Common biochemical properties of metabolic genes recurrently dysregulated in tumors

Abstract

Background: Tumor initiation and progression are associated with numerous metabolic alterations. However, the biochemical drivers and constraints that contribute to metabolic gene dysregulation are unclear. Methods: Here, we present MetOncoFit, a computational model that integrates 142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topology, and reaction flux. MetOncoFit uses genome-scale metabolic modeling and machine-learning to quantify the relative importance of various metabolic features in predicting cancer metabolic gene expression, copy number variation, and survival data.