Partially Ordered Expression Features Improves Survival Prediction in Cancer

Predicting the survival of cancer patients is critical for choosing patient specific treatment strategies. Survival prediction has been traditionally based on clinical or pathological factors such as patient age and tumor stage. With the availability of high-throughput data expression quantities are also incorporated in the models [6, 1, 8]. The survival models that are built with molecular expression profiles rely on the individual expression quantities of the molecules in the tumors. However, in the cell molecules interact with each other and in cancer these interactions are dysregulated in various ways. A better representation of the molecular abundance that accounts for these dysregulations has potential to increase the predictive performance of survival models and help reach biomarkers that are readily interpretable.