CN113053458A - Prediction method and device for tumor neoantigen load - Google Patents

Prediction method and device for tumor neoantigen load Download PDF

Info

Publication number
CN113053458A
CN113053458A CN202110067368.XA CN202110067368A CN113053458A CN 113053458 A CN113053458 A CN 113053458A CN 202110067368 A CN202110067368 A CN 202110067368A CN 113053458 A CN113053458 A CN 113053458A
Authority
CN
China
Prior art keywords
tumor
mutation
somatic
variant
sites
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110067368.XA
Other languages
Chinese (zh)
Other versions
CN113053458B (en
Inventor
高志博
金皓玄
王佳茜
苏小凡
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Yukang Medical Laboratory
Original Assignee
Shenzhen Yukang Medical Laboratory
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Yukang Medical Laboratory filed Critical Shenzhen Yukang Medical Laboratory
Priority to CN202110067368.XA priority Critical patent/CN113053458B/en
Publication of CN113053458A publication Critical patent/CN113053458A/en
Application granted granted Critical
Publication of CN113053458B publication Critical patent/CN113053458B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Biophysics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Biotechnology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Molecular Biology (AREA)
  • Genetics & Genomics (AREA)
  • Bioethics (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Evolutionary Computation (AREA)
  • Public Health (AREA)
  • Software Systems (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

The invention discloses a method and a device for predicting tumor neoantigen load, and provides a specific method for predicting the tumor neoantigen load degree based on a tumor immune editing theory. The method can accurately quantify the tumor neoantigen load, and can be used as a biomarker to predict the curative effect of an immune checkpoint inhibitor anti-PD- (L) 1.

