MinerVa: A high performance bioinformatic algorithm for the detection of minimal residual disease in solid tumors.
10.7507/1001-5515.202303039
- Author:
Piao YANG
1
;
Yaxi ZHANG
1
;
Liang XIA
2
;
Jiandong MEI
2
;
Rui FAN
1
;
Yu HUANG
1
;
Lunxu LIU
2
;
Weizhi CHEN
1
Author Information
1. Genecast Biotechnology Co., Ltd, Wuxi, Jiangsu 214000, P. R. China.
2. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China.
- Publication Type:Journal Article
- Keywords:
Circulating tumor DNA;
Minimal residual disease;
Multi-variant joint confidence analysis;
Single-base resolution;
Technical noise baseline
- MeSH:
Humans;
Carcinoma, Non-Small-Cell Lung/genetics*;
Lung Neoplasms/genetics*;
Neoplasm, Residual/pathology*;
Biomarkers, Tumor/genetics*;
Computational Biology
- From:
Journal of Biomedical Engineering
2023;40(2):313-319
- CountryChina
- Language:Chinese
-
Abstract:
How to improve the performance of circulating tumor DNA (ctDNA) signal acquisition and the accuracy to authenticate ultra low-frequency mutation are major challenges of minimal residual disease (MRD) detection in solid tumors. In this study, we developed a new MRD bioinformatics algorithm, namely multi-variant joint confidence analysis (MinerVa), and tested this algorithm both in contrived ctDNA standards and plasma DNA samples of patients with early non-small cell lung cancer (NSCLC). Our results showed that the specificity of multi-variant tracking of MinerVa algorithm ranged from 99.62% to 99.70%, and when tracking 30 variants, variant signals could be detected as low as 6.3 × 10 -5 variant abundance. Furthermore, in a cohort of 27 NSCLC patients, the specificity of ctDNA-MRD for recurrence monitoring was 100%, and the sensitivity was 78.6%. These findings indicate that the MinerVa algorithm can efficiently capture ctDNA signals in blood samples and exhibit high accuracy in MRD detection.
