The overall goal of this proposal is to integrate advanced imaging and mathematical modeling to optimize combination treatments involving immunotherapy in human epidermal growth factor receptor type 2 positive (HER2+) breast cancer. Current standard-of-care therapeutic regimens and even clinical trials are limited because they are not personalized based on the tumor biology of the individual patient, potentially diminishing the efficacy of the treatment. This proposed research will employ noninvasive, quantitative magnetic resonance imaging (MRI) and positron emission tomography (PET) to inform mathematical models to direct timing for multi-modal therapies in HER2+ breast cancer. Overexpression of HER2 is indicative of more aggressive disease with five times higher risk of metastasis, with increased risk of breast-to-brain metastases, compared to HER2- patients. We have extensive experience and expertise in using quantitative medical imaging techniques to assess and predict treatment response to anti-cancer therapies. Additionally, we have shown that trastuzumab dosing prior to cytotoxic treatment (instead of simultaneous dosing of combination therapies) has potential to improve vascular delivery and oxygenation in HER2+ breast cancer tumors, which in turns sensitizes the tumor for cytotoxic therapies, reduces metastatic potential, improves drug delivery and reduces systemic toxicity. As immunotherapy becomes mainstream for many solid tumors, it is essential to develop techniques to both personalize and optimize therapeutic efficacy and decrease systemic toxicity. Thus, our central hypothesis is that quantitative imaging integrated with mathematical modeling can enhance personalization of treatment strategies and increase efficacy (additive and synergistic) of combination therapies with immunotherapy in HER2+ breast cancer. To achieve this goal, we have identified the following specific aims: 1) Quantify biological changes to immuno- and targeted therapy in HER2+ breast cancer with quantitative imaging, 2) Build a mathematical model of biological alterations to immunotherapy in HER2+ breast cancer, and 3) Employ model forecasting and quantitative imaging to guide combination therapy. We will exploit the alterations in biological changes, such as vascular delivery (evaluated with dynamic contrast enhanced (DCE)- MRI pharmacokinetic parameter, Ktrans) and oxygenation (evaluated with fluoromisonidazole (FMISO)-PET imaging metric, SUV) to inform a mathematical model in order to identify (and validate) optimal sequencing (order, timing, dose) to combination therapy (targeted, immunotherapy) for enhanced synergistic effects. Completion of this project provides a pathway to dramatically improve the efficacy of treatment strategies with immunotherapy for primary HER2+ breast cancer. Importantly, the proposed techniques provide a straightforward route for patient translation and potential to enhance care for HER2+ breast cancer patients.

Public Health Relevance

We propose to integrate advanced?but clinically relevant?imaging with mathematical modeling to personalize, guide, and improve the overall efficacy of anti-HER2 targeted therapy with immunotherapy in murine models of HER2+ breast cancer. Our goal is to provide an approach to exploit the biological and vascular alterations in response to anti-cancer therapies to identify optimal combinations, thereby improving treatment efficacy (while minimizing toxicity) and increasing tumor kill; this has high likelihood to improve patient outcome.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Imaging Guided Interventions and Surgery Study Section (IGIS)
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Redmond, George O
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University of Alabama Birmingham
Schools of Medicine
United States
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