CN116312923A - Automatic processing method, device, equipment and storage medium for gene detection report - Google Patents
Automatic processing method, device, equipment and storage medium for gene detection report Download PDFInfo
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Abstract
The invention belongs to the technical field of gene detection reports, and discloses an automatic processing method, device and equipment for a gene detection report and a storage medium. According to the invention, a disease tumor tree is constructed according to clinical tumor information; constructing a gene tree rule according to clinical gene information; constructing a gene tree targeting annotation according to the disease tumor tree and the gene tree rule; constructing a cell mutation targeting drug use knowledge base according to the gene tree rules and clinical drug use experience; obtaining tumor information and gene information of a patient, and obtaining a corresponding target annotation item according to the tumor information and the gene information; and matching the targeted drug information according to the targeted annotation item, generating a gene detection report, and storing the tumor information and the genetic variation information by constructing a tree, so that corresponding drug information and annotation information can be directly obtained from corresponding tree nodes during detection, and a comprehensive gene detection report can be generated.
Description
Technical Field
The present invention relates to the field of gene detection report technology, and in particular, to a method, an apparatus, a device, and a storage medium for automatically processing a gene detection report.
Background
The tumor itself is a genetic disease, and the genes of tumor cells are continuously mutated. The variant forms of the gene mutation are: SNV (single nucleotide variation), INDEL (small fragment INDEL), CNV (copy number variation), FUSION (gene FUSION or structural variation). Different genes, different variant forms of the genes, and different variant sites may correspond to one or more targeted drugs. After each level of clinical molecular detection laboratory detects specific gene detection sites by various gene detection means or methods, genetic consultants need to find the most suitable targeted drugs according to disease tumors, genes and gene variation forms and gene variation sites to generate a gene detection report. At present, the most suitable targeted drugs are found according to gene detection results, the most suitable targeted drugs have various gene mutation forms, a plurality of mutation sites and difficult drug matching, and the most suitable targeted drugs depend on experience and knowledge reserves of genetic consultants in making diagnosis, so that the diagnosis results are likely to be inaccurate.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide an automatic processing method, device, equipment and storage medium for a gene detection report, and aims to solve the technical problems that in the prior art, the diagnosis is difficult and the diagnosis result is possibly inaccurate because the generation of the gene detection report excessively depends on experience of related personnel.
In order to achieve the above object, the present invention provides an automated processing method for gene detection report, the method comprising the steps of:
constructing a disease tumor tree according to clinical tumor information;
constructing a gene tree rule according to clinical gene information;
constructing a gene tree targeting annotation according to the disease tumor tree and the gene tree rule;
constructing a cell mutation targeting drug use knowledge base according to the gene tree rules and clinical drug use experience;
obtaining tumor information and gene information of a patient, and obtaining a corresponding target annotation item according to the tumor information and the gene information;
and generating a gene detection report according to the targeting annotation item matching targeting drug information.
Optionally, the obtaining a corresponding targeting annotation item according to the tumor information and the gene information includes:
determining a corresponding disease tumor tree according to the tumor information;
determining a corresponding gene tree rule according to the gene information;
searching parent nodes of the tumor information in the disease tumor tree or parent nodes of the gene information in the gene tree rule until target annotation items containing the corresponding tumor information and the gene information are searched, and taking the searched target annotation items as the target annotation items of the tumor information and the gene information.
Optionally, the constructing a disease tumor tree according to clinical tumor information includes:
determining a tumor classification relation in the clinical tumor information, and creating a tumor tree root node;
determining a tumor classification level of the tumor according to the tumor classification relation, wherein the tumor classification level comprises a previous classification and a next classification of the current tumor;
and ordering the clinical tumor information from the root node according to the classification level, and constructing the disease tumor tree.
Optionally, the constructing a gene tree rule according to clinical gene information includes:
determining gene attributes in the clinical gene information, and creating gene tree root nodes;
classifying the gene attributes according to a preset rule strategy, and determining a gene classification level, wherein the gene classification level comprises a previous classification and a next classification of the current gene attributes;
and ordering the clinical gene information from the root node according to the gene classification level, and constructing the gene tree rule.
Optionally, after the gene tree rule is constructed according to the clinical gene information, the method further comprises:
acquiring rule nodes of the created gene tree rule, and matching father nodes in the rule nodes with a plurality of created gene tree rule items;
And when the matching is successful, screening out successfully matched gene tree rule entries, matching the child nodes of the father node with the successfully screened out gene tree rule entries until the last node is successfully matched, and adding the created gene tree rule into the gene tree rule.
Optionally, the constructing a gene tree targeting annotation according to the disease tumor tree and the gene tree rule includes:
acquiring nodes in the disease tumor tree and a plurality of gene tree rules;
and adding targeted annotation comments for the nodes of the acquired disease tumor tree and the plurality of gene tree rules, wherein the targeted annotation comprises a targeted drug name, a clinical type, clinical significance, a evidence grade, approval information details, guideline consensus details, clinical trial, drug prompt, comment and remark.
