Robust gene expression-based classification of cancers without normalization

Abstract

Binary classification using gene expression data is commonly used to stratify cancers into molecular subgroups that may have distinct prognoses and therapeutic options. A limitation of many such methods is the requirement for comparable training and testing data sets. Here, we describe and demonstrate a self-training implementation of probability ratio-based classification prediction score (PRPS-ST) that facilitates the porting of existing classification models to other gene expression data sets. We demonstrate its robustness through application to two binary classification problems in diffuse large B-cell lymphoma using a diverse variety of gene expression data types and normalization methods. BackgroundThe classification of tumors into molecular subgroups using gene expression features has been applied to numerous cancer types and can be used to identify high-risk patients and/or award and the BC Cancer Foundation. The authors thank George Wright for helpful discussions regarding the Scott cohort. We also gratefully acknowledge the patient donors of samples used herein.