WO2022053065A1

WO2022053065A1 - Biomarker used for predicting or evaluating lung cancer patients, detection method, and application

Info

Publication number: WO2022053065A1
Application number: PCT/CN2021/118274
Authority: WO
Inventors: 孙继亚; 彭波; 徐伟
Original assignee: 信达生物制药(苏州)有限公司
Priority date: 2020-09-14
Filing date: 2021-09-14
Publication date: 2022-03-17

Abstract

A lung cancer biomarker, a detection method, and an application thereof. The lung cancer biomarker includes the following genes: CASP8 and/or ADAP2, or further includes one or more of ITGB2, MPP1, PI3K3AP1, GBGT1, RAB27A, CD180, RLN3, CD84, DRAM1, HLA-DMB, OTULIN, HELZ, VDR, HLA-DPA1, MX2, FUCA1, MKRN1, and NEK6, said genes being the genes most significantly associated with immune combination therapy PFS in the tumour microenvironment. Use of the lung cancer biomarker can play a good role in predicting or evaluating the prognostic efficacy of treatment with PD1/PDL1 pathway immune checkpoint inhibitors combined with chemotherapy drugs.

Description

Biomarkers, detection methods and applications for predicting or evaluating lung cancer patients

technical field

The invention belongs to the field of biological diagnosis, in particular to biomarkers and uses thereof, and more particularly to a biomarker that can diagnose PD1/PDL1 pathway immune checkpoint inhibitors combined with chemotherapeutic drugs for predicting or evaluating the prognosis of lung cancer patients.

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. Immune checkpoint inhibitors (such as anti-PD-1 antibody or anti-PD-L1 antibody) are used as treatment NSCLC, especially relapsed or metastatic advanced NSCLC, provides a new clinical avenue for first-line treatment. 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 Also 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 or evaluate the efficacy of combination therapy.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide an effective lung cancer biomarker 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 biomarker for predicting or evaluating the prognosis and efficacy of lung cancer patients, and the biomarker is CASP8 and/or ADAP2.

Preferably, the biomarkers also include: ITGB2, MPP1, PIK3AP1, GBGT1, RAB27A, CD180, NEK6, RLN3, CD84, DRAM1, HLA-DMB, OTULIN, HELZ, VDR, HLA-DPA1, MX2, FUCA1 , one or more of MKRN1.

The inventors unexpectedly found that the above-mentioned genes are the genes most significantly associated with PFS of immune combination therapy in the tumor microenvironment, and their expression in patients who responded to immune combination therapy was higher than that in non-responders. The immune combination can be conventional in the art, for example, a PD1 immune checkpoint inhibitor combined with a second therapeutic agent.

The second technical solution of the present invention is: the application of the biomarker according to the one of the technical solutions in the preparation of a diagnostic reagent for predicting or evaluating the prognosis and curative effect of lung cancer patients, and the patients have been administered PD1/PDL1 pathway immune checkpoints Inhibitor; preferably, the patient has been administered a PD1/PDL1 pathway immune checkpoint inhibitor in combination with a second therapeutic agent.

The third technical solution of the present invention is as follows: the biomarkers described in one of the technical solutions have a therapeutic effect after screening and administering PD1 or PDL1 pathway immune checkpoint inhibitor or PD1 or PDL1 pathway immune checkpoint inhibitor combined with a second therapeutic agent application in lung cancer patients.

The fourth technical solution of the present invention is: a kit for predicting or evaluating the prognosis of a patient after administration of a PD1/PDL1 pathway immune checkpoint inhibitor or a combination of a PD1/PDL1 pathway immune checkpoint inhibitor combined with a second therapeutic agent, The kit contains reagents for detecting the expression level of the biomarkers 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 method, 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 fifth technical solution of the present invention is: a system for predicting or evaluating the prognosis and efficacy of a patient after administration of a PD1/PDL1 pathway immune checkpoint inhibitor or a PD1/PDL1 pathway immune checkpoint inhibitor combined with a second therapeutic agent, said The system includes a tool and a computer for determining the expression level of the biomarker as described in one of the technical solutions.

