Use of gene combinations in the manufacture of products for grading and detection of homologous recombination defects, tumor mutagenesis, and microsatellite instability in human tumors.

Abstract

This application relates to the field of tumor grading and detection, specifically to the use of gene combinations in the manufacture of products for grading and detecting homologous recombination deficiency, tumor mutation burden, and microsatellite instability in human tumors. The gene combination consists of gene set A and gene fragment set B. The gene combination is obtained through specific pairwise clustering analysis from actual high-throughput sequencing data, and the data obtained from real-world data has higher reliability and certainty, enabling accurate grading and prediction of pan-cancer homologous recombination deficiency, tumor mutation burden, and microsatellite instability.

Description

Technical Field

[0001] [Cross - reference to Related Applications] This application claims priority to a Chinese patent application filed with the China National Intellectual Property Administration on May 20, 2022, with application number 202210555507.8 and title of invention "Use of Gene Combinations in Products for Grading and Detection of Homologous Recombination Deficiency, Tumor Mutation Load, and Microsatellite Instability in Human Tumors", and all the contents of said application are incorporated herein by reference.

[0002] [Technical Field] This application relates to the field of tumor grading and detection, specifically to the use of gene combinations in products for grading and detection of homologous recombination deficiency, tumor mutation load, and microsatellite instability in human tumors.

Background Art

[0003] Homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) are important clinical indicators commonly used to assess the efficacy of malignant tumor immunotherapy and PARP inhibitors. Typically, different sequencing methods are required to obtain complete information regarding the determination of these three indicators. For HRD and MSI, it is usually necessary to design special sequencing panels to perform sequencing targeting specific gene regions. Finally, the final HRD and MSI scores are calculated, and the levels of HRD and MSI are determined. For TMB, whole-exome sequencing is usually required to definitively determine the TMB level. While conventional methods for detecting HRD, TMB, and MSI are reliable, there are obvious shortcomings when simultaneously detecting all three indicators. While methods such as whole-exome sequencing are highly accurate in detecting these indicators, they are also costly and incur enormous medical expenses. Therefore, to reduce detection costs while ensuring high accuracy, it is necessary to find a new grading system based on the detection of specific genes for homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) in tumors. [Overview of the project] [Problems that the invention aims to solve]

[0004] Therefore, the technical problem that this application seeks to solve is to provide the use of a gene combination in the manufacture of a product for grading and detecting homologous recombination deficiency (HRD), tumor mutagenesis (TMB), and microsatellite instability (MSI) in pan-oncological tumors. This gene combination can perform grading and prediction of homologous recombination deficiency (HRD), tumor mutagenesis (TMB), and microsatellite instability (MSI) in pan-oncological tumors. To achieve this objective, this application ultimately obtains the gene combination of the present invention by screening sequencing data of patients specifically screened and grouped from Peking University First Hospital using whole-exome sequencing technology. The gene combinations of the present invention, when used for grading and prediction of homologous recombination deficiency (HRD), tumor mutagenesis (TMB), and microsatellite instability (MSI) in pan-oncological diseases, provide clinicians and patients with accurate grading information and disease prediction information for HRD, TMB, and MSI in pan-oncological diseases. Moreover, compared to whole-exome sequencing, the present invention significantly reduces costs at the same sequencing depth because it targets fewer genes. [Means for solving the problem]

[0005] This application relates to the use of gene combinations in the manufacture of products for grading and detecting homologous recombination deficiencies, tumor mutagenesis, and / or microsatellite instability in human tumors, wherein the gene combination comprises a gene set A and a gene fragment set B. The aforementioned gene set A includes ASAH1, ASXL1, BCOR, BRAF, CALML6, CCDC136, CIDEC, COX18, CSF1R, CYP3A5, DEK, DNMT3A, EGR1, FAM71E2, FGFR1, FKBP7, FLT1, FLT3, FLT4, GNAQ, GLIS1, IDH2, IFITM3, IMMT, KDR, KIT, KMT2A, KNOP1, KRT76, KRT9, KRTAP10-10, KRTAP10-8, MAF, MECOM, MFRP, MLLT3, MNS1, M It includes at least one of RTFA, MTOR, MYH11, NF1, NUP214, PDGFRA, PDGFRB, PML, PRB2, PROSER3, RAF1, RARA, RBM15, RET, REXO1, RPN1, RUNX1T1, SCYL1, SLC16A6, SRC, STAG2, TCEAL5, TET2, TMEM82, TP53, TRIM26, U2AF1, U2AF2, UGT1A1, USP35, VEGFA, WBP2NL, WDR44, ZNF20, ZNF700, and ZRSR2, The aforementioned gene fragment set B consists of chr2:179479501-179610249, chr2:207989501-208000249, chr2:219719501-219840249, chr2:3679501-3700249, chr3:126249501-126270249, chr3:129319501-129330249, chr3:138659501-138770249, chr3:183999501-184020249, chr4:1189501-1230249, and chr4:8579501-8 590249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:147 819501-147840249, chr6:157089501-157110249, chr6:164889501-1649 00249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:7165 9501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, chr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-29200249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:17 739501-17760249, chr11:63329501-63350249, chr12:169501-250249, c hr12:54329501-54350249, chr12:63179501-63550249, chr12:7269501-7 310249, chr13:114519501-114530249, chr15:73649501-73670249, chr1 5:74209501-74220249, chr15:78409501-78430249, chr15:83859501-83 880249, chr18:8809501-8820249, chr19:24059501-24070249, chr19:42 29501-4250249, chr19:46879501-46900249, chr20:22559501-22570249,The set of gene fragments includes at least one of chr20:62189501-62200249, chr21:45949501-46110249, chr22:19499501-19760249, chr22:36649501-38700249, and chr22:46309501-47080249. The positions of the gene fragments in the set of gene fragments B are annotated relative to GRCh37. While the number of such fragments may change in GRCh38 and future versions of the human reference genome, the positions of the target fragments and the genes available for detection will remain the same, providing a useful application.

[0006] The detailed genes included in gene fragment set B, which can be selected at will, are shown in the table below. TIFF0007897337000001.tif232163 TIFF0007897337000002.tif248163

[0007] Optionally, gene set A includes at least one of CALML6, CCDC136, EGR1, FAM71E2, GLIS1, IFITM3, KNOP1, KRT76, KRT9, KRTAP10-10, MAF, MNS1, PROSER3, SCYL1, SLC16A6, SRC, TCEAL5, TMEM82, TRIM26, U2AF2, USP35, WBP2NL, WDR44, ZNF20, and ZNF700. The aforementioned gene fragment set B consists of chr2:179479501-179610249, chr2:207989501-208000249, chr2:219719501-219840249, chr2:3679501-3700249, chr3:126249501-126270249, chr3:129319501-129330249, chr3:138659501-138770249, chr3:183999501-184020249, chr4:1189501-1230249, and chr4:8579501-8 590249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:147 819501-147840249, chr6:157089501-157110249, chr6:164889501-1649 00249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:7165 9501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, chr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-29200249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:17 739501-17760249, chr11:63329501-63350249, chr12:169501-250249, c hr12:54329501-54350249, chr12:7269501-7310249, chr13:114519501-1 14530249, chr15:73649501-73670249, chr15:74209501-74220249, chr1 5:78409501-78430249, chr15:83859501-83880249, chr18:8809501-882 0249, chr19:24059501-24070249, chr19:4229501-4250249, chr19:4687 9501-46900249, chr20:22559501-22570249, chr20:62189501-62200249,It includes at least one of the following: chr21:45949501-46110249, chr22:19499501-19760249, chr22:36649501-38700249, and chr22:46309501-47080249.

[0008] Optionally, the target samples for detection in the grading and detection of homologous recombination deficiencies, tumor mutation burdens, and / or microsatellite instability in the human tumors are pan-cancer.

[0009] Optionally, grading and prediction of homologous recombination deficiency (HRD), tumor mutational burden (TMB), and / or microsatellite instability (MSI) of the tumor may be used to guide clinical diagnosis and treatment. Optionally, the aforementioned grading can be divided into high-grade and low-grade groups.

[0010] Optionally, the product includes primers, probes, reagents, kits, gene chips, or detection systems for detecting the genotype of a gene in the gene combination.

[0011] Optionally, the product detects exons and associated intron regions of genes in gene set A and gene fragment set B.

[0012] Optionally, the grading method for homologous recombination deficiency, tumor mutation burden, and / or microsatellite instability of the human tumor is: Step S1 involves evaluating gene mutations and gene copy number variations of genes included in gene set A in the tumor cell tissue, and evaluating gene copy number variations of gene fragment set B in the tumor cell tissue. Step S2 includes determining and predicting the grade of homologous recombination deficiency (HRD), tumor mutational burden (TMB), and / or microsatellite instability (MSI) of the cancer based on the evaluation results of Step S1.

[0013] Optionally, the gene mutations include base substitution mutations, deletion mutations, insertion mutations, and / or fusion mutations, and the gene copy number polymorphisms include gene copy number increases and / or gene copy number decreases.

[0014] Optionally, in step S1, sequencing data of tumor cell tissue and normal tissue are compared to evaluate gene mutations and copy number variations of genes included in gene set A, and gene copy number variations of gene fragment set B are also evaluated.

[0015] Optionally, in step S2, if a gene mutation or copy number variation occurs in at least one gene from gene set A, or if a gene copy number increase occurs in at least one fragment from gene fragment set B, the grading is in the high-grade group. On the other hand, if no gene mutation or copy number variation occurs in any of the genes in gene set A, and no gene copy number increase occurs in any of the fragments in gene fragment set B, the grading is in the low-grade group.

[0016] By arbitrary selection, any gene fragment is selected from the aforementioned gene combinations and combined to form a new gene combination, and the homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) of the tumor are graded and predicted using the same human tumor homologous recombination deficiency, tumor mutation burden, and / or microsatellite instability grading method. [Effects of the Invention]

[0017] The technical solution of this application has the following advantages.

[0018] 1. The gene combination for detection in this application is obtained through specific pairwise clustering analysis from the actual high-throughput sequencing data of Peking University First Hospital. The data obtained from real-world data has higher reliability and accuracy, enabling accurate grading and prediction of pan-cancer homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI).

[0019] 2. The gene combination in this application is diverse, allowing for the selection of multiple gene combinations for grading and prediction of pan-cancer homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI), as well as for various clinical situations.

[0020] 3. Compared with whole-exome sequencing, in this application, target sequencing analysis is performed on specific genes and DNA fragments, enabling significant improvement in the depth and accuracy of sequencing at the same cost. At the same depth and accuracy of sequencing, the cost can be significantly reduced, making it highly versatile.

