Actionable Cancer Network ModelsAnd Open Medical Information Systems               Stephen Friend MD PhD      Sage Bionetw...
Why not use data intensive science   to build models of disease    Current Reward StructuresOrganizational Structures and ...
What	  is	  the	  problem?	  	  	  	  We	  need	  to	  rebuild	  the	  drug	  discovery	  process	  so	  that	  we	       ...
Personalized Medicine 101:Capturing Single bases pair mutations = ID of responders
Reality: Overlapping Pathways
The value of appropriate representations/ maps
“Data Intensive” Science- Fourth Scientific Paradigm       Equipment capable of generating       massive amounts of data  ...
WHY	  NOT	  USE	  	        “DATA	  INTENSIVE”	  SCIENCE	  TO	  BUILD	  BETTER	  DISEASE	  MAPS?	  
what will it take to understand disease?	  	  	  	  	  	  	  	  	  	  DNA	  	  RNA	  PROTEIN	  (dark	  maGer)	  	  MOVING	...
2002 Can one build a “causal” model?
Our ability to integrate compound data into our network analyses    db/db mouse    (p~10E(-30))   = up regulated   = down ...
List of Influential Papers in Network Modeling                                        50 network papers                  ...
(Eric Schadt)
“Data Intensive” Science- Fourth Scientific Paradigm       Score Card for Medical Sciences    Equipment capable of generat...
We still consider much clinical research as if we we hunter gathers - not sharing                        .
 TENURE   	     	  	  FEUDAL	  STATES	  	     	  
Clinical/genomic data are accessible but minimally usableLittle incentive to annotate and curate       data for other scie...
Mathematicalmodels of disease are not built to be   reproduced orversioned by others
Lack of standard forms for sharing dataand lack of forms for future rights and consentss
Publication Bias- Where can we find the (negative) clinical data?
sharing as an adoption of common standards..      Clinical Genomics Privacy IP
Sage Mission      Sage Bionetworks is a non-profit organization with a vision to   create a commons where integrative bion...
Sage Bionetworks Collaborators  Pharma Partners     Merck, Pfizer, Takeda, Astra Zeneca,      Amgen, Johnson &Johnson  ...
PLATFORM                                 Sage Platform and Infrastructure Builders-                              ( Academi...
Developing predictive models of genotype-specificsensitivity to compound treatment                                     Gen...
1	        20	          100	          500	  Feature	   Features	     Features	     Features	                               ...
Bootstrapping retains robust predictive features                                                   29	  
Our approach identifies mutations in genes upstream of MEKas top predictors of sensitivity to MEK inhibition              ...
Other top predictive features include expressionlevels of genes regulated by MEK                                          ...
Model built excluding expression data identifies BRAF, NRAS, and KRAS toppredictive features for both MEK inhibitors      ...
For 11/12 compounds, the #1 predictive feature in an unbiasedanalysis corresponds to the known stratifier of sensitivity  ...
Why not share clinical /genomic data and model building in the        ways currently used by the software industry        ...
Leveraging Existing TechnologiesAddama                                  Taverna               tranSMART
INTEROPERABILITYSYNAPSE	                              Genome Pattern                            CYTOSCAPE                 ...
sage bionetworks synapse project        Watch What I Do, Not What I Say           Reduce, Reuse, Recycle                  ...
Select	  Six	  Pilots	  at	  Sage	  Bionetworks	   CTCAP	   Arch2POCM	   The	  FederaQon	                                 ...
CTCAP	  Clinical Trial Comparator Arm Partnership “CTCAP”Strategic Opportunities For Regulatory Science            Leaders...
Clinical Trial Comparator Arm        Partnership (CTCAP)  Description: Collate, Annotate, Curate and Host Clinical Trial ...
Shared clinical/genomic data sharing and analysis will   maximize clinical impact and enable discovery•  Graphic	  of	  cu...
Arch2POCM	  Restructuring	  the	  PrecompeQQve	      Space	  for	  Drug	  Discovery	     How	  to	  potenQally	  De-­‐Risk...
What	  is	  the	  problem?	  	  	  	  We	  need	  to	  rebuild	  the	  drug	  discovery	  process	  so	  that	  we	       ...
A PPP to generate novel chemical probes                      June 10                      June 09                      Apr...
Academic, scientific, drug       discovery & economic impact  Published Dec 23 2010 - already cited 30 times  Distribute...
The	  FederaQon	  
How can we accelerate the pace of scientific discovery?           2008	         2009	     2010	     2011	   Ways to move b...
(Nolan	  and	  Haussler)	  
human aging:predicting bioage using whole blood methylation•  Independent training (n=170) and validation (n=123) Caucasia...
sage federation:model of biological age                                                        Faster Aging        Predict...
Reproducible	  science==shareable	  science	            Sweave: combines programmatic analysis with narrativeDynamic gener...
Federated	  Aging	  Project	  :	  	         Combining	  analysis	  +	  narraQve	  	                                =Sweave...
Portable	  Legal	  Consent	      (AcQvaQng	  PaQents)	         John	  Wilbanks	  
Sage	  Congress	  Project	       April	  20	  2012	              RA	          Parkinson’s	           Asthma	  (Responders	...
Why not use data intensive science   to build models of disease    Current Reward StructuresOrganizational Structures and ...
Stephen Friend Inspire2Live Discovery Network 2011-10-29
Stephen Friend Inspire2Live Discovery Network 2011-10-29
Stephen Friend Inspire2Live Discovery Network 2011-10-29
Stephen Friend Inspire2Live Discovery Network 2011-10-29
Stephen Friend Inspire2Live Discovery Network 2011-10-29
Stephen Friend Inspire2Live Discovery Network 2011-10-29
Stephen Friend Inspire2Live Discovery Network 2011-10-29
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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

