M-PACT leverages cell-free DNA methylomes to achieve robust classification of pediatric brain tumors

Cerebrospinal fluid (CSF) liquid biopsies serve as a rich source of tumor-derived cell-free DNA (cfDNA) for evaluating persons with central nervous system (CNS) tumors. However, challenges stemming from trace cfDNA yields and low mutational burden have hindered sensitivity, whereas first-generation clinical assays have relied on genetic alterations as biomarkers. Leveraging the diagnostic utility of DNA methylation classification in CNS tumors, we developed M-PACT (methylation-based predictive algorithm for CNS tumors), a robust deep neural network that accurately classifies tumors from subnanogram-input cfDNA methylomes. Across embryonal CNS tumor benchmarking (n = 79) and validation (n = 58) cohorts, M-PACT achieved 92% and 88% accuracy, respectively. We further showcase M-PACT utility in nonembryonal CNS tumors, balanced tumor genomes and nonmalignant CSF. Beyond classification, this workflow enables methylation-based cellular deconvolution and sensitive copy-number variation detection. Altogether, we provide a blueprint for CNS tumor classification from low-input cfDNA methylomes, motivating prospective validation for future clinical implementation.

Authors

Institutions

Publication Details

Journal
Nature Cancer
Published
2026-02-17
DOI
https://doi.org/10.1038/s43018-026-01115-4
Citations
1
Primary Topic
Glioma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
36.93
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

M-PACT leverages cell-free DNA methylomes to achieve robust classification of pediatric brain tumors

Kristiina Nordfors, Martin G. McCabe, Taha Soliman, Sibylle Madlener et al.
1 citations
Nature Cancer
Glioma Diagnosis and Treatment
36.93
article

M-PACT leverages cell-free DNA methylomes to achieve robust classification of pediatric brain tumors

Kristiina Nordfors, Martin G. McCabe, Taha Soliman, Sibylle Madlener, Brent A. Orr, Patrick R. Blackburn, Johannes Gojo, Natalia Stepien, Tom T. Fischer, H. Haapasalo, Giles W Robinson, Paul Northcott, Daniel Senfter, Justina Dargvainiene, Nathalie Schwarz, Judith M. de Bont, Kendra K. Maaß, Anna Kostecka, Stefanie Volz, Maria T. Schmook, Frank Leypoldt, Sandeep K Dhanda, Stefan M. Pfister, Amar Gajjar, Hong Lin, Katie Han, Esther Hulleman, Kyle S. Smith, Christine Haberler, Joonas Haapasalo
article en
1 citations

Abstract

Cerebrospinal fluid (CSF) liquid biopsies serve as a rich source of tumor-derived cell-free DNA (cfDNA) for evaluating persons with central nervous system (CNS) tumors. However, challenges stemming from trace cfDNA yields and low mutational burden have hindered sensitivity, whereas first-generation clinical assays have relied on genetic alterations as biomarkers. Leveraging the diagnostic utility of DNA methylation classification in CNS tumors, we developed M-PACT (methylation-based predictive algorithm for CNS tumors), a robust deep neural network that accurately classifies tumors from subnanogram-input cfDNA methylomes. Across embryonal CNS tumor benchmarking (n = 79) and validation (n = 58) cohorts, M-PACT achieved 92% and 88% accuracy, respectively. We further showcase M-PACT utility in nonembryonal CNS tumors, balanced tumor genomes and nonmalignant CSF. Beyond classification, this workflow enables methylation-based cellular deconvolution and sensitive copy-number variation detection. Altogether, we provide a blueprint for CNS tumor classification from low-input cfDNA methylomes, motivating prospective validation for future clinical implementation.

Nature Cancer
St. Jude Children's Research Hospital (US), Tampere University (FI), German Cancer Research Center (DE), Heidelberg University (DE), University Hospital Heidelberg (DE), Comprehensive Cancer Center Vienna (AT), National Center for Tumor Diseases (DE), University Hospital Schleswig-Holstein (DE), Hopp Children's Cancer Center Heidelberg (DE), Tampere University Hospital (FI), Princess Máxima Center (NL), Tampere University (FI), Fimlab (Finland) (FI), Amsterdam University Medical Centers (NL), Medical University of Vienna (AT), University of Lübeck (DE)
Partnerships for the goals
Openalex Percentile: Top 1%
Glioma Diagnosis and Treatment
36.93
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.