CN111986728A - Breast cancer gene variation and medication reading system, reading method and device - Google Patents
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Abstract
The present specification provides a system, a method and a device for interpretation of breast cancer gene variation and medication, wherein the system comprises a breast cancer database version, the breast cancer database version comprises a plurality of databases which are mutually associated through the same key field or the same key field combination, and the databases are respectively used for storing the related information of breast cancer; when the analysis is carried out, carrying out biological information analysis on the sequencing off-line data of the detected sample to obtain the genetic variation annotation information of the detected sample; the sequencing off-line data comprises sample information of a detected sample; and performing correlation reading on the sample information and the genetic variation annotation information of the detected sample and a database in the breast cancer database version to obtain the corresponding reading information of the detected sample. When the medicine interpretation is carried out based on the breast cancer gene variation and medicine interpretation system, the comprehensive medicine guidance suggestions with clear evidence grades can be quickly and accurately found and provided, and important references are provided for medical decision.
Description
Technical Field
The present disclosure relates to the field of tumor medication information processing technologies, and in particular, to a system, a method and an apparatus for interpreting breast cancer gene variation and medication.
Background
Primary breast cancer, which is abbreviated as breast cancer, refers to malignant tumors originating from mammary ducts and lobules and is classified into different molecular types of HER-2 positive (HR-negative), HER-2 positive (HR-positive), three negative type, Luminal A, Luminal B and the like. The causes of breast cancer are not completely understood, may be related to heredity, physical radiation exposure, early menstruation, short menopause age, infertility, late first birth age, short lactation period and the like, and are malignant tumors with highest morbidity and mortality of women all over the world. With the continuous and deep research of molecular biology and new drug development, the individual treatment based on molecular markers has been applied to the treatment of breast cancer patients, and good effects are achieved.
With the massive genetic variation data produced by the second-generation sequencing, the genetic variation interpretation is the key point for realizing accurate medical treatment of diseases. At present, a unified standard and a unified database interpretation system for interpretation of breast cancer genetic variation and medicines do not exist, and for interpretation, collection and arrangement of related genetic variation data, different processing methods and processes are provided for various major companies and clinical institutions, so that how to establish a perfect and convenient breast cancer genetic variation and medicine database is an urgent problem to be solved.
Disclosure of Invention
In view of this, the present disclosure is directed to a system, a method and a device for interpreting breast cancer genetic variation and medication, so as to establish a perfect and convenient breast cancer genetic variation and medication database for guiding precise medication of breast cancer.
In view of the above, the present specification provides, in a first aspect, a breast cancer genetic variation and medication interpretation system, which includes a breast cancer database version including a plurality of databases related to each other by the same key field or the same key field combination, and the databases are respectively used for storing related information of breast cancer.
Optionally, the multiple databases include a breast cancer information database, a breast cancer gene analysis database, a breast cancer germline gene variation database, a breast cancer system gene variation database, a breast cancer genetic variation clinical significance database, a breast cancer drug information database, a breast cancer targeted therapy and evidence grade database, a breast cancer chemotherapy and evidence grade database, a breast cancer immunotherapy and evidence grade database, a breast cancer endocrine therapy and evidence grade database, a breast cancer prognosis evaluation and evidence grade database, a breast cancer biomarker database, a breast cancer clinical test database, and a breast cancer reference document database;
the breast cancer information database is used for storing basic introduction of breast cancer, relation between the breast cancer and pathogenic genes and treatment progress information; the breast cancer gene analysis database is used for storing gene biological functions and relationship information between genes and breast cancer occurrence and development; the breast cancer germ line gene variation database is used for storing gene variation information of breast cancer germ line cells; the breast cancer system gene variation database is used for storing gene variation information of breast cancer system cells; the breast cancer gene variation clinical significance database is used for storing the clinical significance of breast cancer gene variation; the breast cancer drug information database is used for storing drug information related to breast cancer treatment; the breast cancer targeted therapy and evidence grade database is used for storing targeted therapy information and targeted therapy evidence grade information of breast cancer; the breast cancer chemical treatment and evidence grade database is used for storing chemical treatment information of breast cancer and clinical evidence grade information of chemical treatment; the breast cancer immunotherapy and evidence grade database is used for storing the immunotherapy information and the immunotherapy clinical evidence grade information of the breast cancer; the breast cancer endocrine treatment and evidence grade database is used for storing endocrine treatment information and endocrine treatment clinical evidence grade information of breast cancer; the breast cancer prognosis evaluation and evidence grade database is used for storing the result information of breast cancer prognosis evaluation and breast cancer prognosis evidence grade grading; the breast cancer biomarker database is used for storing genes related to breast cancer immunotherapy and biomarker information; the breast cancer clinical test database is used for storing clinical test information of breast cancer; the breast cancer reference database is used for storing the basis of the reference data of the breast cancer.
Optionally, the breast cancer information database and the breast cancer drug information database are associated by a combination of a disease ID and a drug ID; the breast cancer gene analysis database is respectively associated with the breast cancer germ line gene variation database and the breast cancer germ line gene variation database through gene ID and variation ID combination; the breast cancer germ line gene variation database and the breast cancer system gene variation database are respectively associated with the breast cancer gene variation clinical significance database through variation ID and disease ID combination; the breast cancer gene variation clinical significance database is respectively associated with the breast cancer targeted therapy and evidence grade database, the breast cancer chemotherapeutics and evidence grade database, the breast cancer immunotherapy and evidence grade database, the breast cancer endocrine therapy and evidence grade database and the breast cancer prognosis evaluation and evidence grade database through disease ID and variation ID combination; the breast cancer drug information database is respectively associated with the breast cancer targeted therapy and evidence grade database, the breast cancer chemotherapeutics and evidence grade database, the breast cancer immunotherapy and evidence grade database, the breast cancer endocrine therapy and evidence grade database and the breast cancer prognosis evaluation and evidence grade database through combination of a disease ID and a drug ID; the breast cancer biomarker database is associated with the breast cancer immunotherapy and evidence grade database by a disease ID and biomarker name combination, or by a disease ID and biomarker ID combination; the breast cancer clinical test database is respectively associated with the breast cancer targeted therapy and evidence grade database, the breast cancer chemotherapeutics and evidence grade database, the breast cancer immunotherapy and evidence grade database, the breast cancer endocrine therapy and evidence grade database and the breast cancer prognosis evaluation and evidence grade database through combination of disease ID and drug ID; the breast cancer reference database is respectively associated with the breast cancer information database, the breast cancer gene analysis database, the breast cancer germline gene variation database, the breast cancer system gene variation database, the breast cancer gene variation clinical significance database, the breast cancer drug information database, the breast cancer targeted therapy and evidence grade database, the breast cancer chemotherapeutics and evidence grade database, the breast cancer immunotherapy and evidence grade database, the breast cancer endocrine therapy and evidence grade database, the breast cancer prognosis evaluation and evidence grade database, the breast cancer biomarker database and the breast cancer clinical test database through reference document IDs.
Optionally, the system further includes a gene variation and drug interpretation plate, a biological information data analysis plate, a report management plate, and a storage plate, wherein the gene variation and drug interpretation plate is connected to the breast cancer database plate, the biological information data analysis plate, the report management plate, and the storage plate is connected to the gene variation and drug interpretation plate, the biological information data analysis plate, and the report management plate; the gene variation and medicine interpretation plate is used for executing interpretation of breast cancer gene variation and medicine guidance; the biological information data analysis plate is used for carrying out biological information analysis on second-generation sequencing off-line data of the breast cancer patient; the report management block is used for generating a gene detection report; the storage plate is used for storing breast cancer gene variation and medication reading information, information annotated by the biological information data analysis plate and a gene detection report.
For the same purpose, the present specification provides, in a second aspect, a method for breast cancer genetic variation and medication mapping, the method comprising: performing biological information analysis on the sequencing off-line data of a detected sample to obtain genetic variation annotation information of the detected sample; obtaining sample information of the sample to be detected; and performing correlation reading on the sample information and the genetic variation annotation information of the detected sample and a database in a breast cancer database version to obtain reading information corresponding to the detected sample.
Optionally, the performing biological information analysis on the sequencing offline data of the sample to be tested to obtain the genetic variation annotation information of the sample to be tested includes: obtaining the data of a sample to be detected, and extracting the data of the sample to be detected to obtain input data and process information; responding to an operation instruction sent by a user, confirming the input data and the process information, starting a data analysis process corresponding to the sequencing off-line data, and generating genetic variation detection information of the detected sample; or, responding to an operation instruction sent by a user after confirming the input data and the process information, starting a data analysis process corresponding to the sequencing off-line data, and generating genetic variation detection information of the detected sample; and starting an annotation process corresponding to the sequencing off-line data, and annotating the genetic variation detection information of the detected sample to obtain the genetic variation annotation information of the detected sample.
Optionally, the associating and reading the sample information and the genetic variation annotation information of the sample to be examined with a database in a breast cancer database version to obtain interpretation information corresponding to the sample to be examined includes: and comparing the sample information of the detected sample, the genetic variation annotation information of the detected sample and a database with key fields or key field combinations to obtain corresponding interpretation information of the detected sample.
Optionally, the method further includes: and calling a report template and generating a gene detection report based on the sample information, the genetic variation annotation information and the interpretation information corresponding to the sample to be detected.
Optionally, the method further includes: and storing the sample information, the genetic variation annotation information, the interpretation information and the genetic detection report of the detected sample.
With the same objective in view, the third aspect of the present specification provides a breast cancer genetic variation and medication interpretation device, comprising: the genetic variation annotation information acquisition module is used for carrying out biological information analysis on sequencing off-line data of a detected sample to acquire genetic variation annotation information of the detected sample; the sample information acquisition module is used for acquiring sample information of the detected sample; and the interpretation information acquisition module is used for correlating and reading the sample information and the genetic variation annotation information of the detected sample and a database in a breast cancer database version to acquire the interpretation information corresponding to the detected sample.
Optionally, the genetic variation annotation information acquisition module is specifically configured to: obtaining the data of a sample to be detected, and extracting the data of the sample to be detected to obtain input data and process information; responding to an operation instruction sent by a user, confirming the input data and the process information, starting a data analysis process corresponding to the sequencing off-line data, and generating genetic variation detection information of the detected sample; or, responding to an operation instruction sent by a user after confirming the input data and the process information, starting a data analysis process corresponding to the sequencing off-line data, and generating genetic variation detection information of the detected sample; and starting an annotation process corresponding to the sequencing off-line data, and annotating the genetic variation detection information of the detected sample to obtain the genetic variation annotation information of the detected sample.
Optionally, the interpretation information obtaining module is specifically configured to: and comparing the sample information of the detected sample, the genetic variation annotation information of the detected sample and a database with key fields or key field combinations to obtain corresponding interpretation information of the detected sample.
Optionally, the apparatus further includes a genetic testing report generating module, configured to invoke a report template and generate a genetic testing report based on the sample information of the test sample, the genetic variation annotation information, and the interpretation information corresponding to the test sample.
Optionally, the apparatus further includes a storage module, configured to store sample information, genetic variation annotation information, interpretation information, and the genetic testing report of the test sample.
As can be seen from the above, in the system, the breast cancer database version stores various information such as breast cancer targeted therapy, chemical drug therapy, immunotherapy, endocrine therapy, prognosis evaluation, evidence grade and the like, and each database of the breast cancer database version is associated by the same key field or key field combination, so that an interaction function between the databases is realized.
Drawings
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
FIG. 1 is a schematic diagram of a breast cancer database version of the system for genetic variation and medication interpretation of breast cancer provided in the present specification;
FIG. 2 is a schematic diagram showing the connection of blocks in the breast cancer gene mutation and medication interpretation system provided in the present specification;
FIG. 3 is a schematic flow chart of a method for interpreting breast cancer gene variation and medication provided herein;
fig. 4 is an explanation of step S31 in fig. 3;
fig. 5 is a schematic structural diagram of an interpretation device for breast cancer gene mutation and medication provided in the present specification.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present specification should have the ordinary meaning as understood by those of ordinary skill in the art to which this disclosure belongs. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
In recent years, the diagnosis and treatment of breast cancer have been seriously changed, and the conventional gene detection of breast cancer examinees formally provides the best personalized medication guidance for patients. For example, the 3 rd edition NCCN breast cancer diagnosis and treatment guideline in 2020 recommends that the target points to be detected by breast cancer patients include Ki67, STK15, Survivin, CCNB1, MYBL2, MMP11, CTSL2, GRB7, HER2, ER, PGR, BCL2, SCUBE2, GSTM1, BAG1, CD68, beta-actin, GAPDH, RPLP0, GUS, TFRC and the like, and the guideline also explicitly indicates the recommendable treatment schemes corresponding to the target points. Although the guidelines recommend drug targets, each target comprises hundreds of genetic variation sites, each of which requires interpretation of clinical significance, and the clinical benefit of individualized treatment for clinically significant variation sites is greater. And with the continuous and deep clinical research, a plurality of potential meaningful targets and medicines are continuously emerged, so that the interpretation of the targets and the medicines is very important, and the method is very important for providing a personalized treatment scheme with great clinical benefit for the breast cancer.
With the massive genetic variation data produced by second-generation sequencing, genetic variation interpretation is the key point for realizing accurate disease treatment. At present, a unified standard and a unified database interpretation system for interpretation of breast cancer genetic variation and medicines do not exist, and for interpretation, collection and arrangement of related genetic variation data, different processing methods and processes are provided for various major companies and clinical institutions, so that how to establish a perfect and convenient breast cancer genetic variation and medicine database is an urgent problem to be solved.
In order to solve the above problems, the present specification provides a system, a method and a device for reading breast cancer gene mutation and medication, wherein the system includes a breast cancer database version, the breast cancer database version includes a plurality of databases storing related information of breast cancer, and the databases are associated with each other through the same key field or the same key field combination; when the breast cancer gene variation and medication reading system is used for reading, firstly, biological information analysis is carried out on the sequencing off-machine data of a detected sample to obtain gene variation annotation information of the detected sample, wherein the sequencing off-machine data comprises sample information of the detected sample; and after acquiring the genetic variation annotation information of the detected sample, correlating the sample information of the detected sample, the genetic variation annotation information and the database to read the interpretation information of the detected sample. The method and the device can be applied to electronic equipment such as a computer, a tablet personal computer, a smart phone, a PAD and the like, and are not limited specifically.
For the convenience of understanding, the tumor precise medication reading system is described in detail below with reference to the accompanying drawings. FIG. 1 is a schematic diagram of a database block in a breast cancer gene mutation and medication reading system provided in the present specification; as shown in fig. 1, the system for analyzing breast cancer gene mutation and medication includes a breast cancer database version, which includes a plurality of databases related to each other by the same key field or the same key field combination, and the databases are respectively used for storing the related information of breast cancer.
It can be understood that, according to the correlation between the databases, when a user uses the system for interpreting genetic variation and medication of breast cancer, the retrieval key fields are set in the retrieval frame of any database, so that the description information of the corresponding key fields can be obtained in the database connected with the retrieval frame, and based on the related information of breast cancer stored in each database, the multi-level interpretation of each variation can be performed, the interpretation standard is rigorous and scientific, the system can be applied to the accurate treatment of breast cancer, and the comprehensive medication guidance suggestions with clear evidence levels can be accurately found and provided, so that an important reference is provided for medical decision making.
As shown in fig. 1, the multiple databases in the breast cancer database version 10 include a breast cancer information database 101, a breast cancer gene analysis database 102, a breast cancer germline gene variation database 103, a breast cancer system genetic variation database 104, a breast cancer genetic variation clinical meaning database 105, a breast cancer drug information database 106, a breast cancer targeted therapy and evidence grade database 107, a breast cancer chemotherapeutics and evidence grade database 108, a breast cancer immunotherapy and evidence grade database 109, a breast cancer endocrine therapy and evidence grade database 1010, a breast cancer prognosis evaluation and evidence grade database 1011, a breast cancer biomarker database 1012, a breast cancer clinical trial database 1013, and a breast cancer reference database 1014.
The breast cancer information database 101 is used for storing basic introduction of breast cancer, relationship between breast cancer and pathogenic genes and treatment progress information; the relevant information of the breast cancer information database 101 may be derived from expert consensus, diagnosis and treatment guidelines related to breast cancer, and is not particularly limited. The breast cancer information database 101 may include one or more of the following key fields: the Chinese name of the disease, English name of the disease, ID of the disease, brief introduction of the disease, advice of the disease guidance, reference source, etc., without limitation.
The breast cancer gene analysis database 102 is used for storing gene biological functions and gene and breast cancer occurrence development relation information; the information in the breast cancer gene analysis database 102 may be derived from data obtained by a professional who collates data from the biomedical websites such as HGNC, GeneCards, CIViC, and the like. The breast cancer gene profiling database 102 may include one or more of the following key fields: gene ID, disease ID, gene name, introduction of gene biological function, main mutation types of genes, occurrence and development relation with breast cancer and the like, and the details are not limited.
The breast cancer germ line gene variation database 103 and the breast cancer system gene variation database 104 are used for storing breast cancer germ line and system gene variation information respectively; the information of the breast cancer germ line gene variation database 103 and the breast cancer system gene variation database 104 can be derived from public databases such as COSMIC, CKB, ClinVar, and the like, and is not particularly limited; the annotation rules of the common databases are different, the bioinformatics need to use a script to select a reference Genome version GRCh37 to re-annotate the Gene Variation, the transcript selects the optimal clinical transcript, the unified specification and naming are performed, the genes are named according to the rules of the Human Genome Nomenclature Committee (HGNC), and the mutation sites are named according to the rules of the Human Genome Variation Society (HGVS). The breast cancer germline gene variation database 103 and the breast cancer germline gene variation database 104 may each include one or more of the following key fields: gene ID, variant ID, gene name, chromosomal location, transcription start location, transcription stop location, reference base, altered base, transcript number, gene variant RS number, signaling pathway of gene, gene variant nucleotide variant information, and gene variant amino acid variant information, and the like, without limitation.
The breast cancer gene variation clinical significance database 105 is used for storing the clinical significance of the breast cancer gene variation; one or more of the following key fields may be included: gene ID, variant ID, disease ID, clinical significance description, reference basis and the like, and the details are not limited. Dividing clinical significance of breast cancer gene variation based on a somatic mutation variation site interpretation guide jointly formulated by AMP, ASCO and CAP in 2017, and dividing the clinical significance of the breast cancer gene variation into four levels of Tier I, Tier II, Tier III and Tier IV, wherein the Tier I has stronger clinical significance variation, the Tier II has potential clinical significance variation, the Tier III has unknown clinical significance variation, and the Tier IV is benign or possibly benign; specifically, Tier I has strong clinical significance variation, including evidence levels Level A and B; tier II has potential clinically significant variations, including evidence levels Level C and D. Tier I and Tier II recommend medication to guide efficacy assessments.
The breast cancer drug information database 106 is used for storing drug information related to breast cancer treatment; one or more of the following key fields may be included: the name of the medicine commodity, the common name, the medicine ID, the medicine Pubchem _ ID, the medicine indication information, the adverse reaction data, the approval state, the reference source and the like, and the details are not limited; the information in the breast cancer drug information database 106 may be derived from a database of a food and drug administration and various drug databases, such as CFDA, FDA, EMA, PMDA, drug bank, etc., without limitation.
The breast cancer targeted therapy and evidence grade database 107 is used for storing breast cancer targeted therapy information and breast cancer targeted therapy evidence grade information; the breast cancer targeted therapy and evidence grade database 107 stores the targeted therapy information of clinical approval, guideline recommendation, expert consensus and clinical in-research trials of breast cancer and the results of grading the targeted therapy information according to AMP guideline standards; one or more of the following key fields may be included: gene ID, gene name, mutation ID, drug generic name, sensitivity, clinical efficacy information, clinical evidence rating, reference source, and the like, without limitation; wherein, the sensitivity comprises sensitivity, drug resistance, inconsistent research conclusion and unknown clinical significance of 4 types.
The clinical efficacy information of the breast cancer targeted therapy can be derived from NCCN, CSCO, ASCO, ESMO, FDA, CFDA guidelines, clinical trial websites of Clinicalrials, scientific research literature and the like, and is not particularly limited; the description of clinical efficacy includes: the test type, the clinical test stage, the number of people who enter the group in the clinical test, the gene variation characteristics carried by people who enter the group, the description of the clinical curative effect after treatment (refer to the new standard of the curative effect evaluation of solid tumors: RECIST), the adverse reaction after treatment and the like. The classification of clinical evidence for breast cancer targeted therapy is classified into four classes according to the classification criteria of the cancer sequence variation interpretation and reporting criteria guidelines, as suggested by the association consensus of american society of pathologists (CAP), american association of medical genetics and genomics (ACMG) molecular pathology Association (ASCO): level A: FDA/CFDA approved indications or clinical guideline recommendations; level B: large-scale clinical trial consistency conclusions; level C: FDA/CFDA approval for non-indications or multiple small-scale clinical trial consistency conclusions; level D: preclinical testing or few cases.
The breast cancer chemotherapeutics and evidence grade database 108 is used for storing breast cancer chemotherapeutics information and breast cancer chemotherapeutics clinical evidence grade information; the breast cancer chemotherapeutics and evidence grade database 108 stores clinical information of breast cancer chemotherapeutics and results of classification and classification of the chemotherapeutics clinical evidence grade according to the PharmGKB database; one or more of the following key fields may be included: gene ID, variation ID, gene variation RS number, genotype, drug name, medication prompt, clinical evidence grade, reference source and the like, and the details are not limited; wherein, the medication prompt information is mainly from PharmGKB database, NCCN guideline, clinical trial website of Clinicalrials and scientific research literature, etc.; the medication prompt description includes the aspects of chemotherapy response rate, toxic and side effects and the like.
The classification and classification of clinical evidence of breast cancer chemotherapeutics are referred to PharmGKB database, and are divided into four grades: level 1A: a guideline for clinical pharmacogenomics implementation alliance (CPIC), or for medical society-approved pharmacogenomics, or for a pharmacogenomic research network already in force, or for variation-drug annotation in another major health system; level 1B: comments about variant-drugs have sufficient evidential advantages and show a correlation that must be replicated in multiple cohorts with significant p-values, and preferably have a strong scope of influence; level 2A: annotations for variation-drug reached a 2B level and this variation was defined as VIP (very important pharmacogenetic gene) in PharmGKB, a 2A level variation is a known pharmacogenetic gene and is therefore more likely to have functional significance; level 2B: annotations for variation-drug have some evidence of associations that must be replicated in some studies but may not show statistical significance in these studies, and the scope of influence may be small; level 3: annotations for variant-drugs are based on an important (not yet replicated) study, or have been evaluated in multiple studies, but lack clear evidence of association; level 4: the annotation for variant-drug is based on a case report or nonsensical studies or experimental evidence in vitro, molecular or functional aspects.
The breast cancer immunotherapy and evidence grade database 109 is used for storing breast cancer immunotherapy information and immunotherapy clinical evidence grade information; the information stored in the breast cancer immunotherapy and evidence rating database 109 includes Tumor Mutation Burden (TMB) assessment, mismatch repair gene deficiency (dMMR) assessment, microsatellite instability assessment, and the like; one or more of the following key fields may be included: biomarkers, biomarker IDs, gene names, gene IDs, clinical significance, evidence rating, reference source, and the like, without limitation. The clinical significance of breast cancer mutation load assessment comprises tumor mutation load definition, the relation between TMB and an immune checkpoint inhibitor, the clinical test progress and the like; the mismatch repair gene defect evaluation comprises the detection results of mismatch repair genes and the relationship between the inactivation mutation of the genes and the curative effects of dMMR, MSI-H and an immune checkpoint inhibitor, wherein the detection results of the mismatch repair genes comprise MLH1, MSH2, MSH6, PMS2, POLE and the like, and are not limited specifically; the evaluation of the instability of the microsatellite comprises the definition and the occurrence mechanism of the instability of the microsatellite, the progress of clinical tests of the patients with the microsatellite unstable tumor by using immunotherapy drugs, and the like.
The classification of clinical evidence grades of breast cancer immunotherapy and classification reference evidence-based medical evidence grades are divided into six grades according to the relationship among biomarkers, tumor types and medicines: level 1: FDA/NMPA/EMA/PMDA approved indications; level 2: NCCN/ASCO/ESMO/CSCO guidelines or other guideline consensus recommendations; level 3: clinical study compliance results reported in large-scale clinical trials or major meetings; level 4: reported in case; level 5: preclinical in vitro assays; level 6: function prediction studied.
The breast cancer endocrine treatment and evidence grade database 1010 is used for storing breast cancer endocrine treatment information and clinical evidence grade information of endocrine treatment; the breast cancer endocrine treatment and evidence grade database 110 stores clinical approval, guideline recommendation, expert consensus and endocrine treatment information of clinical in-research trials of breast cancer and grades the endocrine treatment information by referring to AMP guideline standard; one or more of the following key fields may be included: gene ID, gene name, mutation ID, drug generic name, sensitivity, clinical efficacy information, clinical evidence rating, reference source, and the like, without limitation; wherein, the sensitivity comprises sensitivity, drug resistance, inconsistent research conclusion and unknown clinical significance of 4 types.
The clinical curative effect information of the endocrine treatment of the breast cancer can be derived from NCCN, CSCO, ASCO, ESMO, FDA, CFDA guidelines, clinical trial websites of Clinicalrials, scientific research documents and the like, and is not particularly limited; the description of clinical efficacy includes: the test type, the clinical test stage, the number of people who enter the group in the clinical test, the gene variation characteristics carried by people who enter the group, the description of the clinical curative effect after treatment (refer to the new standard of the curative effect evaluation of solid tumors: RECIST), the adverse reaction after treatment and the like. The classification and classification of clinical evidence for endocrine treatment of breast cancer is classified into four classes according to the classification criteria of "cancer sequence variation interpretation and reporting criteria guidelines" as suggested by the association consensus of American Society of Clinical Oncology (ASCO), american society of pathologists (CAP), american association of medical genetics and genomics (ACMG) molecular pathology association: level A: FDA/CFDA approved indications or clinical guideline recommendations; level B: large-scale clinical trial consistency conclusions; level C: FDA/CFDA approval for non-indications or multiple small-scale clinical trial consistency conclusions; level D: preclinical testing or few cases.
