ArticleGenome medicine2026
A computational framework for sensitive tumor detection and accurate subtyping using shallow cell-free DNA methylome sequencing.
Article in Genome medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Early detection of multiple cancers: the era of methylation-based liquid biopsy.Frontiers in oncology · 2026Review
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Authors and funding
17 authors.
Funding
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.
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Registered trials
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