title: Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
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.
authors.affiliation: Division of Hematology-Oncology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America; Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America.
authors.forename: Mary E
authors.initials: ME
authors.lastname: Sehl
authors.affiliation: Waseda University, Shinjuku, Japan.
authors.forename: Miki
authors.initials: M
authors.lastname: Shimada
authors.affiliation: Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America.
authors.forename: Alfonso
authors.initials: A
authors.lastname: Landeros
authors.affiliation: Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America; Department of Statistics, University of California, Los Angeles, Los Angeles, CA, United States of America.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Lange
authors.affiliation: University of Michigan Comprehensive Cancer Center, Ann Arbor, MI, United States of America.
authors.forename: Max S
authors.initials: MS
authors.lastname: Wicha
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000378:metabolism* / Q000473:pathology* / Q000628:therapy; D002471:Cell Transformation, Neoplastic; D003198:Computer Simulation; D058750:Epithelial-Mesenchymal Transition; D005260:Female; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D014411:Neoplastic Stem Cells / Q000187:drug effects / Q000378:metabolism* / Q000473:pathology*; D055153:Stem Cell Niche; D016896:Treatment Outcome; D059016:Tumor Microenvironment