Using the CMS labels of the core consensus samples as a ' gold standard ' , we developed a novel classification framework for predicting CMS subtypes using aggregated gene expression data from all cohorts ( Online Methods ) . CMS labeled samples were split into two equal partitions for training and validation , and a Random Forest classifier was generated from 500 balanced bootstraps of the training data . When applied to the validation data , the classifier demonstrated robust performance across gene expression platforms ( Affymetrix , Agilent , and RNA - sequencing ) and sample collections ( FFPE , fresh - frozen ) with a > 90 % balanced accuracy across all subtypes ( Supplementary Table 4 , Supplementary   Fig . 3 ) . This corroborates both the portability of the classifier as well as the evident subtypespecific signals across datasets .