Description

Prediction method and device for tumor neoantigen load
Technical Field
The invention belongs to the technical field of bioinformatics, relates to a method and a device for predicting tumor neoantigen load, and particularly relates to a method and a device for predicting tumor neoantigen load based on a tumor immune editing theory.
Background
Anti-tumor targeted drugs and immune checkpoint inhibitors are effective means for treating cancers, but at present, compared with accepted effective screening of patients who can not completely benefit the immune checkpoint inhibitors by evaluating potential biomarkers such as Tumor Mutation Burden (TMB), microsatellite instability (MSI) and the like through the curative effect of the immune checkpoint inhibitor anti-PD- (L) 1.
The research of various biomarkers (such as TMB, MSI, HLA (human leukocyte antigen) and the like) sensitive to the curative effect of immunotherapy, which are the binding points of genome and tumor immunotherapy, can be finally concluded to the search of high-quality tumor neoantigens. The basis of tumor immunotherapy is the immunogenicity of tumors, a tumor neoantigen is a protein which is specifically expressed only in tumor cells, can be recognized by T cells of an immune system, and is an ideal target for tumor immunotherapy.
The generation and development of cancer cells in the body are dynamic processes interacting with the immune system, and the immune system not only has the capacity of eliminating tumor cells, but also has the function of promoting the growth of tumors. The understanding of the academic circles on the generation and development of tumors from the viewpoint of immunity has a theory: tumor immune editing theory. Tumor immune editing is a process by which the adaptive and innate immune system controls tumor growth and shapes tumor immunogenicity, which includes three stages: clearance, equilibration and escape. Clearance, or tumor immune surveillance, refers to the process of adaptive and innate immune branches to identify and destroy newly formed cancer cells. The balance is the longest phase, including the balance between preventing tumor growth and shaping the immunogenicity of a small number of tumor cells. During the escape phase, less immunogenic tumor cells gradually grow and spread into visible tumors. The extent of tumor immunoediting can be determined by studying the relationship of tumor neoantigens to tumor mutations.
However, at present, no method which can accurately quantify the tumor neoantigen load, calculate the tumor neoantigen load based on the tumor immune editing theory and can be used as a biomarker to predict the curative effect of an immune checkpoint inhibitor anti-PD- (L)1 exists. The tumor neoantigen load calculated by other similar methods can not be verified or can be verified only in a small data set to be capable of evaluating the efficacy of the immune checkpoint inhibitor anti-PD- (L) 1.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is as follows: the curative effect evaluation of the immune checkpoint inhibitor anti-PD- (L)1 lacks a sufficiently accurate biomarker, and no method for evaluating the curative effect of the immune checkpoint inhibitor PD-1 in a plurality of cancer species in a large range by applying the tumor neoantigen load optimized based on the tumor immune editing theory is available, so that a tumor neoantigen load prediction method based on the tumor immune editing theory is provided.
In order to solve the technical problems, the technical scheme of the invention is as follows:
the invention provides a prediction method of tumor neoantigen load in a first aspect, which comprises the following steps:
obtaining sequencing data of the same tumor tissue sample and a control sample;
respectively carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain somatic mutation sites;
performing clustering analysis on the somatic mutation sites to obtain tumor clones containing different mutation sites;
predicting a potential tumor neoantigen peptide sequence based on the binding affinity of the peptide and HLA according to the somatic mutation site;
calculating the proportion of the newborn antigen peptide fragment to the non-synonymous variation site based on the clustering result of the somatic variation site and the tumor newborn antigen peptide fragment sequence to obtain an immune editing score;
calculating the tumor neoantigen load.
The second aspect of the present invention provides a device for predicting tumor neoantigen, comprising:
the data acquisition module is used for acquiring sequencing data of the same tumor tissue sample and the control sample, and respectively carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain somatic mutation sites;
the detection module is used for respectively carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain a somatic mutation site;
the clustering module is used for clustering the somatic cell mutation sites to obtain tumor clones containing different mutation sites;
the prediction module is used for predicting a potential tumor neogenesis antigen peptide fragment sequence based on the binding affinity of the peptide fragment and HLA according to the somatic mutation site;
the immune editing calculation module is used for calculating the proportion of the newborn antigen peptide fragment to the non-synonymous mutation site based on the clustering result of the somatic mutation site and the tumor newborn antigen peptide fragment sequence to obtain an immune editing score;
and the tumor neoantigen calculating module is used for calculating the tumor neoantigen load.
Compared with the prior art, the technical scheme of the invention has the following advantages:
the prediction method of the tumor neoantigen load provided by the invention provides a specific method for predicting the tumor neoantigen load degree based on the tumor immune editing theory, fully utilizes each mutation site of a tumor tissue sample and potential tumor neoantigens to estimate a specific numerical value corresponding to the tumor neoantigen load predicted based on the tumor immune editing theory, and provides a numerically referable basis for the subsequent prediction of the curative effect of immunotherapy. The method can accurately quantify the tumor neoantigen load, and can be used as a biomarker to predict the curative effect of an immune checkpoint inhibitor anti-PD- (L) 1.
Drawings
In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the embodiments of the present disclosure taken in conjunction with the accompanying drawings, in which
FIG. 1 is a flow chart of a prediction method according to a first embodiment of the present invention;
FIG. 2 is a fifth embodiment of the lung cancer cohort tissue sample iotNL score predictive immunotherapeutic outcome of the present invention;
FIG. 3 is a graphical representation of the outcome of the predictive immunotherapy effect of the IOTNL score for a nasopharyngeal carcinoma cohort tissue sample according to a sixth embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more apparent and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
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 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.
The numbering of the components as such, e.g., "first", "second", etc., is used herein only to distinguish the objects as described, and does not have any sequential or technical meaning.
It must be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise.
As used herein, the term "patient" preferably refers to a human, but also encompasses other mammals. The terms "organism," "individual," "subject," or "patient" are used as synonyms for interchangeable use.
The invention is applicable to the prediction of all cancers. The cancer may be a respiratory system cancer, or subtypes and stages thereof (phase), the respiratory system including the respiratory tract (nasal cavity, pharynx, larynx, trachea, bronchi) and lungs, in some embodiments, the cancer includes, but is not limited to, lung cancer, nasopharyngeal cancer, laryngeal cancer, pharyngeal cancer, tracheal cancer, and the like. In some embodiments, the cancer may also include, but is not limited to, breast cancer, lung cancer, prostate cancer, colorectal cancer, brain cancer, esophageal cancer, gastric cancer, bladder cancer, pancreatic cancer, cervical cancer, head and neck cancer, ovarian cancer, melanoma, and multidrug resistant cancers; or its subtype and stage (phase).
In some embodiments, the subject may also be a solid tumor patient, including but not limited to a lung cancer, nasopharyngeal cancer, or melanoma patient.
