TY - JOUR T1 - Identifying selective drug combinations using Comparative Network Reconstruction JF - bioRxiv DO - 10.1101/2020.12.17.423240 SP - 2020.12.17.423240 AU - Evert Bosdriesz AU - João M. Fernandes Neto AU - Anja Sieber AU - René Bernards AU - Nils Blüthgen AU - Lodewyk F.A. Wessels Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/12/18/2020.12.17.423240.abstract N2 - Inhibition of aberrated signaling processes using targeting inhibitors is an important treatment strategy in cancer. However, due to network effects and the rapid onset of resistance, single drug treatments are often ineffective and responses short-lived. The use of multi-drug combinations has the potential to overcome these problems, but to avoid toxicity such combinations must be selective and the dosage of the individual drugs should be as low as possible while still being effective. Since the search space of possible multi-drug combinations is enormous, especially if the dosing needs to be optimized as well, a systematic method to identify the most promising combinations of drugs and dosages is required. We therefore developed a combined experimental and computational pipeline where we perform a limited set of drug-perturbation experiments, employ these to reconstruct mutant specific signaling networks, and connect changes in signaling output to changes in cell viability. These models can then be used to prioritize selective low-dose multi–drug combinations in silico, based on the mutation profile of the target cell population. As a proof of principle, we applied this approach to a breast cell line and an isogenic clone with an activating PI3K mutation, for which we predicted and validated multiple selective multi-drug combinations. Applying this pipeline to suitably chosen models systems will allow for the identification of biomarker-specific combination treatment regimens.Competing Interest StatementThe authors have declared no competing interest. ER -