Optionally, the cell mutation targeting drug knowledge base is constructed according to the gene tree rules and clinical drug experience, and comprises the following steps:
determining corresponding gene tree rule entries according to the clinical gene variation data;
determining corresponding medication information according to the clinical gene variation data;
binding the rule items of the gene tree with the medication information, and constructing a cell mutation targeting medication knowledge base.
In addition, in order to achieve the above object, the present invention also provides a gene detection report automatic processing apparatus comprising:
the disease tumor tree construction module is used for constructing a disease tumor tree according to clinical tumor information;
the gene tree rule construction module is used for constructing a gene tree rule according to clinical gene information;
the gene tree targeting annotation construction module is used for constructing gene tree targeting annotations according to the disease tumor tree and the gene tree rules;
the somatic variation targeted drug knowledge base construction module is used for constructing a somatic variation targeted drug knowledge base according to the gene tree rules and clinical drug experience;
the targeting annotation item matching module is used for acquiring tumor information and gene information of a patient, and obtaining a corresponding targeting annotation item according to the tumor information and the gene information;
and the gene detection report generation module is used for generating a gene detection report according to the targeting annotation item matching targeting drug information.
In addition, in order to achieve the above object, the present invention also proposes a gene detection report automatic processing apparatus comprising: a memory, a processor, and a gene detection report automation processing program stored on the memory and executable on the processor, the gene detection report automation processing program configured to implement the steps of the gene detection report automation processing method as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a gene detection report automation processing program which, when executed by a processor, implements the steps of the gene detection report automation processing method as described above.
According to the invention, a disease tumor tree is constructed according to clinical tumor information; according to clinicConstructing a gene tree rule by using the gene information; constructing a gene tree targeting annotation according to the disease tumor tree and the gene tree rule; constructing a cell mutation targeting drug use knowledge base according to the gene tree rules and clinical drug use experience; obtaining tumor information and gene information of a patient, and obtaining a corresponding target annotation item according to the tumor information and the gene information; generating a gene detection report according to the targeting annotation item matching targeting drug information ， By storing the tumor information and the genetic variation information construction tree, corresponding medication information and annotation information can be directly obtained from corresponding tree nodes during detection, a comprehensive gene detection report can be generated, and the method and the device for detecting the gene can ensure the accuracy of a diagnosis result and improve the efficiency of gene detection mainly according to experience and knowledge of genetic consultants in the prior art.
Drawings
FIG. 1 is a schematic diagram of a hardware running environment of a genetic testing report automation processing device according to an embodiment of the present invention;
FIG. 2 is a flow chart of a first embodiment of the automated gene detection report processing method of the present invention;
FIG. 3 is a schematic diagram of creating a disease tumor tree according to an embodiment of the automated gene detection report processing method of the present invention;
FIG. 4 is a schematic diagram of rule construction of a gene tree according to an embodiment of the automated processing method of a gene detection report of the present invention;
FIG. 5a is a schematic diagram illustrating the creation of a rule for a gene tree according to an embodiment of the automated processing method for gene detection report of the present invention;
FIG. 5b is a schematic diagram illustrating the creation of a rule for a gene tree according to an embodiment of the automated processing method for gene detection report of the present invention;
FIG. 6 is a schematic diagram of creating groupings of rules of a gene tree according to an embodiment of the automated processing method of a gene detection report of the present invention;
FIG. 7a is a schematic diagram showing variation of the addition satisfying conditions according to an embodiment of the automated processing method for gene detection report of the present invention;
FIG. 7b is a schematic diagram showing variation of the addition of the automated processing method for gene detection report according to an embodiment of the present invention, wherein the addition does not satisfy the condition;
FIG. 7c is a schematic diagram showing the results of the automated processing method for gene detection report according to an embodiment of the present invention;
FIG. 8a is a schematic diagram of targeted drug names, clinical types, clinical meanings, evidence grades, remark information for constructing targeted annotation of a gene tree according to an embodiment of the automated processing method of gene detection report;
FIG. 8b is a diagram of details of consensus manual and clinical studies for constructing targeting annotations of a gene tree according to an embodiment of the automated processing method of a gene detection report of the present invention;
FIG. 8c is a schematic diagram of clinical experiments, drug prompts, comments and remarks for constructing gene tree targeting annotations according to an embodiment of the automated processing method for gene detection report of the present invention;
FIG. 9 is a diagram of a genetic tree targeting annotation according to an embodiment of the automated processing method of the present invention;
FIG. 10 is a schematic diagram of the construction of a knowledge base of a somatic mutation-targeted drug using an embodiment of an automated gene detection report processing method according to the present invention;
FIG. 11 is a block diagram showing the construction of a first embodiment of an automated gene assaying report processing apparatus according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an automated processing apparatus for gene detection report of a hardware operation environment according to an embodiment of the present invention.
As shown in fig. 1, the gene detection report automation processing apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in FIG. 1 does not constitute a limitation of the automated processing equipment for gene detection reports, and may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a gene detection report automation processing program may be included in the memory 1005 as one type of storage medium.
