TY - JOUR
T1 - Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer
AU - Chapman, Margaret P.
AU - Risom, Tyler
AU - Aswani, Anil J.
AU - Langer, Ellen M.
AU - Sears, Rosalie C.
AU - Tomlin, Claire J.
N1 - Funding Information:
MPC is supported by a National Science Foundation Graduate Research Fellowship (www.nsfgrfp.org), and was supported by the Berkeley Fellowship for Graduate Study (grad.berkeley.edu) and the Tau Beta Pi Engineering Honors Society, Williams No. 35 (www.tbp.org). CJT and MPC were supported by the National Institutes of Health (NIH) Center “Systems Biology of Collective Cells Decisions” through Stanford University NIH #P50GM107615, and by the National Cancer Institute (NCI) CSBC consortia “Model-Based Predictions of Responses to RTK Pathway Therapies” through OHSU NCI #U54CA112970 and “Measuring, Modeling and Controlling Heterogeneity” through OHSU NCI #1U54CA209988-01A1. TR was supported by the Ruth L. Kirschstein T32 Program in Molecular and Cellular Biosciences Training Grant 5T32GM071338-09, Vertex Pharmaceuticals Scholarship (www.vrtx.com), and Tartar Trust Fellowship (www.ohsu.edu). EML was supported by the American Cancer Society Postdoctoral Fellowship (www.cancer.org). RCS is supported by the National Institutes of Health, National Cancer Institute R01-CA196228, R01-CA186241, and U54-CA209988, the Department of Defense Breast Cancer Research ProgramBC160550P1, the Colson Family Foundation (Vancouver, WA), and the Prospect Creek Foundation (Minneapolis, MN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2019 Chapman et al.
PY - 2019/3
Y1 - 2019/3
N2 - 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/nonmesenchymal 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.
AB - 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/nonmesenchymal 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.
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U2 - 10.1371/journal.pcbi.1006840
DO - 10.1371/journal.pcbi.1006840
M3 - Article
C2 - 30856168
AN - SCOPUS:85063615070
VL - 15
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 3
M1 - e1006840
ER -