, and trametinib; the novel compounds are CGP-60474, PD-0325901, tubacin, tyrphostin-AG-494, and SB-366791. Among these, CGP-60474 and SB-366791 have the ability to overcome immunotherapy resistance and, when used in combination with commonly used immunotherapy drugs PD-1 and anti-PD-1, exhibit significant therapeutic efficacy against tumors.

[0227] To further explore the mechanisms by which CGP-60474 and SB-366791 alleviate drug resistance, this invention compared the expression profiles of mice treated with CGP-60474 or SB-366791 alone, as well as mice treated with CGP-60474 in combination with anti-PD-1 or SB-366791 in combination with anti-PD-1, with the control group. The results showed that the combination of CGP-60474 with anti-PD-1 or SB-366791 with anti-PD-1 can overcome drug resistance and thus inhibit tumor development by promoting T cell activation and inhibiting the tumor cell cycle.

[0228] The main advantages of this invention include:

[0229] (1) Based on tumor-related transcriptome and cell line data, this invention characterized the differences in tumor microenvironment between immunotherapy responders and non-responders, and constructed an expression profile of 33 immunotherapy-related genes that can predict immunotherapy response.

[0230] (2) Based on the expression characteristics of 33 immunotherapy-related genes, this invention developed a computational framework to quantify the immunotherapy effect of specific compounds in specific cancer types according to interference conditions including cell line, compound dosage, and treatment time, and to screen compounds with the potential to enhance immunotherapy effects based on the effect value.

[0231] (3) Based on the expression characteristics of immunotherapy-related genes and computational framework, this invention screened out 5 new compounds that could potentially enhance the effects of immunotherapy.

[0232] (4) The five new compounds discovered in this invention that have the potential to enhance the effect of immunotherapy can overcome the drug resistance of immunotherapy and can significantly enhance the effect of immunotherapy when used in combination with existing immunotherapy drugs.

[0233] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods in the following embodiments, unless otherwise specified, are generally performed under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or as recommended by the manufacturer. Unless otherwise stated, percentages and parts are weight percentages and parts by weight.

[0234] Materials and methods:

[0235] 1. Pan-cancer Immunotherapy Cohort

[0236] Literature on immune checkpoint inhibitors (anti-PD-1, anti-PD-L1, anti-CTLA-4, or anti-PD-1 + anti-PD-L1) for cancer treatment was manually searched. A total of 17 cohorts with pre-immunotherapy transcriptome data and response information were collected and randomly divided into a discovery set and a validation set. The discovery set included 12 cohorts across 6 cancer types: 5 melanoma datasets, 2 clear cell renal cell carcinoma datasets, 2 lung cancer datasets, 1 gastric cancer dataset, 1 glioblastoma dataset, and 1 bladder cancer dataset. The validation set contained 5 cohorts across 2 cancer types: 4 melanoma datasets and 1 bladder cancer dataset. A unified workflow was used to assess treatment response and process transcriptome data for all cohorts. Transcriptome data was downloaded from the GEO database or literature appendices. Gene expression levels were expressed using read counts, FPKM index, or TPM values. All quantitative indicators were converted to TPM values, and then the logarithm (log2) was taken to make the expression distribution closer to a normal distribution. Batch effect correction was performed on all cohorts in the discovery set using a combinatorial approach, and this was then used as reference data to perform batch correction on each dataset in the validation set. Different studies assessed patient efficacy in slightly different ways. For studies using the RECIST evaluation criteria, patients with complete or partial remission were divided into response groups, and patients with stable or progressive disease were divided into non-responder groups. For studies reporting survival time, patients were divided into response and non-responder groups based on the survival time threshold used in the original study.

[0237] 2. TCGA pan-cancer cohort

[0238] This invention utilized 21 primary solid tumors from the TCGA database, including bladder cancer (BLCA), breast cancer (BRCA), cervical squamous cell carcinoma (CESC), colorectal cancer (COAD), esophageal cancer (ESCA), glioblastoma (GBM), head and neck cancer (HNSC), clear cell renal cell carcinoma (KIRC), renal cell carcinoma (KIRP), low-grade glioma (LGG), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian cancer (OV), pancreatic ductal adenocarcinoma (PAAD), prostate cancer (PRAD), rectal cancer (READ), gastric cancer (STAD), thyroid cancer (THCA), endometrial cancer (UCEC), primary melanoma (pSKCM), and metastatic melanoma (mSKCM). RNA sequencing data and clinical information were downloaded via TCGAbiolinks. Gene expression matrices were converted to TPM values. The concordance tumor purity estimated using the TUMERIC method was downloaded from the literature. Since the TUMERIC method did not provide data on primary melanoma, tumor purity estimated using the CPE method was used. Patients for whom tumor purity was not estimated were excluded from the analysis.