In the automated gene assaying report processing apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the automatic processing device for gene detection report of the present invention may be disposed in the automatic processing device for gene detection report, where the automatic processing device for gene detection report invokes the automatic processing program for gene detection report stored in the memory 1005 through the processor 1001, and executes the automatic processing method for gene detection report provided by the embodiment of the present invention.
The embodiment of the invention provides an automatic processing method for a gene detection report, and referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the automatic processing method for the gene detection report.
In this embodiment, the automated processing method for gene detection report includes the following steps:
step S10: and constructing a disease tumor tree according to the clinical tumor information.
The execution body of the embodiment is a genetic detection report automatic processing device, where the genetic detection report automatic processing device has functions of data processing, data communication, program running, and the like, and the genetic detection report automatic processing device may be an integrated controller, a control computer, or other devices with similar functions, and the embodiment is not limited to this.
It can be understood that the disease tumor tree is a classification mode for clinical tumor information of modern medicine, and can better classify and store the tumor information which is clear now according to different types.
In a specific implementation, it is able to determine what type of tumor is the last stage of a tumor and what type of tumor is included in the current type of tumor, for example, currently there is a tumor type of non-small cell lung cancer, the last type of non-small cell lung cancer is lung cancer, that is, the non-small cell lung cancer belongs to lung cancer, and the next type of non-small cell lung cancer includes lung squamous cell carcinoma, lung adenocarcinoma, large cell carcinoma, and other subtypes. When each tumor is classified according to the above mode, the classification, the included subtypes and the like are clarified, and the tumors can be arranged layer by layer according to the classification level, so that a disease tumor tree is obtained.
Further, the step S10 further includes:
determining a tumor classification relation in the clinical tumor information, and creating a tumor tree root node;
determining a tumor classification level of the tumor according to the tumor classification relation, wherein the tumor classification level comprises a previous classification and a next classification of the current tumor;
and ordering the clinical tumor information from the root node according to the classification level, and constructing the disease tumor tree.
It should be noted that, the root node is the root of the disease tumor tree, the root node is the largest classification type of the disease tumor tree, that is, cancer, and the other various types of cancer types are all subtypes of cancer.
It can be understood that the tumor classification level refers to a level description of a tumor when the tumor is classified, and is illustrated by taking one branch of a disease tumor tree as an example, if the branch is: cancer= > solid tumor= > lung cancer= > non-small cell lung cancer= > lung adenosquamous carcinoma, wherein the cancer is classified as top grade, the solid tumor is classified as first grade subclass, the lung cancer is classified as second grade subclass, and so on, the tumor is in the nth of the tumor chain, and the corresponding classification is the nth-1 subclass.
In a specific implementation, when a disease tumor tree is created, a root node of the disease tumor tree is created first, the root node is expressed by cancers, namely all cancers are subtypes of the cancers, after the root node of the disease tumor tree is created, a first-level subclass can be created under the root node, wherein the first-level subclass can be created into a blood system tumor, a solid tumor and other three first-level subclasses according to clinical tumor information, and similarly, after the first-level subclass is created, a second-level subclass and a third-level subclass can be sequentially created until the subclass cannot be subdivided. For example, there are 2 secondary subclasses of primary subclasses of hematological tumors: medullary system, lymph. There are 29 secondary subclasses of primary subclass solid tumors: thymus cancer, peritoneal cancer, pleural cancer, spent ampulla cancer, eye cancer, peripheral nervous system tumor, esophagus/stomach cancer, pancreas cancer, intestinal cancer, skin cancer, vagina cancer, head and neck cancer, penis cancer, central nervous system tumor, biliary tract tumor, testis cancer, bone cancer, bladder/urothelial cancer, adrenal cancer, uterine cancer, soft tissue tumor, thyroid cancer, kidney cancer, ovary/fallopian tube cancer, prostate cancer, cervical cancer, liver cancer, lung cancer, breast cancer. The other secondary subclasses of the primary subclass have 4: mixed tumor types, cervical in situ adenocarcinoma, extragonadal germ cell tumor, and unknown primary focus. According to the construction mode, a disease tumor tree with luxuriant branches and leaves, good surface and perfect sustainable updating growth can be constructed.
In actual creation, referring to fig. 3, fig. 3 is a schematic diagram of creation of a disease tumor tree according to the present invention. In creating the disease tumor tree, each tumor may be assigned an ID number, which may be considered as being assigned, or may be automatically generated, which is not limited in this embodiment. Each node on the disease tumor tree can provide a function of adding a new node so as to be convenient for coping with the situation that the disease tumor tree can adapt to the medical development along with the medical development, the disease tumor tree is updated in time, and if the new node is added to the node of one secondary subclass, the added new node is the node included under the secondary subclass, namely, the third subclass under the secondary subclass. After the disease tumor tree is established, operations such as adding, deleting, modifying and inquiring can be performed on the disease tumor tree so as to adapt to scenes such as correcting, inquiring and using the disease tumor tree in actual use. When a node of a disease tumor tree is newly built, firstly, a tumor branch to be created needs to be defined, if a disease tumor branch which is cancer= > solid tumor= > lung cancer= > non-small cell lung cancer= > lung adenosquamous carcinoma exists in the current disease tumor tree, and if a new disease tumor branch needs to be created at the moment: cancer= > solid tumor= > lung cancer= > non-small cell lung cancer= > lung adenocarcinoma, because the same node exists in the branch and the existing branch, the same node can be shared, for example, when the branch of lung adenocarcinoma is created, the node comprising the non-small cell lung cancer and the previous node can be shared, and a subclass is newly created at the existing non-small cell lung cancer, which is named as lung adenocarcinoma, so that part of data from the cancer to the non-small cell lung cancer does not need to be created again in actual storage, and the use condition of the storage space can be reduced.
