DeepOS: pan-cancer prognosis estimation from RNA-sequencing data

RNA-sequencing (RNA-seq) analysis offers a tumor-centered approach of interest for personalizing cancer care. However, existing methods – including deep learning models – struggle to reach satisfying performances on survival prediction based upon pan-cancer RNA-seq data. Here, we present DeepOS, a deep learning model that predicts overall survival (OS) from pan-cancer RNA-seq with a concordance-index of 0.714 and a survival AUC of 0.749 across 33 TCGA tumor types whilst tested on an unseen test cohort. DeepOS notably uses (i) prior biological knowledge to condense inputs dimensionality, (ii) mean squared error adapted to survival loss function and (iii) transfer learning to enlarge its training capacity through pre-training on organ prediction to improve the model performances (factors sorted by contributions). Interpretation showed that DeepOS learned biologically-relevant prognosis biomarkers. Altogether, DeepOS achieved competitive and consistent performances on pan-cancer prognosis estimation from individual RNA-seq data.