As used herein, the term "tumor" refers to all tumor cell growth and proliferation, either malignant or benign, as well as all precancerous and cancerous cells and tissues. Such cancers include, but are not limited to, cancers of the respiratory tract including, but not limited to, lung cancer, nasopharyngeal cancer, laryngeal cancer, pharyngeal cancer, tracheal cancer, and the like; the cancer may also be other lymphoproliferative cancers, such as precursor B lymphoblastic leukemia/lymphoblastic lymphoma, follicular B cell non-Hodgkin's lymphoma, Hodgkin's lymphoma precursor T cell lymphoblastic leukemia/lymphoblastic lymphoma, immature T cell neoplasm, post-peripheral thymic T cell neoplasm, T cell prolymphocytic leukemia, peripheral T cell lymphoma, undefined anaplastic large cell lymphoma, adult T cell leukemia/lymphoma, chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, marginal zone lymphoma, hairy cell leukemia, diffuse large B cell lymphoma, Burkitt's lymphoma, lymphoplasmacytic lymphoma, precursor T lymphoblastic leukemia/lymphoblastic lymphoma, lympho, T cell prolymphocytic leukemia, angioimmunoblastic lymphoma or hodgkin's lymphoma mainly comprising nodular lymphocytes.
As used herein, variant allelic sequencing depth ViAlso called mutation depth, variant allelic frequency VAFiAlso known as mutation frequency; somatic mutation sitesvariAlso known as variant variCandidate peptide neoAgiAlso called tumor neoantigen peptide neoAgi。
To solve the problem of lack of sufficiently accurate biomarkers for the efficacy assessment of the anti-PD- (L)1 immune checkpoint inhibitor, a first embodiment of the present application provides a method for the prediction of tumor neoantigen burden (ioTNL), as shown in fig. 1, comprising the steps of:
s1, obtaining the sequencing data of the same tumor tissue sample and the control sample.
Specifically, the tumor tissue sample is detected by the variation detection software, which can use any one of VarScan and mutec, such as VarScan (v2.4.1), mutec (v4.0.12.0), etc., within the whole exon range, the sequencing data includes somatic variation, depth and mutation frequency information. In this example, tumor samples and control samples of a single subject are tested simultaneously. The subject may, for example, be an individual who has been clinically diagnosed as a tumor patient. The tumor sample generally refers to a sample derived from the affected part or tissue of a tumor patient, such as a lung tissue sample of a lung cancer patient. The control sample is generally derived from a non-diseased part or tissue of the same tumor patient, such as a leukocyte sample isolated from peripheral blood. The genome secondary sequencing data of the tumor sample and the control sample are typically aligned first to the reference genome. Thus, in a preferred embodiment, the data acquisition step acquires alignment files of the genomic second generation sequencing data of the tumor sample and the control sample aligned to the reference genome. In a more preferred embodiment, the reference genome may specifically be human reference genome hg 19.
In some embodiments of this example, the tumor tissue sample is sequenced to a depth of > 200 ×, in other embodiments the tumor tissue sample is sequenced to a depth of > 300 ×, in other embodiments the tumor tissue sample is sequenced to a depth of > 400 ×, and in other embodiments the tumor tissue sample is sequenced to a depth of > 500 ×. In addition, in some specific embodiments, the control sample has a sequencing depth of > 50 ×, in other embodiments, the control sample has a sequencing depth of > 100 ×, and in other embodiments, the control sample has a sequencing depth of > 200 ×.
And S2, respectively carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain a somatic mutation site.
As will be appreciated by those skilled in the art, a somatic variation may also be referred to as a somatic mutation vari(i ═ 1, …, n), the mutation site can also be referred to as the mutation site, and in this example, the same tumor tissue sample and control sample refer to those derived from the same subject. And (3) detecting the somatic copy number variation of the sample in the whole exon range through copy number detection software, and obtaining estimated tumor purity information. The copy number detection software can adopt any one of CNVkit and ascatNgs, such as CNVkit (v0.8.1) and ascatNgs (v3.1.0).
And S3, carrying out clustering analysis on the somatic cell variation sites to obtain tumor clones containing different variation sites.
Specifically, PyClone software can be used for cluster analysis, preferably PyClone (v0.13.0). First the somatic mutation (SNV/indel/SV) site is located: and (3) inputting the results obtained in the steps S1 and S2 into Pyclone software, and performing cluster analysis on the mutations according to the somatic mutation sites of the sample, and the variant allelic sequencing depth, the variant allelic frequency, the variant site copy number and the tumor purity of the corresponding mutation sites. Of course other analysis software such as CloneSig (v0.1) may be used by the clustering software.
Wherein the variant allelic sequencing depth (V)i) Refers to the number (pieces) of sequences in which somatic variations occur at the corresponding sites in the sequencing data. Variant allelic frequency VAFi(variable Allole Fraction) is calculated by the following formula:
Figure BDA0002904638200000061
wherein R isiFor reference to allelic sequencing depth, i.e., absence of bulk detail at the corresponding site in the sequencing dataNumber of normal sequences of cellular variation.
The variation point copy number is calculated as follows: according to somatic mutation site variCopy number variation CNV in regionsiCalculation of somatic mutation sites variReference copy number of the region in NCNiAnd actual total copy number TCNi(ii) a Wherein:
Figure BDA0002904638200000062
Figure BDA0002904638200000063
obtaining somatic mutation sites variThe two chromosomes on which the copy number variation CNV is allele-specifici，major、CNVi，minorWherein, CNVi，major≥CNVi，minorFurther calculating the actual copy number of the mutation site CNi，major、CNi，minorWherein:
Figure RE-GDA0003075494810000064
Figure BDA0002904638200000065
the tumor purity refers to the ratio Pur of the number of tumor cells in the total number of cells in a tumor tissue sample, and the value range is (0, 1), the tumor cells refer to the sum of all cells with somatic variation, and the tumor purity can be detected by ascatNgs software, and specifically ascatNgs (v3.1.0) software can be adopted.
In the above clustering process, for any type of somatic variation, the cells in the tumor tissue sample of a subject can be classified into three categories: normal cells (N), tumor cells (T) not carrying the mutationwt) And tumor cells (T) carrying the mutationmut) Tumor cells (T) carrying said somatic variationsmut) Occupying tumor cells that do not carry this variation (T)wt) And tumor cells (T) carrying the mutationmut) If the ratio of the variant tumor cells of two or more variant sites meets the requirement in the same distribution model, the variants in the same distribution model are endowed with the same cluster label and clustered into a cluster, which is called a clone. Each cluster label C for each subjectj(j-1, …, c) all had a proportion of tumor cell clusters corresponding to them
Figure BDA0002904638200000071
Wherein the tumor cell cluster ratio
Figure BDA0002904638200000072
Calculated by the following way:
Figure BDA0002904638200000073
in some embodiments, other versions of PyClone or other variant clustering software such as CloneSig (v0.1) may also be employed in variant clustering.
And S4, predicting the potential tumor neoantigen peptide fragment sequence based on the binding affinity of the peptide fragment and HLA according to the somatic mutation site.
Specifically, the potential tumor neoantigen peptide sequence is predicted based on the binding affinity of the peptide and HLA, and based on the detected variation data, the somatic cell variation site var is used for predicting the potential tumor neoantigen peptide sequenceiTranslated variant amino acid aaiTo this end, polypeptides in the 21-mer range were scanned to identify candidate peptides that bind to HLA class I molecules. Then, the binding affinity of the 8-11-mer peptide to HLA class I molecules is predicted by adopting NetMHCPan3.0 software, and epitopes meeting the following conditions are screened as candidate peptides nevAyi: (1) based on the mutation of RNA sequence data that is not expressed (the mutation of mutant allele reads number ≧ 1 in RNA sequence data is confirmedConsidered expression); (2) epitopes with sequences homologous to themselves; (3) according to netmhcpan3.0, the half maximal inhibitory concentration (IC50) is an epitope greater than 500 nanometers. In this embodiment, the prediction software of the peptide fragment of the tumor neoantigen can be NetMHCPan (3.0), NetMHCPan (4.0), or the like.
S5, calculating the proportion of the new antigen peptide segment to the non-synonymous variable site based on the clustering result of the somatic variation site and the tumor new antigen peptide segment sequence to obtain an immune editing score so as to quantify and determine the immune editing stage (elimination, balance or escape) of each tumor clone.
