Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general highdimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an opensource software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models. Advanced molecular platforms can generate rich descriptions of the genetic, transcriptional, epigenetic and proteomic profiles of cancer specimens, and data from these platforms are increasingly utilized to guide clinical decision-making. Although contemporary platforms like sequencing can provide thousands to millions of features describing the molecular states of neoplastic cells, only a small number of these features have established clinical significance and are used in prognostication 1-4 . Making reliable and accurate predictions of clinical outcomes from high-dimensional molecular data remains a major challenge in realizing the potential of precision genomic medicine. Traditional Cox proportional hazards models require enormous cohorts for training models on high-dimensional datasets containing large numbers of features. Consequently, a small set of features is selected in a subjective process that is prone to bias and limited by imperfect understanding of disease biology. High-dimensional learning problems are common in the machine-learning community, and many machine-learning approaches have been adapted to predicting survival or time to progression 5 . Prior knowledge has been used to reduce dimensionality by learning gene signatures of cancer hallmarks to generate intermediate features that successfully predict outcomes 6,7 . Regularization methods for Cox models like elastic net have been developed to perform objective and data-driven feature selection with time-to-event data 8 . Random forests are reputed to resist overfitting in high-dimensional prediction problems, and have been adapted to survival modeling 9 . Neural network based approaches have been used in low-dimensional survival prediction problems 10 , but subsequent evaluation of these methods found no performance improvement over ordinary Cox regression 11 . The difficulty of deconstructing these black-box models to gain insights into disease progression or biology remains
In REF , Bayesian optimized deep survival models compared with other state of the art machine learning methods for survival like Cox elastic net and random survival forests.
Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models
Scientific Reports
Scientific Reports
Medicine