WO2022012420A1

WO2022012420A1 - Nucleotide combination and use thereof

Info

Publication number: WO2022012420A1
Application number: PCT/CN2021/105374
Authority: WO
Inventors: 冀颜; 徐伟; 周辉; 王树彦; 竺东雷; 彭波
Original assignee: 信达生物制药(苏州)有限公司
Priority date: 2020-07-17
Filing date: 2021-07-09
Publication date: 2022-01-20

Abstract

Provided is a nucleotide combination and a use thereof. The nucleotide combination comprises the following genes: CD74, CTSZ, ACP5, MS4A6A, CD83, NPC2, GPNMB, C1QB, HLA-DPA1, and HLA-DMB, or mutations of said genes; and/or a gene such as LAP3, IFNGR1, TBXAS1, SPI1, SNX10 or mutations of said genes. The genes are genes specifically expressed by a macrophage in a tumor microenvironment. Using the nucleotide combination allows for effective disease prediction or prognostic diagnosis, and especially provides a good predictive effect in combined treatment with a PD1/PDL1 pathway immune checkpoint inhibitor and a chemotherapy drug.

Description

A kind of nucleotide combination and its application

This application claims the priority of Chinese patent application 2020106925834 with an application date of 2020/7/17. This application cites the full text of the above Chinese patent application.

technical field

The invention belongs to the field of biological diagnosis, in particular to a combination of nucleotides and uses thereof, and more particularly to a gene biomarker capable of diagnosing the prognostic effect of PD1/PDL1 pathway immune checkpoint inhibitor combined with chemotherapy drugs.

Background technique

Cancer has always been one of the leading causes of death in the world, and has become a major disease that seriously endangers human life and health and restricts social development. Today, the incidence and death toll of cancer are still rising rapidly. Epidemiological data show that in 2018, there were 18.1 million new cancer cases worldwide and 9.6 million cancer deaths. In 2015, the number of new cancer cases and deaths in my country were about 4.292 million and 2.814 million, respectively, equivalent to an average of 12,000 new cancer cases and 7,500 cancer deaths every day. It can be seen that cancer has become the main cause of death in China, seriously threatening people's health and life, and causing huge public health problems. According to the order of the number of cases, lung cancer ranks first in the incidence of malignant tumors in my country. The estimated results show that there were about 787,000 new lung cancer cases in my country in 2015, the incidence rate was 57.26/100,000, and the winning rate was 35.96/100,000. The other high-incidence malignant tumors are gastric cancer, colorectal cancer, liver cancer and breast cancer, etc. The top 10 malignant tumors account for about 76.70% of all malignant tumors.

Among them, non-small cell lung cancer (NSCLC) accounts for about 80% to 85% of all lung cancer cases, and about 70% of NSCLC patients have locally advanced or metastatic disease that is not suitable for surgical resection at the time of diagnosis. Moreover, a considerable proportion of patients with early-stage NSCLC who received surgical treatment later developed recurrence or distant metastasis, and progressed to death. About 60% of NSCLC in China are non-squamous NSCLC. The treatment of patients with advanced non-squamous NSCLC is mainly chemotherapy, and targeted therapy can be used for some patients. The epidermal growth factor (EGFR) mutation rate in non-squamous NSCLC in China is about 40%. First-line EGFR inhibitors (gefitinib, erlotinib, or icotinib) are recommended for patients with EGFR-mutated advanced NSCLC. In China, the ALK rearrangement rate is about 3%, and the ALK inhibitor crizotinib is recommended for first-line patients with ALK-rearranged advanced NSCLC. The standard first-line treatment for advanced non-squamous NSCLC in China without EGFR mutation and ALK rearrangement is platinum-containing double-drug chemotherapy, and the survival time after failure of first-line chemotherapy is only 6-9 months. Therefore, the development of new drugs for recurrent or advanced non-squamous NSCLC still needs A long way to go.

In recent years, the research on activating the body's own immune system by inhibiting immune checkpoints and making them play the role of attacking tumor cells has progressed rapidly. The use of immune checkpoint inhibitors (such as anti-PD-1/PD-L1 antibodies) is used to treat NSCLC. Especially the first-line treatment of relapsed or metastatic advanced NSCLC provides a new clinical avenue. For example, in June 2015, pembrolizumab was approved for non-small cell lung cancer. In addition, nivolumab was also approved for marketing in non-small cell lung cancer; in December 2018, sintilimab was launched for the indication of Hodgkin's lymphoma, and immunological clinical trials for various indications are currently underway, including First-line non-squamous NSCLC.

Currently in immunotherapy monotherapy, some biomarkers are considered to predict efficacy, such as tumor mutational burden (TMB), PD-L1 protein expression, and microsatellite instability (MSI). On December 21, 2017, Yarchoan et al. published in the New England Journal a study evaluating the relationship between Tumor Mutation Burden (TMB) and Objective Response Rate (ORR) and found 55% different The difference in objective response rate of tumor types can be explained by TMB, the higher the TMB, the higher the objective response rate of the cancer, this study promotes the application of TMB in immunotherapy (Yarchoan, M., Hopkins, A., & Jaffee, EM (2017). Tumor Mutational Burden and Response Rate to PD-1 Inhibition. The New England Journal of Medicine, 377(25), 2500-2501.).

However, in the treatment of anti-PD-1 antibodies combined with chemotherapy drugs, there is not enough evidence to prove that TMB, PD-L1, MSI-H, etc. can predict the efficacy. For example, in combination therapy, PD-L1 expression is not a good biomarker. In addition, taking TMB as an example, although the efficacy of immunotherapy can be predicted, different platforms are used for TMB detection in clinical experiments. The detection and analysis accuracy is not enough, and it is not suitable for the prediction of combination therapy, so we need to find a better biomaker to predict the efficacy of combination therapy.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide some nucleotide combinations and their applications for overcoming the defects in the prior art.

The present invention solves the above technical problems through the following technical solutions.

One of the technical solutions of the present invention is: a nucleotide combination comprising or consisting of the following genes: CD74, CTSZ, ACP5, MS4A6A, CD83, NPC2, GPNMB, C1QB, HLA-DPA1 and HLA-DMB, or mutations in the above genes; and/or

LAP3, IFNGR1, TBXAS1, SPI1, SNX10, LYZ, HCK, ACP5, CTSH, ASAH1, MAN2B1, CD33, RASSF4, MS4A6A, DOK1, PLEK, NPC2, CD80, CCR2, NCKAP1L, ACP2, P2RX4, TLR2, CASP1, IDH1, N4BP2L1, ITGAX, C1orf162, PLA2G7, ATP6V1B2, FERMT3, TMEM86A, MERTK, SLAMF8, FCER1G, ATP6V0D1, SCIMP, TNFSF13, CTSS, MNDA, TMEM144, CYBB, SMCO4, C2, TPP1, P2RY6, CLEC7A, SMPDL3A, C1QB, OLR1, LRRC25, CD163, FUCA1, CSF1R, ADAP2, TMEM106A, ZNF267, FPR3, CARD9, DAPK1, MPEG1, PSAP, FAM49A, FCGR1B, CSF2RA, SLC29A3, GGTA1P, IFI30, HLA-DPA1, GPX1, NFAM1, HLA-DMB, LYN, SLC43A2 and IGSF6, or mutations in the above genes.

The inventors have unexpectedly found that the above-mentioned genes are specifically highly expressed by macrophages in the tumor microenvironment, wherein the "specifically high expression" refers to the expression of the genes in patients who respond to tumor immune combination therapy. Expression was higher than in non-responding patients. The tumor immune combination can be conventional in the art, such as PD1 immune checkpoint inhibitor combined with chemotherapy.