Step S20: and constructing a gene tree rule according to the clinical gene information.
The gene tree rule is a gene tree constructed according to the modern medicine on the gene mutation information, and may include a gene name, a DNA mutation type, a grouping type, a protein mutation type, a gene region, an amino acid start position, etc., for describing various gene mutation types and classifications that have been found today.
In a specific implementation, the genetic variation types may be grouped according to various genetic variation types, and may be classified into conventional groupings or groupings such as exact matches according to whether exact matches can be performed, in a genetic tree rule, the regions of the genetic names, DNA variation types, grouping types, protein variation types, genetic regions, amino acid starting positions, etc. may be classified according to a specific grouping arrangement sequence, for example, a genetic tree rule of the genetic variation type L861Q is now created, and then a genetic tree rule such as a genetic tree rule may be created according to the name, rule name, etc. of the genetic variation type L861Q: the gene mutation= > egfr= > snv_indel= > tkd= > L861Q is preferably constructed by the sequence of gene name, DNA mutation type, domain, and gene mutation type at the time of creation. According to the above gene tree rules, EGFR is the gene name, SNV_indel is the DNA mutation type, TKD is the corresponding domain, and L861Q is the specific gene mutation type.
Further, the step S20 further includes the steps of:
determining gene attributes in the clinical gene information, and creating gene tree root nodes;
classifying the gene attributes according to a preset rule strategy, and determining a gene classification level, wherein the gene classification level comprises a previous classification and a next classification of the current gene attributes;
and ordering the clinical gene information from the root node according to the gene classification level, and constructing the gene tree rule.
The gene attribute includes a mutation type corresponding to the gene and an original inherent attribute of the gene, such as a corresponding gene name, a mutation type of the gene mutation, a domain where the gene is located, pathogenicity, a chromosome range and the like, and the current gene mutation type is classified according to the gene attribute of the mutated gene, so as to construct a gene tree rule.
In a specific implementation, referring to fig. 4, fig. 4 is a schematic diagram of a rule of constructing a gene tree according to the present invention. Firstly, a root node of a gene tree rule can be created, the gene named by the root node is mutated, 19058 first-level subclasses are created under the root node by taking the gene name as a first-level subclass, wherein the gene name comprises EGFR, BRAF, MET, KRAS, ALK and the like, and then, a second-level subclass and a third-level subclass are created in sequence according to the rule until the subclasses cannot be subdivided. These create gene tree rules are configured with: superordinate, rule name, grouping type, germ line/system, DNA mutation type, somatic mutation function, germ line pathogenicity (pathogenic, possibly pathogenic, ambiguous, possibly benign, benign), protein mutation type, chromosome range, domain, gene region, amino acid start position, CDS start position, fusion master gene position, fusion partner, fusion gene coding direction, fusion reading frame, MET exon14 skip, rule terms of these rule configuration panels and grouping type determine how the last mutation is annotated and constructed on the gene rule tree. Such as constructing a gene tree rule: the rule of gene mutation= > egfr= > snv_indel= > tkd= > L858R is created according to= > first-order new-child node. Thus, a genetic rule tree with luxuriant branches and leaves, good surface and perfect sustainable updating growth is constructed. Wherein, referring to fig. 5, fig. 5 is a schematic diagram of creating a rule of a gene tree. The upper level is the parent class of the rule, the rule name is popular and short for short, the grouping type comprises conventional grouping, exact matching and other grouping, the gene mutation belongs to a germ line or system, the DNA mutation type comprises snv, indel, amplification, displacement and fusion, the somatic mutation function comprises Gain-of-function, round Gain-of-function, predictive Gain-of-function, predicted Likely Gain-of-function, loss-of-function, round Loss-of-function, predictive Loss-of-function, predicted Likely Loss-of-function, switch-of-function, likely Switch-of-function, predicted Switch-of-function, predicted Likely Switch-of-function, neutral, like Neutral, predicted Neutral, predicted Likely Neutral, inclusion, predicted Inconclusive, unknown, germ line pathogenicity including pathogenicity, potentially pathogenic, ambiguous, potentially benign, protein variant types including delivery, frame shift, missense, insertion, nonsense, silent, duplex, start-last, unclassified, delivery-insert, stop-last, domain including Growth factor Receptor domain, function-like Neutral-rich domain, protein kinase domain, receptor L-domain, fusion gene encoding orientation including: the fusion reading frame comprises in-frame, frame shift, MET exon14 skip comprises MET exon14-skip, MET PPT-SNV. In creating the genetic tree rules, it is necessary to create groupings of genetic variations, and referring to fig. 6, fig. 6 creates a grouping schematic diagram for the genetic tree rules. When creating a mutation group, determining the upper level corresponding to the current mutation group, inputting the corresponding gene name and the corresponding description information, wherein the creation mode can perform addition operation of new gene mutation, and constructs a complete gene tree rule according to a first-level mode, for example, when creating the new gene tree rule, if one gene rule is already present when creating the new gene tree rule, the new node is created under the original group of gene mutation= > EGFR= > SNV_indel= > TKD= > L858R, when creating the new gene rule, the gene mutation= > EGFR= > SNV_indel= > TKD= > S768I, the group with the same node can be determined first, and when creating the TKI I, the two gene mutation= > EGFR= > SNV_indel= > D is used as an example, therefore, when creating the original group of gene mutation, the new sub-node can be created under the original group of gene mutation= > EGFR= > SNV_indel= > TKI D, the new sub-node is created under the new gene mutation is created by the TKI I, and redundancy data can be stored in a small space when the two parts of TKI D are not needed, and the redundancy data can be stored in a practical space.