The breast cancer prognosis evaluation and evidence grade database 1011 is used for storing result information of breast cancer prognosis evaluation and breast cancer prognosis evidence grade grading; one or more of the following key fields may be included: gene ID, mutation ID, clinical efficacy information, clinical evidence grade, reference source and the like, and the details are not limited; clinical efficacy information is derived from reading personnel's summaries obtained from guidelines such as NCCN, CSCO, ASCO, ESMO, FDA, CFDA, clinicaltralals clinical trials websites, scientific literature, and the like. The description of clinical efficacy includes: the test type, the stage of clinical test, the number of people who enter the group in clinical test, the gene variation characteristics carried by people who enter the group, the description of the clinical curative effect after treatment (refer to the new standard of curative effect evaluation of solid tumor: RECIST), the adverse reaction after treatment and the like; the grade of clinical evidence for breast cancer prognosis evaluation is the same as that of the clinical evidence for targeted medication.
The breast cancer biomarker database 1012 is used for storing breast cancer immunotherapy-related genes and biomarker information; one or more of the following key fields may be included: immunotherapy-related genes, TMB, MSI, PD-L1, and the like, without limitation. Determining the state of the TMB according to the value of the TMB: TMB-H (> ═ 20Muts/Mb), TMB-M (5< TMB <20Muts/Mb), TMB-L (< ═ 5 Muts/Mb). MSI status determined from MSI values: MSI-H indicates that the detected value of the instability level of the microsatellite is high, MSI-L indicates that the detected value of the instability level of the microsatellite is low, and MSI-H: the number of changed STRs is more than or equal to 20 percent, and the MSI-L: the number of STRs that changed was < 20%. The expression and interpretation standard of PD-L1 is that tumor cells TC (Tumor cell) detects the proportion of tumor cells in the white slices which present any intensity of cell staining (PD-L1 expression); TC 3: the expression cell ratio is more than or equal to 50 percent, and the PD-L1 has high expression; TC 2: the expression cell ratio is more than or equal to 5 percent and less than 50 percent, and the expression of PD-L1 is moderate; TC 1: the expression cell ratio is more than or equal to 1 percent and less than 5 percent, the expression of PD-L1 is low, and the like; TC 0: the expression cell ratio is less than 1%, and the expression of PD-L1 is negative.
The breast cancer clinical test database 1013 is used for storing breast cancer clinical test information; one or more of the following key fields may be included: disease ID, test stage, test name, test drug ID, test status, test start date and test completion date, test number NCT, and the like, and the details are not limited.
Breast cancer reference database 1014 is a basis for storing breast cancer reference data; one or more of the following key fields may be included: the reference ID, DOI such as PMID, NCCN, ASCO, CSCO, etc., and the reference title and literature link are not limited.
It can be understood that the breast cancer database version comprises a plurality of databases, and each database respectively comprises data related to different directions of breast cancer, so that a basis is provided for carrying out gene variation and medication interpretation on breast cancer patients, and convenience is brought to carrying out comprehensive interpretation on the breast cancer patients.
In practical application, in order to realize the relevant reading and interaction among different databases, the databases in the breast cancer database version are relevant through the same key field or the combination of the same key field; then, in some possible embodiments, the breast cancer information database 101 is associated with the breast cancer drug information database 106 by a disease ID and drug ID combination; the breast cancer gene analysis database 102 is respectively associated with the breast cancer germ line gene variation database 103 and the breast cancer system gene variation database 104 through gene ID and variation ID combination; the breast cancer germ line gene variation database 103 and the breast cancer system gene variation database 104 are respectively associated with the breast cancer gene variation clinical significance database 105 through combination of variation ID and disease ID; the breast cancer gene mutation clinical significance database 105 is respectively associated with a breast cancer targeted therapy and evidence grade database 107, a breast cancer chemotherapeutics and evidence grade database 108, a breast cancer immunotherapy and evidence grade database 109, a breast cancer endocrine therapy and evidence grade database 1010 and a breast cancer prognosis evaluation and evidence grade database 1011 through the combination of a disease ID and a mutation ID; the breast cancer drug information database 106 is respectively associated with a breast cancer targeted therapy and evidence grade database 107, a breast cancer chemotherapeutics and evidence grade database 108, a breast cancer immunotherapy and evidence grade database 109, a breast cancer endocrine therapy and evidence grade database 1010 and a breast cancer prognosis evaluation and evidence grade database 1011 through the combination of a disease ID and a drug ID; the breast cancer biomarker database 1012 is associated with the breast cancer immunotherapy and evidence rating database 109 by a disease ID and biomarker name combination, or by a disease ID and biomarker ID combination; the breast cancer clinical test database 1013 is respectively associated with the breast cancer targeted therapy and evidence grade database 107, the breast cancer chemotherapeutics and evidence grade database 108, the breast cancer immunotherapy and evidence grade database 109, the breast cancer endocrine therapy and evidence grade database 1010 and the breast cancer prognosis evaluation and evidence grade database 1011 through the combination of a disease ID and a drug ID; the breast cancer reference database 1014 is respectively associated with the breast cancer information database 101, the breast cancer gene analysis database 102, the breast cancer germline gene variation database 103, the breast cancer system gene variation database 104, the breast cancer gene variation clinical significance database 105, the breast cancer drug information database 106, the breast cancer targeted therapy and evidence grade database 107, the breast cancer chemotherapeutics and evidence grade database 108, the breast cancer immunotherapy and evidence grade database 109, the breast cancer endocrine therapy and evidence grade database 1010 and the breast cancer prognosis evaluation and evidence grade database 1011, the breast cancer biomarker database 1012 and the breast cancer clinical test database 1013 through reference IDs.
It can be understood that, a search key word is input into a search unit in the breast cancer database, and the search key word is compared with information in the database to obtain a search result matched with the search key word; because each database of the breast cancer database version is associated based on the same key field or the same key field combination, the information of the corresponding search key field can be obtained in the database associated with the search key field by inputting the search key field in any database in the breast cancer database version, and therefore unscrambling personnel can conveniently obtain comprehensive information of breast cancer medication unscrambling.
Fig. 2 is a schematic diagram illustrating connection of data blocks in a breast cancer gene mutation and medication interpretation system provided in the present specification.
In practical application, after reading each database of the database version block in an associated manner to obtain the interpretation information of the detected sample, the interpretation information of the detected sample needs to be further processed; then, as shown in fig. 2, in some possible embodiments, the breast cancer genetic variation and medication interpretation system further includes a genetic variation and medication interpretation block 20, a biological information data analysis block 30, a report management block 40, and a storage block 50, wherein the genetic variation and medication interpretation block 20 is respectively connected to the breast cancer database block 10, the biological information data analysis block 30, the report management block 40, and the storage block 50 is respectively connected to the genetic variation and medication interpretation block 20, the biological information data analysis block 30, and the report management block 40; the gene variation and drug interpretation block 20 is used for performing interpretation of breast cancer gene variation and drug guidance; the biological information data analysis block 30 is used for performing biological information analysis on second-generation sequencing off-line data of the breast cancer patient; the report management block 40 is used for generating a gene detection report; the storage block 50 is used for storing breast cancer gene variation and medication interpretation information, information annotated by the biological information data analysis block and gene detection reports.
The gene variation and drug interpretation block 20 is respectively connected with each database in the breast cancer database block 10, and the gene variation and drug interpretation block 20 can perform comprehensive interpretation on the breast cancer from the aspects of targeting, chemotherapy, immunity, prognosis evaluation treatment and the like based on each database in the breast cancer database block 10 to obtain breast cancer gene variation and drug interpretation information; the biological information data analysis block 30 can carry out biological information analysis on the second-generation sequencing off-line data of the breast cancer patient; the report management block 40 can automatically generate a gene detection report in response to the instructions of the gene variation and the medicine interpretation block; the storage section 50 can automatically store the breast cancer genetic variation and medication interpretation information in response to the instruction of the genetic variation and medication interpretation section, and can store the annotated information obtained by the biological information data analysis section 30 after performing biological information analysis on the second-generation sequencing offline data of the breast cancer patient, and can store the gene detection report generated by the report management section 40.
The biological information data analysis plate 30 is connected with the genetic variation and medicine interpretation plate 20, after the biological information analysis of second-generation sequencing off-line data of the breast cancer patient is completed, the biological information data analysis plate 30 sends an instruction to the genetic variation and medicine interpretation plate 20, and the genetic variation and medicine interpretation plate 20 responds to the instruction of the biological information data analysis plate 30 and conducts comprehensive interpretation on the genetic variation and medicine use of the breast cancer according to a biological information analysis result of the biological information data analysis plate 30.
The gene variation and drug interpretation plate 20 is connected with the report management plate 40, after the gene variation and drug interpretation plate 20 completes the comprehensive interpretation of the gene variation and drug administration of the breast cancer, an instruction is sent to the report management plate 40, and the report management plate 40 responds to the instruction of the gene variation and drug interpretation plate 20 to automatically generate a gene detection report.
