Fragmentomic-based algorithm to computationally predict tumor-somatic, germline, and clonal hematopoiesis variant origin in liquid biopsy.

ABSTRACT:

Purpose

Genomic profiling of tumors by liquid biopsy (LBx) is a pragmatic alternative to profiling tissue. Despite recent methodologic advances, clonal hematopoiesis (CH) variants arising from hematopoietic stem cells may confound LBx results. Distinguishing the origin of variants detected by LBx will greatly enhance treatment decision-making for patients with cancer.

Experimental design

We sequenced DNA isolated from paired plasma and white blood cells (WBC) at equal depth to train (n = 1977) and validate (n = 658) Variant Origin Prediction (VOP), a machine learning algorithm that leverages fragmentomics to generate probabilities that a short variant (SV) detected by LBx is tumor-somatic, germline, or CH in origin. The algorithm's classifications were validated for accuracy

SUBMITTER: Brown DW 

PROVIDER: S-EPMC12284786 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature