Model-based Prediction of Redox-Modulated Responses to Cancer Treatments

点击翻译按钮获取中文摘要

Project Summary While the arsenal of approaches to selectively killing cancer cells is increasing, the majority of treatments rely on redox alterations of tumor cells and their microenvironment through chemotherapy, radiation, or some combination thereof. Effectively predicting response to these treatments remains a significant challenge in designing successful personalized therapeutic strategies and currently there are no biomarkers of response to chemo/radiation therapies in clinical use. We hypothesize that the response to redox-based chemotherapeutics can be predicted and enhanced by identifying specific metabolic network features contributing to the redox couple NAD(P)+/NAD(P)H and associated with the specific mechanism of action. We will integrate and expand the scope of our prior successful models of drug bioactivation networks and redox metabolic systems in a comprehensive systems-level approach to improve understanding and enhance prediction of phenotype-specific responses to chemotherapeutic strategies. We will investigate the NAD(P)H-driven mechanisms of response to the quinone-based chemotherapeutic, beta-lapachone (ß-lap), in laboratory models and clinical specimens of Head and Neck Squamous Cell Cancer (HNSCC). We propose to 1) Develop and validate a predictive model to quantify ß-lap lethality in matched HNSCC cell lines with altered redox metabolism and response to treatment (SCC-61/rSCC-61); 2) Enhance predictive capabilities of computational model by accounting for metabolic diversity across HNSCC tumors in vitro and in vivo; and, 3) Test model-based predictions of therapeutic outcomes with HNSCC clinical specimens. We anticipate our study will advance precision medicine by accounting for the redox-dependent mechanisms of action for molecular or systemic chemotherapies.