Systems Biology of Cancer: which pathways to choose?

Emmanuel Barillot

Central questions to cancer

• On the clinical side: predict the phenotype

• On the biological side: explain tumorigenesisand tumoral progression

• Classification of tumors (diagnosis andprognosis) based on molecular profiles

3

The case of uveal melanomaClustering on 86 ocular tumors, based on molecular profiles :

Detection of few recurrent alterations + Clustering

Groups and metastasis :

–L1p, L3, G8q : 56% metastasis

–L3, G8q : 72% metastasis

–L3 : 12,5% metastasis

–G6p, G8q : 40% metastasis

–G6p : 14% metastasis

Classification achieves 75%

sensibility and 75% specificity. G6p

L3

L1p

G8q

G8q

G6p

G6p, G8q

L3

L3, G8q

L1p, L3, G8q

Legend : Blue chr3 monosomy - Red chr3 disomyPurple non-metastasis - Green metastasis

Central questions to cancer• On the clinical side: predict the phenotype

– « small n, large p » (JP Vert talk this morning)– Not all answers can be predicted– Is there a treatment?

• On the biological side: explaintumorigenesis and tumoral progression– Identify pathways– Understand their principles– Model their effect on the phenotype

Systems Biology: statistical vs mechanistical models

perturbation perturbation

Systems Biology

• Génomique, post-génomique et biologie des systèmes

• Les propriétés émergentes d’un système complexe sont irréductibles à celles de ses composants

• La fonction n’est pas accessible à l’étude spécifique d’un élément

• Jeux de données exhaustifs, multi-échelles, issus des nouvelles technologies de biologie à haut débit :Génome, transcriptome, protéome, interactome, phénotypes

cellulaires

Biologie des Systèmes du Cancer• Modélisation des voies de régulation associées au cancer:

– Structure : littérature et inférence depuis les données– Dynamique : modèles quantitatifs et qualitatifs– Contrôle : effet des perturbations– Allers-retours entre modèles et expériences

Perturbation

--

??

+

+

?-

?

?

? ??

-

-+

?

?

?

+

?

?

-

?

-

-+

?

+

+?

negative deviations

not observable

Objective of the Systems Biology of Cancer Group

Use available information of molecular structures and interactions

andintegration of heterogeneous multi-level sources of data

for creating mechanistical and statistical models of human cancer

with the aim to contribute to the prevention, diagnosis and treatment of cancer

http://bioinfo.curie.fr/sysbio

Outline

• Building a comprehensive map of the Rb pathway from the literature

• Investigating the pathway modular decomposition

• Studying complexity and robustness of pathways (example of NFkB)

• Reverse-engineering and modelling of an oncogenic process in Ewing tumors

• Modelling qualitatively pathways for identifying intervention points

• Studying the evolution of network motifs

“Problem-oriented” and “support” projects

Studying concrete cancers: EWING, bladder, breast …Pathway modeling in the cancer context: RB, IGF, Wnt, NfκB …

Developing methodology: Reverse engineering, Model reduction, Qualitative modeling, Standards

Software development: BiNoM (Cytoscape platform), NETI

RB-pathway structural analysis

collaboration with:François Radvanyi, Institut Curie/CNRS UMR144

Comprehensive map and model of RB-pathway

Signal Mitogénique

CDK4/6

RB RB -P

E2F

Progression du Cycle Cellulaire

CKI

(p16)

Cyc D

Text book view

Detailed view, closer to reality

Comprehensive map and model of RB-pathway

Progression du Cycle Cellulaire

Created with CellDesigner 3.2(Kitano’s graphical notation system)

Contains84 distinct proteins127 genes337 species (complexes, protein forms, …)436 reactions (binding, modifications, regulations, …)

Compiles information from 245 publications

Creation of RB-pathway is supported by ESBIC-D European project (FP6 CA)

CellDesigner process diagrams

Structural organization of the network

Methods:

1) Decomposition into independent cycles

2) Finding conservation laws from thestoichiometry matrix

3) Block-decomposition of thestoichiometry matrix

External world

Out-layerIn-layer

In- and Out- digraph layers

Cyclic part(Strongly connected

components)

BoundaryConditions

(we can not infer the behaviorof the nodes with no incoming edges)

Network Output

(The nodes with no outcoming edgesdo not have effect on the rest

of the network)

“Non-trivial” network part