Further, after the step S20, the method further includes the following steps:
acquiring rule nodes of the created gene tree rule, and matching father nodes in the rule nodes with a plurality of created gene tree rule items;
and when the matching is successful, screening out successfully matched gene tree rule entries, matching the child nodes of the father node with the successfully screened out gene tree rule entries until the last node is successfully matched, and adding the created gene tree rule into the gene tree rule.
In a specific implementation, when a new gene tree rule needs to be added in the created gene tree rule, the new gene tree rule can be added in a batch manner or one by one, when the new gene tree rule is added one by one, a corresponding group can be selected according to a specific group of the gene variation classifications of Jin Jinyin gentlerules to be added and added into the corresponding group, if the added group belongs to a novel variation, a new child node can be created under the corresponding parent node, the creation of the corresponding gene rule is completed, and the gene tree rule is enriched. When batch addition is performed, the gene rules to be added can be introduced into the gene tree rules together, the nodes in the existing gene tree rules and the nodes in the gene rules to be added are matched and compared, corresponding information is fed back according to the matching condition, and referring to fig. 7 a-7 c, fig. 7a is a variation schematic diagram of the addition meeting the condition, fig. 7b is a variation schematic diagram of the addition not meeting the condition, and fig. 7c is a schematic diagram of the addition result. When a new gene rule is created again, firstly, a father node of the newly created gene rule is obtained, the father node is compared with the father node in the gene tree rule, a successfully matched gene tree rule item is determined, the process is continuously circulated, then, a first-level sub-class node is obtained and is matched with the first-level sub-class node in the successfully matched gene tree rule item until the last node is matched, if the last node is successfully matched, the newly created gene rule can be added to the gene tree, the successfully matched information is fed back, otherwise, the newly created gene rule is not added, and the prompt information of the failed matching is fed back.
Step S30: and constructing gene tree targeting annotation according to the disease tumor tree and the gene tree rule.
The gene tree targeting annotation is a targeting drug name, clinical type, clinical meaning, evidence grade, approval information details, guideline consensus details, clinical research information, clinical trial, drug prompt, comment and remark which are commonly associated with the leaf nodes of the disease tumor tree and the leaf nodes of the gene tree.
In a specific implementation, because the tumor and the genetic variation situation are interrelated, because the genetic mutation has randomness, a tumor may be caused by multiple genetic variations, and meanwhile, multiple different genes have variations, and the possible illness states or medication modes have commonality, so that the tumor can be associated with one tumor in a disease tumor tree when the genetic tree targeting annotation is carried out, namely, a tumor is formed, a plurality of genetic rules are corresponding, and the genetic tree targeting annotation is corresponding, so that the current tumor and the genetic variation situation are annotated and described in sequence, and relevant clinical information of the current genetic mutation type is described.
Further, the step S30 further includes the steps of:
Acquiring nodes in the disease tumor tree and a plurality of gene tree rules;
adding targeted annotation comments to the nodes of the acquired disease tumor tree and the plurality of gene tree rules, wherein the targeted annotation comprises a targeted drug name, a clinical type, clinical significance, a evidence grade, approval information details, guideline consensus details, clinical trials, drug prompts, comments and remarks.
In particular, in the prior art, a corresponding annotation is required for each tumor and each genetic variation type, and if 1000 tumors exist and there are 1000000 genetic variations, 1000 x 1000000=1000000000 annotation entries need to be created, the data size is very large, and it is very difficult to maintain the database. In this embodiment, one node in a disease tumor tree is obtained first, then, several gene rules corresponding to the genetic variation of the tumor information are bound to the corresponding node in the disease tumor tree from the gene tree rules, and the tumor is bound to the targeting drug name, clinical type, clinical meaning, evidence grade, approval information details, guideline consensus details, clinical test, drug prompt, comment and remark of the tumor variation type, so as to achieve the purpose of binding the tumor information, the several genetic variation types causing the tumor and the corresponding targeting comments, wherein the targeting comments are associated with the gene tree. Referring to fig. 8a-8c and fig. 9, fig. 8a-8c are schematic representations of gene tree targeting annotation construction, and fig. 9 is a schematic representation of gene tree targeting annotation. In practical situations, for the occurrence of a tumor, the occurrence of multiple genetic variations may be caused, while for the occurrence of an impossible genetic variation, macroscopic manifestations may be consistent, but there may also be subtle differences, so that when constructing a genetic tree targeting annotation, the annotation should be performed with the smallest common node in several genetic trees, and when there is only one genetic rule, the annotation should be performed with a specific genetic variation.
Step S40: constructing a cell mutation targeting drug use knowledge base according to the gene tree rules and clinical drug use experience.
It should be noted that, the somatic mutation targeted drug knowledge base refers to a large amount of somatic mutation data accumulated by related personnel during clinical second-generation sequencing molecular diagnostic detection, and the somatic mutation data are corresponding to the gene tree rule, and each somatic mutation may correspond to one drug information, so that the somatic mutation targeted drug knowledge base is used for storing the drug information and the corresponding relationship with the gene mutation.
In a specific implementation, the drug administration information corresponding to the somatic cell gene variation can be associated with the somatic cell variation information, so that when knowing that a mutation occurs in a certain gene, the corresponding drug administration information can be queried.
Further, the step S40 further includes the steps of:
determining corresponding gene tree rule entries according to the clinical gene variation data;
determining corresponding medication information according to the clinical gene variation data;
binding the rule items of the gene tree with the medication information, and constructing a cell mutation targeting medication knowledge base.
In a specific implementation, referring to fig. 10, fig. 10 is a schematic diagram of a somatic mutation targeted drug knowledge base construction. When creating the somatic mutation targeted drug application knowledge base, the existing targeted drug for somatic mutation and the corresponding genetic mutation can be associated, so that the genetic mutation can be matched with drug application information, and meanwhile, the corresponding genetic mutation is matched with the genetic tree rule, wherein when the somatic mutation is bound with the genetic tree rule, if the drug application information corresponding to a plurality of genetic mutations is the same, the genetic mutation can be bound with the minimum node shared in the corresponding genetic rule to obtain the somatic mutation targeted drug application knowledge base.
Step S50: and acquiring tumor information and gene information of the patient, and obtaining a corresponding target annotation item according to the tumor information and the gene information.
In specific implementation, according to the tumor type and the genetic variation type of the patient obtained from the detection report of the patient, it is assumed that the genetic variation of the patient is lung adenocarcinoma at present, EGFR G719A mutation exists in the disease tumor tree, and the rule of the disease tumor tree is as follows:
disease tumor tree rule 1: cancer= > solid tumor= > lung cancer= > non-small cell lung cancer= > lung adenosquamous carcinoma,
Disease tumor tree rule 2: cancer= > solid tumor= > lung cancer= > non-small cell lung cancer= > lung adenocarcinoma,
the existing gene tree rules are as follows:
gene tree rule 1: genetic variation= > egfr= > snv_indel= > tkd= > L861Q,
gene tree rule 2: genetic variation= > egfr= > snv_indel= > tkd= > S768I,
gene tree rule 3: genetic variation= > egfr= > snv_indel= > tkd= > G719A,
gene tree rule 4: genetic variation= > egfr= > snv_indel= > other,
then a lung adenocarcinoma can be used to find with EGFR G719A, a match can be made to the target annotation entry corresponding to gene tree rule 3, and then the disease tumor tree rule 2 can be met.
Further, the step S50 further includes:
determining a corresponding disease tumor tree according to the tumor information;
determining a corresponding gene tree rule according to the gene information;
searching parent nodes of the tumor information in the disease tumor tree or parent nodes of the gene information in the gene tree rule until target annotation items containing the corresponding tumor information and the gene information are searched, and taking the searched target annotation items as the target annotation items of the tumor information and the gene information.
In a specific implementation, if the corresponding annotation information cannot be directly obtained according to the tumor information and the gene information, the annotation information can be searched according to the previous parent node of the tumor information or the previous parent node of the gene information, and it can be understood that if one disease is a lower concept of another disease, then in the treatment process, the treatment mode of the disease capable of treating the upper concept is that capable of treating the disease of the lower concept, for example, one drug a is suitable for treating pain, drug B is suitable for treating stomach pain, drug C is suitable for treating headache, when drug B or drug C is absent, drug a can be naturally suitable for treating, and similarly, when the target node cannot find the corresponding annotation information, the annotation information corresponding to the parent node of the node can be used. If the current patient detection report is lung squamous carcinoma, EGFR S768I mutation can be known that the lung squamous carcinoma is different from lung adenocarcinoma, if the gene tree rule corresponding to EGFR S768I does not have corresponding annotation information, the annotation information of father node non-small cell lung cancer of the lung squamous carcinoma can be searched, if the father node non-small cell lung cancer does not have corresponding annotation information, the annotation information corresponding to the father node of the non-small cell lung cancer is continuously searched until the corresponding drug annotation information is searched.
