Method, equipment and medium for constructing a database for interpreting tumor somatic cell mutations

Abstract

The present application relates to a method, device and medium for constructing a tumor somatic cell variation interpretation database. The method includes: obtaining public resource data, obtaining gene information and gene variation data to be interpreted therefrom; making carcinogenicity judgments on gene variation data; obtaining biological function and carcinogenic mechanism data from the carcinogenicity judgment results, interpreting gene variation data, and obtaining gene variation data to be interpreted with clinical significance; obtaining common biological marker data of genes, matching and interpreting the biological marker data, and obtaining the clinical significance data and evidence classification data of the biological marker data corresponding to each gene in different types of cancer; after marking the gene variation data to be interpreted with clinical significance according to the biological marker data, matching and obtaining the clinical significance interpretation data in different types of cancer, and then constructing a tumor somatic cell variation interpretation database. The present application effectively improves the efficiency, accuracy and standardization of tumor somatic cell variation interpretation.

Description

Technical Field

[0001] The present invention relates to the technical field of database construction, and in particular to a method, device and medium for constructing a tumor somatic cell variation interpretation database. Background Art

[0002] In recent years, with the successful completion of the Human Genome Project, the field of gene sequencing has experienced rapid development, and the advancement of sequencing technology has led to a continuous decline in sequencing costs. Genetic testing is increasingly used in guiding personalized medication for cancer patients. Doctors, patients and their relatives are increasingly accepting of genetic testing, and the market prospects are showing a trend of continuous expansion. More and more testing institutions are committed to providing personalized precision treatment testing and interpretation services for cancer patients, prompting a large number of clinical patients to actively choose treatment plans tailored for them. However, the advancement of second-generation sequencing technology has also brought a huge amount of gene variation data. How to efficiently and accurately analyze and interpret these gene variation data has become the key to achieving precision medicine for tumors. Some medical analysis institutions still use manual data collation to generate reports, which is inefficient and has limited accuracy. Although some institutions have tried to build an internal gene variation medication interpretation database to improve work efficiency, they still face many challenges in database structure construction, data collection, updating and utilization.

[0003] At present, the information storage database for tumor precision medication guidance is relatively scattered, and analysts need to screen out key information from massive data for clinical reference, which greatly reduces work efficiency. At the same time, the lack of unified variant interpretation methods and reference standards makes the process of extracting important information from massive data lack of standardization, relying on the subjective judgment of the interpreters, which easily leads to information omissions, thereby reducing the accuracy and standardization of the interpretation results. In addition, when constructing an internal interpretation database, how to effectively establish structural relationships and associations between the same type of gene variants, between gene variants and clinical significance, and between the clinical significance of the same biological markers in different tumor types is still a technical problem that needs to be solved urgently. For example, although the interpretation of the biological function and carcinogenic mechanism of the same gene variant in different cancer types is not much different, its clinical significance in different cancer types may be significantly different. How to organically associate gene variants with clinical significance to ensure the accuracy and personalization of interpretation results still needs further research and exploration.

[0004] Therefore, building a comprehensive, accurate, real-time updated and easy-to-use tumor gene variation interpretation database has become a major technical challenge that needs to be urgently addressed. Summary of the invention

[0005] In order to improve the efficiency, accuracy and standardization of precise interpretation of tumor gene mutations, the present application provides a method, device and medium for constructing a tumor somatic cell mutation interpretation database.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0007] A method for constructing a tumor somatic cell variation interpretation database, characterized in that the method for constructing a tumor somatic cell variation interpretation database comprises:

[0008] Acquire public resource data, and acquire to-be-interpreted gene information and gene variation data corresponding to the to-be-interpreted gene information from the public resource data;

[0009] Performing carcinogenicity judgment on the gene variation data according to the public resource data to obtain a carcinogenicity judgment result;

[0010] Obtaining biological function data and carcinogenic mechanism data from the carcinogenicity judgment result, performing individual or group interpretation of the gene variation data according to the biological function data and the carcinogenic mechanism data to obtain variation interpretation data, and using the variation interpretation data as the gene variation data to be interpreted with clinical significance;

[0011] Acquire common biomarker data of genes, match and interpret the clinical significance of the biomarker data in different cancer types according to the public resource data, and obtain clinical significance data and clinical significance evidence grading and classification data of the biomarker data corresponding to each gene in different cancer types as clinical significance interpretation data;

[0012] After the gene variation data to be interpreted for clinical significance are marked with biomarkers according to the biomarker data, the clinical significance interpretation data of each gene variation data to be interpreted for clinical significance in different cancer types are obtained by matching, and the tumor somatic variation interpretation database is constructed according to the variation interpretation data and the clinical significance interpretation data of the gene variation data.

[0013] By adopting the above technical scheme, by obtaining the gene information to be interpreted and its corresponding gene variation data, and combining the public resource data to make a carcinogenicity judgment to obtain the carcinogenicity judgment result, the accurate and standardized interpretation of the gene variation data is achieved; on the basis of obtaining the carcinogenicity judgment result, the biological function data and carcinogenic mechanism data are further extracted, and the gene data to be interpreted for clinical significance is systematically interpreted by individual interpretation or group interpretation, which makes the interpretation of gene variation data more accurate, efficient and standardized; by obtaining the clinical significance of the gene's biological markers in different cancer types and the classification of the level of evidence, the standardization and specialization of clinical significance interpretation is achieved. By associating the second biological marker to annotate the shared clinical significance of different biological markers of the same gene in the same cancer type, the clinical significance annotation process of the biological marker is simplified and the annotation difficulty is reduced. By marking the genetic data for clinical significance interpretation with biological markers, efficient processing of the matching genetic variation data is achieved, and the complexity of clinical significance matching is simplified; by building a unified tumor somatic cell variation interpretation process, the interpreters can efficiently obtain and use key information, greatly reducing the workload of manual annotation. The automatic matching and processing of specific content in the process by public resource data avoids repeated searches and tedious sorting, and improves the comprehensiveness and speed of annotation. In addition, the existing data in the annotation library is regularly updated and maintained to ensure the accuracy and timeliness of the database, providing reliable data support for subsequent research and application.

[0014] In a preferred example, the present application may be further configured as follows: the obtaining of public resource data, obtaining the gene information to be interpreted from the public resource data, and the gene variation data corresponding to the gene information to be interpreted, specifically includes:

[0015] Obtaining preset gene key field information, screening and classifying the public resource data according to the gene key field information, and obtaining gene interpretation fields and the gene information to be interpreted;

[0016] Gene variation information corresponding to the gene information to be interpreted is obtained, and the gene variation data corresponding to the gene information to be interpreted is obtained through the gene interpretation field and the gene variation information.

[0017] By adopting the above technical solution, by obtaining the preset gene key field information, and screening and classifying the public resource data according to the gene key field information, the gene interpretation field is obtained, thereby standardizing the content and structure of the gene interpretation. The gene variation information corresponding to the gene to be interpreted is obtained, and the gene variation information to be judged for carcinogenicity is obtained through the gene interpretation field and gene variation information, providing accurate site information for the next step of judging the carcinogenicity of the variation, standardizing the variation information content, and reducing errors caused by information redundancy. The above technical solution can avoid tedious manual operations, significantly improve the efficiency of gene variation interpretation, and improve the accuracy and consistency of variation data processing.

[0018] In a preferred example, the present application may be further configured as follows: the carcinogenicity judgment of the gene variation data is performed according to the public resource data to obtain the carcinogenicity judgment result, specifically including:

[0019] Acquire gene variation attribute data of the gene variation data from the public resource data;

[0020] Carcinogenicity judgment is performed based on the gene variation attribute data to obtain the corresponding carcinogenicity judgment result.

[0021] By adopting the above technical solution, public resource data is obtained, and the carcinogenicity of the gene variation data is judged according to the public resource data. According to the gene variation data to be judged for carcinogenicity and the gene variation attribute data obtained from the public resource data, the corresponding carcinogenicity judgment result is obtained, which standardizes the principle of carcinogenicity judgment of variation data and provides a basis for the next step of accurate interpretation.

