Stephen Friend Dana Farber Cancer Institute 2011-10-24
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Stephen Friend Dana Farber Cancer Institute 2011-10-24



Stephen Friend, Oct 24, 2011. Dana Farber Cancer Institute, Boston, MA

Stephen Friend, Oct 24, 2011. Dana Farber Cancer Institute, Boston, MA



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Stephen Friend Dana Farber Cancer Institute 2011-10-24 Stephen Friend Dana Farber Cancer Institute 2011-10-24 Presentation Transcript

  • Actionable Cancer Network ModelsAnd Open Medical Information Systems Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ Amsterdam DFCI October 24th, 2011
  • Why not use data intensive science to build models of disease Current Reward StructuresOrganizational Structures and Tools Six Pilots Opportunities
  • 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
  • What is the problem?We need to rebuild the drug discovery process so that webe6er understand disease biology before tes8ngproprietary compounds on sick pa8ents
  • What is the problem?Most approved cancer therapies assumed tumorindica8ons would represent homogenous popula8onsMost new cancer therapies are in search of single alteredcomponentsOur exis8ng tumor models o>en assume pathwayknowledge sufficinet to infer correct therapies
  • 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 IT Interoperability Open Information System Host evolving Models in a Compute Space- Knowledge Expert
  • what will it take to understand disease? DNA RNA PROTEIN (dark maGer)MOVING BEYOND ALTERED COMPONENT LISTS
  • 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 regulateddb/db mouse(p~10E(-20) p~10E(-100)) AVANDIA in db/db mouse
  • 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.
  • 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 generating massive amounts of data A- IT Interoperability D Open Information System D- Host evolving Models in a Compute Space- Knowledge Expert F
  • hunter gathers - not sharing
  • Clinical/genomic data are accessible but minimally usableLittle incentive to annotate and curate data for other scientists to use
  • Mathematicalmodels of disease are not built to be reproduced orversioned by others
  • Assumption that genetic alterationsin human conditions should be owned
  • 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 bionetworks are evolved by contributor scientists with a shared vision to accelerate the elimination of human diseaseBuilding Disease Maps Data RepositoryCommons Pilots Discovery Platform
  • 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 29
  • 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, M Clinical Trialists, Industrial Trialists, CROs…) S FOR MAP NEW MAPS 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...)
  • Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning
  • Leveraging Existing TechnologiesAddama Taverna tranSMART
  • 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
  • Six Pilots at Sage Bionetworks CTCAP Non-­‐Responders Arch2POCM M S FOR MAP The FederaOon PLAT Portable Legal Consent EW N Sage Congress Project RULES GOVERN
  • CTCAPClinical Trial Comparator Arm Partnership “CTCAP”Strategic Opportunities For Regulatory Science Leadership and Action FDA September 27, 2011
  • 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
  • Shared clinical/genomic data sharing and analysis will maximize clinical impact and enable discovery 
  • 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
  • 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
  • The  Non-­‐Responder  Project  is  an  internaOonal  iniOaOve  with  funding  for  6  iniOal  cancers  anOcipated  from  both  the  public  and  private  sectors   GEOGRAPHY   United  States   China   TARGET   CANCER   Ovarian     Renal   Breast   AML   Colon   Lung   RetrospecOve   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   prospec8ve  studies   Government   5  
  • Arch2POCMRestructuring the PrecompeOOve Space for Drug Discovery How to potenOally De-­‐Risk High-­‐Risk TherapeuOc Areas
  • What is the problem?We need to rebuild the drug discovery process so that webe6er understand disease biology before tes8ngproprietary compounds on sick pa8ents
  • 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)
  • 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
  • The FederaOon
  • 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
  • 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
  • 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)
  • 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
  • Federated  Aging  Project  :     Combining  analysis  +  narraOve     =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  
  • Portable Legal Consent (AcOvaOng PaOents) John Wilbanks
  • Sage Congress Project April 20 2012 RA Parkinson’s Asthma(Responders CompeOOons)
  • Why not use data intensive science to build models of disease Current Reward StructuresOrganizational Structures and Tools Six Pilots Opportunities
  • And Open Medical Information Systems