Stephen Friend Nature Genetics Colloquium 2012-03-24

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Stephen Friend, Mar 24, 2012. Nature Genetics Colloquium, Boston, MA

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Stephen Friend Nature Genetics Colloquium 2012-03-24

  1. 1. Why not use data intensive science to build models of disease within a commons? Power of a Compute Space/Beyond Current Reward Structures Five Pilots- Missing Ingredients
  2. 2. What  is  the  problem?        Most  approved  therapies  assumed  indica2ons  would   represent  homogenous  popula2ons    Our  exis2ng  disease  models  o8en  assume  pathway   knowledge  would  be  sufficient  to  infer  correct  therapies  
  3. 3. Personalized Medicine 101:Capturing Single bases pair mutations = ID of responders
  4. 4. Reality: Overlapping Pathways
  5. 5. WHY  NOT  USE     “DATA  INTENSIVE”  SCIENCE  TO  BUILD  BETTER  DISEASE  MAPS?  
  6. 6. what will it take to understand disease?                    DNA    RNA  PROTEIN  (dark  maGer)    MOVING  BEYOND  ALTERED  COMPONENT  LISTS  
  7. 7. Preliminary Probabalistic Models- Rosetta 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
  8. 8. 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) d ..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.
  9. 9. List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
  10. 10. “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
  11. 11. We still consider much clinical research as if we were hunter gathers - not sharing .
  12. 12. 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
  13. 13. Sage Bionetworks Collaborators  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca,Roche Amgen, Johnson &Johnson  Foundations   Kauffman CHDI, Gates Foundation  Government   NIH, LSDF  Academic   Levy (Framingham)   Rosengren (Lund)   Krauss (CHORI)  Federation   Ideker, Califarno, Butte, Schadt 16
  14. 14. Knowledge NetworkInformation Commons
  15. 15. Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning
  16. 16. Leveraging Existing TechnologiesAddama Taverna tranSMART
  17. 17. INTEROPERABILITYSYNAPSE   Genome Pattern CYTOSCAPE tranSMART I2B2 INTEROPERABILITY  
  18. 18. sage bionetworks synapse project Watch What I Do, Not What I Say
  19. 19. sage bionetworks synapse project Reduce, Reuse, Recycle
  20. 20. sage bionetworks synapse project Most of the People You Need to Work with Don’t Work with You
  21. 21. sage bionetworks synapse project My Other Computer is “The Cloud”
  22. 22. Five  Pilots  around  Sharing  –Leveraging  the  Work  of  others   CTCAP   The  FederaKon   Portable  Legal  Consent   ORM S Sage  Congress  Project   MAP F BRIDGE   PLAT NEW RULES GOVERN
  23. 23. 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
  24. 24. Shared clinical/genomic data sharing and analysis will maximize clinical impact and enable discovery•  Graphic  of  curated  to  qced  to  models  
  25. 25. The  FederaKon  
  26. 26. (Nolan)  
  27. 27. 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)  
  28. 28. 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
  29. 29. Federated  Aging  Project  :     Combining  analysis  +  narraKve     =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  
  30. 30. Portable  Legal  Consent   (AcKvaKng  PaKents)   John  Wilbanks  
  31. 31. Sage  Congress  Project   April  20  2012   RealNames  Parkinson’s  Project  RevisiKng  Breast  Cancer  Prognosis   Fanconi’s  Anemia   (Responders  CompeKKons-­‐  IBM-­‐DREAM)  
  32. 32. BRIDGE  DemocraKzaKon  of  Medical  Research   (Social  Value  Chain)   ASHOKA  
  33. 33. Networking  Disease  Model  Building  
  34. 34. Why not use data intensive science to build models of disease within a commons? Power of a Compute Space/Beyond Current Reward Structures Five Pilots- Missing Ingredients

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