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<p>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 (<a href="https://github.com/caos-lab-unifi/METER">https://github.com/caos-lab-unifi/METER</a>) integrates TC estimation and subtyping in a single framework, enabling sensitive and accurate analyses for precision oncology.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月3日

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  • 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

摘要

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.