Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions

Martin Lukačišin,Tobias Bollenbach

doi:10.1016/j.cels.2019.10.004

Martin Lukačišin, Tobias Bollenbach

Open Access

https://doi.org/10.1016/j.cels.2019.10.004

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Abstract

Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper's transparent peer review process is included in the Supplemental Information.

Highlights

Combinatorial drug treatment is an increasingly important strategy for combating microbial infections and a powerful tool for understanding the molecular biology of the perturbed cell (Chen and Lahav, 2016; Fischbach, 2011; Pemovska et al, 2018)
To rationally design combination therapies, a deeper understanding of the combinatorial effects of drugs on cell physiology and of the general principles guiding the cellular response to drug combinations is necessary (Cohen et al, 2008)
To quantify gene expression changes in drug combinations independently of growth rate changes, we introduce a new methodology, isogrowth profiling, which is based on measurements at constant growth inhibition achieved by varying ratios of two drugs

Summary

Introduction

Combinatorial drug treatment is an increasingly important strategy for combating microbial infections and a powerful tool for understanding the molecular biology of the perturbed cell (Chen and Lahav, 2016; Fischbach, 2011; Pemovska et al, 2018). When two or more drugs are combined, synergistic or antagonistic interactions can occur. These interactions, respectively, correspond to increased or decreased inhibitory effect of the drug combination compared to the null expectation of additivity (Figure 1A; Bollenbach, 2015; Loewe, 1928). High-throughput techniques for identifying drug interactions (Brochado et al, 2018; Cokol et al, 2011, 2014) and their modifiers (Chevereau and Bollenbach, 2015) have considerably advanced our understanding of drug interactions. To rationally design combination therapies, a deeper understanding of the combinatorial effects of drugs on cell physiology and of the general principles guiding the cellular response to drug combinations is necessary (Cohen et al, 2008)

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