[0022] In a preferred example, the present application can be further configured as follows: the biological function data and the carcinogenic mechanism data are obtained from the carcinogenicity judgment result, and the gene variation data are interpreted individually or in groups according to the biological function data and the carcinogenic mechanism data to obtain variation interpretation data, and the variation interpretation data is used as the gene variation data to be interpreted with clinical significance, specifically including:

[0023] Determining the biological function data and carcinogenic mechanism data in the carcinogenic or potentially carcinogenic gene variation data to obtain a determination result;

[0024] storing the gene variation data with inconsistent judgment results as a separate analysis data set, and storing the gene variation data with consistent judgment results as a variation grouping data set;

[0025] The individual analysis data set or the variation grouping data set is interpreted respectively to obtain the gene variation data to be interpreted with clinical significance.

[0026] By adopting the above technical solution, the gene variation data is divided into a separate analysis data set and a variation grouping data set according to the consistency judgment results of the biological function data and the carcinogenic mechanism data, which avoids some repetitive work in the process of gene variation interpretation and reduces the difficulty of variation interpretation; by interpreting these two types of data sets separately, the gene data to be interpreted for clinical significance is obtained, which can significantly improve the efficiency of gene variation annotation. At the same time, the accuracy and standardization of variation annotation results are also guaranteed.

[0027] In a preferred example, the present application may be further configured as follows: interpreting the variation grouping data set to obtain the gene variation data to be interpreted with clinical significance, specifically including:

[0028] Obtaining the gene mutation type, biological function data and carcinogenic mechanism data corresponding to each of the gene variation data;

[0029] The gene variation data are grouped and marked according to the gene mutation type, the biological function data and the carcinogenic mechanism data, and the grouped data are interpreted for variation to obtain the gene variation data to be interpreted for clinical significance.

[0030] By adopting the above technical solution, the gene mutation type, biological function and carcinogenic mechanism corresponding to each of the gene mutation data are obtained; the gene mutation data are grouped and marked according to the gene mutation type, biological function and carcinogenic mechanism, and the grouped data are uniformly interpreted for mutation, thereby significantly improving the accuracy and standardization of mutation annotation, avoiding repetitive work, greatly improving work efficiency, and improving the consistency of annotation content. Specifically, first, the gene mutation data to be interpreted, including biological function data and carcinogenic mechanism data, are obtained from public resource data, ensuring the reliability and diversity of data sources, covering a wide range of public databases and authoritative literature, and laying a solid foundation for subsequent interpretation; then, the gene mutation data with the same or similar biological functions and carcinogenic mechanisms are grouped and marked. This process not only avoids repetitive work, greatly reduces the time and labor cost of data entry, improves work efficiency, but also ensures the consistency and standardization of mutation interpretation. For example, for nonsense / frameshift mutations, splice mutations, stop codon mutations, in-frame insertions and deletions, and copy number loss mutations of tumor suppressor genes, the unified mutation grouping labels are XXtruncating mutations, XX splice site mutations, XX stop loss mutations, XXinsertion / deletion mutations, and XX copy number loss. For copy number gain, fusion mutations, and in-frame insertion and deletion of proto-oncogenes, the unified mutation grouping labels are XX copy number gain, XX fusion, and XXinsertion / deletion mutations. Finally, detailed mutation interpretation is performed on the grouped mutation data, which not only improves the accuracy and depth of the interpretation, but also provides a reliable basis for clinical application.

[0031] In a preferred example, the present application can be further configured as follows: the common biomarker data of genes are obtained, and the clinical significance of the biomarker data in different cancer types is matched and interpreted according to the public resource data, so as to obtain the clinical significance data and clinical significance evidence classification data of the biomarker data corresponding to each gene in different cancer types as clinical significance interpretation data, which specifically includes:

[0032] Extracting common biological markers of the gene from the public resource data to obtain corresponding biological marker information data;

[0033] Obtain cancer type information from commonly used clinical tumor diagnosis and treatment guidelines to obtain cancer type data to be annotated;

[0034] Interpreting the clinical significance of the biological marker information data in different cancer types according to the public resource data to obtain clinical significance data of the biological marker in different cancer types;

[0035] The clinical significance evidence of biological markers in different types of cancer is graded and classified according to preset normative standard data to obtain the clinical significance evidence graded and classified data.

[0036] By adopting the above technical solution, common biomarkers of the genes are obtained from public resource data, and a set of common biomarkers of genes is constructed to prepare for the biomarker labeling of gene variation data to be interpreted for clinical significance. By obtaining cancer information, the clinical significance (prevention, diagnosis, treatment, prognosis) of biomarkers in different cancer types is interpreted according to public resource data, and a set of common biomarkers of genes in different cancer types is constructed to provide a basis for matching the clinical significance of gene variations in specific cancer types, and also provide scientific support for clinical decision-making. By grading and classifying the collected clinical significance evidence, the interpretation of clinical significance is made more professional and standardized, and the work efficiency of clinical significance interpretation is also improved.

[0037] In a preferred example, the present application may be further configured as follows: after interpreting the clinical significance of the biological marker information data in different cancer types according to the public resource data to obtain the clinical significance data of the biological marker in different cancer types, the method for constructing a tumor somatic cell variation interpretation database further includes:

[0038] Acquire different biological markers of the same gene from the public resource data to obtain biological marker information set data of the same gene;

[0039] Normalizing the biological marker information set data of the same gene according to its type to obtain normalized biological marker set data of the same gene;

[0040] According to the normalized biological marker data information, combined with the corresponding annotated cancer information, the markers sharing the same clinical significance are associated with the second biological marker to obtain the corresponding clinical significance association relationship, and the clinical significance data of the biological marker after association is obtained according to the association relationship.

[0041] By adopting the above technical solution, the scope of application is determined according to the type of biological marker, and the naming of different biological markers of the same gene is standardized. According to the standardized biological marker data information, the corresponding second biological marker is associated with the markers that share the same clinical significance, so as to establish a clinical significance association relationship, which significantly improves the accuracy and standardization of clinical significance annotations, avoids duplication of work, and greatly improves work efficiency.

[0042] In a preferred example, the present application can be further configured as follows: after the gene variation data to be interpreted for clinical significance are marked with biomarkers according to the biomarker data set, the clinical significance interpretation data of each gene variation data to be interpreted for clinical significance in different cancer types are matched, and the tumor somatic variation interpretation database is constructed according to the variation interpretation data and the clinical significance interpretation data of the gene variation data.

[0043] By adopting the above technical solutions, when constructing a tumor somatic cell variation interpretation database, by marking and associating the gene variation data with biological markers, the accurate and effective association between gene variation and its clinical significance is achieved, simplifying the database construction process; by constructing historical data on the clinical significance of biological markers in different cancer types, the repeated clinical significance interpretation work is reduced and work efficiency is improved; further, through standardized gene variation interpretation and clinical significance interpretation processes, the standardization of interpretation is improved, thereby reducing the dependence on the knowledge level and experience level of the interpreter. When interpreting tumor somatic cell variation, not only the biological function and carcinogenic mechanism of gene variation are deeply interpreted, but also its clinical significance in the corresponding cancer type is introduced in detail, such as sensitivity to different targeted drugs, drug resistance, and research details of clinical trials. This process ensures the comprehensiveness and practicality of variation interpretation and provides scientific evidence support for clinical decision-making.

[0044] The second object of the invention is achieved by the following technical solutions:

[0045] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for constructing a tumor somatic cell variation interpretation database when executing the computer program.

[0046] The third objective of the present application is achieved through the following technical solutions:

[0047] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for constructing a tumor somatic cell variation interpretation database.

[0048] In summary, the present application includes at least one of the following beneficial technical effects:

[0049] 1. The systematic construction and automated processing of tumor somatic mutation databases greatly improves the efficiency of gene mutation annotation.