Step S60: and generating a gene detection report according to the targeting annotation item matching targeting drug information.
In specific implementation, a target annotation item of the patient disease is obtained according to tumor information and gene information in a detection report of a patient, corresponding target medication information is searched according to the target annotation item, the target annotation item is combined with the target medication information, and the gene detection report is generated together with the tumor information and the gene information of the patient, so that excessive dependence on genetic consultants is greatly reduced, and automatic processing of the gene detection report can be performed quickly and efficiently.
The present embodiment constructs a disease tumor tree by constructing a disease tumor tree according to clinical tumor information; constructing a gene tree rule according to clinical gene information; constructing a gene tree targeting annotation according to the disease tumor tree and the gene tree rule; constructing a cell mutation targeting drug use knowledge base according to the gene tree rules and clinical drug use experience; obtaining tumor information and gene information of a patient, and obtaining a corresponding target annotation item according to the tumor information and the gene information; generating a gene detection report according to the targeting annotation item matching targeting drug information ， By storing the tumor information and the genetic variation information construction tree, corresponding medication information and annotation information can be directly obtained from corresponding tree nodes during detection, a comprehensive gene detection report can be generated, and the method and the device for detecting the gene can ensure the accuracy of a diagnosis result and improve the efficiency of gene detection mainly according to experience and knowledge of genetic consultants in the prior art.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium stores a gene detection report automatic processing program, and the gene detection report automatic processing program realizes the steps of the gene detection report automatic processing method when being executed by a processor.
Referring to FIG. 11, FIG. 11 is a block diagram showing the construction of a first embodiment of an automated gene assaying report processing apparatus according to the present invention.
As shown in fig. 11, the automated gene detection report processing apparatus according to the embodiment of the present invention includes:
a disease tumor tree construction module 10 for constructing a disease tumor tree according to clinical tumor information;
a gene tree rule construction module 20 for constructing a gene tree rule according to clinical gene information;
A gene tree targeting annotation construction module 30 for constructing a gene tree targeting annotation according to the disease tumor tree and the gene tree rules;
the somatic mutation targeted drug application knowledge base construction module 40 is used for constructing a somatic mutation targeted drug application knowledge base according to the gene tree rules and clinical drug application experience;
the targeting annotation item matching module 50 is used for acquiring tumor information and gene information extracted from a patient and obtaining a corresponding targeting annotation item according to the tumor information and the gene information;
the gene detection report generating module 60 is configured to generate a gene detection report according to the targeting annotation item matching the targeting drug information.
The present embodiment constructs a disease tumor tree by constructing a disease tumor tree according to clinical tumor information; constructing a gene tree rule according to clinical gene information; constructing a gene tree targeting annotation according to the disease tumor tree and the gene tree rule; constructing a cell mutation targeting drug use knowledge base according to the gene tree rules and clinical drug use experience; obtaining tumor information and gene information of a patient, and obtaining a corresponding target annotation item according to the tumor information and the gene information; generating a gene detection report according to the targeting annotation item matching targeting drug information ， By storing the tumor information and the genetic variation information construction tree, corresponding medication information and annotation information can be directly obtained from corresponding tree nodes during detection, a comprehensive gene detection report can be generated, and the method and the device for detecting the gene can ensure the accuracy of a diagnosis result and improve the efficiency of gene detection mainly according to experience and knowledge of genetic consultants in the prior art.
In an embodiment, the targeting annotation entry matching module 50 is further configured to determine a corresponding disease tumor tree according to the tumor information;
determining a corresponding gene tree rule according to the gene information;
searching parent nodes of the tumor information in the disease tumor tree or parent nodes of the gene information in the gene tree rule until target annotation items containing the corresponding tumor information and the gene information are searched, and taking the searched target annotation items as the target annotation items of the tumor information and the gene information.
In an embodiment, the disease tumor tree construction module 10 is further configured to determine a tumor classification relationship in the clinical tumor information, and create a tumor tree root node; determining a tumor classification level of the tumor according to the tumor classification relation, wherein the tumor classification level comprises a previous classification and a next classification of the current tumor; and ordering the clinical tumor information from the root node according to the classification level, and constructing the disease tumor tree.
In one embodiment, the gene tree rule construction module 20 is further configured to determine a gene attribute in the clinical gene information and create a gene tree root node; classifying the gene attributes according to a preset rule strategy, and determining a gene classification level, wherein the gene classification level comprises a previous classification and a next classification of the current gene attributes; and ordering the clinical gene information from the root node according to the gene classification level, and constructing the gene tree rule.
In an embodiment, the genetic tree rule construction module 20 is further configured to obtain rule nodes of the created genetic tree rule, and match parent nodes in the rule nodes with the created genetic tree rule entries; and when the matching is successful, screening out successfully matched gene tree rule entries, matching the child nodes of the father node with the successfully screened out gene tree rule entries until the last node is successfully matched, and adding the created gene tree rule into the gene tree rule.
