The discovery of novel members of tumorigenic pathways remains a critical step to fully dissect the molecular biology of cancer. Indeed, because a number of cancer drivers are themselves undruggable, elucidating the signaling apparatuses in which they participate is essential for discovering novel therapeutic targets that will allow the treatment of aggressive neoplastic growth. In the context of oncoproteins and tumor suppressors, novel participants may be upstream regulators, downstream effectors, or physical cognate binding partners. In this work, we develop in silico approaches to more fully elucidate the tumorigenic signaling machinery used by tumor suppressors and oncoproteins. We first report applications of machine-learning algorithms to integrate diverse networkbased information to generate testable hypotheses of proteins involved in canonical oncogenic pathways. We develop the OncoSig algorithm to elucidate novel members of protein-centric maps to elucidate upstream modulators, cognate binding partners, and downstream effectors for any tumor suppressor or oncogene in a tumor-specific fashion. We specifically apply OncoSig to elucidate the oncogenic KRAS regulatory map in Lung adenocarcinoma (LUAD). Oncogenic KRAS is a key driver of aggressive tumor growth in many LUAD patients, yet has no FDA-approved drugs targeting it. Thus, elucidating members of the KRAS protein-centric map is critical for discovering synthetic lethal interactions that may be subject to therapeutic targeting. Critically, 18/22 of novel predicted KRAS interactors elicited synthetic lethality in LUAD organoid cultures that harbored an activating KRAS mutation. We then extend the OncoSig algorithm to 10 oncogenic/tumor suppressor pathways (such as TP53, EGFR, and PI3K), and show that OncoSig is able to recover known regulators and downstream effectors of these critical mediators of tumorigenesis. We then focus specifically on dissecting KRAS’s physical protein-protein interactions. Many cognate binding partners bind to KRAS via a structurally conserved RAS-Binding Domain (RBD), thus propagating KRAS signal transduction. Thus, for example, CRAF, PI3K, and RALGDS, all bind to KRAS via an RBD. To elucidate novel KRAS protein-protein interactors, we use structural and sequence based approaches to discover biophysical properties of known RBDs. We apply the PrePPI algorithm, which predicts novel protein-protein interactions based on structural similarity, and find that PrePPI successfully recovers known RBDs while discriminating from domains structurally similar to the RBD that do not bind to KRAS. Using this information, we develop biophysical features to computationally predict novel KRAS binding partners. Finally, we report computational and experimental work addressing whether KRAS forms a homodimer. The precise mechanism for how KRAS propagates signal transduction after binding to the RBD remains elusive, and KRAS homo-dimerization, for example, may play a key role in KRAS induced tumorigenesis. Using Analytical Utracentrifugation to measure binding affinity, we find that KRAS forms either a weak dimer or a large non-specific multimer. Furthermore, analysis of KRAS protein structures deposited in the Protein Data Bank reveals key regions that have a propensity to form homodimer contacts in the crystal complexes, and may mediate KRAS homo-dimerization in a biological setting as well. These results provide mechanistic insight into how KRAS dimerization may facilitate cellular signal transduction.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8NZ8M76 |
| Date | January 2018 |
| Creators | Broyde, Joshua |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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