Detection reagent for tumor mutation load as well as related product and application thereof

Abstract

The invention discloses a tumor mutation load detection reagent as well as a related product and application thereof, and relates to the field of biological detection. Through gene combination optimization, the core problems of insufficient coverage, standardization deficiency, difficulty in clinical transformation and the like in the existing TMB detection technology are solved, the method is suitable for effective detection of TMB of solid tumors and has high consistency with WES detection results, a more accurate and efficient detection tool is provided for prediction of the curative effect of immunotherapy, and the method is suitable for popularization and application. And popularization and application of TMB detection in clinical practice are promoted.

Description

Technical Field

[0001] This invention relates to the field of biological detection, and more specifically, to a detection reagent for tumor mutation burden and related products and applications. Background Technology

[0002] Tumor mutational burden (TMB) refers to the number of gene mutations in tumor cells, specifically the total number of single nucleotide variants and small insertions / deletions per megabase (Mb) in the tumor genome, measured in muts / Mb. TMB is crucial in cancer research and treatment, closely related to tumor immunogenicity. Studies have found that tumors with high TMB may contain more neoantigens, which are generated by mutations in tumor cells. Unlike protein fragments in normal cells, these neoantigens can be recognized by the immune system, activating an immune response that helps the immune system recognize and attack tumor cells.

[0003] Currently, TMB detection mainly relies on whole-exome sequencing (WES) or targeted sequencing panel technology. WES requires coverage of the entire exome region (approximately 30 Mb), resulting in high detection costs (around 3000-5000 RMB per test) and large sample requirements (≥250 ng DNA), making it difficult to meet the high-frequency clinical testing needs. WES requires processing massive amounts of data (averaging 50-100 Gb of raw data per sample), has a long bioinformatics analysis workflow (typically 3-5 days), and places extremely high demands on computing resources. Existing commercial panel genes suffer from insufficient coverage, lack of standardization, and difficulties in clinical translation, resulting in a relatively high error rate in TMB detection. Reducing costs while maintaining detection sensitivity presents significant technical challenges.

[0004] In view of this, the present invention is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a reagent for detecting tumor mutation burden, as well as related products and applications.

[0006] This invention is implemented as follows: In a first aspect, embodiments of the present invention provide a detection reagent for a gene combination, wherein the gene combination includes at least 500 genes listed in Table 1.

[0007] Table 1. Gene Panels

[0008] Secondly, embodiments of the present invention provide a kit comprising the detection reagents described in the foregoing embodiments.

[0009] Thirdly, embodiments of the present invention provide the application of the detection reagents as described in the foregoing embodiments in the preparation of products for detecting tumor mutation burden.

[0010] Fourthly, embodiments of the present invention provide a device for detecting tumor mutation burden, comprising: An acquisition module is used to acquire the sequencing results of the gene combination sequence; wherein, the gene combination is the gene combination described in the foregoing embodiments; The processing module is used to process the sequencing results and compare the processed data with the reference genome to obtain mutation information; The analysis module is used to count somatic mutations in the gene combination based on the mutation information and to calculate the tumor mutation burden.

[0011] Fifthly, embodiments of the present invention provide an electronic device, which includes a processor and a memory. The memory is used to store a program, which, when executed by the processor, enables the processor to implement a method for detecting tumor mutational burden. The method for detecting tumor mutational burden includes the following steps: obtaining sequencing results of a gene combination; wherein the gene combination is the gene combination described in the foregoing embodiments; processing the sequencing results and aligning the processed data to a reference genome to obtain mutation information; and using the mutation information to count somatic mutations in the gene combination and calculate the tumor mutational burden.

[0012] In a sixth aspect, embodiments of the present invention provide a computer-readable medium storing a computer program, which, when executed by a processor, implements the tumor mutation burden detection method described in the foregoing embodiments.