[0239] 3. Identify gene expression characteristics related to immunotherapy

[0240] 3.1 Identification methods for immunotherapy sensitivity and resistance characteristics: Three gene sets (TME_GS) related to the tumor microenvironment (TME) were manually compiled from the literature. For gene sets where calculation methods or programs were provided in the original literature, the activity scores of TME_GS were calculated using the original methods. For gene sets related to immune cell infiltration, ConsensusTME-gsva, xcell, MCP-Counter, CIBERSORTX, ImmunCellAI, TIMER, EPIC, and Quantiseq were used to calculate the activity scores of TME_GS. For other gene sets, the ssGSEA method in the R package "gsva" was used to calculate the scores. The association between each TME_GS and immunotherapy response was then analyzed. In each cohort, the original scores of each gene set were standardized using Z-transform, with a mean of 0 and a standard deviation of 1. A logistic regression model was established using the R function "glm" to estimate the effect size (regression coefficient) and residuals of each TME_GS, where the standardized score and binary response label were used as independent and dependent variables, respectively. Considering the heterogeneity among cohorts, a random-effects model was used to perform meta-analysis via the R package "meta" to integrate the effect sizes of each gene set in the cohorts, obtaining the odds ratio (OR) and p-value indicating the association between the gene set and the immunotherapy response. Ultimately, TME_GS with p-values ​​less than 0.05 and ORs greater than 1 were defined as features associated with immunotherapy sensitivity (sIGeS), while p-values ​​less than 0.05 and ORs within the range of (0,1) were defined as features associated with immunotherapy resistance (rIGeS).

[0241] 3.2 Expanded Strategy for IGeS Detection: Due to differences between different cancer types and variations in the quantification of IGeS in cancer cell lines induced by subsequent compound use, this invention implements an expanded strategy to detect IGeS in different cancer types and cell lines. For each cancer type in TCGA, tumor purity is used as a confounding variable. The partial Spearman correlation coefficient (PSCC) between each IGeS and each gene is calculated using the R package "ppcor" and then subjected to significance testing and p-value correction to obtain (PSCC p.adj). The percentage (DP) of genes detected in cancer cell lines is calculated from the CCLE database. The top 200 genes meeting the criteria of PSCC p.adj < 0.05, PSCC > 0.4, and DP > 0.8 are selected as IGeS for specific cancer types.

[0242] 4. Construct and evaluate immunotherapy response prediction models

[0243] 4.1 Methods for evaluating the accuracy of IGeS combination in predicting immunotherapy response: To evaluate the accuracy of IGeS combination in predicting immunotherapy response, five multivariate machine learning models were constructed, including Elastic Network Regression (IGeS-EN), Ridge Regression (IGeS-RR), Random Forest (IGeS-RF), Support Vector Machine (IGeS-SVM), and Gradient Boosting Tree (IGeS-XGBoost). Before training, a Combat method was used to eliminate batch effects between queues. Model performance on the discovery set was evaluated using five-fold cross-validation. In each fold, the model was trained on 80% of the data and subjected to an additional ten-fold cross-validation for hyperparameter tuning, then tested on the remaining folds. The results of each test fold were summarized to generate the overall performance on the discovery set. Furthermore, the model was retrained in the same manner across the entire discovery set to optimize hyperparameters and applied on five independent validation sets to evaluate model performance and generalization ability. Within the "mlr" framework, we construct Elastic Net Regression, Random Forest, Support Vector Machine, and XGBoost models; we train the Ridge Regression model using the "glmnet" package, employing the same data partitioning strategy as other models; and we compute the evaluation matrix (i.e., ROC curve and AUC value) using the "pROC" package in R.

[0244] 4.2 Method for comparing the predictive capabilities of the model of this invention with existing models: The model of this invention was compared with published transcriptome-based immunotherapy prediction methods, including the average expression of IFNG characteristic genes, the average expression of CD8 characteristic genes, the score of 18 genes, the TIP score, the immunophenotype score, the T-cell exclusion procedure, and the TIDE model. The scripts of the above methods were rewritten using the code provided in the original literature or based on the description of the original publication and recalculated locally.

[0245] 5. Compound-induced gene expression dataset

[0246] Compound-induced gene expression profiles in cancer cell lines were downloaded from the LINCS project, resulting in two batches of data (GSE70138 and GSE92742). The LINCS tertiary data were parsed using the `parse.gctx()` function to obtain gene expression profiles for each compound at different doses and treatment times, with dimethyl sulfoxide (DMSO) treated on each plate serving as a control. The cancer cell lines from LINCS were matched against patient data from TCGA using the same nomenclature. After matching criteria, 13 cancer types with corresponding drug-induced gene expression profiles in the cancer cell lines were retained.

