No abstract

With the advent of high-throughput sequencing technologies, the genomic platforms generate a vast amount of high dimensional genomic profiles. One of the fundamental challenges of genomic medicine is the accurate prediction of clinical outcomes from these data. Gene expression profiles are established to be associated with overall survival in cancer patients, and this perspective the univariate Cox regression analysis was widely used as primary approach to develop the outcome predictors from high dimensional transcriptomic data for ovarian cancer patient stratification. Recently, the classical Cox proportional hazards model was adapted to the artificial neural network implementation and was tested with The Cancer Genome Atlas (TCGA) ovarian cancer transcriptomic data but did not result in satisfactory improvement, possibly due to the lack of datasets of sufficient size. Nevertheless, this methodology still outperforms more traditional approaches, like regularized Cox model, moreover, deep survival models could successfully transfer information across diseases to improve prognostic accuracy. We aim to extend the transfer learning framework to "pan-gyn" cancers as these gynecologic and breast cancers share a variety of characteristics being female hormone-driven cancers and could therefore share common mechanisms of progression. Our first results using transfer learning show that deep survival models could benefit from training with multi-cancer datasets in the high-dimensional transcriptomic profiles.

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.