Friend NIEHS 2013-03-01
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Friend NIEHS 2013-03-01 Presentation Transcript

  • 1. Integrating genomes and networksto understand health and disease If not
  • 2. Examples of being Naive: Expression Profiles
  • 3. 2000
  • 4. Examples of being Naive: DNA Alterations
  • 5. Examples of being Naive:Synthetic Lethal Screens
  • 6. Examples of being Naieve: Drugs and Trials
  • 7. PARPIGF1-Rm-TORVEGF-RWee-1
  • 8. Reality: Overlapping Pathways
  • 9. • alchemist
  • 10. How often are we hurt by going from the particular to the general in very complex systems driven by context? Is this going from the particular to the general a central problem in Hypothesis Driven Biomedical Research? How often do we inappropriately praisefindings that go on to have awkward adjacencies?
  • 11. .
  • 12. TENURE FEUDAL STATES
  • 13. What could be done by us?
  • 14. BUILDING PRECISION MEDICINE Extensions of Current Institutions Proprietary Short term SolutionsOpen Systems of Sharing in a Commons
  • 15. Massive amount of human “omic’s” and compound data
  • 16. Network Modeling Approaches for Diseases are emerging
  • 17. IT Infrastructure and Cloud compute capacity allowsa generative open approach to solving problems
  • 18. Nascent Movement for patients to Control Sensitive information allowing sharing
  • 19. Open Social Media allows citizens and experts to use gaming to solve problems
  • 20. 1- Now possible to generate massive amount of human “omic’s” data2-Network Modeling Approaches for Diseases are emerging3- IT Infrastructure and Cloud compute capacity allowsa generative open approach to biomedical problem solving4-Nascent Movement for patients to Control Sensitive informationallowing sharing5- Open Social Media allows citizens and experts to use gaming tosolve problems A HUGE OPPORTUNITY -- A HUGE RESPONSIBILITY
  • 21. We focus on a world where biomedical research is aboutto fundamentally change. We think it will be oftenconducted in an open, collaborative way where teams ofteams far beyond the current guilds of experts willcontribute to making better, faster, relevant discoveries
  • 22. Governance Technology PlatformImpactful Models Better Models of Disease: KNOWLEDGE NETWORK Rewards/Challenges
  • 23. 1) Identifying key disease systems and genes- Alzheimer’s Gaiteri et al.1.) Identify groups of genes that move together – coexpressed “modules” - correlated expression of multiple genes across many patients - coexpression calculated separately for Disease/healthy groups - these gene groups are often coherent cellular subsystems, enriched in one or more GO functions Example “modules” of coexpressed genes, color-coded
  • 24. 1) Identifying key disease systems and genes- Alzheimer’s1.) Identify groups of genes that move together – coexpressed “modules”2.) Prioritize the disease-relevance of the modules by clinical and network measures Prioritize modules through expression synchrony with clinical measures or tendency too reconfigure themselves in disease vs
  • 25. 1) Identifying key disease systems and genes- Alzheimer’s1.) Identify groups of genes that move together – coexpressed “modules”2.) Prioritize the disease-relevance of the modules by clinical and network measures3.) Incorporate genetic information to find directed relationships between genes Infer directed/causal relationships Prioritize modules through expression and clear hierarchical structure by synchrony with clinical measures or tendency too reconfigure themselves in disease incorporating eSNP information (no hair-balls here) vs
  • 26. 1) Identifying key disease systems and genes- Alzheimer’s Example network finding: microglia activationModule selection – what identifies these modules as relevant to Alzheimer’s disease?The eigengene of a module of ~400 probes correlates with Braak score, age, cognitivedisease severity and cortical atrophy. Members of this module are on average differentiallyexpressed (both up- and down-regulated).Evidence these modules are related to microglia functionThe members of this module are enriched with GO categories (p<.001) such as “response to bioticstimulus” that are indicative of immunologic function for this module.The microglia markers CD68 and CD11b/ITGAM are contained in the module (this is rare – even when amodule appears to represent a specific cell-type, the histological markers may be lacking).Numerous key drivers (SYK, TREM2, DAP12, FC1R, TLR2) are important elements of microglia signaling . Alzgene hits found in co-regulated microglia module:
  • 27. 1) Identifying key disease systems and genes- Alzheimer’sFigure key:Five main immunologic familiesfound in Alzheimer’s-associatedmoduleSquare nodes in surrounding networkdenote literature-supported nodes.Node size is proportional toconnectivity in the full module. Core family members are shaded.(Interior circle) Width ofconnections between 5immune families arelinearly scaled to thenumber of inter-familyconnections.Labeled nodes are either highlyconnected in the original network,implicated by at least 2 papers asassociated with Alzheimer’s disease,or core members of one of the 5immune families.