[0247] GSE92742 contains 1768 compounds from 12 cell lines representing 8 cancer types (breast cancer (BRCA), liver cancer (LIHC), lung adenocarcinoma (LUAD), pancreatic cancer (PAAD), prostate cancer (PRAD), colon cancer (COAD), rectal cancer (READ), and skin melanoma (SKCM)). It has processed 202133 compounds and has 14304 DMSO-treated expression profiles.

[0248] GSE70138 contains 18,486 compounds from 38 cell lines representing 11 cancer types (breast cancer (BRCA), colon cancer (COAD), liver cancer (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian cancer (OV), prostate cancer (PRAD), rectal cancer (READ), cutaneous melanoma (SKCM), gastric cancer (STAD), and endometrial cancer (UCEC)). It has processed 443,505 compounds and has 23,252 DMSO-treated expression profiles.

[0249] Melanoma cell lines are used for research on primary cutaneous melanoma (pSKCM) and metastatic cutaneous melanoma (mSKCM). Colorectal cancer cell lines are used for research on primary colon cancer (COAD) and rectal cancer (READ).

[0250] 6. Quantifying the effects of compounds on IGeS expression

[0251] The LINCS dataset contains multiple cell lines for each cancer type and multiple perturbation conditions (cell line, drug dose, treatment time) for a given compound. For each perturbation condition (cells, dose, time), genes are ranked based on the expression differences between drug-treated cell lines and the control group. Pre-ranked Gene Set Enrichment Analysis (GSEA) is then applied to calculate the Normalized Enrichment Score (NES) and p-value reflecting the compound's effect on a given IGeS under specific conditions. To integrate the results for each triplet (IGeS, cancer type, compound) under multiple perturbation conditions, the FDR-adjusted p-values ​​are combined using the Aggregated Cauchy Association Test (ACAT); a more robust Gene Expression Perturbation Score (PGES) is obtained by aggregating NES using a published method (with slight modifications to cover cases where only one dose and one time point are provided).

[0252]

[0253] Where n is the number of interfering conditions (cells, dose, time) for a given compound and cancer type; p ,dosep time p These represent the cell line, drug dosage, and treatment time under the given conditions, respectively. Within the same cancer type, cell line p The mean correlation coefficient with the corresponding patient sample in TCGA, divided by its maximum value. If not provided in the literature... The mean correlation coefficient of all cell lines in that cancer type will then be used. The available dosage and time vary between different compounds. (reference) p The values ​​for f(dose) are reference conditions for drug dosage and treatment time, taken as 10 μM and 24 hours, respectively, because most compounds are evaluated under these conditions. p ,time p This represents the change in the perturbation fraction relative to the reference condition when the drug is applied at different doses and times: Where m is the number of corresponding conditions (dose, time).

[0254] 7. Computer simulation ranking of compounds used to enhance immunotherapy response

[0255] Hypothesizing that if certain compounds can increase the expression of sIGeS (the set of genes associated with immunotherapy sensitivity) or decrease the expression of rIGeS (the set of genes associated with immunotherapy resistance), these compounds may help enhance immunotherapy. Based on this hypothesis and the IGeS-EN model, a Boosting Score (BS) is defined to synthesize changes in the expression of all IGeS genes while controlling for multicollinearity among them:

[0256]

[0257] Where i represents the index of IGeS, N is the subset of IGeS with significant expression changes under compound interference (FDR < 0.05), and PGES i It is a triple (IGeS) i A summary of "gene expression perturbation score" (including cancer type and compound), while Coe i IGeS extracted from the IGeS-EN model i The regression coefficients. Compounds containing only sIGeS or rIGeS are ignored in BS score calculation and ranking. The BS value reflects the potential of a drug or compound to enhance the efficacy of immunotherapy in a specific cancer type.

[0258] 8. Evaluate IGeS-BS using prior knowledge

[0259] Annotated information for drugs or compounds, including clinical stage, mechanism of action, and target genes, was downloaded and integrated from LINCS Portal 2.0 API (lincsportal.ccs.miami.edu), DrugBank v5.1, and the Therapeutic Target Database (TTD). First, the ability of compound-induced cell lines to reflect in vivo changes was investigated by comparing expression changes predicted from LINCS with those in cancer mouse models. Five expression datasets were collected from immunocompetent mice before and after treatment with single or combined immune checkpoint inhibitors (ICBs): GSE149825, GSE152925, GSE120500, GSE114601, and GSE160785. Fold changes in expression before and after treatment were calculated using log-transformed expression values ​​(normalized counts or FPKM). Genes were sorted by fold change and GSEA analysis was performed to determine IGeS gene enrichment. IGeS with p.adj < 0.1 were considered to have been regulated after drug treatment. Under the same drug treatment, the concordance between the two was assessed by calculating the proportion of IGeS with the same regulatory direction in cancer cell lines and animal models. Next, the IGeS-BS prediction results were compared with existing knowledge. ICB combination therapies from FDA-approved or clinical trials were manually compiled as supporting evidence from https: / / www.fda.gov and https: / / clinicaltrials.gov. The effect size was defined as the difference in mean scores between compounds with and without supporting evidence. The Wilcoxon rank-sum test was used to assess whether compounds with supporting evidence had significantly higher scores.