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

The sixth technical solution of the present invention is: a method for predicting or evaluating 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 second therapeutic agent , including the following steps:

The biomarkers described in one of the technical solutions are provided, based on the expression of each gene, and according to whether the gene is higher than the median of the expression level, the patients are divided into a high gene expression group and a low gene expression group, and the comparison The PFS of the two groups of patients, of which the patients in the high expression group had curative effect response.

The biomarker described in one of the technical solutions of the present invention, the application described in the second or third technical solution, the kit described in the fourth technical solution, the system described in the fifth technical solution, or the sixth technical solution. 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 biomarker described in one of the technical solutions of the present invention, the application described in the second or third technical solution, the kit described in the fourth technical solution, the system described in the fifth technical solution, or the sixth technical solution. In the method of method, the second therapeutic agent is a chemotherapeutic drug, and the chemotherapeutic drug preferably includes pemetrexed, gemcitabine or paclitaxel, and platinum; wherein, the platinum is preferably cisplatin and/or carboplatin .

The lung cancer is preferably non-small cell lung cancer, more preferably advanced or recurrent non-squamous non-small cell lung cancer.

The marker CASP8 is used to predict or evaluate the prognostic efficacy of lung cancer patients with high CD8-T cell infiltration in the tumor environment, and the marker ADAP2 is used to predict or evaluate the prognostic efficacy of lung cancer patients with low CD8-T cell infiltration in the tumor environment.

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 positive progressive effect of the present invention is:

The biomarkers of the present invention can be used for effective disease prediction or prognostic diagnosis, especially in the treatment of lung cancer with PD1/PDL1 pathway immune checkpoint inhibitor combined with chemotherapeutic drugs.

definition:

As used herein, the term "efficacy" refers to the effect of a drug or medical method for treating a disease.

The term "prognosis" as used herein refers to a prediction of the likely outcome of a clinical condition or disease. A patient's prognosis is generally determined by evaluating factors or symptoms indicative of a favorable or unfavorable disease process or outcome. As will be understood by those of skill in the art, the term "prognosis" refers to an increased probability that a particular process or outcome will occur; that is, that process or outcome is more likely to exhibit a given disorder than individuals who do not exhibit the disorder occurred in patients. Prognosis can be expressed as the amount of time a patient is expected to survive. Alternatively, prognosis can refer to the likelihood of disease remission or the amount of time the disease is expected to remain in remission. Prognosis can be expressed in different ways; for example, prognosis can be expressed as a percentage probability that a patient will survive after one year, five years, ten years, etc. Optionally, prognosis can be expressed as the average number of months that a patient can be expected to survive due to the condition or disease. A patient's prognosis can be considered a relative performance, with many factors influencing the final outcome. For example, for a patient with a particular condition, the prognosis may appropriately be expressed as the likelihood that the condition will be treatable or curable, or the likelihood that the disease will be in remission, while the prognosis of a patient with a more severe condition may be more appropriately expressed as survival specific possibility of time.

The term "good prognosis" as used herein is a relative term for predicting the likely outcome of a condition or disease. A "good prognosis" in the general sense is a relatively good outcome compared to other possible prognosis associated with a particular condition. Typical examples of good prognosis include better-than-average cure rates, lower propensity to metastasize, longer life expectancy, benign processes other than cancer processes, etc.

The term "prediction" as used herein refers to judging the post-administration response based on certain parameters of the patient prior to administration. For example, in the present invention, the patient is administered a PD1/PDL1 pathway immune checkpoint inhibitor combined with a second therapeutic agent (eg, a chemotherapeutic drug) to determine the therapeutic effect of the patient according to the gene expression level of the patient.

The term "assessing" as used herein refers to judging the effect of treatment in a patient after administration. For example, in the present invention, the therapeutic effect of a patient is determined after administration of a PD1/PDL1 pathway immune checkpoint inhibitor combined with a second therapeutic agent (eg, a chemotherapeutic drug).

Description of drawings

Figure 1 is a comparison of the PFS curves of unanalyzed sample patients (226 cases) and analyzed sample patients (171 cases) in Combo and Chemo.

Figure 2 shows the correlation analysis between the expression levels of 20 genes and the PFS of patients in the Combo and Chemo groups, respectively. The line segment shown is the HR 95% confidence interval.