Preferably, the nucleotide combination comprises or consists of the following genes:

CD74, CTSZ, ACP5, MS4A6A, CD83, NPC2, GPNMB, C1QB, HLA-DPA1, and HLA-DMB, and C1QC, C1QA, RGS1, LGMN, APOC1, APOE, HLA-DQA1, GPR183, SGK1, HSPA1B, HLA-DRA , HSPA1A, DNAJB1, ATF3, HLA-DQB1, HLA-DQA2, CCL3, NR4A2, HLA-DPB1, HLA-DRB1, FOSB, HSPB1, HSPH1, HLA-DMA, CTSBCD9, JUN, LMNA, GADD45B, CCL3L3, GSN, CCL4 , SPP1, RNASE1, ZNF331, IER3, CTSL, ARL4C, PLD3, CREM, MS4A4A, FABP5, CREG1, CTSC, CXCL8, HSPD1, HSP90AA1, ITM2B, HSP90AB1, LIPA, YWHAH, CTSD, HSPE1, PPP1R15A, TMEM176B, CALR, CXCL16 , HERPUD1, PRDX1, CD68, TMEM176A, MARCKS, CAPG, TNFAIP3, DUSP2, PLIN2, FCGR2A, C15orf48, CXXC1, FABP5P1 and SCGB1D1, or mutations in one or more of the above genes; and/or

Described nucleotide combination comprises LAP3, IFNGR1, TBXAS1, SPI1, SNX10, LYZ, HCK, ACP5, CTSH, ASAH1, MAN2B1, CD33, RASSF4, MS4A6A, DOK1, PLEK, NPC2, CD80, CCR2, NCKAP1L, ACP2, P2RX4, TLR2, CASP1, IDH1, N4BP2L1, ITGAX, C1orf162, PLA2G7, ATP6V1B2, FERMT3, TMEM86A, MERTK, SLAMF8, FCER1G, ATP6V0D1, SCIMP, TNFSF13, CTSS, MNDA, TMEM144, CYBB, SMCO4, C2, TPP1, P2RY6, CLEC7A, SMPDL3A, C1QB, OLR1, LRRC25, CD163, FUCA1, CSF1R, ADAP2, TMEM106A, ZNF267, FPR3, CARD9, DAPK1, MPEG1, PSAP, FAM49A, FCGR1B, CSF2RA, SLC29A3, GGTA1P, IFI30, HLA-DPA1, GPX1, NFAM1, HLA-DMB, LYN, SLC43A2, and IGSF6, and ABCA1, ABI1, ACAA1, ACER3, ACSL1, ADAMDEC1, ADORA3, ADPGK, AIF1, AKR1A1, ALDH2, ALDH3B1, AMICA1, AMPD3, ANKRD22, AP1B1, APOC1, AQP9, ARAP1 , ARHGAP18, ARHGAP27, ARHGEF10L, ARPC1B, ARRB2, ATF5, ATG3, ATG7, ATP6AP1, ATP6V0B, ATP6V1F, BACH1, BCKDHA, BCL2A1, BID, BLOC1S1, BLVRA, BLVRB, C10orf54, C15orf48, C19orf38, C11QA, C1QC, C3AR , C9orf72, CAPG, CAPZA2, CAT, CCDC88A, CCR1, CCRL2, CD14, CD1D, CD274, CD300C, CD300E, CD300LB, CD300LF, CD302, CD68, CD86, CECR1, CFD, CFP, CLEC10A, CLEC12A, CLEC4A, CLEC4E, CLEC5A , CMKLR1, CMTM6, CNDP2, CNPY3, CORO7, CPVL, CREG1, CSF3R, CST3, CSTA, CTSA, CTSB, C TSC, CTSD, CXCL10, CXCL16, CXCL9, CXCR2P1, CYB5R4, CYBA, CYP2S1, DBNL, DENND1A, DHRS9, DMXL2, DNAJC5B, DOK3, DPYD, EBI3, EMR2, EPSTI1, ETV6, EVI2A, F13A1, FAM105A, FAM157B, FAM26F, FAM96A, FBP1, FCGR1A, FCGR1C, FCGR2A, FCGR2C, FCGR3B, FCGRT, FCN1, FES, FGL2, FKBP15, FLVCR2, FOLR2, FPR1, FPR2, FTH1, FTL, FUOM, GAA, GABARAP, GALC, GATM, GBP1, GCA, GK, GLA, GLB1, GLRX, GLUL, GM2A, GNA13, GNA15, GPBAR1, GPR34, GPR84, GRN, GSTO1, H2AFY, HCAR2, HCAR3, HEIH, HERPUD1, HIST2H2BF, HK2, HK3, HLA-DMA, HLA-DPB1, HLA-DPB2, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DRB6, HMOX1, HN1, HPS1, HSPA6, HSPA7, HSPBAP1, IFI35, IFIT2, IFNGR2, IGFLR1, IL10RB, IL18, IL1B, IL1RN, IL4I1, IL8, IRF5, IRF7, JAK2, KCNMA1, KCNMB1, KYNU, LAIR1, LGALS2, LGALS9, LGMN, LILRA1, LILRA2, LILRA3, LILRA4, LILRA5, LILRA6, LILRB1, LILRB2, LILRB3, LILRB4, LILRB5, LIPA, LST1, LTA4H, M6PR, MAFB, MAPKAPK3, MARCO, MFSD1, MGAT1, MIF4GD, MIIP, MILR1, MKNK1, MOB1A, MPP1, MRC1, MS4A4A, MS4A7, MSR1, MTHFD2, MTMR14, MX1, MX2, MXD1, MYD88, NAAA, NADK, NAGA, NAGK, NAIP, NCF2, NCF4, NCOA4, NFKBID, NINJ1, NLRC4, NLRP3, NMI, NOD2, NPL, NR1H3, OAS1, OAZ1, OSCAR, P2RY12, P2RY13, P2RY14, PAK1, PCK2, PFKFB3, PGD, PILRA, PLA2G15, PLAUR, PLBD1, PLEKHO1, PLEKHO2, PLIN2, PLXDC2, PPM1M, PPT1, PRAM1, PRKCD, PSME2, PTAFR, PTPRE, PYCARD, RAB20, RAB4B, RAB8A, RASGEF1B, RBM47, RBPJ, REEP4, RELT, RGS10, RGS18, RGS19, RGS2, RHBDF2, RHOG, RILPL2, RIPK2, RNASE6, RNASEK, RNASET2, RNF13, RNF130, RNF144B, RNF149, RTN1, S100A11, S100A8, S100A9, SAMHD1, SAT1, SCAMP2, SCO2, SCPEP1, SDS, SECTM1, SEMA4A, SERPINA1, SERPINB1, SFT2D1, SGPL1, SH3BGRL, SHKBP1, SIGLEC1, SIGLEC14, SIGLEC5, SIGLEC7, SIGLEC9, SIRPA, SIRPB1, SIRPB2, SKAP2, SLC11A1, SLC15A3, SLC16A3, SLC1A3, SLC25A19, SLC2A8A5, SLC25A19 SLC2A9, SLC31A2, SLC46A3, SLC7A7, SLC9A9, SLCO2B1, SNX6, SOD2, SPINT2, SQRDL, SRC, STX11, STXBP2, TALDO1, TFRC, TGFBI, THEMIS2, TIFAB, TLR1, TLR4, TLR5, TLR8, TMEM176A, TMEM176B, TMEM37, One of TMEM51, TNFAIP2, TNFAIP8L2, TNFSF13B, TRAFD1, TREM1, TREM2, TRPM2, TTYH3, TWF2, TYMP, TYROBP, UBE2D1, UBXN11, UNC93B1, VAMP8, VMO1, VSIG4, WDFY2, ZEB2, ZNF385A, CTSL, LOC338758, and LOC729737 or multiple, or mutations of the above genes.