[0260] 9. Experimental validation of liver cancer compounds predicted by IGeS-BS

[0261] 9.1 Drug IC50 Assay: Huh7 (NICR; #1101HUM-PUMC000679) cell lines were purchased from the National Cell Resource Infrastructure; Hep3B (ATCC; #HB-8064) cell lines were purchased from the American Type Culture Collection. Cells were cultured in DMEM medium containing 10% FBS (Gibco; #10099141C) and 100 U / L penicillin / streptomycin (Gibco; #15140122) at 37°C in a cell culture incubator with 5% CO2. Cells in logarithmic growth phase were digested, centrifuged, and counted to 5 × 10⁶ cells / year. 4 Cells were seeded at a density of 100 μL / mL into 96-well plates and incubated at 37°C and 5% CO2 for 24 hours. Cell growth and density were observed using an inverted microscope, and cells with good growth, uniform distribution, and appropriate density were selected for further experiments.

[0262] For Hep3B cells, the following solutions were diluted to different concentrations: CGP-60474 was diluted to 0, 10, 20, 30, 50, and 100 nM; Docetaxel was diluted to 0, 0.5, 1, 2, 5, and 10 nM; PD-0325901 was diluted to 0.01, 0.05, 0.1, 0.2, and 1.0 μM; Trametinib was diluted to 0.1, 0.25, 0.5, 0.75, and 1.0 μM; and SB-366791, Phenothiazine, Tyrphostin-AG-494, and Tubacin were diluted to 0, 1, 5, 10, 25, and 50 μM.

[0263] For Huh7 cells, the following solutions were diluted to different concentrations: CGP-60474 was diluted to 0, 10, 50, 100, 150, and 200 nM; docetaxel was diluted to 0, 0.5, 1, 2, 5, and 10 nM; and trametinib, PD-0325901, SB-366791, phenothiazine, Tyrphostin-AG-494, and tubacin were diluted to 0, 1, 5, 10, 25, and 50 μM. Three replicates of each concentration were cultured at 37°C and 5% CO2 for 72 hours. The medium was replaced with 100 μL of 10% CCK-8 solution (Meilun Bio; #MA0218) per well, and incubated at 37°C and 5% CO2 for 2 hours. After incubation, the OD value was measured at 450 nm using enzyme-linked immunosorbent assay (ELISA). Cell viability (%) = (OD value of drug-treated group - OD value of blank group) / (OD value of control group - OD value of blank group) × 100%. Experimental data were processed using Graphpad PRISM 8.0.2 statistical software.

[0264] 9.2 PDOTs Culture and In Vitro Drug Testing: Patient-derived organoid tumor spheroids (PDOTs) were prepared and cultured according to the methods described previously. Fresh hepatocellular carcinoma (HCC) tumor tissue was surgically removed and then minced using a scalpel in a sterile 10 cm culture dish, rinsing three times with PBS before mincing. The minced tumor tissue was diluted in Advanced DMEM / F-12 medium (Thermo Fisher Scientific; #12634010) containing 0.1% collagenase Type 4 (Worthington; #LS004188), 0.05% hyaluronidase (Sigma; #H3506), and 0.01% deoxyribonuclease (Sigma; #DN25). The mixture was placed on a horizontal shaker and shaken at 37°C for 30 minutes. Red blood cells (RBCs) were removed using erythrocyte lysis buffer (Beyotime; C3702). The cell suspension was then precipitated and resuspended in fresh culture medium, and passed sequentially through 100 μM and 40 μM filters to obtain spheroid fractions S1 (>100 μM), S2 (40–100 μM), and S3 (<40 μM). Fraction S2 was resuspended in Matrigel (Corning; #356231) at a concentration of 2.5 mg / ml. The spheroid-Matrixel mixture was then injected into a microfluidic culture device. After incubation at 37°C in a sterile, humidified room for 15 minutes, the substrate gel containing PDOTs was cultured together with the culture medium, or the indicated treatments were added: untreated control group; anti-PD-1 (250 μg / ml pembrolizumab); docetaxel (1.95±0.10 nM), or a combination of docetaxel and anti-PD-1; CGP-60474 (28.59±4.08 nM), or a combination of CGP-60474 and anti-PD-1; trametinib (10 μM), or a combination of trametinib and anti-PD-1. -1; PD-0325901 (10 μM), or in combination with PD-0325901 + anti-PD-1; Tubacin (14.81±0.38 μM), or in combination with Tubacin + anti-PD-1; Tyrphostin-AG-494 (10 μM), or in combination with Tyrphostin-AG-494 + anti-PD-1; SB-366791 (10 μM), or in combination with SB-366791 + anti-PD-1; Phenothiazine (10 μM), or in combination with phenothiazine + anti-PD-1.

