Development and Validation of a Predictive Model of Chemotherapy in Triple Negative Breast Cancer
McKenna, Matthew Thomas
Precision medicine is the concept of incorporating patient-specific variability into prevention and treatment strategies. Precision medicine initiatives in oncology have primarily focused on the use of genetics to classify and pharmaceutically target cancers. While the genetic-centric approach to precision cancer therapy has merit in selecting therapy, an expanded effort is required to guide optimal dosing of those therapies. Fundamentally, treatment response is driven by patient‑, tumor‑, and cell‑specific pharmacologic properties. We posit that mathematical models that explicitly incorporate such processes will improve the ability to deliver precision therapy. In this Dissertation, we develop a combined experimental-mathematical modeling framework to establish a mechanistic mathematical model of doxorubicin treatment response in triple negative breast cancer (TNBC). TNBC is a subgroup of invasive cancers that lack significant expression of the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Lacking specific pharmaceutical targets, the current approach to neoadjuvant therapy for locally advanced disease employs a combination of cytotoxic drugs, including doxorubicin. We present a coupled pharmacokinetic/pharmacodynamic (PK/PD) model that describes how the concentration and duration of doxorubicin therapy shape subsequent cell population dynamics in vitro. The proposed model accurately captures doxorubicin uptake and treatment response dynamics in a panel of cell lines, and the model can be leveraged to predict response to a wide range of doxorubicin treatment timecourses. We propose the equivalent dose metric, a value derived from the mechanistic PK/PD model, to explicitly account for variable cell line pharmacological properties. We demonstrate that the equivalent dose is a more precise means of quantifying combination therapies and comparing cell line response to therapy relative to current approaches. Finally, we extend the model to describe treatment response in heterogeneous populations. We demonstrate that the biological composition of the population significantly impacts treatment response dynamics, and we propose a PK-based mechanism to explain the behavior. By studying various experimental perturbations within a single mechanistic, mathematical framework, this approach provides a means for more efficient discovery of predictive biomarkers and translation of those discoveries into patient care.