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A model for the intrinsic limit of cancer therapy: Duality of treatment-induced cell death and treatment-induced stemness

Monday, July 17 at 6:00pm

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Room assignment: in The Ohio Union.
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Erin Angelini

University of Washington
"A model for the intrinsic limit of cancer therapy: Duality of treatment-induced cell death and treatment-induced stemness"
Intratumor cellular heterogeneity and non-genetic cell plasticity in tumors pose a recently recognized challenge to cancer treatment. Because of the dispersion of initial cell states within a clonal tumor cell population, a perturbation imparted by a cytocidal drug only kills a fraction of cells. Due to dynamic instability of cellular states the cells not killed are pushed by the treatment into a variety of functional states, including a “stem-like state” that confers resistance to treatment and regenerative capacity. This immanent stress-induced stemness competes against cell death in response to the same perturbation and may explain the near-inevitable recurrence after any treatment. This double-edged-sword mechanism of treatment complements the selection of preexisting resistant cells in explaining post-treatment progression. Unlike selection, the induction of a resistant state has not been systematically analyzed as an immanent cause of relapse. Here, we present a generic elementary model and analytical examination of this intrinsic limitation to therapy. We show how the relative proclivity towards cell death versus transition into a stem-like state, as a function of drug dose, establishes either a window of opportunity for containing tumors or the inevitability of progression following therapy. The model considers measurable cell behaviors independent of specific molecular pathways and provides a new theoretical framework for optimizing therapy dosing and scheduling as cancer treatment paradigms move from “maximal tolerated dose,” which may promote therapy induced-stemness, to repeated “minimally effective doses” (as in adaptive therapies), which contain the tumor and avoid therapy-induced progression.
Additional authors: Yue Wang (UCLA); Joseph Xu Zhou (Novartis Institutes); Hong Qian (University of Washington); Sui Huang (Institute for Systems Biology)

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Annual Meeting for the Society for Mathematical Biology, 2023.