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.
Highlights
Heterogeneity of phenotypic states in cancer cell populations is likely driven by both genetic [1] [2] [3] and epigenetic [4] [5] [3] mechanisms, and is linked to the aggressiveness of cancer and its response to therapy
Previous studies have indicated that changes in phenotypic composition of cancer cell populations might be caused by cells transitioning between phenotypes, but details of the transitions are not well-understood due to lack of sufficient time series data
Using a novel data set with wellestablished numerical methods, the results presented here improve our understanding of the phenotypic transitions occurring between drug-treated triple-negative breast cancer cells and have the potential to inform the design of improved cancer treatment strategies
Summary
Heterogeneity of phenotypic states in cancer cell populations is likely driven by both genetic [1] [2] [3] and epigenetic [4] [5] [3] mechanisms, and is linked to the aggressiveness of cancer and its response to therapy. A small subset of cancer cells hypothesized to drive tumorigenesis, were initially implicated as a primary source of phenotypic heterogeneity, since they differentiate generating daughter cells with diverse phenotypic traits [10] [11]. This hierarchical explanation for phenotypic heterogeneity, does not necessarily agree with more recent empirical studies, which suggest that cell-state transition can occur more generally between several types of cancer cells, both stem and non-stem. The same four epithelial differentiation states (two luminal phenotypes and two basal phenotypes) were identified in normal human breast tissues and in human breast cancer tissues, though in altered proportions [14], indicating that the phenotypic states of some epithelial cells switch to different states after the onset of the disease
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