SNUH methylation classifier for CNS tumors.

Abstract

Background: Methylation profiling of central nervous system (CNS) tumors, pioneered by the German Cancer Research Center, has significantly improved diagnostic accuracy. This study aimed to further enhance the performance of methylation classifiers by leveraging publicly available data and innovative machine-learning techniques.

Results: Seoul National University Hospital Methylation Classifier (SNUH-MC) addressed data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm and incorporated OpenMax within a Multi-Layer Perceptron to prevent labeling errors in low-confidence diagnoses. Compared to two published CNS tumor methylation classification models (DKFZ-MC: Deutsches Krebsforschungszentrum Methylation Classifier v11b4: RandomForest, 767-MC: Multi-Layer Perceptron), our SNUH-MC showed improved performance in F1-score. For 'Filtered Test Data Set 1,' the SNUH-MC achieved higher F1-micro (0.932) and F1-macro (0.919) scores compared to DKFZ-MC v11b4 (F1-micro: 0.907, F1-macro: 0.627). We evaluated the performance of three classifiers; SNUH-MC, DKFZ-MC v11b4, and DKFZ-MC v12.5, using specific criteria. We set established 'Decisions' categories based on histopathology, clinical information, and next-generation sequencing to assess the classification results. When applied to 193 unknown SNUH methylation data samples, SNUH-MC notably improved diagnosis compared to DKFZ-MC v11b4. Specifically, 17 cases were reclassified as 'Match' and 34 cases as 'Likely Match' when transitioning from DKFZ-MC v11b4 to SNUH-MC. Additionally, SNUH-MC demonstrated similar results to DKFZ-MC v12.5 for 23 cases that were unclassified by v11b4.

Conclusions: This study presents SNUH-MC, an innovative methylation-based classification tool that significantly advances the field of neuropathology and bioinformatics. Our classifier incorporates cutting-edge techniques such as the SMOTE and OpenMax resulting in improved diagnostic accuracy and robustness, particularly when dealing with unknown or noisy data.