A computational framework for sensitive tumor detection and accurate subtyping using shallow cell-free DNA methylome sequencing

Key Points

METER shows a stronger association with clinical outcomes compared to existing ctDNA methods and circulating tumor cell counts.
Low tumor content below 3% can be sensitively detected using tumor-type specific DNA methylation patterns.
Analysis of longitudinal samples from metastatic breast cancer patients highlights the efficacy of this approach.
The framework integrates tumor content estimation and subtyping, enhancing precision in cancer analyses.

Abstract

Plasma circulating tumor DNA (ctDNA) enables non-invasive monitoring of metastatic cancer. However, the detection of low tumor content (TC) via tumor tissue-agnostic approaches remains challenging. We introduce METER, a computational strategy exploiting tumor-type specific DNA methylation patterns for sensitive ctDNA detection, accurate quantification, and subtyping from plasma low-pass (0.5-1x) whole-methylome sequencing. In longitudinal samples from metastatic breast cancer patients, METER demonstrated a stronger association with clinical outcomes than both state-of-the-art ctDNA methods and matched circulating tumor cell (CTC) counts, even at TC below 3%. METER (https://github.com/caos-lab-unifi/METER) integrates TC estimation and subtyping in a single framework, enabling sensitive and accurate analyses for precision oncology.

A computational framework for sensitive tumor detection and accurate subtyping using shallow cell-free DNA methylome sequencing

Key Points

Abstract

Cite This Study