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

Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, we genetically-engineered breast cancer cell lines express a fluorescently-labelled cell cycle reporter. We then used these cell lines to track drug-induced changes in cell number and cell cycle phase, which revealed drug-specific cell cycle effects that vary across time. We developed a linear chain trick (LCT) computational model, where the cell cycle is partitioned into subphases that can faithfully capture drug-induced dynamic responses. The model correctly infers drug effects and also localizes them to specific cell cycle phases. We used our LCT model to predict the effect of unseen drug combinations and experimentally confirmed the effectiveness of predicted combination treatment strategies. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies.