A method and system for visualizing analysis of tumor neoantigen immune repertoire data

Abstract

This invention relates to the field of immune repertoire data analysis technology, specifically disclosing a method and system for visual analysis of tumor neoantigen immune repertoire data. The method includes: acquiring sequencing data of a tumor neoantigen immune repertoire; extracting immune repertoire information, including CDR3 sequences, from the sequencing data; querying existing databases based on the CDR3 sequences to determine validated neoantigen information in the tumor neoantigen immune repertoire; and performing visual analysis on the immune repertoire information. This invention addresses the problems of high complexity and specificity in tumor neoantigen immune repertoire data, enabling the screening and visual analysis of neoantigen information.

Description

Technical Field

[0001] This invention belongs to the field of immune repertoire data analysis technology, and in particular relates to a method and system for visual analysis of tumor neoantigen immune repertoire data. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Tumor neoantigens, also known as tumor-specific antigens (TSA), are polypeptides produced by mutations in tumor cells and expressed in large quantities only in tumor cells, while being almost entirely absent in normal somatic cells. They possess strong tumor specificity and high immunogenicity. They can activate the killing effect of specific T cells in vivo while avoiding attacking normal cells. Therefore, neoantigens are considered one of the new directions in cancer treatment, mainly applied in tumor vaccines and cell therapy.

[0004] Immune repertoire analysis results are widely used in studies that predict and screen new antigens. Downstream analyses of these immune repertoire analysis results often focus on VDJ gene usage, combination, and some characteristics of clonotype.

[0005] The inventors discovered that, due to the high diversity, complexity, and specificity of tumor neoantigen repositories, existing methods for visualizing immune repositories data encounter difficulties in downstream analysis due to poor specificity representation and visualization. This leads to scattered and complex reference data when predicting and screening neoantigens; therefore, these methods cannot be directly applied to the analysis of tumor neoantigen immune repositories. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a method and system for visual analysis of tumor neoantigen immune repertoire data. Specifically addressing the issues of high complexity and specificity in tumor neoantigen immune repertoire data, this invention enables the screening and visual analysis of neoantigen information.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0008] A method for visualizing and analyzing tumor neoantigen immune repertoire data includes the following steps:

[0009] Obtain sequencing data of tumor neoantigen immune repertoire;

[0010] Immunome library information is extracted from the sequencing data, and the immune memorandum library information includes the CDR3 sequence.

[0011] Based on the CDR3 sequence, existing databases were queried to determine validated neoantigen information in the tumor neoantigen immune repertoire;

[0012] Visual analysis was performed on the aforementioned immune repertoire information.

[0013] Furthermore, after obtaining the sequencing data of the tumor neoantigen immune repertoire, the sequencing data is preprocessed: the sequencing data is sequentially subjected to data filtering, quality control analysis, and UMI analysis.

[0014] Furthermore, the UMI analysis includes:

[0015] Sequences from the same mRNA molecule were retrieved based on UMI tag information;

[0016] For each set of sequencing data, duplicate data, insufficient sequence quantity, and inconsistencies in the forward and reverse strand bases of the sequence are identified and corrected.

[0017] Furthermore, extracting immune repertoire information based on the sequencing data includes: aligning the sequencing sequence to the V, D, J, and C reference sequences of T cell receptors in the reference database, and then using bioinformatics to splice the alignment results to obtain immune repertoire information.

[0018] Furthermore, the visualization analysis includes one or more of the following: VDJ gene usage frequency analysis, VDJ gene combination analysis, CDR3 sequence length analysis, and clonotype distribution analysis.

[0019] One or more embodiments provide a tumor neoantigen immune repertoire data visualization and analysis system, including:

[0020] The immune repertoire construction module is used to obtain sequencing data of tumor neoantigen immune repertoires;

[0021] An immune repertoire information extraction module is used to extract immune repertoire information based on the sequencing data, wherein the immune repertoire information includes the CDR3 sequence.

[0022] The neoantigen information annotation module is used to query existing databases based on the CDR3 sequence to determine the validated neoantigen information in the tumor neoantigen immune repertoire.

