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Vassili Soumelis - Programme d’analyse globale et intégrative du micro-environnement tumoral

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Programme d’analyse globale et intégrative du
micro-environnement tumoral - Vassili SOUMELIS, MD, PhD
Laboratoire d’Immunologie Clinique et Inserm U932

Published in: Science
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Vassili Soumelis - Programme d’analyse globale et intégrative du micro-environnement tumoral

  1. 1. Geneviève Almouzni, PhD Directeur(e) du Centre de Recherche Marc Estève , MD Directeur de l’Hôpital
  2. 2. Programme d’analyse globale et intégrative du micro-environnement tumoral Vassili SOUMELIS, MD, PhD Laboratoire d’Immunologie Clinique et Inserm U932
  3. 3. L’organisation hiérarchique des systèmes vivants
  4. 4. Réseaux de communication intercellulaire dans les tissus inflammatoires
  5. 5. Oncogénèse: vision centrée sur la cellule tumorale Oncogene Tumor supressor geneNormal Tumor
  6. 6. Oncogene Tumor supressor geneNormal Tumor Normal epithelial cells Neoplastic cells Extra-cellular Matrix Fibroblasts/Myofibroblasts CAFs Lymphatic vessels Blood vessels Lymphocytes Macrophages/TAM Dentritic cells Micro-environnement tumoral: une écologie complexe
  7. 7. Oncogene Tumor supressor geneNormal Tumor Influence du micro-environnement sur l’évolution du cancer
  8. 8. Le micro-environnement: nouvelle cible thérapeutique? 1/ Rôle dans la progression tumorale 2/ Implication dans la résistance à la chimiothérapie 3/ Eficacité de nouvelles thérapies ciblant le micro-environnement (exemple: immunothérapie)
  9. 9. Normal epithelial cells Neoplastic cells Extra-cellular Matrix Fibroblasts/Myofibroblasts CAFs Lymphatic vessels Blood vessels Lymphocytes Macrophages/TAM Dentritic cells Comment étudier le micro-environnement tumoral?
  10. 10. T-MEGA: Tumor MicroEnvironment Global Analysis Hospital partners: Surgery Dpt: Fabien Reyal, Pascale Mariani Pathology Dpt: Xavier Sastre-Garau, Anne Vincent- Salomon, Eliane Padoy, Jean-Marie Pléau Centre de ressources biologiques (CRB) : Odette Mariani Bioinformatic and biostatistic: Bioinformatic Unit U900 Biostatistic Unit Technological Platforms: Cytometry Platform Experimental Pathology Platform Preclinical Investigation Laboratories High throughput RTqPCR Platform Affymetrix Platform T-MEGA people: Fatima Mechta-Grigoriou, PhD: Coordinator Vassili Soumelis, MD-PhD: Coordinator Alix Scholer-Dahirel: Project Manager Philemon Sirven: Bioengineer Melissa Cardon: Bioengineer Gerome Jules-Clement: Data Manager Yann Kieffer: Bioinformatician Marine Jeanmougin: Bioinformatician Sofia Honorio-Grand: CRA Researchers involved in T-MEGA initiative: Fatima Mechta-Grigoriou Vassili Soumelis François Radvanyi Fabien Reyal Danijela Vignjevic Arturo Londoño-Vallejo Marc-Henri Stern Marie Dutreix Marie Fernet Janet Hall Virginie Dangles-Marie Didier Decaudin Rosette Lidereau Ivan Bieche Didier Meseure Clotilde Thery Ana-Maria Lennon-Duménil Matthieu Piel
  11. 11. Schéma expérimental: comment générer le maximum de données à partir d’un petit échantilon tumoral (résidu) Macrodissection Transcriptome Histology Molecular Studies TMA Functional Experiments Methylation profiles Labs Patient Frozen (CRB) Fresh Tissue Supernatant Cells MAP dissociationculture FACS T-MEGABioengineer FFPE (PIC BIM) Macrodissection Transcriptome Functional Experiments Methylation profiles Histology Molecular Studies TMA Frozen (CRB) FFPE (PIC BIM) Fresh Tissue Supernatant Cells MAP dissociationculture FACS Patient Clinical Research Associate (T-MEGA)
  12. 12. De l’échantillon clinique aux données biologiques: approche modulaire Clinical sample Cellular TME: - ImmunoHistoChemistry - FACS, 3 Ab panels Soluble TME: - Multiple Analyte Profiling - Tumor infiltrating lymphocyte secretion profiles - Functional effect of tumor-derived supernatants Clinical data Diagnosis / Follow-up Extracellular Matrix composition : ImmunoHistoChemistry Glucose Metabolism Oxidative stress Transcriptomic: Epithelial and Stromal compartments Human breast or ovarian cancer 450 patients included to date
  13. 13. Clinical sample Cellular TME: - ImmunoHistoChemistry - FACS, 3 Ab panels Soluble TME: - Multiple Analyte Profiling - Tumor infiltrating lymphocyte secretion profiles - Functional effect of tumor-derived supernatants Clinical data Diagnosis / Follow-up Extracellular Matrix composition : ImmunoHistoChemistry Glucose Metabolism Oxidative stress Transcriptomic: Epithelial and Stromal compartments Human breast or ovarian cancer 450 patients included to date De l’échantillon clinique aux données biologiques: approche modulaire
  14. 14. Analyse “multi-process” du micro-environnement sécrété Tumor Juxtatumor fragment no treatment RPMI 10% FCS 24h Pro-/Anti-Inflammatory Invasion/MetastasisAngiogenesis Growth factors Metabolism Immune infiltrate, Th subsets Fibroblast infiltrate Oxidative stress Drug sensitivity SASP Apoptosis ECM remodeling Oxidativestress ImmuneInfiltrate Fibroblastinfiltrate Drugsensitivity Pro-Inflammatory Anti-Inflammatory Invasion/Metastasis ECMremodeling Angiogenesis SASP Apoptosis Metabolism GrowthFactors 0 10 20 30 40 50 60 Numberofwantedanalytes
  15. 15. De l’échantillon clinique aux données biologiques: approche modulaire Clinical sample Cellular TME: - ImmunoHistoChemistry - FACS, 3 Ab panels Soluble TME: - Multiple Analyte Profiling - Tumor infiltrating lymphocyte secretion profiles - Functional effect of tumor-derived supernatants Clinical data Diagnosis / Follow-up Extracellular Matrix composition : ImmunoHistoChemistry Glucose Metabolism Oxidative stress Transcriptomic: Epithelial and Stromal compartments Human breast or ovarian cancer 450 patients included to date
  16. 16. Caractérisation de la diversité du microenvironnement cellulaire DC CD4+ T cells, CD8+ T cells, B cells, NK cells Joyce et al. Nat Rev Cancer 2009 + T BMDC Macrophage Neutrophil Mast cell MDSC MSC Fibroblast Lymphocyte TEM Endothelial cell Pericyte Blood vessel LyLymphocyLy Normal epithelial cell Tumor epithelial cell Lymphatic endothelial cell
  17. 17. De l’échantillon clinique aux données biologiques: approche modulaire Clinical sample Cellular TME: - ImmunoHistoChemistry - FACS, 3 Ab panels Soluble TME: - Multiple Analyte Profiling - Tumor infiltrating lymphocyte secretion profiles - Functional effect of tumor-derived supernatants Clinical data Diagnosis / Follow-up Extracellular Matrix composition : ImmunoHistoChemistry Glucose Metabolism Oxidative stress Transcriptomic: Epithelial and Stromal compartments Human breast or ovarian cancer 450 patients included to date
  18. 18. INTEGRATIVE ANALYSIS DATA INTEGRATION 1 2 3 5 4 55 DATA QUERY Sam ple Type + Pathology + Biotechnology + Gene A Scientist / Clinician KDI core system Detection of new target 6 DATAPRE-PROCESSING Web applications m odules Sam ple Patient Clinical data Low throughput biotechnological platform High-throughput biotechnological platform Low-throughput biotechnological platform Analysis pipelines SaSS m ple TyTT pe + PaPP thology + Biotechnology + Gene A KDDDI core syyyyysssssttttteeeeemmmmm Detection of new 66 Anal WEBSERVICES(SOAP) Clinical data Alteration data - DNA copy number - mutations Expression data - gene expression « ClinicalDB » « BIRD » « Bioinfo-Portal » Biological data - Histological Analyzis - Cellular phenotyping - Supernatant Analysis - Functional Experiments «Algebra» «Gersimi» De l’échantillon tumoral à l’intégration des données: un circuit “haute fidélité”
  19. 19. Apply T-MEGA datasetTheoritical modeling Antonio Cappucio 2/ Modeling Today 1/ Biostatistical analysis Prognostic/Predictive Biomarkers 3/ Biomarkers and therapeutic targets T-MEGA: état d’avancement des analyses de données LE.*RE LE.*RS LS.*RE LS.*RS SE Co-segregation of biological parameters Yann Kieffer, Marine Jeanmougin
  20. 20. T-MEGA people: Fatima Mechta-Grigoriou, PhD: Coordinator Vassili Soumelis, MD-PhD: Coordinator Alix Scholer-Dahirel: Project Manager Philemon Sirven: Bioengineer Melissa Cardon: Bioengineer (SIRIC) Gerome Jules-Clement: Data Manager Yann Kieffer: Bioinformatician Marine Jeanmougin: Bioinformatician Sofia Honorio-Grand: CRA Sponsors: T-MEGA: ressources humaines et financières INCAPIC Industrial Partner ARC ITMO PIC ARC Industrial Partner INCA SIRIC ICGEX FRM
  21. 21. Importance du soutien financier: dynamiser et péréniser Ø Personnel: la plupart en CDD financé par des contrats de recherche de durée limitée Ø Analyses biologiques modulaires: financer des modules déjà plannifiés ou ajouter de nouveaux modules Ø Etendre le programme à d’autres tumeurs: cancer du sein, ovaire Ø Permettre la pérénisation pour répondre à des questions cliniques sur l’évolution des cancers Ø Initier le développement de nouvelles thérapeutiques ciblant le micro- environnement Clinical sample Cellular TME: - ImmunoHistoChemistry - FACS, 3 Ab panels Soluble TME: - Multiple Analyte Profiling - Tumor infiltrating lymphocyte secretion profiles - Functional effect of tumor- derived supernatants Clinical data Diagnosis / Follow-up Extracellular Matrix composition : ImmunoHistoChemistry Glucose Metabolism Oxidative stresssupernatants Transcriptomic: Epithelial and Stromal compartments
  22. 22. Merci Fatima
  23. 23. Merci à toutes et à tous pour votre intérêt et votre soutien

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