The gene variation and drug interpretation plate 20 is connected with the storage plate 50, after the gene variation and drug interpretation plate 20 completes the comprehensive interpretation of the breast cancer gene variation and the drug administration, a command is sent to the storage plate 50, and the storage plate 50 automatically stores breast cancer gene variation and drug administration interpretation information in response to the command of the gene variation and drug interpretation plate 20. In practical application, the genetic variation and drug interpretation information stored in the storage block 50 can be fed back to the genetic variation and drug interpretation block 20 and the breast cancer database block 10, and breast cancer genetic variation and drug interpretation data are newly added, so that data iteration is realized. That is, when the user uses the system to analyze and interpret a sample to be examined, the data obtained after the discovery of the genetic variation and the interpretation of the drug interpretation block 20 may not completely cover the clinically significant genetic variation site newly discovered in the sample to be examined, the user supplements the clinical significance of the newly discovered genetic variation site, the genetic variation and drug interpretation block 20 feeds back the information supplemented by the user to the breast cancer database block 10, and the breast cancer database block 10 stores new information of the genetic variation site, clinical significance information of the genetic variation site, clinical efficacy information, and the like.
The storage section 50 is also connected to the bioinformation data analysis section 30 and the report management section 40, respectively, so that the annotated information obtained by the bioinformation data analysis section 30 performing bioinformation analysis on the second-generation sequencing offline data of the breast cancer patient can be stored, and the gene retrieval report generated by the report management section 40 can be stored.
It can be understood that based on the functions and the connection relationship among the breast cancer database edition 10, the gene variation and drug interpretation edition 20, the biological information data analysis edition 30, the report management edition 40 and the storage edition 50, the comprehensive interpretation of the breast cancer gene variation and the drug administration can be realized, the comprehensive and clear drug administration guidance suggestions with clear evidence levels can be quickly and accurately found and provided according to the gene detection results of the breast cancer examinees, and important references are provided for medical decision making.
FIG. 3 is a schematic flow chart of the method for analyzing breast cancer gene mutation and medication; as shown in fig. 3, the method includes:
s31, performing biological information analysis on the sequencing off-line data of the detected sample to obtain the genetic variation annotation information of the detected sample;
s32, obtaining sample information of the detected sample;
and S33, correlating and reading the sample information and the genetic variation annotation information of the detected sample with a database in the breast cancer database version, and acquiring interpretation information corresponding to the detected sample.
In practical application, the interpretation information includes clinical significance of genetic variation corresponding to the sample to be examined, clinical trial information, targeted therapy and evidence grade information of genetic variation corresponding to the sample to be examined, immunotherapy and evidence grade information, chemotherapy and evidence grade information, prognosis evaluation and evidence grade information, and respective corresponding reference guidance bases.
The detected sample refers to the gene sample data to be analyzed of the breast cancer patient; the sample information of the sample to be examined refers to phenotypic information related to the genetic sample, and may include a sample type, a disease name (clinical diagnosis), a sampling time, a delivery unit, a specimen collection date, a delivery sample type, a sample part, a sample number, a sample detection mechanism, and a name, a sex, an age, a family history, a treatment history, and the like of a subject to which the sample belongs, without being limited. The interpretation data corresponding to the detected sample refers to data which is stored in the database block and closely related to the detected sample.
An electronic device (hereinafter referred to as the electronic device) executing the method acquires sample information of a detected sample by a user input method, namely, the user inputs the sample information of the detected sample into the electronic device, and the electronic device acquires the sample information of the detected sample input by the user; the user may input the sample information of the sample to be examined after the electronic device obtains the genetic variation annotation information of the sample to be examined, or may input the sample information of the sample to be examined before the electronic device obtains the genetic variation annotation information of the sample to be examined, which is not limited specifically.
The process of acquiring the annotation information and the interpretation information of the genetic variation of the sample to be examined will be described in detail later, and will not be described herein again.
It can be understood that the interpretation method based on the breast cancer genetic variation and the medicine application can realize the intelligent interpretation of the breast cancer genetic variation and the medicine application based on the second-generation sequencing off-line data of the breast cancer patient, and the interpretation personnel can quickly and accurately find and provide the comprehensive medicine application guidance suggestion with clear evidence grade according to the gene detection result of the breast cancer examined sample.
Fig. 4 is an explanation of step S31 in fig. 3; in practical application, in order to interpret a breast cancer sample, first, acquiring genetic variation annotation information of the sample; then, as shown in fig. 3, in some possible embodiments, performing bioinformatics analysis on the off-sequence data of the sample to be tested to obtain the annotation information of the genetic variation of the sample to be tested includes:
s41, obtaining the sequencing off-line data of the detected sample, and extracting the sequencing off-line data to obtain input data and flow information;
s42, responding to an operation instruction sent by a user, confirming input data and process information, starting a data analysis process corresponding to the sequencing off-line data, and generating genetic variation detection information of the detected sample;
or the like, or, alternatively,
responding to an operation instruction sent by a user after confirming input data and process information, starting a data analysis process corresponding to the sequencing off-line data, and generating genetic variation detection information of the sample to be detected;
and S43, starting an annotation process corresponding to the sequencing off-line data, and annotating the genetic variation detection information of the detected sample to obtain the genetic variation annotation information of the detected sample.
The input data refers to the result data of double-end sequencing, and can be in a compressed form, namely gz ending, or in an uncompressed format, namely fastq format; the input data includes a BED area file that may be used to detect BED area mutations. Depending on the purpose of the analysis, it may be necessary to provide a double-sided primer file when the gene package data is involved.
The process information refers to a tumor whole-external/whole-genome/gene packet analysis process, the analysis items relate to SNV/InDel/CNV/SV, and the specific steps in the analysis process are as follows: filtering original data by using fastp software, comparing and sequencing filtered data with a reference genome version (Grch37) by using BWA software, removing a PCR repeated sequence by using Picard software, performing realign and recall correction on the duplicate bam, obtaining corrected bam by using corresponding variation detection software, obtaining SNV, InDel, CNV and SV analysis results, and storing the analysis results in a variation identification file VCF. For example, the process information of the genetic package detection can be extracted from the "physical pipeline analysis (panel)" analysis process for subsequent operations.
In practical application, in order to obtain the genetic variation annotation information of the sample to be examined, the sequencing off-line data of the sample to be examined can be obtained first, and the sequencing off-line data is extracted to obtain the input data and the process information for starting the genetic variation and the medication reading of the breast cancer sample.
Then, input data and process information are confirmed, and a data analysis process corresponding to the sequencing off-line data is started. In one case, a user confirms whether input data and flow information are correct or not through visual interactive operation, and if so, the user sends an operation instruction; an electronic device (hereinafter referred to as the electronic device) executing the method responds to an operation instruction of a user, starts a data analysis process corresponding to sequencing off-line data of a breast cancer sample to be detected, and obtains genetic variation detection information of the sample to be detected; at this time, the operation instruction of the user is to start the data analysis process. In one case, a user sends an operation instruction through visual interactive operation, the electronic equipment responds to the operation instruction of the user to confirm input data and flow information, starts a data analysis flow corresponding to sequencing off-line data, and obtains genetic variation detection information of a detected sample; at this time, the user's operation instruction is to determine whether the input data and the process information are correct and to start the data analysis process.
And then, starting an annotation process corresponding to the sequencing off-line data of the breast cancer sample, and annotating the genetic variation detection information of the breast cancer sample to obtain the genetic variation annotation information corresponding to the breast cancer sample.
It can be understood that the genetic variation detection information is obtained by performing data analysis on the sequencing off-line data of the breast cancer sample to be detected, and then the genetic variation detection information is annotated to obtain the genetic variation annotation information, which is beneficial to performing correlation reading on each database according to the genetic variation annotation information of the sample to be detected and obtaining accurate interpretation information of the sample to be detected.
In practical application, in order to perform genetic variation and medication interpretation on a breast cancer sample to obtain genetic variation annotation information of the sample to be examined, data in each database needs to be further read; in some possible embodiments, the associating and reading the sample information and the genetic variation annotation information of the sample to be tested with the database in the database version block, and acquiring the interpretation information corresponding to the sample to be tested, includes: and comparing the sample information of the detected sample, the genetic variation annotation information of the detected sample and the database with key fields or key field combinations to obtain corresponding interpretation information of the detected sample.
When obtaining the genetic variation annotation information of the detected sample and then performing correlation reading, performing key field comparison or key field combination comparison on the sample information of the detected sample, the genetic variation annotation information of the detected sample and a certain database in a database version block, obtaining the same key field or key field combination through comparison, screening and obtaining data corresponding to the same key field or key field combination from the data stored in the database, and further performing progressive key field comparison or key field combination comparison among different databases, thereby obtaining the interpretation information corresponding to the detected sample from each database of the database version block.