[0013] The present invention has the following beneficial effects: This invention addresses the core issues of insufficient coverage, lack of standardization, and difficulties in clinical translation in existing TMB detection technologies through gene combination optimization. It is applicable to the effective detection of TMB in solid tumors (bladder urothelial carcinoma, colon cancer, head and neck squamous cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, skin melanoma, gastric cancer, and endometrial cancer), and shows high consistency with WES detection results. It provides a more accurate and efficient detection tool for predicting the efficacy of immunotherapy and promotes the widespread application of TMB detection in clinical practice. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating a method for detecting tumor mutation burden. Figure 2 To verify the consistency of gene sequences and CDS region TMB with WES-TMB for three gene combinations (PanTruth18, PanTruth90V4, PanTruth566) in the TCGA-MC3 (WES) dataset samples of bladder urothelial carcinoma. Figure 3 To verify the consistency of gene sequences and CDS region TMB of three gene combinations (PanTruth18, PanTruth90V4, PanTruth566) with WES-TMB in the TCGA-MC3 (WES) dataset samples of colorectal adenocarcinoma. Figure 4 To verify the consistency of gene sequences and CDS region TMB with WES-TMB for three gene combinations (PanTruth18, PanTruth90V4, PanTruth566) in the TCGA-MC3 (WES) dataset samples of head and neck squamous cell carcinoma. Figure 5 To verify the consistency of gene sequences and CDS region TMB of three gene combinations (PanTruth18, PanTruth90V4, PanTruth566) with WES-TMB in lung adenocarcinoma TCGA-MC3 (WES) dataset samples. Figure 6 To verify the consistency of gene sequences and CDS region TMB of three gene combinations (PanTruth18, PanTruth90V4, PanTruth566) with WES-TMB in lung squamous cell carcinoma TCGA-MC3 (WES) dataset samples. Figure 7 To verify the consistency of gene sequences and CDS region TMB with WES-TMB for three gene combinations (PanTruth18, PanTruth90V4, PanTruth566) in the TCGA-MC3 (WES) dataset of cutaneous melanoma. Figure 8To verify the consistency of gene sequences and CDS region TMB of three gene combinations (PanTruth18, PanTruth90V4, PanTruth566) with WES-TMB in the TCGA-MC3 (WES) dataset samples of gastric adenocarcinoma. Figure 9 To verify the consistency of gene sequences and CDS region TMB of three gene combinations (PanTruth18, PanTruth90V4, PanTruth566) with WES-TMB in endometrial cancer TCGA-MC3 (WES) dataset samples. Figure 10 To verify the consistency between CDS TMB and WES-TMB in the CDS region of the PanTruth566 clinical sample. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Unless otherwise specified, specific conditions in the embodiments are performed under conventional conditions or conditions recommended by the manufacturer. Reagents or instruments used without specified manufacturers are all commercially available conventional products.

[0017] Definition of noun In this article, the term "TMB" (Tumor mutational burden) refers to the number of somatic variants, including single nucleotide variants and small insertions / deletions, per Mb region.

[0018] In this article, the term "WES" refers to whole exome sequencing.

[0019] In this article, the term "SNV" refers to a somatic single nucleotide point mutation.

[0020] In this article, the term "Indel" refers to a small fragment insertion / deletion mutation.

[0021] The term “CNV” in this article refers to copy number variation.

[0022] In this article, the term "Panel" is synonymous with "gene package" or "gene combination," where several genes form a Panel, dozens of genes form a Panel, and hundreds or thousands of genes can also form a Panel. The term "TotalPanel TMB" in this article refers to the estimated TMB value for targeted sequencing of all regions of the probe.

[0023] In this article, the term "CDS TMB" refers to the estimated TMB value for probe-coding sequence (DNA sequence corresponding to the protein sequence) region-targeted sequencing.

[0024] The term "WES TMB" in this article refers to the value of TMB estimated by whole exome sequencing.

[0025] It should be noted that, unless otherwise specified, the features of the embodiments or implementation methods of this disclosure can be combined with each other.

[0026] Technical solution On one hand, embodiments of the present invention provide a detection reagent for a gene panel, the gene panel comprising at least 500 genes shown in Table 1.

[0027] In some implementations, the at least 500 includes at least 500, 550, or 600.

[0028] In some embodiments, the gene combination includes the genes shown in Table 1.

[0029] In some embodiments, the detection region of the detection reagent includes the gene sequence, exon region, or CDS region of each gene in the gene combination.

[0030] In some embodiments, the detection reagent includes primers, probes, and / or chips. There are no special requirements for the design of primers, probes, and chips; they can be obtained based on conventional technical knowledge.

[0031] On the other hand, embodiments of the present invention also provide a kit comprising the detection reagents described in any of the foregoing embodiments.

[0032] On the other hand, embodiments of the present invention also provide the application of the detection reagents as described in any of the foregoing embodiments in the preparation of products for detecting tumor mutational burden.

[0033] In some embodiments, the product includes a reagent kit, a chip, or a device.

[0034] In some embodiments, the detection of tumor mutational burden includes detecting the tumor mutational burden of solid tumors.

[0035] In some embodiments, the solid tumor includes any one or more of the following: bladder urothelial carcinoma, colon cancer, head and neck squamous cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, skin melanoma, gastric cancer, and endometrial cancer.

[0036] On the other hand, embodiments of the present invention also provide a device for detecting tumor mutation burden, comprising: An acquisition module is used to acquire the sequencing results of the gene combination sequence; wherein, the gene combination is the gene combination described in any of the foregoing embodiments; The processing module is used to process the sequencing results and compare the processed data with the reference genome to obtain mutation information; The analysis module is used to count somatic mutations in the gene combination based on the mutation information and to calculate the tumor mutation burden.

[0037] In some embodiments, the detection device further includes a sequencing module for extracting the sequence of the gene combination and performing high-throughput sequencing to obtain sequencing results.

[0038] In some implementations, the sequencing is next-generation sequencing.

[0039] In some implementations, the sequencing platform includes the Illumina platform.

[0040] In some implementations, the sequencing depth is ≥500×.

[0041] In some implementations, the steps of processing the sequencing results include: performing raw data quality control on the sequencing data to remove low-quality data.

[0042] In some implementations, the reference genome is the human reference genome GRCh37 / hg19.

[0043] In some implementations, the step of obtaining mutation information includes: detecting mutations using mutation detection software and annotating the mutations; after annotation, filtering for false positive sites to obtain mutation information.

[0044] In some implementations, the step of detecting mutations using mutation detection software employs a unified mutation analysis software to detect mutations and obtain mutation information, thereby avoiding the influence of different software on the results of mutation site detection.

[0045] In some implementations, the types of somatic mutations include single nucleotide variants (SNVs) and small fragment insertions / deletions (Indels).

[0046] In some implementations, the variant types of the somatic mutations are excluded: synonymous mutations; intron mutations; and splice mutations.

[0047] In some implementations, the tool used for SNV mutation detection is selected from at least one of the software programs Muse, Mutect, Strelka, Varscan, samtools, bcftools, GVC, and SomaticSniper, and the tool used for Indels mutation detection is selected from at least one of the software programs Mutect, Strelka, Varscan, and GVC.

[0048] In some embodiments, the frequency of the mutant alleles in the somatic cell mutation is ≥5%.

[0049] In some embodiments, the mutational allele frequency of the somatic mutation is 5% to 40% (Tumor-only, sequencing and analysis are performed only on tumor tissue samples, without relying on paired normal tissue samples), specifically any one or any two of 5%, 10%, 15%, 20%, 25%, 30%, 35%, and 40%.

[0050] In some implementations, the annotation gene library includes, but is not limited to: 1000Genome (Asia, Europe), dbSNP, COSMIC, and ExAC.

[0051] In some implementations, false positive site filtering includes germline mutation exclusion and / or background noise filtering.

[0052] In some implementations, false positive site filtering includes manual IGV verification. Manual IGV verification includes, but is not limited to, verifying the gene structure annotations of suspicious sites using a local genome browser to reduce erroneous gene structure annotations.

[0053] In some implementations, the TMB calculation formula is as follows: ; In the formula, (1) the numerator is the number of somatic mutations; (2) the denominator is the size of the region where the somatic mutation is located, in “Mb” (the whole exon region annotation analysis of the genome is based on the annotation version of GRCh37 / hg19).

[0054] The modules described in this embodiment of the invention can be stored in memory or embedded in the operating system (OS) of the electronic device provided in this application in the form of software or firmware, and can be executed by the processor in the electronic device. Meanwhile, the data, program code, etc., required to execute the above modules can be stored in memory.

