The long-term vision of this program is to improve patient care by optimizing, validating, and extending quantitative MRI methods for the early prediction of breast cancer response to neoadjuvant therapy (NAT). During the first period of support, we developed several experimental and computational tools for improving quantitative dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) of the breast. These tools were successfully applied in clinical trials and the resulting data were incorporated into a statistical model to predict, after only one cycle of treatment, the eventual pathological complete response (pCR) of breast tumors to NAT. We now have the opportunity to deploy these techniques in two multi-site clinical trials, focused on triple negative breast cancer (TNBC), to be opened simultaneously at Vanderbilt University and the University of Chicago. These trials offer the opportunity to validate and then extend our imaging techniques in both simple and complex trial environments. Thus, we have identified the following Specific Aims:
Aim 1. Optimize quantitative DCE- and DW-MRI for two multi-site breast cancer clinical trials Aim 2. Validate quantitative MRI for predicting breast cancer treatment response early during NAT Aim 3. Extend quantitative MRI by predicting breast cancer treatment response during a complex NAT trial Our overarching hypothesis (guided by the results from the first period of support) is that the synthesis of quantitative DCE- and DW-MRI measured after the first cycle of NAT will achieve an area under the receiver operating characteristic curve of at least 0.87 for predicting the eventual response of TNBC patients to NAT. If this hypothesis is validated, we will be able to provide significant direction on developing personalized treatment strategies for this important patient population. Furthermore, we will be well-positioned to proceed to larger multi-site trials-a necessary step towards adoption into routine clinical algorithms.

Public Health Relevance

The overall goal of this program is to significantly improve patient care by optimizing, validating, and then extending quantitative MRI methods for the early prediction of breast cancer response to neoadjuvant therapy. The knowledge acquired through this study will provide direction on developing personalized treatment strategies for breast cancer patients undergoing neoadjuvant therapy and may motivate a shift in existing paradigms of therapy monitoring and selection in breast cancer. Furthermore, MRI assessment of early response could be broadly applicable to other solid tumors where neoadjuvant therapy is appropriate.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA142565-08
Application #
9132671
Study Section
Special Emphasis Panel (ZCA1-TCRB-9 (J3))
Program Officer
Tata, Darayash B
Project Start
2010-05-01
Project End
2020-08-31
Budget Start
2016-09-01
Budget End
2017-08-31
Support Year
8
Fiscal Year
2016
Total Cost
$462,791
Indirect Cost
$92,788
Name
Vanderbilt University Medical Center
Department
Type
DUNS #
079917897
City
Nashville
State
TN
Country
United States
Zip Code
37232
Sorace, Anna G; Partridge, Savannah C; Li, Xia et al. (2018) Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial. J Med Imaging (Bellingham) 5:011019
Kang, Hakmook; Hainline, Allison; Arlinghaus, Lori R et al. (2018) Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results. J Med Imaging (Bellingham) 5:011015
Jarrett, Angela M; Hormuth, David A; Barnes, Stephanie L et al. (2018) Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results. Phys Med Biol 63:105015
Cardin, Dana B; Goff, Laura W; Chan, Emily et al. (2018) Dual Src and EGFR inhibition in combination with gemcitabine in advanced pancreatic cancer: phase I results : A phase I clinical trial. Invest New Drugs 36:442-450
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
Virostko, John; Hainline, Allison; Kang, Hakmook et al. (2018) Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis. J Med Imaging (Bellingham) 5:011011
Malyarenko, Dariya; Fedorov, Andriy; Bell, Laura et al. (2018) Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging (Bellingham) 5:011006
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
Woodall, Ryan T; Barnes, Stephanie L; Hormuth 2nd, David A et al. (2018) The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains. Magn Reson Med 80:330-340
Bane, Octavia; Hectors, Stefanie J; Wagner, Mathilde et al. (2018) Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study. Magn Reson Med 79:2564-2575

Showing the most recent 10 out of 82 publications