More preferably, the nucleotide combination comprises or consists of the following genes:

C1QC, C1QB, C1QA, RGS1, LGMN, APOC1, APOE, HLA-DQA1, CD74, GPR183, SGK1, HSPA1B, HLA-DRA, GPNMB, HSPA1A, DNAJB1, ATF3, HLA-DQB1, HLA-DQA2, CCL3, NR4A2, HLA-DPB1, HLA-DRB1, HLA-DMB, FOSB, HSPB1, ACP5, HSPH1, HLA-DPA1, HLA-DMA, CTSB CD9, CD83, JUN, LMNA, GADD45B, CCL3L3, GSN, CCL4, SPP1, RNASE1, ZNF331 , IER3, CTSL, ARL4C, PLD3, CREM, NPC2, MS4A4A, FABP5, CREG1, CTSC, CXCL8, HSPD1, HSP90AA1, ITM2B, HSP90AB1, LIPA, CTSZ, YWHAH, CTSD, HSPE1, PPP1R15A, TMEM176B, CALR, CXCL16, HERPUD1 , PRDX1, CD68, TMEM176A, MARCKS, CAPG, TNFAIP3, DUSP2, MS4A6A, PLIN2, FCGR2A, C15orf48, CXXC1, FABP5P1, and SCGB1D1, or mutations in the above genes; and/or,

ABCA1, ABI1, ACAA1, ACER3, ACP2, ACP5, ACSL1, ADAMDEC1, ADAP2, ADORA3, ADPGK, AIF1, AKR1A1, ALDH2, ALDH3B1, AMICA1, AMPD3, ANKRD22, AP1B1, APOC1, AQP9, ARAP1, ARHGAP18, ARHGAP27, ARHGEF10L, ARPC1B, ARRB2, ASAH1, ATF5, ATG3, ATG7, ATP6AP1, ATP6V0B, ATP6V0D1, ATP6V1B2, ATP6V1F, BACH1, BCKDHA, BCL2A1, BID, BLOC1S1, BLVRA, BLVRB, C10orf54, C15orf48, C19orf38, C1orf, QC162, C1QA, C1QBCC1QA C2, C3AR1, C5AR1, C9orf72, CAPG, CAPZA2, CARD9, CASP1, CAT, CCDC88A, CCR1, CCR2, CCRL2, CD14, CD163, CD1D, CD274, CD300C, CD300E, CD300LB, CD300LF, CD302, CD33, CD68, CD80, CD86, CECR1, CFD, CFP, CLEC10A, CLEC12A, CLEC4A, CLEC4E, CLEC5A, CLEC7A, CMKLR1, CMTM6, CNDP2, CNPY3, CORO7, CPVL, CREG1, CSF1R, CSF2RA, CSF3R, CST3, CSTA, CTSA, CTSB, CTSC, CTSD, CTSH, CTSS, CXCL10, CXCL16, CXCL9, CXCR2P1, CYB5R4, CYBA, CYBB, CYP2S1, DAPK1, DBNL, DENND1A, DHRS9, DMXL2, DNAJC5B, DOK1, DOK3, DPYD, EBI3, EMR2, EPSTI1, ETV6, EVI2A, F13A1, FAM105A, FAM157B, FAM26F, FAM49A, FAM96A, FBP1, FCER1G, FCGR1A, FCGR1B, FCGR1C, FCGR2A, FCGR2C, FCGR3B, FCGRT, FCN1, FERMT3, FES, FGL2, FKBP15, FLVCR2, FOLR2, FPR1, FPR2, FPR3, FTH1, FTL, FUCA1, FUOM, GAA, GABARAP, GALC, GATM, GBP1, GCA, GGTA1P, GK, GLA, GLB1, GLRX, GLUL, GM2A, GNA13, GNA15, GPBAR1, GPR34, GPR84, GPX1, GRN, GSTO1, H2AFY, HCAR2, HCAR3, HCK, HEIH, HERPUD1, HIST2H2BF, HK2, HK3, HLA-DMA, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DPB2, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DRB6, HMOX1, HN1, HPS1, HSPA6, HSPA7, HSPBAP1, IDH1, IFI30, IFI35, IFIT2, IFNGR1, IFNGR2, IGFLR1, IGSF6, IL10RB, IL18, IL1B, IL1RN, IL4I1, IL8, IRF5, IRF7, ITGAX, JAK2, KCNMA1, KCNMB1, KYNU, LAIR1, LAP3, LGALS2, LGALS9, LGMN, LILRA1, LILRA2, LILRA3, LILRA4, LILRA5, LILRA6, LILRB1, LILRB2, LILRB3, LILRB4, LILRB5, LIPA, LRRC25, LST1, LTA4H, LYN, LYZ, M6PR, MAFB, MAN2B1, MAPKAPK3, MARCO, MERTK, MFSD1, MGAT1, MIF4GD, MIIP, MILR1, MKNK1, MNDA, MOB1A, MPEG1, MPP1, MRC1, MS4A4A, MS4A6A, MS4A7, MSR1, MTHFD2, MTMR14, MX1, MX2, MXD1, MYD88, N4BP2L1, NAAA, NADK, NAGA, NAGK, NAIP, NCF2, NCF4, NCKAP1L, NCOA4, NFAM1, NFKBID, NINJ1, NLRC4, NLRP3, NMI, NOD2, NPC2, NPL, NR1H3, OAS1, OAZ1, OLR1, OSCAR, P2RX4, P2RY12, P2RY13, P2RY14, P2RY6, PAK1, PCK2, PFKFB3, PGD, PILRA, PLA2G15, PLA2G7, PLAUR, PLBD1, PLEK, PLEKHO1, PLEKHO2, PLIN2, PLXDC2, PPM1M, PPT1, PRAM1, PRKCD, PSAP, PSME2, PTAFR, PTPRE, PYCARD, RAB20, RAB4B, RAB8 A, RASGEF1B, RASSF4, RBM47, RBPJ, REEP4, RELT, RGS10, RGS18, RGS19, RGS2, RHBDF2, RHOG, RILPL2, RIPK2, RNASE6, RNASEK, RNASET2, RNF13, RNF130, RNF144B, RNF149, RTN1, S100A11, S100A8, S100A9, SAMHD1, SAT1, SCAMP2, SCIMP, SCO2, SCPEP1, SDS, SECTM1, SEMA4A, SERPINA1, SERPINB1, SFT2D1, SGPL1, SH3BGRL, SHKBP1, SIGLEC1, SIGLEC14, SIGLEC5, SIGLEC7, SIGLEC9, SIRPA, SIRPB1, SIRPB2, SKAP2, SLAMF8、SLC11A1、SLC15A3、SLC16A3、SLC1A3、SLC25A19、SLC29A3 STXBP2, TALDO1, TBXAS1, TFRC, TGFBI, THEMIS2, TIFAB, TLR1, TLR2, TLR4, TLR5, TLR8, TMEM106A, TMEM144, TMEM176A, TMEM176B, TMEM37, TMEM51, TMEM86A, TNFAIP2, TNFAIP8L2, TNFSF13, TNFSF13B, TPP1, TRAFD1, TREM1, TREM2, TRPM2, TTYH3, TWF2, TYMP, TYROBP, UBE2D1, UBXN11, UNC93B1, VAMP8, VMO1, VSIG4, WDFY2, ZEB2, ZNF267, ZNF385A, CTSL, SMCO4, LOC338758, and LOC729737, or mutations in the above genes.

The second technical solution of the present invention is: the application of the nucleotide combination according to the one of the technical solutions in the preparation of a diagnostic reagent for the prognosis of a patient who has been administered a PD1/PDL1 pathway immune checkpoint inhibitor; Preferably, the patient has been administered a PD1/PDL1 pathway immune checkpoint inhibitor in combination with a chemotherapy drug.

