Stephen Friend HHMI-Penn 2011-05-27

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Stephen Friend, May 27, 2011. University of Pennsylvania - Howard Hughes Medical Institute, Philadelphia, PA

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Stephen Friend HHMI-Penn 2011-05-27

  1. 1. Use of Integrated Genomic Networks to Build Better Maps of Disease Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ San Francisco University of Pennsylvania May 27th, 2011
  2. 2. why consider the fourth paradigm- data intensive science thinking beyond the narrative, beyond pathways advantages of an open innovation compute space it is more about why than what
  3. 3. Alzheimers Diabetes Treating Symptoms v.s. Modifying Diseases Depression Cancer Will it work for me?
  4. 4. The Current Pharma Model is Broken:•  In 2010, the pharmaceutical industry spent ~$100B for R&D•  Half of the 2010 R&D spend ($50B) covered pre-PH III activities•  Half of the pre-PH III costs ($25B) were for program targets that at least one other pharmaceutical company was actively pursuing•  Only 8% of pharma company small molecule PCCs make it to PH III•  In 2010, only 21 new medical entities were approved by FDA April  16-­‐17,  2011   4   San  Francisco  
  5. 5. Familiar but Incomplete
  6. 6. Personalized Medicine 101:Capturing Single bases pair mutations = ID of responders
  7. 7. Reality: Overlapping Pathways
  8. 8. “Data Intensive Science” - Fourth Scientific Paradigm Equipment capable of generating massive amounts of data IT Interoperability Open Information System Evolving Models hosted in a Compute Space- Knowledge expert
  9. 9. WHY NOT USE “DATA INTENSIVE” SCIENCETO BUILD BETTER DISEASE MAPS?
  10. 10. “Data Intensive Science”- “Fourth Scientific Paradigm”For building: “Better Maps of Human Disease” Equipment capable of generating massive amounts of data IT Interoperability Open Information System Evolving Models hosted in a Compute Space- Knowledge Expert
  11. 11. It is now possible to carry out comprehensive monitoring of many traits at the population levelMonitor disease and molecular traits in populations Putative causal gene Disease trait
  12. 12. what will it take to understand disease? DNA RNA PROTEIN (dark matter)MOVING BEYOND ALTERED COMPONENT LISTS
  13. 13. 2002 Can one build a “causal model”?
  14. 14. How is genomic data used to understand biology? RNA amplification Tumors Microarray hybirdization Tumors Gene Index Standard GWAS Approaches Profiling Approaches Identifies Causative DNA Variation but Genome scale profiling provide correlates of disease provides NO mechanism   Many examples BUT what is cause and effect?   Provide unbiased view of molecular physiology as it relates to disease phenotypes trait   Insights on mechanism   Provide causal relationships and allows predictions 19 Integrated Genetics Approaches
  15. 15. Integration of Genotypic, Gene Expression & Trait Data Schadt et al. Nature Genetics 37: 710 (2005) Millstein et al. BMC Genetics 10: 23 (2009) Causal Inference “Global Coherent Datasets” •  population based •  100s-1000s individuals Chen et al. Nature 452:429 (2008) Zhu et al. Cytogenet Genome Res. 105:363 (2004) Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)
  16. 16. Gene Co-Expression Network Analysis Define a Gene Co-expression Similarity Define a Family of Adjacency Functions Determine the AF Parameters Define a Measure of Node Distance Identify Network Modules (Clustering) Relate the Network Concepts to External Gene or Sample Information Zhang B, Horvath S. Stat Appl Genet Mol Biol 2005
  17. 17. Constructing Co-expression Networks Start with expression measures for genes most variant genes across 100s ++ samples 1 2 3 4 Note: NOT a gene expression heatmap 1 1 0.8 0.2 -0.8 Establish a 2D correlation matrix 2 for all gene pairsexpression 0.8 1 0.1 -0.6 3 0.2 0.1 1 -0.1 4 -0.8 -0.6 -0.1 1 Brain sample Correlation Matrix Define Threshold eg >0.6 for edge 1 2 4 3 1 2 3 4 1 1 1 4 1 1 1 0 1 1 0 1 2 2 1 1 1 0 1 1 0 1 1 1 1 0 Hierarchically 3 Identify modules 4 0 0 1 0 2 3 cluster 4 3 0 0 0 1 1 1 0 1 Network Module Clustered Connection Matrix Connection Matrix sets of genes for which many pairs interact (relative to the total number of pairs in that set)
