Abstract 208: Proteomic measurements of protein abundance and phosphorylation identify novel kinase-substrate relationships in ovarian cancer

Abstract

Abstract As part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC), we have recently published the first large-scale proteomic and phosphoproteomic analysis of high-grade serous ovarian tumors. We observed that phosphorylation status was an excellent indicator of pathway activity and could discriminate between patient survival times. This dataset covers tumor samples from 69 patients with deep phosphoproteomic depth (>20,000 phosphopeptides confidently identified). Our continuing analysis of this dataset, reported here, has revealed that the correlation between kinase protein abundance and abundance of phosphorylated target peptides is very low, indicating that kinase abundance is not a good proxy for phosphorylation status overall. However, highly correlated kinase-substrate pairs are significantly more likely to be true relationships (from existing knowledge), demonstrating that this method could be used to predict novel kinase targets in some cases. Using this approach we predicted novel kinase-target relationships and constructed a kinase activity network of ovarian cancer. To better analyze cancer-relevant pathway activity we developed a novel approach that characterizes correlation, differential abundance, and statistical interactions between components to analyze multiple omics types in the context of signaling and functional pathways. We used this approach, called the Layered Enrichment Analysis of Pathways (LEAP), to identify active pathways in molecular subtypes of ovarian cancer, contrasting observations in patients stratified for short versus long overall survival. This analysis resulted in a pre-treatment protein-based signature that is significantly predictive of overall survival. Our results show that integration of multiple omics types has the ability to inform our understanding of novel kinase-substrate interactions, and potentially identify novel interactions associated with patient survival. Note: This abstract was not presented at the meeting. Citation Format: Jason E. McDermott, Tao Liu, Samuel Payne, Vladislav Petyuk, Hui Zhang, Zhen Zhang, Daniel Chan, Richard Smith, Karin Rodland, Clinical Proteomic Tumor Analysis Consortium. Proteomic measurements of protein abundance and phosphorylation identify novel kinase-substrate relationships in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 208. doi:10.1158/1538-7445.AM2017-208