DNA-Methyaltion-Based Deep Learning for Precision Classification of Central Nervous System Tumors: A Comparative Study

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Abstract As part of the advancement in therapeutic decision-making for brain tumor patients at St. Jude Children’s Research Hospital (SJCRH), we develop and compare the performance of three classification models: a deep learning neural network (NN), an exact bootstrap k-nearest neighbor (kNN), and a random forest classifier (RF) model to predict the 82 molecularly distinct central nervous system (CNS) tumor classes based on DNA-methylation profiles of 2,801 patients. We validate their classification accuracy, precision, and recall with 2,054 samples from two independent cohorts. Although all models perform robustly to missing data, the NN model achieves the highest classification accuracy and maintains better balance between precision and recall than kNN and RF. Average precision and recall of NN reduce to that of RF and kNN only when tumor purity was less than 50%. In conclusion, DNA-methylation based deep learning approach provides the most potential advancement toward precision medicine for brain tumors.