Figure 3 shows the Combo group was divided into high-infiltration and low-infiltration groups of CD8-T cells according to the median CD8A expression, and the HR and PValue values of the 20 genes in the two groups were investigated for the correlation analysis with PFS. The line segments shown are the HR 95% confidence intervals.

Figure 4(A, D) shows the association analysis of CASP8 and ADAP2 genes and PFS in all samples in Combo group and Chemo group; (B, C) shows that in Combo group, CASP8 is highly infiltrated and low in CD8-T cells, respectively Association analysis with PFS in the infiltration group; (E, F) In the Combo group, the association analysis of ADAP2 with PFS in the CD8-T cell high infiltration and low infiltration groups, respectively.

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 drugs or reagents in the examples can be obtained commercially unless otherwise specified.

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.

Placebo: including 140mmol/L mannitol, 25mmol/L histidine, 20mmol/L sodium citrate dihydrate, 50mmol/L sodium chloride, 0.02mmol/L disodium edetate (EDTA) sodium), 0.2 mg/mL polysorbate 80, pH 6.0.

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 randomized 2:1 into two groups:

1) Sintilimab

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

2) Placebo

(Pemetrexed for injection) and platinum, this group is called the control group (Chemo), 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

A total of 248 patients' FFPE (Paraffin-Embedded, paraffin-embedded) RNA sequence samples (168 from the treatment group and 80 from the control group) were detected, and 77 samples that failed to meet the detection standards were removed, and 171 patients' FFPE ( Paraffin-Embedded, paraffin-embedded) RNA-seq samples (113 from the treatment group and 58 from the control group) were analyzed, as shown in Figure 1, although only in a subset of patients, from progression free survival (PFS, progression-free survival) For perspective evaluation, FFPE RNA sequences are specifically representative. In either Combo or Chemo, there was no statistical difference in the PFS curves between the two groups of patients with RNA-seq samples and the rest of the patients whose RNA-seq samples were not. This shows that the 171 samples used for analysis can represent the clinical characteristics of all samples with statistical significance.

Example 2 Screening of highly expressed biomarker genes

The 171 patients in Example 1 were subjected to correlation analysis between the expression levels of protein-coding genes and PFS before treatment (113 Combo and 58 Chemo).

In the Combo and Chemo groups, respectively, 19,947 protein-coding genes (https://www.ensembl.org/) known in humans were analyzed. For each gene, patients were classified according to the median gene expression level of 171 patients. Divided into high and low groups. Using the coxph function in the R package survival, the differences in PFS between the two groups of patients were analyzed, and genes significantly correlated with PFS (Pvalue < 0.05) were obtained. When Pvalue<0.05, it was considered that a certain gene could play a predictive or prognostic role as a biomarker.

In order to find the biomarker genes that can well predict the treatment effect of Combo, firstly, in the Combo group, the Pvalue of the PFS survival analysis was sorted from small to large, and the genes with Pvalue<0.0001 and HR<1 were selected as significant candidate biomarkers, and we got 20 genes. The Hazard ratio (HR) and PValue values of each gene in the high expression group relative to the low expression group were then compared in the Combo and Chemo groups, respectively (see Figure 2). As shown in the figure, in the Combo group, 20 genes had PValue values < 0.0001, and HR was less than 1. In the Chemo group, except for NEK6 whose PValue was less than 0.05 and the HR was less than 1, the PValues of the remaining 19 genes in the Chemo group were greater than 0.05 and much greater than 0.0001, and the range of the HR confidence interval was significantly expanded. Therefore, the above 20 genes can be used as significant biomarkers for predicting or evaluating the treatment effect of the Combo group, indicating that lung cancer patients, especially advanced or recurrent non-squamous NSCLC patients can benefit from PD1 inhibitor sintilimab combined with chemotherapy drug pemetrexed Trexed, platinum (cisplatin or carboplatin) therapy; and NEK6 can also be used as a biomarker for predicting or evaluating the prognosis of traditional chemotherapy drugs.

