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NetBioSIG2012 joshstuart

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Keynote lecture for NetBio SIG 2012

Keynote lecture for NetBio SIG 2012

Published in: Health & Medicine, Technology

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  • Pathway Inference in the MiddleArrows pointing out to different uses:differential marker ID for subtypescross-cancer analysisassess mutationssimulate to infer drug targets
  • GBM results:PDGFR predicted as GOF. Exon 8/9 deletion shown to be oncogenic.
  • Pathway Inference in the MiddleArrows pointing out to different uses:differential marker ID for subtypescross-cancer analysisassess mutationssimulate to infer drug targets
  • Pathway Inference in the MiddleArrows pointing out to different uses:differential marker ID for subtypescross-cancer analysisassess mutationssimulate to infer drug targets
  • BR is at Brown University.
  • Transcript

    • 1. Predicting the impact of mutations using pathway-guided integrative genomics Network Biology SIG, ISMB 2012 Josh Stuart, UC Santa Cruz July 12, 2012
    • 2. Overview of pathway-guided approachIntegrate many data sources to gain accurate view of how genes are functioning in pathwaysPredict the functional consequences of mutations by quantifying the effect on the surrounding pathwayUse pathway signatures to implicate mutations in novel genes to (re-)focus targetingIdentify critical “Achilles Heels” in the pathways that distinguish a particular sub-type
    • 3. Flood of Data Analysis ChallengesGenomics, Functional Genomics, Metabolomics, Epigenomics = Exome Sequences Multiple, Possibly Structural Conflicting Signals Variation Expression Copy Numberis What it This Does to You Alterations DNA Methylation
    • 4. Analysis of disease samples like automotive repair (or detective work or other sleuthing) Patient Sample 1 Patient Sample 2 Sleuths use as much knowledge as possible. Patient Sample 3 Patient Sample N …
    • 5. Much Cell Machinery Known:Gene circuitry now available. Curated and/or Collected Reactome KEGG Biocarta NCI-PID Pathway Commons …
    • 6.  Expression of 3 transcription factors: high TF high TF low TF Inference: Inference: Inference: TF is ON TF is OFF TF is ON (expression (high expression (low-expression reflects but inactive) but active ) activity)
    • 7.  BUT, targets are amplifiedExpression -> TF ON Copy Number -> TF OFF TF Lowers our belief in active TF because explained away by cis evidence.
    • 8. Nir Friedman, Science (2004) - Review
    • 9. Integration Approach: Detailed models of gene expression and interaction
    • 10. Integration Approach: Detailed models of expression and interaction Two Parts: 1. Gene Level Model (central dogma) 2. Interaction Model (regulation)
    • 11. 1. Central Dogma-Like Gene Model of Activity 2. Interactions that connect to specific points in gene regulation map Charlie VaskeVaske et al. 2010. Bioinformatics Steve Benz
    • 12. Multimodal Data Pathway Model Cohort Inferred Activities of Cancer CNV mRNA meth …
    • 13. Patient Samples (247)Pathway Concepts (867) TCGA Network. 2011. Nature (lead by Paul Spellman)
    • 14. Patient Samples (247) FOXM1 Transcription NetworkPathway Concepts (867) TCGA Network. 2011. Nature (lead by Paul Spellman)
    • 15. TCGA Network. 2011. Nature (lead by Paul Spellman)
    • 16. Mutated genes are the focus of many targeted approaches.Some patients with “right” mutation don’t respond. Why?Many cancers have one of several “novel” mutations. Can these be targeted with current approaches?Pathway-motivated approaches:  Identify gain-of-function from loss-of-function. Sam Ng  Compare novel signatures ISMB Oral Poster
    • 17. HighInferred Activity Inference using Inference using Inference using all downstream upstream neighbors neighbors neighbors mutated FG gene FG SHIFT FG LowInferred Activity Sam Ng, ECCB 2012
    • 18. FG Sam Ng, ECCB 2012
    • 19. 1. IdentifyFG Local FG Neighbor- hood Sam Ng, ECCB 2012
    • 20. 2a. Regulators Run 1. FG IdentifyFG Local FG Neighbor- 2b. hood Targets FG Run Sam Ng, ECCB 2012
    • 21. 2a. Regulators Run 3. 1. FG Calculate IdentifyFG Local FG FG - FG P-Shift Score Neighbor- 2b. Difference hood Targets FG FG Run (LOF) Sam Ng, ECCB 2012
    • 22. Shift ScorePARADIGM downstreamPARADIGM upstreamExpressionMutation RB1 Sam Ng, ECCB 2012
    • 23. Focus Gene Key P-Shift T-Run R-Run Expression MutationNeighbor Gene Key Activity Expression RB1 Mutation Sam Ng, ECCB 2012
    • 24. RB1 Discrepancy Scores distinguish mutated vs non-mutated samples Signal Score (t-statistic) = -5.78 Sam Ng
    • 25.    Observed SS Background SS Sam Ng
    • 26. TP53 Network Sam Ng
    • 27. P-Shift ScorePARADIGM downstreamPARADIGM upstreamExpressionMutation NFE2L2 Sam Ng
    • 28. Focus Gene Key P-Shift T-Run R-Run Expression MutationNeighbor Gene Key Activity Expression RB1 Mutation NFE2L2 Sam Ng
    • 29. RB1 TP53 NFE2L2Signal Score (t-statistic) = -5.78 Signal Score (t-statistic) = -10.94 Signal Score (t-statistic) = 4.985 Observed SS Background SS Sam Ng
    • 30.  “ ”   “ ” ’ Sam Ng
    • 31. Sam Ng
    • 32. Pathway Discrepancy HIF3A (n=7) TBC1D4 (n=9) (AKT signaling) NFE2L2 (29) MAP2K6 (n=5) LUSC MET (n=7) (gefitinib resistance) GLI2 (n=10) (SHH signaling) CDKN2A (n=30) AR (n=8) EIF4G1 (n=20) Sam Ng
    • 33. Christina Yau, Buck Inst
    • 34. Christina Yau, Buck Inst.
    • 35. Identify sub-pathways that distinguish patients sub-types (e.g. Insight from contrast mutant vs. non-mutant, response to drug, etc)Predict mutation impact on pathway “neighborhood”Identify master control points for drug targeting.Predict outcomes with quantitative simulations. Sam Ng Ted Goldstein
    • 36. “ ”Pathway ActivitiesPathway Activities Ted Goldstein Sam Ng
    • 37. “ ”SuperPathway ActivitiesSuperPathway Activities Pathway Signature Ted Goldstein Sam Ng
    • 38.  Traditional methods treat each gene as a separate feature  Use features reflecting overall pathway activity  Smaller number of features are now fed to predictorsPredictor Artem Sokolov
    • 39.  Traditional methods treat each gene as a separate feature  Use features reflecting overall pathway activity  Smaller number of features are now fed to predictors Artem SokolovPredictor ISMB poster
    • 40. Basal vs. LuminalRecursive feature elimination: we train an SVM, drop the least important half of features and recurseThe number of times each feature survived the elimination across 100 random splits of dataArtem Sokolov
    • 41. MethotrexateSensitivityNon sub-typespecific drugPathway involvingthe target of thedrug.Artem Sokolov
    • 42. One large highly-connected component (size and connectivity significant according to permutation980 pathway concepts analysis) 1048 interactions Characterized by several “hubs’ IL23/JAK2/TYK2 P53 ER tetramer HIF1A/ARNT FOXA1 Myc/Max Higher activity in ER- Lower activity in ER- Sam Ng, Ted Goldstein
    • 43. Identify master controllers using SPIA (signaling pathway impact analysis)Google PageRank for NetworksDetermines affect of a given pathway on each nodeCalculates perturbation factor for each node in the networkTakes into account regulatory logic of interactions. n IF ( g j ) Impact IF ( gi ) = s ( gi ) + å bij × factor: j=1 N up ( g j ) Google’s PageRank-Like Yulia Newton (NetBio SIG Poster)
    • 44. Slight Trick: Run SPIA in reverseReverse edges in Super PathwayHigh scoring genes now those at the “top” of the pathway PageRank finds Reverse to find highly referenced Highly referencing Yulia Newton
    • 45. Master Controller Analysis on Breast Cell Lines Basal Luminal Yulia Newton
    • 46. • DNA damage network is upregulated in basal breast cancers• Basal breast cancers are sensitive to PLK inhibitors GSK-PLKi Basal Claudin-low Luminal Ng, Goldstein Up Heiser et al. 2011 PNAS Down