  • 28. 1) Identifying key disease systems and genes- Alzheimer’sTransforming networks into biological hypotheses
  • 29. 1) Identifying key disease systems and genes- Alzheimer’s Design-stage AD projects at Sage Fusing our expertise in… Gene regulatory networks Diffusion Spectrum Imaging Feedback Microcircuits & neuronal diversityJoin us in uniting genes, circuits and regionsto build multi-scale biophysical disease models.Contact chris.gaiteri@sagebase.org
  • 30. 2) Identifying genetic biomarkers of statin response from cellular expression changes in treated LCLs Clinical simvastatin trial Cellular Simvastatin exposure Control 2M simvastatin N=480 N=944, P<0.0001 Genotypes N=587 P<0.0001 Differential eQTL analysis Identifying local “cis” acting genetic effects Differential network analysis Identifying “trans” acting genetic effects.Lara Mangravite
  • 31. Differential eQTL analysis identifies loci for which genetic association with gene expression is altered by statin treatment Control Simvastatin Difference Control vs. Simvastatin AA AG GG AA AG GG AA AG GG log10BF=0.52 log10BF=7.1* log10BF=5.7* Diff-eQTL locus is associated with reduced incidence of statin-induced myopathyLara Mangravite
  • 32. Differential network analysis: By integrating statin-mediated changes in gene correlation with eQTLs, we identify genes predicted to alter cholesterol homeostatis and lipoprotein metabolism. (including one involved in creatine biosynthesis) 78.1±8.0% gene knockdown, Huh7 cells Knockdown of candidate gene in hepatocytes confirms alterations in lipoprotein metabolismPartial correlation,FDR=5% and PP>0.90 Lara Mangravite
  • 33. 3) Classification of transporter-mediated hepatotoxicity Bile Salt Exporter BSEP (Amgen) 1. Characterization of differential 2. Classification of response to compounds expression following compound by BSEP Inhibitor Status (rat IC50) exposures in rat liver 3. Development of 4. Validation classifier for predicting BSEP inhibition of unknown compounds AUC=0.98Mangravite, Jang, Mecham, Derry 5-fold crossvalidation
  • 34. How It All Fits Together Synapse FEDERATION Access to DREAM Data Sets Challenges PortableLegal Consent BRIDGE Data Data ActivationGeneration 2009-2010On-Line Open Generative 45Communities
  • 35. How It All Fits TogetherFEDERATION Synapse DREAM Challenges PortableLegal Consent BRIDGE Data Data ActivationGeneration 2010-2011On-Line Open Generative 46Communities
  • 36. TECHNOLOGY PLATFORM two approaches to building common scientific knowledge Every code change versioned Every issue trackedText summary of the completed project Every project the starting point for new workAssembled after the fact All evolving and accessible in real time Social Coding
  • 37. Synapse is GitHub for Biomedical Data • Every code change versioned • Every issue tracked • Every project the starting point for new work• Data and code versioned • Social/Interactive Coding• Analysis history captured in real time• Work anywhere, and share the results with anyone• Social/Interactive Science
  • 38. Data Analysis with SynapseRun Any ToolOn Any PlatformRecord in SynapseShare with Anyone
  • 39. “Synapse is a nascent computeplatform for transparent, reproducible,and modular collaborative research.”
  • 40. Currently at 16K+ datasets and ~1M models
  • 41. Download analysis and meta-analysisDownload another Cluster Result Download Evaluation and view more stats • Perform Model averaging • Compare/contrast models • Find consensus clusters • Visualize in Cytoscape
  • 42. Pancancer collaborative subtype discovery
  • 43. Objective assessment of factors influencing modelperformance (>1 million predictions evaluated) Sanger CCLECross validation prediction accuracy (R2) Prediction accuracy improved by… Not discretizing data Including expression data Elastic net regression 130 compounds In Sock Jang 24 compounds
  • 44. How It All Fits Together Synapse DREAM Challenges PortableLegal Consent BRIDGE FEDERATION Data Data ActivationGeneration 2011-2012On-Line Open Generative 55Communities
  • 45. THE FEDERATIONSchadt Ideker Friend Haussler) Nolan Vidal (Nolan and Califano
  • 46. How It All Fits Together DREAM Synapse Challenges PortableLegal Consent BRIDGE FEDERATION Data Data ActivationGeneration 2012-2013On-Line Open Generative 57Communities
  • 47. Sage-DREAM Breast Cancer Prognosis Challenge #1 Building better disease models together Caldos/Aparicio breast cancer data154 participants; 27 countries 334 participants; >35 countries Sep 26 StatusChallenge Launch: July 17 >500 models posted to Leaderboard
  • 48. How It All Fits Together DREAM Synapse ChallengesBRIDGE FEDERATION Portable Data Data Legal Consent ActivationGeneration 2012-2013On-Line Open Generative 59Communities
  • 49. GOVERNANCE: PORTABLE LEGAL CONSENT Control of Private information by Citizens allows sharing weconsent.us John WilbanksJohn Wilbanks • Online educational wizardTED Talk • Tutorial video • Legal Informed Consent Document“Let’s pool our medical data” • Profile registrationweconsent.us • Data upload
  • 50. How It All Fits Together DREAM Synapse Challenges BRIDGE Data Generation FEDERATION Portable Data Legal Consent Activation 2012-2013On-Line Open Generative 61Communities
  • 51. BRIDGEBRIDGE
  • 52. How It All Fits Together On-Line Open Generative Communities DREAM Synapse IMPACT Challenges BRIDGE DataGeneration FEDERATION Portable Data Legal Consent Activation 2013-2014 64
  • 53. A ‘clearScience’ way of sage bionetworksmodeling PI3K pathway metaGenomics/pan-cancer project collaboration with david haussler @ ucsc foractivation in breast cancer “analysis-ready” tcga data tcga breast RNAseq data tcga breast exome seq data R code for a pathway heuristic web-accessible random forest model of pi3k activation DATA web-accessible executable pi3k model SOURCE CODE binary web-accessible MODEL web-accessible PROVENANCE world wide web consortium (w3c) specification PROVENANCE for all the interconnections above all of these elements can be housed in an virtual machine
  • 54. THE DREAM PROJECT JOINSSAGE BIONETWORKS TO ENABLE COLLABORATIVE SCIENCE 66
  • 55. How to incent the joint evolution of ideas in a rapid learning space- prepublication?How to fund where data generators and analysts are not always the same people- repeatedly? Should we considerCentralized Guilds and Distributed Dynamic Teams to perform gene-environment model building?
  • 56. SYNAPSEIf not FEDERATION PORTABLE LEGAL CONSENT CHALLENGES BRIDGE CITIZEN ENGAGEMENT