The third technical solution of the present invention is: a kit for evaluating the prognostic effect of a patient after administration of PD1/PDL1 pathway immune checkpoint inhibitor or administration of PD1/PDL1 pathway immune checkpoint inhibitor combined with chemotherapy drugs, the reagent The kit contains reagents for detecting the expression level of the combination of nucleotides as described in one of the technical solutions.

The expression level can be determined by any convenient method, and many suitable techniques are known in the art. For example, suitable techniques include: real-time quantitative PCR (RT-qPCR), digital PCR, microarray analysis, whole transcriptome shotgun sequencing (RNA-SEQ), direct multiplex gene expression analysis, enzyme-linked immunosorbent assay (ELISA), protein Chips, flow cytometry (eg, Flow-FISH of RNA, also known as FlowRNA), mass spectrometry, Western blots, and northern blots.

The reagents may be reagents suitable for determining the expression of the gene in question using any of the techniques described herein, such as RT-qPCR, digital PCR, microarray analysis, whole transcriptome shotgun sequencing, or direct multiplex gene expression analysis. For example, the kit may include primers suitable for determining the expression of the gene in question using, eg, T-qPCR, digital PCR, microarray analysis, whole transcriptome shotgun sequencing or direct multiplex gene expression analysis. The design of suitable primers is routine and within the skill of the artisan. Kits for direct multiplex gene expression analysis may also or alternatively include fluorescent probes for determining the expression of the gene in question.

In addition to detection reagents, the kits may also include RNA extraction kits and/or reagents for reverse transcription of RNA into cDNA.

The kit may also include one or more articles of manufacture and/or reagents for carrying out the methods, such as buffers, and/or a device for obtaining the test sample itself, such as a device for obtaining and/or isolating the sample, and Containers for processing samples (these components are usually sterile).

The fourth technical solution of the present invention is: a system for evaluating the prognostic effect of a patient after administration of a PD1/PDL1 pathway immune checkpoint inhibitor or a PD1/PDL1 pathway immune checkpoint inhibitor combined with a chemotherapeutic drug, the system comprising a tool And a computer, the tool is used to determine the expression level of the nucleotide combination as described in one of the technical solutions.

Preferably, the computer is programmed to determine the prognostic effect based on the patient's gene expression data.

The fifth technical solution of the present invention is: a method for predicting the response of a patient diagnosed with cancer after administration of a PD1/PDL1 pathway immune checkpoint inhibitor or a PD1/PDL1 pathway immune checkpoint inhibitor combined with a chemotherapeutic drug, comprising the following steps :

The nucleotide combination as described in one of the technical solutions is provided, and based on the expression of each gene in the combination, the patients are divided into a gene high expression group and a gene low expression group, and the PFS conditions of the two groups of patients are compared. Statistically different, high or low expression of the combined gene was considered to be associated with a corresponding therapeutic effect.

The nucleotide combination described in the technical solution one, the application described in the technical solution 2, the kit described in the technical solution 3, the system described in the technical solution 4, or the technical solution described in the fifth solution of the present invention In the method:

The PD1/PDL1 pathway immune checkpoint inhibitor is preferably PD1 antigen binding protein or PDL1 antigen binding protein; wherein the PD1 antigen binding protein or PDL1 antigen binding protein is preferably a monoclonal antibody or a bispecific antibody, Multispecific antibodies; for example, nivolumab, pembrolizumab, silimumab, toripalizumab, camrelizumab, tislelizumab, sintilimab; atezolizumab, avelumab, durvalumab, adebrelimab, pacmilimab, envafolimab;

More preferably, the PD1/PDL1 pathway immune checkpoint inhibitor is sintilimab.

The nucleotide combination described in the technical solution one, the application described in the technical solution 2, the kit described in the technical solution 3, the system described in the technical solution 4, or the technical solution described in the fifth solution of the present invention In the method, the chemotherapeutic drugs preferably include pemetrexed, gemcitabine or paclitaxel, and platinum; wherein, the platinum is preferably cisplatin and/or carboplatin.

The patient preferably has a cancer selected from the group consisting of adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, cholangiocarcinoma, colorectal carcinoma, lymphoma, diffuse large B-cell lymphoma , esophageal cancer, glioma, head and neck squamous cell carcinoma, mixed renal carcinoma, acute myeloid leukemia, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic cancer , pheochromocytoma and paraganglioma, prostate adenocarcinoma, sarcoma, skin melanoma, gastric cancer, gastric and esophageal cancer, testicular cancer, thyroid cancer, thymoma, endometrial cancer, uterine sarcoma, uveal melanoma of the group, preferably the cancer is non-small cell lung cancer, more preferably advanced or recurrent non-squamous non-small cell lung cancer.

The sixth technical solution of the present invention is: the application of the nucleotide combination according to one of the technical solutions in screening PD1/PDL1 pathway immune checkpoint inhibitory drugs.

On the basis of conforming to common knowledge in the art, the above preferred conditions can be combined arbitrarily to obtain preferred examples of the present invention.

The reagents and raw materials used in the present invention are all commercially available.

The positive progressive effect of the present invention is:

Using the nucleotide combination of the present invention can carry out effective disease prediction or prognosis diagnosis, especially in the treatment of PD1/PDL1 pathway immune checkpoint inhibitor combined with chemotherapeutic drugs.

Description of drawings

Figure 1 shows the PFS curves of sample patients with RNA sequences in treatment and control groups and all patients in treatment and control groups.

Figure 2A and Figure 2B are the PFS curves of myloid_gene_set in the treatment group and the control group.

Figures 3A and 3B are PFS curves of TAM_gene_set in the treatment group and the control group.

Figure 4A and Figure 4B are the PFS curves of myloid_gene_set_filtered in the treatment group and the control group.

Figures 5A and 5B are PFS curves of TAM_gene_set_filtered in the treatment group and the control group.

detailed description

The present invention is further described below by way of examples, but the present invention is not limited to the scope of the described examples. The experimental methods that do not specify specific conditions in the following examples are selected according to conventional methods and conditions, or according to the product description.

The present invention is a combination of sintilimab combined with chemotherapy drugs pemetrexed and platinum or placebo combined with chemotherapy drugs pemetrexed and platinum in Chinese subjects with advanced or recurrent non-squamous non-small cell lung cancer. A multicenter, randomized, double-blind phase III study of first-line treatment of

In the present invention, placebo combined with pemetrexed, cisplatin or carboplatin was used as a parallel control, and the patients received sintilimab or placebo combined with pemetrexed, cisplatin or carboplatin for 4 cycles, and then received sintilimab or placebo combined with pemetrexed, cisplatin or carboplatin. Limumab or placebo monotherapy plus pemetrexed maintenance therapy, and the maximum duration of sintilimab treatment was 24 months.

The method used in the following examples:

1) Differential analysis method of gene expression: Suppose there are two groups of samples A and B, and the number of samples is a and b respectively. For the mRNA expression of a gene, there are a and b values in groups A and B, respectively. Use the wilcox in the coin package in the statistical software R (https://cran.r-project.org/mirrors.html). The test function compares whether there is a significant difference in the size distribution between a and b values.

2) Biological pathway enrichment analysis method: set the total number of genes N, there are two groups of genes (query group, that is, favorably predictive gene set; reference group, that is, a biological pathway), and the genes are divided into four groups: A group (a genes, both in query group and reference group), B group (b genes, in query group, not in reference group), C group (c genes, not in query group, in reference group) and D group (d genes, not in the query group, not in the reference group). Use fisher.test to test whether the 2*2 matrix distribution formed by a, b, c, d is significant.

