A computational framework for sensitive tumor detection and accurate subtyping using shallow cell-free DNA methylome sequencing
Year: 2026
Type: article
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.
Source: Genome Medicine
Authors: Marta Paoli, Francesca Galardi, Agostina Nardone, Chiara Biagioni, Dario Romagnoli, Samantha Di Donato, Gian Marco Franceschini, Luca Livraghi, Marta Pestrin, Giuseppina Sanna, Emanuela Risi, Ilenia Migliaccio, Erica Moretti, Luca Malorni, Laura Biganzoli, Francesca Demichelis, Matteo Benelli
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,290
Awards: G3818078400, G8128652777, G997879478
OpenAlex ID: W7127364586