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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.

项目总结