Stephen Friend CRUK-MD Anderson Cancer Workshop 2012-02-28
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Stephen Friend CRUK-MD Anderson Cancer Workshop 2012-02-28

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Stephen Friend, Feb 28, 2012. CRUK-MD Anderson Cancer Workshop, Houston, TX

Stephen Friend, Feb 28, 2012. CRUK-MD Anderson Cancer Workshop, Houston, TX

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    Stephen Friend CRUK-MD Anderson Cancer Workshop 2012-02-28 Stephen Friend CRUK-MD Anderson Cancer Workshop 2012-02-28 Presentation Transcript

    • Open Source pre-competitive drug discovery Moving beyond linear investigations Both of the science and of how we work Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ Amsterdam February 28, 2012
    • Partnering  &  Collabora/on-­‐So  what  has  been  possible?           All  pa&ents  now  >25,000  at  a  Cancer  Center  partnered  provide   consented  expression  on  their  pts  for  classifying  sub-­‐popula&ons    Combina&on  Therapies-­‐  each  at    Ph  I-­‐  joint  development  2  Pharma    Sharing  all  the  CT  Onc  Trial  imagining  files  among  2  Pharma    Link  Parma  with  an  “Ins&tute  for  Applied  Cancer  Center”        Share  genomic  data    on  25,000  samples    with  clinical  records  and   Expression  and  Exomes  among  three  Pharma  
    • Partnering  &  Collabora/on-­‐So  what  has  been  possible?           All  pa&ents  now  >25,000  at  a  Cancer  Center  partnered  provide   consented  expression  on  their  pts  for  classifying  sub-­‐popula&ons    2006      MoffiP  Cancer  Center-­‐  Merck      Combina&on  Therapies-­‐  each  at    Ph  I-­‐  joint  development  2  Pharma    2007    AZ  Merck  (Mek/Akt)    Sharing  all  the  CT  Onc  Trial  imagining  files  among  2  Pharma    2008    BMS  &  Merck    Link  Parma  with  an  ”  Ins&tute  for  Applied  Cancer  Center”    2008    Belfer-­‐  Merck        Share  genomic  data    on  25,000  samples    with  clinical  records  and   Expression  and  Exomes  among  three  Pharma    2010              Asian  Cancer  Research  Group  ACRG-­‐    Lilly  Merck  Pfizer  
    • So  what  is  the  problem?        Most  approved  therapies  were  assumed  to  be   monotherapies  for  diseases  represen&ng  homogenous   popula&ons    Our  exis&ng  disease  models  o]en  assume  pathway   knowledge  sufficient  to  infer  correct  therapies  
    • Familiar but Incomplete
    • Reality: Overlapping Pathways
    • what will it take to understand disease?                    DNA    RNA  PROTEIN  (dark  maCer)    MOVING  BEYOND  ALTERED  COMPONENT  LISTS  
    • DIVERSE  POWERFUL  USE  OF  MODELS  AND  NETWORKS  
    • List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
    • (Eric Schadt)
    • 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
    • Sage Bionetworks Collaborators  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca, Amgen, Roche  Foundations   Kauffman CHDI, Gates Foundation  Government   NIH, LSDF, NCI  Academic   Levy (Framingham)   Rosengren (Lund)   Krauss (CHORI)  Federation   Ideker, Califano, Nolan, Schadt 12
    • S MAP NEWRULES GOVERN PLAT FORM
    • S MAP NEWRULES GOVERN PLAT FORM
    • Why not share clinical /genomic data and model building within teams in 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
    • sage bionetworks synapse project Reduce, Reuse, Recycle
    • sage bionetworks synapse project Most of the People You Need to Work with Don’t Work with You
    • sage bionetworks synapse project My Other Computer is Cloudera Amazon Google
    • Sage Metagenomics Project Processed Data (S3)•  > 10k genomic and expression standardized datasets indexed in SCR•  Error detection, normalization in mG•  Access raw or processed data via download or API in downstream analysis•  Building towards open, continuous community curation
