New	  Approaches	  for	  iden1fica1on	  and	  selec1on	  of	         therapeu1c	  targets	  for	  Complex	  Disease	       ...
Disease	  Preven1on	  and	  Treatment	  •  To	  Prevent	  need	  to:	      –  Have	  clinical	  &	  molecular	  defini1on	 ...
Data-­‐driven	  Target	  Iden0fica0on	     If	  we	  accept	  that	  disease	  is	  driven	  by	  the	  complex	  interplay...
Data-­‐driven	  Target	  Iden0fica0on	     If	  we	  accept	  that	  disease	  is	  driven	  by	  the	  complex	  interplay...
Problem	  is	  Complex	  and	  will	  not	  be	  solved	  by	                      any	  one	  group	  –  New	  Capabili1e...
Two	  recurring	  problems	  in	  AD	  research	  Ambiguous	  pathology	                                                 D...
Two	  recurring	  problems	  in	  AD	  research	  Ambiguous	  pathology	                                                  ...
Iden1fying	  key	  disease	  systems	  and	  genes	  1.)	  Iden1fy	  groups	  of	  genes	  that	  move	  together	  –	  co...
Iden1fying	  key	  disease	  systems	  and	  genes	   1.)	  Iden1fy	  groups	  of	  genes	  that	  move	  together	  –	  c...
Where	  does	  coexpression	  come	  from?	  	                    What	  does	  a	  “link”	  in	  these	  networks	  mean?...
Iden1fying	  key	  disease	  systems	  and	  genes	  1.)	  Iden1fy	  groups	  of	  genes	  that	  move	  together	  –	  co...
Iden1fying	  key	  disease	  systems	  and	  genes	  1.)	  Iden1fy	  groups	  of	  genes	  that	  move	  together	  –	  co...
Iden1fying	  key	  disease	  systems	  and	  genes	  1.)	  Iden1fy	  groups	  of	  genes	  that	  move	  together	  –	  co...
Iden1fying	  key	  disease	  systems	  and	  genes	  1.)	  Iden1fy	  groups	  of	  genes	  that	  move	  together	  –	  co...
Example	  network	  finding:	  microglia	  ac1va1on	  in	  AD	  Module	  selec0on	  –	  what	  iden0fies	  these	  modules	 ...
Figure	  key:	  Five	  main	  immunologic	  families	  found	  in	  Alzheimer’s-­‐associated	  module	  Square	  nodes	  i...
Transforming	  networks	  into	  biological	  hypotheses	  
Tes1ng	  network-­‐based	  hypotheses	  
Tes1ng	  network-­‐based	  hypotheses	  
Tes1ng	  network-­‐based	  hypotheses	  
Current	  AD	  projects	  with	  Sage	  in	  collabora1on	                            Follow-­‐up	  microglia	  experiment...
Design-­‐stage	  AD	  projects	  at	  Sage	      Fusing	  our	  exper1se	  in…	                                     Gene	 ...
List of 50 Influential Papers in Network Modeling                                        http://sagebase.org/research/res...
Now add Dimensions of Circuits, Brain Regions, Individual Dynamic Heterogeneity,                         And Longitudinal ...
Ul1mately	  these	  efforts	  will	  fail	  without	        more	  ambi1ous	  thinking	   –  Ac1vate	  Pa1ents	       •  Pa...
Why not share clinical /genomic data and model building in the ways             currently used by the software industry   ...
sage bionetworks synapse project     Watch What I Do, Not What I Say        Reduce, Reuse, Recycle                        ...
We	  pursue	  Alzheimer’s	  Care	  is	  if	  it	  were	  an	  “Infinite	  Game”	                                           ...
We	  pursue	  Alzheimer’s	  Care	  is	  if	  it	  were	  an	  “Infinite	  Game”	                                           ...
Who will build the datasets/ models capable of providing powerful         insights enabling disease modifying therapies?  ...
