TY - GEN
T1 - A model of phenotypic state dynamics initiates a promising approach to control heterogeneous malignant cell populations
AU - Chapman, Margaret P.
AU - Risom, Tyler T.
AU - Aswani, Anil
AU - Dobbe, Roel
AU - Sears, Rosalie C.
AU - Tomlin, Claire J.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/27
Y1 - 2016/12/27
N2 - 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.
AB - 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.
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U2 - 10.1109/CDC.2016.7798634
DO - 10.1109/CDC.2016.7798634
M3 - Conference contribution
AN - SCOPUS:85010818107
T3 - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
SP - 2481
EP - 2487
BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 55th IEEE Conference on Decision and Control, CDC 2016
Y2 - 12 December 2016 through 14 December 2016
ER -