Integrating genomes and networksto understand health and disease               If not
Examples of being Naive:   Expression Profiles
2000
Examples of being Naive:    DNA Alterations
Examples of being Naive:Synthetic Lethal Screens
Examples of being Naieve:    Drugs and Trials
PARPIGF1-Rm-TORVEGF-RWee-1
Reality: Overlapping Pathways
• alchemist
How often are we hurt by going from           the particular to the general   in very complex systems driven by context? I...
.
TENURE   FEUDAL STATES
What could be done by us?
BUILDING PRECISION MEDICINE  Extensions of Current Institutions   Proprietary Short term SolutionsOpen Systems of Sharing ...
Massive amount of human “omic’s” and compound data
Network Modeling Approaches for Diseases are emerging
IT Infrastructure and Cloud compute capacity allowsa generative open approach to solving problems
Nascent Movement for patients to Control Sensitive information allowing sharing
Open Social Media allows citizens and experts to use gaming to solve problems
1- Now possible to generate massive amount of human “omic’s” data2-Network Modeling Approaches for Diseases are emerging3-...
We focus on a world where biomedical research is aboutto fundamentally change. We think it will be oftenconducted in an op...
Governance                                       Technology PlatformImpactful Models                   Better Models of   ...
1) Identifying key disease systems and genes- Alzheimer’s                              Gaiteri et al.1.) Identify groups o...
1) Identifying key disease systems and genes- Alzheimer’s1.) Identify groups of genes that move together – coexpressed “mo...
1) Identifying key disease systems and genes- Alzheimer’s1.) Identify groups of genes that move together – coexpressed “mo...
1) Identifying key disease systems and genes- Alzheimer’s              Example network finding: microglia activationModule...
1) Identifying key disease systems and genes- Alzheimer’sFigure key:Five main immunologic familiesfound in Alzheimer’s-ass...
1) Identifying key disease systems and genes- Alzheimer’sTransforming networks into biological hypotheses
1) Identifying key disease systems and genes- Alzheimer’s                 Design-stage AD projects at Sage   Fusing our ex...
2) Identifying genetic biomarkers of statin response from                         cellular expression changes in treated L...
Differential eQTL analysis identifies loci for which genetic association            with gene expression is altered by sta...
Differential network analysis:                                     By integrating statin-mediated                         ...
3) Classification of transporter-mediated hepatotoxicity                      Bile Salt Exporter BSEP (Amgen)  1. Characte...
How It All Fits Together Synapse FEDERATION                       Access to   DREAM                          Data Sets  Ch...
How It All Fits TogetherFEDERATION                      Synapse   DREAM  Challenges   PortableLegal Consent BRIDGE        ...
TECHNOLOGY PLATFORM   two approaches to building common scientific knowledge                                            Ev...
Synapse is GitHub for Biomedical Data                                                       •   Every code change versione...
Data Analysis with SynapseRun Any ToolOn Any PlatformRecord in SynapseShare with Anyone
“Synapse is a nascent computeplatform for transparent, reproducible,and modular collaborative research.”
Currently at 16K+ datasets and ~1M models
Download analysis and meta-analysisDownload another Cluster Result   Download Evaluation and view more stats  •   Perform ...
Pancancer collaborative subtype discovery
Objective assessment of factors influencing modelperformance (>1 million predictions evaluated)                           ...
How It All Fits Together                                Synapse   DREAM  Challenges   PortableLegal Consent BRIDGE        ...
THE FEDERATIONSchadt Ideker Friend Haussler) Nolan Vidal         (Nolan and Califano
How It All Fits Together                       DREAM                                     Synapse                      Chal...
Sage-DREAM Breast Cancer Prognosis Challenge #1                                 Building better disease models together   ...
How It All Fits Together                          DREAM                                        Synapse                    ...
GOVERNANCE: PORTABLE LEGAL CONSENT      Control of Private information by Citizens allows sharing                         ...
How It All Fits Together                          DREAM                                        Synapse                    ...