[0050] Through the systematic management of the acquisition of gene variation data, carcinogenicity judgment, variation interpretation, biomarker labeling and clinical significance matching, manual operations are effectively reduced, data processing is automated, and the overall interpretation process is significantly accelerated. Through the automatic matching and processing of specific content in the process with public resource data, repeated searches and tedious sorting are avoided, and the comprehensiveness and speed of annotation are improved. For example, the gene variation data obtained by NGS detection of tumor samples from Maijing Clinical Inspection Center and bioinformatics analysis process are transferred to the annotation library through the interface. During the interpretation process, multiple basic tables such as variation grouping table, biomarker table, tumor type table, clinical grade table, evidence grade table, reference table, drug table, approved drug table, clinical trial table, and clinical impact table are extracted. The current annotation library can meet the automatic annotation of more than 90% of the variations in daily samples, thus completely replacing the traditional manual annotation process. In addition, among the remaining 10% of the data that need to be re-investigated, according to the standard interpretation process of the annotation library and the existing data history records, the time required for the re-investigation of a gene variation is shortened by about half compared with the manual annotation period, which significantly improves the efficiency of single variation annotation.

[0051] 2. The systematic construction and professional interpretation process of the tumor somatic mutation database greatly improves the accuracy and standardization of mutation annotation.

[0052] The construction of the database realizes the systematic processing and standardized management of gene variation data, covering the entire process from data acquisition, carcinogenicity judgment to clinical significance markers and matching, ensuring the consistency and standardization of the interpretation results. The process requires strict interpretation of data according to the established steps, which not only ensures the accuracy of annotations and avoids missing key information, but also reduces the dependence on personnel's knowledge level and experience, ensuring the reliability and scientific rigor of the interpretation results. The interpretation process of tumor somatic variation not only covers the carcinogenicity judgment, biological function and carcinogenic mechanism of gene variation, but also introduces in detail its clinical significance and grading and classification in specific cancer types. This multi-dimensional information integration makes the interpretation of variation more comprehensive and scientific, thereby providing professional and accurate guidance for clinical diagnosis and treatment.

[0053] 3. Solve the complex problems faced when building a database and the association between genetic variation and clinical significance.

[0054] By standardizing the naming of biological markers, biological markers are used as a bridge between genetic variation and clinical significance. This association method simplifies the database construction process and avoids repetitive interpretation work, so that the biological functions and carcinogenic mechanisms of different variations of the same gene can be uniformly interpreted in different cancer types, and the clinical significance of different variations of the same gene can be personalized in different cancer types, ensuring the accuracy and pertinence of the interpretation results.

[0055] 4. Solve the problem of many repetitive entry tasks when building a database, interpreting different variants of the same gene, and interpreting the clinical significance of different biological markers of the same gene in the same cancer type.

[0056] By grouping variants, carcinogenic or potentially carcinogenic variants in the same gene with the same or similar biological functions and carcinogenic mechanisms are grouped and marked, and then interpreted uniformly after grouping, avoiding a large amount of repeated entry of variant interpretation work. At the same time, by standardizing the naming of different biological markers of the same gene, when different biological markers share some of the same clinical significance, their clinical significance can be supplemented and annotated by associating with a second biological marker, further reducing the repeated entry of clinical significance interpretation work and improving interpretation efficiency and consistency.

[0057] Through the above technical scheme, the present invention systematically and standardizedly constructs a tumor somatic cell variation interpretation database, which effectively solves the problems of low efficiency, insufficient accuracy, lack of unified standards and complex database construction in the prior art of gene variation interpretation. It has significant technical advantages and broad application value, and can significantly improve the efficiency, accuracy and standardization of gene variation annotation in tumor precision medicine, and meet the high standards of clinical drug guidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Used to guide the construction of a clinical tumor somatic variation interpretation database method process.

[0059] Figure 2 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The present application is further described in detail below in conjunction with the accompanying drawings.

[0061] In one embodiment, if Figure 1 As shown, the present application discloses a method for constructing a tumor somatic cell variation interpretation database, which specifically includes the following steps:

[0062] S10: Acquire public resource data, and acquire to-be-interpreted gene information and gene variation data corresponding to the to-be-interpreted gene information from the public resource data.

[0063] Specifically, the genes that need to be interpreted are obtained from a preset gene library, and information such as gene type, gene function, carcinogenic mechanism, epidemiology and clinical significance are obtained from a public database as gene interpretation information.

[0064] Furthermore, the tumor samples are tested and analyzed by the internal system to obtain the mutation data of the genes to be interpreted, or the tumor hotspot mutation data are obtained by searching commonly used tumor-related databases, such as Cosmic, CKB, and Clinvar, as the data of the gene mutations to be interpreted.

[0065] Furthermore, the genome location information of the corresponding variation data, the cDNA sequence variation information annotated with the transcript number, and the amino acid variation information are obtained from each gene information to be interpreted as the gene variation related information data.

[0066] S20: Performing carcinogenicity judgment on the gene variation data according to the public resource data to obtain a carcinogenicity judgment result.

[0067] Specifically, by obtaining existing public resource data and referring to the "Guidelines for the Classification of Carcinogenicity of Somatic Mutations" jointly developed by ClinGen, CGC and VICC (https: / / doi.org / 10.1016 / j.gim.2022.01.001), we obtain the carcinogenicity evidence corresponding to each gene variation data, and classify the gene variation data accordingly based on the evidence. The classification of carcinogenicity is: carcinogenic, possibly carcinogenic, of unknown significance, possibly benign, and benign, thereby obtaining the carcinogenicity judgment result.

[0068] S30: Obtain biological function data and carcinogenic mechanism data from the carcinogenicity judgment result, interpret the gene variation data individually or in groups according to the biological function data and the carcinogenic mechanism data, to obtain variation interpretation data, and use the variation interpretation data as the gene variation data to be interpreted for clinical significance.

[0069] Specifically, from the carcinogenicity judgment result, carcinogenic and possible carcinogenic gene variation data are screened out. Further, from the screened gene variation data, the corresponding biological function data and carcinogenic mechanism data are obtained, wherein the biological function data include: loss of function, possible loss of function, gain of function, possible gain of function, function conversion, possible function conversion, uncertain, neutral and possible neutral.

[0070] Furthermore, the consistency of the biological function data and the carcinogenic mechanism data between the different variation data of the gene is judged to obtain a consistency judgment result; the gene variation data with inconsistent consistency judgment results are stored as individually resolved variation, and the gene variation data with consistent consistency judgment results are stored as a variation grouping and resolved variation set; the individually resolved variation and the grouped interpreted variation are interpreted respectively to obtain variation interpretation data, and the variation data after variation interpretation is the gene variation data to be interpreted for clinical significance.

[0071] S40: Obtaining common biological marker data for genes, matching and interpreting the clinical significance of the biological marker data in different types of cancer according to the public resource data, and obtaining clinical significance data and clinical significance evidence grading and classification data of the biological marker data corresponding to each gene in different types of cancer as clinical significance interpretation data.

[0072] Common biological markers of the gene are obtained from public resource data such as NCCN / CSCO, Clinical Trial, PubMed, etc., to obtain common biological marker information data of the gene.

[0073] Furthermore, cancer information is obtained, and the clinical significance (prevention, diagnosis, treatment, and prognosis) of biomarkers in different cancer types is interpreted based on the above public resource data to obtain clinical significance data of common biomarkers of genes in different cancer types. If different biomarkers of the same gene share the same clinical significance in the same cancer type, clinical significance data can be obtained by associating with a second biomarker.

[0074] Furthermore, according to the joint recommendation of AMP&ASCO&CAP "Standards and Guidelines for the Interpretation and Reporting of Cancer Sequence Variants", (http: / / dx.doi.org / 10.1016 / j.jmoldx.2016.10.002), the evidence of the clinical significance (prevention, diagnosis, treatment, prognosis) of biological markers in different types of cancer was graded and classified to obtain the clinical significance evidence grading and classification data.

