PATH-10. MULTI-OMICS PROFILING AND MACHINE LEARNING PROPOSE A REFINED MOLECULAR CLASSIFICATION SYSTEM FOR GLIOMA

Abstract

Abstract Accurate molecular stratification of glioma patients is key to an optimal design of therapeutic strategy to maximize patient survival. Here we leveraged multi-omics analysis of glioma and detailed clinical follow-up to build a refined classification system for glioma patients using support vector machines. The model input included the number of non-synonymous mutations in cancer driver genes, the number of non-synonymous mutations in cancer related genes, the transcriptomic grouping information, the immune infiltrations predicted by RNA-seq dataset, the site of tumor occurrence, as well as other well-known markers including IDH mutation status and 1p19q co-deletion status. We validated key model predictions using TCGA and CGGA datasets. Our refined classification system outperforms current state-of-the-art framework used in clinic. Taken together, we propose a refined molecular classification for glioma combining multi-omics profiling and machine learning approaches.