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Meeting Cancéropoles Clara-Paca - 08092010
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  • Diapo parcours Citer inserm INCa Centre regional de lutte contre le cancer
  • Justification Recherche de meilleurs marqeurs pour rechute a 5 ans Plus de temps sur cette diapos Prédiction du pronostic et adaptation du traitement au patient beacoup de donness/échantillons As it is not possible to accurately predict the risk of metastasis development in individual patients, nowadays more than 80% of them receive adjuvant chemotherapy, although only approximately 40% of the patients relapse and ultimately die of metastatic breast cancer. Therefore, many women who would be cured by local treatment alone, which includes surgery and radiotherapy, will be ‘over-treated’ and suffer the toxic side effects of chemotherapy needlessly.
  • Cartes d’interaction proteines proteins
  • Il n’existe pas d’outil tesls que djjeen Extenstion supplémentaires des informations utilisées dans ITI Mise à disposition
  • Faire un lien direct avec ITI Cet outils est aussi en cytometrie…. Détéction de classes, utilisation des outils du transcriptome Intro cytometrie en flux Outil pour faire sortit l’information des combinatoires, faible taille des populations, Production au CRCM, CIML, J Galon, devel d’un programme bioinfo innovant, on a fédére des projets au niveau national Plateforme IBIsa 3-4 diapos sur infocyt, origine est au CRCM, appui, Perspectives sur l’activation inactivation de voies signalisation Détection automatisee -> suppression de l’intervention manuelle
  • Intégration de donnees -> djeen, infocyt
  • Aucune signature n’est fausse mais comment établir un modèle biologique ?
  • Bax est (BLC2 assiciated X protein) est lié au mauvais pronostic dans le can,cer du sein Amplification of both PPFIA1 and CCND1 were significantly associated with high-grade breast cancer phenotype 14-3-3zeta cooperates with ErbB2 to promote ductal carcinoma in situ progression to invasive breast cancer by inducing epithelial-mesenchymal transition. Mettre une conclusion -set d’apprentissage plus large -l’analyse a permis de sortirs de nouveaux gènes -vérification sur un set indépendant -cht couleurs
  • Passage de gauche a droite Les mécanismes biologiques fondamentaux affectés en biologie du cancer sont retrouvés: prolifération, différentiation, adhésion, division cellulaire.

Meeting Cancéropoles Clara-Paca - 08092010 Meeting Cancéropoles Clara-Paca - 08092010 Presentation Transcript

  • High throughput genomics data integration for cancer Ghislain Bidaut CRCM Integrative Bioinformatics Team Centre de Recherche en Cancérologie de Marseille Inserm U891, Institut Paoli-Calmettes, Université de la Méditerranée
  • CRCM Integrative Bioinformatics team Projects
    • ITI ( Interactome-Transcriptome integration ) Linking interactome to disease: a network-based analysis of metastatic relapse in breast cancer (Maxime Garcia)
    • MAT (Meta Analysis in the Transcriptome) Search for coexpressed genes by mining ArrayExpress and Gene Expression Omnibus (Fanny Blondin)
    • InfoCyt: high throughput bioinformatics for flow cytometry data analysis (Philippe Rouillier, Ph.D., Olivier Stahl)
    • Djeen : A high throughput multi-technological Research Information Management System for the Joomla! CMS (Olivier Stahl, Arnaud Guille)
  • ITI: Biomarkers signing five years metastatic relapse in BC
    • Discovery of biomarkers for improvement of adjuvant chemotherapy decision for BC patients.
    • Two studies:
    • Classical analysis: discriminative power of the two signatures is not reproduced when crossing studies (Ein dor et al., 2006)
    • A strong dependencies of signature over training set has been shown (Michiels et al., 2005)
    • 1) Microarrays do not detect drivers genes (mutations)
    • 2) Curse of dimensionality
    • Network based analysis: Chuang et al (Nat. Biotech. 2007, cross validation), van Vliet et al. (PLoS one, 2007)
    76 gènes 70 gènes Wang et al. (286 patients) van’t Veer et al. van de Vijver et al. (295 patients) 3
    • Subnetworks are scored by correlation:
    • Subnetworks must meet a minimal score Sth over c datasets
    1) Microarrays do not detect drivers genes (mutations) 2) Curse of dimensionality
    • Interaction Data
    • 6 PPI datasets, 137280 interactions over 13777 proteins
      • Gene Expression Data
      • 5 GEP datasets, 780 samples in total (including 31 IPC patients)
  • Classification BC metastatic relapse Average accuracy on CV runs ~77% of correctly classified patients (mixed ER+, ER-) Improvements: -Integration w/ CGH data -Comparison w/ other signatures - Integration of subnetwork in classification
  • Djeen : A high throughput multi-technological Research Information Management System for the Joomla! CMS Hierarchy Right management Joomla! CMS Multitechnological Templates Experiment Design