Example 1 Multicenter, multicenter, first-line treatment of sintilimab combined with pemetrexed and platinum-based chemotherapy or placebo combined with pemetrexed and platinum-based chemotherapy in subjects with advanced or recurrent non-squamous NSCLC Randomized, double-blind phase III study

In this study, a total of 397 patients were randomly divided into two groups according to 2:1: 1) sintilimab combined with libitas

(Pemetrexed disodium for injection) and platinum, this group is called the treatment group (comb), 266 patients.

2) Placebo combined with libitas

(Pemetrexed disodium for injection) and platinum, this group is called the control group (chem), 131 patients.

Table 1 Mode of administration

Q3W: Dosing every 3 weeks

Carboplatin: AUC5 (calculated using Calvert formula), the dose of carboplatin should not exceed 750mg.

AUC (area under the curve, area under the plasma concentration-time curve)

Calvert formula: total dose (mg) = (target AUC) × (CrCl+25)

The estimated CrCl used in the Calvert formula must not exceed 125ml/min

Maximum carboplatin dose (mg) = target AUC5 (mg*min/ml) x (125+25) = 5 x 150/min = 750mg

Of these, 182 patients had FFPE (Paraffin-Embedded, paraffin-embedded) RNA-seq samples, 119 from the treatment group and 63 from the control group.

The FFPE RNA sequence samples of 182 patients were analyzed, as shown in Figure 1. Although only a subset of patients, FFPE RNA sequences were specifically representative from the perspective of progression free survival (PFS, progression-free survival). In the treatment group or the control group, there was no statistical difference in the PFS curves of either the patients with RNA-seq or the whole sample. This shows that the 182 samples used for analysis can represent the clinical characteristics of all samples with statistical significance.

Example 2 Screening of four groups of highly expressed biomarker gene sets

Based on the FFPE RNA sequences of 182 cases in Example 1, it was found that four groups of genes that are specifically highly expressed on macrophages in the tumor immune microenvironment can be used as biomarkers for tumor patient selection, such as for PD1/PDL1 pathway immunity Prediction of response or companion diagnosis of checkpoint inhibitors in combination with chemotherapeutic agents in subjects with advanced or recurrent non-squamous NSCLC.

1) Obtaining two gene sets of myloid_gene_set and TAM_gene_set:

According to the expression levels of all genes of the patients in the comb group and the PFS data of the patients in the comb group, the surv_cutpoint function in the survminer, a software package in the statistical software R, is used to select a cutoff value for each gene, and the samples with a value greater than this cutoff value are selected. The set is called the high group (the better curative effect group), and the sample set below this cutoff value is called the low group (the poor curative effect group). Compare the difference between the PFS curves of the high group and the low group, and take the low group as the reference group. If the hazard ratio<1&p-value<0.05, the gene is called a favorably predictive gene. If hazard ratio>1&p-value<0.05, the gene is called unfavorably predictive gene, and the obtained favorably predictive gene is analyzed by biological pathway enrichment analysis method to find the favorably predictive gene that is significantly enriched in the macrophage pathway. The two groups of top nucleotide combinations are obtained, namely myloid_gene_set and TAM_gene_set, and the specific gene sets are shown in Table 2. The relationship between myloid_gene_set, TAM_gene_set and PFS was then evaluated, see Figures 2 and 3.

2) Obtaining two gene sets of myloid_gene_set_filtered and TAM_gene_set_filtered:

The first step is to evaluate the relationship between gene and PFS for all genes in myloid_gene_set and TAM_gene_set according to the method in 1), and find the favorably predictive gene;

In the second step, the myloid_gene_set and TAM_gene_set are respectively based on the overall expression(OE) score (see https://github.com/livnatje/ImmuneResistance for the method) and the PFS data of the patients in the comb group, using a software package survminer in the statistical software R The surv_cutpoint in , select a cutoff for both myloid_gene_set and TAM_gene_set respectively. The sample set greater than this cutoff is called the high group (better curative effect group), and the sample set lower than this cutoff is called the low group (poor curative effect group). According to the mRNA expression level of the gene, the genes with significantly high expression in the high group were retained by the gene expression differential analysis method.

The third step is to take the intersection of the genes obtained in the first step and the second step, namely myloid_gene_set_filtered and TAM_gene_set_filtered. The specific gene collection is shown in Table 2. Then evaluate the relationship between myloid_gene_set_filtered and TAM_gene_set_filtered and PFS, see Figure 4 and Figure 5.

3) The drawing process of myloid_gene_set, TAM_gene_set, myloid_gene_set_filtered and TAM_gene_set_filtered and PFS curve

①Using the coxph function in the R software package, fit the equation Surv(time,status)～gene_set

②gene_set: run the equation in step ① in comb (treatment group) and chem (control group) in four gene_sets, myloid_gene_set, TAM_gene_set, myloid_gene_set_filtered and TAM_gene_set_filtered, respectively.

In each gene_set, the comb group includes the high group and the low group, and the chem group includes the high group and the low group. The low group is set as the reference, and the statistical results indicate the statistical relationship between the high/low group and the patient's PFS. The values are shown in Figure 2A, Figure 2B, Figure 3A, Figure 3B, Figure 4A, Figure 4B and Figure 5A, Figure 5B, through these figures, it can be found that the hazard ratio of the high/low group in the treatment group (comb) is < 1 and p-value < 0.05, indicating that In the comb group, the patients in the high group (better curative effect group) had a longer progression-free survival than the patients in the low group (poor curative effect group); while in the control group (chem), the hazard ratio of the high/low group was <1, but the p- value>0.05, indicating that in the control group (chem), there was no difference in PFS between the high and low groups.

In addition, it can be seen from the figure that the PFS of the high group in the comb group is significantly longer than that in the chem group. The prognostic effect was significantly better than the control group. Therefore, these four groups of genes can be used as predictive biomarkers (PB) to select patients with better response to tumor immune combination therapy (especially anti-PD1 antibody combined with chemotherapy drugs).

Table 2 myloid_gene_set, TAM_gene_set, myloid_gene_set_filtered and TAM_gene_set_filtered gene sets

Example 3 Predicted effective patients in different tumors

Make predictions by building a supervised machine learning model. Four prediction models were constructed based on clinical sample RNA sequence data and genes in myloid_gene_set, TAM_gene_set, myloid_gene_set_filtered and TAM_gene_set_filtered gene sets, respectively. According to the relationship between the overall expression (OE) score of each gene set and the patient's PFS, the sample set was divided into high group and low group. Using the autoML function in the H2O package that trains the algorithm, the input contains only myloid_gene_set, TAM_gene_set, myloid_gene_set_filtered, or TAM_gene_set_filtered gene expression data in samples in different tumors (https://portal.gdc.cancer.gov/), to predict these The probability of belonging to the high group in the tumor sample. Among them, the high group represents patients with tumor microenvironment subtypes that may have a better prognosis for combination therapy (anti-PD1 antibody combined with chemotherapy drugs). Take all tumors of TCGA (The Cancer Genome Atlas, Tumor Genome Atlas) as an example.

