Sensitive tumor detection, accurate quantification, and cancer subtype classification using low-pass whole methylome sequencing of plasma DNA

Abstract

The analysis of circulating tumor DNA (ctDNA) is increasingly used for monitoring disease in patients with metastatic cancer. Here, we introduce a robust and reproducible strategy combining low-pass whole methylome sequencing of plasma DNA with METER, a novel computational tool. Engaging prediction models trained on independent available datasets, METER enables the detection and quantification of tumor content (TC) and performs molecular cancer subtyping. Applied to plasma methylomes from early metastatic breast cancer patients, our method demonstrated reliable quantification, sensitive tumor detection below 3% of TC, and the ability to perform accurate Estrogen Receptor (ER) subtyping. METER provided clinically relevant predictions, underscored by associations with relevant prognostic factors, robust correlation with matched circulating tumor cells, and highly correlated with patients outcomes in challenging scenarios as TC<3%. Our strategy provides comprehensive and sensitive analysis of plasma samples, serving as a valuable yet cost-effective precision oncology tool.