Uncovering Scaling Laws to Infer Multi-drug Response of Resistant Microbes and Cancer Cells

Abstract

Drug resistance in bacterial infections and cancers constitutes a major threat to human health. Treatments often include several interacting drugs, but even potent therapies can become ineffective in resistant mutants. Here we simplify the picture of drug resistance by identifying scaling laws that unify the multi-drug responses of drug sensitive and drug resistant cells. Based on these scaling relationships, we are able to infer the two-drug response of resistant mutants in previously unsampled regions of dosage space in clinically relevant microbes such as E. coli, E. faecalis, S. aureus and S. cerevisiae, as well as in human non-small cell lung cancer, melanoma, and breast cancer stem cells. Importantly, we find that scaling relations also apply across evolutionarily close strains. Finally, scaling allows one to rapidly identify new drug combinations and predict potent dosage regimes for targeting resistant mutants without any prior mechanistic knowledge of the specific resistance mechanism.