[0023] The visualization analysis module performs visualization analysis on the immune repertoire information.

[0024] One or more embodiments provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for visual analysis of tumor neoantigen immune repertoire data.

[0025] One or more embodiments provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method for visualizing and analyzing tumor neoantigen immune repertoire data.

[0026] The above one or more technical solutions have the following beneficial effects:

[0027] This application provides a method for constructing, processing, and visualizing tumor neoantigen repertoire data. Considering the specificity of tumor neoantigen immune repertoire data, in order to accurately identify neoantigen information, it is verified by comparing CDR3 sequences with existing databases. The algorithm has low complexity and high accuracy.

[0028] By sequentially filtering data, performing quality control analysis, and conducting UMI analysis, the sequencing data was deduplicated and error-corrected, ensuring its accuracy and providing a data foundation for subsequent data analysis. Attached Figure Description

[0029] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0030] Figure 1 This is an overall flowchart of the tumor neoantigen immune repertoire data visualization and analysis method in one or more embodiments of the present invention;

[0031] Figure 2 This is a framework diagram of a tumor neoantigen immune repertoire data visualization and analysis system in one or more embodiments of the present invention. Detailed Implementation

[0032] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0033] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0034] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0035] Terminology Explanation:

[0036] The immune repertoire (IR) refers to the sum of all functionally diverse T cells and B cells in an individual's circulatory system at a specified time. The receptors found on B cells and T cells are called B cell receptors (BCRs) and T cell receptors (TCRs), respectively.

[0037] Basic Structure and Differences between BCR and TCR: TCRs are divided into two types. Most T cell TCRs are composed of α and β chains, while a minority are composed of γ and δ peptide chains. Their peptide chains can be divided into V region, C region, transmembrane region, and cytoplasmic region. BCRs are composed of two heavy chains and two light chains. The heavy chains are divided into a variable region (V region), a constant region (C region), a transmembrane region, and a cytoplasmic region; the light chains only have V and C regions. Both BCRs and TCRs have three hypervariable regions: CDR1, CDR2, and CDR3. CDR3 exhibits the greatest variation and directly determines antigen-binding specificity. Research on the diversity of B cells and T cells has focused on the CDR3 region.

[0038] The CDR3 region of the BCR includes light chain V and J gene segments and heavy chain V, D, and J gene segments, while the CDR3 region of the TCR includes α chain V and J gene segments and β chain V, D, and J gene segments. Therefore, it is called the VDJ segment.

[0039] Each TCR chain contains three hypervariable loops, called complementarity-determining regions (CDR1-3). CDR1 and CDR2 are encoded by the V gene and are crucial for the interaction between the TCR and the MHC complex. However, CDR3 is encoded by the linker region between V and J or D and J, and therefore exhibits greater variability. Since CDR3 is the TCR region that directly contacts the antigen, it plays a vital role in the interaction between the TCR and the peptide-MHC complex.

[0040] Therefore, CDR3 is a common clonal region of T cells. Unless T cells are derived from the same form of expansion, it is generally unlikely that T cells express the same CDR3 sequence.

[0041] Example 1

[0042] This embodiment discloses a method for visualizing and analyzing tumor neoantigen immune repertoire data, such as... Figure 1 As shown, it includes the following steps:

[0043] Step 1: Obtain sequencing data of the tumor neoantigen immune repertoire.

[0044] Specifically, step 1 includes:

[0045] Step 1.1: Obtain the tumor neoantigen immune repertoire dataset, which includes sequencing data.

[0046] Collect published, biologically validated tumor neoantigen-related immune repertoire datasets. For each collected tumor neoantigen immune repertoire dataset, store the neoantigen information, including specific tumor cells, antigens, epitopes, HLA allele information, TCR sequence information, and literature source information, into a database to obtain a tumor neoantigen immune repertoire.

[0047] The TCR sequence information includes V / D / J fragments, CDR3 fragments, etc.

[0048] Step 1.2: Preprocess the sequencing data. This specifically includes: data filtering, quality control analysis, and UMI analysis of the TCR sequence information.

