Comparison of DNA Methylation Based Classification Models for Precision Diagnostics of Central Nervous System Tumors

As part of the advancement in therapeutic decision-making for brain tumor patients at St. Jude Children’s Research Hospital (SJCRH), we developed three robust classifiers, a deep learning neural network (NN), k-nearest neighbor (kNN), and random forest (RF), trained on a reference series DNA-methylation profiles to classify central nervous system (CNS) tumor types. The models’ performance was rigorously validated against 2,054 samples from two independent cohorts. In addition to classic metrics of model performance, we compared the robustness of the three models to reduced tumor purity, a critical consideration in the clinical utility of such classifiers. Our findings revealed that the NN model exhibited the highest accuracy and maintained a balance between precision and recall. The NN model was the most resistant to drops in performance associated with a reduction in tumor purity, showing good performance until the purity fell below 50%. Through rigorous validation, our study emphasizes the potential of DNA-methylation-based deep learning methods to improve precision medicine for brain tumor classification in the clinical setting. We validated the performance of these two models and the RFmod with two independent brain tumor cohorts consisting of 1104 samples from GSE109379 and 950 samples from the St. Jude Children’s Research Hospital. Our results showed that although all models performed robustly to missing data, the deep NN model had the highest CNS classification accuracy and the most favorable performance characteristics, especially in minimizing the proportion of subthreshold scores during testing and validation. Average precision and recall of the NNmod started reducing to similar levels of kNNmod and RFmod when tumor purity was less than 50%. This suggests that a deep NN model can be implemented in clinical laboratories as a reliable and essential diagnostic tool to assist in precision therapy for brain tumors.