CN113106157A

CN113106157A - Kit for prognosis survival prediction of tumor immunotherapy and application thereof

Info

Publication number: CN113106157A
Application number: CN202110564926.3A
Authority: CN
Inventors: 赵滨
Original assignee: Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University
Current assignee: Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University
Priority date: 2021-05-24
Filing date: 2021-05-24
Publication date: 2021-07-13
Anticipated expiration: 2041-05-24
Also published as: CN113106157B9; CN113106157B

Abstract

The invention provides a kit for predicting prognosis survival period of tumor immunotherapy, which comprises a reagent for detecting gene mutation of a gene, wherein the gene is SETD2 gene. The kit comprises a sequencing module and a comparison module, and can be used for respectively extracting and sequencing DNA of a sample to be tested, and comparing a sequencing result with a reference gene, so as to obtain mutation information of a target SETD2 gene. The kit is used for detecting the mutation status of the SETD2 gene of a tumor patient, can realize the advanced screening of the patient, can provide a basis for the formulation of an individual treatment scheme, has the advantage of low cost while keeping the high-efficiency prediction of whether the patient is suitable for immunotherapy compared with hundreds of genome markers or TMB methods in the prior art, and can be widely applied to various tumor diseases.

Description

Kit for prognosis survival prediction of tumor immunotherapy and application thereof

Technical Field

The invention relates to the technical field of biology, in particular to a kit for predicting prognosis survival time of tumor immunotherapy.

Background

In recent years, tumor immunotherapy has led to a major breakthrough in the field of tumor therapy, and in particular, immune checkpoint inhibitors represented by PD-1/PD-L1 are the successful fields and hot spots of research of current tumor immunotherapy, and the therapeutic effects of related immunotherapy drugs such as anti-PD-L1 (avimab, alemtuzumab, delavolumab), anti-PD-1 (nivolumab, pemirolimumab) and anti-CTLA-4 (ipilimumab, tremelimumab) have been clinically confirmed: it can effectively improve the survival rate of tumor patients. However, the overall effective rate of immunotherapy is limited to 10-40%, the proportion of patients who benefit from treatment is low, and the treatment cost is high, so that a specific biomarker needs to be searched to screen out tumor patients who can receive immunotherapy, excessive treatment is avoided, and the curative effect and application of immunotherapy are greatly influenced due to the lack of efficient biomarkers at present.

In the prior art, the Tumor Mutation load (TMB) has a certain correlation with the curative effect of the immune checkpoint inhibitor, and can be used as a biomarker for characterizing the curative effect of the immune checkpoint inhibitor. Wherein, TMB refers to the total number of detected somatic non-synonymous gene mutations in each million bases, and comprises coding errors, base substitution, gene insertion or deletion errors and the like. Somatic mutations are finally expressed at the protein level through transcription and translation, and the mutations generate new antigens such as new protein segments or polypeptide segments, which are recognized as non-self antigens by the autoimmune system to activate T cells and cause immune response. Thus, it is theorized that the higher the TMB, the more tumor associated neoantigen that is produced, the more likely it is to stimulate an immune response, and the better the therapeutic effect will be with immune checkpoint inhibitors. Specific studies have shown that: TMB is in positive correlation with the clinical curative effect of the immune checkpoint inhibitor in malignant melanoma, lung cancer, colorectal cancer and other tumors. However, the calculation of TMB involves negative mutations, the efficacy of predicting efficacy of therapeutic efficacy of immune checkpoint inhibitors is limited, only top 20% of patients are predicted to have a good prognosis (compared to bottom 80%), and the high and low cutoff values for TMB are not uniform among different tumors. These drawbacks greatly affect the widespread use of TMB as a biomarker in immunotherapy.

In addition, the gold standard for TMB detection, Whole Exon Sequencing (WES), is expensive, and currently targeted gene sequencing is mainly used, although studies show that TMB measured and calculated based on targeted gene sequencing of large gene combinations is significantly related to TMB measured and calculated based on WES, but sequencing combinations of different sizes may affect TMB accuracy. At present, the gene sequencing combination for detecting the TMB contains more and more genes and is higher and higher in cost, and meanwhile, the calculated TMB has differences, so that the uniform cutoff value is more difficult to realize for guiding clinical practice.

