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

Abstract

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.

Fig 1. Modeling temporal cell states and single drug dose responses.

(a) Graphical abstract. Schematic showing the idea that integration of single drug dose response experiments with temporal cell state network models might enable prediction of drug combination responses. (b) Schematic of the temporal cell state network comprising G0/G1, late G1/S and G2/M states and the activity of the drugs PD0325901, Abemaciclib and TAK-960 in each state. In this work, we assume that the action of each drug is specific to the state indicated by color. Cells exiting G2/M divide when they re-enter G0/G1. (c) Time courses of cell number in the temporal cell state model for U87 and U251 cells starting with 100 cells for 72 hours. Cell proportions at G0/G1, late G1/S and G2/M states are also shown, which remain constant. (d) Single drug dose responses for PD0325901, Abemaciclib and TAK-960 in U87 and U251 cells at 72 hours (points) compared to model predictions (lines). Error bars denote standard error.

Fig 2. Model prediction vs experiments for drug combination responses.

Predicted and measured combination drug dose responses for Abemaciclib/PD0325901, Abemaciclib/TAK-960 and PD0325901/TAK-960 for U87 cells (top) and U251 cells (bottom). First column is relative cell counts for model simulations, second column is relative cell counts for experiments, and the third column is a scatterplot for model vs experiment for relative cell counts. The drug concentrations (nM) for Abemaciclib and PD0325901 are 0, 1.22, 4.88, 19.53, 78.13, 312.5, 1250, and 5000, and for TAK-960 are 0, 0.012, 0.049, 0.20, 0.78, 3.13, 12.5, and 50. The correlation coefficient for agreement between model and experiment for each cell line/drug combination pair is indicated. Error bars denote standard error.

Fig 3. Excess Over Bliss Analysis.

Excess over Bliss (EOB) for each drug combination dataset in simulations and experiments were calculated as described in Methods, and mean summarized to obtain as single EOB score for each cell line / drug combination pair. Positive scores denote synergy while negative scores denote antagonism. Error bars are standard error.