Brief Description of the Drawings

[0021] To more clearly illustrate the specific embodiments of this application or the technical solutions in the prior art, the following briefly introduces the drawings necessary for the description of the specific embodiments or the prior art. It is clear that the drawings in the following description are some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0022] [Figure 1] In Experimental Example 1 of this application, after grading homologous recombination deficiency (HRD) using the gene combination in Example 1 of this application, the p-value was calculated by the Mann-Whitney U test, and this is a box-and-whisker plot comparing the high-grade group and the low-grade group. [Figure 2]In Experimental Example 1 of the present application, after grading the tumor mutation burden (TMB) using the gene combination in Example 1 of the present application, the p-value was calculated by the Mann-Whitney U test, and it is a box plot comparing the high-grade group and the low-grade group. [Figure 3] In Experimental Example 1 of the present application, after grading the microsatellite instability (MSI) using the gene combination in Example 1 of the present application, the p-value was calculated by the Mann-Whitney U test, and it is a box plot comparing the high-grade group and the low-grade group. [Figure 4] In Experimental Example 2 of the present application, after grading the homologous recombination deficiency (HRD) using Gene Combination 1, the p-value was calculated by the Mann-Whitney U test, and it is a box plot comparing the high-grade group and the low-grade group. [Figure 5] In Experimental Example 2 of the present application, after grading the tumor mutation burden (TMB) using Gene Combination 1, the p-value was calculated by the Mann-Whitney U test, and it is a box plot comparing the high-grade group and the low-grade group. [Figure 6] In Experimental Example 2 of the present application, after grading the microsatellite instability (MSI) using Gene Combination 1, the p-value was calculated by the Mann-Whitney U test, and it is a box plot comparing the high-grade group and the low-grade group.

Mode for Carrying Out the Invention

[0023] The following examples are provided for a better understanding of the present application. The present invention is not limited to the above optimal embodiments, nor does it limit the content and protection scope of the present application. Under the inspiration of the present application, or any product identical or similar to the present application obtained by combining the features of the present application and other prior arts is included in the protection scope of the present application.

[0024] If specific experimental steps or conditions are not specified in the examples, they may be carried out according to the usual experimental steps or conditions described in the literature of this industry. Unless the manufacturer of the reagents or equipment used is specified, they are all common reagent products available through the market.

[0025] Example 1: Gene combination (Panel) for grading homologous recombination deficiency (HRD), tumor mutagenesis (TMB), and microsatellite instability (MSI) in human tumors. The inventors primarily used a high-throughput exon sequencing database of patients from Peking University First Hospital to screen and determine gene combinations (panels) for grading homologous recombination deficiency (HRD), tumor mutagenesis (TMB), and microsatellite instability (MSI) in human tumors, each gene combination comprising gene set A and gene fragment set B. The aforementioned gene set A includes ASAH1, ASXL1, BCOR, BRAF, CALML6, CCDC136, CIDEC, COX18, CSF1R, CYP3A5, DEK, DNMT3A, EGR1, FAM71E2, FGFR1, FKBP7, FLT1, FLT3, FLT4, GNAQ, GLIS1, IDH2, IFITM3, IMMT, KDR, KIT, KMT2A, KNOP1, KRT76, KRT9, KRTAP10-10, KRTAP10-8, MAF, MECOM, MFRP, MLLT3, MNS1, M It includes at least one of RTFA, MTOR, MYH11, NF1, NUP214, PDGFRA, PDGFRB, PML, PRB2, PROSER3, RAF1, RARA, RBM15, RET, REXO1, RPN1, RUNX1T1, SCYL1, SLC16A6, SRC, STAG2, TCEAL5, TET2, TMEM82, TP53, TRIM26, U2AF1, U2AF2, UGT1A1, USP35, VEGFA, WBP2NL, WDR44, ZNF20, ZNF700, and ZRSR2, The aforementioned gene fragment set B consists of chr2:179479501-179610249, chr2:207989501-208000249, chr2:219719501-219840249, chr2:3679501-3700249, chr3:126249501-126270249, chr3:129319501-129330249, chr3:138659501-138770249, chr3:183999501-184020249, chr4:1189501-1230249, and chr4:8579501-8 590249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:147 819501-147840249, chr6:157089501-157110249, chr6:164889501-1649 00249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:7165 9501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, chr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-29200249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:17 739501-17760249, chr11:63329501-63350249, chr12:169501-250249, c hr12:54329501-54350249, chr12:63179501-63550249, chr12:7269501-7 310249, chr13:114519501-114530249, chr15:73649501-73670249, chr1 5:74209501-74220249, chr15:78409501-78430249, chr15:83859501-83 880249, chr18:8809501-8820249, chr19:24059501-24070249, chr19:42 29501-4250249, chr19:46879501-46900249, chr20:22559501-22570249,The set of gene fragments includes at least one of the following: chr20:62189501-62200249, chr21:45949501-46110249, chr22:19499501-19760249, chr22:36649501-38700249, and chr22:46309501-47080249. The positions of the gene fragments in the aforementioned gene fragment set B are annotated relative to GRCh37. While the number of such fragments may change in GRCh38 and future versions of the human reference genome, the positions of the target fragments and the genes available for detection will remain the same.