Specifically, when calculating the immune editing score of each tumor, each tumor clone cluster label C is labeledjAll non-synonymous somatic mutation sites variPredicted candidate peptide neoAgiRespectively accumulating to obtain the number num of non-synonymous variation sitesvar,jAnd number of candidate peptides numneoAg，j. Calculating the ratio of the number of candidate peptides to non-synonymous variation sites to obtain the immune editing score esjThe calculation method is as follows:
numvar，f＝var1+var2...+vart；
numneoAg，j＝neoAg1+neoAg2...+neoAgt；
Figure BDA0002904638200000081
and S6, calculating the load of the tumor neoantigen.
Adding the proportions of various tumor clones defined as immune clearance to the tumor cells to obtain the final tumor neoantigen loading ioTNL. In this example, when calculating the tumor neoantigen burden ioTNL based on the tumor clonal cluster defined as the immune clearance state and the tumor immune editing theory, the immune editing is scored as esjDefining the tumor clone cluster less than 0.9 as "immune clearance" state, and making the tumor cell cluster ratio corresponding to the tumor clone cluster in the immune clearance state
Figure BDA0002904638200000082
Adding and calculating an iotNL value, wherein the value range of the iotNL value is [0,3 ]]The specific calculation method is as follows:
Figure BDA0002904638200000083
and esf＜0.9。
Preferably, after step S6, the method further includes: and setting a threshold according to the tumor neoantigen load ioTNL predicted based on the tumor immune theory in the tumor tissue sample, and determining the subject corresponding to the sample greater than or equal to the threshold as a low-risk subject, otherwise, indicating that the tumor immune escape degree is low. In some embodiments of this embodiment, the tumor sample is preferably lung cancer, nasopharyngeal carcinoma, or melanoma.
In some embodiments of this embodiment, the somatic mutation selects for at least one of a point mutation (SNV), an insertion/deletion (indel), a structural mutation (SV), and a copy number mutation (CNV). For example, in some embodiments, the somatic mutation may be SNV, indel, in other embodiments SNV, indel, SV, CNV.
In some embodiments of this embodiment, the sequencing method for the tumor tissue sample and the control sample is whole genome sequencing, whole exome sequencing or probe capture sequencing, preferably whole exome sequencing.
A second embodiment of the present application provides a device for predicting tumor neoantigen, which includes:
the data acquisition module is used for acquiring sequencing data of the same tumor tissue sample and the control sample, and respectively carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain somatic mutation sites;
the detection module is used for respectively carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain a somatic mutation site;
the clustering module is used for clustering the somatic cell mutation sites to obtain tumor clones containing different mutation sites;
the prediction module is used for predicting a potential tumor neogenesis antigen peptide fragment sequence based on the binding affinity of the peptide fragment and HLA according to the somatic mutation site;
the immune editing calculation module is used for calculating the proportion of the newborn antigen peptide fragment to the non-synonymous mutation site based on the clustering result of the somatic mutation site and the tumor newborn antigen peptide fragment sequence to obtain an immune editing score;
and the tumor neoantigen calculating module is used for calculating the tumor neoantigen load.
In some embodiments of this embodiment, the tumor neoantigen calculation module is configured to determine a tumor clone cluster in an "immune clearance" state, and accumulate a ratio of tumor cell clusters corresponding to the tumor clone in the "immune clearance" state, which is a tumor neoantigen load value predicted based on a tumor immune editing theory.
A third embodiment of the present application provides an electronic apparatus, including:
a memory for storing a program.
A processor for implementing the method as described in the first embodiment by executing the program stored in the memory.
A fourth embodiment of the present application provides a computer-readable storage medium, which can be disposed in the electronic device in the third embodiment, and the computer-readable storage medium can be the memory in the foregoing embodiments. Which stores a program that can be executed by a processor to implement the prediction method according to the first embodiment. 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.
According to the embodiment, the method for predicting the tumor neoantigen load based on the tumor immune editing theory is provided, each mutation site and the potential tumor neoantigen of a sample are fully utilized to estimate a specific numerical value corresponding to the tumor neoantigen load predicted based on the tumor immune editing theory, and a numerically referable basis is provided for the subsequent prediction of the curative effect of immunotherapy.
The fifth example of the present application provides a specific example of prediction of tumor neoantigens:
in the examples, the samples used were a batch of lung cancer immunotherapy cohort tumor tissue samples and control blood samples, in particular, peripheral blood isolated leukocyte samples, for a total of 65 patients. The tumor tissue sample sampling method of this embodiment is to sample pathological puncture single points at lung lesions of each patient and prepare formalin-fixed paraffin-embedded (FFPE) samples, and the sampling unit is the tumor control center of the university of zhongshan.
The prediction method for the sample comprises the following steps:
1. tumor tissue samples and control blood samples were obtained and whole exome sequencing was performed, this sequencing being provided by Nanjing and Gene Biotechnology, Inc. After obtaining the sequencing data, the variation detection software VarScan (v2.4.1) is used to detect the variation (SNV/indel) of the tested sample in the whole exon range, the minimum coverage rate is set to be equal to 20, the minimum support reading is set to be equal to 5, and the default parameters are adopted in other parameters. And obtaining the reference allelic sequencing depth and the variant allelic sequencing depth information of each mutation site. And (3) detecting the somatic copy number variation of the tested sample in the whole exon range by using copy number detection software ascatNgs (v3.1.0), setting the mode as allele _ count, and detecting and obtaining the copy number of each allele specificity and an estimated tumor purity value by using other parameters as default parameters.
2. According to the related parameters obtained in the step S1, carrying out cluster analysis on the mutation by clusters through PyClone (v0.13.0) software, setting a primer parameter as major _ copy _ number, an events parameter as 10,000, a burn-in parameter as 1000 and a tumor contents parameter as the value of the tumor purity Pur of the subject. The mutation number, the reference allelic sequencing depth, the mutation allelic sequencing depth, the reference copy number of the region where the mutation is located, and the actual allelic specific copy number parameter of the mutation are made into a file sample.tsv which is tabulated as a separator as an input of the-in _ files parameter, such as the sample.tsv file content input by one patient (patient number: 17120989277) in the group of lung cancer cohorts as follows. The analysis result of the somatic mutation clusters of each sample in the queue is obtained after PyClone software is operated, wherein mutation _ id is the variation number of each variation site, ref _ counts is the reference allele sequencing depth, var _ counts is the variant allele sequencing depth, normal _ cn is the reference copy number of the region where the variation is located, minor _ cn is the copy number of the minor allele specificity of the region where the variation is located, and major _ cn is the copy number of the major allele specificity of the region where the variation is located, and the detailed contents are shown in table 1.
TABLE 1
Figure BDA0002904638200000111
Figure BDA0002904638200000121
Figure BDA0002904638200000131
Figure BDA0002904638200000141
Figure BDA0002904638200000151
And obtaining the analysis result of the mutation clustering of each sample in the queue by running PyClone software. The PyClone run results for example patient F17120989277 are exemplified below. Wherein sample _ id is the sample number of the subject, cluster _ id is the number of each variation cluster clustered by PyClone, size is the number of variations contained in each variation cluster, mean represents the estimated ratio of the corresponding variation cluster to the tumor cells, std represents the standard deviation of the calculation result, and is shown in table 2.
TABLE 2
Figure BDA0002904638200000152