[0049] The data filtering includes: storing the immune repertoire sequencing data in a FastQ .gz compressed file, removing adapters from the sequences, trimming the sequences according to base quality values, and removing unknown genes marked with N. This embodiment uses Trimmomatic software for data filtering; other methods that can achieve data filtering can also be used, and are not limited here.

[0050] The preprocessed sequencing data undergoes quality control analysis to generate quality control files. The quality control analysis uses FastQC software to generate the quality control files; in this embodiment, these are HTML webpage files and ZIP compressed files.

[0051] The UMI analysis includes: extracting UMI tag information, mapping, and deleting duplicate data based on UMI tags.

[0052] The UMI tag information serves as a unique molecular identifier, aiming to reduce experimental errors in next-generation sequencing and improve detection accuracy. The principle is that a UMI is inserted during cDNA synthesis, ensuring each cDNA molecule has a unique UMI. During PCR amplification, each sequence also copies the same UMI tag, resulting in a set of sequencing sequences for each sequence. Therefore, sequences from the same mRNA molecule can be retrieved after sequencing. Data analysis of the sequences generated from the same mRNA molecule can distinguish between false positive mutations caused by random errors during PCR amplification and sequencing, and genuine mutations in the sample. The sequencing results are then corrected, thereby reducing the impact of random errors occurring during PCR amplification or sequencing on downstream analysis.

[0053] The UMI analysis includes: retrieving sequences from the same mRNA molecule based on UMI tag information; specifically, extracting and mapping UMI information to map sequencing sequences with the same UMI tag to a reference sequence; and for each set of sequencing data, identifying and correcting duplicate data, insufficient sequence quantity, and inconsistencies in the forward and reverse strand bases. Specifically, correcting duplicate data involves deleting duplicate data based on UMI tags.

[0054] In this embodiment, the UMI-tools tool is used for UMI analysis.

[0055] Step 2: Extract immune repertoire information from the preprocessed sequencing data. The immune repertoire information includes the number of clonal types, clonal frequency, CDR3 sequence and amino acid sequence, V, D, J, C genes, fr1, fr2, fr3, fr4 sequences, and CDR1, CDR2 sequences.

[0056] Step 2 specifically includes: aligning the sequencing sequence to the V, D, J, and C reference sequences of the T cell receptor in the reference database (IMGT database), then using bioinformatics to splice the alignment results to obtain the full-length sequence of the immune receptor, and finally outputting the spliced ​​full sequence analysis results as a readable text file.

[0057] Immunome library information was extracted from the preprocessed and UMI-analyzed sequencing files. Specifically, MiXCR software was used to extract the immunoome library information, including the analysis workflow of multiplex PCR and 5'RACE library preparation methods. The full-length coding sequence of the sequencing data, the VDJ gene used, and the nucleotide and amino acid sequences of the antigen complementarity-determining region were obtained.

[0058] Step 3: Query the CDR3 sequence in the extracted immune repertoire information in the databases obtained in Step 1, VDJdb, and McPAS-TCR database to determine the validated neoantigen information in the tumor neoantigen immune repertoire, and annotate the corresponding immune repertoire information obtained in Step 2.

[0059] Step 3 involves downstream analysis of the immune repertoire. Based on the structural characteristics of the TCR and the specificity of neoantigens, and considering the high diversity of the CDR3 sequence and its direct interaction with the antigen, the antigen-binding specificity of the TCR is directly determined. Therefore, in this embodiment, the CDR3 sequence is used as the marker portion of the TCR sequence. Thus, neoantigens that have been discovered can be retrieved using the CDR3 sequence based on existing databases. The downstream analysis includes: querying the CDR3 sequence in the immune repertoire information in the databases included in this invention, VDJdb, and the McPAS-TCR database, thereby marking the verified neoantigen information in the immune repertoire information and providing reference information for the screening and prediction of neoantigens by conducting probability screening.

[0060] Step 4: Perform visualization analysis on the extracted immune repertoire information. This visualization analysis can be performed on the immune repertoire information obtained in Step 2, or on the labeled and validated immune repertoire information obtained in Step 3. It includes the following dimensions: VDJ gene usage frequency, VDJ gene combination, CDR3 sequence length, and clonotype distribution.

