Reactome Functional Interaction Network and

ReactomeFIViz app

Contact: help@reactome.org

LearningObjectivesofModule

• Beabletoperformpathwayandnetwork-baseddataanalysisusingReactomeFIViz app

Reactome Functional Interaction (FI) Network and ReactomeFIViz app

• 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?

Input1-Input2, Input1-Catalyst,Input1-Activator, Input1-

Inhibitor, Input2-Catalyst, Input2-Activator, Input2-Inhibtior,

Catalyst-Activator, Catalyst-Inhibitor, Activator-Inhibitor

Reaction Functional Interactions

• Convert reactions in pathways into pair-wise relationships

• Functional Interaction: an interaction in which two proteins are involved in the same reaction as input, catalyst, activator and/or inhibitor, or as components in a complex

Method and practical application: A human functional protein interaction network and its application to cancer data analysis, Wu et al. 2010 Genome Biology.

ConstructionoftheFINetwork

ReactomeKEGG

Encode TF/TargetTRED TF/Target

NCI-BioCartaNCI-Nature

Data sources for annotated FIs

Naïve Bayes Classifier

Trained by Validated by

Predicted FIs Annotated FIs

FI Network

Human PPI

Fly PPI

Domain InteractionPrieto’s Gene ExpressionLee’s Gene Expression

GO BP Sharing

Yeast PPIWorm PPI

Data sources for predicted FIs

Mouse PPI

Panther Pathways

Featured by

Extracted fromPredicted by

328K interactions 12K proteins

Projecting Experimental Data onto FI Network

127CancerDriverGenes

ACVR1B, ACVR2A, AJUBA, AKT1, APC, AR, ARHGAP35, ARID1A, ARID5B, ASXL1, ATM, ATR, ATRX, AXIN2, B4GALT3, BAP1, BRAF, BRCA1, BRCA2, CBFB, CCND1, CDH1, CDK12, CDKN1A, CDKN1B, CDKN2A, CDKN2C, CEBPA, CHEK2, CRIPAK, CTCF, CTNNB1, DNMT3A, EGFR, EGR3, EIF4A2, ELF3, EP300, EPHA3, EPHB6, EPPK1, ERBB4, ERCC2, EZH2, FBXW7, FGFR2, FGFR3, FLT3, FOXA1, FOXA2, GATA3, H3F3C, HGF, HIST1H1C, HIST1H2BD, IDH1, IDH2, KDM5C, KDM6A, KEAP1, KIT, KRAS, LIFR, LRRK2, MALAT1, MAP2K4, MAP3K1, MAPK8IP1, MECOM, MIR142, MLL2, MLL3, MLL4, MTOR, NAV3, NCOR1, NF1, NFE2L2, NFE2L3, NOTCH1, NPM1, NRAS, NSD1, PBRM1, PCBP1, PDGFRA, PHF6, PIK3CA, PIK3CG, PIK3R1, POLQ, PPP2R1A, PRX, PTEN, PTPN11, RAD21, RB1, RPL22, RPL5, RUNX1, SETBP1, SETD2, SF3B1, SIN3A, SMAD2, SMAD4, SMC1A, SMC3, SOX17, SOX9, SPOP, STAG2, STK11, TAF1, TBL1XR1, TBX3, TET2, TGFBR2, TLR4, TP53, TSHZ2, TSHZ3, U2AF1, USP9X, VEZF1, VHL, WT1

AR

RPL22

NPM1

RPL5

TAF1

CEBPA

RB1CCND1

ERCC2

BRCA2

ATR

EZH2

ASXL1

CHEK2

BAP1

TET2

SIN3A

GATA3

FBXW7

EPHB6

KDM5C

NSD1

HIST1H1C

USP9X

RAD21

BRCA1

KDM6A SMC3

AJUBA

FOXA1

FOXA2

PCBP1

U2AF1

ATM

DNMT3A

STAG2ATRX

CTCF

SMC1A

ELF3

NFE2L2

SF3B1

KEAP1

TP53

WT1

CDKN1B

CDKN2C

CDKN2A

CDKN1A

KIT

FGFR3MAP2K4

PDGFRA

NRAS

BRAF

KRAS

LIFR

PIK3CG

TLR4

MAP3K1

PTPN11

CDH1

FLT3

AKT1

HGF

ARHGAP35

FGFR2

MTORPIK3CA

EGFR

PIK3R1

PPP2R1AERBB4

NF1

PTEN CTNNB1

SOX9

EPHA3

EP300

NOTCH1

ARID5B

NCOR1

HIST1H2BDARID1A

SMAD4

SMAD2

TBL1XR1

CBFB

AXIN2

EIF4A2

RUNX1

APC

STK11

ACVR2A

TGFBR2LRRK2

ACVR1B

MECOM

SOX17

Signaling by EGFR, FGFR, SCF-KIT, etc.

Cell CycleTP53 Pathway

Signaling by Notch, Wnt, TGF-beta, SMAD2/3, etc.