[0075] When interpreting the clinical significance of biomarkers in different cancer types, enter the overall information of the entry, including gene name, biomarker name, associated second biomarker name, cancer type information, clinical grade, whether to export solid tumor annotations, and remarks. Then, based on the interpretation information on clinical significance (prevention, diagnosis, treatment, prognosis) obtained from the public data resource survey, include the type of plan, clinical impact, evidence level, plan details, reference source, and if the plan type is treatment, it also involves the type of treatment and treatment plan.

[0076] S50: After the gene variation data to be interpreted for clinical significance are marked with biomarkers according to the biomarker data, the clinical significance interpretation data of each gene variation data to be interpreted for clinical significance in different cancer types are obtained by matching, and the tumor somatic variation interpretation database is constructed according to the variation interpretation data and the clinical significance interpretation data of the gene variation data.

[0077] Specifically, the gene variation data to be interpreted for clinical significance are marked with biological markers to obtain gene variation data marked with biological markers; and the clinical significance in different types of cancer is matched according to the biological marker marking information to obtain historical data of the clinical significance interpretation of the gene variation in different types of cancer.

[0078] When performing biomarker tagging and clinical significance matching for gene variants to be interpreted for clinical significance, if the clinical significance interpretation data of the corresponding biomarker already exists in the clinical significance database of the biomarker, the historical data can be directly matched. If the corresponding biomarker and its clinical significance do not exist, the biomarker and clinical significance interpretation information can be directly added again, see the clinical significance interpretation module of biomarkers in different cancer types for details.

[0079] Furthermore, a tumor somatic cell variation interpretation database is constructed based on the variation interpretation data and clinical significance interpretation data of the gene variation data. The tumor somatic cell variation interpretation database includes multiple data sections: gene interpretation section, gene variation carcinogenicity judgment section, gene variation interpretation section, biomarker clinical significance interpretation section, and basic data section. The basic data section includes variation grouping table, biological marker table, tumor type table, clinical grade table, evidence grade table, reference table, drug table, approved drug table, clinical trial table, clinical impact table, etc. Each data section is used to store tumor-related information, and multiple data sections are interconnected through the same key field labels.

[0080] The gene interpretation section is used to store information on the biological functions of genes and the relationship between genes and tumor occurrence and development, such as gene type, gene function, carcinogenic mechanism, epidemiology and clinical significance.

[0081] The gene variation carcinogenicity judgment section is used to store the attribute fields used for the gene variation carcinogenicity judgment and the carcinogenicity judgment result information.

[0082] The gene variation interpretation section is used to store basic information on tumor gene variation, carcinogenic mechanisms, and interpretation information on biological functions.

[0083] The section on interpretation of the clinical significance of biomarkers is used to store information related to the clinical significance of biomarkers in different types of cancer, such as prevention, diagnosis, treatment, prognosis, etc.

[0084] The basic data section is used to store basic general information used in the process of gene interpretation, determination of the carcinogenicity of mutations, interpretation of mutations, and analysis of the clinical significance of biomarkers.

[0085] The sections of the database are associated with each other through the same fields. The gene interpretation section, the gene variation carcinogenicity section and the gene variation interpretation section are associated through the gene ID; the gene variation interpretation section is associated with the clinical significance of biomarkers section through the biomarker ID; the gene variation interpretation is associated with the variation grouping table through the variation grouping marker ID; the clinical significance of biomarkers section is associated with the biomarker table, tumor type table, clinical grade table, evidence grade table, reference table, drug table, approved drug table, clinical trial table, and clinical impact table through the biomarker ID, tumor type ID, clinical grade ID, evidence grade ID, reference ID, drug ID, approved drug ID, clinical trial ID, and clinical impact ID, respectively.

[0086] In one embodiment, in step S10, public resource data is obtained, and the gene information to be interpreted and the gene variation data corresponding to the gene information to be interpreted are obtained from the public resource data, specifically including:

[0087] S11: Obtain preset gene key field information, screen and classify the public resource data according to the gene key field information, and obtain gene interpretation fields and the gene information to be interpreted.

[0088] S12: Obtain gene variation information corresponding to the gene information to be interpreted, and obtain the gene variation data corresponding to the gene information to be interpreted through the gene interpretation field and the gene variation information.

[0089] Specifically, the genes to be interpreted are obtained from the preset gene library, and information such as gene type, gene function, carcinogenic mechanism, epidemiology and clinical significance are obtained from public databases as gene interpretation information. Public databases such as GeneCards, OncoKB, CKB, NCBI, COSMIC, TCGA and other databases can be referred to. The clinical significance can refer to the oncology professional guidelines, approval documents of institutions such as FDA and NMPA, expert consensus, etc. For example, the interpretation of the gene to be interpreted EGFR. The EGFR (Epidermal Growth Factor Receptor) gene encodes a transmembrane glycoprotein [PMID: 24691965] and is a member of the ErbB receptor tyrosine kinase family, which includes ERBB1 (EGFR), ERBB2, ERBB3 and ERBB4. EGFR protein binds to ligands to induce receptor dimerization and tyrosine autophosphorylation and activate downstream MAPK and PI3K / AKT / mTOR signaling pathways [PMID: 22239438], participating in the regulation of cell proliferation, growth and differentiation. EGFR activating mutations can promote abnormal cell proliferation, differentiation and angiogenesis, and inhibit tumor cell apoptosis [PMID: 18045542]. EGFR hotspot mutations are mainly concentrated in exons 18-21 of the kinase domain, such as in-frame deletions, L858R, and T790M in exon 19, in addition to EGFR gene amplification. EGFR activating mutations are common in a variety of tumors, including lung adenocarcinoma, esophageal cancer, head and neck epithelial tumors, and glioblastoma. The frequencies of EGFR mutations in patients with non-small cell lung cancer (NSCLC), esophageal squamous cell carcinoma, and head and neck squamous cell carcinoma in the population are 49.5% [PMID: 28210132], 14% [PMID: 21615826], and 7.3% [PMID: 15837736], respectively. In NSCLC, activating mutations of EGFR lead to sensitivity to EGFR tyrosine kinase inhibitors (TKIs), including gefitinib, erlotinib, dacomitinib, afatinib, osimertinib, etc., which have been approved by the FDA and NMPA for first-line treatment of NSCLC patients. Secondary EGFR resistance mutations may occur in patients previously treated with EGFR TKIs, such as EGFR T790M and EGFRG797S [PMID: 29068003]. Currently, approved drugs in China for EGFR T790M resistance mutations include osimertinib, ametinib, and vometinib. Studies have shown that tumors with EGFR gene amplification may also be sensitive to EGFR targeted therapy [PMID: 11426640]. However, in patients with advanced NSCLC who have never been treated and carry EGFR sensitive mutations, concomitant EGFR gene amplification may be a poor prognostic factor [2016 ASCO #9053].In patients with EGFR mutant NSCLC treated with first-generation EGFR TKIs, acquired EGFR amplification (with or without T790M) is a possible mechanism of resistance [2014 ASCO #8053]. In addition, amplification of the wild-type EGFR allele can also lead to resistance to third-generation EGFR TKIs [PMID: 28202511]. In addition, studies have shown that EGFR gene mutations may be associated with poor efficacy of immune checkpoint inhibitors and hyper-progression (HP) [PMID: 28351930, 2017 ESMO #1140PD].

[0090] Furthermore, the mutation data obtained by the internal detection system and analysis of tumor samples, or the tumor hotspot mutation data obtained by searching commonly used tumor-related databases, such as Cosmic, CKB, and Clinvar, are used as the data for gene mutations to be interpreted. The mutation data must be annotated by VEP software and uniformly follow the unified naming method of HGVS, including genomic location information, cDNA sequence change information often annotated with transcript numbers, amino acid change information, etc. For example, the gene mutation information of EGFR L858R, chromosome: chr7; start position: 55259513; end position: 55259515; transcript: NM_005228.5; chgvs naming: c.2571_2573delinsACG; amino acid change (1-letter abbreviation): p.L858R, etc.

