Use of gene combinations in the manufacture of products for grading and detection of homologous recombination deficiency, tumor mutation burden, 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 the priority of a Chinese patent application with an application number of 202210555507.8 and an invention title of "Use of Gene Combinations in Products for Grading and Detecting Homologous Recombination Deficiency, Tumor Mutation Burden, and Microsatellite Instability of Human Tumors", which was filed with the China National Intellectual Property Administration on May 20, 2022. All the contents of the 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 detecting homologous recombination deficiency, tumor mutation burden, and microsatellite instability of human tumors.

Background Art

[0003] Homologous recombination deficiency (HRD), Tumor Mutation Burden (TMB), and Microsatellite Instability (MSI) are important clinical indicators commonly used to determine the therapeutic effects of malignant tumor immunotherapy and PARP inhibitors. Usually, different sequencing methods are required to obtain complete information regarding the determination of the above three types of indicators. Usually, in the case of homologous recombination deficiency (HRD) and microsatellite instability (MSI), it is necessary to design a special sequencing panel to sequence the genes in a specific region. Finally, the final HRD score and MSI score are calculated, and the levels of HRD and MSI are determined. Usually, in the case of Tumor Mutation Burden (TMB), whole exome sequencing is required to finally determine the level of TMB. The conventional detection methods for homologous recombination deficiency (HRD), Tumor Mutation Burden (TMB), and Microsatellite Instability (MSI) are reliable. However, when detecting the above three indicators simultaneously, there are the following obvious defects. When methods such as whole exome sequencing are used to detect these indicators, although the accuracy is high, the cost is high and it incurs huge medical expenses. Therefore, in order to reduce the detection cost while ensuring high accuracy, it is necessary to find a new grading system based on the detection of specific genes for tumor homologous recombination deficiency (HRD), Tumor Mutation Burden (TMB), and Microsatellite Instability (MSI).

Summary of the Invention

Problems to be Solved by the Invention

[0004] Therefore, the technical problem to be solved by the present application is to provide the use of a gene combination in the manufacture of a product for grading and detecting homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) in pan-cancer. This gene combination can perform grading and prediction of homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) in pan-cancer. To achieve this purpose, in the present application, by using the whole-exome sequencing technology to screen the sequencing data of patients specifically screened and grouped from Peking University First Hospital, the gene combination of the present invention is finally obtained. When the gene combination of the present invention is used for grading and prediction of homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) in pan-cancer, it provides accurate grading information and disease prediction information of homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) to clinicians and patients. Moreover, compared with whole-exome sequencing, since the present application has fewer such target genes, at the same sequencing depth, the cost is significantly reduced.

Means for Solving the Problem

[0005] The present application is the use of a gene combination in the manufacture of a product for grading and detecting homologous recombination deficiency, tumor mutation burden, and / or microsatellite instability in human tumors, wherein the gene combination consists of gene set A and gene fragment set B. The gene set A includes at least one of 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, MRTFA, 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 gene fragment set B is located at 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, chr4:8579501-8590249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:147819501-147840249, chr6:157089501-157110249, chr6:164889501-164900249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:71659501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, chr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-29200249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:17739501-17760249, chr11:63329501-63350249, chr12:169501-250249, chr12:54329501-54350249, chr12:63179501-63550249, chr12:7269501-7310249, chr13:114519501-114530249, chr15:73649501-73670249, chr15:74209501-74220249, chr15:78409501-78430249, chr15:83859501-83880249, chr18:8809501-8820249, chr19:24059501-24070249, chr19:4229501-4250249, chr19:46879501-46900249, chr20:22559501-22570249,Provided for use is at least one of chr20:62189501-62200249, chr21:45949501-46110249, chr22:19499501-19760249, chr22:36649501-38700249, and chr22:46309501-47080249, wherein the positions of the gene fragments in the gene fragment set B are annotated based on GRCh37, and although the number may change in GRCh38 or new versions of the human reference genome that may emerge in the future, the positions of the target fragments and the genes that can be used for detection do not change.

[0006] Optionally, the detailed genes included in the gene fragment set B are shown in the following table. TIFF2025516889000002.tif232163 TIFF2025516889000003.tif248163

[0007] Optionally, the 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 gene fragment set B is located at 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, chr4:8579501-8590249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:147819501-147840249, chr6:157089501-157110249, chr6:164889501-164900249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:71659501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, chr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-29200249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:17739501-17760249, chr11:63329501-63350249, chr12:169501-250249, chr12:54329501-54350249, chr12:7269501-7310249, chr13:114519501-114530249, chr15:73649501-73670249, chr15:74209501-74220249, chr15:78409501-78430249, chr15:83859501-83880249, chr18:8809501-8820249, chr19:24059501-24070249, chr19:4229501-4250249, chr19:46879501-46900249, chr20:22559501-22570249, chr20:62189501-62200249,It includes at least one of chr21:45949501-46110249, chr22:19499501-19760249, chr22:36649501-38700249, and chr22:46309501-47080249.

