bioRxivCancer Biology
Cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells
T. M. Zikry, S. C. Wolff, J. S. Ranek, H. Davis, A. Naugle, A. A. Whitman, M. R. Kosorok, P. M. Spanheimer, J. E. Purvis
Abstract
The CDK4/6 inhibitor palbociclib blocks cell cycle progression in ER+/HER2- breast tumor cells. Although these drugs have significantly improved patient outcomes in metastatic breast cancers, a small percentage of tumor cells continues to divide in the presence of palbociclib--a phenomenon we refer to as fractional resistance. It is critical to understand the cellular mechanisms underlying fractional resistance because the precise percentage of resistant cells in patient tissue is a strong predictor of clinical outcome. Here, we hypothesize that fractional resistance arises from cell-to-cell differences in core cell cycle regulators that allow a subset of cells to escape CDK4/6 inhibitor therapy. We used multiplex, single-cell imaging to identify fractionally resistant tumor cells both in a cell culture model of ER+/HER2- breast cancer as well as live primary tumor cells resected from a patient. We found that tumor cells capable of proliferating in the presence of palbociclib showed both expected (e.g., CDK2, E2F1) and unexpected (e.g., Cdt1, p21, cyclin B1) shifts in core cell cycle regulators. Notably, resistant cells in both tumor models showed premature enrichment of the G1 regulators E2F1 and CDK2 protein and, unexpectedly, the G2/M regulator cyclin B1 just before cell cycle entry, suggesting that resistant cells may use noncanonical mechanisms to overcome CDK4/6 inhibition. Using computational data integration and trajectory inference approaches, we show how plasticity in cell cycle regulators gives rise to alternate cell cycle "paths" that allow individual ER+/HER2- tumor cells to escape palbociclib treatment. Understanding drivers of cell cycle plasticity, and how to eliminate resistant cell cycle paths, could lead to improved cancer therapies targeting fractionally resistant cells to improve patient outcomes.
- organism: human
- assay: imaging
- research focus: cell signaling & trafficking
- approach: microscopy & imaging analysis
- approach: statistical model
- approach: machine learning (classical)
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