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

基于模型的氧化还原调节对癌症治疗反应的预测

基本信息

批准号：
9769699
负责人：
Cristina Maria Furdui
金额：
$ 70.6万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-04 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9769699
关键词：
Accounting Animal Model Automobile Driving Biochemical Pathway Biological Assay Biological Markers Biology Biopsy Cancer Patient Cancer cell line Cell Line Chemosensitivity Assay Clinical Computer Simulation DNA Damage DNA lesion Data Drug Modelings Enzymes Equilibrium FDA approved Family Glutathione Head and Neck Cancer Head and neck structure Hydrogen Peroxide Hydroquinones In Vitro Knock-out Laboratories Lead Malignant Neoplasms Malignant Squamous Cell Neoplasm Mass Spectrum Analysis Measures Mediating Mediator of activation protein Metabolic Metabolism Methods Modeling Molecular Molecular Mechanisms of Action NADP NQO1 gene Operative Surgical Procedures Outcome Oxidation-Reduction Oxidoreductase Patient Selection Patients Pharmaceutical Preparations Phenotype Proteomics Quinones Radiation Radiation therapy Reactive Oxygen Species Research Personnel Resistance Series Solid Specimen System Systems Biology Testing Therapeutic Time Tumor Tissue Validation Xenobiotics base beta-Lapachone cancer cell cancer therapy catalase chemotherapeutic agent chemotherapy clinically relevant computational network modeling cytotoxic cytotoxicity design drug efficacy drug metabolism high throughput screening improved in vivo inhibitor/antagonist metabolic profile multidisciplinary neoplastic cell network models novel therapeutic intervention oncology personalized therapeutic precision medicine predicting response predictive marker predictive modeling repaired response response biomarker standard of care targeted agent targeted treatment therapy outcome treatment response tumor tumor growth

项目摘要

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.

项目摘要虽然选择性杀死癌细胞的方法的武器库正在增加，但大多数疗法依赖通过化学疗法，放射线或某些肿瘤细胞及其微环境的氧化还原改变组合。有效预测对这些治疗的反应仍然是一个重大挑战设计成功的个性化理论策略，目前没有对临床使用中的化学/辐射疗法。我们假设对基于氧化还原的化学治疗药的反应可以通过识别有助于氧化还原的特定代谢网络特征来预测和增强夫妇nad（p）+/nad（p）h，并与特定的作用机理相关联。我们将整合和扩展我们先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，以提高理解并增强表型特异性的预测对化学治疗策略的反应。我们将研究NAD（P）H驱动的反应机制实验室模型和临床标本中的基于奎因酮的化学治疗β-拉帕酮（ß-LAP）头颈部鳞状细胞癌（HNSCC）。我们建议1）开发和验证一个预测模型量化氧化还原代谢改变的匹配的HNSCC细胞系中的β-宽圈致死性和对治疗的反应（SCC-61/RSCC-61）; 2）通过考虑代谢来增强计算模型的预测能力体外和体内HNSCC肿瘤的多样性； 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通过计算分子或全身化学疗法的氧化还原依赖性机理。