[0008] Optionally, the sample to be detected in the grading and detection of homologous recombination deficiency, tumor mutation burden, and / or microsatellite instability of the human tumor is pan-cancer.

[0009] Optionally, the grading and prediction of homologous recombination deficiency (HRD), tumor mutation burden (TMB), and / or microsatellite instability (MSI) of the tumor are used as a guide for clinical diagnosis and treatment. Optionally, the grading is divided into a high-grade group and a low-grade group.

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

[0011] Optionally, the product detects the exons and related 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 Evaluating gene mutations and gene copy number polymorphisms of genes included in gene set A in the tumor cell tissue, and evaluating the gene copy number polymorphisms of gene fragment set B in the tumor cell tissue, step S1; Based on the evaluation results of step S1, determining the grades of cancer homologous recombination deficiency (HRD), tumor mutation burden (TMB), and / or microsatellite instability (MSI), and performing prediction, step S2.

[0013] Optionally, the gene mutation includes a base substitution mutation, a deletion mutation, an insertion mutation, and / or a fusion mutation, and the gene copy number polymorphism includes an increase in gene copy number and / or a decrease in gene copy number.

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

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

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

Advantages of the Invention

[0017] The technical solution of the present 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, accuracy, and credibility, enabling accurate grading and prediction of pan-cancer homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI).

[0019] 2. The gene combinations in this application are diverse, allowing 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, and significant cost savings at the same depth and accuracy of sequencing, thus having high versatility.

Brief Description of the Drawings

[0021] To more clearly explain 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 obvious 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

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Modes for Carrying Out the Invention

[0023] The following examples are provided to better understand 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] When specific experimental steps or conditions are not specified in the examples, they can be carried out according to the operations or conditions of the normal experimental steps described in the literature in the art. Reagents or equipment whose manufacturers are not specified are all ordinary reagent products that can be obtained through commercial channels.

[0025] Example 1 Gene combination (Panel) for grading homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) of human tumors The inventors mainly performed screening using the high-throughput database of exon sequencing from patients in Peking University First Hospital to determine a gene combination (panel) for grading homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) of human tumors. This gene combination includes gene set A and gene fragment set B. The gene set A includes at least one of 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, MRTFA, 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 gene fragment set B is located at 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, chr4:8579501-8590249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:147819501-147840249, chr6:157089501-157110249, chr6:164889501-164900249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:71659501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, chr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-29200249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:17739501-17760249, chr11:63329501-63350249, chr12:169501-250249, chr12:54329501-54350249, chr12:63179501-63550249, chr12:7269501-7310249, chr13:114519501-114530249, chr15:73649501-73670249, chr15:74209501-74220249, chr15:78409501-78430249, chr15:83859501-83880249, chr18:8809501-8820249, chr19:24059501-24070249, chr19:4229501-4250249, chr19:46879501-46900249, chr20:22559501-22570249,It 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 gene fragment set B are annotated based on GRCh37. In GRCh38 or new versions of the human reference genome that will appear in the future, the number may change, but the positions of the target fragments and the genes that can be used for detection do not change.

[0026] Optionally, the detailed genes included in the gene fragment set B are shown in the following table.

[0027]

Table 1

[0028] Example 2 Method for Grading and Predicting Homologous Recombination Deficiency (HRD), Tumor Mutation Burden (TMB), and Microsatellite Instability (MSI) in Human Pancancer This example provides a method for grading and detecting homologous recombination deficiency, tumor mutation burden, and microsatellite instability in human tumors. This method includes using the gene combination (panel) in Example 1 to grade and predict homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) in human pancancer. The specific steps are as follows.