[0091] In one embodiment, in step S20, the carcinogenicity of the gene variation data is judged according to the public resource data to obtain a carcinogenicity judgment result, which specifically includes:

[0092] S21: Acquire gene variation attribute data of the gene variation data from the public resource data.

[0093] S22: Carcinogenicity judgment is performed according to the gene variation attribute data to obtain the corresponding carcinogenicity judgment result.

[0094] Specifically, after obtaining the gene variation fields to be interpreted, investigate public data resources, refer to the "Somatic Variation Carcinogenicity Classification Guidelines" jointly formulated by ClinGen, CGC and VICC, score the evidence, and then judge the carcinogenicity of the gene variation. Benign: ~-7; possibly benign: -6~-1; unknown significance: 0~5; possibly carcinogenic: 6~9; carcinogenicity: 10 or above. For the judgment of population frequency, you can refer to the database gnomAD, functional data can refer to the database oncoKB, hotspot data can refer to Cancerhotspots, COSMIC, and computer prediction can refer to phyloP, phastCons, spliceAI, varSEAK, etc. According to the unified standardized entry rules, the information corresponding to the above key fields is entered into the corresponding field positions of the database structure framework to obtain the variant carcinogenicity result data set. The specific carcinogenicity evidence scores are shown in the following table.

[0095]

[0096] In one embodiment, in step S30, biological function data and carcinogenic mechanism data are obtained from the carcinogenicity judgment result, and the gene variation data are interpreted individually or in groups according to the biological function data and the carcinogenic mechanism data to obtain variation interpretation data, and the variation interpretation data is used as the gene variation data to be interpreted for clinical significance, specifically including:

[0097] S31: judging the biological function data and carcinogenic mechanism data in the carcinogenic or potentially carcinogenic gene variation data to obtain a judgment result.

[0098] Specifically, biological functions include loss of function / likely loss of function / gain of function / likely gain of function / switch of function / likely switch of function / inconclusive / neutral / likely neutral. The biological function descriptions in gene variation data can be judged by acquiring and identifying oncoKB, CKB and other databases and literature.

[0099] The biological function of the variant was determined according to the definition of oncoKB. Type IA Gain of function: 1. The variant is associated with increased protein function. 2. Increased gene dosage. 3. Increased / abnormal mRNA expression. 4. Increased / constitutive protein activity. 5. Dominant negative effect. 6. Structural protein. 7. Toxic protein. B Loss of function: 1. The variant is associated with decreased protein function. 2. Haploinsufficiency. C Switch of function: 1. The variant is associated with a new function of the protein. 2. New protein. 3. Altered substrate specificity. D Neutral function: 1. The variant does not change the function of the protein. 2. There is no difference in the measurable properties of cells expressing wild-type and mutant genes. E Uncertain function: 1. The available data on the function of the variant are inconsistent. 2. The evidence describing the function of the variant is limited to weak experimental data (small, under-powered experimental studies in one or more papers). 3. The data are limited to describing sensitivity or resistance to a drug in patients and / or in vitro experiments. 4. The data are limited to in silico studies that predict the function of the variant. Type II A: Known function 1. Convincing experimental data from one or more studies directly establish the function of the mutation. 2. Multiple lines of data from one or more studies, including but not limited to experimental data and statistical replication, together provide strong evidence to establish the function of the mutation. 3. The variant is a known hotspot (Chang et al., 2016. Chang et al., 2018) and at least one experimental data provides strong evidence that the alteration results in gain-, loss-, or switch-, or neutral function. 4. Rescue experiments provide evidence that the variant is neutral. 5. The variant has been found in a patient who responded to a targeted inhibitor and at least one experimental data provides strong evidence that the alteration results in gain-, loss-, or switch-, or neutral function. 6. Strong evidence-based data show that there is no difference in measurable properties of cells expressing the wild-type or mutant forms of the gene. B: Possible function. 1. One or more experiments in one study, including but not limited to experimental data or statistical replication, establish the function of the variant. 2. The alteration is a known hotspot (Chang et al., 2016. Chang et al., 2018), but there are currently no known functional studies describing the function of the mutation. 3. The variant occurs in a domain of a gene that is not frequently mutated, and the domain of the paralogous gene has been confirmed to be oncogenic. 4. Although there may be conflicting evidence, a reasonable hypothesis based on the data suggests that the alteration leads to gain-, loss-, switch-, or neutral function. 5. The alteration has been found in a patient who responded to a targeted inhibitor, and at least one experimental data provides limited evidence that the alteration leads to gain-, loss-, switch-, or neutral function.6. Probable, possible, and / or evidence-based data suggest that there is no difference in measurable properties of cells expressing wild-type or mutant forms (likely neutral) of the gene.

[0100] Criteria for judging biological functions:

[0101]

[0102] S32: storing the gene variation data with inconsistent judgment results as a separate analysis data set, and storing the gene variation data with consistent consistency judgment results as a variation grouping data set.

[0103] S33: interpreting the individual analysis data set or the variation grouping data set respectively to obtain the gene variation data to be interpreted with clinical significance.

[0104] Specifically, if the carcinogenic and possibly carcinogenic variants of the same gene do not have the same biological function and carcinogenic mechanism, they will be analyzed separately, and the corresponding gene variant data will be stored as a separate analysis dataset. If the carcinogenic and possibly carcinogenic variants of the same gene have the same or similar biological function and carcinogenic mechanism, the variant grouping labels will be unified, and the corresponding gene variant data will be stored as a variant grouping dataset.

[0105] A detailed and comprehensive description of the biological functions and carcinogenic mechanisms of the variants in the individual analysis data set and the variant data in the group interpretation set is performed respectively, so as to form variant interpretation data and enter them into the corresponding positions of the database to obtain genetic variant data to be interpreted for clinical significance.

[0106] In one embodiment, in step S32, the variant grouping data set is interpreted to obtain the gene variant data to be interpreted with clinical significance, specifically including:

[0107] S321: Obtain the gene mutation type, biological function data and carcinogenic mechanism data corresponding to each of the gene variation data.

[0108] S322: Grouping and labeling the gene variation data according to the gene mutation type, the biological function data and the carcinogenic mechanism data, and interpreting the variation of the grouped data to obtain the gene variation data to be interpreted for clinical significance.

[0109] The rule for mutation grouping is as follows: nonsense / frameshift mutations, splice mutations, stop codon mutations, in-frame insertion / deletion mutations, and copy number loss mutations of tumor suppressor genes are uniformly grouped as XX truncating mutations, XX splice site mutations, XX stop loss mutations, XX insertion / deletion mutations, and XX copy number loss. Copy number gain, fusion mutations, and in-frame insertion / deletion mutations of proto-oncogenes are uniformly grouped as XX copy number gain, XX fusion, and XX insertion / deletion mutations.

[0110] Specifically, if the oncogenic and possibly oncogenic gene mutations of the same gene do not have the same biological function and carcinogenic mechanism, they will be analyzed separately. For example, in the interpretation of EGFR L858R, the EGFR L858R mutation is located in the kinase domain of the EGFR protein. This mutation is a gain-of-function mutation, which will lead to increased EGFR kinase activity, promote cell transformation, show growth advantage in cultured IL-3-deficient cells, and promote tumor formation in mouse models [PMID: 29533785, 28979142, 30952700,16187797]. If the oncogenic and possibly oncogenic mutations of the same gene have the same or similar biological functions and carcinogenic mechanisms, the mutation grouping mark will be unified and interpreted by the mutation grouping method. For example, the interpretation of EGFR c.2239_2248del (p.L747_A750delinsP) can establish the mutation group EGFR exon19 inframe deletion, and all forms of EGFR exon19 inframe deletion can be uniformly annotated by this grouping mark. The variation interpretation of EGFRexon19 inframe deletion is: This mutation is located in exon 19, in the kinase domain of EGFR protein. The literature reports that the in-frame deletion mutation located in exon 19 of EGFR is a gain-of-function mutation, which can lead to constitutive activation of EGFR receptor, increase the phosphorylation level of downstream MAPK and AKT, promote cell transformation, and promote tumor development [PMID: 16187797, 17349580]. For example, the interpretation of APC c.1530dup (p.G511Wfs*26) can establish a variation group APCtruncating mutation. All nonsense or frameshift mutations that affect the function of APC protein can be uniformly annotated through this grouping tag. The variation interpretation of APC truncating mutation is: This mutation is expected to lead to missing or non-functional protein, which is a possible loss-of-function mutation. APC gene loss-of-function mutations can lead to activation of the Wnt signaling pathway. In addition, it can also lead to chromosomal instability, promote proliferation and inhibit differentiation. Genetic studies using multiple APC mutant mouse models suggest that mutations in the APC gene may be causative for intestinal tumorigenesis [PMID: 28423402].