For each tumor type in TCGA, Table 3 below shows the proportion of samples belonging to the high group, indicating that these four groups of genes are present in a variety of tumor types, indicating that they can play a predictive role in a variety of tumors. Specifically, ACC (Adrenocortical carcinoma, adrenal cortical carcinoma), BLCA (Bladder Urothelial Carcinoma, bladder urothelial carcinoma), BRCA (Breast invasive carcinoma, breast invasive carcinoma), CESC (Cervical squamous cell carcinoma and endocervical adenocarcinoma, cervical squamous cell carcinoma) and adenocarcinoma), CHOL (Cholangiocarcinoma, cholangiocarcinoma), COADREAD (Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma, colorectal cancer), DLBC (Lymphoid Neoplasm Diffuse Large B-cell Lymphoma, diffuse large B-cell lymphoma), ESCA (Esophageal carcinoma, esophageal cancer), GBMLGG (Glioma, glioma), HNSC (Head and Neck squamous cell carcinoma, head and neck squamous cell carcinoma), KIPAN (Pan-kidney cohort (KICH+KIRC+KIRP), mixed kidney carcinoma), LAML (Acute Myeloid Leukemia, acute myeloid leukemia), LIHC (Liver hepatocellular carcinoma, hepatocellular carcinoma), LUAD (Lung adenocarcinoma, lung adenocarcinoma), LUSC (Lung squamous cell carcinoma, lung squamous cell carcinoma), MESO (Mesothelioma, mesothelioma), OV (Ovarian serous cystadenocarcinoma, ovarian serous cystadenocarcinoma), PAAD (Pancreatic adenocarcinoma, pancreatic cancer), PCPG (Pheochromocytoma and Paraganglioma, pheochromocytoma and paraganglioma), PRAD (Prostate adenocarcinoma, prostate adenocarcinoma), SARC (Sarcoma, sarcoma), SKCM (Skin Cutaneous Melanoma, skin melanoma), STAD (Stomach adenocarcinoma, gastric cancer), STES (Stomach and Esophageal carcinoma, stomach and esophagus) cancer), TGCT (Testicular Germ Cell Tumors, testicular cancer), THCA (Thyroid carcinoma, thyroid cancer), THYM (Thymoma, thymoma), UCEC (Uterine Corpus Endometrial Carcinoma, endometrial cancer), UCS (Uterine Carcinosarcoma, uterine cancer) sarcoma) and UVM (Uveal Melanoma, uveal melanoma), it can be found that the proportion of patients with corresponding curative effects can be predicted in the above-mentioned indications, which also shows that the combination therapy can have a therapeutic effect in the above-mentioned indications.

Table 3: Proportion of samples predicted to be likely to respond to anti-PD1 antibodies and chemotherapeutics in TCGA multiple tumor types by myloid_gene_set, TAM_gene_set, myloid_gene_set_filtered, and TAM_gene_set_filtered

Therefore, these four gene sets can not only play a predictive role in PD1/PDL1 pathway immune checkpoint inhibitors combined with chemotherapy drugs, especially in the combination of sintilimab combined with pemetrexed, platinum (cisplatin or carboplatin) in the It plays an obvious predictive role in the treatment of patients with advanced or recurrent non-squamous NSCLC, and can also play a role in predicting efficacy in the above-mentioned indications.

Claims

A nucleotide combination, characterized in that the nucleotide combination comprises or consists of the following genes: CD74, CTSZ, ACP5, MS4A6A, CD83, NPC2, GPNMB, C1QB, HLA-DPA1 and HLA-DMB, or Mutations in the above genes; and/or

LAP3, IFNGR1, TBXAS1, SPI1, SNX10, LYZ, HCK, ACP5, CTSH, ASAH1, MAN2B1, CD33, RASSF4, MS4A6A, DOK1, PLEK, NPC2, CD80, CCR2, NCKAP1L, ACP2, P2RX4, TLR2, CASP1, IDH1, N4BP2L1, ITGAX, C1orf162, PLA2G7, ATP6V1B2, FERMT3, TMEM86A, MERTK, SLAMF8, FCER1G, ATP6V0D1, SCIMP, TNFSF13, CTSS, MNDA, TMEM144, CYBB, SMCO4, C2, TPP1, P2RY6, CLEC7A, SMPDL3A, C1QB, OLR1, LRRC25, CD163, FUCA1, CSF1R, ADAP2, TMEM106A, ZNF267, FPR3, CARD9, DAPK1, MPEG1, PSAP, FAM49A, FCGR1B, CSF2RA, SLC29A3, GGTA1P, IFI30, HLA-DPA1, GPX1, NFAM1, HLA-DMB, LYN, SLC43A2 and IGSF6, or mutations in the above genes.
The nucleotide combination of claim 1, wherein the nucleotide combination comprises or consists of the following genes:

CD74, CTSZ, ACP5, MS4A6A, CD83, NPC2, GPNMB, C1QB, HLA-DPA1, and HLA-DMB, and C1QC, C1QA, RGS1, LGMN, APOC1, APOE, HLA-DQA1, GPR183, SGK1, HSPA1B, HLA-DRA , HSPA1A, DNAJB1, ATF3, HLA-DQB1, HLA-DQA2, CCL3, NR4A2, HLA-DPB1, HLA-DRB1, FOSB, HSPB1, HSPH1, HLA-DMA, CTSB CD9, JUN, LMNA, GADD45B, CCL3L3, GSN, CCL4, SPP1, RNASE1, ZNF331, IER3, CTSL, ARL4C, PLD3, CREM, MS4A4A, FABP5, CREG1, CTSC, CXCL8, HSPD1, HSP90AA1, ITM2B, HSP90AB1, LIPA, YWHAH, CTSD, HSPE1, PPP1R15A, TMEM176B, CALR, One or more of CXCL16, HERPUD1, PRDX1, CD68, TMEM176A, MARCKS, CAPG, TNFAIP3, DUSP2, PLIN2, FCGR2A, C15orf48, CXXC1, FABP5P1 and SCGB1D1, or mutations in the above genes; and/or