[0029] (1) Specimens of pan-cancer tissues (herein, pan-cancer is defined as all cancer types in the pan-cancer data of TCGA, including adrenal cancer, urothelial cancer, breast cancer, cervical cancer, cholangiocarcinoma, colon cancer, lymphoma, esophageal cancer, glioblastoma, head and neck squamous cell carcinoma, clear cell renal cell carcinoma, renal papillary cell carcinoma, 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, choroidal melanoma. Since all pan-cancers mentioned in this application are defined as such, they will not be repeatedly described.) and healthy control tissue specimens were collected. The pan-cancer tissue specimens may be pan-cancer cell lines, fresh pan-cancer specimens, frozen pan-cancer specimens, or paraffin-embedded pan-cancer specimens. The healthy control tissue may be tissue from known healthy individuals, or tissue adjacent to the cancer or other non-tumor tissue from pan-cancer patients. In this example, paraffin-embedded pan-cancer specimens were selected, normal tissue adjacent to the cancer was used as the healthy control tissue, DNA was extracted by conventional methods, libraries were constructed by conventional methods, and finally, target high-throughput sequencing was performed using the gene combination (panel) of Example 1 to compare the sequencing data of pan-cancer tissue and healthy tissue, and gene mutations (Mutations) and copy number variations (CNVs) of each gene in gene set A in the gene combination of the pan-cancer tissue, as well as copy number variations (CNVs) of each gene in gene fragment set B, were obtained.

[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) It was determined based on the mutations of each gene in the gene combination of the pan-cancer tissue obtained in step (1), and / or the mutations.

[0032] When there is a gene mutation or copy number polymorphism in at least one gene of gene set A, or when there is an increase in gene copy number in at least one region of gene fragment set B, the pan-cancer patient belongs to the high-grade group, and has a high homologous recombination deficiency (HRD) score, tumor mutation burden (TMB) score, and microsatellite instability (MSI) score. On the other hand, when there is no gene mutation or copy number polymorphism in the genes of gene set A, and there is no increase in gene copy number in any of the fragments of gene fragment set B, the pan-cancer patient belongs to the low-grade group, and has a low homologous recombination deficiency (HRD) score, tumor mutation burden (TMB) score, and microsatellite instability (MSI) score.

[0033] Example 3 As an alternative embodiment, in the present application, it is possible to select and recombine the genes in the gene panel in Example 1 to form a new gene combination. The evaluation criteria are as follows. In the case of genes selected from gene set A, if there is a gene mutation or copy number polymorphism in at least one of them, it indicates that the pan-cancer patient belongs to the high-grade group. Or, in the case of gene fragments selected from gene fragment set B, if there is an increase in gene copy number in at least one of the regions, it indicates that the pan-cancer patient belongs to the high-grade group. On the other hand, if there is no gene mutation or copy number polymorphism in the genes selected from gene set A, and there is no increase in copy number in the fragments selected from gene fragment set B, the pan-cancer patient belongs to the low-grade group.

[0034] Experimental Example 1 Verification of the Practicality of Gene Combinations and Detection Methods for Grading Homologous Recombination Deficiency (HRD), Tumor Mutation Burden (TMB), and Microsatellite Instability (MSI) in Human Tumors, and Evaluation of Grading and Prediction of Homologous Recombination Deficiency (HRD), Tumor Mutation Burden (TMB), and Microsatellite Instability (MSI) in Human Pan-Cancer Tumors The TCGA (PanCancer Atlas) pan-cancer database is a globally recognized pan-cancer database and is useful for verifying the utility and reliability of the present application in the evaluation of the grading and prediction of homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) in pan-cancer.

[0035] The TCGA (PanCancer Atlas) pan-cancer data contains a total of 10,967 cases of pan-cancer data, of which 9,896 cases have complete gene mutation and copy number polymorphism data and meet the usage conditions of the present application.

[0036] Performed according to the method described in Example 2. In this experimental example, all the genes of gene set A and all the fragments of gene fragment set B in Example 1 were selected for implementation. The genes actually used for detection in gene fragment set B 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 a high-grade group and a low-grade group. Among them, the proportion of the high-grade group is 77.0%, and the proportion of the low-grade group is 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, the Mann-Whitney U test was performed. As a result, in both the high-grade group and the low-grade group, there are statistical differences in the homologous recombination deficiency score (Figure 1), tumor mutation burden score (Figure 2), and microsatellite instability score (Figure 3), which is as expected when grouping in this application. In the high-grade group, the homologous recombination deficiency score (p-value = 6.936 e-231), tumor mutation burden score (p-value = 1.954 e-293), and microsatellite instability score (p-value = 4.654 e-90) are significantly higher. Clinically, usually, when the homologous recombination deficiency (HRD) score is 42 or more, it is defined as the HRD high-score group. Defining this criterion as the HRD gold standard and performing a test in the TCGA pan-cancer database, if it is determined whether the high-grade group classified for homologous recombination deficiency (HRD) using the gene combination of this application is the HRD high-score group, the sensitivity is 0.979, and the negative predictive value is 0.989. Clinically, when the tumor mutation burden (TMB) is 10 / Mb or more, it is defined as the TMB high-score group. Defining this criterion as the TMB gold standard and performing a test in the TCGA pan-cancer database, if it is determined whether the high-grade group classified for tumor mutation burden (TMB) using the gene combination of this application is the TMB high-score group, the sensitivity is 0.989, and the negative predictive value is 0.994.Clinically, a microsatellite instability (MSI) score of 10 or more is defined as the high MSI score group. When this criterion is defined as the MSI gold standard and tested in the TCGA pan-cancer database, if it is determined whether the high-grade group classified by microsatellite instability (MSI) using the gene combination of the present application is the high MSI score group, the sensitivity is 0.973 and the negative predictive value is 0.996. Therefore, when grading and predicting homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) for pan-cancer patients using the gene combination of the present application, the accuracy and reliability are high.

