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.
Artículo
en Chino
| WPRIM
| ID: wpr-981544
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.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Biomarcadores de Tumor
/
Carcinoma de Pulmón de Células no Pequeñas
/
Neoplasia Residual
/
Biología Computacional
/
Neoplasias Pulmonares
Límite:
Humanos
Idioma:
Chino
Revista:
Journal of Biomedical Engineering
Año:
2023
Tipo del documento:
Artículo
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