[0061] This embodiment analyzes key information and important sequence fragments of interest in the neoantigen prediction and screening process to obtain a multi-dimensional immune repertoire data matrix. The obtained multi-dimensional immune repertoire data is then visualized to generate multiple charts.

[0062] The extracted immune repertoire information is read as a DataFrame using the pandas library.

[0063] For the VDJ gene usage frequency dimension, the data are grouped and statistically analyzed according to the V, D, and J genes to obtain the corresponding gene usage frequency matrix.

[0064] For the VDJ gene combination dimension, if it is a TRA chain, the data is grouped and statistically analyzed according to the VJ gene combination to obtain the VJ gene combination usage frequency matrix. If it is a TRB chain, the data is grouped and statistically analyzed according to the VJ gene and DJ gene combinations respectively to obtain the two gene combination usage frequency matrices.

[0065] For the CDR3 nucleic acid sequence length dimension, the data is grouped and statistically analyzed according to clone type, and the length of the CDR3 nucleic acid sequence for each clone type is calculated. The clone types are then sorted by frequency to obtain the top 20 clone types' CDR3 nucleic acid sequence lengths and frequencies. The remaining clone types are then grouped according to nucleic acid sequence length to obtain the frequency matrix of the remaining clone types' nucleic acid lengths.

[0066] For the clone frequency distribution dimension, the data was grouped according to frequency, with three groups for frequencies of 1, 2, and 3+, and their frequencies were counted separately. For data with frequencies of 3+, each 20% was grouped into a clone frequency distribution group, for a total of 5 groups. The frequencies of the top 5 clones were then counted, resulting in 3 sets of frequency data.

[0067] Data analysis is performed based on the extracted immune repertoire information to generate visualization charts. In this embodiment, the ECharts library is used to draw corresponding visualization charts based on the obtained frequency matrix, including a histogram of V / D / J gene usage frequency distribution, a VJ gene circular combination chart, a VDJ gene combination Sankey diagram, a CDR3 sequence length stacking chart, and a clonal distribution snail chart.

[0068] The aforementioned methods focus on tumor neoantigens, specifically immune repertoire data for these neoantigens. They involve constructing an immune repertoire database, comparing database information, performing downstream analysis, and visualizing data, which aids in the prediction and screening of tumor neoantigens. Furthermore, by employing multidimensional visualization analysis methods, the obtained immune repertoire information can be analyzed based on specific characteristics, yielding data information from various dimensions and improving the intuitiveness of the analysis.

[0069] Example 2

[0070] Based on the method described in Embodiment 1, such as Figure 2 As shown, this embodiment provides a tumor neoantigen immune repertoire data visualization and analysis system, including:

[0071] The immune repertoire construction module is used to obtain sequencing data of tumor neoantigen immune repertoires;

[0072] An immune repertoire information extraction module is used to extract immune repertoire information based on the sequencing data, wherein the immune repertoire information includes the CDR3 sequence.

[0073] The neoantigen information annotation module is used to query existing databases based on the CDR3 sequence to determine the validated neoantigen information in the tumor neoantigen immune repertoire.

[0074] The visualization analysis module performs visualization analysis on the immune repertoire information.

[0075] The system is web-based, has good interactivity, provides interactive analysis processes and visualization results, and facilitates later system or database maintenance and updates.

[0076] Example 3

[0077] The purpose of this embodiment is to provide an electronic device.

[0078] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in Embodiment 1.

[0079] Example 4

[0080] The purpose of this embodiment is to provide a computer-readable storage medium.

[0081] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in Embodiment 1.

[0082] The steps and methods involved in Examples 2 to 4 above correspond to those in Example 1. For specific implementation details, please refer to the relevant description section of Example 1.

[0083] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0084] The above one or more embodiments disclose a tumor neoantigen immune repertoire data visualization and analysis system and method. The method includes the following steps: screening and collecting published, biologically validated tumor neoantigen-related immune repertoire datasets, where each record includes neoantigen-related information, TCR sequence information, and literature source; acquiring an immune repertoire sequencing data set, performing data preprocessing, UMI analysis, and extracting immune repertoire information from the current sequencing data to obtain the full-length coding sequence, the VDJ gene used, and the nucleotide and amino acid sequences of the antigen complementarity-determining region; querying the CDR3 sequence in the obtained immune repertoire information in the databases included in this invention, VDJdb, and McPAS-TCR database, and labeling the extracted immune repertoire information with validated neoantigen information; performing downstream analysis on the obtained immune repertoire information, analyzing the key information and important sequence fragments of interest in the neoantigen prediction and screening process to obtain a multi-dimensional immune repertoire data matrix; and visualizing the obtained multi-dimensional immune repertoire data information to generate multiple visualization charts.

[0085] The above one or more embodiments include manually collected, published, biologically validated tumor neoantigen-related immune repertoire data. They also implement TCR sequence analysis, extract immune repertoire information, perform database retrieval of CDR3 sequences in the immune repertoire information, annotate with validated neoantigen information, perform downstream analysis, and provide intuitive data visualization results, thereby enhancing the intuitiveness of tumor neoantigen immune repertoire data analysis.

[0086] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for visualizing and analyzing tumor neoantigen immune repertoire data, characterized in that, The method comprises the following steps: obtaining tumor neoantigen immune repertoire sequencing data; After obtaining the tumor neoantigen immune repertoire sequencing data, the sequencing data is further preprocessed: the sequencing data is sequentially subjected to data filtering, quality control analysis and UMI analysis; The UMI analysis comprises: retrieving sequences from the same mRNA molecule based on UMI tag information; For each set of sequencing data, repeated data, insufficient sequence quantity and sequence forward and reverse strand base inconsistency are identified and corrected; extracting immune repertoire information from the sequencing data, wherein the immune repertoire information comprises CDR3 sequences; querying an existing database based on the CDR3 sequences to determine verified neoantigen information in the tumor neoantigen immune repertoire; The neoantigen information comprises specific tumor cell, antigen, epitope, HLA allele information, TCR sequence information and literature source information; performing visual analysis on the immune repertoire information.

2. The method for visualizing and analyzing tumor neoantigen immune repertoire data as described in claim 1, characterized in that, The extraction of the immune repertoire information from the sequencing data comprises aligning the sequencing sequences to V, D, J and C reference sequences of T cell receptors in a reference database, and then splicing the alignment results to obtain the immune repertoire information.

3. The method for visualizing and analyzing tumor neoantigen immune repertoire data as described in claim 1, characterized in that, The visual analysis comprises one or more of VDJ gene usage frequency analysis, VDJ gene combination analysis, CDR3 sequence length analysis and clonotype distribution analysis.

4. A tumor neoantigen immune repertoire data visualization analysis system, characterized in that, The method comprises the following steps: an immune repertoire construction module for obtaining tumor neoantigen immune repertoire sequencing data; After obtaining the tumor neoantigen immune repertoire sequencing data, the sequencing data is further preprocessed: the sequencing data is sequentially subjected to data filtering, quality control analysis and UMI analysis; The UMI analysis comprises: retrieving sequences from the same mRNA molecule based on UMI tag information; For each set of sequencing data, repeated data, insufficient sequence quantity and sequence forward and reverse strand base inconsistency are identified and corrected; an immune repertoire information extraction module for extracting immune repertoire information from the sequencing data, wherein the immune repertoire information comprises CDR3 sequences; a neoantigen information annotation module for querying an existing database based on the CDR3 sequences to determine verified neoantigen information in the tumor neoantigen immune repertoire; The neoantigen information comprises specific tumor cell, antigen, epitope, HLA allele information, TCR sequence information and literature source information; a visual analysis module for performing visual analysis on the immune repertoire information. 5.An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the tumor neoantigen immune repertoire data visualization analysis method according to any one of claims 1-3 when executing the program. 6.A computer readable storage medium having a computer program stored thereon, wherein the program is executable on a processor to implement the tumor neoantigen immune repertoire data visualization analysis method according to any one of claims 1-3.

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