[0111] In one embodiment, in step S40, common biomarker data of genes are obtained, and the clinical significance of the biomarker data in different cancer types is matched and interpreted according to the public resource data, so as to obtain clinical significance data and clinical significance evidence classification data corresponding to each gene in different cancer types as clinical significance interpretation data, which specifically includes:

[0112] S41: Extracting common biological markers of the gene from the public resource data to obtain corresponding biological marker information data.

[0113] S42: Obtain cancer type information from commonly used clinical tumor diagnosis and treatment guidelines to obtain cancer type data to be annotated.

[0114] S43: interpreting the clinical significance of the biological marker information data in different cancer types according to the public resource data to obtain clinical significance data of the biological marker in different cancer types.

[0115] S44: Classify the clinical significance evidence of biological markers in different types of cancer according to preset normative standard data to obtain the clinical significance evidence classification data.

[0116] Wherein, after step S43, the method for constructing a tumor somatic cell variation interpretation database further includes:

[0117] S431: Acquire different biological markers of the same gene from the public resource data to obtain biological marker information set data of the same gene.

[0118] S432: performing normalization processing on the biological marker information set data of the same gene according to its type to obtain normalized biological marker data of the same gene.

[0119] S433: Based on the normalized biological marker data information and in combination with the corresponding annotated cancer type information, the markers sharing the same clinical significance are associated with a second biological marker to obtain a corresponding clinical significance association relationship, and the clinical significance data of the associated biological markers is obtained based on the association relationship.

[0120] Common biological markers of the gene are obtained from public resource data such as NCCN / CSCO, Clinical Trial, PubMed, etc., and processed according to the normalization principle to obtain common biological marker information data of the gene. With reference to public data resources, for variants with their own specific clinical significance, the biological marker is the variant itself, otherwise it is named according to a unified standard. The normalization principle is that the biological markers of the activation mutations of oncogenes are named XX Oncogenic Mutations, XX fusion, and XX amplification. The biological markers of the inactivation mutations of tumor suppressor genes are defined as XX Oncogenic Mutations. In principle, the biological markers of missense mutations of oncogenes or tumor suppressor genes are themselves.

[0121] Furthermore, information on cancer types is obtained, and the clinical significance (prevention, diagnosis, treatment, and prognosis) of biological markers in different cancer types is interpreted based on the above-mentioned public resource data, so as to obtain data on the clinical significance of common biological markers of genes in different cancer types.

[0122] If different biomarkers of the same gene share the same clinical significance in the same cancer, clinical significance data can be obtained by associating with the second biomarker.

[0123] According to the "Standards and Guidelines for Interpretation and Reporting of Cancer Sequence Variants" jointly recommended by AMP, ASCO, and CAP, the evidence of the clinical significance (prevention, diagnosis, treatment, and prognosis) of biological markers in different types of cancer was graded and classified to obtain the clinical significance evidence grading and classification data.

[0124] The specific implementation process includes first determining the overall information of the entry, including gene name, biomarker name, associated second biomarker, cancer type information, clinical grade, whether to export solid tumor annotations, and remarks. Then enter the interpretation information about clinical significance (prevention, diagnosis, treatment, prognosis) obtained from public data resources, including the type of plan, clinical impact, evidence level, plan details, and reference source. If the plan type is treatment, it also involves the type of treatment and treatment plan.

[0125] The gene name must be consistent with the gene name in the gene interpretation module and share the same gene ID.

[0126] The names of biomarkers are uniformly named according to the standardization principles of biomarkers, such as EGFR L858R, APC Ocogenic Mutations, etc.

[0127] When different biomarkers share the same clinical significance, the clinical significance of the biomarkers can be annotated by associating with the second biomarker. For example, the clinical significance of EGFR K860I in non-small cell lung cancer can be annotated. When entering data, the biomarker is EGFR K860I, and the interpretation is "Preclinical studies have shown that EGFR K860I mutations are sensitive to osimertinib and afatinib, and partially sensitive to gefitinib and erlotinib [PMID: 33034420] (Evidence level: D). In a case report, a patient with non-small cell lung cancer (NSCLC) carrying EGFR K860I mutations received afatinib treatment, and the primary lung tumor shrank, but multiple brain metastases worsened, suggesting that afatinib may have a certain effect on NSCLC patients with EGFR K860I mutations, but its efficacy is limited [PMID: 33942527] (Evidence level: D)." At the same time, the second biomarker EGFR Oncogenic Mutations, which is interpreted as "Currently, there are clinical trials for EGFR mutations in non-small cell lung cancer: Pyrotinib (Phase II, NCT03574402); Poziotinib (Phase II, NCT03318939) (Evidence Level: C)." The content of this section can be annotated by associating it with a second biological marker to avoid repeated entry and annotation and facilitate unified updates.

[0128] Cancer type information, the annotated cancer type or annotated pathological subtype corresponding to the clinical significance of the biological marker, must be obtained through public database resources, expert consensus guidelines, literature review, etc.

[0129] The clinical grade system will be automatically generated according to the level of evidence. The evidence classification and grading standards refer to the "Standards and Guidelines for the Interpretation and Reporting of Cancer Sequence Variants" jointly recommended by AMP&ASCO&CAP. The details are as follows: Class I: Variants with clear clinical significance (treatment, prognosis, and diagnosis). Level A: FDA approved; included in expert guidelines. Level B: Strong research support recognized by experts in the field. Class II: Variants with potential clinical significance. Level C: Cross-indication use of FDA-approved drugs; multiple published small-scale studies that have reached consensus. Level D: Preclinical studies; a few case reports without consensus. Class III: Variants of unknown clinical significance. No significant allele frequency has been observed in general or specific subpopulation databases, or pan-cancer or specific tumor databases; no convincing evidence of cancer association has been published. Class IV: Benign or potentially benign variants. Significant allele frequency has been observed in general or specific subpopulation databases, or pan-cancer or specific tumor databases; there is currently no evidence of cancer association.

[0130] Furthermore, whether to export solid tumor annotations, for annotations on the clinical significance of a biological marker in a specific cancer type (non-solid tumor), the annotation library system will automatically export the clinical significance of the biological marker in solid tumors, and the system defaults to "yes". If after manual review, the clinical significance of solid tumors is not suitable for this cancer type, it is necessary to manually change whether to export solid tumors to "no". For example, the clinical significance of TP53 Y220C in non-small cell lung cancer. At present, there is no relevant report on TP53 Y220C in non-small cell lung cancer, but it has been reported in solid tumors. Through this function, the system can automatically annotate the interpretation of TP53 Y220C in solid tumors, that is, preclinical studies have shown that the small molecule p53 activator PC14586 can significantly inhibit the growth of tumor xenograft models carrying TP53Y220C mutations [2021 AACR Abstract LB006], and the anti-tumor efficacy has been preliminarily confirmed in clinical studies. Among 21 evaluable patients with solid tumors, 5 achieved partial remission [2022 ASCOAbstract 3003]. Currently, the FDA has granted PC14586 fast track qualification for the treatment of patients with locally advanced or metastatic solid tumors carrying TP53 Y220C mutations (evidence level: Grade C).

[0131] For the clinical significance module of biological markers, the interpretation information about clinical significance (prevention, diagnosis, treatment, prognosis) obtained from the above-mentioned public data resources is entered.