Based on the above disadvantages, chinese patent CN110229894B discloses a combination of 55 genes, and a Tumor Mutation Score (TMS) method is developed by the combination of genes for predicting the prognosis of patients receiving immune checkpoint inhibition therapy, which has higher prediction efficiency in predicting the overall survival after immune therapy compared to TMB, has a uniform cutoff value, and is easy to be popularized and applied in clinical practice. However, the gene combinations of the present invention involve a large number of genes, and the detection cost is increased in practice.

Further, chinese patent CN110423820A discloses a marker for predicting sensitivity of bladder cancer chemotherapy and its application, and provides a marker for predicting sensitivity of bladder cancer chemotherapy, which is a combination of SOCS1 and CYLD genes, and compared with the above patent CN110229894B, the number of involved genes is greatly reduced, but the invention only aims at bladder cancer chemotherapy, and does not relate to anti-tumor treatment of late-stage bladder cancer.

Further, chinese patent CN109879956A discloses a tumor immune biomarker, which is PGLYRP2 gene or its expressed protein, and also significantly reduces the number of genes/proteins for predicting the effect of immunotherapy, but the present invention is only directed to immunotherapy of liver cancer tumor, and cannot be generalized to other cancers.

Furthermore, markers in current research relating to whether immunotherapy is effective also include: PD-L1 expression, immune cell infiltration, DNA mismatch repair (mismatch repair) deficiency. However, in view of the "false progress" and "hyper-progress" phenomena characteristic of immunotherapy, the criteria for using survival to judge the efficacy of tumor therapy is undoubtedly a better approach.

In addition, no research has been made to disclose or suggest the use of the SETD2 mutation as a marker for predicting the prognostic survival of tumor immunotherapy, and although the SETD2 gene is also mentioned in the combination comprising 55 genes disclosed in the above patent CN110229894B, the invention does not disclose the functional localization of SETD2 in the 55 genes, nor does it relate to the specific mode of action of SETD2, and the number of genes involved in the patent is large, which increases the cost of detection and further increases the economic burden of patients.

At present, the lack of efficient and low-cost biomarkers remains a major problem hindering the application of tumor immunotherapy.

Disclosure of Invention

The invention provides a kit for predicting prognosis survival time of tumor immunotherapy, aiming at the defects in the prior art, and the kit can be used for predicting prognosis of a tumor patient in advance so as to help a doctor to select a treatment scheme more accurately, thereby improving the objective effective rate of the tumor immunotherapy and reducing the treatment cost of the patient.

In a first aspect, a kit for prognosis survival prediction of tumor immunotherapy is provided, the kit comprising a reagent for detecting a gene mutation of a gene, wherein the gene is SETD2 gene.

In other preferred embodiments, the kit comprises:

the sequencing module is used for extracting and sequencing the DNA of the sample to be tested to obtain a sequencing result;

the comparison module is used for comparing the sequencing result with the reference gene to obtain mutation information; the reference gene is human SETD2 gene.

In other optimized technical solutions, the comparison module is configured to obtain mutation information including at least non-synonymous mutations, or further including synonymous mutations.

In a second aspect, the application of a reagent for detecting gene mutation of a gene in preparing a prognosis survival prediction kit for tumor immunotherapy is provided, wherein the gene is SETD2 gene.

In other preferred embodiments, the tumor types include melanoma, renal cancer, lung cancer, and digestive system tumors.

The invention has the beneficial effects that:

in a first aspect, the kit for predicting prognostic survival provided by the present invention can provide a basis for the formulation of a personalized treatment regimen by detecting the mutation status of the marker SETD2 gene and screening patients. Compared with the genome marker or TMB method which is up to dozens and hundreds in the prior art, the method has the advantages of low cost and wide application in various tumor diseases while keeping high-efficiency prediction on whether a patient is suitable for immunotherapy.

In a second aspect, the detection kit provided by the invention is used for detecting the mutation condition of the SETD2 gene, so that the DNA sample of a tumor patient can be conveniently detected, and an accurate treatment scheme can be timely formulated for the patient.

Drawings

In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description of the embodiments will be briefly described below. The drawings in the following description are only some of the embodiments described in the present invention, and it is obvious to those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.