Friend NIH Alzheimers Summit 2012-05-14
Friend NIH Alzheimers Summit 2012-05-14
Friend NIH Alzheimers Summit 2012-05-14
Friend NIH Alzheimers Summit 2012-05-14
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Friend NIH Alzheimers Summit 2012-05-14

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Stephen Friend, May 14, 2012. NIH Alzheimer’s Disease Research Summit Bethesda, MD

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Friend NIH Alzheimers Summit 2012-05-14

  1. 1. New  Approaches  for  iden1fica1on  and  selec1on  of   therapeu1c  targets  for  Complex  Disease   Stephen  H  Friend  MD  PhD   Sage  Bionetworks   Alzheimer’s  Disease  Research  Summit   May  14-­‐15  2012   NIH  
  2. 2. Disease  Preven1on  and  Treatment  •  To  Prevent  need  to:   –  Have  clinical  &  molecular  defini1on  of  disease     –  Be  able  to  predict  progression   –  Have  drugs  that  target  mechanisms  that  drive   progression  •  To  Treat  need  to:   –  Have  clinical  &  molecular  defini1on  of  disease     –  Disease  modifying  therapies   For  Alzheimer’s  we  need  work  to  develop  all  of  these!  
  3. 3. Data-­‐driven  Target  Iden0fica0on   If  we  accept  that  disease  is  driven  by  the  complex  interplay  of  gene1cs  and  environment   mediated  through  molecular  networks…….     Gene1cs  Gene1cs   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  
  4. 4. Data-­‐driven  Target  Iden0fica0on   If  we  accept  that  disease  is  driven  by  the  complex  interplay  of  gene1cs  and  environment   mediated  through  molecular  networks…….     Gene1cs  Gene1cs   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  
  5. 5. Problem  is  Complex  and  will  not  be  solved  by   any  one  group  –  New  Capabili1es   •  Informa1on  Commons   •  Portable  Legal  Consent  –  New  Ways  to  Work  Together   •  Public-­‐Private  Partnerships  eg  ADNI  –  Recognize  new  Roles  for:   •  Pa1ents   •  Ci1zens   •  Funders   •  Scien1sts  
  6. 6. Two  recurring  problems  in  AD  research  Ambiguous  pathology   Diverse  mechanisms  Are  disease-­‐associated  molecular  systems  &   How  do  diverse  muta1ons  and  environmental  factors  genes  destruc1ve,  adap1ve,  or  both?   combine  into  a  core  pathology?  Boom  line:  We  need  to  iden1fy  causal  factors   Boom  line:  There  is  no  rigorous  /  consistent  global  vs  correla1ve  or  adap1ve  features  of  disease.   framework  that  integrates  diverse  disease  factors.         7  
  7. 7. Two  recurring  problems  in  AD  research  Ambiguous  pathology   Diverse  mechanisms  Are  disease-­‐associated  molecular  systems  &   How  do  diverse  muta1ons  and  environmental  factors  genes  destruc1ve,  adap1ve,  or  both?   combine  into  a  core  pathology?  Boom  line:  We  need  to  iden1fy  causal  factors   Boom  line:  There  is  no  rigorous  /  consistent  global  vs  correla1ve  or  adap1ve  features  of  disease.   framework  that  integrates  diverse  disease  factors.         One  consequence…  "There  are  very  few  new  molecular  en22es,  very  few  novel  ideas,  and  almost  nothing  that  gives  any  hope  for  a  transforma2on  in  the  treatment  of  mental  illness.”          -­‐  Thomas  Insel,  Science  2010     8  
  8. 8. Iden1fying  key  disease  systems  and  genes  1.)  Iden1fy  groups  of  genes  that  move  together  –  coexpressed  “modules”                                -­‐  correlated  expression  of  mul1ple  genes  across  many  pa1ents                                -­‐  coexpression  calculated  separate  for  Disease/healthy  groups                                  -­‐  these  gene  groups  are  ofen  coherent  cellular  subsystems,  enriched  in  one  or  more  GO  func1ons   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  
  9. 9. Iden1fying  key  disease  systems  and  genes   1.)  Iden1fy  groups  of  genes  that  move  together  –  coexpressed  “modules”                                -­‐  correlated  expression  of  mul1ple  genes  across  many  pa1ents                                -­‐  coexpression  calculated  separate  for  Disease/healthy  groups                                  -­‐  these  gene  groups  are  ofen  coherent  cellular  subsystems,  enriched  in  one  or  more  GO  func1ons  Alzheimer’s-­‐specific  regulatory  rela1onship   Microarray  result   Transcription factor Gene A Gene B
