[MinerVa: A high performance bioinformatic algorithm for the detection of minimal residual disease in solid tumors].
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
; 40(2): 313-319, 2023 Apr 25.
Article
en Zh
| MEDLINE
| ID: mdl-37139763
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Carcinoma de Pulmón de Células no Pequeñas
/
Neoplasias Pulmonares
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
Zh
Revista:
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
Asunto de la revista:
ENGENHARIA BIOMEDICA
Año:
2023
Tipo del documento:
Article