Assistant Clinical Professor
Department of Medicine
Division of Hematology-Oncology

Project title: Modeling of EMT/MET transitions in breast cancer stem cells

Kenneth Lange, Ph.D. - UCLA
Gay Crooks, M.B.B.S. - UCLA
Max Wicha, M.D. – University of Michigan

Multidisciplinary Expertise:
biomathematics, oncology, cancer stem cells

Project description:
Breast cancer is the most common type of cancer diagnosis in women, with the majority of deaths caused by distant recurrence.
Stem cell-targeted therapies offer new hope in eradicating breast cancer stem-like cells that lead to recurrence after standard therapies fail. Mathematical models have proven useful in studying the population dynamics of cancer stem cells under targeted therapy and are informative in assessing safety and duration of therapy. However, the complexities of the stem cell microenvironment limit the predictive ability of analytic models and suggest the necessity of more informed modeling strategies.

Stochastic simulation methods model rare events that are important in cancer modeling, such as extinction and mutation, and have the ability to address complex dynamics and incorporate feedback of reaction networks in systems biology.  We postulate a model in which breast cancer stem-like cells freely convert between an epithelial (proliferative) and mesenchymal (quiescent, invasive) state, through epithelial-to-mesenchymal transition (EMT) and the reverse process (MET). The mesenchymal state is identified by CD44, a marker of cell adhesion and invasive potential, while epithelial stem cells can be CD44 negative and express ALDH, a marker of mammary stem cells and predictor of poor clinical outcome (6). Cytokines such as IL-6 and TGF-beta, as well as intracellular signaling and miRNAs, may regulate the interconversion of breast cancer stem-like cells between EMT-like and MET-like states.  

We propose a combination of theoretical modeling with experimental validation to study the rates and regulators of EMT/MET transitions in breast cancer stem-like cells. We apply our model to predict alterations in stem cell populations and their regulatory pathways in response to niche-targeted therapy. Ultimately, predictions of therapeutic efficacy and safety could be validated in clinical trials and used to guide drug development and plan therapy duration. We envision that extinction models will provide a novel approach to therapeutic design.