[0265] 9.3 PDOTs Live / Dead Cell Analysis: A dual-labeling method was used to analyze the live / dead cells of PDOTs by replacing the culture medium with Nexcelom ViaStain AO / PI staining solution (Nexcelom, #CS2-0106). Cells were co-incubated with the dye for 15 minutes at room temperature in the dark, and then images were captured using a high-throughput screening system (HCS, PerkinElmer). Image capture and analysis were performed using the Harmony™ analysis system (PerkinElmer). Live and dead cells were quantified by measuring the total cell area for each dye, where green areas represent live cells and red areas represent dead cells. The cell area was calculated using the formula: Cell area = Green area / Total area.

[0266] 9.4 Animal Studies: Male 6-week-old C57BL / 6J mice used in this study were purchased from Charles River (Shanghai, China). All animals were housed in a specific pathogen-free facility. All animal care and experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of Zhongshan Hospital, Fudan University. To establish a spontaneous hepatocellular carcinoma tumor model, each mouse was injected via tail vein with 20 μg pT3-EF1a-c-myc (Addgene; #92046), 20 μg pX330 p53 (Addgene; #59910), and 10 μg SleepingBeauty SB100x transposase-encoding plasmid (Addgene; #34879) dissolved in 2 ml of 0.9% NaCl solution over a period of 1 to 3 seconds. Two weeks after injection, mice that developed spontaneous tumors were randomly assigned to designated groups. For drug treatment, C57BL / 6J tumor mice were injected intraperitoneally with 10 mg / kg CGP-60474 (MedChemExpress; #HY-11009), 20 mg / kg SB-366791 (MedChemExpress; #HY-12245), and / or 10 mg / kg anti-PD-1 monoclonal antibody (Bio X Cell; #BE0146) or an equivalent control monoclonal antibody (Bio X Cell; #BE0089). Specific protocols are detailed below. Figure 6 A. Medication treatment will be administered every two days, for a total of 7 times.

[0267] 9.5 RNA Sequencing Analysis: RNA sequencing was performed on tumor tissues from three mice under each condition, generating expression profiles for 18 samples under six conditions (control group, PD1, CGP-60474, CGP-60474 and PD1, SB-366791, SB-366791 and PD1). Differentially expressed genes between the treatment and control groups were identified using DESeq2, with filters based on "adjusted p-value < 0.1" and "log2 fold change > 1". Gene set enrichment analysis was performed using the R package clusterProfiler.

[0268] Example 1: Identifying stable and predictive features associated with immunotherapy using pan-cancer analysis

[0269] To obtain stable and predictive characteristics associated with the efficacy of pan-cancer immunotherapy ( Figure 1 A) In this embodiment, 371 tumor microenvironment-related gene sets (TME_GS) were first collected. Then, a meta-analysis was performed on 12 cohorts (discovery set) to evaluate the association between TME_GS and immunotherapy efficacy. After that, the best feature combination was selected and finally validated through another 5 independent cohorts (validation set).

[0270] Meta-analysis of 12 clinical cohorts of immunotherapy revealed 130 TME_GS that were significantly associated with immunotherapy response, and these were named immunotherapy-related gene expression signatures (IGeS) (p<0.05). Figure 1 B). Compared to single-cohort studies, meta-analysis can identify IGeS that were missed in a specific cohort and correct for conflicting IGeS results across multiple cohorts. Of the 130 IGeS, 117 had an odds ratio (OR) greater than 1 for TME_GS, indicating an association with immunotherapy sensitivity (sIGeS, Figure 1 B (left side); while the OR for 13 TME_GS was less than 1, indicating an association with immunotherapy resistance (rIGeS, Figure 1 B (right side).

[0271] Because each IGeS contains a small number of genes and the matched cancer types are not clearly defined, this embodiment further employs an expansion strategy to make IGeS detectable and robust in tissues and cell lines of pan-cancer entities (see 3.2 Expansion Strategy for Detecting IGeS). These expanded features reflect the scores of the original features well.

[0272] Because IGeS may be correlated, this embodiment further explores the optimal IGeS combination for predicting immunotherapy response. This embodiment uses five methods to reduce the effects of multicollinearity to build a classification model to distinguish between immunotherapy responders and non-responders in five validation cohorts. The methods used include Elastic Network Regression (EN), Ridge Regression (RR), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Tree (XGBoost). The results of the five cross-validations show that Elastic Network Regression (IGeS-EN) performs best, with an overall AUC of 0.653. Figure 2 A, 2B). In the validation set, IGeS-EN still performs best, with an overall AUC of 0.745 ( Figure 2 (C, 2D). Regularization of elastic network regression enables automatic variable selection. IGeS-EN selected 33 IGeS (26 sIGeS and 7 rIGeS), reflecting the importance of each IGeS while reducing redundancy (Table 1). Compared with seven published prediction methods, IGeS-EN achieved better or similar results in all cohorts, indicating that the combination of the above 33 IGeS is more robust and accurate in estimating the efficacy of pan-cancer immunotherapy.

