![MethyLYZR enables tumor class prediction on sparse data without model retraining
a, Simplified schematic of the timeline of a brain surgery procedure. The stages encompass the following: (1) induction, involving anesthesia and patient positioning with neuronavigation adjustments (approximately 45–60 min); (2) incision and progression to the tumor (approximately 30 min); (3) tumor resection (approximately 60 min) and (4) retraction and completion of suturing (approximately 30 min). Notably, the 60-min tumor resection stage is the critical time window for obtaining a molecular diagnosis. However, the turnaround times of established molecular diagnostics extend beyond the length of the surgical procedure. b, Illustration of the training and prediction process of the naive Bayes algorithm. Multiple tumor classes (m classes) with several samples contribute CpG methylation ratios (p features) for algorithm training. The training involves generating m centroids (μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}) based on the provided samples (S1,…,Snm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{1},\ldots ,{S}_{{n}_{m}}$$\end{document}), describing the average methylation probability of each of the n CpGs (features) per tumor class. Additionally, weights (w\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$w$$\end{document}) are calculated per CpG and class, reflecting the predictive power of a CpG for a specific tumor class. For tumor class prediction in a given sample, sparse, binary methylation values from individual molecules—for example, obtained through Nanopore sequencing—serve as input for the pre-trained Bernoulli naive Bayes model. The output comprises a ranked list of posterior probabilities of all tumor classes in the model. c, Benchmarking analysis of MethyLYZR training time on published CNS 450k methylation arrays across 91 tumor classes with a total of 2,801 samples⁵. The training was executed on a single core using a Dell PowerEdge R7525 server (3 GHz AMD 64-Core Processor, 256 CPUs, 1,031.3 GB DDR4 RAM, Linux distribution) and an Apple iMac Pro (3 GHz 10-Core Intel Xeon W, 64 GB 2,666 MHz DDR4 RAM, 1 TB APFS SSD, Radeon Pro Vega 56 GPU with 8 GB VRAM, macOS 13.2.1). Notably, centroids and weight training were achieved on the server in under 20 min and on the iMac Pro in under 40 min.](https://www.researchgate.net/publication/389439073/figure/fig1/AS:11431281317718694@1742467842363/MethyLYZR-enables-tumor-class-prediction-on-sparse-data-without-model-retraining-a_Q320.jpg)
February 2025
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161 Reads
Nature Medicine
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.
![The eligible participants (ITT (intention-to-treat) population) in the BDNF trial, recruited from the five university hospitals, will undergo testing for their BDNF CpG -87 methylation status. Baseline characteristics, including measures such as the Hamilton Depression Rating Scale (HDRS-24) and Montgomery–Åsberg Depression Rating Scale (MADRS) score, will also be collected. Subsequently, participants will be randomly assigned to one of two treatment arms: a the Marker group, where test results will be communicated to both participants and treating physicians, and the marker status will be considered in treatment decisions, or b TAU (treatment-as-usual) group, where no information about the test result will be provided to participants or physicians, and treatment decisions will be based on the national guidelines [1]](https://www.researchgate.net/publication/379694541/figure/fig1/AS:11431281235261206@1712715724362/The-eligible-participants-ITT-intention-to-treat-population-in-the-BDNF-trial_Q320.jpg)