3. According to the result of somatic variation detection analysis, polypeptides within the range of 21-mers are translated centering on the variant amino acid corresponding to each variant site. The binding affinity of the 8-11 mer range of peptides to HLA class I was predicted using the NetMHCPAN3.0 binding algorithm. Screening epitopes satisfying the following conditions as candidate peptides neoAgi: : (1) according to the RNA sequence data of unexpressed mutation (RNA sequencing data mutant allele reads number ≧ 1 was confirmed as expression); (2) the sequence is homologous with itself; (3) according to netmhcpan3.0, the half maximal inhibitory concentration (IC50) is greater than 500 nm. The prediction of the tumor neoantigen peptide fragment of the exemplary patient F17120989277 is exemplified as follows. Wherein NeoRank is the predicted sequence number of the Peptide fragment of the nascent antigen, HLA is a class I HLA molecule combined with the predicted Peptide fragment, Peptide is the predicted Peptide fragment of the nascent antigen, Neoepiptorcore and WildtypeCore are respectively the affinity values of the HLA molecules combined with the predicted variant Peptide fragment and the wild type Peptide fragment, the lower the value is, the stronger the affinity is, WildtypePrepid is the wild type Peptide fragment, Gene and CDSMutation are respectively the original variant Gene and the original variant site corresponding to the predicted Peptide fragment of the nascent antigen, as shown in Table 3.
TABLE 3
Figure BDA0002904638200000161
Figure BDA0002904638200000171
4. From the results of the previous step, the immunoeditorial score of each tumor clone was calculated, as shown in example F17120989277, where sample _ id is the number of the subject sample, cluster _ id is the number of each variant cluster clustered by PyClone, size is the number of variants contained in each variant cluster, and edit _ score is the immunoeditorial score of the clone, as shown in table 4. None of the other clones from this patient found a tumor neoantigen that matched step 3) above, and thus only clone No. 4 gave an immunoeditorial score.
TABLE 4
Figure BDA0002904638200000172
5. Judging the immune editing state of each clone, defining the tumor clone cluster with the immune editing score less than 0.9 as an 'immune clearance' state, adding the proportions of the tumor cell clusters corresponding to the tumor clone cluster in the 'immune clearance' state, and calculating the iotNL value. After the tumor neogenesis antigen load (ioTNL) value predicted based on the tumor immune edit theory of each sample is calculated, the ioTNL threshold value is set to 60, samples with the ioTNL less than or equal to 60 are judged as low-ioTNL (ioTNL-L), and samples with the ioTNL greater than 60 are judged as high-ioTNL (ioTNL-H). After collecting the therapeutic efficacy and Progression Free Survival (PFS) information of the test patients, the samples were subjected to survival analysis (see fig. 2, time unit of abscissa is day) as shown in table 5 below, and the tumor immunoeditorial degree results evaluated by ioTNL were found to have a significant predictive effect on the PFS prognosis of the patients (p ═ 0.00068), and patients with high ioTNL had a higher risk of progression (HR ═ 2.83). The result verifies the effectiveness and accuracy of the tumor neoantigen load predicted by using the tumor immune editing theory, and also shows that the ioTNL can be used as a biomarker to predict the curative effect of the lung cancer immunotherapy.
TABLE 5
Figure BDA0002904638200000181
Figure BDA0002904638200000191
The sixth embodiment of the present application provides another specific example of prediction of tumor neoantigens:
in this example, the samples used were a batch of nasopharyngeal carcinoma immunotherapy cohort tumor tissue samples and a control blood sample, specifically a leukocyte sample isolated from peripheral blood, of a total of 61 patients. The tumor tissue sample is sampled in a single point at the lung lesion of each patient. The tumor tissue sample sampling method of the present embodiment is a single-point sampling of pathological puncture at the lung lesion of each patient and a prepared formalin-fixed paraffin-embedded (FFPE) sample, and the sampling unit is the tumor prevention and treatment center of the university of zhongshan.
The prediction method is substantially the same as the fifth embodiment, and includes the steps of:
1. tumor tissue samples and control blood samples were obtained and whole exome sequencing was performed, this sequencing being provided by Nanjing and Gene Biotechnology, Inc.
2. According to the relevant parameters obtained in step 1, the mutations were clustered by Pyclone (v0.13.0) software. The contents of the sample. tsv file entered for one of the subjects (patient number: F17120989297) in the nasopharyngeal carcinoma cohort are exemplified as follows. Analysis of the somatic mutation clusters for each sample in the cohort was obtained after running PyClone software, as shown in table 6.
TABLE 6
Figure BDA0002904638200000201
Figure BDA0002904638200000211
Figure BDA0002904638200000221
Figure BDA0002904638200000231
And operating PyClone software to obtain the analysis result of the mutation clustering of each sample in the queue. The PyClone operating results for the exemplary patient are exemplified below. Wherein sample _ id is the sample number of the subject, cluster _ id is the number of each variation cluster clustered by PyClone, size is the number of variations contained in each variation cluster, mean represents the estimated proportion of tumor cells occupied by the corresponding variation cluster, std represents the standard deviation of the calculation result, and the analysis result of the mutation cluster of the sample is shown in Table 7.
TABLE 7
Figure BDA0002904638200000232
Figure BDA0002904638200000241
3. Prediction of potential tumorigenic antigenic peptides, as exemplified below by the tumorigenic antigenic peptide prediction of exemplary patient F17120989297. Wherein NeoRank is the predicted sequence number of the Peptide segment of the new antigen, HLA is a class I HLA molecule combined with the predicted Peptide segment, Peptide is the predicted Peptide segment of the new antigen, NeoepiptoScore and WildtypeScore are respectively the affinity values of the HLA molecules combined with the predicted variant Peptide segment and the wild type Peptide segment, the lower the value is, the stronger the affinity is, WildtypePtide is the wild type Peptide segment, Gene and CDSMutation are respectively the original variant Gene and the original variant site corresponding to the predicted Peptide segment of the new antigen, as shown in Table 8.
TABLE 8
Figure BDA0002904638200000242
Figure BDA0002904638200000251
4. From the results of the previous step, the immunoeditorial score of each tumor clone of each sample was calculated, as shown in table 9, for example, in the case of patient F17120989297, where sample _ id is the subject sample number, cluster _ id is the number of each variation cluster clustered by PyClone, size is the number of variations contained within each variation cluster, and edit _ score is the immunoeditorial score of the clone. None of the other clones from this patient found a tumor neoantigen that matched step 3 above, and therefore only clone No. 4 gave an immunoeditorial score.
TABLE 9
Figure BDA0002904638200000252
5. And (3) calculating an ioTNL value, setting an ioTNL threshold value to be 24.5 after ROC curve analysis and correction, judging a sample with the ioTNL less than or equal to 24.5 as a low-ioTNL (ioTNL-L), and judging a sample with the ITH greater than 24.5 as a high-ioTNL (ioTNL-H). After collecting the efficacy and progression-free survival (PFS) information of the test patients, the samples were subjected to survival analysis (see fig. 3, time unit of abscissa is day) as shown in table 10 below, and the results of tumor immunoeditorial degree evaluated by ioTNL were found to have a significant predictive effect on the prognosis of PFS of patients (p ═ 0.047), and patients with high ioTNL had a higher risk of progression (HR ═ 1.77). The result successfully verifies the effectiveness and accuracy of the tumor neoantigen load predicted based on the tumor immune editing theory by using the ioTNL analysis technology in another nasopharyngeal carcinoma cohort, and also shows that the ioTNL can be used as a biomarker to predict the curative effect of immunotherapy in nasopharyngeal carcinoma.
Watch 10
Figure BDA0002904638200000261
Figure BDA0002904638200000271
In summary, the embodiments of the present invention provide a specific method for calculating tumor neoantigen load of a sample predicted based on a tumor immunoediting theory based on Whole Exome Sequencing (WES) data, and fully utilize each mutation site detected by the sample and the predicted tumor neoantigen to estimate the immunoediting state of each tumor clone, thereby calculating a specific value of tumor immunoediting degree, and providing a numerically referable basis for subsequent prediction of immunotherapy efficacy.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. All embodiments need not be, and cannot be, given poor exemplification here. And obvious variations or modifications therefrom are within the scope of the invention.

