RNA sequencing (RNAseq) analysis offers a tumor centered approach of growing interest for personalizing cancer care. However, existing methods , including deep learning models, struggle to reach satisfying performances on survival prediction based upon pan-cancer RNAseq data. Here, we present DeepOS, a novel deep learning model that predicts overall survival (OS) from pancancer RNAseq with a concordance index of 0.715 and a survival AUC of 0.752 across 33 TCGA tumor types whilst tested on an unseen test cohort. DeepOS notably uses (i) prior biological knowledge to condense inputs dimensionality, (ii) transfer learning to enlarge its training capacity through pretraining on organ prediction, and (iii) mean squared error adapted to survival loss function; all of which contributed to improve the model performances. Interpretation showed that DeepOS learned biologically relevant prognosis biomarkers. Altogether, DeepOS achieved unprecedented and consistent performances on pan-cancer prognosis estimation from individual RNA-seq data.
Peer Review #2 of "Pan-cancer systematic identification of lncRNAs associated with cancer prognosis (v0.1)"
