Network and Pathway Based Analysis of Cancer Progression
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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: [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:// • 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] Genomically-defined subtypes [1] Pathways TCGA_XXXX iTRAQ 114 TCGA_YYYY iTRAQ 115 TCGA_ZZZZ iTRAQ 116 Universal Reference iTRAQ 117 Phosphoproteomics WRI TCGA Proteomi cs Phosphoproteo mics 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 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 <0.01) * * * • Different types of data reveal different aspects of the system • Common pathways provide hypotheses for biological drivers • Network approaches can reveal novel modules that correlate with survival outcomes • Topological analysis predicts points of transition and potential disease drivers Correlated with short survival Correlated with long survival mRNA abundance protein abundance Not observed phosphorylation Weak correlation Weak correlation HDAC Class I E2F ATR Integrin A9B1 PDGFRB RAC1 IL8-CXCR1 Alphasynuclein Caspase PI3 Kinase Pathways Degree Betweenness Closeness 0.0 0.5 1.0 1.5 2.0 2.5 Cancer Drivers Topological Position Fold Enrichment Topological positions in the association network were assessed for statistical enrichment in known cancer drivers and pathogen targets. Asterisks indicate significance (p <0.001). This approach identifies genes/proteins that control information flow in the system and are therefore potential drivers of disease progression [4]. Topological Analysis of Association Networks * * *
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Network and Pathway Based Analysis of Cancer Progression - PowerPoint PPT Presentation
Transcript of Network and Pathway Based Analysis of Cancer Progression
Network and Pathway Based Analysis of Cancer ProgressionJason E. McDermott1, Vladislav A. Petyuk1, Feng Yang1, Marina A. Gritsenko1, Matthew E. Monroe1, Joshua T. Aldrich2, Ronald J. Moore1, Therese R. Clauss1, Anil K. Shukla1, Athena A. Schepmoes1, Rosalie K. Chu2, Samuel H. Payne1, Tao Liu1, Karin D. Rodland1, Richard D. Smith1, 1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA; 2Environmental Molecular Sciences Laboratory, Richland, WA
Overview
AcknowledgementsThis 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.
References1. 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 McDermottBiological Sciences DivisionPacific Northwest National LaboratoryE-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 toInfer drivers of cancer progression- Module members- Topologically important locations- Underlying genetic alterations
Pathway Enrichment
Association Networks
Data Integration
Correlation between mRNA and protein abundanceWithin samplesAcross 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 phosphoproteomicsGenomically-defined subtypes [1] Genomically-defined subtypes [1]
Pathways
TCGA_XXXX
iTRAQ 114
TCGA_YYYY
iTRAQ 115
TCGA_ZZZZ
iTRAQ 116
Universal Reference
iTRAQ 117
Proteomics Phosphoproteomics
WRITCGA
Proteomics Phosphoproteomics
GenomicGene expressionClinical outcomesGenomic 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 pathwayIl12-2 pathwayIl12-STAT4 pathway
AP-1 pathwayNFAT 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
ProteomicsTranscriptomicsProteogenomics aberrant
PDGFRBAndrogen receptor TCR pathway FAKE-cadherin/keratinocyteHIF1 TF
Enriched pathways (adjusted p <0.01)
**
*
• Different types of data reveal different aspects of the system
• Common pathways provide hypotheses for biological drivers
• Network approaches can reveal novel modules that correlate with survival outcomes
• Topological analysis predicts points of transition and potential disease drivers
Correlated with short survival
Correlated with long survival
mRNA abundance
protein abundance
Not observed
phosphorylation
Weak correlation
Weak correlation
HDAC Class IE2FATR
Integrin A9B1
PDGFRBRAC1IL8-CXCR1Alphasynuclein
Caspase
PI3 Kinase
Pathways
Degree Betweenness Closeness0.0
0.5
1.0
1.5
2.0
2.5Cancer DriversPathogen Targets
Topological Position
Fold
Enr
ichm
ent
Topological positions in the association network were assessed for statistical enrichment in known cancer drivers and pathogen targets. Asterisks indicate significance (p <0.001). This approach identifies genes/proteins that control information flow in the system and are therefore potential drivers of disease progression [4].
Topological Analysis of Association Networks
** *
