Friend NIGM 2012-05-23
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Friend NIGM 2012-05-23



Stephen Friend May 23, 2012 Northwest Institute of Genetic Medicine 2012 Retreat Seattle, WA

Stephen Friend May 23, 2012 Northwest Institute of Genetic Medicine 2012 Retreat Seattle, WA



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    Friend NIGM 2012-05-23 Friend NIGM 2012-05-23 Presentation Transcript

    • Building  Be*er  Models  of  Disease  Together   Stephen  H  Friend  MD  PhD   Sage  Bionetworks   NIGM  4th  Annual  Retreat   May  23  2012   Sea*le  
    • What  is  the  problem?        Most  approved  therapies  assume  indica2ons  would   represent  homogenous  popula2ons    Our  exis2ng  disease  models  o8en  assume  pathway   knowledge  sufficient  to  infer  correct  therapies  
    • Personalized Medicine 101:Capturing Single bases pair mutations = ID of responders
    • Reality: Overlapping Pathways
    • The value of appropriate representations/ maps
    • Disease  PrevenLon  and  Treatment  •  To  Prevent  need  to:   –  Have  clinical  &  molecular  definiLon  of  disease     –  Be  able  to  predict  progression   –  Have  drugs  that  target  mechanisms  that  drive   progression  •  To  Treat  need  to:   –  Have  clinical  &  molecular  definiLon  of  disease     –  Disease  modifying  therapies   For  Alzheimer’s  we  need  work  to  develop  all  of  these!  
    • Data-­‐driven  Target  Iden2fica2on   If  we  accept  that  disease  is  driven  by  the  complex  interplay  of  geneLcs  and  environment   mediated  through  molecular  networks…….     GeneLcs  GeneLcs   Disease  progression   Disease  Modifying     Therapy   Healthy     Disease     Environment  Environment   State   State   ………………………….then  it  follows  we  must  study  these  networks  and  how  they  respond  to   perturbagens,  how  they  differ  in  disease,  etc  
    • what will it take to understand disease?                    DNA    RNA  PROTEIN  (dark  ma*er)    MOVING  BEYOND  ALTERED  COMPONENT  LISTS  
    • 2002 Can one build a “causal” model?
    • 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 but Genome scale profiling provide correlates of disease provides NO mechanism   Many examples BUT what is cause and effect?   Provide unbiased view of molecular physiology as it relates to disease phenotypes trait   Insights on mechanism   Provide causal relationships and allows predictions 14 Integrated Genetics Approaches
    • Preliminary Probabalistic Models- Rosetta /Schadt 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
    • 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.
    • 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 18
    • Alzheimer’s  Disease:  IdenLfying  key  disease  systems  and  genes  1.)  IdenLfy  groups  of  genes  that  move  together  –  coexpressed  “modules”                                -­‐  correlated  expression  of  mulLple  genes  across  many  paLents                                -­‐  coexpression  calculated  separate  for  Disease/healthy  groups                                  -­‐  these  gene  groups  are  ogen  coherent  cellular  subsystems,  enriched  in  one  or  more  GO  funcLons   Data  source:   Harvard  Brain   Tissue  Resource  Center   SNPs,   Gene  Expression,   Clinical  Traits   AD   n  =  284   Pre  Frontal  Cortex   Control   153   AD   168   Visual  Cortex   Control   116   AD   220   Cerebellum   Control   122  
    • IdenLfying  key  disease  systems  and  genes   1.)  IdenLfy  groups  of  genes  that  move  together  –  coexpressed  “modules”                                -­‐  correlated  expression  of  mulLple  genes  across  many  paLents                                -­‐  coexpression  calculated  separate  for  Disease/healthy  groups                                  -­‐  these  gene  groups  are  ogen  coherent  cellular  subsystems,  enriched  in  one  or  more  GO  funcLons  Alzheimer’s-­‐specific  regulatory  relaLonship   Microarray  result   Transcription factor Gene A Gene B
