Evidence map›Paper›PMID 42236545›Full record

ArticleNPJ precision oncology2026

Detection and classification of lymphoma from cell-free methylome data.

Victoria B Shelton, Mohamed Alias, Ting Liu, Davidson Zhao, Pamela Alamilla, Althaf Singhawansa, Michael Hong, Yong Zeng, Vanessa Murad, Ibrahim Alrekhais and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Victoria B SheltonPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Mohamed AliasPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Ting LiuPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Davidson ZhaoPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Pamela AlamillaPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Althaf SinghawansaPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Michael HongPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Yong ZengPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Vanessa MuradPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Ibrahim AlrekhaisPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Ur MetserPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
David HodgsonPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Tomohiro AokiPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Anca PricaPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
John KuruvillaPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Michael CrumpPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Bernard LamOntario Institute for Cancer Research, Toronto, ON, Canada.
Michael M HoffmanPrincess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Scott V Bratman *Princess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada.
Robert Kridel *Princess Margaret Cancer Centre-University Health Network, Toronto, ON, Canada. robert.kridel@uhn.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diagnosing lymphoma traditionally relies on invasive tissue biopsies, which can yield insufficient material for histopathological evaluation and carry a risk of complications. Minimally invasive assessment of cell-free DNA (cfDNA) in plasma offers a promising alternative for lymphoma detection that could aid the rapid evaluation of malignant vs. benign lymphadenopathy. Here, we examine the methylome of plasma samples from 165 lymphoma patients and 47 controls using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). Differential methylation analysis of a discovery cohort (142 out of 212 samples) revealed 13,897 hypermethylated genomic regions in lymphoma cases, which were subsequently used for classification using regularized binomial generalized linear models. In a validation cohort (70 samples), we identified lymphomas with an accuracy of 0.89, positive predictive value (PPV) of 0.90 and negative predictive value (NPV) of 0.87. cfDNA methylation scores were significantly associated with orthogonal measures of cfDNA tumor burden, stage, and clinical outcomes. Our results highlight the feasibility of cfDNA methylation profiling as a sensitive and minimally invasive method for detecting lymphoma.

Identifiers

PMID42236545
PMCPMC13542107

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