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Friend WIN Symposium 2012-06-28


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Stephen Friend, June 28, 2012. WIN Symposium, Paris, FR

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Friend WIN Symposium 2012-06-28

  1. 1. The  Marriage  of  Transla/onal  Medicine   to  “Big  Data”:  Key  Opportuni/es  to   change  how  we  do  our  Science   Stephen  H  Friend   June  28,  2012   WIN    
  2. 2. Background:  Informa/on  Commons  for  Biological  Func/ons  
  3. 3. Oncogenes only make good targets in particular molecularcontexts : EGFR story ERBB2 •  EGFR  Pathway  commonly  mutated/ac/vated  in  Cancer   EGFRi EGFR •  30%  of  all  epithelial  cancers   BCR/ABL •  Blocking  Abs  approved  for  treatment  of  metasta/c   colon  cancer   KRAS NRAS •  Subsequently  found  that  RASMUT  tumors  don’t  respond   –  “Nega/ve  Predic/ve  Biomarker”   BRAF •  However  s/ll  EGFR+  /  RASWT  pa/ents  who  don’t   MEK1/2 respond?  –  need  “Posi/ve  Predic/ve  Biomarker”   •  And  in  Lung  Cancer  not  clear  that  RASMUT  status  is   Proliferation, Survival useful  biomarker   Predic/ng  treatment  response  to  known  oncogenes  is   complex  and  requires  detailed  understanding  of  how   different  gene/c  backgrounds  func/on  
  4. 4. what will it take to understand disease?                                        DNA    RNA  PROTEIN    MOVING  BEYOND  ALTERED  COMPONENT  LISTS  
  5. 5. Familiar but Incomplete
  6. 6. Reality: Overlapping Pathways
  7. 7. Preliminary Probabalistic Models- Rosetta 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
  8. 8. 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.
  9. 9. List of Influential Papers in Network Modeling   50 network papers 
  10. 10. Sage Bionetworks A non-profit organization with a vision to enable networked team approaches to building better models of disease BIOMEDICINE INFORMATION COMMONS INCUBATORBuilding Disease Maps Data RepositoryCommons Pilots Discovery Platform
  11. 11. Sage Bionetworks Collaborators  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca, Amgen,Roche, Johnson &Johnson  Foundations   Kauffman CHDI, Gates Foundation  Government   NIH, LSDF, NCI  Academic   Levy (Framingham)   Rosengren (Lund)   Krauss (CHORI)  Federation   Ideker, Califano, Nolan, Schadt 12
  12. 12. Fundamentally  Biological  Science  hasn’t  changed  yet  because  of  the  ‘Omics  Revolu/on……  …  is  s/ll  about  the  process  of  linking  a  system  to  a  hypothesis  to  some  data  to    some  analyses     Biological Data Analysis System
  13. 13. Driven  by  molecular  technologies  we  have  become  more  data  intensive  leading  to  more  specializa/on:  data  generators  (centralized  cores),  data  analyzers  (bioinforma/cians),  validators  (experimentalists:  lab  &  clinical)  This  is  reflected  in  the  tendency  for  more  mul/  lab  consor/um  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
  14. 14. Iterative Networked ApproachesTo Generating Analyzing and Supporting New Models Data Biological System Analysis Uncouple the automatic linkage between the data generators, analyzers, and validators  
  15. 15. Networked Approaches BioMedicine Information Commons Patients/ Citizens Data Generators CURATED DATA Data TOOLS/ Analysts METHODS RAW DATA ANALYZES/ MODELS Clinicians SYNAPSE Experimentalists