Since immune cell infiltration plays an important role in the clinical treatment of PD-1 inhibitors, CD8-T cells are an important indicator for observing immune cell infiltration in cancer tissues, and CD8A is a marker gene in CD8-T cells. The median expression of CD8A gene in 171 patients divided the patients in Combo group into high CD8-T cell infiltration group and CD8-T cell low infiltration group, and further screened 20 genes for better biomarkers. In the high CD8-T cell infiltration group and the CD8-T cell low infiltration group, the associations between these 20 genes and PFS were investigated respectively. As shown in Figure 3, it was found that ADAP2 had the smallest HR value in the CD8-T cell low infiltration group and The PValue value was the smallest and most significant; the HR value of CASP8 in the CD8-T cell high infiltration group was the smallest and the PValue value was the smallest and most significant.

The relationship between high and low expression levels and PFS of ADAP2 and CASP8 genes was investigated in 171 patients (combo: 113, chemo: 58), respectively, as shown in Figure 4 (A and D). In the Combo group, CASP8 In the Chemo group, there was no significant difference in PFS between the high and low expression groups of CASP8 and ADAP2. It shows that CASP8 and ADAP2 can predict or evaluate the treatment effect in the combo group, but cannot predict or evaluate the treatment effect in the Chemo group.

Further, using CD8A in CD8-T cells, the Combo group was divided into a high CD8-T cell infiltration group and a CD8-T cell low infiltration group, and the relationship between the high and low expression of CASP8 and PFS was investigated, as shown in Figure 4(B) As shown in Figure 4(C), in the CD8-T cell high infiltration group, the CASP8 high expression group and the low expression group had significant differences in PFS; as shown in Figure 4(C), in the CD8-T cell low infiltration group, the CASP8 high expression group There was no significant difference between the PFS of the low expression group.

The relationship between ADAP2 high and low expression levels and PFS was then investigated. As shown in Figure 4(E), in the CD8-T cell high infiltration group, there was no significant difference in PFS between the ADAP2 high expression group and the low expression group; Figure 4 As shown in (F), there was a significant difference in PFS between the CD8-T cell low infiltration group, the ADAP2 high expression group and the low expression group.

In addition, before the Combo group was divided into CD8-T cell high infiltration group and CD8-T cell low infiltration group without CD8A, the HR of CASP8 in the Combo group was 0.26 (Fig. 4A). The relationship between the CASP8 high-expression group and the low-expression group and PFS was investigated in the high T cell infiltration group and the CD8-T cell low infiltration group. It can be found that the HR in the CD8-T cell high infiltration group in the Combo group increased to 0.09 (Fig. 4B).

Before the Combo group was divided into CD8-T cell high infiltration group and CD8-T cell low infiltration group without CD8A, the HR of APAD2 in Combo group was 0.24 (Fig. 4D); CD8A was used to divide Combo group into CD8-T cell group After examining the relationship between ADAP2 high expression group and low expression group and PFS in high infiltration group and low CD8-T cell infiltration group, it can be found that HR in Combo group in CD8-T cell low infiltration group increased to 0.1 (Fig. 4F). ).

In conclusion, CASP8 and ADAP2 can be used as predictive biomarkers to select patients with better response to tumor immune combination therapy (especially anti-PD1 antibody combined with chemotherapy drugs).

In particular, after using CD8A to divide the Combo group into a high CD8-T cell infiltration group and a CD8-T cell low infiltration group, CASP8 was more able to predict the treatment effect of the high CD8-T cell infiltration group; ADAP2 was the best predictor of CD8-T cell infiltration. Treatment effect in the low infiltration group.

In summary, the above CASP8, ADAP2, ITGB2, MPP1, PIK3AP1, GBGT1, RAB27A, CD180, RLN3, CD84, DRAM1, HLA-DMB, OTULIN, HELZ, VDR, HLA-DPA1, MX2, FUCA1, MKRN1, NEK6 genes, especially It is CASP8 and/or ADAP2 gene that can predict or evaluate the therapeutic effect of PD1/PDL1 pathway immune checkpoint inhibitors combined with chemotherapy drugs in the treatment of lung cancer patients, especially sintilimab combined with pemetrexed, platinum (Cisplatin or carboplatin) plays a significant role in predicting or evaluating the therapeutic effect in the treatment of patients with advanced or recurrent non-squamous NSCLC, that is, when the expression of these genes is higher than the median expression level, it means that these patients will be treated with subsequent treatment. PD1/PDL1 pathway immune checkpoint inhibitors combined with chemotherapy drugs, especially sintilimab combined with pemetrexed, platinum (cisplatin or carboplatin), can have a good curative effect.

