Triple negative breast cancer (TNBC) accounts for 15% of all breast cancers, and patients with this disease have an increased likelihood of distant recurrence and shorter overall survival compared to non-TNBC patients. The standard of care for TNBC is neoadjuvant therapy (NAT), consisting of a panel of cytotoxic therapies, followed by surgery. However, the field currently lacks a consensus on the appropriate combination of therapies and an ability to predict how any patient will response to a given therapeutic regimen. This proposal addresses these issues through construction of a mathematical model utilizing tumor-specific imaging data. Several attempts have been made to capture tumor growth and treatment response within a mathematical framework, but many of those attempts have relied on parameters and data that are difficult or impossible to measure with the requisite temporal and spatial resolution. This effort is distinguished by proposing a model parameterized exclusively with experimentally available data. Specifically, the proposal builds on recent advances in time-resolved automated fluorescent microscopy and diffusion-weighted magnetic resonance imaging (DW-MRI) to populate the proposed model. Fluorescent microscopy can track cell populations in two dimensions over time, and DW-MRI can provide quantitative information on cell density in three dimensions. We have developed in vitro assays to leverage each of these imaging modalities to track tumor status noninvasively throughout the course of therapy. We hypothesize that this data can be used to initialize a computational model to predict: 1) the temporospatial response of TNBC to therapy and 2) optimal NAT regimens for a given tumor. To test this hypothesis, we propose three specific aims: 1) to measure and model TNBC cell line response to NAT in 2D using fluorescent microscopy, 2) to measure and model TNBC cell line response to NAT in 3D using MRI, and 3) to evaluate model predictions in an in vivo model of TNBC. The proposal will subject a representative sample of TNBC cell lines to a panel of clinically relevant therapies evaluating tumor response in both 2D and 3D. Preliminary data indicates that data collected via fluorescent microscopy can describe tumor-scale data collected via MRI with appropriate temporospatial scaling factors. The last decade has produced significant advances in the genetic and molecular characterization of tumors and their response to therapy. Our proposal will provide insight into how these cell-scale observations translate to clinically relevant measures of tumor status. Further, this proposal will quantitatively characterize the response of TNBC to NAT. The field requires this quantitative understanding to properly evaluate next- generation therapies. Finally, this proposal will demonstrate the utility of a computational approach to therapy design through in vivo experiments. Ultimately, these Aims will move the field towards the goal of precision medicine: delivering the optimal drug in its optimal dose on an optimal schedule to each patient.

Public Health Relevance

Despite the prevalence of cytotoxic drugs in the treatment of triple negative breast cancer, the field lacks a quantitative understanding of how cytotoxic drugs affect tumor growth. The proposed research uses computational approaches in conjunction with advanced imaging techniques to develop a model that describes the effect of clinically relevant cytotoxic therapies on a panel of breast cancer cell lines. This understanding would allow for personalization of current breast cancer therapies, and the proposed work holds the potential to hasten the clinical evaluation of new breast cancer therapeutics.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
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Special Emphasis Panel (ZRG1)
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Damico, Mark W
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Vanderbilt University Medical Center
Biomedical Engineering
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United States
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McKenna, Matthew T; Weis, Jared A; Brock, Amy et al. (2018) Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Transl Oncol 11:732-742
Jarrett, Angela M; Lima, Ernesto A B F; Hormuth 2nd, David A et al. (2018) Mathematical models of tumor cell proliferation: A review of the literature. Expert Rev Anticancer Ther 18:1271-1286
McKenna, Matthew T; Weis, Jared A; Quaranta, Vito et al. (2018) Variable Cell Line Pharmacokinetics Contribute to Non-Linear Treatment Response in Heterogeneous Cell Populations. Ann Biomed Eng 46:899-911
McKenna, Matthew T; Weis, Jared A; Barnes, Stephanie L et al. (2017) A Predictive Mathematical Modeling Approach for the Study of Doxorubicin Treatment in Triple Negative Breast Cancer. Sci Rep 7:5725