[0273] Table 1. 33 IGeS and their weights in the IGeS-EN model

[0274]

[0275]

[0276] Example 2: Computerized screening of immunotherapy enhancers

[0277] This embodiment relates to computer screening of immunotherapy enhancers.

[0278] This embodiment assumes that a certain small molecule compound can significantly upregulate sIGeS and significantly downregulate rIGeS, thus potentially enhancing the efficacy of immunotherapy. Based on this assumption, this embodiment develops a computational framework (IGeS-BS) for computer-based priority screening of immunotherapy enhancers. Figure 3 A) First, gene expression profiles before and after compound intervention were obtained from high-throughput perturbation experiments in cancer models. Second, a perturbation gene expression score (PGES) was defined to quantify the effect of compounds on the expression changes of each IGeS. The PGES of multiple IGeS were weighted and summed to obtain the ICB-enhancement score (BS) for each compound, which reflects the compound's potential to enhance immunotherapy efficacy. Finally, all drugs / compounds were ranked according to the BS for each cancer type.

[0279] Due to the relatively high cost and low efficiency of animal models, data from large-scale perturbation experiments are typically derived from cancer cell lines. Therefore, this embodiment implements the proposed computational framework based on the large cell line perturbation database LINCS. To evaluate whether PGES calculated from the LINCS database can reproduce in vivo expression changes, this embodiment collects five mouse datasets containing gene expression profiles before and after drug treatment. The results show that the consistency rate of drug-induced IGeS expression changes between the mouse datasets and the LINCS datasets ranges from 84.62% to 100%, indicating that applying LINCS to IGeS-BS for large-scale compound screening across multiple cancer types is reasonable.

[0280] Applying two LINCS datasets (GSE70138 and GSE92742) to the IGeS-BS framework generated a cancer-compound landscape demonstrating the potential to enhance immunotherapy. Compounds with higher BS values ​​are more likely to enhance immunotherapy and produce synergistic effects when combined with immune checkpoint inhibitors (ICBs).

[0281] Example 3: IGeS-BS can find known immunotherapy enhancers

[0282] This embodiment relates to validating IGeS-BS using known immunotherapy enhancers.

[0283] To validate the effectiveness of the IGeS-BS strategy, this embodiment first compiled known immunotherapy combinations for different cancer types, including 19 FDA-approved combinations and 258 combinations in clinical trials, of which 87 combinations were in Phase III trials. For each cancer type, this embodiment compared the IGeS-BS scores of known immunotherapy enhancers with those of other compounds. Among seven cancer types, known immunotherapy enhancers showed significantly higher BS values; these seven cancer types were cutaneous melanoma (pSKCM), melanoma (mSKCM), hepatocellular carcinoma (LIHC), prostate cancer (PARD), rectal cancer (READ), breast cancer (BRCA), and colon cancer (COAD). Figure 4 A, 4B, 4C (effect size > 1 and p < 0.05). This indicates that the IGeS-BS strategy can discover known enhancers.

[0284] This embodiment further utilizes drug combination experiment data conducted in mouse models to verify the predictive performance of IGeS-BS and explore the potential mechanisms of drug enhancement reflected by IGeS.

[0285] Birinapant was initially used, which has been reported to enhance the immune checkpoint inhibitor (ICB) response in primary melanoma by upregulating the activity of major histocompatibility complex (MHC) class I molecules. Based on the IGeS-BS enhancement score in primary cutaneous melanoma (pSKCM), birinapant ranked 20th (top 1.2% in GSE70138). Figure 4 D). IGeS-BS analysis showed that, under birenapa treatment, 80% of sIGeS (21 / 26) were upregulated, including MHC characteristics ( Figure 4 E). Expression profile data of melanoma mice treated with birenapa and / or ICB and control groups were downloaded from GSE149825 in this embodiment. MHC feature scores were increased in the birenapa-treated mouse data ( Figure 4 F), which is highly consistent with the synergistic mechanism of pirenapa and ICB.

[0286] Secondly, olaparib was used, and the prediction results of IGeS-BS ( Figure 4 Both G) and mouse experiments demonstrated the enhancing effect of olaparib combined with ICB. IGeS-BS prediction results indicated that olaparib primarily upregulated sIGeS, including dendritic cells, CD8 cells, IFNγ, etc. Figure 4 H), which is consistent with the increased dendritic cell and CD8 signature scores in the expression profile of mice treated with olaparib. Figure 4 I).

[0287] Example 4: Validating the effect of the predicted liver cancer immunotherapy enhancer

[0288] This embodiment involves selecting candidate immunotherapy enhancers for liver cancer through a screening strategy and verifying the predicted efficacy of the immunotherapy enhancers for liver cancer through in vivo and in vitro experiments.