Claims

A biomarker for predicting or evaluating the prognosis and efficacy of lung cancer patients, the biomarker is CASP8 and/or ADAP2.
The biomarker of claim 1, further comprising ITGB2, MPP1, PIK3AP1, GBGT1, RAB27A, CD180, RLN3, CD84, DRAM1, HLA-DMB, OTULIN, HELZ, VDR, HLA-DPA1, One or more of MX2, FUCA1, MKRN1, NEK6.
The application of the biomarker according to any one of claims 1 to 2 in the preparation of a reagent for predicting or evaluating the prognosis of a lung cancer patient, wherein the patient is administered a PD1/PDL1 pathway immune checkpoint inhibitor or PD1/ PDL1 pathway immune checkpoint inhibitor combined with a second therapeutic agent.
The biomarker according to any one of claims 1-2 in screening lung cancer patients with therapeutic effect after administration of PD1/PDL1 pathway immune checkpoint inhibitor or PD1/PDL1 pathway immune checkpoint inhibitor combined with a second therapeutic agent application.
A kit for predicting or evaluating the prognostic effect of PD1/PDL1 pathway immune checkpoint inhibitor or PD1/PDL1 pathway immune checkpoint inhibitor combined with a second therapeutic agent in lung cancer patients, characterized in that the kit A reagent for detecting the expression level of the biomarker of any one of claims 1-2 is included.
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 predicting or evaluating the prognostic effect of administration of a PD1/PDL1 pathway immune checkpoint inhibitor or a PD1/PDL1 pathway immune checkpoint inhibitor combined with a second therapeutic agent in a lung cancer patient, the system comprising a tool and a computer, the system comprising: The tool is used to determine the expression level of the biomarker of any one of claims 1-2.
8. The system of claim 7, wherein the computer is programmed to predict patient treatment outcomes based on patient gene expression data.
A method for predicting or evaluating the prognostic effect of administration of a PD1/PDL1 pathway immune checkpoint inhibitor or a PD1/PDL1 pathway immune checkpoint inhibitor combined with a second therapeutic agent in a lung cancer patient, comprising the following steps: providing the method according to any one of claims 1-2 The biomarkers described in item, based on the expression of each gene, according to whether the gene is higher than the median expression level, the patients are divided into high gene expression group and low gene expression group, and the PFS of the two groups of patients is compared. situation, in which patients in the high expression group were curatively responsive.
The biomarker of any one of claims 1-2, the use of claim 3 or 4, the kit of claim 5 or 6, the system of claim 7 or 8, or 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, Bispecific or multispecific antibodies; eg, nivolumab, pembrolizumab, cilimumab, toripalizumab, camrelizumab, tislelizumab, Dilimumab; atezolizumab, avelumab, durvalumab, adebrelimab, pacmilimab, envafolimab; preferably, the PD1/PDL1 pathway immune checkpoint inhibitor is sintilimab anti.
The use of claim 3 or 4, the kit of claim 5 or 6, the system of claim 7 or 8, or the method of claim 9, wherein the second therapeutic agent It is a chemotherapy drug, including pemetrexed, gemcitabine or paclitaxel, and platinum; wherein, the platinum is preferably cisplatin and/or carboplatin.
The biomarker of any one of claims 1-2, the use of claim 3 or 4, the kit of claim 5 or 6, the system of claim 7 or 8, or claim 9 The method is characterized in that the lung cancer is non-small cell lung cancer, preferably advanced or recurrent non-squamous non-small cell lung cancer.
The biomarker of any one of claims 1-2, the use of claim 3 or 4, the kit of claim 5 or 6, the system of claim 7 or 8, or claim 9 The method is characterized in that the marker CASP8 is used to predict or evaluate the prognosis and efficacy of lung cancer patients with high CD8-T cell infiltration in the tumor environment, and the marker ADAP2 is used to predict or evaluate the CD8-T cells in the tumor environment. The prognosis of patients with low-invasive lung cancer.