Described nucleotide combination comprises LAP3, IFNGR1, TBXAS1, SPI1, SNX10, LYZ, HCK, ACP5, CTSH, ASAH1, MAN2B1, CD33, RASSF4, MS4A6A, DOK1, PLEK, NPC2, CD80, CCR2, NCKAP1L, ACP2, P2RX4, TLR2, CASP1, IDH1, N4BP2L1, ITGAX, C1orf162, PLA2G7, ATP6V1B2, FERMT3, TMEM86A, MERTK, SLAMF8, FCER1G, ATP6V0D1, SCIMP, TNFSF13, CTSS, MNDA, TMEM144, CYBB, SMCO4, C2, TPP1, P2RY6, CLEC7A, SMPDL3A, C1QB, OLR1, LRRC25, CD163, FUCA1, CSF1R, ADAP2, TMEM106A, ZNF267, FPR3, CARD9, DAPK1, MPEG1, PSAP, FAM49A, FCGR1B, CSF2RA, SLC29A3, GGTA1P, IFI30, HLA-DPA1, GPX1, NFAM1, HLA-DMB, LYN, SLC43A2, and IGSF6, and ABCA1, ABI1, ACAA1, ACER3, ACSL1, ADAMDEC1, ADORA3, ADPGK, AIF1, AKR1A1, ALDH2, ALDH3B1, AMICA1, AMPD3, ANKRD22, AP1B1, APOC1, AQP9, ARAP1 , ARHGAP18, ARHGAP27, ARHGEF10L, ARPC1B, ARRB2, ATF5, ATG3, ATG7, ATP6AP1, ATP6V0B, ATP6V1F, BACH1, BCKDHA, BCL2A1, BID, BLOC1S1, BLVRA, BLVRB, C10orf54, C15orf48, C19orf38, C11QA, C1QC, C3AR , C9orf72, CAPG, CAPZA2, CAT, CCDC88A, CCR1, CCRL2, CD14, CD1D, CD274, CD300C, CD300E, CD300LB, CD300LF, CD302, CD68, CD86, CECR1, CFD, CFP, CLEC10A, CLEC12A, CLEC4A, CLEC4E, CLEC5A , CMKLR1, CMTM6, CNDP2, CNPY3, CORO7, CPVL, CREG1, CSF3R, CST3, CSTA, CTSA, CTSB, C TSC, CTSD, CXCL10, CXCL16, CXCL9, CXCR2P1, CYB5R4, CYBA, CYP2S1, DBNL, DENND1A, DHRS9, DMXL2, DNAJC5B, DOK3, DPYD, EBI3, EMR2, EPSTI1, ETV6, EVI2A, F13A1, FAM105A, FAM157B, FAM26F, FAM96A, FBP1, FCGR1A, FCGR1C, FCGR2A, FCGR2C, FCGR3B, FCGRT, FCN1, FES, FGL2, FKBP15, FLVCR2, FOLR2, FPR1, FPR2, FTH1, FTL, FUOM, GAA, GABARAP, GALC, GATM, GBP1, GCA, GK, GLA, GLB1, GLRX, GLUL, GM2A, GNA13, GNA15, GPBAR1, GPR34, GPR84, GRN, GSTO1, H2AFY, HCAR2, HCAR3, HEIH, HERPUD1, HIST2H2BF, HK2, HK3, HLA-DMA, HLA-DPB1, HLA-DPB2, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DRB6, HMOX1, HN1, HPS1, HSPA6, HSPA7, HSPBAP1, IFI35, IFIT2, IFNGR2, IGFLR1, IL10RB, IL18, IL1B, IL1RN, IL4I1, IL8, IRF5, IRF7, JAK2, KCNMA1, KCNMB1, KYNU, LAIR1, LGALS2, LGALS9, LGMN, LILRA1, LILRA2, LILRA3, LILRA4, LILRA5, LILRA6, LILRB1, LILRB2, LILRB3, LILRB4, LILRB5, LIPA, LST1, LTA4H, M6PR, MAFB, MAPKAPK3, MARCO, MFSD1, MGAT1, MIF4GD, MIIP, MILR1, MKNK1, MOB1A, MPP1, MRC1, MS4A4A, MS4A7, MSR1, MTHFD2, MTMR14, MX1, MX2, MXD1, MYD88, NAAA, NADK, NAGA, NAGK, NAIP, NCF2, NCF4, NCOA4, NFKBID, NINJ1, NLRC4, NLRP3, NMI, NOD2, NPL, NR1H3, OAS1, OAZ1, OSCAR, P2RY12, P2RY13, P2RY14, PAK1, PCK2, PFKFB3, PGD, PILRA, PLA2G15, PLAUR, PLBD1, PLEKHO1, PLEKHO2, PLIN2, PLXDC2, PPM1M, PPT1, PRAM1, PRKCD, PSME2, PTAFR, PTPRE, PYCARD, RAB20, RAB4B, RAB8A, RASGEF1B, RBM47, RBPJ, REEP4, RELT, RGS10, RGS18, RGS19, RGS2, RHBDF2, RHOG, RILPL2, RIPK2, RNASE6, RNASEK, RNASET2, RNF13, RNF130, RNF144B, RNF149, RTN1, S100A11, S100A8, S100A9, SAMHD1, SAT1, SCAMP2, SCO2, SCPEP1, SDS, SECTM1, SEMA4A, SERPINA1, SERPINB1, SFT2D1, SGPL1, SH3BGRL, SHKBP1, SIGLEC1, SIGLEC14, SIGLEC5, SIGLEC7, SIGLEC9, SIRPA, SIRPB1, SIRPB2, SKAP2, SLC11A1, SLC15A3, SLC16A3, SLC1A3, SLC25A19, SLC2A8A5, SLC25A19 SLC2A9, SLC31A2, SLC46A3, SLC7A7, SLC9A9, SLCO2B1, SNX6, SOD2, SPINT2, SQRDL, SRC, STX11, STXBP2, TALDO1, TFRC, TGFBI, THEMIS2, TIFAB, TLR1, TLR4, TLR5, TLR8, TMEM176A, TMEM176B, TMEM37, One of TMEM51, TNFAIP2, TNFAIP8L2, TNFSF13B, TRAFD1, TREM1, TREM2, TRPM2, TTYH3, TWF2, TYMP, TYROBP, UBE2D1, UBXN11, UNC93B1, VAMP8, VMO1, VSIG4, WDFY2, ZEB2, ZNF385A, CTSL, LOC338758, and LOC729737 or multiple, or mutations of the above genes.
The nucleotide combination of claim 2, wherein the nucleotide combination comprises or consists of the following genes:

C1QC, C1QB, C1QA, RGS1, LGMN, APOC1, APOE, HLA-DQA1, CD74, GPR183, SGK1, HSPA1B, HLA-DRA, GPNMB, HSPA1A, DNAJB1, ATF3, HLA-DQB1, HLA-DQA2, CCL3, NR4A2, HLA-DPB1, HLA-DRB1, HLA-DMB, FOSB, HSPB1, ACP5, HSPH1, HLA-DPA1, HLA-DMA, CTSB CD9, CD83, JUN, LMNA, GADD45B, CCL3L3, GSN, CCL4, SPP1, RNASE1, ZNF331 , IER3, CTSL, ARL4C, PLD3, CREM, NPC2, MS4A4A, FABP5, CREG1, CTSC, CXCL8, HSPD1, HSP90AA1, ITM2B, HSP90AB1, LIPA, CTSZ, YWHAH, CTSD, HSPE1, PPP1R15A, TMEM176B, CALR, CXCL16, HERPUD1 , PRDX1, CD68, TMEM176A, MARCKS, CAPG, TNFAIP3, DUSP2, MS4A6A, PLIN2, FCGR2A, C15orf48, CXXC1, FABP5P1, and SCGB1D1, or mutations in the above genes; and/or,