    • Where  does  coexpression  come  from?     What  does  a  “link”  in  these  networks  mean?  •  What  is  the  evidence  that  coexpression  is  produced  by  regulatory                rela6onships?  •  Gene  coexpression  has  mulLple  biophysical  sources:   1:  TranscripLonal  overrun    /    chromosome  locaLon  (Ebisuya  2008)   2:  Common  transcripLon  factor  binding  sites  (Marco  2009)   3:  EpigeneLc  regulaLon  (Chen  2005)   4:  3D  Chromosome  configuraLon  (Deng  2010)   Chromosome  segment   –  VariaLon  in  cell-­‐type  density  (Oldham  2008)   #1   #4   #2/TF   Gene  A   Gene  B   Gene  C   Promoter  x     Promoter  y   #3   21  
    • IdenLfying  key  disease  systems  and  genes  1.)  IdenLfy  groups  of  genes  that  move  together  –  coexpressed  “modules”                                -­‐  correlated  expression  of  mulLple  genes  across  many  paLents                                -­‐  coexpression  calculated  separate  for  Disease/healthy  groups                                  -­‐  these  gene  groups  are  ogen  coherent  cellular  subsystems,  enriched  in  one  or  more  GO  funcLons   Example  “modules”  of  coexpressed  genes,  color-­‐coded  
    • IdenLfying  key  disease  systems  and  genes  1.)  IdenLfy  groups  of  genes  that  move  together  –  coexpressed  “modules”  2.)  PrioriLze  the  disease-­‐relevance  of  the  modules  by  clinical  and  network  measures   PrioriLze  modules  through  expression   synchrony  with  clinical  measures  or  tendency   too  reconfigure  themselves  in  disease   vs  
    • IdenLfying  key  disease  systems  and  genes  1.)  IdenLfy  groups  of  genes  that  move  together  –  coexpressed  “modules”  2.)  PrioriLze  the  disease-­‐relevance  of  the  modules  by  clinical  and  network  measures   PrioriLze  modules  through  expression   CombinaLon  of  cogniLve  funcLon,  Braak  score,   synchrony  with  clinical  measures  or  tendency   corLcal  atrophy  with  differenLal  expression         too  reconfigure  themselves  in  disease   and  differenLal  coexpression  rank  modules.   vs  
    • IdenLfying  key  disease  systems  and  genes  1.)  IdenLfy  groups  of  genes  that  move  together  –  coexpressed  “modules”  2.)  PrioriLze  the  disease-­‐relevance  of  the  modules  by  clinical  and  network  measures  3.)  Incorporate  geneLc  informaLon  to  find  directed  relaLonships  between  genes   PrioriLze  modules  through  expression   Infer  directed/causal  relaLonships   synchrony  with  clinical  measures  or  tendency   and  clear  hierarchical  structure  by   too  reconfigure  themselves  in  disease   incorporaLng  eSNP  informaLon   (no  hair-­‐balls  here)   vs  
    • Example  network  finding:  microglia  acLvaLon  in  AD  Module  selec2on  –  what  iden2fies  these  modules  as  relevant  to  Alzheimer’s  disease?  The  eigengene  of  a  module  of  ~400  probes  correlates  with  Braak  score,  age,  cogniLve  disease  severity  and  corLcal  atrophy.    Members  of  this  module  are  on  average  differenLally  expressed  (both  up-­‐  and  down-­‐regulated).  Evidence  these  modules  are  related  to  microglia  func2on  The  members  of  this  module  are  enriched  with  GO  categories  (p<.001)  such  as  “response  to  bioLc  sLmulus”  that  are  indicaLve  of  immunologic  funcLon  for  this  module.    The  microglia  markers  CD68  and  CD11b/ITGAM  are  contained  in  the  module  (this  is  rare  –  even  when  a  module  appears  to  represent  a  specific  cell-­‐type,  the  histological  markers  may  be  lacking).  Numerous  key  drivers  (SYK,  TREM2,  DAP12,  FC1R,  TLR2)  are  important  elements  of  microglia  signaling.   Alzgene  hits  found  in  co-­‐regulated  microglia  module:  
    • Figure  key:  Five  main  immunologic  families  found  in  Alzheimer’s-­‐associated  module  Square  nodes  in  surrounding  network  denote  literature-­‐supported  nodes.  Node  size  is  propor6onal  to  connec6vity  in  the  full  module.  Core    family  members  are  shaded.  (Interior    circle)  Width  of  connec6ons  between  5  immune  families  are  linearly  scaled  to  the  number  of  inter-­‐family  connec6ons.  Labeled  nodes  are  either  highly  connected  in  the  original  network,  implicated  by  at  least  2  papers  as  associated  with  Alzheimer’s  disease,  or  core  members  of  one  of  the  5  immune  families.    
