阅读说明：本技术 肿瘤个体化免疫治疗基因检测结果的解读方法、系统和存储介质 (Interpretation method, system and storage medium for tumor individualized immunotherapy gene detection result ) 是由 高志博 王鹏 吴东方 杨洁 陈丽 何雨鸣 徐丽 杨露 邓乙晓 于 2021-02-03 设计创作，主要内容包括：肿瘤个体化免疫治疗基因检测结果的解读方法、系统和存储介质。该解读方法可以对基因检测中的突变进行分类判级,解读基因突变相关的靶向药物信息、种系单核苷酸多态性相关的化疗药物信息,并结合患者的基因检测结果个性化推荐符合条件的临床试验信息等,将结果以可视化形式进行展示。该方法包括目前已知的绝大部分肿瘤免疫治疗预后相关biomarker的解读,可以一次性为患者提供包含肿瘤免疫治疗、靶向治疗、化疗、临床试验的全面指导信息,同时对解读结果进行可视化展示,更加直观简洁、方便患者理解。(Interpretation method, system and storage medium of tumor individualized immunotherapy gene detection result. The interpretation method can classify and judge the mutation in gene detection, interpret the targeted drug information related to the gene mutation and the chemotherapeutic drug information related to the germline SNP, and individually recommend qualified clinical test information and the like according to the gene detection result of a patient, and display the result in a visual form. The method comprises the interpretation of the currently known biomarker related to the prognosis of most tumor immunotherapy, can provide comprehensive guidance information including tumor immunotherapy, targeted therapy, chemotherapy and clinical tests for patients at one time, and simultaneously visually displays the interpretation result, so that the method is more visual and concise and is convenient for the patients to understand.)

1. A interpretation method of tumor individualized immunotherapy gene detection results is characterized by comprising the following steps:

the information processing step comprises the steps of obtaining the detection result of the censorship information gene and the immunohistochemical detection result of the patient;

a classification judging step, which comprises matching the submission information, the gene detection result and the immunohistochemical detection result obtained in the information processing step with information in an interpretation knowledge base, determining the biomar classification of the patient according to the matching result, and classifying and judging the mutation in the gene detection result according to the matching result to obtain mutation classification;

an evidence reading step, which comprises designing reading logic for different types of biorarers based on evidence-following medical principles according to the matching results, biorarer classification and mutation classification of the classification judging step, acquiring evidence corresponding to the matching results from a reading knowledge base according to the reading logic, reading the evidence and determining the grade of the reading evidence;

the visualization display step comprises the steps of scoring the prediction value of the biorarer detection result based on the interpretation evidence grade matched with each biorarer and the tumor type of the patient, and then displaying the scoring result of each biorarer; the visual display comprises at least one group of information of each biomarker position distribution, interpretation evidence grade, tumor type, tumor immunity status of the patient and immunotherapy scheme selection;

wherein the interpretation knowledge base comprises: tumor immunotherapy biomurarker-related evidence, tumor targeting therapy-related evidence, chemotherapy-related evidence, clinical trial information, tumor classification trees, and variant annotation information.

2. The method for interpreting the results of gene therapy for individualized immunotherapy according to claim 1, wherein in the information processing step, the submission information includes pathological diagnosis results of tumor type, age, sex, and identification information of the patient.

3. The method for interpreting gene test results of personalized immunotherapy for tumors according to claim 2, wherein in the information processing step, the gene test results comprise at least one of: the method comprises the following steps of performing NGS detection on a Somatic mutation result, a Germline single nucleotide polymorphism result, a TMB result calculated by using the mutation number, an MSI result obtained based on microsatellite sequence state analysis, an HLALOH result obtained based on HLA typing and copy number analysis, and a TNB result calculated based on a mutation peptide segment prediction result.

4. The method for interpreting a tumor individualized immunotherapy gene test result according to claim 3, wherein in the information processing step, the immunohistochemical test result includes at least one of: detecting the expression level of PD-L1 in the tumor tissue and the infiltration degree of immune cells in the tumor tissue by using an immunohistochemical technology to obtain the result of CD8 TILs.

5. The method for interpreting gene test results of personalized immunotherapy for tumors according to claim 4, wherein in the classification step, said mutation classification comprises Somatic mutation and Germine mutation, wherein,

the Somatic mutations are divided into four classes,

1) tier I: mutation with clear clinical significance,

2) Tier II: mutation with potential clinical significance,

3) Tier III: mutation of unknown clinical significance,

4) Tier IV: benign mutations;

the Germline mutations are divided into five classes,

1) pathogenic diseases,

2) Suspected pathopoiesia,

3) Has no clear clinical significance,

4) Benign,

5) Suspected benign;

preferably, the biomar classification comprises a tumor immune microenvironment biomar, an immune checkpoint biomar, a tumor neoantigen biomar.

