Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects

Abstract

Identifying effective therapeutic strategies that can prevent tumor cell proliferation is a major challenge to improving outcomes for patients with breast cancer. Here we sought to deepen our understanding of how clinically relevant anti-cancer agents modulate cell cycle progression. We genetically engineered breast cancer cell lines to express a cell cycle reporter and then tracked drug-induced changes in cell number and cell cycle phase, which revealed drug-specific cell cycle effects that varied across time. This suggested that a computational model that could account for cell cycle phase durations would provide a framework to explore drug-induced changes in cell cycle changes. Toward that goal, we developed a linear chain trick (LCT) computational model, in which the cell cycle was partitioned into subphases that faithfully captured drug-induced dynamic responses. The model inferred drug effects and localized them to specific cell cycle phases, which we confirmed experimentally. We then used our LCT model to predict the effect of unseen drug combinations that target cells in different cell cycle phases. Experimental testing confirmed several model predictions and identified combination treatment strategies that may improve therapeutic response in breast cancer patients. Overall, this integrated experimental and modeling approach opens new avenues for assessing drug responses, predicting effective drug combinations, and identifying optimal drug sequencing strategies.