Rapid brain tumor classification from sparse epigenomic data

Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer's maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.

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Publication Details

Journal
Nature Medicine
Published
2025-02-28
DOI
https://doi.org/10.1038/s41591-024-03435-3
Citations
22
Primary Topic
RNA modifications and cancer
Type
article
Field-Weighted Citation Impact
16.93

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article

Rapid brain tumor classification from sparse epigenomic data

Gaojianyong Wang, Skarphéðinn Halldórsson, Henning Leske, Carolin Kubelt et al.
22 citations
Nature Medicine
RNA modifications and cancer
16.93
article

Rapid brain tumor classification from sparse epigenomic data

Gaojianyong Wang, Skarphéðinn Halldórsson, Henning Leske, Carolin Kubelt, Claudia Quedenau, Bernhard Schuldt, Michael Synowitz, Ole Ammerpohl, Einar Osland Vik-Mo, Amy Lum, Derek A. Wong, Ann‐Kristin Afflerbach, Mara Steiger, Martin Proescholdt, Stephen Yip, Romualdas Smičius, Christian Rohrandt, Alena van Bömmel, Luna Djirackor, Nils Ole Schmidt, Björn Brändl, Ulrich Schüller, Gelareh Mohammed Zadeh, Helene Kretzmer, Markus J. Riemenschneider, Zhihan Zhu, Frank Müller, Maximilian Evers, Svetlana Magadeeva
article en
22 citations

Abstract

Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer's maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.

Nature MedicineVol. 31(3)
Oslo University Hospital (NO), University Health Network (CA), Children's Hospital of Philadelphia (US), University of British Columbia (CA), Hasso Plattner Institute (DE), Universität Hamburg (DE), University of Potsdam (DE), University of Oslo (NO), University of Toronto (CA), Universität Ulm (DE), Max Delbrück Center (DE), University of Münster (DE), Princess Margaret Cancer Centre (CA), University Hospital Regensburg (DE), Christian-Albrechts-Universität zu Kiel (DE), Asklepios Klinik Altona (DE), University Medical Center Hamburg-Eppendorf (DE), Kinderkrebs-Zentrum Hamburg (DE), University Hospital Schleswig-Holstein (DE), Brain (Germany) (DE), Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) (DE), Max Planck Institute for Molecular Genetics (DE), Freie Universität Berlin (DE), University of Lübeck (DE)
Bundesministerium für Bildung und Forschung
Good health and well-being
Openalex Percentile: Top 1%
RNA modifications and cancer
16.93
DOIOpen Access PDFOpenAlex
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