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Stephen Friend Inspire2Live Discovery Network 2011-10-29

  1. 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. 2. Why not use data intensive science to build models of disease Current Reward StructuresOrganizational Structures and Tools Pilots Opportunities
  3. 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. 4. Personalized Medicine 101:Capturing Single bases pair mutations = ID of responders
  5. 5. Reality: Overlapping Pathways
  6. 6. The value of appropriate representations/ maps
  7. 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. 8. WHY  NOT  USE     “DATA  INTENSIVE”  SCIENCE  TO  BUILD  BETTER  DISEASE  MAPS?  
  9. 9. what will it take to understand disease?                    DNA    RNA  PROTEIN  (dark  maGer)    MOVING  BEYOND  ALTERED  COMPONENT  LISTS  
  10. 10. 2002 Can one build a “causal” model?
  11. 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. 12. List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
  13. 13. (Eric Schadt)
  14. 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. 15. We still consider much clinical research as if we we hunter gathers - not sharing .
  16. 16.  TENURE      FEUDAL  STATES      
  17. 17. Clinical/genomic data are accessible but minimally usableLittle incentive to annotate and curate data for other scientists to use
  18. 18. Mathematicalmodels of disease are not built to be reproduced orversioned by others
  19. 19. Lack of standard forms for sharing dataand lack of forms for future rights and consentss
  20. 20. Publication Bias- Where can we find the (negative) clinical data?
  21. 21. sharing as an adoption of common standards.. Clinical Genomics Privacy IP
  22. 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. 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. 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. 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. 26. 1   20   100   500  Feature   Features   Features   Features   Elastic net regression   28  
  27. 27. Bootstrapping retains robust predictive features 29  
  28. 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. 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. 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. 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. 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. 33. Leveraging Existing TechnologiesAddama Taverna tranSMART
  34. 34. INTEROPERABILITYSYNAPSE   Genome Pattern CYTOSCAPE tranSMART I2B2 INTEROPERABILITY  
  35. 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. 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. 37. CTCAP  Clinical Trial Comparator Arm Partnership “CTCAP”Strategic Opportunities For Regulatory Science Leadership and Action FDA September 27, 2011
  38. 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. 39. Shared clinical/genomic data sharing and analysis will maximize clinical impact and enable discovery•  Graphic  of  curated  to  qced  to  models  
  40. 40. Arch2POCM  Restructuring  the  PrecompeQQve   Space  for  Drug  Discovery   How  to  potenQally  De-­‐Risk       High-­‐Risk  TherapeuQc  Areas  
  41. 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. 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. 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. 44. The  FederaQon  
  45. 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. 46. (Nolan  and  Haussler)  
  47. 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. 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. 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. 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. 51. Portable  Legal  Consent   (AcQvaQng  PaQents)   John  Wilbanks  
  52. 52. Sage  Congress  Project   April  20  2012   RA   Parkinson’s   Asthma  (Responders  CompeQQons)  
  53. 53. Why not use data intensive science to build models of disease Current Reward StructuresOrganizational Structures and Tools Six Pilots Opportunities

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