Pathway-Based Genomics Prediction using Generalized Elastic Net.

Genetic markers have become a mainstay in modern cancer diagnosis. They enable a quickidentification of a tumor’s subtype to suggest treatment options. Unfortunately, genetic markers provide little insight into a tumor’s inner workings. Genes don’t function in isolation, but rather belong to interacting parts, the collection of which define pathways. Knowing which pathways become dysregulated in cancer can help guide the development of drugs that shut down those pathways and kill tumor cells. However, gene markers are often selected in a way to produce the strongest diagnostic signal, with little consideration of how much the genes interact. This calls for new markers that leverage pathway structure. We introduced a method that selects gene markers in a way that maximizes the proximity of selected genes according to a provided gene-gene interaction network. As an analogy, if genes were people in a social network, our proposed method would build a team of diagnostic experts in a way that maximizes the number of friendships among the team members. Using experiments on synthetic data, we demonstrate that guiding marker selection towards genes that interact with each other maintains, and often improves, the accuracy of predictions even in cases where the full interaction network is unknown. We applied our method to predict the response of breast cancer cells to various chemical compounds and identified a novel pathway that may be related to cancer cells’ resistance to a drug that targets a family of epidermal growth factor receptors.