MinerVa: A high performance bioinformatic algorithm for the detection of minimal residual disease in solid tumors / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 313-319, 2023.
Article
in Zh
| WPRIM
| ID: wpr-981544
Responsible library:
WPRO
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.
Key words
Full text:
1
Index:
WPRIM
Main subject:
Biomarkers, Tumor
/
Carcinoma, Non-Small-Cell Lung
/
Neoplasm, Residual
/
Computational Biology
/
Lung Neoplasms
Limits:
Humans
Language:
Zh
Journal:
Journal of Biomedical Engineering
Year:
2023
Type:
Article