The sample information of the detected sample comprises disease information of the gene sample; the genetic variation annotation information of the detected sample comprises detected biomarkers and marker level state results of the detected sample; the sample information and the genetic variation annotation information of the sample to be detected are read in a correlation manner with the database in the database version block to obtain the interpretation information of the sample to be detected, and the method can be carried out by adopting the following method:
comparing the chromosome position, the reference base, the changed base, the transcription start position and the transcription termination position in the genetic variation annotation information of the breast cancer examined sample with the corresponding chromosome position, the reference base, the changed base, the transcription start position and the transcription termination position in the breast cancer germ line genetic variation database 103 and the breast cancer system genetic variation database 104 respectively to obtain the genetic variation ID of the genetic variation annotation information stored in the breast cancer germ line genetic variation database 103 and the breast cancer system genetic variation database 104; then, acquiring the gene variation clinical significance of the gene variation annotation information of the detected sample according to the obtained gene variation ID corresponding to the gene variation ID in the breast cancer gene variation clinical significance database 105, and judging the gene variation clinical significance as gene variation with stronger clinical significance and gene variation with potential clinical significance so as to carry out medication guidance including targeted therapy, immunotherapy, endocrine therapy and prognosis evaluation in the next step; according to the fact that the gene variation ID and the drug ID in the breast cancer drug information database 106 directly correspond to the gene variation ID and the drug ID in the breast cancer targeted therapy and evidence grade database 107, the breast cancer immunotherapy and evidence grade database 109, the breast cancer endocrine therapy and evidence grade database 1010 and the breast cancer prognosis evaluation and evidence grade database 1011 respectively, the targeted therapy drug administration guide information corresponding to the detected sample and the evidence and evidence grade information of the drug administration guide information, the immunotherapy drug administration guide information and the evidence and evidence grade information of the drug administration guide information, the endocrine therapy drug administration guide information and the evidence and evidence grade information of the drug administration guide information, the prognosis evaluation drug administration guide information and the evidence and evidence grade information of the drug administration guide information are obtained, and the reference guide ID corresponding to each drug administration guide information can be obtained respectively, the reference guidance documents are retrieved from the breast cancer reference database 1014 according to the reference guidance ID.
The gene variation ID and the drug ID in the breast cancer chemotherapeutics and evidence grade database 108 are directly matched according to the gene variation ID and the drug ID in the breast cancer drug information database 106, so that chemotherapeutics medication guide information, evidence and evidence grade of the medication guide information are obtained, a reference guide ID of the medication guide information is obtained at the same time, and a reference guide document can be further obtained from the breast cancer reference document database 1014 according to the reference guide ID of the medication guide information.
The breast cancer biomarker database 1012 is matched according to the detected biomarker and marker level state result corresponding to the breast cancer sample, the immunotherapy ID corresponding to the gene variation annotation information is obtained from the breast cancer biomarker database 1012, the corresponding immunotherapy medication guide and the medication guide evidence and evidence grade are obtained according to the matching of the immunotherapy ID to the immunotherapy and evidence grade database 109 corresponding to the breast cancer, meanwhile, the reference guide ID of the medication guide can be obtained, and the reference guide document is further obtained from the breast cancer reference document database 1014 according to the reference guide ID of the medication guide information.
The explanation method will be described in detail with reference to specific examples below:
table 1 shows the interpretation information of the clinical significance of the acquired genetic variation of breast cancer.
Obtaining a gene variation ID of the annotation information of the gene variation stored in the database 103 of the breast cancer germline gene variation and the database 104 of the breast cancer systemic gene variation based on the record of the gene variation, such as record 1 in Table 1, for example, chromosome position, reference base, altered base, transcription start position, transcription end position, etc., of the gene variation of BRCA1 NM-007294.3: c.1729G > T (p.Glu577Ter) gene variation, chromosome position chr17, reference base G, altered base T, transcription start position 41245819, transcription end position 41245819 are matched to the record of the gene variation of the corresponding chromosome position chr17, reference base G, altered base T, transcription start position 41245819, transcription end position 41245819 in the database 103 of the breast cancer germline gene variation and the database 104 of the breast cancer systemic gene variation, the annotation information of the breast cancer germline gene variation ID is corresponding to the gene variation ID in the database 105 of the breast cancer genetic variation clinical significance, and the gene ID under the gene variation is obtained The clinical significance of the variation is stronger clinical significance, the gene variation with stronger clinical significance and the gene variation with potential clinical significance are judged to have stronger clinical significance according to the clinical significance of the preset gene variation, and the next step of the medication guidance rules including targeted therapy, immunotherapy and prognosis evaluation is carried out on the gene variation, and the gene variation can enter the next step of the medication guidance including targeted therapy, immunotherapy and prognosis evaluation. The clinical significance of the genetic variation was read in Table 1 for record 2 as for record 1.
Table 1.
Table 2 shows the analysis and interpretation information of the obtained targeted treatment and evidence grade of breast cancer.
According to the gene variation ID and the clinical significance (stronger clinical significance) of the gene variation of BRCA1, c.1729G > T (p.Glu577Ter), the gene variation ID and the clinical significance (stronger clinical significance) of the gene variation of CDKN2A, c.250G > T (p.Asp84Tyr), the drug ID in the breast cancer drug information database 106 is respectively matched with the records of the gene variation ID and the targeted therapy of the drug ID in the breast cancer targeted therapy and evidence grade database 107, so that BRCA1, c.1729G > T (p.Glu577Ter) gene variation and the targeted therapeutic effect information of CDKN2A, c.250G > T (p.Asp84Tyr) gene variation are obtained, and the corresponding reference guidance ID of the drug guidance information can also be obtained, and the reference guidance document is obtained from the breast cancer reference document database 1012 according to the reference guidance ID. The screening results are shown in table 2:
table 2.
Table 3 shows interpretation information of the obtained chemotherapy and evidence grade of breast cancer.
Annotation of information on genetic variation of breast cancer: for example, the gene name CYP2D6, the detection site rs3892097 or the chromosome position, the reference base, the changed base, the transcription start position, and the transcription stop position are matched to the gene variation of the corresponding gene name CYP2D6, the detection site rs3892097 or the chromosome position, the reference base, the changed base, the transcription start position, and the transcription stop position in the breast cancer germ line gene variation database 103 and the breast cancer germ line gene variation database 104, to obtain the gene variation ID, the drug treatment information of the gene variation ID and the drug ID (tamoxifen) in the breast cancer chemotherapeutics and evidence grade database 108 is matched according to the gene variation ID and the drug ID (tamoxifen) in the breast cancer drug information database 106, and also to obtain the reference guidance ID of the corresponding medication guidance information, and the reference guidance document is obtained from the breast cancer reference database 1014 according to the reference guidance ID. Screening results such as
Table 3:
table 3.
Tables 4 and 5 are interpretation information of the obtained immunotherapy and evidence grade of breast cancer.
The detection result of the immune check point is that the biomarker is TMB and the value thereof is 21.00Muts/Mb, the biomarkers TMB and TMB 21.00Muts/Mb are matched with 2 key fields of the biomarker name and the biomarker state in the breast cancer biomarker database 1012, the TMB state is determined to be TMB-H and the immunotherapy ID of the biomarker, the immunotherapy ID is matched with the immunotherapy ID in the breast cancer immunotherapy and evidence grade database 109, and the immunotherapy and evidence grade records of the biomarkers TMB and TMB-H under the immunotherapy ID are screened out; reference guidance IDs for corresponding immunotherapy and evidence ratings can also be obtained, and reference guidance documents are obtained from the breast cancer reference database 1014 according to the reference guidance IDs. The screening results are shown in table 4:
table 4.
The detection result of the immune check point is that the biomarker is PD-L1 and the expression cell ratio is 56%, the biomarkers PD-L1 and PD-L156% are matched with 2 key fields of the biomarker name and the biomarker state in the breast cancer biomarker database 1012, the state of PD-L1 is determined to be high expression and the immunotherapy ID of the biomarker, the immunotherapy ID is matched with the immunotherapy ID in the breast cancer immunotherapy and evidence grade database 109, and the records of the immunotherapy and the evidence grade of the biomarker PD-L1 and the high expression state under the immunotherapy ID are screened; reference guidance IDs for corresponding immunotherapy and evidence ratings can also be obtained, and reference guidance documents are obtained from the breast cancer reference database 1014 according to the reference guidance IDs. The screening results are shown in table 5:
table 5.
Table 6 is interpretation information of endocrine treatment and evidence grade of breast cancer obtained.
According to ESR1, c.1610A > G (p.Tyr537Cys) gene variation chromosome position chr6, reference base A, changed base G, transcription start position 152419923 and transcription end position 152419923 are matched with the gene variation records of corresponding chromosome position chr6, reference base A, changed base G, transcription start position 152419923 and transcription end position 152419923 in the breast cancer germ-line gene variation database 103 and the breast cancer system gene variation database 104, the gene variation ID of the gene variation annotation information stored in the breast cancer germ-line gene variation database 103 and the breast cancer system gene variation database 104 is obtained, according to the gene variation ID, the gene variation ID corresponds to the gene variation ID in the breast cancer gene variation clinical significance database 105 and the clinical significance of the gene variation under the gene variation ID is obtained, the clinical significance is potential clinical significance variation, and according to the clinical significance of the preset gene variation, the gene variation with stronger clinical significance and the gene variation with potential clinical significance are judged And (3) carrying out the next medication guidance rule of the endocrine treatment, matching the gene variation ID, the clinical significance (potential clinical significance variation) of the gene variation and the drug ID in the drug database to the record of the gene variation ID and the drug ID in the endocrine treatment of the breast cancer and the endocrine treatment of the evidence grade database according to ESR1, c.1610A > G (p.Tyr537Cys) gene variation to obtain the endocrine treatment information of the ESR1, c.1610A > G (p.Tyr537Cys) gene variation, and also obtaining a reference guidance ID of corresponding medication guidance information, and obtaining a reference guidance document according to the reference guidance ID. The screening results are shown in table 6:
table 6.
Table 7 is the interpretation of the prognostic assessments and evidence ratings obtained for breast cancer.
Matching gene variation records of corresponding chromosome positions chr17, reference base G, changed base A, transcription starting position 7577539 and transcription stopping position 7577539 in the breast cancer germ line gene variation database 103 and the breast cancer system gene variation database 104 according to the chromosome positions chr17, reference base G, changed base A, transcription starting position 7577539 and transcription stopping position 7577539 of TP53, c.742C > T (p.Arg248Trp) gene variation, obtaining the gene variation ID of the gene variation annotation information stored in the breast cancer germ line gene variation database 103 and the breast cancer system gene variation database 104, obtaining the clinical significance of the gene variation under the gene variation ID according to the gene variation ID corresponding to the gene variation ID in the breast cancer gene variation clinical significance database 105, wherein the clinical significance is potential clinical significance variation, and judging the gene variation with stronger clinical significance and the potential clinical significance according to the clinical significance of the preset gene variation And (3) carrying out a medication guidance rule of next prognosis evaluation, matching the gene variation ID, the clinical significance (potential clinical significance variation) of the gene variation and the drug ID in the drug database to the record of the gene variation ID and the drug ID in the breast cancer prognosis evaluation and evidence grade database according to TP53, c.742C > T (p.Arg248Trp) gene variation, obtaining the prognosis treatment information of the TP53, c.742C > T (p.Arg248Trp) gene variation, also obtaining the reference guidance ID of the corresponding medication guidance information, and obtaining a reference guidance document according to the reference guidance ID. The screening results are shown in table 7:
table 7.
It can be understood that by comparing the sample information and the genetic variation annotation information based on the detected sample with the database to perform key fields or key field combinations, the genetic variation can be quickly and accurately interpreted according to different levels, the interpretation standard is strict, the method is scientific, and the method can be applied to accurate treatment of breast cancer.
In practical application, after the interpretation information of a sample to be detected is obtained, the interpretation information needs to be further processed; then, in some possible embodiments, the method further comprises: and calling a report template and generating a gene detection report based on the sample information, the genetic variation annotation information and the interpretation information corresponding to the sample to be detected.
After the interpretation information corresponding to the detected sample is obtained, a gene detection report module can be called, the interpretation information is read and filled into the gene detection report module, and meanwhile, the sample information of the detected sample, the genetic variation annotation information of the detected sample and the interpretation information of the detected sample are filled into the gene detection report template to generate a gene detection report. The filling process of the gene detection report template is as follows: the gene detection report template comprises basic information, detection results, medication and prognosis analysis, statement, reference documents, annexes and other main items. When filling in the gene detection report template, extracting from the sample information, the genetic variation annotation information and the reading information respectively; for example, basic information is extracted from sample information of a gene sample, and detection results, drug administration and prognosis analysis, references, and the like are extracted from gene mutation annotation information and interpretation information. Specifically, the detection result items are retrieved from the variation annotation information and the related information of the variation clinical meaning unit, the medication and prognosis analysis, the related information of the variation annotation information and the variation medication/prognosis and related evidence grade unit, and the like.
The user can further modify, check and download the gene detection report, so that the user can conveniently obtain a more exact gene detection report.
In some possible embodiments, the method further comprises: storing sample information, genetic variation annotation information, reading information and a gene detection report of a detected sample; when the same detected sample is encountered subsequently, the gene variation annotation information, the interpretation information and the gene detection report can be obtained quickly.
In summary, the breast cancer database version in the system stores various information such as breast cancer targeted therapy, immunotherapy, chemical drug therapy, prognosis evaluation and evidence grade, and each database of the breast cancer database version is associated through the same key field or key field combination, so that the interaction function among the databases is realized, and when the medication interpretation system is used for medication interpretation based on the breast cancer gene variation and medication interpretation system, medication guidance suggestions with comprehensive evidence grade and clear evidence grade can be quickly and accurately found and provided according to the gene detection result of a breast cancer examinee, so that an important reference is provided for medical decision making.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
FIG. 5 is a schematic structural diagram of an interpretation device for breast cancer gene mutation and medication provided in the present specification; as shown in fig. 5, the apparatus includes: a genetic variation annotation information acquisition module 51, configured to perform biological information analysis on the sequencing offline data of the sample to be tested, and acquire genetic variation annotation information of the sample to be tested; a sample information acquiring module 52, configured to acquire sample information of a sample to be tested; the interpretation information obtaining module 53 is configured to associate and read the sample information and the genetic variation annotation information of the sample to be tested with the database in the breast cancer database version, and obtain the interpretation information corresponding to the sample to be tested.
In some possible embodiments, the genetic variation annotation information obtaining module 51 is specifically configured to: obtaining the data of the sample to be detected, and extracting the data of the sample to be detected to obtain input data and process information; responding to an operation instruction sent by a user, confirming input data and flow information, starting a data analysis flow corresponding to the sequencing off-line data, and generating genetic variation detection information of the sample to be detected; or, responding to an operation instruction sent by a user after confirming input data and process information, starting a data analysis process corresponding to the sequencing off-line data, and generating genetic variation detection information of the sample to be detected; and starting an annotation process corresponding to the sequencing off-line data, and annotating the genetic variation detection information of the detected sample to obtain the genetic variation annotation information of the detected sample.
In some possible embodiments, the interpretation information obtaining module 53 is specifically configured to: and comparing the sample information of the detected sample and the genetic variation annotation information of the detected sample with the database to obtain corresponding interpretation information of the detected sample.
In some possible embodiments, the apparatus further includes a genetic testing report generating module (not shown in the figure) for invoking the report template and generating a genetic testing report based on the sample information of the sample to be tested, the genetic variation annotation information, and the interpretation information corresponding to the sample to be tested.
In some possible embodiments, the apparatus further includes a storage module (not shown in the figure) for storing the sample information, the genetic variation annotation information, the interpretation information, and the genetic testing report of the test sample.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the modules may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.
The apparatus of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the spirit of the present disclosure, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present description as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures, for simplicity of illustration and discussion, and so as not to obscure one or more embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the understanding of one or more embodiments of the present description, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the one or more embodiments of the present description are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that one or more embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description.
It is intended that the one or more embodiments of the present specification embrace all such alternatives, modifications and variations as fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. The breast cancer gene variation and medication reading system is characterized by comprising a breast cancer database version, wherein the breast cancer database version comprises a plurality of databases which are mutually related through the same key field or the same key field combination, and the databases are respectively used for storing related information of breast cancer.
2. The breast cancer genetic variation and medication reading system according to claim 1, wherein the plurality of databases comprise a breast cancer information database, a breast cancer gene analysis database, a breast cancer germline genetic variation database, a breast cancer system genetic variation database, a breast cancer genetic variation clinical significance database, a breast cancer drug information database, a breast cancer targeted therapy and evidence grade database, a breast cancer chemotherapeutics and evidence grade database, a breast cancer immunotherapy and evidence grade database, a breast cancer endocrine therapy and evidence grade database, a breast cancer prognosis assessment and evidence grade database, a breast cancer biomarker database, a breast cancer clinical trial database, and a breast cancer reference database;
the breast cancer information database is used for storing basic introduction of breast cancer, relation between the breast cancer and pathogenic genes and treatment progress information;
the breast cancer gene analysis database is used for storing gene biological functions and relationship information between genes and breast cancer occurrence and development;
the breast cancer germ line gene variation database is used for storing gene variation information of breast cancer germ line cells;
the breast cancer system gene variation database is used for storing gene variation information of breast cancer system cells;
the breast cancer gene variation clinical significance database is used for storing the clinical significance of breast cancer gene variation;
the breast cancer drug information database is used for storing drug information related to breast cancer treatment;
the breast cancer targeted therapy and evidence grade database is used for storing targeted therapy information and targeted therapy evidence grade information of breast cancer;
the breast cancer chemical treatment and evidence grade database is used for storing chemical treatment information of breast cancer and clinical evidence grade information of chemical treatment;
the breast cancer immunotherapy and evidence grade database is used for storing the immunotherapy information and the immunotherapy clinical evidence grade information of the breast cancer;
the breast cancer endocrine treatment and evidence grade database is used for storing endocrine treatment information and endocrine treatment clinical evidence grade information of breast cancer;
the breast cancer prognosis evaluation and evidence grade database is used for storing the result information of breast cancer prognosis evaluation and breast cancer prognosis evidence grade grading;
the breast cancer biomarker database is used for storing genes related to breast cancer immunotherapy and biomarker information;
the breast cancer clinical test database is used for storing clinical test information of breast cancer;
the breast cancer reference database is used for storing the basis of the reference data of the breast cancer.
