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Network and Pathway Based Analysis of Cancer Progression Jason E. McDermott 1, Vladislav A. Petyuk 1, Feng Yang 1, Marina A. Gritsenko 1, Matthew E. Monroe 1, Joshua T. Aldrich 2, Ronald J. Moore 1, Therese R. Clauss 1, Anil K. Shukla 1, Athena A. Schepmoes 1, Rosalie K. Chu 2, Samuel H. Payne 1, Tao Liu 1, Karin D. Rodland 1, Richard D. Smith 1, 1 Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA; 2 Environmental Molecular Sciences Laboratory, Richland, WA Overview Acknowledgements This work was supported by grant U24-CA-160019 from the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the DoD under MIPR2DO89M2058. Experimental work was performed in the Environmental Molecular Science Laboratory, a DOE/BER national scientific user facility at Pacific Northwest National Laboratory (PNNL) in Richland, Washington. PNNL is operated for the DOE by Battelle under contract DE-AC05-76RLO-1830. References 1.Verhaak RG, Tamayo P, Yang JY, Hubbard D, Zhang H, et al. (2013) Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J Clin Invest 123: 517-525. 2.Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102: 15545-15550. 3.Ideker T, Ozier O, Schwikowski B, Siegel AF (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18 Suppl 1: S233-240 4.McDermott JE, Costa M, Janszen D, Singhal M, Tilton SC (2010) Separating the drivers from the driven: Integrative network and pathway approaches aid identification of disease biomarkers from high-throughput data. Dis Markers 28: 253-266 Conclusions CONTACT: Jason McDermott Biological Sciences Division Pacific Northwest National Laboratory E-mail: jason.mcdermott@pnnl.gov Generate an integrated co-expression/co-abundance network - Integrated transcriptomics, proteomics, and phosphoproteomics data - Statistical network inference across all samples - Hold out survival data and other genomic data Identifies active subnetworks [3] from co-abundance network - Searches for regions of network enriched in correlation with survival Assesses functional coherence of subnetwork modules to Infer drivers of cancer progression - Module members - Topologically important locations - Underlying genetic alterations Pathway Enrichment Association Networks Data Integration Correlation between mRNA and protein abundance Within samples Across samples mRNA alone protein alone Data Availability P = 0.007 P = 0.005 Kaplan-Meier survival based on mutation, CNV, and mRNA expression for five gene signatures from network modules (http://www.cbioportal.org) Ovarian cancer as a test case Multiple layers of omic data for the same samples Integration of data to investigate correlates of survival Traditional approaches do not appear to give robust results Hypothesis: Considering disease processes at the network and pathway level will improve ability to elucidate biological drivers of disease Activated in short survival Activated in long survival PDGFRB Pathway Subtype Analysis Global proteomics Global phosphoproteomics Genomically-defined subtypes [1] Pathways TCGA_XXXX iTRAQ 114 TCGA_YYYY iTRAQ 115 TCGA_ZZZZ iTRAQ 116 Universal Reference iTRAQ 117 Proteomics Phosphoproteomics WRI TCGA WRI TCGA Proteomics Phosphoproteomics Genomic Gene expression Clinical outcomes Genomic subtypes Subtype analysis Comparisons Functional pathway analysis Network analysis What are the functional- and pathway-level correlates of survival in ovarian cancer? PNNL/CPTAC Module 1 (short survival) Module 2 (long survival) CD8 T cell receptor downstream pathway Il12-2 pathway Il12-STAT4 pathway AP-1 pathway NFAT TF pathway Correlated with short survival Correlated with long survival Protein Phosphorylated protein mRNA Comparison of NCI Protein Interaction Database pathways enriched in tumors from short- or long-term survivors based on GSEA [2] across all tumors examined. PDGFRB IL-12/2 CD8 TCR Angiopoietin receptor AP-1 ARF-3 AVB3 OPN ERBB1 downstream IL-6/7 Lysophospholipid Netrin WNT NFAT TF PDGFRB IL-12/2 CXCR4 FAK AMB2 Neutrophils Thrombin PAR1 TXA2 TCPTP Proteomics Transcriptomics Proteogenomics aberrant PDGFRB Androgen receptor TCR pathway FAK E-cadherin/keratinocyte HIF1 TF Enriched pathways (adjusted p