NetBioSIG2012 joshstuart
- 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 Challenges Genomics, 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 amplified Expression -> 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 Vaske Vaske 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 Network Pathway 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. High Inferred Activity Inference using Inference using Inference using all downstream upstream neighbors neighbors neighbors mutated FG gene FG SHIFT FG Low Inferred Activity Sam Ng, ECCB 2012
- 18. FG Sam Ng, ECCB 2012
- 19. 1. Identify FG Local FG Neighbor- hood Sam Ng, ECCB 2012
- 20. 2a. Regulators Run 1. FG Identify FG Local FG Neighbor- 2b. hood Targets FG Run Sam Ng, ECCB 2012
- 21. 2a. Regulators Run 3. 1. FG Calculate Identify FG Local FG FG - FG P-Shift Score Neighbor- 2b. Difference hood Targets FG FG Run (LOF) Sam Ng, ECCB 2012
- 22. Shift Score PARADIGM downstream PARADIGM upstream Expression Mutation RB1 Sam Ng, ECCB 2012
- 23. Focus Gene Key P-Shift T-Run R-Run Expression Mutation Neighbor 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 Score PARADIGM downstream PARADIGM upstream Expression Mutation NFE2L2 Sam Ng
- 28. Focus Gene Key P-Shift T-Run R-Run Expression Mutation Neighbor Gene Key Activity Expression RB1 Mutation NFE2L2 Sam Ng
- 29. RB1 TP53 NFE2L2 Signal 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 Activities Pathway Activities Ted Goldstein Sam Ng
- 37. “ ” SuperPathway Activities SuperPathway 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 predictors Predictor 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 Sokolov Predictor ISMB poster
- 40. Basal vs. Luminal Recursive feature elimination: we train an SVM, drop the least important half of features and recurse The number of times each feature survived the elimination across 100 random splits of data Artem Sokolov
- 41. Methotrexate Sensitivity Non sub-type specific drug Pathway involving the target of the drug. Artem Sokolov
- 42. One large highly-connected component (size and connectivity significant according to permutation 980 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. HotLink Genomic Perturbations Gene Activity (Mutations, Methylation, Focal Copy Number) (Expression, RPPA, PARADIGM) ? Evan Paull
- 50. HotLink Genomic Perturbations Gene Activity (Mutations, Methylation, Focal Copy Number) (Expression, RPPA, PARADIGM) 1. Add heat 2. Diffuse heat 3. Cut out linkers Evan Paull
- 51. HotLink Genomic Perturbations Gene Activity (Mutations, Methylation, Focal Copy Number) (Expression, RPPA, PARADIGM) Linking Sub-Pathway Evan Paull
- 52. HotLink “Double Diffusion” Overview
- 54. Significance of Interlinking Network
- 55. Double diffusion better than single
- 56. Basal-LumA HotLink Basal LumA
- 57. Basal-LumA HotLink MAP, 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 Neighborhood Double feedback loop involving TP53, 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
- 70. UCSC Integrative Genomics Group Please See Posters! Sam Ng Dan Carlin Evan Paull Marcos Woehrmann Ted Golstein James Durbin Artem Sokolov Yulia Newton Chris Szeto Chris Wong
- 71. David Haussler Buck Institute for Aging Chris Benz, • Christina Yau • Sean Mooney UCSC 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 Agencies UCSC 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
- 72. UCSC Cancer Browser genome-cancer.ucsc.edu Jing Zhu
- 73. supplemental
- 74. 325 Genes, 2213 Interactions
- 75. “Backbone” of 43 genes, 90 connections
- 76. “Backbone” of 43 genes, 90 connections Master regulators: Cell-Cell Signaling, AKT1, Cyclin D1
- 77. “Backbone” of 43 genes, 90 connections Major PARADIGM hubs included: MYC, FOXM1, FOXA1, HIF1A/ARNT
- 78. “Backbone” of 43 genes, 90 connections Signaling 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
- 79. Top 20 basal master controllers Yulia Newton
- 80. 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
Editor's Notes
- #13: Pathway Inference in the MiddleArrows pointing out to different uses:differential marker ID for subtypescross-cancer analysisassess mutationssimulate to infer drug targets
- #33: GBM results:PDGFR predicted as GOF. Exon 8/9 deletion shown to be oncogenic.
- #37: Pathway Inference in the MiddleArrows pointing out to different uses:differential marker ID for subtypescross-cancer analysisassess mutationssimulate to infer drug targets
- #38: Pathway Inference in the MiddleArrows pointing out to different uses:differential marker ID for subtypescross-cancer analysisassess mutationssimulate to infer drug targets
- #49: BR is at Brown University.