Discovering anticancer drug target combinations via network-informed signaling-based approach

Oncologists deciding on cancer treatments must make difficult decisions as to which prescription and implementation strategies would best suit each patient. Much is still unknown about combinations of prescription drugs as there are many to choose from. At the outset, the oncologist reckons with at least two established facts: (i) patients receiving successive single molecules treatments are likely to experience drug resistance, and (ii), to select optimal drug combinations requires to pick the 'best' protein drug target combinations. Intuitively, target selection should precede drug selection, implying that well-informed strategies would opt to first consider drug targets - not drugs - combinations. Nowadays, drug combinations that oncologists consider are empirical and limited. They are restricted primarily by observations and praxis, that is, scant clinical experience with their application.

Here we develop a strategy for selecting optimal drug target combinations following nature. We use protein-protein interaction networks and shortest paths to discover communication pathways in cells based on interaction network topology. Our strategy mimics cancer signaling in drug resistance, which commonly harnesses pathways parallel to those blocked by drugs, thereby bypassing them.

We select key communication nodes as combination drug targets inferred from topological features of networks. We test our network-informed signaling-based approach to discover anticancer drug target combinations on available clinical data, patient-derived breast and colorectal cancers. Alpelisib + LJM716 and alpelisib + cetuximab + encorafenib combinations diminish tumors in breast and colorectal cancers, respectively.

Our network-based approach discovers optimal protein co-target combinations to counter resistance, selecting co-targets from alternative pathways and their connectors.