Stephen Friend Norwegian Academy of Science and Letters 2011-11-02

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Stephen Friend, Nov 2, 2011. Norwegian Academy of Science and Letters, Oslo, Norway

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Stephen Friend Norwegian Academy of Science and Letters 2011-11-02

  1. 1. Future of Genetics in Medicine Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ AmsterdamThe Norwegian Academy of Science and Letters, Oslo November 2, 2011
  2. 2. Why not use data intensive science to build models of disease Current Reward StructuresOrganizational Structures and Tools Pilots Opportunities
  3. 3. Alzheimers Diabetes Treating Symptoms v.s. Modifying DiseasesAutism 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 7 7
  5. 5. What is the problem?•  Regulatory hurdles too high?•  Low hanging fruit picked?•  Companies not large enough to execute on strategy?•  Internal research costs too high?•  Clinical trials in developed countries too expensive?In fact, all are true but none is the real problem
  6. 6. What is the problem?We need a better understand disease biologybefore testing proprietary compounds on sickpatients
  7. 7. What is the problem?Most approved therapies assumed indicationsrepresent homogenous populationsOur existing disease models often assumepathway knowledge sufficient to infer correcttherapies
  8. 8. Personalized Medicine 101:Capturing Single bases pair mutations = ID of responders
  9. 9. Reality: Overlapping Pathways
  10. 10. The value of appropriate representations/ maps
  11. 11. “Data Intensive” Science- Fourth Scientific Paradigm Equipment capable of generating massive amounts of data IT Interoperability Open Information System Host evolving computational models in a “Compute Space”
  12. 12. WHY NOT USE “DATA INTENSIVE” SCIENCETO BUILD BETTER DISEASE MAPS?
  13. 13. what will it take to understand disease? DNA RNA PROTEIN (dark ma>er)MOVING BEYOND ALTERED COMPONENT LISTS
  14. 14. 2002 Can one build a “causal” model?
  15. 15. 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 21 Integrated Genetics Approaches
  16. 16. Causal Inference
  17. 17. Constructing Co-expression Networks Start with expression measures for ~13K genes most variant genes across 100-150 samples 1 2 3 4 Note: NOT a gene expression heatmap 1 1 0.8 0.2 -0.8 Establish a 2D correla9on 2 matrix 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 Correla9on 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 Iden9fy 4 1 1 1 0 Hierarchically 3 0 0 1 0 2 3 modules cluster 3 0 0 0 1 4 1 1 0 1 Network Module Clustered Connec9on Matrix Connec9on Matrix sets of genes for which many pairs interact (relaPve to the total number of pairs in that set)
  18. 18. Constructing Bayesian Networks
  19. 19. Preliminary Probalistic Models- Rosetta- Eric 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
  20. 20. Map compound signatures to disease networks Sub-­‐network contains genes Compound Gene expression associated with diabetes traits signaturesSub-­‐networkcontains genesassociatedwith toxici5es 1 2 3 Sub-­‐network contains genesTissue Disease Networks associated with obesity traits Compound 1: Drug signature significantly enriched in subnetwork associated with diabetes traits Compound 2: Drug signature significantly enriched in subnetwork associated with obesity traits Compound 3: Drug signature significantly enriched in subnetwork associated with obesity traits BUT also in subnetwork associated with toxicities
  21. 21. Extensive Publications now Substantiating Scientific Approach Probabilistic Causal Bionetwork Models• >80 Publications from Rosetta Genetics Metabolic "Genetics of gene expression surveyed in maize, mouse and man." Nature. (2003) Disease "Variations in DNA elucidate molecular networks that cause disease." Nature. (2008) "Genetics of gene expression and its effect on disease." Nature. (2008) "Validation of candidate causal genes for obesity that affect..." Nat Genet. (2009) ….. Plus 10 additional papers in Genome Research, PLoS Genetics, PLoS Comp.Biology, etc CVD "Identification of pathways for atherosclerosis." Circ Res. (2007) "Mapping the genetic architecture of gene expression in human liver." PLoS Biol. (2008) …… Plus 5 additional papers in Genome Res., Genomics, Mamm.Genome Bone "Integrating genotypic and expression data …for bone traits…" Nat Genet. (2005) ..approach to identify candidate genes regulating BMD…" J Bone Miner Res. (2009) Methods "An integrative genomics approach to infer causal associations ... Nat Genet. (2005) "Increasing the power to detect causal associations… PLoS Comput Biol. (2007) "Integrating large-scale functional genomic data ..." Nat Genet. (2008) …… Plus 3 additional papers in PLoS Genet., BMC Genet.
  22. 22. List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
  23. 23. (Eric Schadt)
  24. 24. “Data Intensive” Science- Fourth Scientific Paradigm Score Card for Medical Sciences Equipment capable of generating massive amounts of data A- IT Interoperability D Open Information System D- Host evolving computational models in a “Compute Space F
  25. 25. hunter gathers - not sharing
  26. 26. TENURE FEUDAL STATES
  27. 27. Clinical/genomic data are accessible but minimally usableLittle incentive to annotate and curate data for other scientists to use
  28. 28. Mathematicalmodels of disease are not built to be reproduced orversioned by others
  29. 29. Assumption that genetic alterationsin human conditions should be owned
  30. 30. Publication Bias- Where can we find the (negative) clinical data?
  31. 31. 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
  32. 32. Sage Bionetworks Collaborators  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca, Amgen, Johnson &Johnson  Foundations   Kauffman CHDI, Gates Foundation  Government   NIH, LSDF  Academic   Levy (Framingham)   Rosengren (Lund)   Krauss (CHORI)  Federation   Ideker, Califarno, Butte, Schadt 38
  33. 33. NEW MAPS Disease Map and Tool Users- ( Scientists, Industry, Foundations, Regulators...) PLATFORM Sage Platform and Infrastructure Builders- ( Academic Biotech and Industry IT Partners...) PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots- (Federation, CCSB, Inspire2Live....) NEW TOOLS M S FOR MAP Data Tool and Disease Map Generators- (Global coherent data sets, Cytoscape, PLAT Clinical Trialists, Industrial Trialists, CROs…)NEW RULES GOVERN RULES AND GOVERNANCE Data Sharing Barrier Breakers- (Patients Advocates, Governance and Policy Makers,  Funders...)
  34. 34. Sage Datasets- 49+ 40
  35. 35. Sage Neuro Collaborations Neurodegenerative • Huntington’s Disease : Marcy MacDonald/Jim Gusella (MGH) •  $2.5M Grant to CHDI to fund generation of RNA-seq data for brain regions and peripheral tissues for well phenotyped cohorts (also methyl genome studies) •  HD/AD/PD : MacDonald/Gusella (MGH), Myers (BU), Paulsen (Iowa) •  $6.8M RC4 grant to NIH to fund generation of SNP and RNA-seq data for brain regions from HD, AD, PD for well phenotyped cohorts •  Alzheimer’s Disease: Green (BU), Johnson (UWM) •  Collaboration opportunity around longitudinal neuroimaging & cognitive phenotyped cohorts (ADNI, ADGC, etc). Intersect gene expression studies. Funding for further data generation Psychiatric •  Autism/ Schizophrenia- Consortium Sleep & Stress •  Genetics of Sleep: Turek (Northwestern) •  Collaboration with Turek lab & Merck focused on mouse. DARPA •  Enabling Stress Resistance •  DARPA-funded collaboration with Turek lab to look at genetic and brain molecular mechanisms that regulate physical and emotional stress responses in mouse •  Currently looking to expand to human 41
  36. 36. Alzheimer’s  Disease  •  Cross-­‐Pssue  coexpression  networks  for   both  normal  and  AD  brains   –  prefrontal  cortex,  cerebellum,  visual   cortex  •  DifferenPal  network  analysis  on  AD  and   normal  networks  •  Integrate  coexpression  networks  and   Bayesian  networks  to  idenPfy  key   regulators  for  the  modules  associated   with  AD     42  
  37. 37. IdenPficaPon of Disease (AD) Pathways via ComparaPve Gene Network Analysis40,000 genes from three Pssues Glutathione transferase Gain connecPvity by 91 fold AD (PFC, CB, VC) nerve ensheathment Control Lose connecPvity by 40% (PFC, CB, VC) Module ConnecPvity Change (AD/Normal) 43 Bayesian Subnetworks
  38. 38. DifferenPally  Connected  Modules  in  AD   • Unfolded  protein  response  (UPR)     • AKT    HIF1    VEGF   • Olfactory  receptor  acPvity   • Sensory  percepPon  of  smell  chemical  sPmulus   • Inflammatory  Response   • Extra  cellular  matrix  (ECM)   • SynapPc  transmission  (suppressed)   • Nerve  ensheathment   44   44   44  
  39. 39. Key RegulatorsGlutathioneTransferase NerveEnsheathment ExtracellularMatrixPECAM1: Platelet-­‐endothelial cell adhesion molecule, a tyrosine phosphatase acPvator that plays a role in the platelet acPvaPon, increased expression correlates with MS, Crohn disease, chronic B-­‐ cell leukemia, rheumatoid arthriPs, and ulceraPve coliPs ENPP2: Phosphodiesterase I alpha, a lysophospholipase that acts in chemotaxis, phosphaPdic acid biosynthesis, regulates apoptosis and PKB signaling; aberrant expression is associated with Alzheimer type demenPa, major depressive disorder, and various cancers SLC22A25: solute carrier family 22, member 25, Protein with high similarity to mouse Slc22a19, which is a renal steroid sulfate transporter that plays a role in the uptake of estrone sulfate, member of the sugar (and other) transporter family and the major facilitator superfamilyGlutathione Transferase Module (Pink)•  983 probes from all three brain regions (9% from CB, 15% from PFC and 76% from VC) 45•  Most predicPve of Braak severity score
  40. 40. Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning
  41. 41. Leveraging Existing TechnologiesAddama Taverna tranSMART
  42. 42. INTEROPERABILITYSYNAPSE   Genome Pattern CYTOSCAPE tranSMART I2B2 INTEROPERABILITY  
  43. 43. sage bionetworks synapse project Watch What I Do, Not What I Say Reduce, Reuse, Recycle My Other Computer is Amazon Most of the People You Need to Work with Don’t Work with You
  44. 44. Six Pilots at Sage Bionetworks CTCAP Arch2POCM The FederaPon M S FOR MAP Portable Legal Consent PLAT Sage Congress Project EW N Ashoka/Sage MedXChange RULES GOVERN
  45. 45. CTCAPClinical Trial Comparator Arm Partnership “CTCAP”Strategic Opportunities For Regulatory Science Leadership and Action FDA September 27, 2011
  46. 46. 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. Started Sept 2010
  47. 47. Arch2POCMRestructuring the PrecompePPve Space for Drug Discovery How to potenPally De-­‐Risk High-­‐Risk TherapeuPc Areas
  48. 48. The FederaPon
  49. 49. How can we accelerate the pace of scientific discovery? 2008   2009   2010   2011   Ways to move beyond “traditional” collaborations? Intra-lab vs Inter-lab Communication Colrain/ Industrial PPPs Academic Unions
  50. 50. human aging:predicting bioage using whole blood methylation•  Independent training (n=170) and validation (n=123) Caucasian cohorts•  450k Illumina methylation array•  Exom sequencing•  Clinical phenotypes: Type II diabetes, BMI, gender… Training Cohort: San Diego (n=170) Validation Cohort: Utah (n=123) RMSE=3.35 RMSE=5.44 100 100 ! ! !! ! ! ! !! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! !!!! !! ! ! ! !! ! ! !! ! ! !!! ! ! !! ! 80 Biological Age Biological Age !! ! !!!! !! ! !! ! ! ! ! !!! ! 80 ! !!! !! ! ! ! ! !! ! ! !!!! !!! ! !! ! ! ! !!! !! ! ! ! !!! ! !! !!!! ! ! ! ! ! !! !! !! ! ! ! !! ! !!! ! ! !! ! !! ! !! ! ! !!! ! !! ! ! !!!! ! ! ! ! !! ! !! ! ! ! !! !! !!! !!! ! ! ! !! ! ! ! !!! ! !! ! ! !!! !! !! ! ! ! ! !! ! ! ! 60 !!! ! ! ! ! ! ! ! ! ! ! !! ! 60 ! ! ! !! !! ! ! ! ! ! !! ! !!!! ! ! ! ! ! !! ! ! !! !! ! !! ! ! ! ! ! ! 40 40 ! ! 40 50 60 70 80 90 100 40 50 60 70 80 90 Chronological Age Chronological Age
  51. 51. sage federation:model of biological age Faster Aging Predicted Age (liver expression) Slower Aging Clinical Association -  Gender -  BMI -  Disease Age Differential Genotype Association Gene Pathway Expression Chronological Age (years)
  52. 52. Reproducible  science==shareable  science   Sweave: combines programmatic analysis with narrativeDynamic 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
  53. 53. Federated  Aging  Project  :     Combining  analysis  +  narraPve     =Sweave Vignette Sage Lab R code + PDF(plots + text + code snippets) narrative HTML Data objectsCalifano Lab Ideker Lab Submitted Paper Shared  Data   JIRA:  Source  code  repository  &  wiki   Repository  
  54. 54. Portable Legal Consent (AcPvaPng PaPents) John Wilbanks
  55. 55. Sage Congress Project April 20 2012 RA Parkinson’s Asthma(Responders CompePPons)
  56. 56. Ashoka/SageMedXChange
  57. 57. Why not use data intensive science to build models of disease Current Reward StructuresOrganizational Structures and Tools Six Pilots Opportunities
  58. 58. IMPACT ON PATIENTS
  59. 59. IMPACT ON PATIENTS
  60. 60. IMPACT ON PATIENTS

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