[0055] In some implementations, the correlation between the TMB of the gene combination and the WES TMB is ≥80%, 82%, 84%, 86%, 88%, 90%, 92%, 94%, 96%, 98%, or 99%.

[0056] On the other hand, embodiments of the present invention also provide an electronic device, which includes a processor and a memory, the memory being used to store a program that, when executed by the processor, causes the processor to implement a method for detecting tumor mutation burden; The method for detecting tumor mutation burden includes the following steps: obtaining sequencing results of a gene combination; wherein the gene combination is the gene combination described in any of the foregoing embodiments; processing the sequencing results and aligning the processed data to a reference genome to obtain mutation information; and using the mutation information to count somatic mutations in the gene combination and calculate the tumor mutation burden.

[0057] In some embodiments, the detection method includes detecting the tumor mutational burden of solid tumors.

[0058] In some embodiments, the solid tumor includes any one or more of the following: bladder urothelial carcinoma, colon cancer, head and neck squamous cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, skin melanoma, gastric cancer, and endometrial cancer.

[0059] In some embodiments, the specific steps and parameter selections in the tumor mutation burden detection method are the same as those described in the foregoing embodiments, and will not be repeated here.

[0060] Electronic devices may include memory, processor, bus, and communication interface, which are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components may be electrically connected to each other via one or more buses or signal lines.

[0061] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.

[0062] The processor can be an integrated circuit chip with signal processing capabilities. The processor 120 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0063] The electronic device can be a server, cloud platform, mobile phone, tablet computer, laptop computer, ultra-mobile personal computer (UMPC), handheld computer, netbook, personal digital assistant (PDA), wearable electronic device, virtual reality device, etc. Therefore, the embodiments of this application do not limit the types of electronic devices.

[0064] Furthermore, embodiments of the present invention also provide a computer-readable medium storing a computer program, which, when executed by a processor, implements the tumor mutation burden detection method described in any of the foregoing embodiments.

[0065] In some implementations, the computer-readable medium can be a general-purpose storage medium, such as a removable disk or hard disk.

[0066] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0067] In the embodiments of the present invention, the real samples involved undergo gene sequencing by a probe provider, DNA extraction and fragmentation according to the experimental procedure, sequencing library construction, sequencing on the Illumina platform, and generation of detection result data.

[0068] Example 1 This embodiment provides a method for detecting tumor mutation burden, which includes the following steps.

[0069] I. Sample Preparation Sequencing data from eight common cancer types (bladder urothelial carcinoma, BLCA; colon cancer, COAD; head and neck squamous cell carcinoma, HNSC; lung adenocarcinoma, LUAD; lung squamous cell carcinoma, LUSC; skin melanoma, SKCM; gastric cancer, STAD; endometrial cancer, UCEC) in the TCGA dataset were downloaded as samples for TMB detection and evaluation. The sample sizes were: BLCA 428 cases, COAD 441 cases, HNSC 568 cases, LUAD 614 cases, LUSC 525 cases, SKCM 470 cases, STAD 480 cases, and UCEC 540 cases.

[0070] TCGA-MC3 (WES) data download path (Download site): https: / / api.gdc.cancer.gov / data / 1c8cfe5f-e52d-41ba-94da-f15ea1337efc.

[0071] Inclusion criteria for samples: 1. Untreated primary tumor tissue; 2. Sufficient proportion of tumor cells (tumor cell content ≥ 60% in tumor tissue); 3. Matched normal control tissue.

[0072] II. TMB Calculation The formula for calculating TMB is as follows: ; In the formula, (1) represents the number of somatic cell mutations; the screening criteria for somatic cells are as follows: 1. Variation of allele frequency (VAF): Tumor-only mode: 5% ≤ somatic mutation VAF ≤ 40%; Tumor-normal mode: somatic mutation VAF ≥ 5%; 2. Included variant types: Single nucleotide variants (SNVs) and small insertions / deletions (Indels) are limited to this category. 3. Excluded mutation types: Synonymous mutation; Intron mutation; Splice mutation.

[0073] (2) Denominator: Size of the region where somatic mutations are located, in “Mb” (The whole exon region annotation analysis of the genome is based on the annotation version of GRCh37 / hg19: https: / / ftp.ensembl.org / pub / grch37 / release-100 / gtf / homo_sapiens / Homo_sapiens.GRCh37.87.gtf.gz).

[0074] The process can be referred to Figure 1 .

[0075] III. TMB Assessment Based on the genes described in Table 1, three gene combinations—PanTruth18, PanTruth90V4, and PanTruth566—and their probes were developed. The distribution of TotalPanel TMB (gene sequences covering the probe regions of the three gene combinations) or CDSTMB (probe coding region sequences of the three gene combinations) with WES TMB was statistically analyzed. The correlation between TotalPanel TMB / CDS TMB and WES TMB, as well as the correlation between site accuracy and TMB, were calculated for evaluation.

[0076] The algorithms for TotalPanel TMB / CDS TMB and WES TMB Pearson correlation coefficients are as follows: ; Linear regression analysis was performed on TotalPanel TMB / CDS TMB and WES TMB, and the calculation formulas are as follows: y = ax + b; where x is the WES TMB value, and y is the TotalPanel TMB or CDS TMB value, with a regression interval of 95%.

[0077] Evaluation results In samples from eight common cancer types, a comparative analysis was conducted on the Total Panel TMB, CDS TMB, and the currently accepted WES-TMB for three gene combinations (PanTruth18, PanTruth90V4, and PanTruth566). The genes covered by the three gene combinations PanTruth18, PanTruth90V4, and PanTruth566 are detailed in Tables 2, 3, and 1.

[0078] The results show that the PanTruth566 CDS TMB is consistent with the currently accepted WES-TMB threshold and can accurately reflect the TMB status of the sample. See Tables 4, 5, and 6 for details. Figures 2 to 9 .

[0079] Table 2 List of PanTruth18 gene combinations

[0080] Table 3. List of PanTruth90V4 gene combinations

[0081] Table 4. Validation results of the correlation between CDS TMB and WES-TMB thresholds in the PanTruth18 CDS region of TCGA-MC3 (WES) data.

[0082] Table 5. Validation results of the correlation between CDS TMB and WES-TMB threshold in the PanTruth90V4 CDS region of TCGA-MC3 (WES) data.

[0083] Table 6. Validation results of the correlation between CDS TMB and WES-TMB thresholds in the PanTruth566 CDS region of TCGA-MC3 (WES) data.

[0084] Note: The regression model was established by calculating TMB using the three gene combinations PanTruth18 CDS region, PanTruth90V4 CDS region, and PanTruth566 CDS region as the denominator; in the regression model, x represents WES TMB [mut / Mb].

[0085] Example 2 This embodiment provides a method for detecting tumor mutation burden, which includes the following steps.

[0086] I. Sample Preparation In this embodiment, the clinical samples selected were 41 tumor tissues from lung adenocarcinoma (LUAD) with high tumor purity and good DNA quality, along with control adjacent normal tissues. These samples were untreated primary tumor tissues with a sufficient proportion of tumor cells (tumor cell content ≥ 60%). Tumor content was confirmed by HE sectioning. Sufficient DNA was extracted in a single step (generally recommended to be greater than 10 μg, and thoroughly mixed).

[0087] II. Testing Panel analysis based on the PanTruth566 CDS region of the gene combination was performed on the samples to construct the sequencing library: DNA extraction and quality control, DNA fragmentation, end repair and A-tailing, adapter ligation, and PCR amplification.

[0088] III. Sequencing Sequencing was performed on the Illumina sequencing platform. The following content uses the Illumina platform as an example to obtain sample sequencing data. The format is FASTQ file, the effective sequencing depth is greater than 500×, and the data volume is no less than 80G.

[0089] IV. Sequencing Data Analysis First, the FASTQ file is preprocessed using the fastp (v2.7.0r) software to generate Clean FASTQ, including removing adapter sequences and filtering low-quality sequences.

[0090] Then, the Clean FASTQ file was aligned to the GRCh37 / hg19 reference genome using bwa-mem (0.7.17) software, and the generated BAM file was quality controlled using GVCQC (quality control tool). The BAM file was preprocessed by marking duplicates using Picard (2.21.9) MarkDuplicates software, and calibrating the base quality score using GATK BaseRecalibrator software and ApplyBQSR (4.1.5.0) software.

