Genomic technologies have uncovered a wide array of genes that are mutated in cancer, but understanding of how these genes contribute to cancer progression is limited. Protein-protein interactions (PPIs) play a critical role in regulating both normal tissue homeostasis and malignant growth and have now emerged as a new class of drug targets. However, only a small portion of the PPI landscape has been described. A unique high-throughput screening platform was developed and used to generate OncoPPi v1, a lung cancer-associated protein-protein interaction network. OncoPPi includes more than three hundred high confidence PPIs and reveals prominent protein interaction hubs with new partners. Additionally, it uncovers interactions for non-enzymatic proteins, suggests cross-talk between oncogenic pathways and implicates novel mechanisms of action for major oncogene drivers and tumor suppressors.
The c-Myc (MYC) transcription factor is a major cancer driver and a well-validated therapeutic target. However, directly targeting MYC has been challenging. Thus, identifying proteins that interact with and regulate MYC may provide alternative strategies to inhibit its oncogenic activity. To address this critical issue we have developed a NanoLuc®-based protein-fragment complementation assay (NanoPCA) that allows to detect protein-protein interactions (PPI) at the endogenous level as well as weak interactions. Importantly, NanoPCA allows the study of PPI dynamics with reversible interactions. We have utilized NanoPCA to examine MYC interaction with eighty-three cancer-associated proteins in live lung cancer H1299 and colon cancer HCT116 cell lines. Our new MYC PPI data confirmed known MYC-interacting proteins, such as MAX, GSK3A, and SMARCA4, and revealed a panel of novel MYC interaction partners.
The PPIs mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. A recently developed computational method (MEDICI) allows to predict the effect of disruption of individual PPI on cell viability. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. The current version of PPI Essentiality dataset include the data calculated for 7,906 PPIs in 206 cell lines of 19 tumor types.
The OncoPPi protein-drug connectivity network allows to explore the connectivity between the tumor suppressors, oncogenes, and the FDA approved drugs to facilitate development of new intervention strategies for tumor suppressors. A direct linkage of tumor suppressors with actionable cancer targets may reveal tumor dependencies linked to tumor suppressor status.