Systems Biology of Cancer: which pathways to choose? Emmanuel Barillot

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1 Systems Biology of Cancer: which pathways to choose? Emmanuel Barillot

2 Central questions to cancer On the clinical side: predict the phenotype On the biological side: explain tumorigenesis and tumoral progression Classification of tumors (diagnosis and prognosis) based on molecular profiles

3 The case of uveal melanoma Clustering on 86 ocular tumors, based on molecular profiles : Legend : Blue chr3 monosomy - Red chr3 disomy Purple non-metastasis - Green metastasis Detection of few recurrent alterations + Clustering Groups and metastasis : L1p, L3, G8q : 56% metastasis L3, G8q : 72% metastasis L1p, L3, G8q L1p L3 : 12,5% metastasis L3 G6p, G8q : 40% metastasis G6p : 14% metastasis Classification achieves 75% L3, G8q L3 G8q sensibility and 75% specificity. G6p, G8q G8q G6p G6p 3

4 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: explain tumorigenesis and tumoral progression Identify pathways Understand their principles Model their effect on the phenotype

5 Systems Biology: statistical vs mechanistical models perturbation perturbation

6 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

7 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?? not observable?? + - -?? + -????? ? + + +? + +??? negative deviations Perturbation

8 Objective of the Systems Biology of Cancer Group Use available information of molecular structures and interactions and integration 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

9 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

10 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

11 RB-pathway structural analysis collaboration with: François Radvanyi, Institut Curie/CNRS UMR144

12 Comprehensive map and model of RB-pathway Detailed view, closer to reality Text book view Signal Mitogénique Cyc D CDK4/6 RB RB -P CKI (p16) E2F Progression du Cycle Cellulaire

13 Comprehensive map and model of RB-pathway Created with CellDesigner 3.2 (Kitano s graphical notation system) Contains 84 distinct proteins 127 genes 337 species (complexes, protein forms, ) 436 reactions (binding, modifications, regulations, ) Compiles information from 245 publications Progression du Cycle Cellulaire Creation of RB-pathway is supported by ESBIC-D European project (FP6 CA)

14 CellDesigner process diagrams

15 Structural organization of the network Methods: 1) Decomposition into independent cycles 2) Finding conservation laws from the stoichiometry matrix 3) Block-decomposition of the stoichiometry matrix

16 In- and Out- digraph layers External world Non-trivial network part In-layer Cyclic part (Strongly connected components) Out-layer Boundary Conditions (we can not infer the behavior of the nodes with no incoming edges) Network Output (The nodes with no outcoming edges do not have effect on the rest of the network)

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