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Stephen Friend Inspire2Live Discovery Network 2011-10-29

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Stephen Friend, Oct 29, 2011. Inspire2Live Discovery Network, Cambridge, UK

Stephen Friend, Oct 29, 2011. Inspire2Live Discovery Network, Cambridge, UK

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  • 1. Actionable Cancer Network ModelsAnd Open Medical Information Systems Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ Amsterdam Discovery Networks October 29th, 2011
  • 2. Why not use data intensive science to build models of disease Current Reward StructuresOrganizational Structures and Tools Pilots Opportunities
  • 3. What  is  the  problem?        We  need  to  rebuild  the  drug  discovery  process  so  that  we   be6er  understand  disease  biology  before  tes8ng   proprietary  compounds  on  sick  pa8ents  
  • 4. Personalized Medicine 101:Capturing Single bases pair mutations = ID of responders
  • 5. Reality: Overlapping Pathways
  • 6. The value of appropriate representations/ maps
  • 7. “Data Intensive” Science- Fourth Scientific Paradigm Equipment capable of generating massive amounts of data IT Interoperability Open Information System Host evolving Models in a Compute Space- Knowledge Expert
  • 8. WHY  NOT  USE     “DATA  INTENSIVE”  SCIENCE  TO  BUILD  BETTER  DISEASE  MAPS?  
  • 9. what will it take to understand disease?                    DNA    RNA  PROTEIN  (dark  maGer)    MOVING  BEYOND  ALTERED  COMPONENT  LISTS  
  • 10. 2002 Can one build a “causal” model?
  • 11. Our ability to integrate compound data into our network analyses db/db mouse (p~10E(-30)) = up regulated = down regulateddb/db mouse(p~10E(-20) p~10E(-100)) AVANDIA in db/db mouse
  • 12. List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
  • 13. (Eric Schadt)
  • 14. “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 Models in a Compute Space- Knowledge Expert F
  • 15. We still consider much clinical research as if we we hunter gathers - not sharing .
  • 16.  TENURE      FEUDAL  STATES      
  • 17. Clinical/genomic data are accessible but minimally usableLittle incentive to annotate and curate data for other scientists to use
  • 18. Mathematicalmodels of disease are not built to be reproduced orversioned by others
  • 19. Lack of standard forms for sharing dataand lack of forms for future rights and consentss
  • 20. Publication Bias- Where can we find the (negative) clinical data?
  • 21. sharing as an adoption of common standards.. Clinical Genomics Privacy IP
  • 22. 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
  • 23. 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 25
  • 24. 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 Data Tool and Disease Map Generators- (Global coherent data sets, Cytoscape, Clinical Trialists, Industrial Trialists, CROs…) ORM APS NEW MAPSM F PLAT Disease Map and Tool Users- NEW ( Scientists, Industry, Foundations, Regulators...) RULES GOVERN RULES AND GOVERNANCE Data Sharing Barrier Breakers- (Patients Advocates, Governance and Policy Makers,  Funders...)
  • 25. Developing predictive models of genotype-specificsensitivity to compound treatment Gene8c  Feature  Matrix     Expression,  copy  number,   somaQc  mutaQons,  etc.  Predic8ve  Features   (biomarkers)   Cancer  samples  with  varying   degrees  of  response  to  therapy   Sensi8ve   Refractory   (e.g.  EC50)   27  
  • 26. 1   20   100   500  Feature   Features   Features   Features   Elastic net regression   28  
  • 27. Bootstrapping retains robust predictive features 29  
  • 28. Our approach identifies mutations in genes upstream of MEKas top predictors of sensitivity to MEK inhibition #3  Mut  NRAS   !"#$% &"#$% #1  Mut  BRAF   PD-­‐0325901   "#(% #312  Mut  NRAS   )*!+,-% #./0-11% 2/345-674+% #9  Mut  BRAF   30   PD-­‐0325901  
  • 29. Other top predictive features include expressionlevels of genes regulated by MEK #19  ETV5  expr   #8  DUSP6  expr   #5  ETV4  expr   #3  NRAS  mut   #2  SPRY2  expr   !"#$% &"#$% #1  BRAF  mut   PD-­‐0325901   "#(% )*!+,-% #./0-11% 2/345-674+% 31   PraQlas  et  al.,  (2009),  PNAS  
  • 30. Model built excluding expression data identifies BRAF, NRAS, and KRAS toppredictive features for both MEK inhibitors KRAS RAS  mut   #3  K mut NRAS RAS  mut   #2  N mut BRAF RAF  mut   #1  B mut PD-­‐0325901   !"#$% &"#$% "#(% #3  KRAS  mut   KRAS mut NRAS mut mut   #2  NRAS   BRAF mut mut   #1  BRAF   )*!+,-% #./0-11% AZD6244   2/345-674+% 32  
  • 31. For 11/12 compounds, the #1 predictive feature in an unbiasedanalysis corresponds to the known stratifier of sensitivity #2  CML  lineage   CML lineage #1  EGFR  mut   EGFR mut #1  EGFR  mut   EGFR mut #1  CML  lineage   #1  EGFR  mut   CML linage EGFR mut #1  ERBB2  expr   ERBB2 expr How  accurate  would  predic8ve   #1  BRAF  mut   models  perform  for  diagnos8cs?   BRAF mut #1  HGF  expr   HGF expr #2  NRAS  mut   NRAS mut BRAF mut #1  BRAF  mut   #3  KRAS  mut   KRAS mut #2  NRAS  mut   NRAS mut BRAF mut #1  BRAF  mut   #3  KRAS  mut   KRAS mut #2  NRAS  mut   NRAS mut BRAF mut #1  BRAF  mut   #2  TP53  mut   TP53 mut #3  CDKN2A  copy   CDKN2A copy #1  MDM2  expr   MDM2 expr 33  
  • 32. Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning
  • 33. Leveraging Existing TechnologiesAddama Taverna tranSMART
  • 34. INTEROPERABILITYSYNAPSE   Genome Pattern CYTOSCAPE tranSMART I2B2 INTEROPERABILITY  
  • 35. 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
  • 36. Select  Six  Pilots  at  Sage  Bionetworks   CTCAP   Arch2POCM   The  FederaQon   ORM S MAP Portable  Legal  Consent   F PLAT Sage  Congress  Project   NEW RULES GOVERN
  • 37. CTCAP  Clinical Trial Comparator Arm Partnership “CTCAP”Strategic Opportunities For Regulatory Science Leadership and Action FDA September 27, 2011
  • 38. 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
  • 39. Shared clinical/genomic data sharing and analysis will maximize clinical impact and enable discovery•  Graphic  of  curated  to  qced  to  models  
  • 40. Arch2POCM  Restructuring  the  PrecompeQQve   Space  for  Drug  Discovery   How  to  potenQally  De-­‐Risk       High-­‐Risk  TherapeuQc  Areas  
  • 41. What  is  the  problem?        We  need  to  rebuild  the  drug  discovery  process  so  that  we   be6er  understand  disease  biology  before  tes8ng   proprietary  compounds  on  sick  pa8ents  
  • 42. A PPP to generate novel chemical probes June 10 June 09 April 09 Jan 09 Pfizer OICR UNC Novartis(8FTEs) (2FTEs) Well. Trust (£4.1M) (3FTEs) (8FTEs) NCGC (20HTSs) GSK (8FTEs) Ontario ($5.0M) 15 acad. labs Sweden ($3.0M) ….more than £30M of resource….now Lilly (8FTEs)
  • 43. Academic, scientific, drug discovery & economic impact  Published Dec 23 2010 - already cited 30 times  Distributed to >100 labs/companies - profile in several therapeutic areas  Pharmas - started proprietary efforts  Harvard spin off - $15 M seed funding  Opened new area:Zuber et al : BRD4/ JQ1 in acute leukaemia Nature, 2011 Aug 3Delmore et al: BRD4/ JQ1 in multiple myeloma Cell, 2011 Volume 146, 904-917, 16Dawson et al: BRD4/ JQ1 in MLL Nature 2011, in press.Floyed et al: BRD4 in DNA damage response Cell, revisedFilippakopoulos et al:Bromodomains structure and function Cell, revisedNatoli et al: BRD4 in T-cell differentiation manuscript in preparationBradner et al: BRDT in spermatogenesis submittedcollaborations with SGC
  • 44. The  FederaQon  
  • 45. 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
  • 46. (Nolan  and  Haussler)  
  • 47. 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
  • 48. 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)  
  • 49. 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
  • 50. Federated  Aging  Project  :     Combining  analysis  +  narraQve     =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  
  • 51. Portable  Legal  Consent   (AcQvaQng  PaQents)   John  Wilbanks  
  • 52. Sage  Congress  Project   April  20  2012   RA   Parkinson’s   Asthma  (Responders  CompeQQons)  
  • 53. Why not use data intensive science to build models of disease Current Reward StructuresOrganizational Structures and Tools Six Pilots Opportunities