  10. 10. Where  does  coexpression  come  from?     What  does  a  “link”  in  these  networks  mean?  •  What  is  the  evidence  that  coexpression  is  produced  by  regulatory                rela2onships?  •  Gene  coexpression  has  mul1ple  biophysical  sources:   1:  Transcrip1onal  overrun    /    chromosome  loca1on  (Ebisuya  2008)   2:  Common  transcrip1on  factor  binding  sites  (Marco  2009)   3:  Epigene1c  regula1on  (Chen  2005)   4:  3D  Chromosome  configura1on  (Deng  2010)   Chromosome  segment   –  Varia1on  in  cell-­‐type  density  (Oldham  2008)   #1   #4   #2/TF   Gene  A   Gene  B   Gene  C   Promoter  x     Promoter  y   #3   11  
  11. 11. Iden1fying  key  disease  systems  and  genes  1.)  Iden1fy  groups  of  genes  that  move  together  –  coexpressed  “modules”                                -­‐  correlated  expression  of  mul1ple  genes  across  many  pa1ents                                -­‐  coexpression  calculated  separate  for  Disease/healthy  groups                                  -­‐  these  gene  groups  are  ofen  coherent  cellular  subsystems,  enriched  in  one  or  more  GO  func1ons   Example  “modules”  of  coexpressed  genes,  color-­‐coded  
  12. 12. Iden1fying  key  disease  systems  and  genes  1.)  Iden1fy  groups  of  genes  that  move  together  –  coexpressed  “modules”  2.)  Priori1ze  the  disease-­‐relevance  of  the  modules  by  clinical  and  network  measures   Priori1ze  modules  through  expression   synchrony  with  clinical  measures  or  tendency   too  reconfigure  themselves  in  disease   vs  
  13. 13. Iden1fying  key  disease  systems  and  genes  1.)  Iden1fy  groups  of  genes  that  move  together  –  coexpressed  “modules”  2.)  Priori1ze  the  disease-­‐relevance  of  the  modules  by  clinical  and  network  measures   Priori1ze  modules  through  expression   Combina1on  of  cogni1ve  func1on,  Braak  score,   synchrony  with  clinical  measures  or  tendency   cor1cal  atrophy  with  differen1al  expression         too  reconfigure  themselves  in  disease   and  differen1al  coexpression  rank  modules.   vs  
  14. 14. Iden1fying  key  disease  systems  and  genes  1.)  Iden1fy  groups  of  genes  that  move  together  –  coexpressed  “modules”  2.)  Priori1ze  the  disease-­‐relevance  of  the  modules  by  clinical  and  network  measures  3.)  Incorporate  gene1c  informa1on  to  find  directed  rela1onships  between  genes   Priori1ze  modules  through  expression   Infer  directed/causal  rela1onships   synchrony  with  clinical  measures  or  tendency   and  clear  hierarchical  structure  by   too  reconfigure  themselves  in  disease   incorpora1ng  eSNP  informa1on   (no  hair-­‐balls  here)   vs  
  15. 15. Example  network  finding:  microglia  ac1va1on  in  AD  Module  selec0on  –  what  iden0fies  these  modules  as  relevant  to  Alzheimer’s  disease?  The  eigengene  of  a  module  of  ~400  probes  correlates  with  Braak  score,  age,  cogni1ve  disease  severity  and  cor1cal  atrophy.    Members  of  this  module  are  on  average  differen1ally  expressed  (both  up-­‐  and  down-­‐regulated).  Evidence  these  modules  are  related  to  microglia  func0on  The  members  of  this  module  are  enriched  with  GO  categories  (p<.001)  such  as  “response  to  bio1c  s1mulus”  that  are  indica1ve  of  immunologic  func1on  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:  
  16. 16. Figure  key:  Five  main  immunologic  families  found  in  Alzheimer’s-­‐associated  module  Square  nodes  in  surrounding  network  denote  literature-­‐supported  nodes.  Node  size  is  propor2onal  to  connec2vity  in  the  full  module.  Core    family  members  are  shaded.  (Interior    circle)  Width  of  connec2ons  between  5  immune  families  are  linearly  scaled  to  the  number  of  inter-­‐family  connec2ons.  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.    
