Robin Haw15th December 2012 9th Annual Cytoscape Workshop www.reactome.org

Reactome Functional Interaction Network Cytoscape Plugin

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.

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

Construction of the Reactome FI Network

Curated data sets

Naïve Bayes Classifier

Annotated Functional Interactions

Predicted Functional Interactions

Pairwise data setshuman PPI

PPI inferred from fly, worm & yeast

PPI from text mining

Gene co-expression

GO annotation onbiological processes

Protein domain-domain interactions

Reactome FI Network: 273K interactions and 11K proteins

ENCODE interactions

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

Software Architecture – Reactome FI plugin

Server Side in Spring Container

Server Side in Spring Container

CytoscapeCytoscapeDatabase in MySQLDatabase in MySQL

hibernateXML

Messaging

• FIs and Pathways

• Cancer Gene Index

Reactome API

• 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

• 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

RESTful WS

File Formats

MSI2PTPRTPELOSLC18A1TACC2FAM148BPRC1MSTNATP6V1G2APOEIMPA2AGERXPO5MESTRREB1BAT1WIPI1CATSPERBSSR1VEGFA

Simple Gene ListGene/Sample Number PairsNCI MAF (mutation annotation file)Microarray (array) data file

• Choose Plugins, Reactome FIs.• FI plug-in supports four file formats:

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.

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).

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.

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.

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.

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.

HGS OvCa-Reactome FI Network

Find and Annotate Network Modules

Module 0:DNA Repair, TP53 signaling, Cell Cycle Regulation

Module 1:Insulin & ErbB signaling

Module 2:Integrin signaling

Module 3:Rho GTPase signaling

Module 4:GPCR signaling

Module 5:Wnt & cadherin signaling

Module 6:Calcium signaling

Module 7:Cell cycle checkpoints

Module 8:PI3K signaling & metabolism

Module 6

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.

HGS OvCa Module Map

CoxPH Kaplan-Meyer

Patient samples with mutated Module 6 genes

Calcium signalingModule 6

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.

Ministry of Economic Development and

Innovation

Acknowledgements