  • Djeen : A high throughput multi-technological Research Information Management System for the Joomla! CMS (2)
    • User/Group rights management
    • Hierarchical data organisation (Project, Experiment Files)
    • Follows wet laboratories workflow
    • Multitechnological databases
    • Templates
    • Experimental design and export to instruments
    • CMS-based system
    Doc & download: http:// bioinformatique.marseill.inserm.fr / djeen CRCM instance: http:// bioinformatique.marseill.inserm.fr /code Stahl et al. (Submitted to Bioinformatics)
  • InfoCyt: high throughput bioinformatics for flow cytometry data analysis Fluorochromes coupled to surface markers CD98
    • Exploration of all combination
    • Unbiased and reproducible analysis
    • New classes discovery
    CD8 CD44 Automated decection (Mixture Models, package FlowCore) CD4
  • InfoCyt: high throughput bioinformatics for flow cytometry data analysis (2) Experimental design [DJEEN] Tumor microenvironment (Colon Cancer) [J Galon, CRC] Tumor microenvironment (Colon and Breast Cancer) [D Olive, CRCM]
    • - Data integration
    • - Visualisation
    • Sample Classification
    • - Prognosis prediction
    -Population characterisation -Population/sample matching Projects T-cell development, (mouse models) [M Malissen, CIML] - Automated population detection (Gating) by mixture models [Package Bioconductor FloClust, Finak et al., 2009 ] High throughput Analysis Export experimental structure and instrument configuration to instrument [DJEEN] (BD ® FACSDiva) Data import [DJEEN] Experimentation
  • Summary & future directions
    • ITI
      • Submission to Bioinformatics
      • Selected for oral presentation at the Cancer Bioinformatics Workshop (2-4 sept 2010, Cambridge)
      • Todo: CGH, classif with subnetworks,
    • InfoCyt
      • Based on the BioConductor package Flowcore
      • Current: Assignation of labels to detected population
      • Todo: streamline pipeline to perform classification on large patients cohorts
    • DJEEN
      • Manuscript under review ( Bioinformatics )
      • Production version released this month (CRCM, CIML)
      • Todo: links to ITI & InfoCyt pipeline
    • Recent publications from the group
      • Garcia M . Et al (in press) Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications .
      • Bidaut G. Biomedical Informatics for Cancer Research . 315-333.
      • Bidaut G & Stoeckert CJ. Proc. of the Pacific Symp. on Biocomp. 2009:356-67.
      • Bidaut G & Stoeckert CJ. Methods Enzymol. 2009;467:229-45.
  • Acknowledgements
    • ITI
      • D. Birnbaum (IPC, CRCM)
      • F. Bertucci (IPC)
      • S. Carpentier (Ipsogen)
      • M. Chaffanet (IPC)
      • Junwen Wang (HKU)
    • InfoCyt & Djeen
      • M. Malissen (CIML)
      • S. Granjeaud (TAGC)
      • D. Olive (IPC, CRCM)
    • IB Team
      • O. Stahl (IE, InfoCyt, DJEEN)
      • M. Garcia (Doctorant, ITI)
      • P. Rouillier (Post-doc, InfoCyt)
      • A. Guille (M2, DJEEN)
  • Data workflow
    • Input data
      • 5 GEP datasets, 780 samples in total (including 31 IPC patients)
      • 6 PPI dataset, 137280 interactions over 13777 proteins
    Null score distribution on Loi et al Step 1: Subnetwork Detection (ITI) Input 2: Protein-Protein Interaction data Input 1: 780 GE samples 10-fold cross validation Step 2: Statistical validation - type 1 p-value : random subnetworks - type 2 p-value : shuffled expression data - type 3 p-value : random interactome Step 3: Subnetwork intersection - type 1 (p-value < 10 ˉ² on 1 dataset) - type 2 (p-value < 10 ˉ³ on 1 dataset) - type 3 (p-value < 10 ˉ¹ on 1 dataset) SVM classification (majority voting) Test data Training data score Gene signature Outcome Prediction
  • Classification IBC 65% of correctly classified patients
    • Training set:
    • Dresman et al. (36 patients, Affy U133)
    • Nguyen et al. (36 patients, Affy U133)
    • Test set:
    • 196 IPC patients
  • Alternative analysis by integration interactome-transcriptome
    • Exemple: voie Ras
    • Variabilité technologique, expérimentale, et biologique
    Dataset 1 Dataset 2
  • New biomarkers linked to metastasis were found
    • Apoptosis [Subnetworks 291, 5714]
    • Cell adhesion [Subnetwork 6513]
    • Cell cycle control [Subnetworks 1537, 581,7013, 5339]
    • Immune response[Subnetworks 291, 2810, 3251]
    • Developpement [Subnetworks 387, 58, 3420,7013,60312,3251,375]
    • Metabolism [Subnetworks 29959, 3420, 581,4291,5339, 2068,374291]
    20 YWHAZ 19 PRKCI 18 CRMP1 17 SFN 16 PPFIA1 15 BAX 14 CYCS 13 AGTPBP1 12 MAPKAPK2 11 HSPB1 10 ACTN1 9 HNRNPA1 8 TSC1 7 TK1 6 SF3B3 5 LUC7L3 4 GRB2 3 STMN2 2 CCND1 1 CDC2 Rang Gene
  • ITI web resource for functional exploration: ITIDB
    • Subnetwork DB
    • http:// bioinformatique.marseille.inserm.fr/iti )
    • Global exploration of subnetworks and their components –link to NCBI, sorting by discriminative score
    • Database contains subnetworrks linked to
      • IBC (Inflammatory form of breast cancer)
      • BC Metastatic Relapse