3. The breast cancer genetic variation and medication reading system according to claim 2, wherein the breast cancer information database is associated with the breast cancer medication information database by a combination of disease ID and medication ID;
the breast cancer gene analysis database is respectively associated with the breast cancer germ line gene variation database and the breast cancer germ line gene variation database through gene ID and variation ID combination;
the breast cancer germ line gene variation database and the breast cancer system gene variation database are respectively associated with the breast cancer gene variation clinical significance database through variation ID and disease ID combination;
the breast cancer gene variation clinical significance database is respectively associated with the breast cancer targeted therapy and evidence grade database, the breast cancer chemotherapeutics and evidence grade database, the breast cancer immunotherapy and evidence grade database, the breast cancer endocrine therapy and evidence grade database and the breast cancer prognosis evaluation and evidence grade database through disease ID and variation ID combination;
the breast cancer drug information database is respectively associated with the breast cancer targeted therapy and evidence grade database, the breast cancer chemotherapeutics and evidence grade database, the breast cancer immunotherapy and evidence grade database, the breast cancer endocrine therapy and evidence grade database and the breast cancer prognosis evaluation and evidence grade database through combination of a disease ID and a drug ID;
the breast cancer biomarker database is associated with the breast cancer immunotherapy and evidence grade database by a disease ID and biomarker name combination, or by a disease ID and biomarker ID combination;
the breast cancer clinical test database is respectively associated with the breast cancer targeted therapy and evidence grade database, the breast cancer chemotherapeutics and evidence grade database, the breast cancer immunotherapy and evidence grade database, the breast cancer endocrine therapy and evidence grade database and the breast cancer prognosis evaluation and evidence grade database through combination of disease ID and drug ID;
the breast cancer reference database is respectively associated with the breast cancer information database, the breast cancer gene analysis database, the breast cancer germline gene variation database, the breast cancer system gene variation database, the breast cancer gene variation clinical significance database, the breast cancer drug information database, the breast cancer targeted therapy and evidence grade database, the breast cancer chemotherapeutics and evidence grade database, the breast cancer immunotherapy and evidence grade database, the breast cancer endocrine therapy and evidence grade database, the breast cancer prognosis evaluation and evidence grade database, the breast cancer biomarker database and the breast cancer clinical test database through reference document IDs.
4. The breast cancer genetic variation and medication interpretation system according to claim 1, further comprising a genetic variation and medication interpretation section, a bioinformatic data analysis section, a report management section, and a storage section, the genetic variation and medication interpretation section being connected to the breast cancer database section, the bioinformatic data analysis section, the report management section, and the storage section, respectively, the storage section being connected to the genetic variation and medication interpretation section, the bioinformatic data analysis section, and the report management section, respectively;
the gene variation and medicine interpretation plate is used for executing interpretation of breast cancer gene variation and medicine guidance;
the biological information data analysis plate is used for carrying out biological information analysis on second-generation sequencing off-line data of the breast cancer patient;
the report management block is used for generating a gene detection report;
the storage plate is used for storing breast cancer gene variation and medication reading information, information annotated by the biological information data analysis plate and a gene detection report.
5. A breast cancer genetic variation and medication reading method, comprising:
performing biological information analysis on the sequencing off-line data of a detected sample to obtain genetic variation annotation information of the detected sample;
obtaining sample information of the sample to be detected;
and performing correlation reading on the sample information and the genetic variation annotation information of the detected sample and a database in a breast cancer database version to obtain reading information corresponding to the detected sample.
6. The method for analyzing genetic variation and medication interpretation of breast cancer according to claim 5, wherein the obtaining of annotation information of genetic variation of a test sample by performing bioinformatics analysis on the data obtained by performing sequencing and downloading of the test sample comprises:
obtaining the data of a sample to be detected, and extracting the data of the sample to be detected to obtain input data and process information;
responding to an operation instruction sent by a user, confirming the input data and the process information, starting a data analysis process corresponding to the sequencing off-line data, and generating genetic variation detection information of the detected sample;
or the like, or, alternatively,
responding to an operation instruction sent by a user after confirming the input data and the process information, starting a data analysis process corresponding to the sequencing off-line data, and generating genetic variation detection information of the detected sample;
and starting an annotation process corresponding to the sequencing off-line data, and annotating the genetic variation detection information of the detected sample to obtain the genetic variation annotation information of the detected sample.
7. The method for interpreting genetic variation and genetic variation in breast cancer according to claim 5, wherein the step of reading the sample information and the annotation information of the genetic variation of the test sample in association with a database in a breast cancer database version to obtain interpretation information corresponding to the test sample comprises:
and comparing the sample information of the detected sample, the genetic variation annotation information of the detected sample and a database with key fields or key field combinations to obtain the interpretation information corresponding to the detected sample.
8. The breast cancer gene variation and medication reading method of claim 5, further comprising:
and calling a report template and generating a gene detection report based on the sample information, the genetic variation annotation information and the interpretation information corresponding to the sample to be detected.
9. The breast cancer gene variation and medication reading method of claim 8, further comprising:
and storing the sample information, the genetic variation annotation information, the interpretation information and the genetic detection report of the detected sample.
10. A breast cancer genetic variation and medication interpretation apparatus, the apparatus comprising:
the genetic variation annotation information acquisition module is used for carrying out biological information analysis on sequencing off-line data of a detected sample to acquire genetic variation annotation information of the detected sample;
the sample information acquisition module is used for acquiring sample information of the detected sample;
and the interpretation information acquisition module is used for correlating and reading the sample information and the genetic variation annotation information of the detected sample and a database in a breast cancer database version to acquire the interpretation information corresponding to the detected sample.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112735520A (en) * | 2021-02-03 | 2021-04-30 | 深圳裕康医学检验实验室 | Interpretation method, system and storage medium for tumor individualized immunotherapy gene detection result |
| CN114974412A (en) * | 2022-07-05 | 2022-08-30 | 至本医疗科技（上海）有限公司 | Method, apparatus, and medium generating tumor detection data of target object |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060275844A1 (en) * | 2005-04-19 | 2006-12-07 | Linke Steven P | Diagnostic markers of breast cancer treatment and progression and methods of use thereof |
| CN109036572A (en) * | 2018-06-29 | 2018-12-18 | 迈凯基因科技有限公司 | A kind of multiple database exchange method and device |
| CN109063420A (en) * | 2018-06-29 | 2018-12-21 | 迈凯基因科技有限公司 | A kind of colorectal cancer genetic mutation and drug interpret multiple database interactive system |
| CN109686456A (en) * | 2018-12-26 | 2019-04-26 | 博奥生物集团有限公司 | A kind of accurate medication interpretation system and method for tumour |
| CN110364266A (en) * | 2019-06-28 | 2019-10-22 | 深圳裕策生物科技有限公司 | For instructing the database and its construction method and device of clinical tumor personalized medicine |
| CN110957006A (en) * | 2019-12-14 | 2020-04-03 | 杭州联川基因诊断技术有限公司 | Interpretation method of BRCA1/2 gene variation |
-
2020
- 2020-09-02 CN CN202010912233.4A patent/CN111986728A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060275844A1 (en) * | 2005-04-19 | 2006-12-07 | Linke Steven P | Diagnostic markers of breast cancer treatment and progression and methods of use thereof |
| CN109036572A (en) * | 2018-06-29 | 2018-12-18 | 迈凯基因科技有限公司 | A kind of multiple database exchange method and device |
| CN109063420A (en) * | 2018-06-29 | 2018-12-21 | 迈凯基因科技有限公司 | A kind of colorectal cancer genetic mutation and drug interpret multiple database interactive system |
| CN109686456A (en) * | 2018-12-26 | 2019-04-26 | 博奥生物集团有限公司 | A kind of accurate medication interpretation system and method for tumour |
| CN110364266A (en) * | 2019-06-28 | 2019-10-22 | 深圳裕策生物科技有限公司 | For instructing the database and its construction method and device of clinical tumor personalized medicine |
| CN110957006A (en) * | 2019-12-14 | 2020-04-03 | 杭州联川基因诊断技术有限公司 | Interpretation method of BRCA1/2 gene variation |
Non-Patent Citations (2)
| Title |
|---|
| SCOTT MAYER MCKINNEY等: "international evaluation of an AI system for breast cancer screening", NATURE, vol. 577, pages 89 - 94, XP037269950, DOI: 10.1038/s41586-019-1799-6 * |
| 于鑫淼等: "二代测序技术在乳腺癌精专治疗中的应用", 中华乳腺病杂志, vol. 12, no. 4, pages 238 - 241 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112735520A (en) * | 2021-02-03 | 2021-04-30 | 深圳裕康医学检验实验室 | Interpretation method, system and storage medium for tumor individualized immunotherapy gene detection result |
| CN114974412A (en) * | 2022-07-05 | 2022-08-30 | 至本医疗科技（上海）有限公司 | Method, apparatus, and medium generating tumor detection data of target object |
| CN114974412B (en) * | 2022-07-05 | 2023-02-10 | 至本医疗科技（上海）有限公司 | Method, apparatus, and medium generating tumor detection data of target object |
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