  17. 17. Transforming  networks  into  biological  hypotheses  
  18. 18. Tes1ng  network-­‐based  hypotheses  
  19. 19. Tes1ng  network-­‐based  hypotheses  
  20. 20. Tes1ng  network-­‐based  hypotheses  
  21. 21. Current  AD  projects  with  Sage  in  collabora1on   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)   Addi0onal  microarrays  from  model  systems     Check  network  predic2ons  to  refine  both  algorithm  &  biology     (Schadt/Neumann)   Larger  cohorts,  proteomics  Building  networks  in  3x  larger  dataset,  newer  plaorm,  w/  detailed  clinical  info   (Myers,  Gaiteri)  
  22. 22. Design-­‐stage  AD  projects  at  Sage   Fusing  our  exper1se  in…   Gene  regulatory  networks   Diffusion  Spectrum  Imaging   Feedback   Microcircuits  &     neuronal  diversity  To  build  mul1-­‐scale  biophysical  disease  models.    Join  us  in  uni1ng  genes,  circuits  and  regions!  Contact  chris.gaiteri@sagebase.org  
  23. 23. List of 50 Influential Papers in Network Modeling   http://sagebase.org/research/resources.php
  24. 24. Now add Dimensions of Circuits, Brain Regions, Individual Dynamic Heterogeneity, And Longitudinal Variations
  25. 25. Ul1mately  these  efforts  will  fail  without   more  ambi1ous  thinking   –  Ac1vate  Pa1ents   •  Pa1ents  want  to  be  involved,  to  fund  research,  to  direct  the   research  ques1ons,  to  hold  the  scien1fic  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  Informa1on  Commons   •  Synapse   –  Engage  in  Collabora1ve  Challenges   •  Breast  Cancer  Challenge-­‐  IBM/Google/  Science  Transl  Med  
  26. 26. Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning
  27. 27. 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
  28. 28. We  pursue  Alzheimer’s  Care  is  if  it  were  an  “Infinite  Game”   and  We  pursue  Alzheimer’s  Research  as  if  it  were  a  “Finite  Game”  
  29. 29. We  pursue  Alzheimer’s  Care  is  if  it  were  an  “Infinite  Game”   and   We  pursue  Alzheimer’s  Research  as  if  it  were  a  “Finite  Game”   YET   We  should  pursue  Alzheimer’s  Care  is  if  it  were  a  “Finite  Game”   and  We  should  pursue  Alzheimer’s  Research  as  if  it  were  an  “Infinite  Game”  
  30. 30. Who will build the datasets/ models capable of providing powerful insights enabling disease modifying therapies? Power  of  Collabora1ve  Challenges   Evolving  Models  from  Deep  Data  Driven  Longitudinal  Cohorts    in  Worldwide  Open  Informa1on  Commons   Ins1tutes   Industry   Founda1ons   NETWORK   PLATFORM   PPP   Or   ??????  Scientists Physicians Citizens “Knowledge Expert”

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