Predicting somatic mutation origins in cell-free DNA by semi-supervised GAN models.

ABSTRACT:

Motivation

Distinguishing between pathogenic cancer-associated mutations and other somatic variants present in cell-free DNA (cfDNA) is one of the challenges in the field of liquid biopsy. This distinction is critical, since the misclassification of mutations stemming from clonal hematopoiesis (CH) as tumor-derived and vice versa could result in inaccurate diagnoses and inappropriate therapeutic interventions for patients.

Results

We addressed this by developing a specialized machine learning technique to differentiate tumor- or CH-related mutations in cfDNA. We established a comprehensive in-house reference catalog, comprising approximately 25,000 single nucleotide variants (SNVs), each linked to either tumor or CH origin. This reference serves as a foundation for training

SUBMITTER: Palizban F 

PROVIDER: S-EPMC11530920 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature