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High-throughput prediction method for neoantigens of pan-cancer tumor and application thereof

A prediction method and tumor technology, applied in the fields of bioinformatics and tumor immunotherapy, can solve problems such as time-consuming and labor costs, and difficult problems of tumor neoantigen screening methods, so as to reduce workload, save prediction time, and reduce redundancy The effect of steps

Active Publication Date: 2020-01-17
中生康元生物科技(北京)有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] It can be seen that the immunotherapy based on tumor neoantigens has broad prospects, but the screening method of tumor neoantigens based on the whole genome high-throughput method has always been a difficult problem
Tumor neoantigen screening often consumes a lot of time and labor costs

Method used

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  • High-throughput prediction method for neoantigens of pan-cancer tumor and application thereof
  • High-throughput prediction method for neoantigens of pan-cancer tumor and application thereof
  • High-throughput prediction method for neoantigens of pan-cancer tumor and application thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] Example 1 Prediction of tumor neoantigens

[0058] The flow chart of the present invention predicting tumor neoantigens is shown in figure 1 shown. The detailed process is as follows:

[0059] 1. Material preparation

[0060] The tumor tissue of the patient with tumor number AO001 (patient with hepatocellular carcinoma) was obtained, and WES and RNA-seq sequencing of the tumor tissue were completed through the illumina high-throughput sequencing platform.

[0061] 2. Data quality control

[0062] The original fastq data of DNA and RNA sequencing were quality controlled by FastQC software to obtain the data AO001.clean.fq.gz after quality control filtering.

[0063] 3. Data comparison

[0064] The DNA data after quality control was compared with the reference genome using BWA software to obtain bam files of tumor and normal tissue DNA data, and the RNA after quality control was compared with the reference genome using hisat2 software to obtain bam files of tumor RNA...

Embodiment 2

[0087] Example 2 Candidate tumor neoantigen verification

[0088] According to the scoring order in Table 1 in Example 1, some tumor neoantigens were selected to undergo tetramer verification experiments to test the accuracy and reliability of the prediction method of the present invention.

[0089] Steps: experiment according to QuickSwitch TM The operating instructions of the quant tetramer kit were carried out.

[0090] Results: 5 positive peptides were obtained, and the 5 positive peptides were: SLK, ETAA1, DOCK7, CYP2C8, TPR, Figure 4-8 It represents the results of flow cytometry detection of the above five positive peptide tetramer displacement experiments. Figure 2-3 Respectively are positive control polypeptide, negative control polypeptide tetramer displacement experiment flow cytometer detection results.

[0091] It can be seen that the positive polypeptides obtained in the verification are tumor neoantigens with high scores evaluated by the prediction method of ...

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Abstract

The invention discloses a high-throughput prediction method for neoantigens of pan-cancer tumor and application thereof. According to the prediction method disclosed by the invention, mutation and MHC(Major Histocompatibility Complex) detection are carried out on the basis of a next-generation sequencing original data file, and candidate tumor neoantigens are scored from multiple dimensions, so that the false positive of neoantigen screening can be reduced, and the neoantigens with high credibility can be screened out through scoring sorting. The method disclosed by the invention can be suitable for multiple cancer species, can predict the tumor neoantigen without distinguishing the cancer species, and lays a foundation for immunotherapy based on the tumor neoantigen.

Description

technical field [0001] The invention belongs to the fields of bioinformatics and tumor immunotherapy, and relates to one-stop prediction and identification of tumor neoantigens based on a high-throughput sequencing platform and nucleic acid sequencing data. Background technique [0002] Tumor-specific antigens (TSAs) refer to antigens unique to tumor cells, also known as neoantigens. Tumor-specific antigens were proposed in the first half of the last century. Later, with the development of molecular biology and the in-depth understanding of the molecular functions of the major histocompatibility complex (MHC), Boon et al. first discovered that in tumors , the complexes of specific peptides produced by tumors and MHC molecules can be recognized by T cells such as CD8+ or CD4+. Subsequent studies have recognized that these antigens that can be recognized by T cells come from the genomic variation of tumors expressed as tumor-specific peptides (neo-epitopes), which are defined...

Claims

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Application Information

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IPC IPC(8): G16B15/30G16B20/20G16B30/10
CPCG16B15/30G16B20/20G16B30/10
Inventor 程旭东管旭东
Owner 中生康元生物科技(北京)有限公司
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