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A method and prediction model for detecting tumor mutational burden

A mutation load and tumor technology, applied in the field of biomedicine, can solve the problems of cumbersome operation, difficulty in conventional clinical methods, high cost of WES, etc., and achieve the effects of simplified analysis steps, high specificity, and cost saving

Active Publication Date: 2021-01-05
求臻医学科技(浙江)有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the high cost and cumbersome operation of WES, it is difficult to use it as a routine clinical method for detecting TMB.

Method used

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  • A method and prediction model for detecting tumor mutational burden
  • A method and prediction model for detecting tumor mutational burden
  • A method and prediction model for detecting tumor mutational burden

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] In an embodiment, the samples to be tested are paraffin-embedded tissue samples known to have a high tumor mutation burden and corresponding blood control samples.

[0053] The test steps are as follows:

[0054] 1. Sample extraction and fragmentation: Genomic DNA was extracted from blood samples and paraffin-embedded tissue samples using a nucleic acid extraction kit (Magen or Promega), and quantified using Qubit; the sample DNA was fragmented using a Covaris M220 instrument to make The size of the DNA fragment is between 100 and 500 bp, and the QIAxcel (QIAGEN) instrument is used to detect whether the fragment size meets the requirements.

[0055] 2. Double UMI library construction:

[0056] 1) End repair and "A" addition: The fragmented sample DNA is subjected to end repair and "A" addition. The reaction system is shown in Table 1 below. Vortex and mix well, then centrifuge, and place on a thermal cycler. ℃ for 30 minutes, then 65 ℃ for 30 minutes.

[0057] Table ...

Embodiment 2

[0094] Whole exome sequencing and ChosenOne599 were performed on 1453 lung cancer samples Ⓡ Targeted sequencing, analyzed according to the above process, and counted ChosenOne599 Ⓡ The number of SNV synonymous mutations, the number of SNV non-synonymous mutations, the number of frameshift insertion mutations, the number of non-frameshift insertion mutations, the number of frameshift deletion mutations, and the number of non-frameshift deletion mutations in the The TMB value of the panel is used as the gold standard, and the TMB of the panel is calculated according to the multiple linear model.

[0095] Obtain the mutation data of the sample to be tested through the detection process, and calculate ChosenOne599 Ⓡ The number of SNV synonymous mutations Nsys, the number of SNV non-synonymous mutations Nnon, the number of frameshift insertion mutations Nis, the number of non-frameshift insertion mutations Nns, the number of frameshift deletion mutations Nds and the number of non-...

Embodiment 3

[0098] This embodiment carries out ChosenOne599 to 6 routine TMB standard items Ⓡ For targeted region sequencing, predict the results according to the TMB model, and compare the results with the gold standard as shown in Table 9 and Figure 6 shown.

[0099] Table 9 Results prediction and comparison results with gold standard

[0100]

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Abstract

The present invention relates to the field of biomedical technology, in particular to a method for detecting tumor mutation load and a prediction model; it includes the following steps: S1: extracting DNA from tumor tissue and blood samples to be tested; S2: constructing target regions for tumor-related genes Targeted capture sequencing library; S3: Using a high-throughput sequencing platform to obtain the original off-machine data; S4: Obtain the tumor mutation load of the sample through the detection process. The purpose of the present invention is to provide a method for detecting tumor mutation load and a prediction model, through which the detection and calculation of gene mutations can achieve the same results as the TMB detection of whole exome sequencing, so as to improve Accuracy of Tumor Mutational Burden Estimation.

Description

technical field [0001] The invention relates to the field of biomedical technology, in particular to a method for detecting tumor mutation load and a prediction model. Background technique [0002] In recent years, immunotherapy has received extensive attention from the medical community, and it has achieved remarkable clinical efficacy in a variety of tumor treatments. Approved by the Drug Administration (FDA) and recommended by the National Comprehensive Cancer Network (NCCN) guidelines, it has become the first-line treatment option for advanced non-small cell lung cancer (NSCLC). Tumor immunotherapy mainly achieves the efficacy of identifying, controlling and eliminating tumors by activating the human immune system, and the most studied ones are monoclonal antibody immune checkpoint inhibitors such as cytotoxic T lymphocyte-associated protein 4 (CTLA-4 ) monoclonal antibody, programmed death inhibitor protein and its ligand (PD-1 / PD-L1) monoclonal antibody, so far, seven...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): C12Q1/6886C12Q1/6869G16B30/00G16B20/00G16H50/50
CPCC12Q1/6869C12Q1/6886C12Q2600/156G16H50/50G16B20/00G16B30/00C12Q2535/122
Inventor 段小红辛琳杨春燕张腾龙柳毅王冬冬王东亮周启明
Owner 求臻医学科技(浙江)有限公司
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