Stephen Friend MIT 2011-10-20

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Stephen Friend, Oct 20, 2011. Massachusetts Institute of Technology, Cambridge, MA

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Stephen Friend MIT 2011-10-20

  1. 1. Novel Structures (and Non-Structures) to Facilitate Translational ResearchIntegrating layers of omics data models and compute spaces needed to build a “Knowledge Expert” Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ Amsterdam MIT/Whitehead October 10th, 2011
  2. 2. Why not use data intensive science to build models of diseaseOrganizational Structures and Tools How not What Six Pilots Opportunities
  3. 3. Alzheimer’s Diabetes Treating Symptoms v.s. Modifying DiseasesCancer Obesity Will it work for me? Biomarkers?
  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” SCIENCETO BUILD BETTER DISEASE MAPS?
  9. 9. what will it take to understand disease? DNA RNA PROTEIN (dark ma>er)MOVING BEYOND ALTERED COMPONENT LISTS
  10. 10. 2002 Can one build a “causal” model?
  11. 11. 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 Genome scale profiling provide correlates of disease   Many examples BUT what is cause and effect? but provides NO mechanism   Provide unbiased view of molecular physiology as it relates to disease phenotypes trait   Insights on mechanism   Provide causal relationships and allows predictions Integrated Genetics Approaches
  12. 12. Integration of Genotypic, Gene Expression & Trait Data Schadt et al. Nature Genetics 37: 710 (2005) Millstein et al. BMC Genetics 10: 23 (2009) Causal Inference “Global Coherent Datasets” •  population based •  100s-1000s individuals Chen et al. Nature 452:429 (2008) Zhu et al. Cytogenet Genome Res. 105:363 (2004) Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)
  13. 13. Association of SNPs at 1p13.3 with Coronary Artery Disease SNP rs599839 in the 1p13.3 locus associated with CAD: PSRC1 highlighted as candidate suscepUbility gene
  14. 14. Schadt et al, PLoS Biol. 2008
  15. 15. Mouse network around Sort1, Psrc1, and Celsr2Schadt et al, PLoS Biol. 2008
  16. 16. Human network around Sort1, Psrc1, and Celsr2Schadt et al, PLoS Biol. 2008
  17. 17. Map compound signatures to disease networks Sub-network contains genes Compound Gene expression associated with diabetes signatures traits Sub- network contains genes 1associated with toxicities 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
  18. 18. Case Study – Target A/Drug B NO CELL DYNAMICS NEEDED Identified compound whose signature Fasting Insulin significantly intersected with Islet module * * * HF-DRUG * * * Fasting Glucose•  Test carried out in a Diet-Induced Obesity model on the B6 background HF-DRUG •  Model for obesity and insulin resistance•  Animals treated with compound over an 8 week interval, starting at 8 weeks of age•  No significant Adverse Events in 30 day human clinical trial for another indication
  19. 19. 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
  20. 20. Extensive Publications now Substantiating Scientific Approach Probabilistic Causal Bionetwork Models• >80 Publications from Rosetta Genetics Group (~30 scientists) over 5 years including high profile papers in PLoS Nature and Nature 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.
  21. 21. List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
  22. 22. (Eric Schadt)
  23. 23. Recognition that the benefits of bionetwork based molecularmodels of diseases are powerful but that they requiresignificant resourcesAppreciation that it will require decades of evolvingrepresentations as real complexity emerges and needs to beintegrated with therapeutic interventions
  24. 24. 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
  25. 25. Board of Directors- Sage Bionetworks Lee Hartwell Hans Wizgell WangJun Jeff Hammerbacher CEO ClouderaEx President FHCRC ExPresident Karolinska Executive Director Built and HeadedCo-Founder Rosetta Head SAB Rosetta BGI Facebook Data Architecture
  26. 26. 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 28
  27. 27. 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...)
  28. 28. Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning
  29. 29. Evolution of a Software Project
  30. 30. Biology Tools Support Collaboration
  31. 31. Potential Supporting TechnologiesAddama Taverna tranSMART
  32. 32. Platform for Modeling SYNAPSE
  33. 33. 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
  34. 34. -­‐-­‐ Implement customTrain() andcustomPredict() funcUons-­‐-­‐ Everything else handled instandardized workflow(performance evaluaUon,biomarker outputs, evaluaUonagainst other methods, loading ofdifferent datasets, etc).
  35. 35. INTEROPERABILITY Genome Pattern CYTOSCAPE tranSMART I2B2INTEROPERABILITY  
  36. 36. NOT JUST WHAT BUT HOW
  37. 37. hunter gathers - not sharing
  38. 38. TENURE FEUDAL STATES
  39. 39. Clinical/genomic data are accessible but minimally usableLittle incentive to annotate and curate data for other scientists to use
  40. 40. Mathematicalmodels of disease are not built to be reproduced orversioned by others
  41. 41. Assumption that genetic alterationsin human conditions should be owned
  42. 42. Lack of standard forms for sharing dataand lack of forms for future rights and consentss
  43. 43. Publication Bias- Where can we find the (negative) clinical data?
  44. 44. sharing as an adoption of common standards.. Clinical Genomics Privacy IP
  45. 45. Six Pilots at Sage Bionetworks CTCAP Non-Responders Arch2POCM ORM S MAP The Federation F PLAT Portable Legal Consent NEW Sage Congress Project RULES GOVERN
  46. 46. CTCAPClinical Trial Comparator Arm Partnership “CTCAP”Strategic Opportunities For Regulatory Science Leadership and Action FDA September 27, 2011
  47. 47. 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
  48. 48. Shared clinical/genomic data sharing and analysis will maximize clinical impact and enable discovery 
  49. 49. Non-­‐Responders Project To identify Non-Responders to approved Oncology drug regimens in order to improve outcomes, spare patients unnecessary toxicitiesfrom treatments that have no benefit to them, and reduce healthcare costs
  50. 50. The Non-­‐Responder Cancer Project Leadership Team Stephen Friend, MD, PhD Todd Golub, MD President and Co-Founder of Founding Director Cancer Biology Program Broad Institute, Charles Dana Sage Bionetworks, Head of Merck Oncology 01-08, Investigator Dana-Farber Cancer Founder of Rosetta Institute, Professor of Pediatrics Harvard Inpharmatics 97-01, co- Medical School, Investigator, Howard Founder of the Seattle Project Hughes Medical Institute Richard Schilsky, MD Garry Nolan, PhD Chief, Hematology- Oncology, Deputy Professor, Baxter Laboratory of Stem Director, Comprehensive Cancer Cell Biology, Department of Microbiology Center, University of Chicago; Chair, and Immunology, Stanford University National Cancer Institute Board of Director, Proteomics Center at Stanford Scientific Advisors; past-President University ASCO, past Chairman CALGB clinical trials group 11
  51. 51. The  Non-­‐Responder  Project  is  an  internaUonal  iniUaUve  with  funding  for  6  iniUal  cancers  anUcipated  from  both  the  public  and  private  sectors   GEOGRAPHY   United  States   China   TARGET   CANCER   Ovarian     Renal   Breast   AML   Colon   Lung   RetrospecUve   FUNDING   SOURCE   Seeking  private  sector  and   study;  likely  to   Funded  by  the  Chinese  government   philanthropic  funding  for   be  funded  by   and  private  sector  partners   the  Federal   prospec:ve  studies   Government   5  
  52. 52. For each tumor-­‐type, the non-­‐responder project will follow a commonworkflow, with paUent idenUficaUon and sample collecUon the mostvariable across studiesNon-­‐Responder Project WorkflowIdenUficaUon and enrollment, and data The remaining parts of the study will be and sample collecUon may differ by largely similar, and potenUally shared, across tumor-­‐type all projects Data and ClinicalIdenUficaUon and Sample Disease Feedback Sample Data Enrollment Processing Modeling and Results CollecUon ReporUng Payment and Reimbursement Project Management 7
  53. 53. A consorUum of collaborators has been constructed to execute the non-­‐responder project Physicians & AMCs PaUent Advocacy Groups 8
  54. 54. A consorUum of collaborators has been constructed to execute the non-­‐responder project (conUnued) PaUent Consent Pathology Genome Sequencing TBD Core BioinformaUcs Analysis and Disease Modeling 9
  55. 55. Arch2POCMRestructuring Drug DiscoveryHow to potenUally De-­‐RiskHigh-­‐Risk TherapeuUc Areas
  56. 56. What is the problem?•  Regulatory hurdles too high?•  Low hanging fruit picked?•  Payers unwilling to pay?•  Genome has not delivered?•  Valley of death?•  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
  57. 57. What is the problem?We need to rebuild the drug discovery process so that webeMer understand disease biology before tes:ngproprietary compounds on sick pa:ents
  58. 58. The FederaUon
  59. 59. 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
  60. 60. Rules of the game:transparency & trust   Shared data tools models and prepublications   Conflict of interests   Intellectual property   Authorship
  61. 61. sage federation:human aging project Justin Guinney Stephen Friend* Mariano Alvarez Celine Lefebrev Greg Hannum Andrea Califano* Januz Dutkowski Trey Ideker* Kang Zhang*
  62. 62. sage federation:what is the impact of disease/environment on “biological age” ? Biological Age 2009 2001 Chronological Age
  63. 63. 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
  64. 64. 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)
  65. 65. Reproducible science==shareable science Sweave: combines programmatic analysis with narrative Dynamic 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
  66. 66. Federated Aging Project : Combining analysis + narrative =Sweave Vignette Sage Lab R code + PDF(plots + text + code snippets) narrative HTML Data objectsCalifano Lab Ideker Lab Submitted PaperShared Data JIRA: Source code repository & wikiRepository
  67. 67. Portable Legal Consent (Activating Patients) John Wilbanks
  68. 68. Sage Congress Project April 20 2012 RA Parkinson’s Asthma(Responders Competitions)
  69. 69. Why not use data intensive science to build models of diseaseOrganizational Structures and Tools How not What Six Pilots Opportunities

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