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

机译：与三阴性乳腺癌的治疗逃逸相关的分化状态转换建模

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摘要

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-basalon-luminalon-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.
机译：已经显示出乳腺癌细胞群中的耐药性是通过癌细胞向耐受性状态的表型转变而产生的，例如通过上皮-间充质转变或向癌干细胞状态的转变。但是，许多乳腺肿瘤是细胞类型的异质混合物，除了茎样和间充质表型外，还具有许多表观遗传状态，并且这种异质混合物对药物治疗的动态行为尚不清楚。最近，我们发现分化状态之间的可塑性与细胞内标记物（如细胞角蛋白）所鉴定的抗性相关。在这种情况下，了解分化状态转变的动力学可能有助于开发出表现出表型异质性和可塑性的癌症更有效的治疗方法。在这项工作中，我们开发了一种药物治疗的，表型异质的三阴性乳腺癌（TNBC）细胞系的计算模型，以阐明分化状态转变作为该肿瘤亚型治疗性逃逸机制的可行性。具体而言，我们使用建模来预测分化状态转变的变化，这些变化是我们最近在HCC1143细胞系中观察到的特异性治疗诱导的分化状态标记表达变化的基础。我们报告了HCC1143细胞群体中基础，管腔，间充质和非基础/非管腔/非间充质分化状态之间过渡率的几种统计学上显着的治疗诱导的变化。此外，我们凭经验验证了有关细胞分裂和细胞死亡的模型预测，并在独立的数据集上测试了模型。总的来说，我们证明了靶向治疗诱导的分化状态转变速率的变化可以引起耐药细胞的独特分化状态聚集，这可能是设计具有表型异质性的癌症的改良治疗方案的基础。

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