Systematic, network-based characterization of therapeutic target inhibitors

A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.Author summary: Most transcription factors are considered “undruggable” in conventional drug discovery. However, a large number of them are discovered to be key tumor dependencies. Thus, targeting these difficult targets has been a challenge for cancer drug discovery. Here, we introduce a novel method, OncoLead, that applies biological networks to identify candidate inhibitors that either directly or in-directly block the activities of these targets. This approach is confirmed by known target-inhibitor interactions in public databases. Furthermore, we predicted new inhibitors for MYC and STAT3, which are validated by in vitro assays.