The ability to identify-early in the course of therapy-patients that are not responding to a particular neoadjuvant regimen would provide the opportunity to switch to a potentially more efficacious treatment and transform current practice. Unfortunately, existing methods of determining early response are inadequate. The vision for this program is to develop tumor-forecasting methods for predicting response in individual breast cancer patients after a single cycle of neoadjuvant therapy. We propose to combine time-resolved drug- response cell scale data with physiological and tissue scale imaging data in order to initialize and constrain a multi-scale angiogenesis-cell proliferation model designed to predict both size and spatial characteristics of breast tumors at the completion of therapy. To achieve this goal, we will pursue the following specific aims: 1. (Pre-clinical validation) In the BT-474 HER2+ human breast cancer cell line, we will obtain: 1a. (cell scale) in vitro data quantifying rates of entry of proliferating cells into quiescence and apoptosis; 1b. (physiologica scale) in vivo MRI and PET measurements of cellularity, vascularity, and metabolism; 1c. (tissue scale) in vivo MR elastography measurements to quantify the tumor mechanical properties; 1d. (all scales) in situ data from fixed tumor tissue to corroborate cell and imaging-based metrics. These data will be integrated into the multi-scale model to predict tumor response after one cycle of the targeted anti-HER2 agents trastuzumab and lapatinib. 2. (Clinical application) In HER2+ patients receiving neoadjuvant trastuzumab and lapatinib, we will obtain: 2a. (physiological scale) in vivo MRI and PET measurements of cellularity, vascularity, and metabolism; 2b. (tissue scale) in vivo MR elastography measurements to quantify tumor mechanical properties. Guided by the results from Aim 1, these data will be integrated into the multi-scale model and make predictions on breast tumor response outcomes after a single cycle of trastuzumab and/or lapatinib. If successful, our approach would be the foundation for high-impact, large-scale application in clinical settings.

Public Health Relevance

We hypothesize that patient specific imaging data combined with an estimate of the effect of the neoadjuvant regimen on tumor cell phenotype, will enable a multi-scale model to accurately predict the response of breast tumors after a single cycle of therapy on an individual basis. Our goal is to provide the breast cancer community with a rigorous, practical method of predicting therapeutic response that is appropriate for incorporation into clinical trials and, ultimately, clinical practice.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZEB1-OSR-C (J2))
Program Officer
Zhang, Huiming
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Vanderbilt University Medical Center
Schools of Medicine
United States
Zip Code
Yankeelov, Thomas E; Miga, Michael I (2016) Introduction to the Special Section on Clinical Applications of Multi-Scale Modeling. Ann Biomed Eng 44:2589-90
Sorace, Anna G; Quarles, C Chad; Whisenant, Jennifer G et al. (2016) Trastuzumab improves tumor perfusion and vascular delivery of cytotoxic therapy in a murine model of HER2+ breast cancer: preliminary results. Breast Cancer Res Treat 155:273-84
Yankeelov, Thomas E; An, Gary; Saut, Oliver et al. (2016) Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success. Ann Biomed Eng 44:2626-41
Harris, Leonard A; Frick, Peter L; Garbett, Shawn P et al. (2016) An unbiased metric of antiproliferative drug effect in vitro. Nat Methods 13:497-500
Sorace, Anna G; Syed, Anum K; Barnes, Stephanie L et al. (2016) Quantitative [(18)F]FMISO PET Imaging Shows Reduction of Hypoxia Following Trastuzumab in a Murine Model of HER2+ Breast Cancer. Mol Imaging Biol :
Abramson, Richard G; Arlinghaus, Lori R; Dula, Adrienne N et al. (2016) MR Imaging Biomarkers in Oncology Clinical Trials. Magn Reson Imaging Clin N Am 24:11-29
Conley, Rebekah H; Meszoely, Ingrid M; Weis, Jared A et al. (2015) Realization of a biomechanical model-assisted image guidance system for breast cancer surgery using supine MRI. Int J Comput Assist Radiol Surg 10:1985-96
Abramson, Richard G; Lambert, Katrina F; Jones-Jackson, Laurie B et al. (2015) Prone Versus Supine Breast FDG-PET/CT for Assessing Locoregional Disease Distribution in Locally Advanced Breast Cancer. Acad Radiol 22:853-9
Yankeelov, Thomas E; Quaranta, Vito; Evans, Katherine J et al. (2015) Toward a science of tumor forecasting for clinical oncology. Cancer Res 75:918-23
Barnes, Stephanie L; Sorace, Anna G; Loveless, Mary E et al. (2015) Correlation of tumor characteristics derived from DCE-MRI and DW-MRI with histology in murine models of breast cancer. NMR Biomed 28:1345-56

Showing the most recent 10 out of 24 publications