Claims (10)

1. A method for predicting tumor neoantigen burden, comprising the steps of:
obtaining sequencing data of the same tumor tissue sample and a control sample;
respectively carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain somatic mutation sites;
performing clustering analysis on the somatic mutation sites to obtain tumor clones containing different mutation sites;
predicting a potential tumor neogenesis antigen peptide segment sequence based on the binding affinity of the peptide segment and HLA according to the somatic mutation site;
calculating the proportion of the newborn antigen peptide fragment to the non-synonymous variable locus based on the clustering result of the somatic cell variable locus and the tumor newborn antigen peptide fragment sequence to obtain an immune editing score;
calculating the tumor neoantigen load.
2. The method for predicting tumor neoantigen burden according to claim 1, wherein the clustering analysis of the somatic mutation sites to obtain tumor clones containing different mutation sites comprises:
calculating variant allelic sequencing depth, variant allelic frequency, variant locus copy number and tumor purity value based on the somatic cell variant locus, performing cluster analysis according to the calculated variant allelic sequencing depth, variant allelic frequency, variant locus copy number and tumor purity value, and clustering the somatic cell variant locus according to clusters.
3. The method of predicting tumor neoantigen burden according to claim 2, wherein the sequencing depth of variant alleles is the number of variant sequences in the sequencing data at which somatic variations occur at corresponding sites; variant allelic frequency VAFiCalculated by the following formula:
Figure RE-FDA0003075494800000011
wherein, ViFor variant allelic sequencing depth, RiIs a reference allelic sequencing depth.
4. The method for predicting tumor neoantigen burden according to claim 3, wherein the copy number of the mutation site is calculated by:
according to somatic mutation site variIn a regionCopy number variation CNViCalculation of somatic mutation sites variReference copy number of the region in NCNiAnd actual total copy number TCNi；
Obtaining somatic mutation sites variThe two chromosomes on which the copy number variation CNV is allele-specifici，major、CNVi，minorWherein, in the step (A),
Figure RE-FDA0003075494800000026
further calculating the actual copy number of the mutation site CNi，major、CNi，minorWherein:
Figure RE-FDA0003075494800000021
Figure RE-FDA0003075494800000022
5. the method of any one of claims 2-4, wherein the somatic cells in the clustering analysis include normal cells N and tumor cells T that do not carry the mutationmutAnd tumor cells T carrying the mutationwtThe tumor cell T carrying the mutationwtOccupying tumor cells T not carrying the variationmutAnd tumor cells T carrying the mutationwtIf the ratios of the two or more than two variable sites of the variable tumor cells are in the same distribution model, marking the same cluster labels for the variable cells in the same distribution model, and clustering into a cluster; each cluster label cjHas a corresponding tumor cell cluster ratio
Figure FDA0002904638190000023
The ratio of the tumor cell clusters
Figure FDA0002904638190000024
Calculated by the following way:
Figure FDA0002904638190000025
6. the method for predicting the tumor neoantigen burden according to claim 5, wherein the step of predicting the potential tumor neoantigen peptide sequence based on the HLA binding affinity of the peptide according to the somatic mutation site comprises:
based on the somatic mutation site variTranslated variant amino acid aaiCentered on, scanning polypeptides in the 21-mer range to identify candidate peptides that bind to HLA class i molecules;
predicting the binding affinity of the 8-11 mer peptide to HLA class i molecules;
screening candidate peptides neoAgi。
7. The method for predicting tumor neoantigen burden according to claim 6, wherein the candidate peptide neoAg is screenediThe method specifically comprises the following steps: screening the RNA data for unexpressed mutation, epitope with sequence homologous with the RNA data and epitope with half maximum inhibitory concentration larger than 500 nm.
8. The method for predicting tumor neoantigen burden according to claim 7, wherein the calculating a ratio of neoantigen peptide fragments to non-synonymous variant sites based on the clustering result of the somatic variant sites and the sequence of the tumor neoantigen peptide fragments to obtain the immune editing score comprises:
labeling each cluster with CjAll non-synonymous somatic mutation sites variAnd the predicted candidate peptide neoAgiRespectively accumulating to obtain the number of non-synonymous variation sites nurnvar，jAnd number of candidate peptides numneoAg，j；
Calculation of candidate peptide number non-synonymyThe ratio of the number of the mutation sites is the immune editing score esj；
The calculating the tumor neoantigen load comprises:
scoring the immune edits as esjA tumor clonal cluster of less than 0.9 is defined as an immune clearance state;
the ratio of tumor cell clusters corresponding to the tumor clone cluster in the immune clearance state
Figure FDA0002904638190000031
Adding to obtain the tumor neoantigen load.
9. The method for predicting tumor neoantigen burden according to claim 8, wherein the somatic mutation site is at least one selected from the group consisting of a point mutation, an insertion or deletion, a structural mutation, and a copy number mutation; the method for acquiring the sequencing data comprises one of whole genome sequencing, whole exome sequencing or probe capture sequencing.
10. A device for predicting tumor neoantigen, comprising:
the data acquisition module is used for acquiring sequencing data of the same tumor tissue sample and the control sample, and respectively carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain a somatic mutation site;
the detection module is used for respectively carrying out somatic mutation detection on the tumor tissue sample and the control sample to obtain somatic mutation sites;
the clustering module is used for clustering the somatic cell mutation sites to obtain tumor clones containing different mutation sites;
the prediction module is used for predicting a potential tumor neogenesis antigen peptide fragment sequence based on the binding affinity of the peptide fragment and HLA according to the somatic mutation site;
the immune editing calculation module is used for calculating the proportion of the newborn antigen peptide fragment to the non-synonymous variation site based on the clustering result of the somatic variation site and the tumor newborn antigen peptide fragment sequence to obtain an immune editing score;
and the tumor neoantigen calculating module is used for calculating the tumor neoantigen load.
CN202110067368.XA 2021-01-19 2021-01-19 Method and device for predicting tumor neoantigen load Active CN113053458B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110067368.XA CN113053458B (en) 2021-01-19 2021-01-19 Method and device for predicting tumor neoantigen load