[0037]

Table 2

[0038] Experimental Example 2 Verification of the practicality of a preferred gene combination and detection method in the evaluation of grading and prediction of homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) in human pan-cancer In this application, gene fragments are arbitrarily selected and combined from a gene panel to form a new gene combination, and using the same judgment criteria, it is possible to grade and predict pan-cancer homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI). Here, gene set A1 (see Table 3) is selected from gene set A of the gene panel, gene fragment set B1 (see Table 4) is selected from gene fragment set B, and gene combination 1 (panel 1) is constructed and used for grading and prediction of pan-cancer homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI), and the practicality is analyzed using the TCGA (PanCancer Atlas) pan-cancer database. Similarly, the judgment criteria are as follows. If there is a gene mutation or copy number polymorphism in at least one gene in gene set A1, or if there is a gene copy number increase in at least one region in gene fragment set B1, the pan-cancer patient belongs to the high-grade group, indicating a high homologous recombination deficiency (HRD) score, tumor mutation burden (TMB) score, and microsatellite instability (MSI) score. On the other hand, if there is no gene mutation or copy number polymorphism in the genes of gene set A1 and no gene copy number increase occurs in any of the fragments of gene fragment set B1, such a pan-cancer patient belongs to the low-grade group, with a low homologous recombination deficiency (HRD) score, tumor mutation burden (TMB), and microsatellite instability (MSI) score. In this experimental example, gene combination 1 (panel 1) is a preferred one based on the gene panel, and because it has fewer targets, the cost is lower.

[0039]

Table 3

[0040]

Table 4

[0041] Performed according to the method described in Example 2, using gene combination 1 (panel 1), for 9,896 patients in the above-mentioned pan-cancer database, homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) were graded and divided into a high-grade group and a low-grade group. Among them, the proportion of the high-grade group was 39.1%, and the proportion of the low-grade group was 60.9%. Based on the homologous recombination deficiency (HRD) score, tumor mutation burden (TMB) score, and microsatellite instability (MSI) score in the TCGA pan-cancer database, the Mann-Whitney U test was performed. As a result, in both the high-grade group and the low-grade group, there were statistical differences in the homologous recombination deficiency score (Figure 4), tumor mutation burden score (Figure 5), and microsatellite instability score (Figure 6), which was as expected when grouping in the present application. The high-grade group had significantly higher homologous recombination deficiency scores (p-value = 8.594 e-265), tumor mutation burden scores (p-value = 8.281 e-263), and microsatellite instability scores (p-value = 7.486 e-107). Clinically, usually, a homologous recombination deficiency (HRD) score of 42 or more is defined as the HRD high-score group. Defining this criterion as the HRD gold standard and performing a test in the TCGA pan-cancer database, if it is determined whether the high-grade group classified for homologous recombination deficiency (HRD) using gene combination 1 of the present application is the HRD high-score group, the sensitivity is 0.712, and the negative predictive value is 0.944. Clinically, usually, a tumor mutation burden (TMB) of 10 / Mb or more is defined as the TMB high-score group. Defining this criterion as the TMB gold standard and performing a test in the TCGA pan-cancer database, if it is determined whether the high-grade group classified for tumor mutation burden (TMB) using gene combination 1 of the present application is the TMB high-score group, the sensitivity is 0.692, and the negative predictive value is 0.935.Clinically, a microsatellite instability (MSI) score of 10 or more is defined as the high MSI score group. When this criterion is defined as the MSI gold standard and tested in the TCGA pan-cancer database, if it is determined whether the high-grade group classified by using the gene combination 1 of the present application for microsatellite instability (MSI) is the high MSI score group, the sensitivity is 0.704 and the negative predictive value is 0.985. Therefore, even if other gene combinations selected from the gene panel of the present application are used, for pan-cancer patients, the grading and prediction of homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) can be performed.