[0289] This embodiment uses liver cancer as a model to verify the efficacy of the predicted immunotherapy enhancer through in vitro and in vivo experiments. The top 20 compounds of IGeS-BS in two LINCS expression sets are as follows: Figure 5 As shown in A. Candidate compounds were selected based on the following strategy: (1) positive ICB enhancement scores, ranking in the top 20 in both GSE70138 and GSE92742; (2) a known mechanism of action (MoA) and the same MoA as some known immune enhancers. Through the above screening, this example yielded 8 compounds (Table 2), including 3 FDA-approved drugs (phenothiazine, docetaxel, trametinib), which are more likely to be used in clinical practice, and 5 new compounds.

[0290] Experimental verification was conducted using the aforementioned eight compounds. First, in this embodiment, the half-maximal inhibitory concentrations (IC50) of these eight compounds were determined in Huh7 and Hep3B cell lines. Figure 5 B). Based on the experimental results, the drug sensitization effect was further tested using patient-derived organoid tumor spheroids (PDOTs) in a microfluidic culture device. PDOTs were tested based on surgically resected samples from three patients with hepatocellular carcinoma (HCC) who were unresponsive to anti-PD-1 therapy. Figure 5 C). The results showed that all eight compounds exhibited some degree of ability to overcome immunotherapy resistance. Among them, SB-366791 and CGP-60474 showed the most significant effects. Figure 5 D).

[0291] Table 2. Eight candidate compounds to enhance immunotherapy for liver cancer

[0292]

[0293] Example 5: SB-366791 and CGP-60474 overcome drug resistance to immunotherapy

[0294] This embodiment involves the investigation of the role of SB-366791 and CGP-60474 in tumors and the investigation of their mechanisms for overcoming drug resistance.

[0295] Based on the PDOTs detection results, this embodiment further validates the role of SB-366791 and CGP-60474 in a spontaneous hepatocellular carcinoma (HCC) mouse model.

[0296] Drug treatment begins 14 days after tail vein injection, administered every 2 days for a total of 7 doses. Figure 6 A). The results showed that this HCC mouse model was resistant to anti-PD-1 treatment, and the combined use of SB-366791 or CGP-60474 significantly alleviated the resistance. Figure 6 B, 6C, P<0.01). To explore the underlying mechanism, the expression profiles of mice treated alone or in combination were compared with those of the control group.

[0297] Following treatment with anti-PD-1 antibody, CGP-60474, CGP-60474 + anti-PD-1, SB-366791, and SB-366791 + anti-PD-1, 355 (296), 145 (47), 838 (693), 195 (129), and 1232 (816) genes were upregulated (downregulated), respectively. Combination therapy induced more differentially expressed genes (DEGs) than monotherapy, with similar functional enrichment results. Specifically, upregulated genes in combination therapy were enriched in T cell activation, while downregulated genes were enriched in the cell cycle (…). Figure 6 D). Many sIGeS-related genes, such as CD8 T cell activation (Gzmk), MHC class II (H2-Aa, H2-Ab1, H2-DMa, H2-Eb1) and M1 macrophages (Ccl5, Cxcl9, Cxcl10), were significantly increased after two combination therapies. Figure 6 Similarly, some rIGeS-related genes, such as exhausted CD8 T cells (Dgkh), were significantly reduced after combination therapy.

[0298] Differences also existed between CGP-60474 and SB-366791 when combined with anti-PD-1 antibodies. CGP-60474 + anti-PD-1 increased the expression of some angiogenesis genes (Vegfa, Fgf1, Flt1); while SB-366791 + anti-PD-1 had a stronger effect on immune cell activation, achieved by increasing the expression of more immune genes. Furthermore, the ratio of M2 to M1 macrophages was significantly reduced in SB-366791 + anti-PD-1, indicating that macrophages were biased towards the pro-inflammatory M1 phenotype. Figure 6 E).

[0299] In summary, both in vitro and in vivo experiments have demonstrated that IGeS-BS has discovered a novel compound that activates immune cells and overcomes resistance to immunotherapy in liver cancer.

[0300] All documents mentioned in this invention are incorporated herein by reference as if each document were individually incorporated by reference. Furthermore, it should be understood that after reading the foregoing teachings of this invention, those skilled in the art can make various alterations or modifications to this invention, and these equivalent forms also fall within the scope defined by the appended claims.