Module-Based Prognostic Biomarker in ER+ Breast Cancer

Cell Cycle M Phase

Aurora B Signaling

Low expression

High expression

Measure levels of expression of the genes in this network

moduleGuanming Wu

Visualize Cancer Targetome in the Reactome FI Network

TCGA GBM Mutation Profile

3) Pathway-Based Modeling

• Apply list of altered {genes, proteins, RNAs} to biological pathways.

• Preserve detailed biological relationships.

• Attempt to integrate multiple molecular alterations together to yield lists of altered pathway activities.

• Pathway modeling shades into Systems Biology

Types of Pathway-Based Modeling

• Partial differential equations/boolean models, e.g. CellNetAnalyzer

• Mostly suited for biochemical systems (metabolomics)

• Network flow models, e.g. NetPhorest, NetworKIN

• Mostly suited for kinase cascades (phosphorylation info)

• Transcriptional regulatory network-based reconstruction methods, e.g. ARACNe (expression arrays)

• Probabilistic graph models (PGMs), e.g. PARADIGM

• Most general form of pathway modeling for cancer analysis at this time.

Probabilistic Graphical Model (PGM) based Pathway Analysis

• Attempt to integrate multiple molecular alterations together to yield lists of altered pathway activities.

• Many omics data types available for one single patient

• CNV, gene expression, methylation, somatic mutations, etc.

• Pathway and network-based Simulation

• Find significantly impacted pathways for diseases

• Link pathway activities to patient phenotypes

• Predict drug effects: one drug or multiple drugs together?

PARADIGM

MDM2

TP53

Apoptosis

Mutation

mRNA Change

CNV

Mass spec

Apoptosis

MDM2gene

MDM2RNA

MDM2protein

TP53gene

TP53RNA

TP53protein

MDM2Active protein

TP53Active protein

w1

w2

w3

w4

w5

w6w7

Adapted from Vaske, Benz et al. Bioinformatics 26:i237 (2010)

Probabilistic Graphical Models (PGMs) for Reactome Pathways

1

2

PGM-basedSinglePatientPathwayView

Building Quantitative Models for ReactomePathways

ReactomePathwayODE Model

Probabilistic Graphical Model

Boolean Network Model

Future Direction

Tutorial

MajorFeaturesinReactomeFIViz

A1) Reactome Pathway Enrichment Analysis

Displaying Reactome Pathways in the FI Network View

Pathway Enrichment Analysis

Visualize Cancer Targetome in Reactome Pathways

A2) De Novo Subnetwork Construction & Clustering

Extract and Cluster, and Annotate Altered

GenesDisease “modules” (10-30)

CuratedPathway DBs

UncuratedInteractionEvidence

+Reactome Functional Interaction Network

(~12K proteins; ~328K interactions)

Machine Learning

Cytoscape using ReactomeFIViz app

Project your data into Reactome FI

Network

+

Uploadyourdata

• FI plug-in supports four file formats:• Simple gene set: one line per gene• Gene/sample number pair. Contains two required columns, gene and

number of samples having gene mutated, and an optional third column listing sample names (delimited by semi-colon ;)

• NCI MAF (mutation annotation file)• Sample Gene Expression data file

FileFormats• Choose Plugins, Reactome FIs.

• FI plug-in supports four file formats:

• Gene list

• Gene-Sample gene

• NCI Mutation Annotation File

• Microarray Expression Data

GeneSet-basedAnalysis

FIResultsDisplay

• Constructed network is displayed in the Network View panel using an FI specific visual style

The main features ofthe plug-in are invokedfrom a popup menu,which can be displayedby right clicking a whitespace, a node or edgein the the network viewpanel.

FIAnnotations

• Provides detailed information on selected FIs.

• 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.

The main features of the plug-in are invoked from a popup menu, which can be displayed by right clicking a node or edge in the the network view panel.

Query FI Source

Annotated FIs Predicted FIs

• Runs spectral partition based network clustering (Newman, 2006) on the displayed FI network.

• Nodes in different network modules will be shown in different colours (max 15 colours).

• Analyze cancer mutation data with HotNet algorithm (Vandin, 2012)

Cluster FI Network

• Pathway or GO term enrichment analysis on individual network modules. • Use filter to remove small network modules

• Filter by FDR

Analyze ModuleFunctions

• Select a pathway in "Pathways in Network/Modules" tabs, right click, select "Show Pathway Diagram”

Other Features – Show Pathway Diagrams

• View detailed annotations for the selected gene or protein.

• Annotations are sortable by PubMed ID, Cancer type, status, and other criteria.

Other Features – NCI Cancer Gene Index

• Load the NCI disease terms hierarchy in the left panel.

• Select a disease term in the tree to select all nodes that have this annotation or one of its sub-terms.

Other Features – Overlay Cancer Gene Index

• View detailed variant annotations for the selected gene or protein from COSMIC database.

Other Features – COSMIC

ModuleBasedSurvivalAnalysis

• Discover Prognostic Signatures in Disease Module Datasets.

• Based on a server-side R script that runs either CoxPH or Kaplan-Meyer survival analysis.

• Requires appropriate clinical data file.

For any questions or feedback, please contact help@reactome.org