6. The method for interpreting genetic measurements of personalized immunity to tumor treatment according to claim 5, wherein in the step of interpreting evidence, the interpretation logic comprises:

1) and tumor immunotherapy biomararker interpretation logic, which comprises the following 7 types of sub-logic,

a) matching TMB and bTMB interpretation logic to TMB-related tumor immunotherapy evidence for TMB and bTMB results;

b) matching MSI-related tumor immunotherapy evidence to MSI, bMSI results using MSI, bMSI interpretation logic;

c) matching TNB-related tumor immunotherapy evidence to TNB results using TNB interpretation logic;

d) matching PD-L1-related tumor immunotherapy evidence with PD-L1 interpretation logic on PD-L1 test results;

e) matching tumor immunotherapy evidence related to the CD8TILs with CD8TILs interpretation logic on the detection result of the CD8 TILs;

f) matching HLALOH-associated tumor immunotherapy evidence to HLALOH results using HLALOH interpretation logic;

g) matching targeted drug evidence with the Tier I and Tier II mutations by using targeted interpretation logic, and matching immune positive and negative related evidence based on tumor immune positive and negative interpretation logic, wherein Tier III and Tier IV mutations are not interpreted;

2) matching ongoing clinical trials of targeting and immunotherapy using clinical trial interpretation logic for patient tumor type, age, sex and all measurements;

3) reading logic matching chemotherapy evidence by using the curative effect and toxic and side effect of the chemotherapy medicament for the Germline mononucleotide polymorphism typing result;

4) personalized clinical trial interpretation logic.

7. The method for interpreting the gene test result of tumor individualized immunotherapy according to claim 1, wherein in the step of visual display, the visual display means that the score of biomar is displayed in the form of radar chart, pie chart or bar chart.

8. An interpretation system for gene test results of tumor individualized immunotherapy, the interpretation system comprising:

the information processing module is used for acquiring the submission information, the gene detection result and the immunohistochemical detection result of the patient;

the classification judging module is used for matching the submission information, the gene detection result and the immunohistochemical detection result with information in the interpretation knowledge base, determining the biomaker classification of the patient according to the matching result, and classifying and judging the mutation in the gene detection result according to the matching result to obtain mutation classification;

the evidence interpretation module is used for designing interpretation logics for different types of biorarers based on evidence-based medical principles according to the matching results, the biorarer classification and the mutation classification, acquiring evidences corresponding to the matching results from the interpretation knowledge base according to the interpretation logics, interpreting the evidences and determining the grade of the interpreted evidences;

the visualization display module is used for scoring the prediction value of the biorarer detection result based on the interpretation evidence grade matched with each biorarer and the tumor type of the patient, and then displaying the scoring result of each biorarer; the visual display comprises at least one group of information of each biomarker position distribution, interpretation evidence grade, tumor type, tumor immunity status of the patient and immunotherapy scheme selection;

wherein the interpretation knowledge base comprises: tumor immunotherapy biomurarker-related evidence, tumor targeting therapy-related evidence, chemotherapy-related evidence, clinical trial information, tumor classification trees, and variant annotation information.

9. An interpretation device for gene detection results of tumor individualized immunotherapy, comprising:

a memory for storing a program;

a processor for executing the memory-stored program to implement the interpretation method as defined in any one of claims 1 to 7.

10. A computer-readable storage medium characterized by: the medium has stored thereon a program executable by a processor to implement the interpretation method as defined in any one of claims 1 to 7.

Technical Field

The invention relates to the field of gene detection, in particular to a method, a system and a storage medium for interpreting tumor individualized immunotherapy gene detection results.

Background

With the development of gene detection technology, the application of guiding accurate medication by using gene detection in the treatment process of tumor patients is more and more extensive, but at present, most products of gene detection mechanisms are mainly limited to the detection and interpretation of targeted therapy and chemotherapy. In recent years, the development of tumor immunotherapy is rapid, 8 immune checkpoint inhibitors have been approved in China, and more than 200 tumor immunotherapy I-III Chinese clinical trials are in progress. Unlike other anti-tumor treatment schemes, tumor immunotherapy can provide lasting benefits to patients, and is a treatment scheme with a very broad prospect. Cancer patients receiving tumor immunotherapy also require precise treatment, i.e., by determining the defects in the tumor immune process of each patient, and administering the most precise immunotherapy will improve the prognosis of cancer patients. Few companies currently provide accurate immunotherapeutic gene detection of tumors, and have the following limitations:

(1) the interpretation range of the relevant indexes of tumor immunotherapy prognosis is limited: at present, most products only contain tumor mutation load (TMB), PD-L1 expression and microsatellite instability (MSI) interpretation, and lack other indexes closely related to tumor immunotherapy prognosis, including tumor tissue immune cell infiltration degree indexes CD8TILs, tumor immune escape related indexes HLA heterozygosity loss states and tumor immune positive and negative related gene mutation (positive and negative related gene mutation comprises therapy resistance and super-progression related indexes).

(2) The results of a plurality of prognosis-related indexes of tumor immunotherapy are not displayed clearly and intuitively: at present, most products only make a text description on relevant indexes of immunotherapy prognosis, but the indexes of tumor immunotherapy are more, so that a client cannot clearly and intuitively see the overall state of each index, and the interpretation result is difficult to understand.

Disclosure of Invention

The invention aims to provide a novel interpretation method, a device and a storage medium for tumor individualized immunotherapy gene detection results.

In order to achieve the purpose, the following technical scheme is adopted in the application:

the first aspect of the application discloses a method for interpreting the detection result of tumor individualized immunotherapy genes, which comprises the following steps:

and the information processing step comprises the step of acquiring the submission information, the gene detection result and the immunohistochemical detection result of the patient. The inspection information includes pathological diagnosis results of the tumor type of the patient, i.e., the tumor type of the patient can be preliminarily determined, and the genetic detection result generally includes genetic variation results, i.e., related information including genetic mutation.

And a classification judging step, which comprises the steps of matching the submission information, the gene detection result and the immunohistochemical detection result acquired in the information processing step with information in an interpretation knowledge base, determining biomaker classification according to the matching result, and classifying and judging the mutation in the gene detection result according to the matching result to acquire mutation classification. By classifying and grading the mutations and then performing evidence interpretation on the mutations of different types or grades in the next step, reference can be provided for guidance of follow-up tumor immunotherapy medicaments.

And the evidence reading step comprises the steps of designing reading logic for different types of biorarers based on evidence-following medical principles according to the matching results, the biorarer classification and the mutation classification of the classification judging step, acquiring evidence corresponding to the matching results from a reading knowledge base according to the reading logic, reading the evidence and determining the grade of the reading evidence.

And a visual display step, which comprises scoring the prediction value of the biomarker detection result based on the interpretation evidence grade matched with each biomarker and the tumor type of the patient, and then displaying the scoring result of each biomarker. The visual display means that the reading result is visually displayed in a chart form so that the patient can better understand the reading result. The visual display comprises at least one group of information of each biomarker position distribution, interpretation evidence grade, tumor type, tumor immunity status of the patient and immunotherapy scheme selection.

It should be noted that the biomar is a biomarker, also called a biological index, which marks the relevant information of the tumor. And the interpretation knowledge base comprises: tumor immunotherapy biomurarker-related evidence, tumor targeting therapy-related evidence, chemotherapy-related evidence, clinical trial information, tumor classification trees, and variant annotation information. The interpretation knowledge base in the application comprises a plurality of information (various evidences, variation annotation information and the like), especially comprises other evidence information closely related to tumor immunotherapy prognosis, so as to cover the therapy prognosis range of different tumor types and provide more accurate and personalized therapy guidance.

Specifically, the information sources in the interpretation knowledge base include:

1) FDA and NMPA website drug approval information,

2) NCCN, CSCO, ASCO, EMSO guideline information,

3) Published literature data,

4) Published clinical trial data,

5) Public databases of drug information, such as PharmGKB,

6) Public databases of variant information, e.g. OncoKB or CIVIC,

7) Disclosed is tumor naming and classification information.

Preferably, before the information processing step, the method further comprises: and information storage, namely uploading the gene detection result and the immunohistochemical detection result to a result database respectively for storage. And when the interpretation is needed, directly acquiring corresponding data from the result database, and performing corresponding interpretation. For example, the gene detection result is automatically uploaded to the result database, and the immunohistochemical detection result is manually uploaded to the result database, so that data loss or confusion can be prevented, and the interpretation accuracy is ensured. In addition, each patient detection result has a unique detection result ID, and the detection result ID can be corresponding to the submission information.

Because the relevant indexes of tumor immunotherapy prognosis are more, if only one or two indexes are read, data errors are easily caused, and accurate medication cannot be realized. The innovation of the application is that the interpretation method covers the interpretation of most of the tumor immunotherapy biorarers known at present, an independent interpretation logic is designed for each tumor immunotherapy biorarer, and the interpretation logic of the new biorarer can be added in real time to update the interpretation range of the tumor immunotherapy biorarer along with the discovery of the new biorarer of the tumor immunotherapy in the future. In addition, the method and the device have the advantages that the visual scheme is adopted, the scoring condition of the biorarker is displayed in a graphical mode, the method and the device are concise and visual, the detection result understanding threshold is effectively reduced, and a common user can conveniently understand the detection result.

Preferably, in the information processing step, the censorship information includes a pathological diagnosis result of a tumor type of the patient, age, sex, and identification information. The identification information is used for identifying the identity of the patient, and can be information which can uniquely determine the identity, such as an identification number, a mobile phone number or a given code number during information entry, and the owner of the patient can be determined according to the age, the sex and the identification information. The most important information in the examination is the pathological diagnosis result of the tumor type, such as lung adenocarcinoma and cervical cancer, and when the patient is suspected to have the cancer, the tumor type is determined by the pathological diagnosis of the tumor type and the immunohistochemical detection result. Genetic testing is a technique for detecting DNA by blood, other body fluids, or cells, and can be used for diagnosis of disease, and also for prediction of disease risk.

Preferably, in the information processing step, the gene detection result comprises at least one of: the method comprises the following steps of detecting a Somatic mutation result by NGS, detecting a Germline single nucleotide polymorphism result, calculating a tumor mutation load result (TMB) by using the mutation number, analyzing a result (MSI) based on a microsatellite sequence state, analyzing an HLA heterozygosity loss result (HLALOH) based on HLA typing and copy number, and calculating a tumor neoantigen load TNB based on a mutant peptide prediction result. The results of the above gene tests are processed and interpreted separately. In a more preferred embodiment, the interpretation method of the present application interprets all the above gene detection results to provide more accurate treatment guidance.

The above immune-related biomarkers are explained in english as follows: tumor Mutation Burden (TMB), Microsatellite instability (MSI), Tumor Neoantigen Burden (tmo biological Burden, TNB), Human Leukocyte Antigen (HLA) heterozygosity loss of Human Leukocyte Antigen (HLALOH), blood TMB (TMB in blood, bmmb), Microsatellite instability (MSI), blood MSI (MSI in blood, bMSI).

Immunohistochemical detection refers to the study of locating, characterizing and quantifying antigens in tissue cells by specifically binding labeled specific antibodies (or antigens) with antigens (or antibodies) in the tissue and developing the color of the labeled antibodies through chemical reaction. Preferably, in the information processing step, the immunohistochemical detection result includes at least one of: detecting the expression level of PD-L1 in the tumor tissue and the infiltration degree of immune cells in the tumor tissue by using an immunohistochemical technology to obtain the result of CD8 TILs.

