Predicting anti-cancer drug combination responses with a temporal cell state network model

摘要

Author summaryCancer chemotherapy combines multiple drugs, but determining which drugs would be efficacious for particular patients remains extremely challenging. Experimental solutions to this problem are not yet possible due to the large space of possible combinations of hundreds of anti-cancer drugs. Computational models may help, but it is not yet clear how such models should be built and what elements they need to capture to predict drug combination response. In this work, we explored the idea that if we knew something about the proportions of different types of cancer cells in a population, how fast transitions happen between the different types, and how individual drugs affect those transitions, that we might be able to build a computational model that predicts drug combination responses based only on feasible single drug response experiments. We tested this idea using simple cell culture systems with two different cancer cell lines and three different anti-cancer drugs, and found surprisingly good agreement between computational model predictions and experimentally measured drug combination responses. While further application to different cell lines, more drugs, and more complex experimental systems remain, this work demonstrates how currently available or attainable information could be sufficient for prediction of drug combination responses. Cancer chemotherapy combines multiple drugs, but predicting the effects of drug combinations on cancer cell proliferation remains challenging, even for simple in vitro systems. We hypothesized that by combining knowledge of single drug dose responses and cell state transition network dynamics, we could predict how a population of cancer cells will respond to drug combinations. We tested this hypothesis here using three targeted inhibitors of different cell cycle states in two different cell lines in vitro. We formulated a Markov model to capture temporal cell state transitions between different cell cycle phases, with single drug data constraining how drug doses affect transition rates. This model was able to predict the landscape of all three different pairwise drug combinations across all dose ranges for both cell lines with no additional data. While further application to different cell lines, more drugs, additional cell state networks, and more complex co-culture or in vivo systems remain, this work demonstrates how currently available or attainable information could be sufficient for prediction of drug combination response for single cell lines in vitro.