Kimberly Glass, Network model - Ovarian Cancer, fged_seattle_2013
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A Network Model for Controlling and Potentially Reversing Angiogenic Progression in Ovarian Cancer
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Kimberly Glass, Network model - Ovarian Cancer, fged_seattle_2013
- 1. A Network Model for Controlling and Potentially Reversing Angiogenic Progression in Ovarian Cancer Kimberly Glass Functional Genomics Data Society June 20, 2013
- 2. • Biological processes are driven not by individual genes but by the networks linking those genes • Ultimately, we look to develop models that describe the interactions driving different biological systems • We want to find networks using available genomic data (largely expression data) • Correlations in gene expression can be considered to be the result of network interactions • The question is not “Is this model right?” Rather, the question is “Is the model useful?” Why We Care About Networks
- 3. Normal Tissue Network Chemosensitive Tumor Chemoresistant Tumor What can we learn from networks?
- 4. EpigeneticsProtein-protein interactions Protein-DNA interactions gene expression Data- Specific Regulatory Network data integration Regulation of Transcription
- 7. Another Idea: Message Passing Transcription Factor Downstream Target The TF is Responsible for communicating with its Target The Target must be Available to respond to the TF GC Yuan, Curtis Huttenhower, John Quackenbush
- 8. Passing Messages between Biological Networks Protein-protein interactions Protein-DNA interactions Genomic Data Gene Expression Network Representation Cooperation between TFs Potential Regulatory Events genes genes Potential Co- Regulatory Events Use Message Passing to find a consensus among the networks InitialNetwork Information Message Passing LearnedNetwork Information
- 9. Message-Passing Networks: PANDA (Passing Attributes between Networks for Data Assimilation) PPI0 Expression0 Network1 Responsibility Availability Network0 Motif Data Expression1PPI1 Glass et. al. “Passing Messages Between Biological Networks to Refine Predicted Interactions.” PLoS One. 2013 May 31;8(5):e64832. Implementation available on sourceforge: http://sourceforge.net/projects/panda-net/
- 10. Application of PANDA to Ovarian Cancer Subyptes
- 11. A new subtype of ovarian cancer
- 12. A new subtype of ovarian cancer • mRNA/miRNA and DNA were extracted from 132 well-annotated FFPE samples and profiled on arrays • A technique called ISIS was used find robust splits in the data • A major, robust split was associated with expression of angiogenesis genes • Published gene expression data was curated and used to validate the split and signature
- 13. Genes Conditions Expression data (Angiogenic) Genes Conditions Expression data (Non-angiogenic) Application of PANDA to Ovarian Cancer • Downloaded expression data from 510 OvCa patients from TCGA. Normalized data using fRMA and mapped probes to EnsEMBL IDs using BiomaRt • Assigned subtypes using a Gaussian Mixture Model using Mclust: Identified 188 angiogenic, 322 non- angiogenic patient samples. • Combined with TF motif and PPI data and used PANDA to map out networks. Network for Angiogenic Subtype Network for Non-angiogenic Subtype Interaction data Motif data Compare and Identify Differences GC Yuan, Dimitrios Spentzos, John Quackenbush
- 14. 12631 unique edges, Including 56 TFs Targeting 4081 genes 15735 unique edges, Including 49 TFs Targeting 4419 genes Each point: TF→gene edge An individual gene can actually be targeted in both subnetworks, although by different upstream transcription factors. Gene Overlap 1828 25912253 Genes Targeted in Angiogenic Subnetwork Genes Targeted in Non- Angiogenic Subnetwork Network Differences are Captured in Edges
- 15. Key Regulators of Angiogenesis in OvCa TFTF Edges from Regulator in Angiogenic Subnetwork Edges from Regulator in Non-Angiogenic Subnetwork Calculate an “Edge Enrichment” and corresponding significance
- 16. TF Potential Connection with Angiogenesis/Cancer Publication(s) PMID NFKB1 important chromatin remodeler in angiogenesis 20203265 ARID3A required for hematopoetic development 21199920 SOX5 involved in prostate cancer progression, responsive to estrogen 19173284, 16636675 TFAP2A increases MMP2 expression and angiogenesis in melanoma 11423987 NKX2-5 regulates heart development 10021345 PRRX2 deletion cause vascular anomalies 10664157 AHR knock-out impairs angiogenesis 19617630 SPIB inhibits plasma cell differentiation 18552212 MZF1 represses MMP-2 in cervical cancer 22846578 BRCA1 inhibits VEGF and represses IGF1 in breast cancer 12400015, 22739988 Key Regulators of Angiogenesis in OvCa
- 17. TF differential Expression Target differential Expression TF differential Methylation Target differential Methylation Target genes’ availability to be regulated is made possible through epigenetic modifications Key Regulators of Angiogenesis in OvCa Some TFs are acting as transcriptional repressors.
- 18. A+ A- A+;N- N+;A- N- N+ yes yes yes yes no no no no yes yes yes yes yes no yes no yes no no yes no yes no yes 927 1326 624 1204 982 1609 Gene Group Nickname Gene targeted in Angiogenic Subnetwork Gene targeted in non-Angiogenic Subnetwork Gene’s expression increases in Angiogenic tumors Gene’s expression increases in non-Angiogenic tumors Number of Genes in Group 1828 25912253 Genes Targeted in Angiogenic Subnetwork Genes Targeted in Non- Angiogenic Subnetwork Both Activation and Repression of Pathways is Important in Angiogenesis • "A+/A-" genes targeted and more highly/lowly expressed in angiogenic subtype • "A+;N-" genes are targeted in both subnetworks and more highly expressed in angiogenic subtype • "N+;A-" genes are targeted in both subnetworks and more highly expressed in non-angiogenic subtype • "N-/N+" genes targeted in the non-angiogenic subnetwork but are more highly/lowly expressed in angiogenic subtype
- 19. Both Activation and Repression of Pathways is Important in Angiogenesis
- 20. Transcription Factors Genes (expression higher in angiogenic subtype) Edges unique to Angiogenic Subnetwork Edges unique to Non-Angiogenic Subnetwork Genes (expression higher in non-angiogenic subtype) A- A+;N- N- A+ N+;A- N+ A Network Model of Angiogenesis
- 21. Complex Regulatory Patterns Emerge Key TF Co-TF P-value # ARID3A PRRX2 1.16E-23 244 ARID3A SOX5 1.01E-14 155 PRRX2 SOX5 3.83E-12 157 ARID3A PRRX2 SOX5 129 115 26 48 162 2828 A+ ARID3A PRRX2 SOX5 Top Combinatorial Pairs: A+ 46 AHR ARNT ETS1 MZF1 273 10 1 82 1 1 53 33 3 60 17 68 27 50 N- AHR ARNT MZF1 ETS1 Top Combinatorial Pairs: N- Key-TF Co-TF P-value # MZF1 ARNT 5.83E-23 92 AHR ARNT 6.13E-16 382 MZF1 ETS1 9.08E-16 148 TF1 TF2 P-value # ARNT ETS1 2.19E-23 149 AHR ETS1 9.08E-16 101 AHR MZF1 2.68E-7 58 What is the role of ETS1 and ARNT in angiogenesis?
- 22. • ARNT and ETS1 dimerization with HIF1a and HIF2a, respectively, play a role VEGF production. However, AHR can inhibit this by competing as an alternate dimerzation partner. There is a small molecule ligand that dimerizes with HIF2a and which may block angiogenesis AHR agonists may also help to prevent activation of the angiogenic pathway • ARID3A, SOX5 and PRRX2 activate many genes through CpG-poor promoters Therapies altering methylation may alter some of these transcriptional programs Regulatory Patterns Suggest Therapies X X VEGF production and angiogenesis HIF1a ARNT HIF2a ETS1 HIF1a ARNT HIF2a ETS1 AHR AHR AHR AHR AHR (1) Prevent ARNT/HIF1a and ETS1/HIF2a dimerization (2) Promote ARNT/AHR and ETS1/AHR dimerization TREATMENT MODEL ANGIOGENIC BEHAVIOR (3) Decrease genome- wide methylation ARID3A SOX5 PRRX2 High levels of CpG methylation TF2TF1 A+ ARID3A PRRX2 SOX5 N- AHR ARNT MZF1 ETS1
- 23. Other Disease Datasets Provide Validation Standard platinum-based therapies may actually be priming the cellular network to towards angiogenesis. GEO treatment conditions control conditions 2) RMA- normalize 3) Compute differential expression (T-statistic) T-statistic #genes 1) Download data 4) Compute summary statistic for Gene Group
- 24. Other Disease Datasets Provide Validation The effects of the VEGF-inhibiting drug Sorafenib directly correspond to groups of network-identified genes.
- 25. Other Disease Datasets Provide Validation Expression of Network-identified A+ genes decreases with proposed treatments.
- 26. What’s Next? • Represent other genomic data in the network model • Investigate networks underlying other cancers/diseases • Continue to think about how biological mechanisms are represented in network models • Use network predictions to hypothesize on treatments • The question is not “Is this model right?” Rather, the question is “Is the model useful?”
- 27. Acknowledgements PANDA Development Guo-Cheng Yuan John Quackenbush Curtis Huttenhower Angiogenic Subtyping Benjamin Haibe-Kains John Quackenbush Ursula Matulonis Ovarian Network Analysis Guo-Cheng Yuan John Quackenbush Dimitrios Spentzos Others Zhen Shao Jeremy Bellay Michelle Girvan Cristian Tomasetti Emanuele Mazzola Luca Pinello Eugenio Marco-Rubio Matthew Tung Funding: NIH R01HL111759 PANDA Availability: sourceforge.net/projects/panda-net/