Live Brain Cancer Diagnostics from Sparse Epignomic Data

Event Details
Date: 16.01.2024, 17:30 o'clock - 18:30 o'clock 
Location: N2045, Universitätsstraße 2, 86159 Augsburg
Organizer(s): Lehrstuhl für Biomedizinische Informatik, Data Mining und Data Analytics
Topics: Informatik, Gesundheit und Medizin
Series of events: Medical Information Sciences
Event Type: Vortragsreihe
Speaker(s): Dr. Helene Kretzmer
BIOINF ASFDASDF DSFASF ASDF ASDF © University of Augsburg

In diesem Wintersemester wird die im letzten Jahr erfolgreich gestartete Vortragsreihe Medical Information Sciences fortgesetzt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben jeden Dienstag ab 17:30 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes Medical Information Sciences.


Epigenetic mechanisms play a crucial role in establishing and preserving cellular states and function throughout an organism’s life. DNA methylation is an essential part of this multilayered regulation and displays a highly conserved, characteristic bimodal distribution across most somatic cell types. For yet unknown reasons, the distribution of DNA methylation is fundamentally different tumors, an intermediate gain of DNA methylation at certain CpG islands and a partial loss of methylation across gene-poor regions. These two features make DNA methylation an ideal readout for tumor-type prediction from sequencing data.

Specifically in the field of neuropathology, DNA methylation-based tumor type classification has recently become part of the WHO. However, even though the field aimed to achieve an intraoperative differential diagnosis for decades, accomplishing this within a clinically relevant timeframe has remained elusive.

Recent advances in third-generation sequencing technologies have brought this goal within reach. To allow for intraoperative CNS tumor type classification within less than one hour, we developed MethyLYZR, a Naïve Bayesian framework enabling live classification of cancer epigenomes using fully tractable single-CpG resolution modeling while avoiding the need for feature selection or ad hoc model training. MethyLYZR can be run in parallel to an ongoing Nanopore experiment with negligible computational cost and provides clinically relevant and accurate cancer classification results within 15 minutes of sequencing (94% accuracy).

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