A large-scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression-free survival.

ABSTRACT: Monitoring levels of circulating tumour-derived DNA (ctDNA) provides both a noninvasive snapshot of tumour burden and also potentially clonal evolution. Here, we describe how applying a novel statistical model to serial ctDNA measurements from shallow whole genome sequencing (sWGS) in metastatic breast cancer patients produces a rapid and inexpensive predictive assessment of treatment response and progression-free survival. A cohort of 149 patients had DNA extracted from serial plasma samples (total 1013, mean samples per patient = 6.80). Plasma DNA was assessed using sWGS and the tumour fraction in total cell-free DNA estimated using ichorCNA. This approach was compared with ctDNA targeted sequencing and serial CA15-3 measurements. We identified a transition point of 7% estimated tumour f

SUBMITTER: Beddowes EJ 

PROVIDER: S-EPMC12688166 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature