Search

Breast cancer treatment response varies by subtype, treatment regiment, and additionally by vasculature characteristics. For this reason, breast cancer is a model disease for the development of both targeted therapy and prognostic and predictive biomarkers. Mathematical modeling allows for personalized patient specific prediction of treatment outcome based on parameters found to be important to the cancer type. Mathematical modeling is beneficial in providing insight into why cancer treatment fails and in what cases, additionally determining what characteristics result in a successful treatment. Presented in Chapter 1 is a scientific introduction and discussion focusing on representative modeling works specified towards breast cancer which give quantitative insight into chemotherapy resistance and how drug resistance can be overcome or minimized to optimize chemotherapy treatment. Demonstrated in Chapter 2, a modeling tool was created to predict the likelihood of response to neoadjuvant chemotherapy using patient specific tumor vasculature biomarkers measured in a total of 48 patients. To perform accurate and rapid throughput, a semi-automated analysis was implemented, improving on previous methods requiring hand-made measurements. In effort to translate this model towards clinical practice, 48 patients undergoing neoadjuvant chemotherapy were evaluated, collecting clinically relevant data including pre- and post-treatment pathology specimens, and dynamic contrast-enhanced magnetic resonance imaging. Analysis of histology parameters, specifically radius of drug source divided by diffusion penetration distance (L/rb), a normalization penetration distance, and blood volume fraction (BVF), provides a separation of patients obtaining a pathologic complete response (pCR) and those that do not, with 80% accuracy (p= 0.0269), providing a personalized approach to breast cancer treatment. Nanoparticles are shown to improve on cancer treatment efficacy, demonstrating improved cell kill when compared to free drug. Due to drug resistance and patient heterogeneity, patient outcome can vary greatly, in order to explore this phenomenon mouse treatment outcome relative to tumor and organ nanoparticle deposition is analyzed. A mouse study is presented in Chapter 3 as a proof-of-concept demonstrating the heterogeneous distribution of nanoparticles, and the improved cancer cell kill efficacy in an exponential fashion relative to accumulation of nanoparticles in the tumor. The combination of using nanoparticles as improved drug delivery vehicles, analysis of tumor biomarkers, and mathematical modeling to understand the underlying phenomena of treatment efficacy can be used in the clinical setting to help improve cancer treatment, and identify patients likely to respond well to differing and improved cancer treatment. Lastly, future directions are discussed in Chapter 4 whereby the application of chemotherapy, nanotherapeutics, and mathematical modeling may greatly improve and connect the theoretical and clinical side of cancer science.