Large scale genome sequencing efforts have identified tens of thousands of natural and disease variants in large druggable gene families such as protein kinases. However, translating this knowledge into therapeutic strategies remains a challenge due to an incomplete understanding of the mechanisms by which genomic variants alter kinase structure and function. In this talk, I will describe our ongoing efforts combining computational and experimental techniques to identify and characterize statistically significant variants in tyrosine kinases. Using the oncogenic Epidermal Growth Factor Receptor (EGFR) kinase as an experimental system, I will demonstrate how disease variants distal from the active site alter EGFR kinase dynamics and drug sensitivity. I will specifically focus on our recent studies characterizing recurring insertion mutations in a conserved regulatory loop (alphaC-beta4) of EGFR kinase, demonstrating how even subtle variations distal from the active site dramatically impact EGFR signaling and drug sensitivity. Our studies highlight the challenges in predicting disease mutation impact using sequence or structure information alone. To incorporate additional contextual information in disease mutation impact prediction, we are building integrative knowledge graphs that link kinase disease variants with structure, function, pathway, and disease data in human and machine-readable formats. I will demonstrate the application of knowledge graphs in cancer kinome mining and in predicting genome-phenome relationships in large gene families.

Mapping genome-phenome relationships in druggable gene families using a structural systems approach

ACS Spring Meeting 2021

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