[0026] The detailed genes included in gene fragment set B, which can be selected at will, are shown in the table below.

[0027] [Table 1] TIFF0007897337000004.tif248163

[0028] Example 2: Method for grading and predicting homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) in human pancancer. This embodiment provides a method for grading and detecting homologous recombination deficiencies, tumor mutagenesis, and microsatellite instability in human tumors, the method comprising grading and predicting homologous recombination deficiencies (HRD), tumor mutagenesis (TMB), and microsatellite instability (MSI) in human pancancer using the gene combination (panel) in Example 1, the specific steps being as follows:

[0029] (1) Pan-cancer (Pan-cancer here is defined as all cancer types in the TCGA pan-cancer data, including adrenal cancer, urothelial cancer, breast cancer, cervical cancer, cholangiocarcinoma, colon cancer, lymphoma, esophageal cancer, glioblastoma, head and neck squamous cell carcinoma, chromophobe renal cell carcinoma, clear cell carcinoma of the kidney, papillary cell carcinoma of the kidney, leukemia, glioma, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic cancer, pheochromocytoma and paraganglioma, prostate cancer, rectal cancer, sarcoma, cutaneous melanoma, gastric cancer, testicular cancer, thyroid cancer, thymic cancer, endometrial cancer, uterine sarcoma, and uveal melanoma; all pan-cancers referred to in this application are defined in this way and will not be repeated) tissue samples and healthy control tissue samples were collected. The pan-cancer tissue samples may be pan-cancer cell lines, fresh pan-cancer samples, frozen pan-cancer samples, or paraffin-embedded pan-cancer samples. Healthy control tissue may be from a known healthy individual, or it may be tissue adjacent to cancer or other non-tumor tissue from a pan-cancer patient. In this example, paraffin-embedded pan-cancer specimens were selected, normal tissue adjacent to cancer was used as healthy control tissue, DNA was extracted by conventional methods, libraries were constructed by conventional methods, and finally, targeted high-throughput sequencing was performed using the gene combination (panel) of Example 1, and the sequencing data of the pan-cancer tissue and healthy tissue were compared to obtain gene mutations (Mutations) and copy number variations (CNVs) of each gene in gene set A of the pan-cancer tissue gene combination, as well as copy number variations (CNVs) of each gene in gene fragment set B.

[0030] The gene mutations include base substitution mutations, deletion mutations, insertion mutations, and fusion mutations, and the gene copy number variations include gene copy number increases and gene copy number decreases.

[0031] (2) The determination was made based on the mutations and / or mutations of each gene in the gene combination of the pan-cancer tissue obtained in step (1).

[0032] If at least one gene in gene set A has a gene mutation or copy number variation, or if at least one region in gene fragment set B has a gene copy number increase, the patient with pan-cancer is classified as high-grade and has high homologous recombination deficiency (HRD) scores, tumor mutation burden (TMB) scores, and microsatellite instability (MSI) scores. On the other hand, if no gene mutations or copy number variations occur in the genes of gene set A, and no gene copy number increase occurs in any of the fragments of gene fragment set B, the patient with pan-cancer is classified as low-grade and has low homologous recombination deficiency (HRD) scores, tumor mutation burden (TMB) scores, and microsatellite instability (MSI) scores.

