We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyze their enrichment in cancer mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes

Alfonso Valencia

Anais Baudot

Enrico Glaab

Natalio Krasnogor

English

Nature Precedings

E. Glaab, A. Baudot, N. Krasnogor, A. Valencia
PathExpand: Extending biological pathways 
using molecular interaction networks
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Overview:
• Introduction / Motivation
• Pathway extension procedure and criteria
• Validation methods
• Biological application: Alzheimer and cancer pathways
• Conclusion
Outline
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Introduction
Introduction / Motivation:
Why do we want to extend classical
biological pathway definitions?
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Introduction
Biological pathways and
processes:
Rich sources of information,
but partly subjective and
inconsistent.
BioCarta (p53 signalling) KEGG (p53 signalling)
Reactome (VEGF signalling)
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Goals
Include functional genomics data:
• protein-protein interactions
• genetic interactions
• gene co-expression
 large-scale, less biased
Questions / Goals:
• Can we improve pathway
definitions (compactness,
connectivity, density)?
• How are pathways
communicating (“cross-talk”)?
Human unweighted binary 
protein interaction network
(9392 proteins, 38857 
interactions)
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Data representation / integration
Model data as a graph G=(V,E):
V:= molecules; E:= interactions
Molecular interactions:
Modelling and combining the data
Pathway diagrams:
Normalisation 
of gene/protein
names
= annotated node
= unweighted edge
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Pathway extension procedure and criteria:
How do we recognize “good” pathway
definitions and improve them?
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Outline
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PPI-based pathway extension
Extend pathways by adding genes that are „strongly connected“ to the 
pathway-nodes or increase the pathway-“compactness“ in a PPI.
Pathway extension criteria: Add a node v to set P if:
• v has a pathway-neighbour and degree(v) > 1; and
• #pathway-links(v,p) / #outside-links(v,p) > T1; or
• #triangle-links(v,p) / #possible_triangles(v,p) > T2; or
• #pathway-links(v,p) / #pathway-nodes(p) > T3; and
• avg. shortest path distance in {P,v} smaller than in P
black = pathway-nodes
red blue green = nodes added based on different criteria
...
...
...
PathExpand – Idea:
Roadmap:
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Example pathway extension
Example extension:
Pathway: BioCarta “BTG family proteins and cell cycle regulation“
original pathway
added nodes
 Disconnected nodes  
become connected
 increased pathway-
compactness
Added cancer 
gene
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Validation:
How to validate pathway extensions
without a real “gold standard”?
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Evaluation (1)
Cross-validation
Can randomly deleted genes in the original pathways be recovered
by the expansion procedure?
 3-step cross-validation procedure:
1. Randomly remove 10% of the pathway members (among
proteins with at least one partner in the pathway)
2. Apply the proposed extension procedure as well as 100
random extensions (random sampling among candidates)
3. Estimate p-value-like significance scores:
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Evaluation (2)
Semantic similarity analysis (Gene Ontology)
• Quantify pairwise 
similarities between 
protein annotations 
using Jiang & Conrath‘s
semantic similarity 
measure for GO-terms
• Compute avg. GO-term 
similarity between 
pathway-proteins and 
added proteins
 compare to random  
extension model
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Extension statistics
Extension statistics across all databases
Statistics on added proteins across 3 pathway databases:
 pathways increase to 113% - 126% of original size
 many proteins added to multiple pathways
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Topological Analysis
Topological properties of added proteins
Network topological properties for proteins added to BioCarta pathways
The added proteins are more central, more densily clustered and
have shorter distances between them in comparison to matched-
size random proteins and the global network average.
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Biological application:
Which insights do we gain when applying
the approach to Alzheimer and cancer
pathways?
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Biological results (1)
• More than 20 proteins
annotated in our
PPI-network
• 5 proteins added by the 
extension process (circled)
• 3 known to be associated
with the disease
• 2 novel candidates:
METTL2B, TMED10
Application: Alzheimer disease pathway
KEGG Alzheimer disease pathway 
mapped on human PPI-network
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Biological results (2)
• Complex system of
intracellular signalling
cascades
• New putative pathway 
regulators identified 
• New “crosstalk proteins”
identified (associated
with multiple pathways)
Application: Interleukin signalling pathways
Two functions: pathway-regulation & 
pathway-communication?
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Biological results (3)
Using extended pathways for functional 
enrichment analysis
• classical approach:
Test enrichment of experimentally derived gene sets in 
cellular pathway members (one-sided Fisher exact test)
 idea: replace original pathways by extended versions
Example: Enrichment analysis for pancreatic mutated genes
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Cancer mutated genes
Pancreatic mutated genes in expanded pathways
• “Cell cycle G1/S check 
point process” - extension 
procedure adds 7 proteins
• 6 of the added proteins are 
involved in cell cycle regulation
• the 7th (TGIF2) is known to be
mutated in pancreatic cancer
• points to functional role
of added proteins
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Comparison to previous methods
Relation to other network analysis methods
(Disease) candidate 
gene prioritization
(Ab initio) pathway
prediction
Shamir & Ulitsky (2007)
Ma & Tarone & Li (2008)
Brady et al. (2009)
Cerami et al. (2010)
Nitsch et al. (2009)
Aerts et al. (2006)
... ...
similar
criteria
similar
outcome
Kelley & Ideker (2005)
approach
discussed
here
(PathExpand)
Sander et al. (2010)
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Conclusion
• The method integrates two sources of information, 
extending canonical pathways using large-scale 
protein interaction data
• Three validation methods: cross-validation, GO-term
semantic similarity and enrichment analysis
• Extended pathways have advantages in terms of 
network-compactness and can provide new insights 
on pathway regulators, the cross-talk between 
pathways and gene set functional enrichment
Conclusion & Summary
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References
References
1. E. Glaab, A. Baudot, N. Krasnogor, A. Valencia. Extending pathways and processes using molecular 
interaction networks to analyse cancer genome data, BMC Bioinformatics, 11(1), 597, 2010
2. E. Glaab, A. Baudot, N. Krasnogor, A. Valencia. TopoGSA: network topological gene set analysis, 
Bioinformatics, 26(9):1271-1272, 2010
3. E. Glaab, J. M. Garibaldi and N. Krasnogor. ArrayMining: a modular web-application for microarray 
analysis combining ensemble and consensus methods with cross-study normalization, BMC 
Bioinformatics,10:358, 2009
4. E. Glaab, J. M. Garibaldi, N. Krasnogor. Learning pathway-based decision rules to classify microarray 
cancer samples, German Conference on Bioinformatics 2010, Lecture Notes in Informatics (LNI), 173, 
123-134
5. H. O. Habashy, D. G. Powe, E. Glaab, G. Ball, I. Spiteri, N. Krasnogor, J. M. Garibaldi, E. A. Rakha, A. R. 
Green, C. Caldas, I. O. Ellis. RERG (Ras-like, oestrogen-regulated, growth-inhibitor) expression in 
breast cancer: a marker of ER-positive luminal-like subtype, Breast Cancer Research and Treatment,  
(Epub ahead of print)
6. E. Glaab, J. M. Garibaldi and N. Krasnogor. VRMLGen: An R-package for 3D Data Visualization on the 
Web, Journal of Statistical Software, 36(8),1-18, 2010
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Extending pathways and processes using molecular interaction networks to analyse cancer genome data,

oestrogen-regulated, growth-inhibitor) expression in breast cancer: a marker of ER-positive luminal-like subtype, Breast Cancer Research and Treatment, (Epub ahead of print) 6.

PathExpand: Extending biological pathways using molecular interaction networks

http://precedings.nature.com/documents/5639/version/1

PathExpand: Extending biological pathways using molecular interaction networks

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