Efficient Agony Based Transfer Learning Algorithms for Survival Forecasting

Abstract

Progression modeling is a mature subfield of cancer bioinformatics, but it has yet to make a proportional clinical impact. The majority of the research in this area has focused on the development of efficient algorithms for accurately reconstructing sequences of (epi)genomic events from noisy data. We see this as the first step in a broad pipeline that will translate progression modeling to clinical utility, with the subsequent steps involving inferring prognoses and optimal therapy programs for different cancers and using similarity in progression to enhance decision making. In this paper we take some initial steps in completing this pipeline. As a theoretical contribution, we introduce a polytime-computable pairwise distance between progression models based on the graph-theoretic notion of "agony". Focusing on a particular progression model we can then use this agony distance to cluster (dis)similarities via multi-dimensional scaling. We recover known biological similarities and dissimilarities. Finally, we use the agony distance to automate transfer learning experiments and show a large improvement in the ability to forecast time to death.