  18. 18. Preliminary Probabalistic Models- Rosetta /Schadt Networks facilitate direct identification of genes that are causal for disease Evolutionarily tolerated weak spots Gene symbol Gene name Variance of OFPM Mouse Source explained by gene model expression* Zfp90 Zinc finger protein 90 68% tg Constructed using BAC transgenics Gas7 Growth arrest specific 7 68% tg Constructed using BAC transgenics Gpx3 Glutathione peroxidase 3 61% tg Provided by Prof. Oleg Mirochnitchenko (University of Medicine and Dentistry at New Jersey, NJ) [12] Lactb Lactamase beta 52% tg Constructed using BAC transgenics Me1 Malic enzyme 1 52% ko Naturally occurring KO Gyk Glycerol kinase 46% ko Provided by Dr. Katrina Dipple (UCLA) [13] Lpl Lipoprotein lipase 46% ko Provided by Dr. Ira Goldberg (Columbia University, NY) [11] C3ar1 Complement component 46% ko Purchased from Deltagen, CA 3a receptor 1 Tgfbr2 Transforming growth 39% ko Purchased from Deltagen, CANat Genet (2005) 205:370 factor beta receptor 2
  19. 19. Genomic Literature Complexes Transcriptional SignalingThe Evolution of Systems Biology Mol. Profiles Structure Model Evolution Disease Models Physiologic / Model Topology Pathologic Phenotype Model Dynamics Regulation
  20. 20. List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
  21. 21. (Eric Schadt)
  22. 22. Recognition that the benefits of bionetwork based molecularmodels of diseases are powerful but that they requiresignificant resourcesAppreciation that it will require decades of evolvingrepresentations as real complexity emerges and needs to beintegrated with therapeutic interventions
  23. 23. Sage Mission Sage Bionetworks is a non-profit organization with a vision to create a commons where integrative bionetworks are evolved by contributor scientists with a shared vision to accelerate the elimination of human diseaseBuilding Disease Maps Data RepositoryCommons Pilots Discovery Platform Sagebase.org
  24. 24. Engaging Communities of Interest NEW MAPS Disease Map and Tool Users- ( Scientists, Industry, Foundations, Regulators...) PLATFORM Sage Platform and Infrastructure Builders- ( Academic Biotech and Industry IT Partners...) RULES AND GOVERNANCE Data Sharing Barrier Breakers- (Patients Advocates, Governance M and Policy Makers,  Funders...) APS FORM NEW TOOLS PLAT NEW Data Tool and Disease Map Generators- (Global coherent data sets, Cytoscape, RULES GOVERN Clinical Trialists, Industrial Trialists, CROs…) PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots- (Federation, CCSB, Inspire2Live....)
  25. 25. Platform Commons Research Cancer Neurological Disease Metabolic DiseaseCuration/Annotation Building Data Disease Repository Maps CTCAP Public Data Pfizer Merck Data Outposts Merck TCGA/ICGC Federation Takeda CCSB Astra Zeneca CHDI Commons Gates NIH Pilots LSDF-WPP Inspire2Live Hosting Data POC Hosting Tools Bayesian Models Co-expression Models Hosting Models Discovery Tools & Platform Methods KDA/GSVA LSDF 30
  26. 26. Sage Bionetworks Collaborators  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca, Amgen  Foundations   CHDI, Gates Foundation  Government   NIH, LSDF  Academic   Levy (Framingham)   Rosengren (Lund)   Krauss (CHORI)  Federation   Ideker, Califarno, Butte, Schadt 31
  27. 27. Bin ZhangIntegration of Multiple Networks for Jun ZhuPathway and Target Identification CNV Data Gene Expression Clinical Traits Co-Expression Bayesian Network Network Integration of Coexp. & Bayesian Networks 32 Key Driver Analysis 32
  28. 28. Bin ZhanKey Driver Analysis Jun Zhu Justin Guinney http://sagebase.org/research/tools.php 33
  29. 29. Justin GuinneyGene Set Variation Analysis (GSVA) Sonja Haenzelmann ! *#$ %%"! ! ! ! % %! $! !%&+ %& & % ! ! Meta-pathways * * # %$ * "!%&$ & &$ * % # * ! & & % ( & "! " & $ ! " ) ! % & ! %&"$ % "$ % $" , $" ! % % % ! ! ! % % % ! % &% % !% % % Pathway CNV Cross-tissue Pathway Clustering Pathways 34
  30. 30. Bin ZhangModel of Breast Cancer: Co-expression Xudong Dai Jun Zhu A) Miller 159 samples B) Christos 189 samplesNKI: N Engl J Med. 2002 Dec 19;347(25):1999.Wang: Lancet. 2005 Feb 19-25;365(9460):671.Miller: Breast Cancer Res. 2005;7(6):R953.Christos: J Natl Cancer Inst. 2006 15;98(4):262. C) NKI 295 samples E) Super modules Cell cycle Pre-mRNA ECM D) Wang 286 samples Blood vessel Immune response 35 Zhang B et al., Towards a global picture of breast cancer (manuscript).
  31. 31. Bin ZhangModel of Breast Cancer: Integration Xudong Dai Jun Zhu Conserved Super-modules mRNA proc. = predictive Breast Cancer Bayesian Network Chromatin of survival Extract gene:gene relationships for selected super-modules from BN and define Key Drivers Pathways & Regulators (Key drivers=yellow; key drivers validated in siRNA screen=green) Cell Cycle (Blue) Chromatin Modification (Black) Pre-mRNA proc. (Brown) mRNA proc. (red) 36 Zhang B et al., Key Driver Analysis in Gene Networks (manuscript)
  32. 32. Bin ZhangModel of Breast Cancer: Mining Xudong Dai Jun ZhuCo-expression sub-networks predict survival; KDA identifies drivers Co-expression Map to Bayesian Define Key modules correlate Network Drivers with survival 37
  33. 33. Bin ZhangModel of Alzheimer’s Disease Jun Zhu AD normal AD normal AD normal Cell cyclehttp://sage.fhcrc.org/downloads/downloads.php
  34. 34. Liver Cytochrome P450 Regulatory Network Xia Yang Bin Zhang Models Jun Zhu http://sage.fhcrc.org/downloads/downloads.php Regulators of P450 network 39Yang et al. Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver. 2010. Genome Research 20:1020.
  35. 35. AndersNew Type II Diabetes Disease Models Rosengren Global expression data 340 genes in islet-specific from 64 human islet donors open chromatin regions Blue module: 3000 genes Associated with Type 2 diabetes Elevated HbA1c Reduced insulin secretion 168 overlapping genes, which have •  Higher connectivity •  Markedly stronger association with •  Type 2 diabetes •  Elevated HbA1c •  Reduced insulin secretion •  Enrichment for beta-cell transcription factors and exocytotic proteins 40
  36. 36. New Type II Diabetes Disease Models Anders Rosengren•  Search across 1300 datasets in MetaGEO at Sage for similar expression profiles Top hit: Islet dedifferentiation study where the 168 genes were upregulated in mature islets and downregulated in dedifferentiated islets (Kutlu et al., Phys Gen 2009)•  Analyses of expression-SNPs and clinical SNPs as well as Causal Inference Test•  Identification of candidate key genes affecting beta-cell differentiation and chromatinWorking hypothesis:Normal beta-cell: open chromatin in islet-specific regions,high expression of beta-cell transcription factors,differentiated beta-cells and normal insulin secretionDiabetic beta-cell: lower expression of beta-cell transcriptionfactors affecting the identified module, dedifferentiation,reduced insulin secretion and hyperglycemiaNext steps: Validation of hypothesis and suggested key genes in human islets 41
  37. 37. Brig MechamValidating Prostate Cancer Models Xudong Dai Pete Nelson Rich Klingoffer classification Gene Expression Data on >1000 prostate cancer samples (GEO) CNV Gene Expression & CNV Data Data ~200 prostate cancers Gene Expression (Taylor et al) Clinical Traits Co-Expression Bayesian Network Network Integration of Coexp. & Gene Expression & CNV Bayesian Networks 42 Data ~120 rapid autopsy Mets (Nelson) Key Driver Analysis Integrated network analysis Gene Expression & CNV Data ~30 prostate Key Drivers Matched to xenografts Xenografts for validations (Nelson) with Presage Technology siRNA Screen Data (Nelson) 42
  38. 38. Lara MangraviteSystems biology approach to pharmacogenomics Ron KraussMolecular simvastatin response Integrative Ongoing: Genomic Analysis Cellular validation of novel genes and SNPs involved in statin efficacy and cellular cholesterol homeostasisClinical simvastatin response 100 -41 % 80 60 40 20 0 -100 -80 -60 -40 -20 0 20 Percent change LDLC Simon et al, Am J Cardiol 2006 43
  39. 39. Clinical Trial Comparator Arm Partnership (CTCAP)  Description: Collate, Annotate, Curate and Host Clinical Trial Data with Genomic Information from the Comparator Arms of Industry and Foundation Sponsored Clinical Trials: Building a Site for Sharing Data and Models to evolve better Disease Maps.  Public-Private Partnership of leading pharmaceutical companies, clinical trial groups and researchers.  Neutral Conveners: Sage Bionetworks and Genetic Alliance [nonprofits].  Initiative to share existing trial data (molecular and clinical) from non-proprietary comparator and placebo arms to create powerful new tool for drug development.