  16. 16. Networked Approaches 2   1   REWARDS   USABLE   RECOGNITION   DATA   BioMedical Information Commons Patients/ Citizens Data Generators CURATED DATA Data 5   TOOLS/ 3   Analysts REWARDS   METHODS HOW  TO   FOR   RAW DISTRIBUTE   SHARING   DATA TASKS   ANALYZES/ MODELS Clinicians 4   PRIVACY   SYNAPSE Experimentalists BARRIERS  
  17. 17. Open and Networked Approaches:Democratization of Science 1   USABLE   DATA   SYNAPSE   2   REWARDS   RECOGNITION   SYNAPSE  
  18. 18. Two approaches to building common scientific and technical knowledge Every code change versioned Every issue trackedText summary of the completed project Every project the starting point for new workAssembled after the fact All evolving and accessible in real time Social Coding
  19. 19. Synapse is GitHub for Biomedical Data Every code change versioned Every issue trackedData and code versioned Every project the starting point for new workAnalysis history captured in real time All evolving and accessible in real timeWork anywhere, and share the results with anyone Social CodingSocial Science
  20. 20. Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning
  21. 21. Leveraging Existing TechnologiesAddama Taverna tranSMART
  22. 22. sage bionetworks synapse project Watch What I Do, Not What I Say
  23. 23. sage bionetworks synapse project Most of the People You Need to Work with Don’t Work with You
  24. 24. sage bionetworks synapse project My Other Computer is “The Cloud”
  25. 25. Data Analysis with SynapseRun Any ToolOn Any PlatformRecord in SynapseShare with Anyone
  26. 26. •  Automated  workflows  for  cura/on,  QC,  and  sharing  of   1%/2* 53,6%(* !7"(%,2/"* large-­‐scale  datasets.  -./#"++0%(* (3&4"#* •  All  of  TCGA,  GEO,  and  user-­‐submined  data   processed  with  standard  normaliza/on  methods.   1%/2* 53,6%(* !7"(%,2/"* •  Searchable  TCGA  data:  -./#"++0%(* (3&4"#* •  23  cancers   •  11  data  plaoorms   •  Standardized  meta-­‐data  ontologies  -./#"++0%(* -./#"++0%(* !7"(%,2/"* !7"(%,2/"* 1%/2* 1%/2* (3&4"#* (3&4"#* 53,6%(* 53,6%(* !#"80)69"*&%8":* ;"("#6%(* !"#$%#&()"* ++"++&"(,*
  27. 27. 1%/2* 53,6%(* !7"(%,2/"* •  Comparison  of  many  modeling  approaches  applied  -./#"++0%(* (3&4"#* to  the  same  data.   •  Models  transparently  shared  and  reusable  through  -./#"++0%(* 1%/2* 53,6%(* !7"(%,2/"* Synapse.   (3&4"#* •  Displayed  is  comparison  of  6  modeling  approaches   to  predict  sensi/vity  to  130  drugs.   •  Extending  pipeline  to  evaluate  predic/on  of  -./#"++0%(* -./#"++0%(* !7"(%,2/"* !7"(%,2/"* TCGA  phenotypes.   1%/2* 1%/2* (3&4"#* (3&4"#* •  Hos/ng  of  collabora/ve  compe//ons  to  compare   53,6%(* 53,6%(* models  from  many  groups.   1--&2-3$4567$ !#"80)69"*&%8":* *&+%,-./0$ ;"("#6%(* !"#$%#&()"* ++"++&"(,* !"#$%&()$
  29. 29. What  is the problem?Our current models of disease biology are primitive and limit doctor’s understanding and ability to treat patientsCurrent incentives reward those whosilo information and work in closedsystems 30  
  30. 30. The Solution: Competitions to crowd-source researchin biology and other fields  Why competitions? •  Objective assessments •  Acceleration of progress •  Transparency •  Reproducibility •  Extensible, reusable models  Competitions in biomedical research •  CASP (protein structure) •  Fold it / EteRNA (protein / RNA structure) •  CAGI (genome annotation) •  Assemblethon / alignathon (genome assembly / alignment) •  SBV Improver (industrial methodology benchmarking) •  DREAM (co-organizer of Sage/DREAM competition)  Generic competition platforms •  Kaggle, Innocentive, MLComp 31  
