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PLoS Comput Biol. 2019 Mar 11;15(3):e1006840. doi: 10.1371/journal.pcbi.1006840. eCollection 2019 Mar.

Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer.

Author information

1
Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, California, United States of America.
2
Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, United States of America.
3
Department of Industrial Engineering and Operations Research, University of California Berkeley, Berkeley, California, United States of America.
4
Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon, United States of America.
5
Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, Oregon, United States of America.

Abstract

Drug resistance in breast cancer cell populations has been shown to arise through phenotypic transition of cancer cells to a drug-tolerant state, for example through epithelial-to-mesenchymal transition or transition to a cancer stem cell state. However, many breast tumors are a heterogeneous mixture of cell types with numerous epigenetic states in addition to stem-like and mesenchymal phenotypes, and the dynamic behavior of this heterogeneous mixture in response to drug treatment is not well-understood. Recently, we showed that plasticity between differentiation states, as identified with intracellular markers such as cytokeratins, is linked to resistance to specific targeted therapeutics. Understanding the dynamics of differentiation-state transitions in this context could facilitate the development of more effective treatments for cancers that exhibit phenotypic heterogeneity and plasticity. In this work, we develop computational models of a drug-treated, phenotypically heterogeneous triple-negative breast cancer (TNBC) cell line to elucidate the feasibility of differentiation-state transition as a mechanism for therapeutic escape in this tumor subtype. Specifically, we use modeling to predict the changes in differentiation-state transitions that underlie specific therapy-induced changes in differentiation-state marker expression that we recently observed in the HCC1143 cell line. We report several statistically significant therapy-induced changes in transition rates between basal, luminal, mesenchymal, and non-basal/non-luminal/non-mesenchymal differentiation states in HCC1143 cell populations. Moreover, we validate model predictions on cell division and cell death empirically, and we test our models on an independent data set. Overall, we demonstrate that changes in differentiation-state transition rates induced by targeted therapy can provoke distinct differentiation-state aggregations of drug-resistant cells, which may be fundamental to the design of improved therapeutic regimens for cancers with phenotypic heterogeneity.

PMID:
30856168
PMCID:
PMC6428348
DOI:
10.1371/journal.pcbi.1006840
[Indexed for MEDLINE]
Free PMC Article

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