[0132] Program types include prevention, diagnosis, treatment, and prognosis.

[0133] Clinical impacts include positive regulation by immunotherapy, negative regulation by immunotherapy, immune hyperprogression, tumor susceptibility, good prognosis, poor prognosis, molecular typing, sensitivity, resistance, reduced sensitivity, adverse reactions, and unclear results.

[0134] If the treatment type is treatment, it should include treatment type and treatment plan. The treatment type can be targeted therapy, immunotherapy, or chemotherapy, and the treatment plan is the specific clinical medication.

[0135] The level of evidence is classified according to the "Standards and Guidelines for Interpretation and Reporting of Cancer Sequence Variants" jointly recommended by AMP, ASCO and CAP, and is divided into four levels: A / B / C / D.

[0136] The protocol details are a detailed description of the clinical significance of the article. Preclinical trials, clinical trials, case reports, etc. are all described in a standardized format to standardize the annotation content and facilitate unified updates.

[0137] Reference sources include NMPA / FDA approval documents, guidelines and expert consensus, clinical trials, and literature references in public resource libraries.

[0138] Among them, approved drugs include but are not limited to the fields of drug English name, Chinese name, trade name, original research manufacturer, drug type, first time on the market, instruction issuing agency, instruction version, instruction publishing time, link address, target gene, indication, mechanism of action, remarks, etc.

[0139] Among them, the reference guide includes but is not limited to the fields guideline name, cancer type, guideline version, guideline evidence level, evidence details, etc. Among them, the NCCN guidelines classify evidence and consensus into categories 1, 2A, 2B, and 3. Category 1 is based on high-level evidence, and NCCN unanimously believes that this treatment is reasonable. 2A is based on low-level evidence, and NCCN unanimously believes that this treatment is reasonable. 2B is based on low-level evidence, and NCCN basically believes that this treatment is reasonable. Category 3 is based on any level of evidence, and NCCN has major differences on whether this treatment is reasonable. The CSCO diagnosis and treatment guidelines are divided into level I, level II, and level III recommendations. The CSCO guidelines regard class 1A evidence and some class 2A evidence with high expert consensus and good accessibility in China as level I recommendations. Specifically: clear indications, good accessibility, stable tumor treatment value, and included in the diagnosis and treatment measures of the "National Basic Medical Insurance, Work-related Injury Insurance and Maternity Insurance Drug List". The CSCO guidelines regard class 1B evidence and some class 2A evidence with poor accessibility in China but high expert consensus as level II recommendations. Specifically: Randomized controlled studies at home and abroad provide high-level evidence but have poor accessibility or low cost-effectiveness; for measures with obvious clinical benefits but high prices, considering that patients may benefit, they can also be recommended as Level II. For some diagnostic and treatment measures that are commonly used in clinical practice or have exploratory value, although evidence-based medicine is relatively insufficient, if the expert group believes that they are acceptable, they can be recommended as Level III.

[0140] Among them, clinical trials include but are not limited to fields such as trial ID, trial name, trial stage, enrolled population, trial results, and trial status.

[0141] The document reference includes but is not limited to the fields document ID, document name, document abstract, document details, citation information, full text address, etc.

[0142] In one embodiment, in step S50, after the gene variation data to be interpreted with clinical significance are marked with biomarkers according to the biomarker data, the clinical significance interpretation data of each gene variation data to be interpreted with clinical significance in different cancer types are matched, and the tumor somatic variation interpretation database is constructed according to the variation interpretation data and the clinical significance interpretation data of the gene variation data, which specifically includes:

[0143] S51: Marking the gene variation data to be interpreted for clinical significance with biological markers to obtain gene variation data marked with biological markers.

[0144] Biomarker labeling is performed on the data of gene variants that are judged to be carcinogenic, possibly carcinogenic and have biological functions and are to be interpreted clinically, so as to match the variant to the correct clinical significance of the biomarker. The biomarker of the gene variant whose carcinogenicity is judged to be of unknown significance is marked as unknown. The biomarker of the gene variant whose carcinogenicity is judged to be likely benign or benign is marked as XXX Likely Benign mutation or XXX Benignmutation respectively. The biomarker labeling principle of gene variants that are carcinogenic, possibly carcinogenic and have biological functions is: the biomarker of the inactivating mutation of tumor suppressor gene is defined as XX Oncogenic Mutations; the biomarker of the activating mutation of proto-oncogene is named XX Oncogenic Mutations, XX fusion, XX amplification. In principle, the missense mutation of proto-oncogene or tumor suppressor gene has its own biomarker, thereby obtaining the biomarker labeling data of the gene variant of clinical significance to be matched.

[0145] S52: According to the matching of biological marker information to the clinical significance in different cancer types, the clinical significance interpretation data of the gene variation in different cancer types are obtained.

[0146] When performing biomarker tagging and clinical significance matching for gene variants to be interpreted for clinical significance, if the clinical significance interpretation data of the corresponding biomarker already exists in the clinical significance database of the biomarker, the historical data can be directly matched. If the corresponding biomarker and its clinical significance do not exist, the biomarker and clinical significance interpretation information can be directly added again, see the clinical significance interpretation module of biomarkers in different cancer types for details.

[0147] S53: Construct the tumor somatic variation interpretation database based on the variation interpretation data and clinical significance interpretation data of the gene variation data.

[0148] Specifically, the tumor somatic cell variation interpretation database includes multiple data sections: gene interpretation section, gene variation carcinogenicity judgment section, gene variation interpretation section, biomarker clinical significance interpretation section, and basic data section. The basic data section includes variation grouping table, biological marker table, tumor type table, clinical grade table, evidence grade table, reference table, drug table, approved drug table, clinical trial table, and clinical impact table. Each data section is used to store tumor-related information, and multiple data sections are interconnected through the same key field tags.

[0149] The gene interpretation section is used to store information on the biological functions of genes and the relationship between genes and the occurrence and development of tumors.

[0150] The gene variation carcinogenicity judgment section is used to store the fields used for the gene variation carcinogenicity judgment and the carcinogenicity judgment result information.

[0151] The gene variation interpretation section is used to store basic information on tumor gene variation, carcinogenic mechanisms, and interpretation information on biological functions.

[0152] The section on interpretation of the clinical significance of biomarkers is used to store relevant information on the clinical significance of biomarkers, such as prevention, diagnosis, treatment, and prognosis.

[0153] The basic data section is used to store basic general information used in the process of gene interpretation, determination of the carcinogenicity of mutations, interpretation of mutations, and analysis of the clinical significance of biomarkers.

[0154] The sections of the database are associated with each other through the same fields. The gene interpretation section, the gene variation carcinogenicity section and the gene variation interpretation section are associated with the gene ID; the gene variation interpretation section is associated with the clinical significance of biomarkers section through the biomarker ID; the gene variation interpretation and the variation grouping table are associated with the variation grouping marker ID; the clinical significance of biomarkers section is associated with the biomarker table, tumor type table, clinical grade table, evidence grade table, reference table, drug table, approved drug table, clinical trial table, and clinical impact table through the biomarker ID, tumor type ID, clinical grade ID, evidence grade ID, reference ID, drug ID, approved drug ID, clinical trial ID, and clinical impact ID, respectively.

[0155] The entered information is reviewed and / or amended multiple times. The information is entered and reviewed by at least two different people. If the entered information is inaccurate or non-standard, the reviewer can reject the evidence on this platform, record the unreasonableness of the information and provide suggestions for the inputter to modify it. For the modification of the entered information, the system will record the specific content of the modification and provide subsequent users with the modification process of the source information.

[0156] Update and maintain the entered data. Crawl the latest research progress data on tumor chemotherapy, targeted therapy and immunotherapy according to the time period. After obtaining the updated data, replace and add the updated data to the information in the database. For the fixed update time of gene interpretation, cancer interpretation and mutation interpretation, for example, update once a month. For the clinical significance section of biomarkers, continuously track and update the status according to the time period.