  40. 40. CTCAP Workstreams Uncurated GCD Curated GCD •  Single common identifier to link datatypes •  Gender mismatches removed Public Sage Domain GCDs Curated & QC d GCD  Curated GCD •  Gene expression data corrected for batch Uncurated effects, etc GCD  Curated & QC’d GCDCollaborators Database GCDs (Sage)  Network Models •  Public • Collaboration •  Internal Co- Private expressio Domain n Public Databases GCDs Network  dbGAP Analysis Integrate d Network Bayesian Analysis Network Analysis
  41. 41. Developing predictive models of genotype specific sensitivityto Perturbations- Margolin Predictive model
  42. 42. Examples: The Sage Federation •  Founding Lab Groups –  Seattle- Sage Bionetworks –  New York- Columbia: Andrea Califano –  Palo Alto- Stanford: Atul Butte –  San Diego- UCSD: Trey Ideker –  San Francisco: UCSF/Sage: Eric Schadt •  Initial Projects –  Aging –  Diabetes –  Warburg •  Goals: Share all datasets, tools, models Develop interoperability for human data
  43. 43. Human Aging Project Data Transformations Machine LearningBrain A(n=363) Interactome Elastic NetBrain B(n=145)Brain C TF Activity Profile Age(n=400) Network Prior Model Models Blood A(n=~1000) Gene Set / Pathway Variation Analysis Blood B Tree Classifiers(n=~1000)Adipose(n=~700)
  44. 44. Preliminary Results Adipose Age Predictionmultivariate logistic regression model predicting age in human adipose dataMaster Regulator Analysis (MARINa) from Califanos lab.
  45. 45. Federation s Genome-wide Network and Modeling ApproachCalifano group at Columbia Sage Bionetworks Butte group at Stanford
  46. 46. Deriving Master Regulators from Transcription FactorsRegulatory Networks Glycolysis & Glycogenesis Metabolism Pathway
  47. 47. Genes Associated with Poor Prognosis are disproportionally found among the networks regulating the glycolysis Genes P-Value<0.005 Size of the node proportional to -log10 P value for recurrence free survival. Inferred regulatory module for GGMSE Inferred regulatory module for Oxidative Phosphorylation and Sphingolipid >5 fold enrichment of recurrence free prognostic genes with Metabolism genes the Glycolysis BN module than random selection (p<1e-100)
  48. 48. THE FEDERATIONButte Califano Friend Ideker Schadt vs
  49. 49. http://sagecongress.org
  50. 50. We still consider much clinical research as if we were!hunter gathers" not sharing soon enough - .
  51. 51. Assumption that genetic alterationsin human conditions should be owned
  52. 52.   a =Sweave Vignette Sage Lab R code + PDF(plots + text + code snippets) narrative HTML Data objectsCalifano Lab Ideker Lab Submitted Paper
  53. 53. Reproducible science==shareable science Sweave: combines programmatic analysis with narrative Dynamic generation of statistical reports using literate data analysis Sweave.Friedrich Leisch. Sweave: Dynamic generation of statistical reportsusing literate data analysis. In Wolfgang Härdle and Bernd Rönz,editors, Compstat 2002 – Proceedings in Computational Statistics,pages 575-580. Physica Verlag, Heidelberg, 2002. ISBN 3-7908-1517-9
  54. 54. Software Tools Support Collaboration
  55. 55. Biology Tools Support Collaboration
  56. 56. Potential Supporting TechnologiesAddama Taverna tranSMART
  57. 57. Platform for Modeling SYNAPSE
  58. 58. Synapse as a Github for building models of disease
  59. 59. INTEROPERABILITYINTEROPERABILITY
  60. 60. TENURE FEUDAL STATES
  61. 61. IMPACT ON PATIENTS
  62. 62. How free are you to gather all the data you need? How interoperable and accessible are the tools to build models of disease?How fast could we get to cures if patients begancontributing their data while demanding sharing? How aware do you think patients are of current reward structures in academia? “”Ul9mate  excellence  lies   not  in  winning   every  ba@le   but  in  defea9ng  that     which  should  not  be  allowed   without  ever  figh9ng.”
  63. 63. why consider the fourth paradigm- data intensive science thinking beyond the narrative, beyond pathways advantages of an open innovation compute space it is more about why than what

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