In an embodiment, the genetic tree targeting annotation construction module 30 is further configured to obtain nodes in the disease tumor tree and a plurality of genetic tree rules; and adding targeted annotation comments for the nodes of the acquired disease tumor tree and the plurality of gene tree rules, wherein the targeted annotation comprises a targeted drug name, a clinical type, clinical significance, a evidence grade, approval information details, guideline consensus details, clinical trial, drug prompt, comment and remark.
In one embodiment, the somatic mutation targeting drug knowledge base construction module 40 is further configured to determine a corresponding gene tree rule entry according to clinical gene mutation data; determining corresponding medication information according to the clinical gene variation data; binding the rule items of the gene tree with the medication information, and constructing a cell mutation targeting medication knowledge base.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A method for automated processing of a gene detection report, the method comprising:
constructing a disease tumor tree according to clinical tumor information;
constructing a gene tree rule according to clinical gene information;
constructing a gene tree targeting annotation according to the disease tumor tree and the gene tree rule;
constructing a cell mutation targeting drug use knowledge base according to the gene tree rules and clinical drug use experience;
obtaining tumor information and gene information of a patient, and obtaining a corresponding target annotation item according to the tumor information and the gene information;
and generating a gene detection report according to the targeting annotation item matching targeting drug information.
2. The method of claim 1, wherein the deriving a corresponding targeted annotation entry from the tumor information and the genetic information comprises:
determining a corresponding disease tumor tree according to the tumor information;
determining a corresponding gene tree rule according to the gene information;
searching parent nodes of the tumor information in the disease tumor tree or parent nodes of the gene information in the gene tree rule until target annotation items containing the corresponding tumor information and the gene information are searched, and taking the searched target annotation items as the target annotation items of the tumor information and the gene information.
3. The method of claim 1, wherein constructing a disease tumor tree from clinical tumor information comprises:
determining a tumor classification relation in the clinical tumor information, and creating a tumor tree root node;
determining a tumor classification level of the tumor according to the tumor classification relation, wherein the tumor classification level comprises a previous classification and a next classification of the current tumor;
and ordering the clinical tumor information from the root node according to the classification level, and constructing the disease tumor tree.
4. The method of claim 1, wherein constructing a gene tree rule from clinical gene information comprises:
determining gene attributes in the clinical gene information, and creating gene tree root nodes;
classifying the gene attributes according to a preset rule strategy, and determining a gene classification level, wherein the gene classification level comprises a previous classification and a next classification of the current gene attributes;
and ordering the clinical gene information from the root node according to the gene classification level, and constructing the gene tree rule.
5. The method of claim 1, wherein after constructing the gene tree rule according to the clinical gene information, further comprising:
Acquiring rule nodes of the created gene tree rule, and matching father nodes in the rule nodes with a plurality of created gene tree rule items;
and when the matching is successful, screening out successfully matched gene tree rule entries, matching the child nodes of the father node with the successfully screened out gene tree rule entries until the last node is successfully matched, and adding the created gene tree rule into the gene tree rule.
6. The method of claim 1, wherein said constructing a gene tree targeting annotation according to the disease tumor tree and the gene tree rules comprises:
acquiring nodes in the disease tumor tree and a plurality of gene tree rules;
and adding targeted annotation comments for the nodes of the acquired disease tumor tree and the plurality of gene tree rules, wherein the targeted annotation comprises a targeted drug name, a clinical type, clinical significance, a evidence grade, approval information details, guideline consensus details, clinical trial, drug prompt, comment and remark.
7. The method of claim 1, wherein constructing a cell variation targeting drug knowledge base based on the gene tree rules and clinical drug experience comprises:
Determining corresponding gene tree rule entries according to the clinical gene variation data;
determining corresponding medication information according to the clinical gene variation data;
binding the rule items of the gene tree with the medication information, and constructing a cell mutation targeting medication knowledge base.
8. An automated gene test report processing apparatus, comprising:
the disease tumor tree construction module is used for constructing a disease tumor tree according to clinical tumor information;
the gene tree rule construction module is used for constructing a gene tree rule according to clinical gene information;
the gene tree targeting annotation construction module is used for constructing gene tree targeting annotations according to the disease tumor tree and the gene tree rules;
the somatic variation targeted drug knowledge base construction module is used for constructing a somatic variation targeted drug knowledge base according to the gene tree rules and clinical drug experience;
the targeting annotation item matching module is used for acquiring tumor information and gene information of a patient, and obtaining a corresponding targeting annotation item according to the tumor information and the gene information;
and the gene detection report generation module is used for generating a gene detection report according to the targeting annotation item matching targeting drug information.
9. An automated gene detection report processing apparatus, the apparatus comprising: a memory, a processor, and a gene detection report automation processing program stored on the memory and executable on the processor, the gene detection report automation processing program configured to implement the steps of the gene detection report automation processing method of any one of claims 1 to 7.
10. A storage medium having stored thereon a gene detection report automation processing program which, when executed by a processor, implements the steps of the gene detection report automation processing method according to any one of claims 1 to 7.
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