[0091] Finally, variant detection for SNVs and Indels was performed on the BQSR BAM files using recommended default parameters from multiple software programs, and mutations were annotated. The variant detection software included GATK Mutect2 (4.1.5.0), Muse (1.0rc), Varscan (2.3.9), Strelka (2.9.9), and SomaticSniper (1.5). Variant results were annotated using ANNOVAR, with annotation gene libraries including, but not limited to, 1000 Genomes (Asian and European), dbSNP, COSMIC, and ExAC.

[0092] The unified mutation analysis software GVC was used to detect mutations in the raw data FASTQ of the test samples. The true positive, false positive and false negative sites detected by SNV and Indels in the real tumor sample test data were calculated. After filtering by false positive sites, false negative sites and germline database, the number of somatic mutations (SNV and Indels) was counted.

[0093] It should be noted that the mutation detection data from WES are filtered as follows: SNPs with a maximum population frequency ≥0.001 in 1000 Genomes (Asia, Europe) and ExAC are removed; synonymous mutations are removed; intron region mutations are removed; splice region mutations are removed; and CDS region mutations with a mutation frequency of ≥5% are retained.

[0094] V. TMB Calculation See Example 1.

[0095] VI. TMB Assessment See Example 1.

[0096] Analysis results show that the CDS TMB in the PanTruth566 CDS region of clinical samples is highly consistent with the WES-TMB, and can accurately reflect the TMB status of clinical samples. See details... Figure 10 .

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A reagent for detecting gene combinations, characterized in that, The gene combination includes at least 500 of the following 619 genes:

2. The detection reagent according to claim 1, characterized in that, The gene combination includes at least 600 of the 619 genes.

3. The detection reagent according to claim 1, characterized in that, The detection region of the detection reagent includes the gene sequence, exon region and / or CDS region of each gene in the gene combination; Optionally, the detection reagent includes primers, probes, and / or chips.

4. A reagent kit, characterized in that, It includes the detection reagent as described in any one of claims 1 to 3.

5. The use of the detection reagent as described in any one of claims 1 to 3 in the preparation of products for detecting tumor mutation burden.

6. The application according to claim 5, characterized in that, The detection of tumor mutational burden includes the detection of tumor mutational burden in solid tumors; Optionally, the solid tumor includes any one or more of the following: bladder urothelial carcinoma, colon cancer, head and neck squamous cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, skin melanoma, gastric cancer, and endometrial cancer.

7. A device for detecting tumor mutation burden, characterized in that, It includes: An acquisition module is used to acquire the sequencing results of the gene combination sequence; wherein the gene combination is the gene combination described in claim 1 or 2; The processing module is used to process the sequencing results and compare the processed data with the reference genome to obtain mutation information; The analysis module is used to count somatic mutations in the gene combination based on the mutation information and to calculate the tumor mutation burden.

8. An electronic device, characterized in that, It includes a processor and a memory, the memory being used to store a program that, when executed by the processor, enables the processor to implement a method for detecting tumor mutational burden; The method for detecting tumor mutational burden includes the following steps: obtaining sequencing results of a gene combination; wherein the gene combination is the gene combination described in claim 1 or 2; processing the sequencing results and aligning the processed data to a reference genome to obtain mutation information; and using the mutation information to count somatic mutations in the gene combination and calculate the tumor mutational burden.

9. The electronic device according to claim 8, characterized in that, The detection method includes detecting the tumor mutation burden of solid tumors; Optionally, the solid tumor includes any one or more of the following: bladder urothelial carcinoma, colon cancer, head and neck squamous cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, skin melanoma, gastric cancer, and endometrial cancer; Optionally, the reference genome is the human reference genome GRCh37 / hg19; Optionally, the types of somatic mutations include: single nucleotide variants and small fragment insertions / deletions; Optionally, the frequency of the mutant alleles in the somatic cell mutation is ≥5%; Optionally, the sequencing is next-generation sequencing; Optionally, the sequencing platform includes the Illumina platform; Optionally, the sequencing depth is ≥500×.

10. A computer-readable medium, characterized in that, The computer-readable medium stores a computer program that, when executed by a processor, implements the tumor mutation burden detection method of claim 8 or 9.

Images