Classification of colon cancer patients into Consensus Molecular Subtypes using Support Vector Machines

Abstract

AbstractObjectiveThe molecular heterogeneity of colon cancer has made classification of tumors a requirement for effective treatment. One of the approaches for molecular subtyping of colon cancer patients is the Consensus Molecular Subtypes (CMS) developed by the Colorectal Cancer Subtyping Consortium (CRCSC). CMS-specific RNA-Seq dependent classification approaches are recent with relatively low sensitivity and specificity. In this study, we aimed to classify patients into CMS groups using RNA-seq profiles.MethodsWe first identified subtype specific and survival associated genes using Fuzzy C-Means (FCM) algorithm and log-rank test. Then we classified patients using Support Vector Machines with Backward Elimination methodology.ResultsWe optimized RNA-seq based classification using 25 genes with minimum classification error rate. Here we report the classification performance using precision, sensitivity, specificity, false discovery rate and balanced accuracy metrics.ConclusionWe present the gene list for colon cancer classification with minimum classification error rates. We observed the lowest sensitivity but highest specificity with CMS3-associated genes, which is significant due to low number of patients in the clinic for this group.