BRIDGEBRIDGE
How It All Fits Together                    On-Line Open                     Generative                    Communities    ...
A ‘clearScience’ way of                           sage bionetworksmodeling PI3K pathway                             metaGe...
THE DREAM PROJECT JOINSSAGE BIONETWORKS TO ENABLE   COLLABORATIVE SCIENCE                             66
How to incent the joint evolution of ideas in a rapid         learning space- prepublication?How to fund where data genera...
SYNAPSEIf not   FEDERATION         PORTABLE LEGAL CONSENT         CHALLENGES         BRIDGE         CITIZEN ENGAGEMENT
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
Friend NIEHS 2013-03-01
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Friend NIEHS 2013-03-01

  1. 1. Integrating genomes and networksto understand health and disease If not
  2. 2. Examples of being Naive: Expression Profiles
  3. 3. 2000
  4. 4. Examples of being Naive: DNA Alterations
  5. 5. Examples of being Naive:Synthetic Lethal Screens
  6. 6. Examples of being Naieve: Drugs and Trials
  7. 7. PARPIGF1-Rm-TORVEGF-RWee-1
  8. 8. Reality: Overlapping Pathways
  9. 9. • alchemist
  10. 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. 11. .
  12. 12. TENURE FEUDAL STATES
  13. 13. What could be done by us?
  14. 14. BUILDING PRECISION MEDICINE Extensions of Current Institutions Proprietary Short term SolutionsOpen Systems of Sharing in a Commons
  15. 15. Massive amount of human “omic’s” and compound data
  16. 16. Network Modeling Approaches for Diseases are emerging
  17. 17. IT Infrastructure and Cloud compute capacity allowsa generative open approach to solving problems
  18. 18. Nascent Movement for patients to Control Sensitive information allowing sharing
  19. 19. Open Social Media allows citizens and experts to use gaming to solve problems
  20. 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. 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. 22. Governance Technology PlatformImpactful Models Better Models of Disease: KNOWLEDGE NETWORK Rewards/Challenges
  23. 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. 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. 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. 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. 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. 28. 1) Identifying key disease systems and genes- Alzheimer’sTransforming networks into biological hypotheses
  29. 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. 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. 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. 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. 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. 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. 35. How It All Fits TogetherFEDERATION Synapse DREAM Challenges PortableLegal Consent BRIDGE Data Data ActivationGeneration 2010-2011On-Line Open Generative 46Communities
  36. 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. 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. 38. Data Analysis with SynapseRun Any ToolOn Any PlatformRecord in SynapseShare with Anyone
  39. 39. “Synapse is a nascent computeplatform for transparent, reproducible,and modular collaborative research.”
  40. 40. Currently at 16K+ datasets and ~1M models
  41. 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. 42. Pancancer collaborative subtype discovery
  43. 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. 44. How It All Fits Together Synapse DREAM Challenges PortableLegal Consent BRIDGE FEDERATION Data Data ActivationGeneration 2011-2012On-Line Open Generative 55Communities
  45. 45. THE FEDERATIONSchadt Ideker Friend Haussler) Nolan Vidal (Nolan and Califano
  46. 46. How It All Fits Together DREAM Synapse Challenges PortableLegal Consent BRIDGE FEDERATION Data Data ActivationGeneration 2012-2013On-Line Open Generative 57Communities
  47. 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. 48. How It All Fits Together DREAM Synapse ChallengesBRIDGE FEDERATION Portable Data Data Legal Consent ActivationGeneration 2012-2013On-Line Open Generative 59Communities
  49. 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. 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. 51. BRIDGEBRIDGE
  52. 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. 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. 54. THE DREAM PROJECT JOINSSAGE BIONETWORKS TO ENABLE COLLABORATIVE SCIENCE 66
  55. 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. 56. SYNAPSEIf not FEDERATION PORTABLE LEGAL CONSENT CHALLENGES BRIDGE CITIZEN ENGAGEMENT
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