    • Transforming  networks  into  biological  hypotheses  
    • TesLng  network-­‐based  hypotheses  
    • TesLng  network-­‐based  hypotheses  
    • TesLng  network-­‐based  hypotheses  
    • Current  AD  projects  with  Sage  in  collaboraLon   Follow-­‐up  microglia  experiments   Confirming  TYROBP  relevance  in  human-­‐derived  microglia-­‐neuron  co-­‐culture   Similar  microglia  experiments  with  Fc  receptor   (Neumann,  Gaiteri)   Novel  genes  validated  with  in  vitro  and  in  vivo  model  systems   Cell  culture  &  transgenic  FAD  crosses  with  novel  gene  KO’s   (Wang,  Kitazawa,  Gaiteri)   Addi2onal  microarrays  from  model  systems     Check  network  predic6ons  to  refine  both  algorithm  &  biology     (Schadt/Neumann)   Larger  cohorts,  proteomics  Building  networks  in  3x  larger  dataset,  newer  plaZorm,  w/  detailed  clinical  info   (Myers,  Gaiteri)  
    • Design-­‐stage  AD  projects  at  Sage   Fusing  our  experLse  in…   Gene  regulatory  networks   Diffusion  Spectrum  Imaging   Feedback   Microcircuits  &     neuronal  diversity  To  build  mulL-­‐scale  biophysical  disease  models.    Join  us  in  uniLng  genes,  circuits  and  regions!  Contact  
    • List of 50 Influential Papers in Network Modeling 
    • Fundamentally  Biological  Science  hasn’t  changed  because  of  the  ‘Omics  RevoluLon……  …  is  about  the  process  of  linking  a  system  to  a  hypothesis  to  some  data  to  some  analyses     Biological Data Analysis System But  the  way  we  do  it  has  changed…………………………………………  
    • Driven  by  molecular  technologies  we  have  become  more  data  intensive  leading  to  more  specializaLon:  data  generators  (centralized  cores),  data  analyzers  (bioinformaLcians),  validators  (experimentalists:  lab  &  clinical)  This  is  reflected  in  the  tendency  for  more  mulL  lab  consorLum  style  grants  in  which  the  data  generators,  analyzers,  validators  may  be  different  labs.   Single Lab Model Data •  R01 Funding •  Hypothesis->data->analysis->paper •  Small-scale data / analysis •  Reproducible? Biological Analysis System Multiple Lab Model Data •  P01 Funding •  Hypothesis->data->analysis->paper •  Medium-scale data / analysis •  Data Generators/Analysts/Validators maybe different groups Biological Analysis •  Reproducible? System
    • What  does  this  New  Model  Enable   “Open Market” Model Data•  Democratization of Biology •  Large scale data, compute, analysis open to all•  Dissociation of Data Generators from Analysts from Validators – if scientists want to work on other people’s data they can, or validate someone else’s findings? Biological Analysis System•  New ways to fund and incentivize research •  BRIDGE •  Collaborative Competitions
    • Open and Networked Approaches and the “Democratization” of Science BioMedicine Information Commons Patients/ Citizens Data•  “Open” access to Generators CURATED data, tools, DATA models Data TOOLS/ Analysts METHODS•  Wide constituency of RAW DATA users and contributors ANALYZES/•  Break the “link” MODELS between data and ownership Clinicians SYNAPSE Experimentalists
