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

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.

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Publication Details

Journal
Genome Medicine
Published
2026-02-03
DOI
https://doi.org/10.1186/s13073-026-01603-3
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
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article

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

L. Biganzoli, Marta Pestrin, Matteo Benelli, Gian Marco Franceschini et al.
Genome Medicine
Cancer Genomics and Diagnostics
article

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

L. Biganzoli, Marta Pestrin, Matteo Benelli, Gian Marco Franceschini, Giuseppina Sanna, Marta Paoli, Agostina Nardone, Francesca Galardi, Chiara Biagioni, Erica Moretti, Luca Malorni, Ilenia Migliaccio, Samantha Di Donato, Luca Livraghi, Francesca Demichelis, Emanuela Risi, Dario Romagnoli
article en

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.

Genome MedicineVol. 18(1)
University of Trento (IT), Ospedale SS. Annunziata (IT), Azienda Usl Toscana Centro (IT), Hospital of Prato (IT), University of Florence (IT)
Good health and well-being
Openalex Percentile: Top 69%
Cancer Genomics and Diagnostics
DOIOpen Access PDFOpenAlex
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A computational framework for sensitive tumor detection and accurate subtyping using shallow cell-free DNA methylome sequencing — L. Biganzoli, Marta Pestrin, et al. · Genome Medicine (2026) | TGRS Research Map | TGRS