FIG. 1 is the mutation position, distribution and mutation subtype of the SETD2 gene mutation in the cancer genomic map (TCGA) in example 1;

FIG. 2 is the TMB of the TCGA patient of example 1 with the SETD2 gene mutated and unmutated;

FIG. 3 is the relationship between the SETD2 mutation frequency and TMB median in patients with a mutation in the SETD2 gene among TCGA patients in example 1;

FIG. 4 is a survival curve for patients with the SETD2 gene mutation versus unmutated patient in example 1;

FIG. 5 is a survival curve for patients with TMB less than median and greater than or equal to median in example 1;

FIG. 6 is a survival curve for patients with SETD2 gene mutation versus non-mutated lung cancer in example 3;

FIG. 7 is a survival curve for patients with TMB less than median and greater than or equal to median in lung cancer patients from example 3;

FIG. 8 is a survival curve for patients with melanoma according to example 4 with mutations and unmutated SETD2 gene;

FIG. 9 is the survival curves for patients with melanoma in example 4 with TMB less than median and greater than or equal to median;

FIG. 10 is a survival curve for patients with tumors of the digestive system of example 5 with SETD2 gene mutation versus unmutated patients;

FIG. 11 is the survival curves for patients with digestive system tumors from example 5 with TMB less than median and greater than or equal to median;

fig. 12 is a two-dimensional code address storing the color original of fig. 1 to 11.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

First, a description is given of english abbreviations referred to in the present application:

1, SETD 2: histone methyltransferase

TCGA: tumor genome map

TMB: tumor mutational burden

4, MSI: instability of microsatellite

ICI: immune checkpoint inhibitors

PD-1: targeted apoptosis protein 1

PD-L1: programmed cell death ligand 1

CTLA-4: cytotoxic T lymphocyte-associated antigen 4

And 9, OS: overall survival rate

MSISensor: tool for deducing MSI state

HR: ratio of risks

12, CI: a confidence interval.

Next, a description is given of the data sources used in the analysis in the following examples:

2734 tumor patients treated with an immunosuppressive agent used in the examples were from 8 studies, and the 8 studies are disclosed in 9 journal articles, the information of the 9 references being as follows:

1.Van Allen EM,Miao D,Schilling B,et al.Genomic correlates of response to CTLA-4blockade in metastatic melanoma.Science.2015；350(6257):207-211.

2.Hugo W,Zaretsky JM,Sun L,et al.Genomic and Transcriptomic Features of Response to Anti-PD-1Therapy in Metastatic Melanoma.Cell.2016；165(1):35-44.

3.Riaz N,Havel JJ,Makarov V,et al.Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab.Cell.2017；171(4):934-949.e916.

4.Miao D,Margolis CA.Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma.Science.2018；359(6377):801-806.

5.Miao D,Margolis CA,Vokes NI,et al.Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors.Nature genetics.2018；50(9):1271-1281.

6.Samstein RM,Lee C-H,Shoushtari AN,et al.Tumor mutational load predicts survival after immunotherapy across multiple cancer types.Nature Genetics.2019；51(2):202-206.

7.Fehrenbacher L,Spira A,Ballinger M,et al.Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer(POPLAR):a multicentre,open-label,phase 2randomised controlled trial.Lancet(London,England).2016；387(10030):1837-1846.

8.Gandara DR,Paul SM,Kowanetz M,et al.Blood-based tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab.Nature medicine.2018；24(9):1441-1448.

9.Rittmeyer A,Barlesi F,Waterkamp D,et al.Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer(OAK):a phase 3,open-label,multicentre randomised controlled trial.Lancet(London,England).2017；389(10066):255-265.

these documents are obtained by screening the PubMed database after the inventor carries out system search on the database in 11 months in 2020, and the screened documents meet the following criteria: the clinical trial included more than 30 adult solid tumor patients; (2) and (3) intervention: at least one cohort in the trial received treatment for ICI regardless of dose and duration of treatment; (3) as a result: information about the state of the SETD2 mutation and the OS is reported. When the same study is published in multiple publications, the most complete and/or up-to-date literature is selected and the clinical data from three melanoma studies, two lung cancer trials, one renal cancer dataset and two cohort studies of cancer patient samples including multiple tumors are finally screened, with the data involved in these eight studies being interspersed among the 9 above-mentioned literatures, with specific correspondence as shown in table 1:

TABLE 1 sources of immunotherapeutic tumor patients

Corresponding documents	Name of drug	Tumor type
			Ref
1	Yipima (easily priama)	Melanoma (MEA)
			Ref 2	Pabolilizumab/nivolumab	Melanoma (MEA)
Ref 3	Nivolumab	Melanoma (MEA)
			Ref 4	Nivolumab	Renal cancer
Ref
	5	Multiple medicines	Multiple tumors
Ref 6				Multiple medicines	Multiple tumors
	Ref 7,8	Abiralizumab	Lung cancer
Ref 8,9				Abiralizumab	Lung cancer

Example 1:

in this example, the characteristics of SETD2 somatic mutation in the cohort of TCGA pan-carcinomas were first investigated, and specifically, statistical analysis was performed based on the data of 10427 tumor patients in the cohort of TCGA pan-carcinomas, resulting in the following results:

there were 451 (4.33%) carrying the SETD2 mutation. And the SETD2 mutation occurred in a small fraction of most tumor types, and there was a significant difference in mutation frequency among the various tumors (P < 0.001);

a total of 569 SETD2 mutations were identified, of which 375 (65.9%) were missense mutations, 193 (33.9%) were truncation mutations, and 1 (0.2%) was an integer mutation, as shown in figure 1, wherein the X-axis represents the sequence number of SETD2 amino acids; the Y axis represents the number of gene mutations corresponding to each amino acid mutation site in the cancer genomic map (TCGA), the light gray box is the SET domain (1561-1667), the medium gray box is the WW domain (2391-2420), the dark gray box is the SRI domain (2466-2558), and the light gray point represents missense mutations; black dots represent truncation mutations; the medium gray dots represent integer mutations.

For clarity, reference may be made to the color artwork of FIG. 1 stored in FIG. 12, wherein the green boxes are SET domains (1561-1667); the red box is WW domain (2391-2420); blue boxes are SRI domains (2466-2558); green dots represent missense mutations; black dots represent truncation mutations; orange dots represent integer mutations, and these mutations occur in a dispersed manner throughout the sequence and 3D protein structure.

Further analysis as shown in figure 2, patients with the SETD2 mutation had significantly higher TMB than patients with SETD2 non-mutated.

The intensive analysis of the ten most common tumorigenic sites including prostate, ovary, head and neck, sarcoma, breast, brain, liver, stomach, colorectal, skin, bladder, lung, cervix, kidney, etc. was continued as shown in fig. 3, in which the mutation frequency of SETD2 was linearly related to the median value of TMB (correlation coefficient of 0.62).

It can be seen that despite the deletion of a large number of negative and unnecessary mutant genes, SETD2 has significant correlation with TMB, and the correlation is the basis for the SETD2 gene to be used as a predictive marker.

Next, 2734 tumor patients in table 1 were subjected to SETD2 mutation detection and survival analysis, wherein the mutation results are shown in table 2:

TABLE 2 SETD mutation results in tumor patients

Survival analysis was performed on this 2734 tumor patients using MedCalc 18.2.1 (MedCalc software, belgium).

Specifically, the Kaplan-Meier method was used for survival analysis and the log rank test was used for comparison. Patients were censored for the last date of examination without death. The risk ratio (HR) was calculated by the Cox proportional hazards model and 95% CI was reported. Median total survival time and 95% CI were presented where necessary. The Spearman's ρ correlation coefficient was calculated. The relationship between various clinical features and the SETD2 mutation was evaluated by the χ 2 test, Student's t test or Fisher's exact test, as the case may be. The results are shown in FIG. 4, which is a survival curve of the patients with SETD2 gene mutation and those without mutation in 2734 patients, wherein the x-axis of the survival curve represents the overall survival time and the y-axis represents the overall survival rate. The more open the curves in the different groups represent the higher the predictive potency of their survival markers. In fig. 4, the survival curve for patients with the SETD2 mutation was significantly higher than that of patients with the SETD2 non-mutated (P < 0.0001). More importantly, the risk ratio (HR) is an important indicator in survival analysis: HR values greater than 1, representing a correlation with poor prognosis; HR values less than 1 are indicative of good prognosis, and with HR less than 1, the smaller the value, the higher the predictive potency. The HR value for the SETD2 mutant patient was 0.55 (95% confidence interval: 0.46-0.65) compared to the patient with the non-mutant SETD 2. Further, this significant correlation between the SETD2 mutation and better OS remains stable after adjusting factors including age, sex, cancer type, treatment strategy and TMB.

As can be seen from FIG. 5, patients with high TMB also had longer survival times when analyzed using the TMB method compared to patients with low TMB, however, patients with high TMB had HR values of 0.81 (95% confidence interval: 0.72-0.91).

It can be seen that the use of mutations in the SETD2 gene to predict prognostic survival is advantageous over the use of the TMB method: the SETD2 has a lower HR value (SETD 20.55 vs TMB 0.81) and a more significant P value (SETD 2P <0.0001 vs TMB P ═ 0.0004) in survival analysis, and the SETD2 gene as a predictive marker has the advantage of low cost while maintaining high efficiency in predicting whether a patient is suitable for immunotherapy as compared to several tens or hundreds of genomic markers or TMB methods in the prior art, and can be widely applied to various tumor diseases.

From a theoretical point of view, the SETD2 gene can be used as a marker for prognosis survival prediction of tumor immunotherapy and has close relation with the role of SETD2 in maintaining genome integrity and stability. The SETD2 mutant patients had higher TMB and microsatellite instability (MSI) compared to the non-mutant patients with SETD 2. Where MSI status can be deduced using MSISensor, the MSISensor score (0.12; 0.01-0.84) for patients with SETD2 mutations was significantly higher than the MSISensor score (0.05, 0.00-0.31; P <0.0001) for patients without SETD2 mutations.

In addition, transcription of most genes was correlated with the upregulation of immunoreactive expression in patients with the SETD2 mutant tumor. And this example also demonstrates that the SETD2 mutation is associated with good clinical outcome of immunotherapy by further investigating cancer patients receiving Immune Checkpoint Inhibitor (ICI) therapy.

Example 2

The embodiment provides a kit for predicting prognosis survival of tumor immunotherapy, which comprises a reagent for detecting the mutation condition of SETD2 gene, and specifically comprises the following components:

For specific setting parameters of two functional modules in the kit, the following scheme can be referred to:

pretreatment: extracting a DNA sample of a focus of a tumor patient, and performing quality control inspection on the DNA sample;

wherein the collected tumor sample is obtained before receiving immune checkpoint inhibitor treatment, and comprises primary focus, metastatic lymph node and distant metastasis specimen, and the tumor cells in the malignant effusion can also be grouped if enough target gene detection is available. Providing 5-micron anti-drop slices for the wax stone specimen, wherein the surgical specimen is not less than 5 slices, and the puncture specimen is not less than 10 slices; for formalin-fixed specimens, the surgical specimens are not less than 50mg, and the puncture specimens are not less than 1 needle; malignant pleural effusion/cerebrospinal fluid/pericardial effusion and the like are collected by using a STREK tube, and the volume is not less than 8 ml. If the peripheral blood sample matched with qi needs to be collected by a STREK tube, the volume is not less than 2 ml. And carrying out post-treatment within 30min after the blood specimen is separated from the body.

The specific content of the quality control inspection of the DNA sample is as follows: the DNA sample needs to satisfy the conditions that the nucleic acid quality is more than or equal to 300ng, the OD260/280 ratio is between 1.8 and 2.2, the concentration is more than or equal to 10 ng/mu L, and the volume is more than or equal to 10 mu L; the agarose gel electrophoresis DNA band is clear without degradation and RNA and protein pollution.

A sequencing module: performing SETD2 gene sequencing on the DNA sample;

a comparison module: after sequencing, the BCL files were converted to FastQ format files by BCL2FASTQ, quality control was performed by FastQC, and reference gene (version: hg19 or b37) alignment was performed using BWA software. The GATK is used for searching the difference between the sequencing data of the sample and the reference gene, the difference points are listed, Annovar mutation is used for functional annotation, the mutant gene list of the sample is obtained, and the mutation result of the SETD2 gene is analyzed. Wherein, the SETD2 gene mutation result comprises nonsynonymous mutation, and the prognosis of the patient with the SETD gene having nonsynonymous mutation has longer survival time.

The prognosis survival prediction method for tumor immunotherapy specifically comprises the steps of carrying out SETD2 gene sequencing on a DNA sample of a tumor patient, comparing the sequencing with a reference gene to obtain a mutation result of a SETD2 gene, and analyzing the mutation result of a SETD2 gene to predict the prognosis survival of the patient, wherein compared with a method for indirectly detecting the expression condition of the SETD2 gene, the method for predicting the prognosis survival of the patient comprises the following steps: the direct sequencing method has stronger reliability, and the second time is that: because the expression quantity difference of the SETD2 gene in different tumors is large, a uniform threshold value is difficult to determine, and the direct sequencing method can be widely applied to various cancer species and is convenient to popularize and apply in various disease species.

Example 3

In this example, survival analysis was performed on lung cancer patients, and all analysis methods and tools were the same as those in examples 1 and 2, and the results are shown in fig. 6 and 7.

Referring first to fig. 6, the survival curve for patients with the SETD2 mutation was significantly higher than that of patients with the SETD2 non-mutated (P < 0.0001). And the HR value of the SETD2 mutant patient was 0.42 (95% confidence interval: 0.31-0.60) compared to the patient without the mutation of SETD 2.

As can be seen in FIG. 7, patients with high TMB also had longer survival times when analyzed using the TMB method compared to patients with low TMB, however, the HR values for patients with high TMB were 0.66 (95% confidence interval: 0.54-0.81).

Example 4

In this example, survival analysis was performed on melanoma patients, and all analysis methods and tools were the same as those in examples 1 and 2, and the results are shown in fig. 8 and 9.

Referring first to fig. 8, the survival curve for patients with the SETD2 mutation was significantly higher than that of patients with the SETD2 non-mutated (P < 0.0001). And the HR value of the SETD2 mutant patient was 0.50 (95% confidence interval: 0.38-0.63) compared to the patient without the mutation of SETD 2.

As can be seen in FIG. 9, patients with high TMB also had longer survival times when analyzed using the TMB method compared to patients with low TMB, however, patients with high TMB had HR values of 0.73 (95% confidence interval: 0.58-0.90).

Example 5

In this example, survival analysis was performed on patients with digestive system tumor, and all analysis methods and tools were the same as those in examples 1 and 2, and the results are shown in FIGS. 10 and 11.

Referring first to fig. 10, the survival curve for patients with the SETD2 mutation was significantly higher than that of patients with the SETD2 non-mutated (P < 0.004). And the HR value of the SETD2 mutant patient was 0.32 (95% confidence interval: 0.18-0.55) compared to the patient without the mutation of SETD 2.

As can be seen in FIG. 11, patients with high TMB also had longer survival times when analyzed using the TMB method compared to patients with low TMB, however, patients with high TMB had HR values of 0.57 (95% confidence interval: 0.39-0.84).

From examples 3 to 5, it is clear that, when data of a lung cancer patient, a melanoma patient, and a digestive system tumor patient are analyzed, prediction of prognosis survival using mutations of the SETD2 gene is more advantageous than using the TMB method, and at the same time, the method has an advantage of low cost.

Finally, to more clearly illustrate the technical solutions and the technical effects achieved by the embodiments of the present invention, the two-dimensional code addresses of the color original drawings of fig. 1 to 11 are attached, as shown in fig. 12.

It should be noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.

Claims

1. A kit for predicting prognosis survival of tumor immunotherapy, which is characterized in that:

the kit comprises a reagent for detecting gene mutation of a gene, wherein the gene is SETD2 gene.

2. The kit for predicting prognostic survival of immunological therapy for tumors according to claim 1, wherein:

the kit comprises:

3. The kit for predicting prognostic survival of immunological therapy for tumors according to claim 2, wherein:

the comparison module is used for obtaining mutation information at least comprising non-synonymous mutations or further comprising synonymous mutations.

4. The application of a reagent for detecting gene mutation of a gene in preparing a prognosis survival prediction kit for tumor immunotherapy is disclosed, wherein the gene is SETD2 gene.

5. Use according to claim 4, characterized in that:

the tumor types include melanoma, renal cancer, lung cancer, and digestive system tumor.