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

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. Author summary Some classes of breast cancer tumors are composed of cells with different sets of observable traits, or phenotypes. The phenotype corresponds to particular cellular functionality and can arise due to the genetic/epigenetic code inside the cell, the environment outside the cell, and the genotype-environment interaction. Interestingly, treating a population of cancer cells with specific targeted therapies can stimulate changes in the phenotypic make-up of the population, contributing to resistance against the drug. 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 well-established 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.