KiRNet: Kinase-centered network propagation of pharmacological screen results

Abstract

Summary: The ever-increasing size and scale of biological information have popularized network-based approaches as a means to interpret these data. We develop a network propagation method that integrates kinase-inhibitor-focused functional screens with known protein-protein interactions (PPIs). This method, dubbed KiRNet, uses an a priori edge-weighting strategy based on node degree to establish a pipeline from a kinase inhibitor screen to the generation of a predictive PPI subnetwork. We apply KiRNet to uncover molecular regulators of mesenchymal cancer cells driven by overexpression of Frizzled 2 (FZD2). KiRNet produces a network model consisting of 166 high-value proteins. These proteins exhibit FZD2-dependent differential phosphorylation, and genetic knockdown studies validate their role in maintaining a mesenchymal cell state. Finally, analysis of clinical data shows that mesenchymal tumors exhibit significantly higher average expression of the 166 corresponding genes than epithelial tumors for nine different cancer types. Motivation: We have previously developed a powerful ensemble modeling approach called Kinome Regularization (KiR) that uses a functional kinase inhibitor screen to predict key kinases contributing to a phenotype, such as cell migration. However, kinase-mediated protein-protein interactions (PPIs) might also contribute to the phenotype. We sought to extend the method by integrating hits with known PPIs to accurately model relationships among them and robustly identify additional non-kinase targets. By adapting existing network propagation principles and methods and optimizing them for KiR's kinase predictions, we develop a complete, robust pipeline to go from a drug screen to kinase-centered, functional network modules.

Keywords

• kinase
• network
• propagation
• computational
• modeling
• protein