    • Sage Metagenomics using Amazon Simple Workflow Full case study at http://aws.amazon.com/swf/testimonials/swfsagebio/
    • Amazon SWF and Synapse•  Maintains state of analysis •  Hosts raw and processed data for•  Tracks step execution further reuse in public or private projects•  Logs workflow history •  Provides visibility into•  Dispatches work to Amazon or intermediate results and remote worker nodes algorithmic details•  Efficiently match job size to •  Allows programmatic access to hardware data; integration with R•  Provides error handling and •  Provides standard terminologies recovery for annotations •  Search across data sets
    • Synapse Roadmap•  Data Repository•  Projects and security Synapse Platform Functionality•  R integration •  Workflow templates•  Analysis provenance •  Social networking •  Publishing figures •  User-customized • Search •  Wiki & collaboration tools dashboards • Controlled Vocabularies •  Integrated management •  R Studio integration • Governance of restricted of cloud resources •  Curation tool integration data Internal Alpha Public Beta Testing Synapse 1.0 Synapse 1.5 Future Q1-2012 Q2-2012 Q3-2012 Q4-2012 Q1-2013 Q2-2013 Q3-2013 Q4-2013 • TCGA •  Predictive modeling •  TBD: Integrations with other •  METABRIC breast workflows visualization and analysis cancer challenge •  Automated processing of packages common genomics platforms•  40+ manually curated clinical studies•  8000 + GEO / Array Express datasets•  Clinical, genomic, compound sensitivity•  Bioconductor and custom R analysis Data / Analysis Capabilities
    • INTEROPERABILITYSYNAPSE   Genome Pattern CYTOSCAPE tranSMART I2B2 INTEROPERABILITY  
    • Five  Pilots  involving  Sage  Bionetworks   CTCAP   The  Federa/on   Portable  Legal  Consent   ORM S Sage  Congress  Project   MAP F PLAT NEW Arch2POCM   RULES GOVERN
    • 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•  Graphic  of  curated  to  qced  to  models  
    • The  Federa/on  
    • 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
    • (Nolan  and  Haussler)  
    • 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
    • For 11/12 compounds, the #1 predictive feature in an unbiasedanalysis corresponds to the known stratifier of sensitivity #2  CML  lineage   CML lineage #1  EGFR  mut   EGFR mut #1  EGFR  mut   EGFR mut #1  CML  lineage   #1  EGFR  mut   CML linage EGFR mut #1  ERBB2  expr   ERBB2 expr Can  the  approach  make  new   mut   #1  BRAF   discoveries?   BRAF mut #1  HGF  expr   HGF expr #2  NRAS  mut   NRAS mut BRAF mut #1  BRAF  mut   #3  KRAS  mut   KRAS mut #2  NRAS  mut   NRAS mut BRAF mut #1  BRAF  mut   #3  KRAS  mut   KRAS mut #2  NRAS  mut   NRAS mut BRAF mut #1  BRAF  mut   #2  TP53  mut   TP53 mut #3  CDKN2A  copy   CDKN2A copy #1  MDM2  expr   MDM2 expr 35  
    • Presentation outline1)  Predic&ng  drug  response   2)  Future  approaches:   3)  Standardized  from  cancer  cell  lines   network-­‐based  predictors   workflows  for  data   and  mul&-­‐task  learning   management,   Cancer  cell  line   versioning  and   encyclopedia   method  comparison  Molecular characterization Network  /  pathway  (1,000 cell lines) prior  informa&on   Currently   mRNA   copy number   somatic mutations (36 cancer-related genes) In progress   targeted exon sequencing Vaske,  et  al.     epigenetics   microRNA TCGA  /ICGC     lncRNA Transfer  Molecular characterization learning  (50 tumor types)   phospho-tyrosine kinase   metabolitesViability screens (500 cell   genomicslines, 24 compounds)   transcriptomicsSmall molecule screen   epigenetics Predic&ve   Clinical data model   Vaske,  et  al.  
    • 1)  Data  management  APIs  to  load  standaridzed  objects,  e.g.   R  ExpressionSets  (MaP  Furia):            ccleFeatureData  <-­‐  getEn/ty(ccleFeatureDataId)            ccleResponseData  <-­‐  getEn/ty(ccleResponseDataId)   2)      tAutomated,  standardized  workflows  for  cura&on  and  QC  of   large-­‐scale  datasets  (-­‐  getEn/ty(tcgaFeatureDataId)           cgaFeatureData  < Brig  Mecham).            tcgaResponseData  <-­‐  getEn/ty(tcgaResponseDataId)   A.  TCGA:  Automated  cloud-­‐based  processing.   B. GEO  /  Array  Expression:  Normaliza/on  workflows,  cura/on   of  phenotype  using  standard  ontologies.   C. Addi/onal  studies  with  gene/c  and  phenotypic  data  in   Sage  repository  (e.g.  CCLE  and  Sanger  cell  line  datasets)   Observed Data!=! Systematic Variation! +! Random Variation! =! +! +! 3)  Pluggable  API  to  implement  predic&ve  modeling   algorithms.  Normalization: Remove the influence of adjustment variables on data...! A)  Support  for  all  commonly  used  machine  learning  methods  4)  Sta&s&cal  performance  assessment  across   (for  automated  benchmarking  against  new  methods)   models.   and  mustomPredict()  methods.   B)  Pluggable  custom  =! ethods  as  R  classes  implemen/ng   customTrain()   c +!custom  model  1   be  arbitrarily  complex  (e.g.  pathway  and  other   A)  Can   custom  model  2   custom  model  N   priors)   5)  Output  of  candidate  biomarkers  aoops.   B)  Support  for  paralleliza/on  in  for  each  lnd   feature  evalua&on  (e.g.  GSEA,  pathway   analysis)  custom  model  1   custom  model  2   custom  model  N   6)  Experimental  follow-­‐up  on  top  predic&ons  (TBD)        E.g.  for  cell  lines:  medium  throughput  suppressor  /  enhancer   screens  of  drug  sensi/vity  for  knockdown  /  overexpression  of   predicted  biomarkers.  
    • Portable  Legal  Consent   (Ac/va/ng  Pa/ents)   John  Wilbanks  
    • Sage  Congress  Project   April  20  2012   RealNames  Parkinson’s  Project  Revisi/ng  Breast  Cancer  Prognosis   Fanconi’s  Anemia   (Responders  Compe//ons-­‐  IBM-­‐DREAM)  
    • THE QUICK WIN, FAST FAIL DRUG DEVELOPMENT PARADIGM Test each scarce TRADITIONAL Preclinical molecule development Phase I thoroughly Phase II Phase III Scarcity of drug discovery $ $ $$ $$$$ PD Launch FHD FED CS •  Increase critical information content early to shift attrition to cheaper phase QUICK WIN, FAST FAIL •  Use savings from shifted attrition to re-invest in the R&D ‘sweet spot’ Preclinical development POC Confirmation, Higher p(TS) dose finding Commercialization Abundance of drug discovery PD Launch FHDSource: Nature Publishing Group CS R&D ‘sweet spot’ March 1, 2012 Confidential | © 2012 Third Rock Ventures PAGE 40
    • Arch2POCM  Restructuring  the  Precompe//ve   Space  for  Drug  Discovery   How  to  poten/ally  De-­‐Risk       High-­‐Risk  Therapeu/c  Areas  
    • Arch2POCM: Highlights A PPP To De-Risk Novel Targets That The Pharmaceutical Industry Can Then Use To Accelerate The Development of New and Effective Medicines•  The Arch2POCM will be a charitable Public Private Partnership (PPP) that will file no patents and whose scientific plan (including target selection) will be endorsed by its pharmaceutical, private and public funders•  Arch2POCM will de-risk novel targets by developing and using pairs of test compounds (two different chemotypes) that interact with the selected targets: the compounds will be developed through Phase IIb clinical trials to determine if the selected target plays a role in the biology of human disease•  Arch2POCM will work with and leverage patient