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110067368.XA CN113053458B (en) 2021-01-19 2021-01-19 Method and device for predicting tumor neoantigen load

Publications (2)

Publication Number Publication Date
CN113053458A true CN113053458A (en) 2021-06-29
CN113053458B CN113053458B (en) 2023-08-04

Family

ID=76508497

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110067368.XA Active CN113053458B (en) 2021-01-19 2021-01-19 Method and device for predicting tumor neoantigen load

Country Status (1)

Country Link
CN (1) CN113053458B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114141304A (en) * 2021-12-15 2022-03-04 上海科技大学 Tumor sample immune editing state quantification method and application thereof
CN114882951A (en) * 2022-05-27 2022-08-09 深圳裕泰抗原科技有限公司 Method and device for detecting MHC II tumor neoantigen based on next generation sequencing data
CN115424740A (en) * 2022-09-30 2022-12-02 四川大学华西医院 Tumor immunotherapy effect prediction system based on NGS and deep learning

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013192339A1 (en) * 2012-06-19 2013-12-27 The Regents Of The University Of California Biomarkers for the immunoediting-escape phase
WO2016047715A1 (en) * 2014-09-24 2016-03-31 北海道公立大学法人札幌医科大学 Tumor antigen peptides
CN109584960A (en) * 2018-12-14 2019-04-05 上海鲸舟基因科技有限公司 Predict the method, apparatus and storage medium of tumor neogenetic antigen
CN109706065A (en) * 2018-12-29 2019-05-03 深圳裕策生物科技有限公司 Tumor neogenetic antigen load detection device and storage medium
CN110387419A (en) * 2019-08-20 2019-10-29 裕策医疗器械江苏有限公司 Solid tumor polygenes detects genetic chip and preparation method thereof and detection device
CN111402952A (en) * 2020-03-27 2020-07-10 深圳裕策生物科技有限公司 Method and system for detecting tumor heterogeneity degree
WO2020253643A1 (en) * 2019-06-19 2020-12-24 上海交通大学医学院 Tumor neoantigen polypeptide and use thereof

