Prediction of metabolic drug targets for cancer patients with poor prognosis by using genome-scale metabolic models and survival analyses

Abstract

Cancer metabolism has been extensively explored to identify biomarkers and novel drug targets for effective cancer treatment. In particular, it is expected that metabolism-derived data somehow reflects unique features associated with a cancer patient’s prognosis. Here, we introduce a computational framework that predicts metabolic drug targets by considering metabolites associated with poor prognosis. For inputs, the computational framework requires genome-scale metabolic models (GEMs) and survival data that represent each of cancer patients with diverse prognoses. GEM is a computational model that allows predicting fluxes of entire metabolic reactions in a patient-specific manner. By using this computational framework, a risk score, which reflects the prognosis of cancer patients, was first established on the basis of metabolites significantly associated with survival. Finally, metabolic reaction targets were predicted that could reduce the risk score (i.e., improving the prognosis) upon their knockdown in silico. In this study, this framework was applied to bladder cancer patients as a proof-of-concept demonstration, and predicted 13 metabolic reaction targets. These metabolic reaction targets were expected to be also effective to bladder cancer patients with poor prognosis. The computational framework developed in this study is distinct from previous drug targeting methods because it also considers unique metabolic features associated with cancer patients with poor prognosis in order to identify drug targets that are effective to both low-risk and high-risk groups of cancer patients.