Abstract
Cancer stem cells (CSCs) possess capacity to both self-renew and generate all cells within a tumor, and are thought to drive tumor recurrence. Targeting the stem cell niche to eradicate CSCs represents an important area of therapeutic development. The complex nature of many interacting elements of the stem cell niche, including both intracellular signals and microenvironmental growth factors and cytokines, creates a challenge in choosing which elements to target, alone or in combination. Stochastic stimulation techniques allow for the careful study of complex systems in biology and medicine and are ideal for the investigation of strategies aimed at CSC eradication. We present a mathematical model of the breast cancer stem cell (BCSC) niche to predict population dynamics during carcinogenesis and in response to treatment. Using data from cell line and mouse xenograft experiments, we estimate rates of interconversion between mesenchymal and epithelial states in BCSCs and find that EMT/MET transitions occur frequently. We examine bulk tumor growth dynamics in response to alterations in the rate of symmetric self-renewal of BCSCs and find that small changes in BCSC behavior can give rise to the Gompertzian growth pattern observed in breast tumors. Finally, we examine stochastic reaction kinetic simulations in which elements of the breast cancer stem cell niche are inhibited individually and in combination. We find that slowing self-renewal and disrupting the positive feedback loop between IL-6, Stat3 activation, and NF-κB signaling by simultaneous inhibition of IL-6 and HER2 is the most effective combination to eliminate both mesenchymal and epithelial populations of BCSCs. Predictions from our model and simulations show excellent agreement with experimental data showing the efficacy of combined HER2 and Il-6 blockade in reducing BCSC populations. Our findings will be directly examined in a planned clinical trial of combined HER2 and IL-6 targeted therapy in HER2-positive breast cancer.
Highlights
Breast cancer is the most common type of cancer in women, with over 230,000 new cases diagnosed and nearly 40,000 deaths in the United States every year [1]
We explore the effects of varying the rate of symmetric self-renewal in breast cancer stem cell (BCSC), while holding the rates of division, differentiation, and death constant in the progenitor and differentiated cell populations
We have developed a mathematical model to study key regulators of epithelial to mesenchymal transition (EMT) and MET conversions in BCSCs and to predict response to therapies targeting these regulators
Summary
Breast cancer is the most common type of cancer in women, with over 230,000 new cases diagnosed and nearly 40,000 deaths in the United States every year [1]. The majority of deaths are caused by distant recurrence [1]. Stem cell-targeted therapies offer new hope in eradicating breast cancer stem-like cells that lead to recurrence after standard therapies fail [2,3,4]. Mathematical models have proven useful in studying the population dynamics of cancer stem cells under targeted therapy [5, 6]. Models are informative in assessing safety and duration of therapy [7]. The complexities of the stem cell microenvironment limit the predictive ability of analytic models and suggest the necessity of more detailed models pursued through simulation
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