    • 47. • HDAC Network is down- regulated in basal breast cancer cell lines• Basal/CL breast cancers are resistant to HDAC inhibitors HDAC inhibitor VORINOSTAT Heiser et al. 2011 PNAS Ng, Goldstein
    • 48. Connect genomic alterations to downstream expression/activity ?• What circuitry connects mutations to transcriptional changes? – Mutations  general (epi-) genomic perturbation – Expression  activity• Mutation/perturbation and expression/activity treated as heat diffusing on a network – HotNet, Vandin F, Upfal E, B.J. Raphael, 2008. Evan Paull – HotNet used in ovarian to implicate Notch pathway• Find subnetworks that link genetic to mRNA and ISMB protein-level changes. Oral Poster
    • 49. HotLinkGenomic Perturbations Gene Activity(Mutations, Methylation, Focal Copy Number) (Expression, RPPA, PARADIGM) ? Evan Paull
    • 50. HotLinkGenomic Perturbations Gene Activity(Mutations, Methylation, Focal Copy Number) (Expression, RPPA, PARADIGM) 1. Add heat 2. Diffuse heat 3. Cut out linkers Evan Paull
    • 51. HotLinkGenomic Perturbations Gene Activity(Mutations, Methylation, Focal Copy Number) (Expression, RPPA, PARADIGM) Linking Sub-Pathway Evan Paull
    • 52. HotLink “Double Diffusion” Overview
    • 53. HotLink Causal Paths
    • 54. Significance of Interlinking Network
    • 55. Double diffusion better than single
    • 56. Basal-LumA HotLink Basal LumA
    • 57. Basal-LumA HotLinkMAP, PI3K, AKT Map Basal LumA TP53, RB1 MYC, FOXM1
    • 58. AKT/PI3K MAPK8, MAPK14 (p38- alpha) identified as mediators.
    • 59. TP53, RB1 Basal LumA
    • 60. MYC NeighborhoodDouble feedback loop involvingTP53, RB1, CDK4, FOXM1, and MYC Basal LumA
    • 61. AKT signaling Basal LumA
    • 62.  Ted Goldstein
    • 63.  ’ Mutations PARADIGM Signatures Ted Goldstein
    • 64.  ’ Mutations APC and other Wnt PARADIGM Signatures Ted Goldstein (Note: CRC figure below; soon for BRCA)
    • 65.  ’ Mutations PARADIGM Signatures Ted Goldstein
    • 66.  ’ Mutations PARADIGM Signatures TGFB Pathway mutations Ted Goldstein (Note: CRC figure below; soon for BRCA)
    • 67.  ’ Mutations PARADIGM Signatures PIK3CA, RTK pathway, KRAS Ted Goldstein (Note: CRC figure below; soon for BRCA)
    • 68.  ’ Mutations PARADIGM Signatures Evidence for AHNAK2 acting PI3KCA-like? (Note: CRC figure below; soon for BRCA) Ted Goldstein
    • 69. UCSC Integrative Genomics Group Please See Posters! Sam Ng Dan Carlin Evan Paull Marcos Woehrmann Ted GolsteinJames Durbin Artem Sokolov Yulia Newton Chris Szeto Chris Wong
    • 70. David Haussler Buck Institute for Aging Chris Benz, • Christina Yau • Sean MooneyUCSC Cancer Genomics Jing Zhu • Janita Thusberg• Kyle Ellrott Collaborators• Brian Craft• Chris Wilks • Joe Gray, LBL• Amie Radenbaugh • Laura Heiser, LBL• Mia Grifford • Eric Collisson, UCSF• Sofie Salama • Nuria Lopez-Bigas, UPF• Steve Benz • Abel Gonzalez, UPF Broad Institute Funding AgenciesUCSC Genome Browser Staff • Gaddy Getz• Mark Diekins • NCI/NIH • Mike Noble • SU2C• Melissa Cline • Daniel DeCara • NHGRI• Jorge Garcia • AACR• Erich Weiler • UCSF Comprehensive Cancer Center • QB3
    • 71. UCSC Cancer Browser genome-cancer.ucsc.edu Jing Zhu
    • 72. supplemental
    • 73. 325 Genes, 2213 Interactions
    • 74. “Backbone” of 43 genes, 90 connections
    • 75. “Backbone” of 43 genes, 90 connectionsMaster regulators: Cell-Cell Signaling, AKT1, Cyclin D1
    • 76. “Backbone” of 43 genes, 90 connectionsMajor PARADIGM hubs included: MYC, FOXM1, FOXA1, HIF1A/ARNT
    • 77. “Backbone” of 43 genes, 90 connectionsSignaling through beta-catenin explains MYC activity in basals:-deletions in CDKN2A de-repress CTNNB1 in basals or-lower expression of Cyclin D1 de-repress CTNNB1
    • 78. Top 20 basal master controllers Yulia Newton
    • 79. RNAi vs. Master Controller (after recurring runs)Basal vs Luminal RNAi Growth AKT2 RPS6KA3 - p90 S6 kinase PDPK1 AKT1 High-scoring after Iterative runs. RAF1 Basals differentially RPS6KA3 Sensitive to RNAi Inhibitors available. Master Controller Score Yulia Newton