    • UlLmately  these  efforts  will  fail  without   more  ambiLous  thinking   –  AcLvate  PaLents   •  PaLents  want  to  be  involved,  to  fund  research,  to  direct  the   research  quesLons,  to  hold  the  scienLfic  community  to   account   •  Portable  Legal  Consent   –  Collect  Large  Scale  Longitudinal  Data   •  We  need  to  collect  the  right  kind  of  data.  Molecular  and   Phenotypic  in  a  longitudinal  fashion  on  10s-­‐100,000s  of   individuals   •  Real  Names  Discovery  Project   –  Build  an  InformaLon  Commons   •  Synapse   –  Engage  in  CollaboraLve  Challenges   •  Breast  Cancer  Challenge-­‐  IBM/Google/  Science  Transl  Med  
    • Networked Approaches and the 3   2   REWARDS   “Democratization” of Science USABLE   RECOGNITION   DATA   1   PRIVACY   BARRIERS   BioMedical Information Commons Patients/ Citizens Data•  “Open” access to Generators CURATED data, tools, DATA models Data TOOLS/ Analysts METHODS•  Wide constituency of RAW DATA users and contributors 6   ROLES   ANALYZES/•  Break the “link” MODELS 4   FOR     between data and CITIZENS   GOVERNANCE   ownership Clinicians 5   HOW  TO   SYNAPSE DISTRIBUTE   Experimentalists TASKS  
    • Open and Networked Approaches:Democratization of Science 1   PRIVACY   PORTABLE  LEGAL  CONSENT:   BARRIERS   John  Wilbanks  
    • Open and Networked Approaches:Democratization of Science 2   USABLE   DATA   SYNAPSE   3   REWARDS   RECOGNITION   SYNAPSE  
    • Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning
    • 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
    • sage bionetworks synapse project
    • sage bionetworks synapse project
    • Open and Networked Approaches:Democratization of Science THE  FEDERATION   4   RULES   GOVERNANCE  
    • Pipeline  Strategy   A   B   C   Divide  and  Conquer  Strategy   D  A   B   C   Parallel/IteraLve  Strategy   A   B   C  
    • 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)  
    • sage federation: Google- SageBionetworks / The Federation
    • Open and Networked Approaches:Democratization of Science 5   HOW  TO   COLLABORATIVE   DISTRIBUTE   CHALLENGES   TASKS  
    • Open and Networked Approaches:Democratization of Science 6   ROLES   BRIDGE   FOR     CITIZENS  
    • We  pursue  Medical  Care  is  if  it  were  an  “Infinite  Game”   and  We  pursue  Medical  Research  as  if  it  were  a  “Finite  Game”  
    • We  pursue  Medical  Care  is  if  it  were  an  “Infinite  Game”   and   We  pursue  Medical  Research  as  if  it  were  a  “Finite  Game”   YET   We  should  pursue  Medical  Care  is  if  it  were  a  “Finite  Game”   and  We  should  pursue  Medical  Research  as  if  it  were  an  “Infinite  Game”  
    • Who will build the datasets/ models capable of providing powerful insights enabling disease modifying therapies? Power  of  CollaboraLve  Challenges   Evolving  Models  from  Deep  Data  Driven  Longitudinal  Cohorts    in  Worldwide  Open  InformaLon  Commons   InsLtutes   Industry   FoundaLons   NETWORK   PLATFORM   PPP   Or   ??????  Scientists Physicians Citizens “Knowledge Expert”