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

项目摘要 虽然选择性杀死癌细胞的方法越来越多，但大多数治疗方法都依赖于 化疗、放疗等对肿瘤细胞及其微环境氧化还原的影响 两者的结合。有效预测对这些治疗的反应仍然是一个巨大的挑战 设计成功的个性化治疗策略，目前还没有反应的生物标志物 临床应用中的化疗/放射治疗。我们假设对氧化还原类化疗药物的反应 可以通过确定有助于氧化还原的特定代谢网络特征来预测和增强 将NAD(P)+/NAD(P)H偶联，并与特定的作用机制相关联。我们将整合和拓展 我们以前成功的药物生物活化网络和氧化还原代谢系统模型的范围 全面的系统级方法，以改进对特定表型的理解和预测 对化疗策略的反应。我们将研究NAD(P)H驱动的反应机制 在实验室模型和临床标本中，以苯醌为基础的化疗药物β-拉帕酮(？) 头颈部鳞癌(HNSCC)。我们建议1)开发和验证预测模型以 定量检测氧化还原代谢和治疗反应改变的匹配HNSCC细胞株的？-LAP致死率 (SCC-61/RSCC-61)；2)通过考虑代谢来增强计算模型的预测能力 体内和体外HNSCC肿瘤的多样性；以及，3)基于测试模型的治疗预测 HNSCC临床标本的结果。我们预计我们的研究将推动精准医学 解释了分子或系统化疗中氧化还原依赖的作用机制。

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.