Network and Pathway Based Analysis of Cancer Progression Jason E. McDermott 1, Vladislav A. Petyuk...
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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
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:
[email protected] 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