GSE138256
Emergent gene expression responses to drug combinations predict higher-order drug interactions
Effective design of combination therapies requires understanding the changes in cell physiology resulting 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. Overall design: Sequencing of polyA RNA from yeast cells grown in 2D discretised gradients of all pairwise combinations of four growth inhibitors using automated reinoculation system to ensure control of cell density and a detailed sampling of conditions with varied growth rate. No drug controls as well as single drug controls at various drug concentrations were also included.