Preferably, in the classification step, based on the tumor type (such as the obtained cancer species of the patient), the detection result (gene detection result and immunohistochemical detection), known mutation annotation information, evidence information of mutation matching, and the like, the mutation in the gene detection result is classified and classified according to ACMG rules (ACMG genetic mutation classification standard and guideline, revised 2015; ACMG system mutation classification standard and guideline, 2017), so as to determine the mutation classification. Mutation refers to permanent changes in the nucleotide sequence of a gene, and includes both Somatic and Germline mutations.

The Somatic mutations were classified into four classes, four grades:

1) tier I: mutation with clear clinical significance,

2) Tier II: mutation with potential clinical significance,

3) Tier III: mutation of unknown clinical significance,

4) Tier IV: benign mutations;

the Germline (Germline) mutations are divided into five classes, five grades:

1) pathogenic diseases,

2) Suspected pathopoiesia,

3) Has no clear clinical significance,

4) Benign,

5) Is suspected to be benign.

Preferably, in the evidence interpretation step, the interpretation logic comprises:

1) tumor immunotherapy biorarker interpretation logic, comprising the following 7 classes of sub-logic: .

TMB, bTMB result interpretation logic;

MSI, bMSI result interpretation logic;

TNB result interpretation logic;

PD-L1 result interpretation logic;

CD8TILs result interpretation logic;

HLALOH result interpretation logic;

immune positive and negative direction-related gene mutation interpretation logic;

2) tumor targeted therapy related gene mutation interpretation logic;

3) interpretation logic of curative effect and toxic and side effect of chemotherapy drugs related to Germline mononucleotide polymorphism;

4) personalized clinical trial interpretation logic.

Further, the specific interpretation process of the interpretation logic includes:

1) and tumor immunotherapy biomerarker interpretation logic,

a) matching TMB and bTMB interpretation logic to TMB-related tumor immunotherapy evidence for TMB and bTMB results;

b) matching MSI-related tumor immunotherapy evidence to MSI, bMSI results using MSI, bMSI interpretation logic;

c) matching TNB-related tumor immunotherapy evidence to TNB results using TNB interpretation logic;

d) matching PD-L1-related tumor immunotherapy evidence with PD-L1 interpretation logic on PD-L1 test results;

e) matching tumor immunotherapy evidence related to the CD8TILs with CD8TILs interpretation logic on the detection result of the CD8 TILs;

f) matching HLALOH-associated tumor immunotherapy evidence to HLALOH results using HLALOH interpretation logic;

g) matching targeted drug evidence with the Tier I and Tier II mutations by using targeted interpretation logic, and matching immune positive and negative related evidence based on tumor immune positive and negative interpretation logic, wherein Tier III and Tier IV mutations are not interpreted;

2) matching ongoing clinical trials of targeting and immunotherapy using clinical trial interpretation logic for patient tumor type, age, sex and all measurements;

3) reading logic matching chemotherapy evidence by using the curative effect and toxic and side effect of the chemotherapy medicament for the Germline mononucleotide polymorphism typing result;

4) personalized clinical trial interpretation logic; personalized interpretation logic is designed for a patient's clinical trial based on the patient's actual condition, e.g., based on tumor type, family history, etc.

The interpretation logic may be classified into the above categories, and the interpretation logic may be adaptively adjusted according to the categories.

Preferably, in the visualization step, the visualization refers to displaying the score of the biomaker in the form of a radar chart, a pie chart or a bar chart.

At present, most of tumor immunotherapy prognosis indexes are described by characters, but the prognosis indexes are more, the description is not visual, and a patient cannot easily know related analysis results from reports quickly. Therefore, the application adopts a form of combining pictures and texts to display the result. For example, a radar chart, which is similar to a dashboard, is used for displaying, the dashboard is equally divided into a plurality of parts, each part represents a biomar, meanwhile, a plurality of isocentric circles are drawn on the dashboard, the circle center is taken as a starting point and is marked as 0 minute, the isocentric circles are sequentially and equidistantly divided along the radius, and the circle to the outermost side is marked as 100 minutes. See in particular fig. 3. It should be noted that other figures can be used for displaying as long as the visual and visual display can be satisfied.

Tumor immunotherapy biorarker is divided into three classes based on its role in the tumor immune process: the tumor immune microenvironment biorarker, the immune check point biorarker and the tumor neogenesis antigen biorarker are respectively displayed in different areas of the instrument panel.

The second aspect of the present application discloses a system for interpreting tumor individualized immunotherapy gene detection results, which specifically includes:

and the information processing module is used for acquiring the submission information, the gene detection result and the immunohistochemical detection result of the patient.

And the classification judging module is used for matching the submission information, the gene detection result, the immunohistochemical detection result and the information in the interpretation knowledge base, determining the biomaker classification according to the matching result, and classifying and judging the mutation in the gene detection result according to the matching result to obtain the mutation classification.

The evidence interpretation module is used for designing interpretation logics for different types of biorarers based on evidence-based medical principles according to the matching results, the biorarer classification and the mutation classification, acquiring evidences corresponding to the matching results from the interpretation knowledge base according to the interpretation logics, interpreting the evidences and determining the grade of the interpreted evidences; wherein the interpretation knowledge base comprises: tumor immunotherapy biomurarker-related evidence, tumor targeting therapy-related evidence, chemotherapy-related evidence, clinical trial information, tumor classification trees, and variant annotation information.

The visualization display module is used for scoring the prediction value of the biorarer detection result based on the interpretation evidence grade matched with each biorarer and the tumor type of the patient, and then displaying the scoring result of each biorarer; the visual display comprises at least one group of information of each biomarker position distribution, interpretation evidence grade, tumor type, tumor immunity status of the patient and immunotherapy scheme selection.

Preferably, the interpretation system further comprises an information storage module, wherein the information storage module comprises a result database and an interpretation knowledge base. The result database is used for storing detection results, including gene detection results and immunohistochemical detection results; the interpretation knowledge base is used for storing information required for interpreting each item of biomarker.

After the gene detection and the immunohistochemical detection of the patient are finished, the gene detection result and the immunohistochemical detection result are analyzed and uploaded to a result database for storage, the classification judging module carries out automatic classification judging on mutation in the gene detection result by matching the submission information (including diagnosis and treatment results such as tumor types and identity identification information of the patient) of the patient and the detection result with information in a reading knowledge base, then the evidence reading module matches the corresponding reading evidence with the gene detection result and the immunohistochemical detection result, and finally the visual display module displays the grading result of the biomarker related to the tumor immunotherapy (the grading is carried out based on the reading evidence grade and the cancer type of the patient).

The third aspect of the present application discloses a device for interpreting tumor individualized immunotherapy gene detection results, which specifically comprises:

a memory for storing a program;

a processor for executing the program stored in the memory to implement the interpretation method described herein.

A fourth aspect of the present application discloses a computer-readable storage medium having a program stored thereon, the program being executable by a processor to implement the interpretation method described in the present application.

In the present application, the interpretation of the information related to the gene of the ex vivo sample is not intended for a living human or animal body; moreover, the obtained interpretation information is only used for subsequent disease diagnosis reference, belongs to intermediate reference information and is not a final diagnosis result; in practical applications, the final diagnosis result or health condition can be obtained by combining the current subjective symptoms, the past medical history and other information of the subject. Therefore, the technical scheme of the invention does not belong to a method for diagnosing diseases, and does not belong to a method for treating diseases.

By adopting the technical means, the reading method has the advantages that all mutations in the gene detection of the tumor patients can be classified and judged, targeted drug information related to cell mutation and chemotherapeutics information related to germline mononucleotide polymorphism are read, and qualified clinical test information and the like are recommended in a personalized manner by combining the gene detection results of the patients. The method comprises the interpretation of the currently known biomarker related to the prognosis of most tumor immunotherapy, can provide comprehensive guidance information including tumor immunotherapy, targeted therapy, chemotherapy and clinical tests for patients at one time, and simultaneously visually displays the interpretation result in image-text forms such as radar pictures and the like, so that the method is more intuitive and concise and is convenient to understand.

Drawings

FIG. 1 is a flow chart of an interpretation method described herein;

FIG. 2 is a block diagram of an interpretation system according to the present application;

fig. 3 is a schematic structural diagram of the visualization module in the embodiment of the present application.

Detailed Description

The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances.

Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the method descriptions may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences in the specification and drawings are for the purpose of describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where such sequence must be followed.

With the popularization and development of accurate medical treatment and the reduction of sequencing cost, numerous organizations at home and abroad begin to provide detection and interpretation services of individualized accurate treatment for tumor patients. As the industry belongs to emerging industries, interpretation standards are not perfect enough, and result interpretation is a core link for restricting the application of accurate treatment and transformation of tumors.

The interpretation system is improved to provide the interpretation method for the tumor individualized immunotherapy gene detection result, which has wider interpretation range, more accurate interpretation result and more convenience for the patient to read and understand. The interpretation method comprises the following steps: the method comprises an information processing step, a classification and grading step, an evidence reading step and a visualization display step. In other words, the method and the device determine the type of the tumor suffered by the patient and acquire the mutation information of the gene by processing information such as various detection results, submission information and the like, classify and grade the mutation, determine the grade of the mutation, match and read the mutation with various evidences, score the relevant biological index, namely biomar, and visually display the biomar in a visual form.

The steps of the interpretation method are explained below, please refer to fig. 1.

S100, an information processing step, namely acquiring the submission information, the gene detection result and the immunohistochemical detection result of the patient.

In the step, the censorship information and the detection result are subjected to primary processing, and the mutation condition of the gene and the tumor type are obtained primarily. Most tumors are associated with gene mutation, so that the detection of genes can provide reference directions for the diagnosis and treatment of tumors by further exploring the gene mutation situation. Mutations contemplated in this application include cellular and germline mutations. The most important of the information is the pathological diagnosis of the tumor type, which is combined with the immunohistochemical detection result and analyzed to determine the tumor type of the patient.

S200, a classification and classification step, namely matching the inspection information, the gene detection result and the immunohistochemical detection result with information in an interpretation knowledge base, and determining a plurality of biomar classifications related to immunotherapy; and mutation classification can be determined (i.e., mutations are classified and ranked according to certain logic, such as according to severity, referential degree, etc.).

In this step, ACMG rules are mainly used to determine the level of gene mutation to provide clinical guidance for tumor treatment. Specifically, this step classifies mutations for classification based on their nature, tumor type, known annotation information for mutations, evidence of mutation matches, and the like. The classification and classification rules are referred to in the summary of the invention and will not be described herein.

S300, an evidence interpretation step, namely, the conditions such as the matching result, the biomarker classification and the mutation classification of the classification judgment step are interpreted through interpretation logic, and the corresponding interpretation evidence grade is matched in an interpretation knowledge database.

Different interpretation logics are designed based on evidence-based medical principles, and interpretation of various different biorarers is realized through various interpretation logics. Comprises the explanation of biomar relevant to tumor immunotherapy, the explanation of gene mutation relevant to tumor targeted therapy, the explanation of curative effect and toxic and side effect of chemotherapy drugs relevant to Germline mononucleotide polymorphism and the explanation of personalized clinical tests. The evidence interpretation step covers relevant interpretation information in the whole process of diagnosis and treatment of the tumor, and one interpretation can provide comprehensive guide information including tumor immunotherapy, targeted therapy, chemotherapy and clinical trials for patients.

S400, visually displaying, namely scoring the prediction value of the biorarer detection result based on the interpretation evidence grade matched with each biorarer and the tumor type of the patient, and then displaying the scoring result of each biorarer. Wherein the visual display comprises at least one group of information of biomarker position distribution, evidence grade interpretation, tumor type, tumor immunity status of the patient and immunotherapy scheme selection.

The step adopts a visual scheme to display the scoring condition of the biological indexes, so that the detection result understanding threshold is reduced, and the common user can conveniently understand the detection result. The visualization scheme is to display the result through imaging, so that the interpretation result is more visual and concise. As shown in fig. 3, a schematic structural diagram of a visualization module is provided to realize visualization. The visualization module is specifically in the shape of a circular IO instrument panel, can objectively display biological indexes related to tumor immune microenvironment, immune check points and 3 major immune circulation steps of tumor neoantigens (see radar maps), provides reference for tumor immune condition evaluation and immunotherapy scheme selection of patients, and position distribution of each index in the graph is related to evidence grade and tumor type.

Those skilled in the art will appreciate that all or part of the functions of the various systems described in the above embodiments may be implemented in hardware or may be implemented in computer programs. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.

Therefore, as shown in fig. 2, the present application also provides a system for interpreting the detection result of tumor-specific immunotherapy genes, comprising: an information processing module 100, a classification ranking module 200, an evidence interpretation module 300, and a visualization presentation module 400.

The information processing module 100 is configured to obtain censorship information, a genetic test result, and an immunohistochemical test result of a patient; a classification judging module 200, configured to match the censored information, the gene detection result, the immunohistochemical detection result with information in the interpretation knowledge base, determine a biomar classification according to the matching result, and classify and judge mutations in the gene detection result according to the matching result to obtain a mutation classification; the evidence interpretation module 300 is used for designing interpretation logics for different types of biorarers based on evidence-following medical principles according to the matching results, the biorarer classification and the mutation classification, acquiring evidences corresponding to the matching results from an interpretation knowledge base according to the interpretation logics, interpreting the evidences and determining the grade of the interpreted evidences; the visualization display module 400 is used for scoring the prediction value of the biorarer detection result based on the interpretation evidence grade matched with each biorarer and the tumor type of the patient, and then displaying the scoring result of each biorarer; the visual display comprises at least one group of information of each biomarker position distribution, interpretation evidence grade, tumor type, tumor immunity status of the patient and immunotherapy scheme selection.

Compared with the interpretation systems of most companies in the prior art, the interpretation system has the following advantages:

1. the interpretation system covers interpretation of most tumor immunotherapy biorarers, independent interpretation logics are designed for each tumor immunotherapy biorarer, the interpretation logics of the new biorarers can be added in real time along with discovery of the new biorarers of the tumor immunotherapy in the future, and interpretation ranges of the tumor immunotherapy biorarers are updated.

2. The interpretation system adopts the visual module, namely, the image-text form similar to an instrument panel (radar chart) is displayed, the interpretation results of various tumor immunotherapy biomarkers are simply and intuitively displayed, the detection result understanding threshold is reduced, and the detection results can be conveniently understood by common users.

In addition, the application also provides an interpretation device of the tumor individualized immunotherapy gene detection result, which comprises a memory and a processor.

Wherein, the memory is used for storing programs; the processor is used for executing the program stored in the memory to realize the following method: the information processing step comprises the steps of obtaining the submission information, the gene detection result and the immunohistochemical detection result of a patient; a classification judging step, which comprises matching the submission information, the gene detection result and the immunohistochemical detection result obtained in the information processing step with information in an interpretation knowledge base, determining biomar classification according to the matching result, and classifying and judging the mutation in the gene detection result according to the matching result to obtain mutation classification; an evidence reading step, which comprises the steps of designing reading logic for different types of biorarers based on evidence-following medical principles according to the matching results, the biorarer classification and the mutation classification of the classification judging step, obtaining evidence corresponding to the matching results from a reading knowledge base according to the reading logic, reading the evidence and determining the grade of the reading evidence; the visualization display step comprises the steps of scoring the prediction value of the biorarer detection result based on the interpretation evidence grade matched with each biorarer and the tumor type of the patient, and then displaying the scoring result of each biorarer; the visual display comprises at least one group of information of each biomarker position distribution, interpretation evidence grade, tumor type, tumor immunity status of the patient and immunotherapy scheme selection; wherein the interpretation knowledge base comprises: tumor immunotherapy biomurarker-related evidence, tumor targeting therapy-related evidence, chemotherapy-related evidence, clinical trial information, tumor classification trees, and variant annotation information.

The specific process of interpretation of the tumor-specific immunotherapy gene detection described in the present application is described below. The interpretation method specifically comprises the following operation steps:

and S101, uploading analysis results.

The gene detection result is automatically uploaded to a result database, the immunohistochemical detection result is manually uploaded to the result database, the detection result of each patient has a unique detection result ID, and the interpretation report corresponding to the patient can be determined according to the detection result ID.

And S102, automatically classifying and grading the variation.

And matching and interpreting evidence and known variation annotation information of the knowledge base by using the mutation result (obtained by combining gene detection and immunohistochemical detection) detected by the patient, judging whether the tumor type of pathological diagnosis of the patient is matched with an indication in the evidence, and finally giving a mutation grading result adapted to the tumor type of the patient, namely determining the grade of the mutation. According to the step, through automatic matching, errors in the manual matching process are reduced, and the accuracy of detection and judgment is improved.

And S103, manually reviewing and supplementing variant annotations.

And (4) manually auditing the mutation after automatic grading by professional interpretation personnel, and supplementing new mutation annotation information which is not covered by the interpretation knowledge base information.

And S104, interpretation.

And matching and reading the mutation grading result after manual examination, the patient inspection information and the reading knowledge base on the target evidence, the tumor immunotherapy evidence, the chemotherapy evidence and the clinical test respectively.

The interpretation process specifically includes:

matching targeted drug evidence for Tier I and Tier II mutations based on targeted interpretation logic, and matching immune positive and negative related evidence based on tumor immune positive and negative interpretation logic, wherein Tier III and Tier IV mutations are not interpreted;

matching TMB and bTMB interpretation logic to TMB-related tumor immunotherapy evidence for TMB and bTMB results;

matching MSI-related tumor immunotherapy evidence to MSI, bMSI results using MSI, bMSI interpretation logic;

matching TNB-related tumor immunotherapy evidence to TNB results using TNB interpretation logic;

matching HLALOH-associated tumor immunotherapy evidence to HLALOH results using HLALOH interpretation logic;

matching PD-L1-related tumor immunotherapy evidence with PD-L1 interpretation logic on PD-L1 test results;

the CD8TILs detection results were matched to CD8 TILs-related tumor immunotherapy evidence using CD8TILs interpretation logic.

The results of the Germline SNP typing are logically matched with chemotherapy evidence by using the curative effect and the toxic and side effect interpretation of chemotherapy drugs.

The type of tumor, age, sex and all measurements diagnosed for a patient are matched to the ongoing clinical trials of targeted and immunotherapeutic therapies using clinical trial interpretation logic.

And S105, manually checking the interpretation result.

And the professional reading personnel reviews the reading result and supplements the uncovered new reading evidence in the reading knowledge base.

S106, displaying the immune biomar on an instrument panel.

And scoring each immune biomar based on the evidence grade of each immune biomar, and whether the tumor type diagnosed by the patient is matched with the cancer species in the evidence, and automatically drawing an IO dashboard chart based on the scoring result. The variation grade and the interpretation result which are manually checked are finally displayed in the interpretation report in a concise and intuitive mode. And (4) displaying the mutation subjected to grading in a sequencing manner according to a grading result, and displaying the medicament subjected to mutation reading in a sequencing manner according to an evidence grade.

And manually checking the display content of the report, checking the display result, and if the abnormal condition is found, refusing to enter the step S102 again. If the report content shows normal, the final interpretation report can be generated after the audit is passed.

The technical scheme of the present invention will be described in detail by specific examples, which are given as an example of a 53-year-old male stage 3 lung adenocarcinoma sample numbered 21 x 13, and it should be understood that the examples are only illustrative and should not be construed as limiting the scope of the present invention.

Examples

The interpretation method of the tumor individualized immunotherapy gene detection result of the patient specifically comprises the following operation steps:

and S101, uploading analysis results.

73 individual cell mutations, 2 germline gene mutations and 565 chemotherapy-related SNP site typing results are obtained through gene detection analysis, and the detected TMB result is TMB-H, MSI result, the detected MSI-H, TNB result is TNB-H result, the detected HLALOH result is negative, and the results of the gene detection are automatically uploaded to a result database after the analysis is completed.

The immunohistochemistry detects that the sample PD-L1 is positive (TPS 20-25%), the infiltration degree of the CD8TILs is Low (Low), and the immunohistochemistry detection result is manually uploaded to a result database.

And S102, automatically classifying and grading the variation.

And matching and interpreting evidence and known variation annotation information of the knowledge base by using the gene detection result (combined with the pathological diagnosis result of the patient) detected by the patient, judging whether the tumor type of the pathological diagnosis of the patient is matched with the indication in the evidence, and finally giving a mutation grading result adapted to the tumor type of the patient, namely determining the grade of the mutation. According to the step, through automatic matching, errors in the manual matching process are reduced, and the accuracy of detection and judgment is improved. Taking EML4-ALK gene rearrangement and ATM R35-embryonic line mutation as examples, the gene is classified as Tier 1 (or named TierI class; similarly, Tier II class, Tier III class and Tier IV class can be named as Tier2 class, Tier3 class and Tier4 class respectively) and pathogenic mutation respectively. As shown in table 1 below:

TABLE 1

Variation number	Gene	Variation results	Biological function	Type of variation	Grade of variation
						VI000000311	EML4-ALK	Rearrangement	GOF	Somatic	Tier 1
VI000075503	ATM	R35*	LOF	Germline	Pathogenic factor

And S103, manually reviewing and supplementing variant annotations.