ABCA1, ABI1, ACAA1, ACER3, ACP2, ACP5, ACSL1, ADAMDEC1, ADAP2, ADORA3, ADPGK, AIF1, AKR1A1, ALDH2, ALDH3B1, AMICA1, AMPD3, ANKRD22, AP1B1, APOC1, AQP9, ARAP1, ARHGAP18, ARHGAP27, ARHGEF10L, ARPC1B, ARRB2, ASAH1, ATF5, ATG3, ATG7, ATP6AP1, ATP6V0B, ATP6V0D1, ATP6V1B2, ATP6V1F, BACH1, BCKDHA, BCL2A1, BID, BLOC1S1, BLVRA, BLVRB, C10orf54, C15orf48, C19orf38, C1orf, QC162, C1QA, C1QBCC1QA C2, C3AR1, C5AR1, C9orf72, CAPG, CAPZA2, CARD9, CASP1, CAT, CCDC88A, CCR1, CCR2, CCRL2, CD14, CD163, CD1D, CD274, CD300C, CD300E, CD300LB, CD300LF, CD302, CD33, CD68, CD80, CD86, CECR1, CFD, CFP, CLEC10A, CLEC12A, CLEC4A, CLEC4E, CLEC5A, CLEC7A, CMKLR1, CMTM6, CNDP2, CNPY3, CORO7, CPVL, CREG1, CSF1R, CSF2RA, CSF3R, CST3, CSTA, CTSA, CTSB, CTSC, CTSD, CTSH, CTSS, CXCL10, CXCL16, CXCL9, CXCR2P1, CYB5R4, CYBA, CYBB, CYP2S1, DAPK1, DBNL, DENND1A, DHRS9, DMXL2, DNAJC5B, DOK1, DOK3, DPYD, EBI3, EMR2, EPSTI1, ETV6, EVI2A, F13A1, FAM105A, FAM157B, FAM26F, FAM49A, FAM96A, FBP1, FCER1G, FCGR1A, FCGR1B, FCGR1C, FCGR2A, FCGR2C, FCGR3B, FCGRT, FCN1, FERMT3, FES, FGL2, FKBP15, FLVCR2, FOLR2, FPR1, FPR2, FPR3, FTH1, FTL, FUCA1, FUOM, GAA, GABARAP, GALC, GATM, GBP1, GCA, GGTA1P, GK, GLA, GLB1, GLRX, GLUL, GM2A, GNA13, GNA15, GPBAR1, GPR34, GPR84, GPX1, GRN, GSTO1, H2AFY, HCAR2, HCAR3, HCK, HEIH, HERPUD1, HIST2H2BF, HK2, HK3, HLA-DMA, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DPB2, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DRB6, HMOX1, HN1, HPS1, HSPA6, HSPA7, HSPBAP1, IDH1, IFI30, IFI35, IFIT2, IFNGR1, IFNGR2, IGFLR1, IGSF6, IL10RB, IL18, IL1B, IL1RN, IL4I1, IL8, IRF5, IRF7, ITGAX, JAK2, KCNMA1, KCNMB1, KYNU, LAIR1, LAP3, LGALS2, LGALS9, LGMN, LILRA1, LILRA2, LILRA3, LILRA4, LILRA5, LILRA6, LILRB1, LILRB2, LILRB3, LILRB4, LILRB5, LIPA, LRRC25, LST1, LTA4H, LYN, LYZ, M6PR, MAFB, MAN2B1, MAPKAPK3, MARCO, MERTK, MFSD1, MGAT1, MIF4GD, MIIP, MILR1, MKNK1, MNDA, MOB1A, MPEG1, MPP1, MRC1, MS4A4A, MS4A6A, MS4A7, MSR1, MTHFD2, MTMR14, MX1, MX2, MXD1, MYD88, N4BP2L1, NAAA, NADK, NAGA, NAGK, NAIP, NCF2, NCF4, NCKAP1L, NCOA4, NFAM1, NFKBID, NINJ1, NLRC4, NLRP3, NMI, NOD2, NPC2, NPL, NR1H3, OAS1, OAZ1, OLR1, OSCAR, P2RX4, P2RY12, P2RY13, P2RY14, P2RY6, PAK1, PCK2, PFKFB3, PGD, PILRA, PLA2G15, PLA2G7, PLAUR, PLBD1, PLEK, PLEKHO1, PLEKHO2, PLIN2, PLXDC2, PPM1M, PPT1, PRAM1, PRKCD, PSAP, PSME2, PTAFR, PTPRE, PYCARD, RAB20, RAB4B, RAB8 A, RASGEF1B, RASSF4, RBM47, RBPJ, REEP4, RELT, RGS10, RGS18, RGS19, RGS2, RHBDF2, RHOG, RILPL2, RIPK2, RNASE6, RNASEK, RNASET2, RNF13, RNF130, RNF144B, RNF149, RTN1, S100A11, S100A8, S100A9, SAMHD1, SAT1, SCAMP2, SCIMP, SCO2, SCPEP1, SDS, SECTM1, SEMA4A, SERPINA1, SERPINB1, SFT2D1, SGPL1, SH3BGRL, SHKBP1, SIGLEC1, SIGLEC14, SIGLEC5, SIGLEC7, SIGLEC9, SIRPA, SIRPB1, SIRPB2, SKAP2, SLAMF8、SLC11A1、SLC15A3、SLC16A3、SLC1A3、SLC25A19、SLC29A3 STXBP2, TALDO1, TBXAS1, TFRC, TGFBI, THEMIS2, TIFAB, TLR1, TLR2, TLR4, TLR5, TLR8, TMEM106A, TMEM144, TMEM176A, TMEM176B, TMEM37, TMEM51, TMEM86A, TNFAIP2, TNFAIP8L2, TNFSF13, TNFSF13B, TPP1, TRAFD1, TREM1, TREM2, TRPM2, TTYH3, TWF2, TYMP, TYROBP, UBE2D1, UBXN11, UNC93B1, VAMP8, VMO1, VSIG4, WDFY2, ZEB2, ZNF267, ZNF385A, CTSL, SMCO4, LOC338758, and LOC729737, or mutations in the above genes.
The application of the nucleotide combination according to any one of claims 1 to 3 in the preparation of a diagnostic reagent for the prognosis of a patient, the patient has been administered a PD1/PDL1 pathway immune checkpoint inhibitor; preferably, the patient of patients had been administered PD1/PDL1 pathway immune checkpoint inhibitors in combination with chemotherapy drugs.
A kit for evaluating the prognosis of patients after administration of PD1/PDL1 pathway immune checkpoint inhibitor or PD1/PDL1 pathway immune checkpoint inhibitor combined with chemotherapeutic drugs, characterized in that the kit comprises detection The agent for the expression level of the nucleotide combination according to any one of claims 1 to 3.
The kit of claim 5, wherein the kit comprises establishing the nucleosides by RT-qPCR, microarray analysis, digital PCR, whole transcriptome shotgun sequencing or direct multiplex gene expression analysis Reagents for expression levels of acid combinations.
A system for evaluating the prognosis of a patient after administration of a PD1/PDL1 pathway immune checkpoint inhibitor or a PD1/PDL1 pathway immune checkpoint inhibitor combined with a chemotherapeutic drug, the system includes a tool and a computer, the tool is used for Determining the expression level of the nucleotide combination of any one of claims 1-3.
8. The system of claim 7, wherein the computer is programmed to determine the prognostic effect based on the patient's gene expression data.
A method for predicting the response of a patient diagnosed with cancer after administration of a PD1/PDL1 pathway immune checkpoint inhibitor or a PD1/PDL1 pathway immune checkpoint inhibitor combined with a chemotherapy drug, comprising the following steps:

The nucleotide combination according to any one of claims 1 to 3 is provided, and based on the expression amount of each gene in the combination, the patients are respectively divided into a gene high expression group and a gene low expression group, and the two groups of patients are compared. In the case of PFS, if there was a statistical difference, it was considered that the high or low expression of the combined gene was associated with the therapeutic effect.
The nucleotide combination of any one of claims 1 to 3, the use of claim 4, the kit of claim 5 or 6, the system of claim 7 or 8, or the method of claim 9 The method is characterized in that the PD1/PDL1 pathway immune checkpoint inhibitor is PD1 antigen-binding protein or PDL1 antigen-binding protein; the PD1 antigen-binding protein or PDL1 antigen-binding protein is preferably a monoclonal antibody or a dual antibody. Specific antibodies, multispecific antibodies; e.g., nivolumab, pembrolizumab, cilimumab, toripalizumab, camrelizumab, tislelizumab, sindi Limumab; atezolizumab, avelumab, durvalumab, adebrelimab, pacmilimab, envafolimab;

Preferably, the PD1/PDL1 pathway immune checkpoint inhibitor is sintilimab.
The application according to claim 4, the kit according to claim 5 or 6, the system according to claim 7 or 8, or the method according to claim 9, wherein the chemotherapeutic drug comprises pemetrexed Troxet, gemcitabine or paclitaxel, and platinum; wherein, the platinum is preferably cisplatin and/or carboplatin.
The use of claim 4, the kit of claim 5 or 6, the system of claim 7 or 8, or the method of claim 9, wherein the patient has cancer, the The cancer is selected from adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and adenocarcinoma, cholangiocarcinoma, colorectal carcinoma, lymphoma, diffuse large B-cell lymphoma, esophageal carcinoma, glioma, head and neck cancer Squamous cell carcinoma, mixed renal carcinoma, acute myeloid leukemia, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic carcinoma, pheochromocytoma, and paraganglia tumor, prostate adenocarcinoma, sarcoma, skin melanoma, gastric cancer, gastric and esophageal cancer, testicular cancer, thyroid cancer, thymoma, endometrial cancer, uterine sarcoma, uveal melanoma, preferably the cancer Non-small cell lung cancer, more preferably advanced or recurrent non-squamous non-small cell lung cancer.
The application of the nucleotide combination according to any one of claims 1 to 3 in screening PD1/PDL1 pathway immune checkpoint inhibitory drugs.