Claims

1. A method for screening compounds that enhance the efficacy of tumor immunotherapy based on transcriptome analysis, characterized in that, Including the following steps: (1) Collect published tumor microenvironment-related gene sets (TME_GS), and calculate the original scores of the TME_GS using tumor immune infiltration analysis tools; in the discovery set, standardize the original scores of the TME_GS through mathematical transformation, establish a logistic regression model, estimate the regression coefficients and residuals of each TME_GS, then integrate the regression coefficients using meta-analysis to obtain a threshold, and classify the expression features of immunotherapy-related genes (IGeS) according to the threshold; then, based on the dataset, use an expansion strategy to obtain IGeS for specific cancer types, and use machine learning algorithms to construct an immunotherapy response prediction model, which generates the weights of the IGeS; (2) In the compound-induced gene expression dataset, the compound-induced gene expression profile is compared with the control expression profile to obtain the differential expression between the compound-induced gene expression profile and the control expression profile under multiple interference conditions. Genes are ranked according to the differential expression. Through pre-ranked gene set enrichment analysis, the normalized enrichment score (NES) and p-value of the compound's effect on the specific IGeS under the specific interference conditions are calculated. Then, multiple p-values ​​are integrated using a combined Cauchy association test. Data with insignificant p-values ​​after integration are not included in subsequent calculations. Multiple NES values ​​are weighted and integrated to obtain the gene expression perturbation score (PGES) of the compound in the specific cancer type. (3) Based on the weight of the IGeS, the PGES of multiple IGeS of the compound in the specific cancer type are weighted and summed using the Enhanced Scoring (BS) algorithm, and the compound in the specific cancer type is sorted according to the BS to obtain the compound that enhances the efficacy of tumor immunotherapy.

2. The method as described in claim 1, characterized in that, In (1), the following steps are also included: (1.1) Evaluate the immunotherapy response prediction model based on the validation set; and (1.2) Compare the immunotherapy response prediction model with published immunotherapy prediction methods.

3. The method as described in claim 1 or 2, characterized in that, The expression features of the immunotherapy-related genes include: immunotherapy sensitivity-related features (sIGeS) and immunotherapy resistance-related features (rIGeS); there are 117 immunotherapy sensitivity-related features and 13 immunotherapy resistance-related features.

4. The method as described in claim 1 or 2, characterized in that, The immunotherapy response prediction models include: IGeS-EN, IGeS-RR, IGeS-RF, IGeS-SVM, and IGeS-XGBoost; wherein, in the validation set described in (1.1), IGeS-EN selected 33 IGeS.

5. The method as described in claim 1, characterized in that, The enhanced score is calculated as follows: Where i is the index of the IGeS; N is a subset of the IGeS that exhibit significant expression changes under the interference of the compound; Coe i It is the weight of the IGeS, that is, the regression coefficient of the i-th IGeS extracted from the immunotherapy response prediction model; PGES i It is the perturbation score of the compound on the gene expression of the i-th IGeS in the cancer.

6. A framework or system for screening compounds that enhance the efficacy of tumor immunotherapy, characterized in that, include: An input unit is configured to input a compound-induced gene expression dataset, the compound-induced gene expression dataset including a compound-induced gene expression profile and a control treatment expression profile; A processing unit, configured to process the gene expression dataset induced by the compound, includes the following steps: (I) The gene expression profile induced by the compound is compared with the expression profile of the control treatment to obtain the differential expression between the gene expression profile induced by the compound and the expression profile of the control treatment under multiple interference conditions. The genes are ranked according to the differential expression. Through pre-ranked gene set enrichment analysis, the standardized enrichment score (NES) and p-value of the effect of the compound on the expression characteristics (IGeS) of specific immunotherapy-related genes (IGeS) under specific interference conditions are calculated. Then, multiple p-values ​​are integrated using a polymerized Cauchy association test. Data with insignificant p-values ​​after integration are not included in subsequent calculations. The multiple NES values ​​are weighted and integrated to obtain the gene expression perturbation score (PGES) of the compound in a specific cancer type. (II) Based on the weights of IGeS generated by the immunotherapy response prediction model, the PGES of multiple IGeS of the compound in the specific cancer type are weighted and summed using the Enhanced Scoring (BS) algorithm, and the compounds in the specific cancer type are sorted according to the BS to obtain the compound that enhances the efficacy of tumor immunotherapy; wherein, the immunotherapy response prediction model is IGeS-EN; An output unit, configured to output the result of the processing unit.

7. A compound that enhances the efficacy of tumor immunotherapy, characterized in that, The compound was obtained by screening using the method described in claim 1; The compounds that enhance the efficacy of tumor immunotherapy include compounds having the following structures:

8. A drug or drug composition, characterized in that, The drug or drug composition comprises the compound of claim 7.

9. A medicine box, characterized in that, include: (A) A first container, and a compound in the first container that enhances the efficacy of tumor immunotherapy; and (B) A second container, and other drugs for treating tumors located in the second container, or drugs containing other drugs for treating tumors.

10. Use of the compound of claim 7, the drug or pharmaceutical composition of claim 8, or the pillbox of claim 9, characterized in that, Used to prepare immunotherapy enhancers for treating tumors.

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