Abstract High grade serous ovarian carcinoma (HGSOC) is a highly lethal gynecological malignancy, in large part because most patients develop resistance to the standard of care chemotherapy with platinum and taxanes. We performed deep proteomic and phosphoproteomic characterization on ovarian high-grade serous carcinomas (HGSC) previously characterized genomically by the The Cancer Genome Atlas (TCGA) as part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC; http://proteomics.cancer.gov/programs/cptacnetwork). We constructed an integrated proteogenomic landscape of HGSC and identified functional modules statistically associated with outcomes including platinum resistance and overall survival. Integration of genomic data such as copy number alterations (CNA) with global protein abundance data revealed several chromosomal regions that have significant trans effects on protein expression. These genetically affected proteins were used to construct statistical models capable of predicting survival outcomes. Trans-affected proteins were enriched in proliferation, cell motility and invasion, and immune regulation, hallmarks of cancer. We used statistical methods to identify functional pathways able to discriminate between short surviving and long surviving patients. We found that protein abundance and phosphorylation indicated pathway activity that was able to clearly discriminate between these two groups, but that transcriptional expression and CNAs were not. We integrated proteomic abundance and phosphorylation state to elucidate specific signaling interactions and pathways differentially active in tumors from patients with short overall survival as well as to predict novel kinase substrate relationships important in HGSC. Finally, we identified druggable pathways that are differentially active across proteomic subtypes. A key point arising from our multimodal analysis is that combining different types of molecular data from these tumors greatly increases the ability to identify biologically relevant differences between groups. Using a Lasso-based regression approach to integrate data we show that the most robustly predictive survival models can be obtained using integrated CNA, transcriptome, proteome, and phosphoproteome data than with any of the individual sources of data. Citation Format: Jason E. McDermott, Samuel Payne, Debjit Ray, Vladislav Petyuk, Ronald Moore, Marina Gritsenko, Richard Smith, Karin Rodland. Proteogenomic characterization of high-grade serous ovarian cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 774.