[0033] Example 3 As an alternative embodiment, in this application, it is possible to select genes from the gene combination (panel) in Example 1 and combine them again to form a new gene combination. The evaluation criteria are as follows: If a gene is selected from gene set A, the presence of a gene mutation or copy number variation in at least one of the genes indicates that the pan-cancer patient is in the high-grade group. Alternatively, if a gene fragment is selected from gene fragment set B, the occurrence of a gene copy number increase in at least one region indicates that the pan-cancer patient is in the high-grade group. On the other hand, if no gene mutation or copy number variation occurs in the gene selected from gene set A, and no copy number increase occurs in the fragment selected from gene fragment set B, the pan-cancer patient is in the low-grade group.

[0034] Experimental Example 1: Verification of the practicality of gene combinations and detection methods for grading homologous recombination deficiencies (HRD), tumor mutagenesis (TMB), and microsatellite instability (MSI) in human tumors in evaluating the grading and prediction of homologous recombination deficiencies (HRD), tumor mutagenesis (TMB), and microsatellite instability (MSI) in human pancancerous tumors. The TCGA (PanCancer Atlas) pancancer database is a globally recognized pancancer database and is useful for verifying the practicality and reliability of this invention in evaluating the grading and prediction of homologous recombination deficits (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) in pancancers.

[0035] The TCGA (PanCancer Atlas) pancancer data includes data from a total of 10,967 cases of pancancer, of which 9,896 cases have complete gene mutation and copy number variation data, thus meeting the conditions for use of this application.

[0036] The procedure was carried out according to the method described in Example 2. In this experiment, all genes from gene set A and all fragments from gene fragment set B of Example 1 were selected and used. The genes from gene fragment set B actually used for detection are shown in Table 2. For the above 9896 patients, homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) were graded and divided into high-grade and low-grade groups. Of these, the proportion of the high-grade group was 77.0% and the proportion of the low-grade group was 23.0%. Based on the homologous recombination deficiency (HRD) score, tumor mutation burden (TMB) score, and microsatellite instability (MSI) score in the TCGA pan-cancer database, a Mann-Whitney U test was performed. The results showed that there were statistically significant differences between the high-grade and low-grade groups in homologous recombination deficiency score (Figure 1), tumor mutation burden score (Figure 2), and microsatellite instability score (Figure 3), as expected when grouping in this application. In the high-grade group, the homologous recombination deficiency score (p=6.936 e-231), tumor mutation burden score (p=1.954 e-293), and microsatellite instability score (p=4.654 e-90) were significantly higher. Clinically, a homologous recombination deficiency (HRD) score of 42 or higher is usually defined as the high-score HRD group. When this criterion is defined as the HRD gold standard and tested in the TCGA pan-cancer database, the sensitivity to determine whether the high-grade group classified for homologous recombination deficiency (HRD) using the gene combination of this application is the high-score HRD group is 0.979, and the negative predictive value is 0.989. Clinically, a tumor mutational burden (TMB) of 10 / Mb or higher is defined as a high TMB score group. When this criterion is defined as the TMB gold standard and tested in the TCGA pan-cancer database, the sensitivity is 0.989 and the negative predictive value is 0.994 when determining whether a high-grade group classified using the gene combination of this invention corresponds to a high TMB score group.Clinically, a microsatellite instability (MSI) score of 10 or higher is defined as the high-score MSI group. When this criterion is defined as the MSI gold standard and tested in the TCGA pan-cancer database, the sensitivity to determine whether a high-grade group classified using the gene combination of this application corresponds to the high-score MSI group is 0.973, and the negative predictive value is 0.996. Therefore, the gene combination of this application provides high accuracy and reliability when grading and predicting homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) in pan-cancer patients.

[0037] [Table 2] TIFF0007897337000006.tif98135

[0038] Experimental Example 2: Verification of the practicality of preferred gene combinations and detection methods in evaluating the grading and prediction of homologous recombination deficiency (HRD), tumor mutagenesis (TMB), and microsatellite instability (MSI) in human pan-oncology. In this application, it is possible to arbitrarily select and combine gene fragments from a gene combination (panel) to form a new gene combination, and to use the same criteria to grade and predict homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) in pan-cancer. Here, gene set A1 (see Table 3) was selected from gene set A of gene combination (panel), and gene fragment set B1 (see Table 4) was selected from gene fragment set B to construct gene combination 1 (panel 1), which was used for grading and predicting homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) in pan-cancer, and its practicality was analyzed using the TCGA (PanCancer Atlas) pan-cancer database. Similarly, the criteria are as follows. If at least one gene in gene set A1 has a gene mutation or copy number variation, or if at least one region in gene fragment set B1 has a gene copy number increase, then the patient with pan-cancer is classified as high-grade and shows high homologous recombination deficiency (HRD) scores, tumor mutation burden (TMB) scores, and microsatellite instability (MSI) scores. On the other hand, if no gene mutations or copy number variations occur in the genes of gene set A1, and no gene copy number increase occurs in any of the fragments of gene fragment set B1, then such a patient with pan-cancer is classified as low-grade and shows low homologous recombination deficiency (HRD) scores, tumor mutation burden (TMB) scores, and microsatellite instability (MSI) scores. In this experimental example, gene combination 1 (panel 1) is a preferred gene combination (panel) and has fewer targets, resulting in lower costs.

[0039] [Table 3]

[0040] [Table 4] TIFF0007897337000009.tif143139

[0041] The procedure described in Example 2 was followed, and using gene combination 1 (panel 1), homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) were graded for 9896 patients in the above-mentioned pan-cancer database. The patients were then divided into high-grade and low-grade groups, with 39.1% in the high-grade group and 60.9% in the low-grade group. Based on the homologous recombination deficiency (HRD) score, tumor mutational burden (TMB) score, and microsatellite instability (MSI) score in the TCGA pan-cancer database, a Mann-Whitney U test was performed. The results showed that there were statistically significant differences between the high-grade and low-grade groups in homologous recombination deficiency score (Figure 4), tumor mutational burden score (Figure 5), and microsatellite instability score (Figure 6), as expected for the grouping described in this application. The high-grade group had significantly higher homologous recombination deficiency scores (p=8.594 e-265), tumor mutation burden scores (p=8.281 e-263), and microsatellite instability scores (p=7.486 e-107). Clinically, a homologous recombination deficiency (HRD) score of 42 or higher is usually defined as the high-score HRD group. When this criterion is defined as the HRD gold standard and tested in the TCGA pan-cancer database, the sensitivity to determine whether the high-grade group classified for homologous recombination deficiency (HRD) using gene combination 1 of this application is the high-score HRD group is 0.712, and the negative predictive value is 0.944. Clinically, a tumor mutational burden (TMB) of 10 / Mb or higher is usually defined as a high TMB score group. When this criterion is defined as the TMB gold standard and tested in the TCGA pan-cancer database, the sensitivity is 0.692 and the negative predictive value is 0.935 when determining whether a high-grade group classified using gene combination 1 of the present invention corresponds to a high TMB score group.Clinically, a microsatellite instability (MSI) score of 10 or higher is defined as the high-score MSI group. When this criterion is defined as the MSI gold standard and tested in the TCGA pan-cancer database, the sensitivity to determine whether a high-grade group classified using gene combination 1 of this application corresponds to the high-score MSI group is 0.704, and the negative predictive value is 0.985. Therefore, even when using other gene combinations selected from the gene combinations (panel) in this application, grading and prediction of homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) can be performed for pan-cancer patients.

[0042] It is clear that the above embodiments are merely illustrative for clarity and do not limit the embodiments. Those skilled in the art can make other different forms of variations or modifications based on the above description. It is not necessary, and impossible, to list all embodiments here. Any obvious variations or modifications resulting therefrom are considered to fall within the scope of protection of the present invention.

