Reactome Functional Interaction Network Cytoscape Plugin

1. Robin Haw15th December 2012 9th Annual Cytoscape Workshop www.reactome.org Reactome Functional Interaction Network Cytoscape Plugin Ministry of Economic Development and Innovation

2. Network Module Based Analysis of Disease Datasets • No single mutated gene is necessary and sufficient to cause cancer. • Typically one or two common mutations (e.g. TP53) plus rare mutations. • Analyzing mutated genes in a network context: • reveals relationships among these genes. • can elucidate mechanism of action of drivers. • facilitates hypothesis generation on roles of these genes in disease phenotype. • Network analysis reduces hundreds of mutated genes to < dozen mutated pathways.

3. What is a Functional Interaction Network? • A high coverage, reliable interaction network based on manually curated pathways extended with predicted interactions. • The plugin is a resource for the constructing FI sub-networks based on gene lists. • Tools that: • Provide the underlying evidence for FIs. • Identify network modules of highly-interacting groups. • Perform functional enrichment to annotate modules. • Display source pathway diagrams and overlay with a variety of information sources such as cancer gene index annotations. • Method and practical application: A human functional protein interaction network and its application to cancer data analysis, Wu et al. 2010 Genome Biology. http://wiki.reactome.org/index.php/Reactome_FI_Cytoscape_Plugin

4. Construction of the Reactome FI Network Curated data sets Pairwise data sets human PPI PPI inferred from fly, worm & yeast PPI from text mining Gene co-expression GO annotation on biological processes Protein domain-domain interactions ENCODE interactions Naïve Bayes Classifier Annotated Functional Interactions Predicted Functional Interactions Reactome FI Network: 273K interactions and 11K proteins Wu et al. (2010) Genome Biology

5. FI Network Analysis Pipeline Your gene list (e.g. mutated, over-expressed, down-regulated, amplified or deleted genes in disease samples) Project genes of interest onto Reactome F.I. Network Identify Disease/Cancer Subnetwork Apply Clustering Algorithms Apply Pathway/GO Annotation to each cluster Perform Survival Analysis (optional) Generate Biological Hypothesis! Predict Disease Gene Function Classify Patients & Samples

6. Software Architecture – Reactome FI plugin Database in MySQL Server Side in Spring Container Cytoscape XML Messaging hibernate RESTful WS Reactome API • Network module display: color genes based on network modules • Pathway/GO enrichment result display: table view • FI annotations: directions, and scores • View of cancer gene index annotation • Pathway diagram view: highlight genes in pathway diagrams • FIs and Pathways • Cancer Gene Index • Bridge to fetch FIs and Pathways • Network clustering: spectral and edge-betweenness • Pathway and GO term enrichment analysis • Cancer gene annotations: caBIG cancer gene index

7. File Formats • Choose Plugins, Reactome FIs. • FI plug-in supports four file formats: NCI MAF (mutation annotation file) Gene/Sample Number Pairs Simple Gene List MSI2 PTPRT PELO SLC18A1 TACC2 FAM148B PRC1 MSTN ATP6V1G2 APOE IMPA2 AGER XPO5 MEST RREB1 BAT1 WIPI1 CATSPERB SSR1 VEGFA Microarray (array) data file

8. Reactome Functional Interactions • Three edge attributes are created: • FI Annotation. • FI Direction. • FI Score (for predicted FI). • Edges display direction attribute values. • ‘>’ for activating/catalyzing. • ‘|’ for inhibition. • solid line for complexes or inputs. • --- for predicted FIs. • Additional features • Query FI Source. • Fetch FIs for particular node.

9. Cluster FI network • Plugin runs spectral partition based network clustering (Newman, 2006) on the displayed FI network. • MCL graph clustering algorithm is used with the gene expression data. • After network clustering, nodes in different network modules will be shown in different colours (max 15 colours).

10. Analyze module functions • Pathway or GO term enrichment analysis on individual network modules. • Use filter to remove small network modules. • Filter by FDR. • Select nodes in the network highlights the corresponding gene sets • Select rows in Data panel highlights contributing genes in network.

11. Overlay Cancer Gene Index • Load the NCI disease terms hierarchy in the Control panel. • Select a disease term in the tree to select all nodes that have this annotation or one of its sub-terms. • View the NCI gene annotations for an individual node.

12. Module Based Survival Analysis • Discover Prognostic Signatures in Disease Module datasets. • Requires appropriate clinical data file. • Based on a server-side R script that runs either CoxPH or Kaplan-Meyer survival analysis.

13. Example: Analysis of Cancer Genome • HGS OvCa Exome Sequencing data. • 316 Patient Samples. • MAF contains 8420 NS mutations. • Survival data. • Publication: TCGA Consortium, Nature 2011.

14. HGS OvCa-Reactome FI Network

15. Find and Annotate Network Modules Module 3: Rho GTPase signaling Module 6: Calcium signaling Module 0: DNA Repair, TP53 signaling, Cell Cycle Regulation Module 8: PI3K signaling & metabolism Module 7: Cell cycle checkpoints Module 2: Integrin signaling Module 4: GPCR signaling Module 1: Insulin & ErbB signaling Module 5: Wnt & cadherin signaling

16. Module Based Survival AnalysisDiscovering Prognostic Signatures in Cancer Module Datasets • FI plugin performs CoxPH and Kaplan-Meyer survival analysis if clinical data is available for the samples used in the network construction. Calcium signaling Module 6 Module 6 Patient samples with mutated Module 6 genes HGS OvCa Module Map CoxPH Kaplan-Meyer

17. Future Work and Conclusions • Increase the size and functionality of the Reactome Fl network. • add additional sources of functional interactions and annotations. • employ other clustering algorithms. • Cytoscape FI network plugin provides a powerful way to analyze cancer and disease datasets • lets anyone perform the workflow of discovering and annotating network modules. • reveals functional relationships amongst cancer/disease genes. • to identify cancer prognostic signatures to predict patient survival.

18. Acknowledgements • GuanmingWu • Adrian Duong • MarijaOrlic-Milacic • Karen Rothfels • Lisa Matthews • Marc Gillespie • Irina Kalatskaya • Christina Yung • Michael Caudy • David Croft • PhaniGarapati • BijayJassal • Steve Jupe • Bruce May • Antonio FabregatMundo • Veronica Shamovsky • Heeyeon Song • Joel Weiser • Mark Williams • Henning Hermjakob • Peter D’Eustachio • Lincoln Stein Ministry of Economic Development and Innovation