[0042] It is obvious that the above embodiments are merely examples for clear explanation and do not limit the embodiments. For those skilled in the art, based on the above description, other different forms of changes or modifications can be made. Here, it is not necessary to list all the embodiments limitedly, and this is also impossible. The obvious changes or modifications derived therefrom shall be included within the protection scope of the present invention.

Claims

1. 1. Use of a gene combination in the manufacture of a product for grading and detecting homologous recombination deficiency, tumor mutation load, and / or microsatellite instability in human tumors, comprising: the gene combination consists of a gene set A and a gene fragment set B, The gene set A includes ASAH1, ASXL1, BCOR, BRAF, CALML6, CCDC136, CIDEC, COX18, CSF1R, CYP3A5, DEK, DNMT3A, EGR1, FAM71E2, FGFR1, FKBP7, FLT1, FLT3, FLT4, GLIS1, GNAQ, IDH2, IFITM3, IMMT, KDR, KIT, KMT2A, KNOP1, KRT76, KRT9, KRTAP10-10, KRTAP10-8, MAF, MECOM, MFRP, MLLT3, MNS1, M 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 gene fragment set B includes 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, 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, chr8:29189501-292 00249, 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 gene fragment set B includes at least one of chr20:62189501-62200249, chr21:45949501-46110249, chr22:19499501-19760249, chr22:36649501-38700249, and chr22:46309501-47080249, and the positions of the gene fragments in the gene fragment set B are annotated with reference to GRCh37.

2. The detailed genes included in the gene fragment set B are shown in the following table: Optionally, said set of genes 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 gene fragment set B includes 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, 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, chr8:29189501-292 00249, 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,The use according to claim 1, characterized in that it comprises at least one of chr21:45949501-46110249, chr22:19499501-19760249, chr22:36649501-38700249, and chr22:46309501-47080249.

3. The use according to claim 1 or 2, characterized in that the sample to be detected in the grading and detection of homologous recombination deficiency, tumor mutation load, and / or microsatellite instability of human tumors is pan-cancer.

4. Grading and predicting homologous recombination deficiency, tumor mutational burden, and / or microsatellite instability of said human tumors for use in guiding clinical diagnosis and treatment; 4. The use according to claim 3, wherein optionally the grading is divided into a high-grade group and a low-grade group.

5. The use according to any one of claims 1 to 4, characterized in that the product comprises a primer, a probe, a reagent, a kit, a gene chip, or a detection system for detecting the genotype of the genes in the gene combination.

6. The use according to claim 5, characterized in that the product detects exons and associated intron regions of genes in gene set A and gene fragment set B.

7. The method for grading homologous recombination deficiency, tumor mutation burden, and / or microsatellite instability of a human tumor comprises: Step S1: 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 fragment set B in tumor cell tissue; The use according to any one of claims 1 to 6, characterized in that it comprises a step S2 of determining and predicting the grade of homologous recombination deficiency, tumor mutation load, and / or microsatellite instability of the cancer based on the evaluation results of step S1.

8. The use according to claim 7, characterized in that the gene mutation comprises a base substitution mutation, a deletion mutation, an insertion mutation, and / or a fusion mutation, and the gene copy number variation comprises a gene copy number increase and / or a gene copy number decrease.

9. The use according to claim 7 or 8, characterized in that in step S1, the sequencing data of the tumor cell tissue and the normal tissue are compared to evaluate gene mutations and copy number variations of the genes included in the gene set A, and evaluate gene copy number variations of the gene fragment set B.

10. The use described in any one of claims 7 to 9, 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 fragment of gene fragment set B, the grading is a high-grade group, while if a gene mutation or copy number variation does not occur in any gene of gene set A, and if a gene copy number increase does not occur in any fragment of gene fragment set B, the grading is a low-grade group.

11. The use of any one of claims 1 to 10, characterized in that any gene fragments are selected and combined from the gene combinations to form new gene combinations, and then the homologous recombination deficiency, tumor mutation load, and microsatellite instability of tumors are graded and predicted by the same method for grading homologous recombination deficiency, tumor mutation load, and / or microsatellite instability of human tumors, thereby guiding clinical diagnosis and treatment.

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