Claims

1. The use of gene combinations in the manufacture of products for grading and detecting homologous recombination defects, tumor mutagenesis, and / or microsatellite instability in human tumors, The aforementioned gene combination consists of gene set A and gene set B. The gene set A includes CALML6, CCDC136, EGR1, FAM71E2, GLIS1, IFITM3, KNOP1, KRT76, KRT9, KRTAP10-10, MAF, MNS1, PROSER3, SCYL1, SLC16A6, SRC, TCEAL5, TMEM82, TRIM26, U2AF2, USP35, WBP2NL, WDR44, ZNF20, and ZNF700, and The aforementioned gene set B includes TTN, KLF7, WNT6, COLEC11, CHST13, PLXND1, FOXL2, PSMD2, CTBP1, GPR78, USP17L5, FAT2, SAMD5, ARID1B, C6orf118, E2F3, hCG11, B3GAT2, KCNQ5, ACHE, KBTBD11, and BMP1. , including DUSP4, SHC3, PI4K2A, MYOD1, PLAAT2, IQSEC3, HOXC13, CLSTN3, GAS6, HCN4, LOXL1, CIB2, HDGFL3, MTCL1, ZNF726, EBI3, PPP5C, FOXA2, HELZ2, TSPER, SEPTIN5, MYH9, and WNT7B, The use is characterized in that the sample to be detected in the grading and detection of homologous recombination deficiencies, tumor mutational burdens, and / or microsatellite instability in the aforementioned human tumors is pan-cancer.

2. The grading and prediction of homologous recombination deficiencies, tumor mutational burdens, and / or microsatellite instability in the aforementioned human tumors are used to guide clinical diagnosis and treatment. The use according to claim 1, characterized in that, at the discretion of the user, the grading is divided into a high-grade group and a low-grade group.

3. The use according to claim 1 or 2, characterized in that the product includes primers, probes, reagents, kits, gene chips, or detection systems for detecting the genotype of a gene in the gene combination.

4. The use of the product according to claim 3, characterized in that it detects exons and associated intron regions of genes in gene set A and gene set B.

5. The grading method for homologous recombination deficiencies, tumor mutation burdens, and / or microsatellite instability in human tumors is: Step S1 involves evaluating gene mutations and gene copy number variations of genes included in gene set A in tumor cell tissue, and evaluating gene copy number variations of gene set B in tumor cell tissue. The use according to claim 1 or 2, characterized by comprising step S2, which determines and predicts the grade of homologous recombination deficiency, tumor mutation burden, and / or microsatellite instability of cancer based on the evaluation results of step S1.

6. The use according to claim 5, characterized in that the gene mutation includes base substitution mutations, deletion mutations, insertion mutations, and / or fusion mutations, and the gene copy number variation includes gene copy number increase and / or gene copy number decrease.

7. The use according to claim 5, characterized in that in step S1, sequencing data of tumor cell tissue and normal tissue are compared, gene mutations and copy number variations of genes included in gene set A are evaluated, and gene copy number variations of gene set B are evaluated.

8. The use according to claim 5, characterized in that in step S2, if a gene mutation or copy number variation occurs in at least one gene of gene set A, or if a gene copy number increase occurs in at least one gene of gene set B, the grading is in the high-grade group, on the other hand, if no gene mutation or copy number variation occurs in any of the genes of gene set A, and no gene copy number increase occurs in any of the genes of gene set B, the grading is in the low-grade group.

9. The use according to claim 1 or 2, characterized by selecting and combining any gene fragment from the aforementioned gene combination to form a new gene combination, and grading and predicting homologous recombination deficiency, tumor mutation burden, and / or microsatellite instability of a tumor by the same human tumor homologous recombination deficiency, tumor mutation burden, and / or microsatellite instability grading method, thereby guiding clinical diagnosis and treatment.

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