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Systems biology in cancer
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What is systems biology?
= Molecular physiology?
“…physiology is the science of the mechanical,
physical, and biochemical functions of humans …”
Wikipedia
“Systems biology is a … study field that focuses on the systematic study of
complex interactions in biological systems, thus using a new perspective
(holism instead of reduction) to study them. … Because the scientific method
has been used primarily toward reductionism, one of the goals of systems
biology is to discover new emergent properties that may arise from the
systemic view used by this discipline in order to understand better the entirety
of processes that happen in a biological system.“
Wikipedia
What is cancer?
A disease of many genes
and their interactions
Cancer attractors: A systems view of tumors…
Sui Huang, Ingemar Ernberg, Stuart Kauffman
Biological complexity: reduction is crucial.
Tool complexity ≠ Vision complexity
Modelling. What is a model?
•Topological vs. quantitative
•Relevance vs. causality
The Cancer Genome Atlas Research Network.
2008
Wholesome vision:
All proteins?
All interactions?
All diseases?
All organisms?
Human Protein Atlas
Oncomine
FunCoup: a data integration framework to discover
functional coupling
Amouse
Rat
Fly
?
Yeast
Bmouse
*
Human
Andrey Alexeyenko and Erik L.L. Sonnhammer. Global networks of functional
coupling in eukaryotes from comprehensive data integration. Genome Research.
Published in Advance February 25, 2009
FunCoup: recapitulation of known cancer
pathways
Figure 5 from:
The Cancer Genome Atlas Research Network
Comprehensive genomic characterization defines human
glioblastoma genes and core pathways.
Nature. 2008 Sep 4. [Epub ahead of print]
The same genes submitted to FunCoup
No TCGA data were used.
Outgoing links are not shown.
TGFβ <-> cancer pathway cross-talk
FunCoup was queried for any links between members of
TGFβ pathway (left blue circle) and habituées of known
cancer pathways (members of at least 7 out of 18
groups; right blue circle). MAPK1 and MAPK3 belonged
to both categories.
What is FEASIBLE in systems biology?
• Holistic view?
• Comparison between healthy and ill?
• Disease prevention?
• Drug targets?
From genes to pathways
Inositol phosphate metabolism
Glioblastoma (TCGARN, 2008)
Enrichment of functional groups
Group 1
Group 2
Enrichment analysis in the networks turns to
be more powerful than on gene lists
Discerning 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
co-expression under normal conditions
+ ALL human & mouse data.
Red lines: evidence from mRNA
co-expression in expO tumor samples
+ ALL human data + mouse PPI.
Node
size: number of pathway
members in the network.
Edge opacity: p0.
ss: number of
Edge thickne
gene-gene links.
Level of functional groups
Zebrafish transcriptome under dioxin treatment
Accounting for edge features:
dioxin- “enabled” vs. “sensitive” links
Andrey Alexeyenko, Deena M Wassenberg, Edward K Lobenhofer, Jerry Yen, Erik LL
Sonnhammer, Elwood Linney, Joel N Meyer Transcriptional response to dioxin in
the interactome of developing zebrafish. PLoS One
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?
Cancer data for basic research: a testbed
Sonic hedgehog pathway
Functional coupling
transcription ? transcription
transcription ? methylation
methylation
? methylation
mutation

methylation
mutation

transcription
mutation
? mutation
+
mutated gene
Cancer individuality
Tumour tcga-02-0114-01a-01w
There is a CAUSATIVE gene network behind each individual cancer
Cancer individuality in clinic
Functional coupling
transcription ? transcription
transcription ? methylation
methylation
? methylation
mutation

methylation
mutation

transcription
mutation
? mutation
+
mutated gene
Conclusions:
• Cancer is a disease of multiple alternatives,
hence PERSONALIZED medicine.
• Systems biology: enormous complexity, great
challenge.
• Focus on feasible today, think of possible in
the future.
• Descriptive and analytic HUMAN language?