groups and clinical CROs to enable patient recruitment, and with regulators to design novel studies and to validate novel biomarkers•  Arch2POCM will make its GMP test compounds available to academic groups and foundations so they can use them to perform clinical studies and publish on a multitude of additional indications•  Arch2POCM will release all reagents and data to the public at pre-defined stages in its drug development process. To ensure scientific quality, data and reagents will be released once they have been vetted by an independent scientific committee •  Arch2POCM will publish all negative POCM data immediately in order to reduce the number of ongoing redundant proprietary studies (in pharma, biotech and academia) on an invalidated target and thereby –  minimize unnecessary patient exposure –  provide significant economic savings for the pharmaceutical industry•  In the rare instance in which a molecule achieves positive POCM, Arch2POCM will ensure that the compound has the ability to reach the market by arranging for exclusive access to the proprietary IND database for the molecule 42
    • Arch2POCM: scale and scope•  Proposed Goal: Initiate 2 programs. One for Oncology/Epigenetics/ Immunology. One for Neuroscience/Schizophrenia/Autism. Both programs will have 6-8 drug discovery projects (targets) - ramped up over a period of 2 years –  It is envisioned that Arch2POCM’s funding partners will select targets that are judged as slightly too risky to be pursued at the top of pharma’s portfolio, but that have significant scientific potential that could benefit from Arch2POCM’s crowdsourcing effort•  These will be executed over a period of 5 years making a total of 16 drug discovery projects –  Projected pipeline attrition by Year 5 (assuming 12 targets loaded in early discovery) •  30% will enter Phase 1 •  20% will deliver Ph 2 POCM data 43
    • Arch2POCM: proposed funding strategy–  Arch2POCM funding will come from a combination of public funding from governments and private sector funding from pharmaceutical and biotechnology companies and from private philanthropists–  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’s12- 16 drug discovery projects 3.  have the strategic opportunity to expand their overall portfolio 44
    • Entry points for Arch2POCM programs: Two compounds (different chemotypes) will be advanced per target Pioneer targets - genomic/ genetic - disease networks - academic partners - private partners - SAGE, SGC, Lead Lead Preclinical Phase I Phase II identification optimisation Assay in vitro probe Lead Clinical Phase I Phase II candidate asset assetStage-gate 1: Early Discovery and Stage-gate 2: Pharma’s re-PCC Compounds (75%) purposed clinical assets (25%) 45
    • Pipeline flow for Arch2POCMFive Year Objective: Initiate ≈ 8 drug discovery projects with 6 entering in Early Discovery, one entering inpre-clinical and one entering in PH IMonths → 0-6 7-12 13-18 19-24 25-30 31-36 37-42 43-48 49-54 55-60 Early discovery (2) Pre-clinical Ph 11.3 Ph 2Year #1 Pre-clinical (1) Ph 1 Ph 2Arch2POCMTarget Load 11 Early discovery (4) Pre-clinical Ph 1 Year #2 Ph 1 (1) Ph 2 Arch2POCM Target Load 1 Early discovery (45% PTRS) Arch2POCM Snapshot at Year 5 Pre-clinical (70% PTRS) Targets  Loaded   8   Ph I (65% PTRS) Projected  INDs  filed   3-­‐4   Ph II (10% PTRS) Ph  1  or  2  Trials  In  Progress   2   Projected  Complete  Ph  2  (POCM)  Data   1   *PTRS = Probability of technical and regulatory success Sets   46
    • 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 47
    • The current epigenetics universe Domain Family Typical substrate class* