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The Network is a Biomarker in Cancer Signatures

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  1. The Network is a Biomarker in Cancer Signatures Sol Efroni The Mina & Everard Goodman Faculty of Life Sciences Bar Ilan University

  2. My Lab Cancer Genomics Rep-Seq Systems Immunology • Vainas et al,Autoimmunity 2011 • Mascanfroni et al,Nature Immunology 2013 Benichouet al,Immunology 2012

  3. Introduction•Biomarkers• Regulation • Implementation Complex Biomarkers Clinically motivated multi gene markers

  4. The problem with Multi-gene mRNA markers Introduction•Biomarkers•Regulation• Implementation Robustness


  5. Introduction•Biomarkers•Regulation• Implementation

  6. Introduction•Biomarkers• Regulation • Implementation TCGA “...a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing”

  7. Introduction•Biomarkers• Regulation • Implementation

  8. Phenotype comparison Introduction•Biomarkers• Regulation •Implementation Tumor Normal

  9. Introduction•Biomarkers• Regulation •Implementation Phenotype comparison Tumor Normal

  10. Introduction•Biomarkers• Regulation •Implementation ? ? ? ? ? ? A single number to represent the Pathway within this sample ?

  11. Introduction•Biomarkers •Regulation• Implementation Regulation as metric Example

  12. Gene 1is a perfectbiomarker Gene 1 1 The pathway is enriched with the interesting genes Gene 1and Gene 2 and and is therefore important 2 Gene 2is a perfectbiomarker Gene 2

  13. Gene 1is a poorbiomarker Gene 1 Network 1 Perfect corr The two gene network is a perfectbiomarker 1 ? -1 Perfect anti corr 2 Gene 2is a poorbiomarker Gene 2

  14. Introduction•Biomarkers• Regulation •Implementation

  15. Introduction•Biomarkers• Regulation •Implementation Efroni et al PLoS ONE 2009 Greenblum et al. BMC Bioinformatics 2011 Efroni et al IET Systems Biology 2013

  16. Pathways as biomarkersandpathways as targets inGBMand in Ovarian cancer

  17. RotemBen-Hamo Dr. HelitCohen Introduction•Biomarkers• Regulation •Implementation ovarian

  18. Methodology Introduction•Biomarkers• Regulation •Implementation ovarian Data Collection Ovarian Cancer – 511 patients, 348 whole exomes

  19. Ovarian cancer gene signature Introduction•Biomarkers• Regulation •Implementation ovarian

  20. Introduction•Biomarkers• Regulation •Implementation ovarian Survival Analysis: Gene based Ben-Hamo R, Efroni S. BMC Systems Biology (2012)

  21. A pathway view highlights a single pathway Introduction•Biomarkers• Regulation •Implementation ovarian

  22. Introduction•Biomarkers• Regulation •Implementation ovarian Ovarian Cancer: PDGF Signaling Pathwayprovides a robust signature TCGA 511 patients Duke1 Dataset 119 patients Duke2 Dataset 42 patients

  23. GBM Introduction•Biomarkers• Regulation •Implementation GBM Again, a collection of sources for clinical and molecular data

  24. Introduction•Biomarkers• Regulation •Implementation GBM Pathway 1 Pathway 2 Pathway 3 Pathway 4 Pathway 5 Pathway 6 … Pathway 579

  25. The p38 pathway is most significant Introduction•Biomarkers• Regulation •Implementation GBM “p38 signaling mediated by mapkapkinases”

  26. The p38 pathway is robust across multiple datasets Preliminary Results Introduction•Biomarkers• Regulation •Implementation GBM Ben-Hamo R, Efroni S. Genome Medicine (2011)Ben-Hamo R, Efroni S. Systems Biomedicine (2013)

  27. Another type of regulation has been suggested:microRNA Control over Pathways Introduction•Biomarkers• Regulation •Implementation GBM Inui M et al. Nature Reviews Molecular Cell Biology (2010)

  28. Introduction•Biomarkers• Regulation •Implementation GBM

  29. Introduction•Biomarkers• Regulation •Implementation GBM P38/MAPKP Pathway AND hsa-miR-9 P38 signaling pathway P38 signaling pathway R2 = -0.67 R2 = -0.21 hsa-miR-9 hsa-miR-9

  30. Preliminary Results - Computational Introduction•Biomarkers• Regulation •Implementation GBM P38/MAPKP Pathway AND hsa-miR-9 P38 signaling pathway P38 signaling pathway R2 = -0.67 R2 = -0.21 hsa-miR-9 hsa-miR-9

  31. Preliminary Results - Experimental Introduction•Biomarkers• Regulation •Implementation GBM microRNAs regulation of pathways - GBM Expression levels of P38 pathway genes in HeLa Vs. HMEC cell lines • HeLa: cervical cancer cell line. • HMEC: (Human mammary epithelial cell) primary cell line.

  32. Introduction•Biomarkers• Regulation •Implementation GBM microRNAs regulation of pathways - GBM Expression levels of P38 pathway genes in HeLa cell lines after miR-9 transfection

  33. miR-9 down regulation of the p38 network improves prognosis Introduction•Biomarkers• Regulation •Implementation GBM ? Can we see the same effect with drug response?

  34. Drug response Introduction•Biomarkers• Regulation •Implementation GBM • Patients are treated using a wide spectrum of 69 different drugs • Drugs are classified into two groups: • drugs that target genes in the p38 pathway And • drugs that do not target genes in the pathway

  35. Introduction•Biomarkers• Regulation •Implementation GBM Out of the 69 drugs given to the patients 6 drugs target genes that are part of the p38 network

  36. Group1 • Low survival • 169 patients • Average overall survival time – 433 days • Median survival time – 310 days • All patients did not received p38 targeted drugs • Group2 • High survival • 63 patients • Average overall survival – 896 days • Median survival time – 691 days • All patients received p38 targeted drugs Ben-Hamo R, Efroni S. Genome Medicine (2011) Ben-Hamo R, Efroni S. Systems Biomedicine (2013) (CAMDA first prize)

  37. Summary Measurethe Network Targetthe Network

  38. Summary Core biology hides in functional wiring of the network By selecting for robust signatures we achieve significant markers for prognosis By following the outline of these signatures we discover biology that may lead to treatment

  39. Acknowledgements Helit Cohen Rotem Ben Hamo AlonaZilberberg Jennifer Benichou RivkaCashman MoriahCohen MiriGordin DrorHibsh IdoSloma Hagit PhilipRenanaKozol

  40. Systems biomedicine journal

  41. Mechanisms