[0157] The following shows some important interpretation information about EGFR c.2235_2249del (p.E746_A750del) in non-small cell lung cancer constructed by the above method in the construction of the tumor somatic variation interpretation database.

[0158]

[0159]

[0160] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0161] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 2 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for constructing a tumor somatic cell variation interpretation database is implemented.

[0162] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0163] Acquire public resource data, and acquire to-be-interpreted gene information and gene variation data corresponding to the to-be-interpreted gene information from the public resource data;

[0164] Performing carcinogenicity judgment on the gene variation data according to the public resource data to obtain a carcinogenicity judgment result;

[0165] Obtaining biological function data and carcinogenic mechanism data from the carcinogenicity judgment result, performing individual or group interpretation of the gene variation data according to the biological function data and the carcinogenic mechanism data to obtain variation interpretation data, and using the variation interpretation data as the gene variation data to be interpreted with clinical significance;

[0166] Acquire common biomarker data of genes, match and interpret the clinical significance of the biomarker data in different cancer types according to the public resource data, and obtain clinical significance data and clinical significance evidence grading and classification data of the biomarker data corresponding to each gene in different cancer types as clinical significance interpretation data;

[0167] After the gene variation data to be interpreted for clinical significance are marked with biomarkers according to the biomarker data, the clinical significance interpretation data of each gene variation data to be interpreted for clinical significance in different cancer types are obtained by matching, and the tumor somatic variation interpretation database is constructed according to the variation interpretation data and the clinical significance interpretation data of the gene variation data.

[0168] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0169] Acquire public resource data, and acquire to-be-interpreted gene information and gene variation data corresponding to the to-be-interpreted gene information from the public resource data;

[0170] Performing carcinogenicity judgment on the gene variation data according to the public resource data to obtain a carcinogenicity judgment result;

[0171] Obtaining biological function data and carcinogenic mechanism data from the carcinogenicity judgment result, performing individual or group interpretation of the gene variation data according to the biological function data and the carcinogenic mechanism data to obtain variation interpretation data, and using the variation interpretation data as the gene variation data to be interpreted with clinical significance;

[0172] Acquire common biomarker data of genes, match and interpret the clinical significance of the biomarker data in different cancer types according to the public resource data, and obtain clinical significance data and clinical significance evidence grading and classification data of the biomarker data corresponding to each gene in different cancer types as clinical significance interpretation data;

[0173] After the gene variation data to be interpreted for clinical significance are marked with biomarkers according to the biomarker data, the clinical significance interpretation data of each gene variation data to be interpreted for clinical significance in different cancer types are obtained by matching, and the tumor somatic variation interpretation database is constructed according to the variation interpretation data and the clinical significance interpretation data of the gene variation data.

[0174] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0175] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0176] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for constructing a tumor somatic cell variation interpretation database, characterized in that: The method for constructing a tumor somatic cell variation interpretation database comprises: Acquire public resource data, and acquire to-be-interpreted gene information and gene variation data corresponding to the to-be-interpreted gene information from the public resource data; Performing carcinogenicity judgment on the gene variation data according to the public resource data to obtain a carcinogenicity judgment result; Obtaining biological function data and carcinogenic mechanism data from the carcinogenicity judgment result, performing individual or group interpretation of the gene variation data according to the biological function data and the carcinogenic mechanism data to obtain variation interpretation data, and using the variation interpretation data as the gene variation data to be interpreted with clinical significance; Obtain gene biomarker data, match and interpret the clinical significance of the biomarker data in different cancer types according to the public resource data, and obtain clinical significance data and clinical significance evidence classification data corresponding to each gene in different cancer types as clinical significance interpretation data, specifically including: Extracting the gene biomarker from the public resource data to obtain corresponding biomarker information data; Obtain cancer type information from commonly used clinical tumor diagnosis and treatment guidelines to obtain cancer type data to be annotated; Interpreting the clinical significance of the biological marker information data in different cancer types according to the public resource data to obtain clinical significance data of the biological marker in different cancer types; Acquire different biological markers of the same gene from the public resource data to obtain biological marker information set data of the same gene; Normalizing the biological marker information set data of the same gene according to its type to obtain normalized biological marker set data of the same gene; According to the normalized biological marker data information, combined with the corresponding annotated cancer type information, the second biological marker is associated with the markers sharing the same clinical significance to obtain the corresponding clinical significance association relationship, and the clinical significance data of the biological marker after association is obtained according to the association relationship; Grading and classifying the clinical significance evidence of biological markers in different types of cancer according to preset normative standard data to obtain the clinical significance evidence grading and classification data; After the gene variation data to be interpreted for clinical significance are marked with biomarkers according to the biomarker data, the clinical significance interpretation data of each gene variation data to be interpreted for clinical significance in different cancer types are obtained by matching, and the tumor somatic variation interpretation database is constructed according to the variation interpretation data and the clinical significance interpretation data of the gene variation data.

2. The method for constructing a tumor somatic cell variation interpretation database according to claim 1, characterized in that: The step of obtaining public resource data and obtaining the gene information to be interpreted and the gene variation data corresponding to the gene information to be interpreted from the public resource data specifically includes: Obtaining preset gene key field information, screening and classifying the public resource data according to the gene key field information, and obtaining gene interpretation fields and the gene information to be interpreted; Gene variation information corresponding to the gene information to be interpreted is obtained, and the gene variation data corresponding to the gene information to be interpreted is obtained through the gene interpretation field and the gene variation information.

3. The method for constructing a tumor somatic cell variation interpretation database according to claim 1, characterized in that: The performing carcinogenicity judgment on the gene variation data according to the public resource data to obtain the carcinogenicity judgment result specifically includes: Acquire gene variation attribute data of the gene variation data from the public resource data; Carcinogenicity judgment is performed based on the gene variation attribute data to obtain the corresponding carcinogenicity judgment result.

4. The method for constructing a tumor somatic variation interpretation database according to claim 1, characterized in that: The step of obtaining biological function data and carcinogenic mechanism data from the carcinogenicity judgment result, interpreting the gene variation data individually or in groups according to the biological function data and the carcinogenic mechanism data to obtain variation interpretation data, and using the variation interpretation data as the gene variation data to be interpreted with clinical significance, specifically includes: Determining the biological function data and carcinogenic mechanism data in the carcinogenic or potentially carcinogenic gene variation data to obtain a determination result; The gene variation data with inconsistent judgment results are stored as a separate analysis data set, and the gene variation data with consistent judgment results are stored as a variation grouping data set; The individual analysis data set or the variation grouping data set is interpreted respectively to obtain the gene variation data to be interpreted with clinical significance.

5. The method for constructing a tumor somatic cell variation interpretation database according to claim 4, characterized in that: The interpreting of the variation grouping data set to obtain the gene variation data to be interpreted with clinical significance specifically includes: Obtaining the gene mutation type, biological function data and carcinogenic mechanism data corresponding to each of the gene variation data; The gene variation data are grouped and marked according to the gene mutation type, the biological function data and the carcinogenic mechanism data, and the grouped data are interpreted for variation to obtain the gene variation data to be interpreted for clinical significance.

6. The method for constructing a tumor somatic cell variation interpretation database according to claim 1, characterized in that: After the gene variation data to be interpreted with clinical significance are marked with biomarkers according to the biomarker data, the clinical significance interpretation data of each gene variation data to be interpreted with clinical significance in different cancer types are matched, and the tumor somatic variation interpretation database is constructed according to the variation interpretation data and the clinical significance interpretation data of the gene variation data, specifically including: Marking the gene variation data to be interpreted for clinical significance with biomarkers to obtain gene variation data marked with biomarkers; According to the matching of biological marker information to the clinical significance in different cancer types, the clinical significance interpretation data of the gene variation in different cancer types are obtained; The tumor somatic cell variation interpretation database is constructed based on the variation interpretation data and clinical significance interpretation data of the gene variation data.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for constructing a tumor somatic variation interpretation database according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for constructing a tumor somatic cell variation interpretation database as claimed in any one of claims 1 to 6 are implemented.

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