A model of phenotypic state dynamics initiates a promising approach to control heterogeneous malignant cell populations

Margaret P. Chapman, Tyler T. Risom, Anil Aswani, Roel Dobbe, Rosalie C. Sears, Claire J. Tomlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

A growing body of experimental evidence indicates a strong link between intratumoral heterogeneity and therapeutic resistance in cancer. In particular, tumor cells may survive therapy by switching their phenotypic identities to more resistant, drug-tolerant states. Computational models of phenotypic plasticity in response to cytotoxic therapy are needed: (1) to strengthen understanding of the interplay between phenotypic heterogeneity and therapeutic resistance, and (2) to identify potential strategies in silico that weaken resistance prior to in vitro testing. This work presents a linear time-invariant model of phenotypic state dynamics to deduce subpopulation-level behavior likely to affect temporal phenotypic composition and thus drug resistance. The model was identified under different therapeutic conditions with authentic biological data from a breast cancer cell line. Subsequent analysis suggested drug-induced effects on phenotypic state switching that could not be deduced directly from empirical observations. A bootstrap algorithm was implemented to identify statistically significant results: reduction in cell division under each therapeutic condition versus control. Further, Monte Carlo simulation was used to evaluate quality of model fit for two-way switching and net switching on synthetically generated data to determine the limitations of the latter assumption for subsequent modeling. Most importantly, the simple model structure initiated a control-theoretic approach for identifying promising combination treatments in silico to guide future laboratory testing.

Original languageEnglish (US)
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2481-2487
Number of pages7
ISBN (Electronic)9781509018376
DOIs
StatePublished - Dec 27 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

Name2016 IEEE 55th Conference on Decision and Control, CDC 2016

Other

Other55th IEEE Conference on Decision and Control, CDC 2016
CountryUnited States
CityLas Vegas
Period12/12/1612/14/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization

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    Chapman, M. P., Risom, T. T., Aswani, A., Dobbe, R., Sears, R. C., & Tomlin, C. J. (2016). A model of phenotypic state dynamics initiates a promising approach to control heterogeneous malignant cell populations. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 2481-2487). [7798634] (2016 IEEE 55th Conference on Decision and Control, CDC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7798634