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013192339A1 (en) * 2012-06-19 2013-12-27 The Regents Of The University Of California Biomarkers for the immunoediting-escape phase
WO2016047715A1 (en) * 2014-09-24 2016-03-31 北海道公立大学法人札幌医科大学 Tumor antigen peptides
CN109584960A (en) * 2018-12-14 2019-04-05 上海鲸舟基因科技有限公司 Predict the method, apparatus and storage medium of tumor neogenetic antigen
CN109706065A (en) * 2018-12-29 2019-05-03 深圳裕策生物科技有限公司 Tumor neogenetic antigen load detection device and storage medium
WO2020253643A1 (en) * 2019-06-19 2020-12-24 上海交通大学医学院 Tumor neoantigen polypeptide and use thereof
CN110387419A (en) * 2019-08-20 2019-10-29 裕策医疗器械江苏有限公司 Solid tumor polygenes detects genetic chip and preparation method thereof and detection device
CN111402952A (en) * 2020-03-27 2020-07-10 深圳裕策生物科技有限公司 Method and system for detecting tumor heterogeneity degree

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
杨琳，等: "肿瘤精准免疫诊断综合评估", 《中国生物工程杂志》 *
杨琳，等: "肿瘤精准免疫诊断综合评估", 《中国生物工程杂志》, vol. 39, no. 02, 28 February 2019 (2019-02-28), pages 62 - 73 *
高强等: "多发肝癌免疫基因组特征及免疫逃逸机制的探究", 《临床肝胆病杂志》 *
高强等: "多发肝癌免疫基因组特征及免疫逃逸机制的探究", 《临床肝胆病杂志》, no. 06, 15 June 2020 (2020-06-15), pages 152 - 155 *
高志博，等: "肿瘤精准免疫治疗与基因检测", 《生物产业技术》 *
高志博，等: "肿瘤精准免疫治疗与基因检测", 《生物产业技术》, no. 02, 31 March 2017 (2017-03-31), pages 27 - 33 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114141304A (en) * 2021-12-15 2022-03-04 上海科技大学 Tumor sample immune editing state quantification method and application thereof
CN114141304B (en) * 2021-12-15 2024-04-30 上海科技大学 Tumor sample immune editing state quantification method and application thereof
CN114882951A (en) * 2022-05-27 2022-08-09 深圳裕泰抗原科技有限公司 Method and device for detecting MHC II tumor neoantigen based on next generation sequencing data
CN114882951B (en) * 2022-05-27 2022-12-27 深圳裕泰抗原科技有限公司 Method and device for detecting MHC II tumor neoantigen based on next generation sequencing data
CN115424740A (en) * 2022-09-30 2022-12-02 四川大学华西医院 Tumor immunotherapy effect prediction system based on NGS and deep learning
CN115424740B (en) * 2022-09-30 2023-11-17 四川大学华西医院 Tumor immunotherapy effect prediction system based on NGS and deep learning

Also Published As

Publication number Publication date
CN113053458B (en) 2023-08-04

Similar Documents

Publication Publication Date Title
CN113053458B (en) Method and device for predicting tumor neoantigen load
CN111402952A (en) Method and system for detecting tumor heterogeneity degree
JP2019516407A (en) Methods for subtyping lung adenocarcinoma
CN113228190A (en) Tumor classification based on predicted tumor mutation burden
CN107709636A (en) For diagnosing or detecting the method and composition of lung cancer
WO2012125712A2 (en) Lung tumor classifier for current and former smokers
CN107849613A (en) Method for lung cancer parting
Zheng et al. Identification and validation of immunotherapy for four novel clusters of colorectal cancer based on the tumor microenvironment
CN111933219A (en) Detection method of molecular marker tumor deletion mutation load
CN115410713A (en) Hepatocellular carcinoma prognosis risk prediction model construction based on immune-related gene
Cao et al. Identification and development of a novel 4-gene immune-related signature to predict osteosarcoma prognosis
WO2015184461A1 (en) Methods for typing of lung cancer
CN113544288A (en) DNA methylation marker for predicting liver cancer recurrence and application thereof
Bouhaddou et al. Caveolin-1 and Sox-2 are predictive biomarkers of cetuximab response in head and neck cancer
Pagadala et al. Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response
Singh et al. Differentially expressed full-length, fusion and novel isoforms transcripts-based signature of well-differentiated keratinized oral squamous cell carcinoma
EP2558594A2 (en) Methods for the analysis of breast cancer disorders
CN116206681A (en) Method for evaluating prognostic gene pair value of immune infiltration cell model
CN112210596B (en) Tumor neoantigen prediction method based on gene fusion event and application thereof
Ma et al. A more novel and powerful prognostic gene signature of lung adenocarcinoma determined from the immune cell infiltration landscape
CN112992266A (en) Method, device and storage medium for evaluating tumor immune exhaustion state
Nassar et al. Tumor mutation burden prediction model in Egyptian breast cancer patients based on next generation sequencing
Wilmott et al. Tumour procurement, DNA extraction, coverage analysis and optimisation of mutation-detection algorithms for human melanoma genomes
Nie et al. Comprehensive analysis of an individualized immune-related lncRNA pair signature in gastric cancer
CN116072258A (en) Device for developing bladder cancer tumor antigen and predicting guiding medication and prognosis of bladder cancer patient

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant