Abstract PO-075: Kinotype to phenotype: Perturbed phosphoproteomic state predicts cancer cell growth rates in vitro

Abstract

Abstract Numerous aspects of cellular signaling are regulated by the kinome - the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation being a key driver of many diseases, particularly cancer. Linked with their role in disease, the druggability of kinases has led to increased interest in the development of kinase inhibitors, with over fifty now having achieved FDA approval. These and other yet to be approved compounds have been used in a variety of contexts, including in screens to test their efficacy as potential therapeutics. Recent proteomics techniques including Kinobeads and multiplexed inhibitor beads linked with mass spectroscopy (MIB/MS) are a relatively new mechanism which affords the ability to assess the state of the protein kinome en masse. When combined with perturbation with targeted kinase inhibitors, the quantification of the dynamic response of the kinome provides a novel platform for the study of cell signaling and potential design of drug therapies. Here, we describe an approach that links the state of the kinome, or kinotype, with a downstream cancer cell phenotype, in this case, cellular growth. Integrating independent perturbation and growth response data sets, we find that the kinotype of a cell has a significant and predictive relationship with cell growth. More specifically, we characterize kinome response to perturbation via Kinobeads within models aimed at predicting cellular growth inhibition rates. Integrating 82 drugs, 237 kinases, and 380 cell lines (both malignant and nonmalignant) into a sparse GLM predicts growth rate inhibition at multiple doses of an unseen small molecule with an r^2 value of 0.624 (Bonferroni corrected p-value of 5.7 × 10^−69), even with an unmatched dataset. We further explore the use of higher-level kinase relationships, i.e., kinome subnetworks, along with manifold learning approaches as a component of these models to better ascertain the potential impact of kinome architecture in cellular response. Together, this systems view of the kinome presents potential opportunities for improved cancer disease classification, the identification of new drug targets, as well as impacting on the broader design of combination therapies for cancer treatment. Citation Format: Isaac S. Robson, Matthew E. Berginski, Shawn M. Gomez. Kinotype to phenotype: Perturbed phosphoproteomic state predicts cancer cell growth rates in vitro [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-075.