  31. 31. The Sage/DREAM breast cancer prognosischallengeGoal: Challenge to assess the accuracy of computational models designed topredict breast cancer survival using patient clinical and genomic dataWhy this is unique:  This Sage/DREAM Challenge is a pre-collated cohort: 2000 breast cancer samples from the Metabric cohort  Accessible to all: A cloud-based common compute architecture is being made available by Google to support the computational models needed to develop and test challenge models  New Rigor: •  Contestants will evaluate their models on a validation data set composed of newly generated data (provided by Dr. Anne-Lise Borreson Dale) •  Contestants must demonstrate their models can be reproduced by others  New incentives: leaderboard to energize participants, Science Translational Medicine publication for winning team  Breast cancer patients, funders and researchers can track this Challenge on BRIDGE, an open source online community being built by Sage and Ashoka Changemakers and affiliated with this Challenge 32  
  32. 32. Sage/DREAM Challenge: Details and TimingPhase  1: Apr thru end-Sep 2012 Phase  2:  Oct 1 thru Nov 12, 2012  Training data: 2,000 breast cancer   Evaluation of models in novel samples from METABRIC cohort dataset. •  Gene expression •  Copy number   Validation data: ~500 fresh frozen •  Clinical covariates tumors from Norway group with: •  10 year survival •  Clinical covariates •  10 year survival  Supporting data: Other Sage- curated breast cancer datasets   Gene expression and copy number •  >1,000 samples from GEO data to be generated for model •  ~800 samples from TCGA evaluation •  ~500 additional samples from •  Sent to Cancer Research UK to Norway group generate data at same facility as •  Curated and available on METABRIC Synapse, Sage’s compute •  Models built on training data platform evaluated on newly generated data  Data released in phases on Synapse from now through end-   Winners announced at November September 12 DREAM conference  Will evaluate accuracy of models built on METABRIC data to predict survival in: •  Held out samples from METABRIC 33   •  Other datasets
  33. 33. SummaryTransparency,   Valida;on  in  novel  reproducibility   -./#"++0%(* 1%/2* (3&4"#* 53,6%(* !7"(%,2/"* dataset   1%/2* 53,6%(* !7"(%,2/"* -./#"++0%(* (3&4"#* -./#"++0%(* -./#"++0%(* !7"(%,2/"* !7"(%,2/"* 1%/2* 1%/2* (3&4"#* (3&4"#* 53,6%(* 53,6%(* !#"80)69"*&%8":* ;"("#6%(* !"#$%#&()"* ++"++&"(,*Publica;on  in  Science   Dona;on  of  Google-­‐Transla;onal  Medicine   scale  compute  space.   For  the  goal  of  promo;ng  democra;za;on  of  medicine…   Registra;on  star;ng  NOW…   sign  up  at:   34  
  34. 34. Open and Networked Approaches 4   PRIVACY   PORTABLE  LEGAL  CONSENT:   BARRIERS   John  Wilbanks  
  35. 35. 5  REWARDS     FOR  SHARING   Arch2POCM   An  approach  to  speed  our  basic   understanding  of  the  consequences  of   targe/ng  novel  high  risk  drivers   of  disease  states     Clinical  valida/on  (Ph  IIa)  of     pioneer  targets  
  36. 36. The Current R&D Ecosystem Is In Need of a New Approach to Drug Development•  $200B per year in biomedical and drug discovery R&D•  Only a handful of new medicines are approved each year•  Productivity in steady decline since 1950•  >90% of novel drugs entering clinical trials fail, and negative POC information is not shared•  Significant pharma revenues going off patent in next 5 years•  >30,000 pharma employees laid off from downsizing in each of last four years•  90% of 2013 prescriptions will be for generic drugs 37  
  37. 37. Issues With Drug Discovery1.  The greatest attrition is at clinical proof-of-concept – once a “target” is linked to a disease in the clinic, the risk of failure is far lower2.  Most novel targets are pursued by multiple companies in parallel (and most fail at clinical POC)3.  The complete data from failed trials are rarely, if ever, released to the public 38  
  38. 38. Open access research tools drive science 39  
  39. 39. SGC: Open Access Chemical Biology a great success•  PPP:      -­‐  GSK,  Pfizer,  Novar/s,  Lilly,  Abbon,  Takeda    -­‐  Genome  Canada,  Ontario,  CIHR,  Wellcome  Trust  •  Based  in  Universi/es  of  Toronto  and  Oxford  •  200  scien/sts  •  Academic  network  of  more  than  250  labs  •  Generate  freely  available  reagents  (proteins,  assays,  structures,  inhibitors,   an/bodies)  for  novel,  human,  therapeu/cally  relevant  proteins  •  Give  these  to  academic  collaborators  to  dissect  pathways  and  disease   networks,  and  thereby  discover  new  targets  for  drug  discovery   40  
  40. 40. Some SGC Achievements•  Structural  impact   –  SGC  contributed  ~25%  of  global  output  of  human  structures  annually     –  SGC  contributes  >40%  of  global  output  of  human  parasite  structures  annually  •  High  quality  science  (some  publica/ons  from  2011)          Vedadi  et  al,  Nature  Chem  Biol,  in  press  (2011);  Evans  et  al,  Nature  Gene;cs  in   press  (2011);  Norman  et  al  Science  Transl  Med.  3(88):88mr1  (2011);  Kochan  G   et  al  PNAS  108:7745  (2011);  Clasquin  MF  et  al  Cell  145:969  (2011);  Colwill  et  al,   Nature  Methods  8:551  (2011);  Ceccarelli  et  al,  Cell  145:1075  (2011;   Strushkevich  et  al,  PNAS  108:10139  (2011);  Bian  et  al  EMBO  J  in  press  (2011)   Norman  et  al  Science  Trans.  Med.  3:76cm10  (2011);  Xu  et  al  Nature  Comm.  2:   art.  no.  227  (2011);  Edwards  et  al  Nature  470:163  (2011);  Fairman  et  al  Nature   Struct,  and  Mol.  Biol.  18:316  (2011);  Adams-­‐Cioaba  et  al,  Nature  Comm.  2  (1)   (2011);  Carr  et  al  EMBO  J  30:317  (2011);  Deutsch  et  al    Cell  144:566  (2011);   Filippakopoulos  et  al  Cell,  in  press;  Nature  Chem.  Biol.  in  press,  Nature  in  press   41  
  41. 41. Open access to the clinic? 42  
  42. 42. Most Novel Targets Fail at Clinical POC Hit/ Target HTS Probe/ LO Clinical Tox./ Phase Phase ID/ candidate Lead Pharmacy I IIa/ bDiscovery ID ID 50% 10% 30% 30% 90+% this is killing our industry …we can generate “safe” molecules, but they are not developable in chosen patient group 43  
  43. 43. This Failure Is Repeated, Many Times Hit/ Target HTS Probe/ LO Clinical Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ bDiscovery Hit/ ID Target ID Clinical Probe/ Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ bDiscovery Hit/ ID 30% 30% 90+% Target ID Clinical Probe/ Toxicology/ Phase Phase ID/ Hit/ candidate Target Lead Clinical Pharmacy I IIa/ bDiscovery Probe/ ID Toxicology/ Phase Phase ID/ ID candidate 30% 30% 90+% Lead Pharmacy I IIa/ bDiscovery Hit/ ID Target ID Clinical Probe/ Toxicology/ 30% Phase 30% Phase 90+% ID/ candidate Lead Pharmacy I IIa/ bDiscovery Hit/ ID Target ID Clinical 30% 30% 90+% Probe/ Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ bDiscovery Hit/ ID 30% 30% 90+% Target ID Clinical Probe/ Toxicology/ Phase Phase ID/ candidate Lead Pharmacy I IIa/ bDiscovery ID ID 30% 30% 90+% 50% 10% 30% 30% 90+% …and outcomes are not shared 44  
  44. 44. A Possible Soution:Arch2POCM An Open Access Clinical Validation PPP•  PPP  to  clinically  validate  (Ph  IIa)  pioneer  targets  •  Pharma,  public,  academia,  regulators  and  pa/ent  groups  are  ac/ve   par/cipants  •  Cul/vate  a  common  stream  of  knowledge   –  Avoid  patents     –  Place  all  data  into  the  public  domain   –  Crowdsource  the  PPP’s  druglike  compounds  •  In  –validated  targets  are  iden/fied  before  pharma  makes  a  substan/al   proprietary  investment   –  Reduces  the  number  of  redundant  trials  on  bad  targets     –  Reduces  safety  concerns  •  Validated  targets  are  de-­‐risked  for  pharma  investment   –  Pharma  can  ini/ate  proprietary  effort  when  risks  are  balanced  with  returns   –  PPP  pharma  members  can  acquire  Arch2POCM  IND  for  validated  targets  and  benefit  from   shorter  development  /meline  and  data  exclusivity  for  sales   45  
  45. 45. Arch2POCM: Scale and Scope•  Original Goal: –  Initiate 2 programs. One for Oncology/Epigenetics/Immunology. One for Neuroscience/Schizophrenia/Autism. –  Both programs will have 8 drug discovery projects (targets) –  By Year 5, 30% of projects will have started Ph 1 and 20% will have completed Ph Iia –  $200-250M over five years is projected as necessary to advance up to 8 drug discovery projects within each of the two therapeutic programs –  By investing $1.6 M annually into one or both of Arch2POCM’s selected disease areas, partnered pharmaceutical companies: 1.  obtain a vote on Arch2POCM target selection 2.  gain real time data access to Arch2POCM’s 16 drug discovery projects 3.  have the strategic opportunity to expand their overall portfolio•  Revised Goal: –  Initiate 1-2 projects, (1-2 novel target mechanisms), as pilots to assess Arch2POCM principle of sharing data and reagents till clinical validation –  In either Oncology or Neuroscience –  Specific target mechanisms to be determined by funders’ interest –  Interested funders include pharma, public research foundations and venture philanthropists 46  
  46. 46. Epigenetics: Exciting Science and Also A New Area For Drug Discovery Lysine DNA Histone Modification Write Read Erase Acetyl HAT Bromo HDAC Methyl HMT MBT DeMethyl 47  
  47. 47. The Case For Epigenetics/Chromatin Biology1.  There are epigenetic oncology drugs on the market (HDACs)2.  A growing number of links to oncology, notably many genetic links (i.e. fusion proteins, somatic mutations)3.  A pioneer area: More than 400 targets amenable to small molecule intervention - most of which only recently shown to be “druggable”, and only a few of which are under active investigation4.  Open access, early-stage science is developing quickly – significant collaborative efforts (e.g. SGC, NIH) to generate proteins, structures, assays and chemical starting points 48  
  48. 48. Poten;al  Targets-­‐  Bromodomain  Family     Evidence  that  this  target  plays  an  important   Maturity  of  the   Posi;ve   Data  showing   Mouse  knockout  model    (MGI)   role  in  tumors  (in  vitro,  in  vivo,  animal   program   evidence  of   a  failed  result   model  data)   the   of  the   compound   compound  for   playing  a  role   the  given   in  the  given   disease   disease   Expression  correlates  with  development  of   potent,   NA   NA   Homozygotes  for  a  null  allele  die  in  utero  before  SMARCA4   prostate  cancer     selec/ve,  cell   implanta/on.  Embryos  heterozygous  for  this  null   BUT  SMARCA4  in  general  acts  as  tumor   ac/ve   allele  and  an  ENU-­‐induced  allele  show  impaired   suppressor  and  is  necessary  for  genome   compound   defini/ve  erythropoiesis,  anemia  and  lethality   stability;  targeted  knockdown  of  SMARCA4   iden/fied   during  organogenesis.  Heterozygotes  show   poten/ates  lung  cancer  development;     cyanosis  and  cardiovascular  defects  and  are  pre-­‐ disposed  to  breast  tumors   Gastric  cancer;  mutated  in  CLL;  deple/on  of   potent,   NA   NA   Mice  homozygous  for  a  targeted  muta/on  in  this  SMARCA2A   BRM  causes  accelerated  progression  to  the   selec/ve,  cell   gene  may  exhibit  infer/lity  and  a  slightly  increased   differen/a/on  phenotype   ac/ve   body  weight  in  some  gene/c  backgrounds.   BUT  targeted  dele/on  is  causa/ve  for  the   compound   development  of  prosta/c  hyperplasia  in  mice   iden/fied   Transloca/on  of  CBP  with  MOZ,  monocy/c   potent,   NA   NA   Homozygotes  for  null  or  altered  alleles  die  around  CBP   leukemia  zinc  finger  protein    cause    acute   selec/ve,  cell   midgesta/on  with  defects  in  hemopoiesis,  blood   myeloid  leukemia  ;  other  transloca/ons   ac/ve   vessel  forma/on,  and  neural  tube  closure.   involve  MLL  (HRX);  Mutated  in  ALL  BUT  CBP   compound   Heterozygotes  may  exhibit  skeletal,  cardiac,  and   has  also  been    proposed  as  a  classical  tumor   iden/fied   hematopoie/c  defects,  retarded  growth,  and   suppressor     hematologic  tumors.   Correlated  with  survival  of  high-­‐grade   Weak  hits   NA   NA   NA  ATAD2   osteosarcoma  pa/ents  ayer  chemo-­‐therapy;   required  for  breast  cancer  cell  prolifera/on  ;   differen/ally  expressed  in  NSCLC     Transloca/ons  produce  BRD4-­‐NUT  fusion   JQ1   JQ1  in  BRD-­‐ NA   Homozygotes  for  a  gene-­‐trap  null  muta/on  die  BRD4   oncogene  causing  midline  carcinoma   NUT  fusion   soon  ayer  implanta/on.  Heterozygotes  exhibit   and  MLL   impaired  pre-­‐  and  postnatal  growth,  head   malforma/ons,  lack  of  subcutaneous  fat,   cataracts,  and  abnormal  liver  cells.       In  transgenic  mice,  cons/tu/ve  lymphoid   JQ1   JQ1  in  BRD-­‐ NA   Mice  homozygous  for  a  null  muta/on  display  BRD2   expression  of  Brd2  causes  a  malignancy  most   NUT  fusion   embryonic  lethality  during  organogenesis  with   similar  to  human  diffuse  large  B  cell   and  MLL   decreased  embryo  size,  decreased  cell   lymphoma   prolifera/on,  a  delay  in  the  cell  cycle,  and   increased  cell  death.  Heterozygous  mice  also   display  decreased  cell  prolifera/on.  
  49. 49. Poten;al  Targets-­‐  Demethylases   Evidence  that  this  target  plays  an  important  role  in   Maturity  of   Posi;ve   Data  showing  a   Mouse  model    (MGI)   tumors  (in  vitro,  in  vivo,  animal  model  data)   the  program   evidence  of  the   failed  result  of   compound   the  compound   playing  a  role  in   for  the  given   the  given   disease   disease   Upregulated  in  prostate  cancer;  expression  is  higher   potent,   NA;  inhibits     NA   Mice  homozygous  for  a  knock-­‐out  allele  JMJD3   in  metasta/c  prostate  cancer   selec/ve,   TNF-­‐alpha   exhibit  perinatal  lethality  associated  with   BUT  JMJD3  contributes  to  the  ac/va/on  of  the   cell  ac/ve   produc/on  in   thick  alveolar  septum  and  absences  of  air   INK4A-­‐ARF  tumor  suppressor  locus  in  response  to   compound   macrophages  of   space  in  the  lungs.  Bone  marrow  chimera   oncogene  -­‐  and  stress-­‐induced  senescence.     iden/fied   RA  pa/ents   mice  derived  from  fetal  liver  cells  exhibit   impaired  eosinophil  recruitment  and   abnormal  response  to  helminth  infec/on.   High  levels  in  breast  cancer  cell  lines,  strong   No  progress   NA   NA   NA  JARID1B   expression  in  the  invasive  but  not  in  the  benign   components  of  primary  breast  carcinomas.  BUT   tumor  suppressor  in  melanoma  cells  
  50. 50. Poten;al  Targets-­‐  Histone  Methyltransferases   Evidence  that  this  target  plays  an  important  role  in   Maturity  of  the   Posi;ve  evidence   Data  showing  a   tumors  (in  vitro,  in  vivo,  animal  model  data)   program   of  the  compound   failed  result  of  the   playing  a  role  in   compound  for  the   the  given  disease   given  disease   Recent  data  indicates  that  SETD8  deregulates  PCNA   Weak  inhibitors   NA   NA  SETD8   expression  by  degrada/on  accelerated  by  methyla/on  at   iden/fied  (8  microM)   K248.    Expression  levels  of  SETD8  and  PCNA  upregulated  in   in  chemistry   cancer  cells.    Cancer  Research  May  2012  Takawa  et  al.   op/miza/on.   EZH2  upregulated  in  cancer  cells.    Studies  on  mutants   potent,  selec/ve,  cell   NA   NA  EZH2   indicates  an  interes/ng  profile  where  both  wild-­‐type  and   ac/ve  compound   mutant  (Y641F)  are  required  for  malignant  phenotype.     iden/fied.       Sneeringer  et  al.  PNAS  2012.    Compounds  iden/fied  in  GSK   patents  WO  2011/140324  and  140315  and  WO  2012/005805   and  075080.   MMSET,  WHSC1,  NSD2  is  overexpressed  in  cancer  cells.     No  hits—currently   NA   NA  MMSET   Hudlebusch  et  al.  Clinical  Cancer  Res  2011   screening   Daigle  et  al.  Cancer  Cell  2011  elegantly  show  that  potent   potent,  selec/ve,  cell   Transgenic  mouse  DOT1L   DOT1L  inhibitors  kill  cells  containing  MLL  transloca/ons   ac/ve  compound   model  tumors   and  do  not  kill  cell  not  containing  the  transloca/ons   iden/fied.   shrunk  by  SC   dosing  of  inhibitor  
  51. 51. Program Activities Grid For Arch2POCMAc;vity     Arch2POCM  Loca;on/Inves;gator  (TBD)  Target  Structure  Compound  libraries  Assay  development  for  epigene/c  screens  and  biomarkers  HTP  screens  for  epigene/c  hits  Med  Chem  SAR  To  ID  Two  Suitable  Binding  Arch2POCM  Test  Compounds  Non-­‐GLP  scaleup  of  Arch2POCM  Test  Compounds  and  associated  analy/cs  Distribu/on  of  Arch2POCM  Test  Compounds  PK,  PD,  ADME,  Tox  Tes/ng  GMP  Manufacturing  of  Arch2POCM  Test  Compounds  GMP  Formula/on  GMP  Drug  Storage  and  Distribu/on  IND  Prepara/on  Support  Clinical  Assay  Development  and  Qualifica/on  Ph  I-­‐II  Clinical  Trials  Ph  I-­‐II  Database  Management  and  CSR  Produc/on   52  
  52. 52. Networked Approaches 2   1   REWARDS   USABLE   RECOGNITION   DATA   BioMedical Information Commons Patients/ Citizens Data Generators CURATED DATA Data 5   TOOLS/ 3   Analysts REWARDS   METHODS HOW  TO   FOR   RAW DISTRIBUTE   SHARING   DATA TASKS   ANALYZES/ MODELS Clinicians 4   PRIVACY   SYNAPSE Experimentalists BARRIERS