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Gene network approach in
epidemiology
Andrey Alexeyenko
M
E
B
edical
pidemiology and
iostatistics
Network is just a graph!
The fact that we can draw a
network does not yet make
it a biological reality!..
Why the network approach is
an advancement compared to differential
expression analysis?
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Functional context
“Anchoring”, i.e. interdependence
Biological interpretability
Accounts for more statistical properties
Data integration
More data = flexibility!
Gene network discovery:
high-throughput experiments
Gene network discovery:
probabilistic data integration
Bayesian inference:
rps14 and rps8 coexpressed
P(C|E) = (P(C) * P(E|C)) / P(E)
rps14<->rps8
rps14 and rps8
functionally coupled
FunCoup is a data integration
framework to discover
?
BHuman
Mouse
Fly
Worm
Yeast
High-throughput
evidence
AHuman
Find orthologs*
functional coupling
Conversion “data pieces  confidence”
in a Bayesian framework
Data components in FunCoup
D. rerio, 17.3%
D. melanogaster, 9.8%
C. elegans, 9.3%
R. norvegicus, 5.1%
S. cerevisiae, 10.2%
A
M. musculus, 25.4%
A. thaliana, 6.5%
H. sapiens, 16.5%
Phylogenetic profiling, 18.6%
Protein interactions, 10.6%
Protein expression, 6.1%
T F targeting, 12.3%
miRNA targeting, 2.0%
Sub-cellular localization, 7.3%
mRNA expression, 43.1%
http://FunCoup.sbc.su.se
FunCoup: on-line interactome resource
Andrey Alexeyenko and Erik L.L. Sonnhammer (2009) Global networks of functional
coupling in eukaryotes from comprehensive data integration. Genome Research.
Gene network discovery:
getting rid of spurious links
0.7
0.5
0.4
Data processing inequality:
“Direct links convey more information than indirect ones”
Mutations: distinguishing drivers from
passengers
Functional coupling
transcription  transcription
transcription  methylation
methylation
 methylation
mutation

methylation
mutation

transcription
mutation
 mutation
+
mutated gene
Network curation:
cancer viewed by KEGG database curators
Prostate cancer:
recapitulated by FunCoup
Network reconstruction:
combination of methods
Combination of methods:
edges with different features
A Alexeyenko, DM Wassenberg, EK Lobenhofer, J Yen, ELL Sonnhammer, E Linney,
JN Meyer (2010) Transcriptional response to dioxin in the interactome of
developing zebrafish. PLoS One.
Verification of
single gene lists
•Yellow diamonds: somatic
mutations in prostate cancer
•Pink crosses: also mutated in
glioblastome (TCGA)
Subtyping cancer.
Personalized medicine.
Power of clinical trials
AZD
2281
Salt Lake City, UT, June 19, 2007—
Myriad Genetics, Inc. today
announced the start of two Phase II
trials for a new compound being
tested to treat patients with BRCA1 &
BRCA2 positive breast and ovarian
cancer.
Biomarker signatures in the network
×
Severity,
Optimal treatment,
Prognosis
etc.
Single molecular markers are often far from perfect.
Combinations (signatures) should perform better.
How to select optimal combinations?
Candidate signature in the network
Biomarker candidates
Ready network-based signature
RELAPSE = γ1EIF3S9+ γ2CRHR1 + γ3LYN + … + γNKCNA5
Identical genotypes can go different ontogenetic ways
Gene a
Protein A
Disease
Development
Birth
Adult
Current gene expression
results from
inherited genotype,
ontogenesis, and
disease etiology
Gene
Gene a
Protein A
Gene
Disease
Gene
Physiological
condition
Gene
Pathway cross-talk
Analysis of cancer-specific wiring
Pathway network of normal
vs. tumor tissues
Edges connect pathways given a higher
(N>9; p0<0.01; pFDR<0.20) number
of gene-gene links (pfc>0.5) between
them (seen as edge labels).
Known pathways (circles) are classified as:
•signaling,
•metabolic,
•cancer,
•other disease.
Blue lines: evidence from mRNA coexpression under normal conditions + ALL
human & mouse data.
Red lines: evidence from mRNA coexpression in expO tumor samples + ALL
human data + mouse PPI.
si
Node
ze: number of pathway
members in the network.
Edge opacity: p0.
ss: number of gene-
Edge thickne
gene links.
Arrow of time: network prospective
A Alexeyenko, DM Wassenberg, EK Lobenhofer, J Yen, ELL Sonnhammer, E Linney,
JN Meyer (2010) Transcriptional response to dioxin in the interactome of
developing zebrafish. PLoS One.
Thanks to:
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Erik Sonnhammer
Martin Klammer
Sanjit Roopra
Joel Meyer
Thank you for listening!
http://FunCoup.sbc.su.se
Summary:
• Predicting gene networks is realistic.
• Proposed applications:
– Genetic heterogeneity of cancer
– Communication between different cells, tissues,
processes etc.
– Evaluation of candidate biomarkers
– Expression signatures
Ask concrete practical questions, not global ones!
Prostate cancer
a cancer regulatory network +
proteomics (HPA) data
Cyan: up-regulated in normal glandular cells
Red/green: up/down-regulated in malignant cells
Yellow&magenta: (potential) regulators of prostate cancer