The Emory Molecular Interaction Center for Functional Genomics (MicFG), with its expertise in high throughput technologies for the study of protein-protein interactions (PPI) , productive track record of innovative HTS assay development for chemical lead discovery, and proven cancer genomics mining, database, and data integration capabilities, seeks to become a node in the NCI Cancer Target Discovery and Development (CTDD) Network. Genomic alterations in various tumor types, as revealed by cancer genomics initiatives such as The Cancer Genome Atlas (TCGA), often lead to re-wired protein-protein interaction networks, which in turn drive tumor initiation and progression. Thus, identifying prominent PPI nodes and networks among oncoproteins and tumor suppressors as enriched by various genomics datasets and the validation of their critical roles in tumorigenesis and progression are expected to reveal an entirely new class of PPI-based cancer targets for therapeutic development. To achieve this goal, we propose to utilize our established high throughput PPI technologies to interrogate cancer genomics information through a team science approach (i) to rapidly establish oncogenic PPI networks of selected cancer types based on TCGA and other genomic datasets, (ii) to validate functional roles of key PPI nodes or hubs in tumorigenesis and progression, (iii) to develop HTS assays for critical tumor-associated PPIs to enrich the therapeutic target pipeline of the NCI and the drug discovery field, and (iv) to leverage our informatics capability for genomics data mining for prioritized PPI mapping, functional PPI validation, and trans-network data sharing and collaboration.
We aim to bridge the gap between vast genomic datasets and therapeutic discovery by establishing and interrogating the cancer PPI target space. With our PPI expertise and state-of-the-art high throughput technologies, which is highly adaptable to a variety of outputs, we intend to function as an active, synergistic member in collaborative trans- Network projects.
While current drug discovery is primarily focused on a single molecular target, such as a kinase or a transmembrane receptor, our project intends to target the interface between two proteins (protein-protein interactions) to disrupt the signaling networks that cancers rely on. Genomic alterations in cancer often lead to re-wired protein interaction networks. Interrogation of these cancer-associated protein-protein interactions with high throughput methods offers new opportunities for the discovery of novel therapeutics.
|Qi, Q; Kang, S S; Zhang, S et al. (2017) Co-amplification of phosphoinositide 3-kinase enhancer A and cyclin-dependent kinase 4 triggers glioblastoma progression. Oncogene 36:4562-4572|
|Ivanov, A A; Gonzalez-Pecchi, V; Khuri, L F et al. (2017) OncoPPi-informed discovery of mitogen-activated protein kinase kinase 3 as a novel binding partner of c-Myc. Oncogene 36:5852-5860|
|Li, Zenggang; Ivanov, Andrei A; Su, Rina et al. (2017) The OncoPPi network of cancer-focused protein-protein interactions to inform biological insights and therapeutic strategies. Nat Commun 8:14356|
|Mo, Xiulei; Qi, Qi; Ivanov, Andrei A et al. (2017) AKT1, LKB1, and YAP1 Revealed as MYC Interactors with NanoLuc-Based Protein-Fragment Complementation Assay. Mol Pharmacol 91:339-347|
|Umstead, MaKendra; Xiong, Jinglin; Qi, Qi et al. (2017) Aurora kinase A interacts with H-Ras and potentiates Ras-MAPK signaling. Oncotarget 8:28359-28372|
|Zhu, G; Qi, Q; Havel, J J et al. (2017) PRAS40 promotes NF-?B transcriptional activity through association with p65. Oncogenesis 6:e381|
|Ivanov, Andrei A; Revennaugh, Brian; Rusnak, Lauren et al. (2017) The OncoPPi Portal: an integrative resource to explore and prioritize protein-protein interactions for cancer target discovery. Bioinformatics :|
|Cancer Target Discovery and Development Network (2016) Transforming Big Data into Cancer-Relevant Insight: An Initial, Multi-Tier Approach to Assess Reproducibility and Relevance. Mol Cancer Res 14:675-82|
|Gu, Lubing; Zhang, Hailong; Liu, Tao et al. (2016) Discovery of Dual Inhibitors of MDM2 and XIAP for Cancer Treatment. Cancer Cell 30:623-636|
|Mo, Xiu-Lei; Luo, Yin; Ivanov, Andrei A et al. (2016) Enabling systematic interrogation of protein-protein interactions in live cells with a versatile ultra-high-throughput biosensor platform. J Mol Cell Biol 8:271-81|
Showing the most recent 10 out of 15 publications