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

Information

  • Research Project
  • 10247074
  • ApplicationId
    10247074
  • Core Project Number
    U01CA215848
  • Full Project Number
    5U01CA215848-05
  • Serial Number
    215848
  • FOA Number
    PAR-16-131
  • Sub Project Id
  • Project Start Date
    9/4/2017 - 6 years ago
  • Project End Date
    8/31/2022 - a year ago
  • Program Officer Name
    HUGHES, SHANNON K
  • Budget Start Date
    9/1/2021 - 2 years ago
  • Budget End Date
    8/31/2022 - a year ago
  • Fiscal Year
    2021
  • Support Year
    05
  • Suffix
  • Award Notice Date
    9/9/2021 - 2 years ago

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

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.

IC Name
NATIONAL CANCER INSTITUTE
  • Activity
    U01
  • Administering IC
    CA
  • Application Type
    5
  • Direct Cost Amount
    573162
  • Indirect Cost Amount
    53584
  • Total Cost
    626746
  • Sub Project Total Cost
  • ARRA Funded
    False
  • CFDA Code
    396
  • Ed Inst. Type
    BIOMED ENGR/COL ENGR/ENGR STA
  • Funding ICs
    NCI:626746\
  • Funding Mechanism
    Non-SBIR/STTR RPGs
  • Study Section
    ZCA1
  • Study Section Name
    Special Emphasis Panel
  • Organization Name
    GEORGIA INSTITUTE OF TECHNOLOGY
  • Organization Department
    ENGINEERING (ALL TYPES)
  • Organization DUNS
    097394084
  • Organization City
    ATLANTA
  • Organization State
    GA
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    303320415
  • Organization District
    UNITED STATES