And (4) manually auditing the mutation after automatic grading by professional interpretation personnel, and supplementing new mutation annotation information which is not covered by the interpretation knowledge base information.

And S104, interpretation.

And matching and reading the mutation grading result after manual examination, the patient inspection information and the reading knowledge base on the target evidence, the tumor immunotherapy evidence, the chemotherapy evidence and the clinical test respectively.

The interpretation process specifically includes:

matching targeted drug evidence for Tier I and Tier II mutations based on targeted interpretation logic, and matching immune positive and negative related evidence based on tumor immune positive and negative interpretation logic, wherein Tier III and Tier IV mutations are not interpreted;

evidence of the rearrangement interpretation of the EML4-ALK gene is shown in Table 2 below:

TABLE 2

ATM R35 embryonic line mutation interpretation evidence is shown in Table 3 below (note that sensitive evidence levels Level 1- > Level 5, wherein Level3 is subdivided into three levels of Level 3A, Level 3B, Level3C, i.e., 7 levels of sensitive evidence: 1, 2, 3A, 3B, 3C, 4, 5):

TABLE 3

Evidence of read of the immunologically positive and negative correlated mutations is shown in table 4 below (note that Level R1 is evidence of drug resistance, and R represents resistance. there are two levels of Level R1 and Level R2):

TABLE 4

Matching TMB and bTMB interpretation logic to TMB-related tumor immunotherapy evidence for TMB and bTMB results; the TMB results in this example are TMB-H, which are read as shown in Table 5 below:

TABLE 5

Matching MSI-related tumor immunotherapy evidence to MSI, bMSI results using MSI, bMSI interpretation logic; the MSI result in this example is MSI-H, which is read as shown, for example, in Table 6 below:

TABLE 6

Matching TNB-related tumor immunotherapy evidence to TNB results using TNB interpretation logic; the TNB results in this example are TNB-H, which are read as shown in Table 7 below:

TABLE 7

Matching HLALOH-associated tumor immunotherapy evidence to HLALOH results using HLALOH interpretation logic; the HLALOH results in this case are negative and the interpretation results are shown, for example, in Table 8 below:

TABLE 8

Marker substance

State of marker

Name of drug

Tumor type

Clinical tips

Evidence rating

Sources of evidence

HLALOH

Negative of

Is free of

Is free of

Is free of

Is free of

Is free of

Matching PD-L1-related tumor immunotherapy evidence with PD-L1 interpretation logic on PD-L1 test results; in this example, the PD-L1 test was positive (TPS 20-25%), and the interpretation results are shown in Table 9 below:

TABLE 9

The CD8TILs detection results were matched to CD8 TILs-related tumor immunotherapy evidence using CD8TILs interpretation logic. In this example, the detection of CD8TILs was negative, and the interpretation is shown in Table 10 below:

watch 10

Marker substance

State of marker

Name of drug

Tumor type

Clinical tips

Evidence rating

Sources of evidence

CD8TILs

Low

Is free of

Is free of

Is free of

Is free of

Is free of

The results of the Germline SNP typing are logically matched with chemotherapy evidence by using the curative effect and the toxic and side effect interpretation of chemotherapy drugs. The interpretation results of the SNP sites of this example are shown in Table 11 below:

TABLE 11

Medicine

Gene

Detection site

The result of the detection

Therapeutic effect of medicine

Toxic and side effects

Evidence rating

Carboplatin + vinorelbine

MTHFR

rs1801133

AA

↑

/

Level 2A

Carboplatin + vinorelbine

CASP7

rs12415607

CA

↑

/

Level 3

Carboplatin + vinorelbine

CASP7

rs7921977

TT

↑

/

Level 3

Carboplatin + vinorelbine

CASP7

rs2227310

CG

↑

/

Level 3

Carboplatin + vinorelbine

CASP7

rs4353229

CT

↑

/

Level 3

The type of tumor, age, sex and all measurements diagnosed for a patient are matched to the ongoing clinical trials of targeted and immunotherapeutic therapies using clinical trial interpretation logic. Some of the clinical trial interpretation results in this example are shown in table 12 below:

TABLE 12

And S105, manually checking the interpretation result.

And the professional reading personnel reviews the reading result and supplements the uncovered new reading evidence in the reading knowledge base.

S106, displaying the immune biomar on an instrument panel.

And scoring each immune biomar based on the evidence grade of each immune biomar, and whether the tumor type diagnosed by the patient is matched with the cancer species in the evidence, and automatically drawing an IO dashboard chart based on the scoring result. For an example of the IO dashboard diagram in this example, refer to fig. 3.

The variation grade and the interpretation result which are manually checked are finally displayed in the interpretation report in a concise and intuitive mode. And (4) displaying the mutation subjected to grading in a sequencing manner according to a grading result, and displaying the medicament subjected to mutation reading in a sequencing manner according to an evidence grade. The target interpretation results in this example are shown in table 13 below, for example:

watch 13

The immunobiomarker interpretation results are shown in table 14 below, for example:

TABLE 14

The chemotherapeutic read results are shown, for example, in table 15 below:

watch 15

The clinical trial interpretation results are shown, for example, in table 16 below:

TABLE 16

And manually checking the display content of the report, checking the display result, and if the abnormal condition is found, refusing to enter the step S102 again. If the report content shows normal, the final interpretation report can be generated after the audit is passed.

The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.