Learning to train and to explain a deep survival model with large scale ovarian cancer transcriptomic data

Abstract Ovarian cancer is a complex disease with poor outcome affecting women worldwide. The lack of successful therapeutic options for ovarian cancer patients results in the strong need to identify new biomarkers for patient selection. The development of outcome predictors based on gene expression is important not only for patient stratification but also to recognize categories of patients that are more likely to respond to particular therapies. In this paper, we proposed a new deep learning survival model trained on the high-dimensional transcriptomic data for the task of ovarian cancer prognostication. We validated our deep learning survival model on an independent clinical and molecular datatset. Finally, we illustrated the way our model can be interpreted, by calculating the contributions of the input features to the network outputs. We demonstrated how these contributions can be related to the molecular pathways to uncover biological processes associated with ovarian cancer patients survival.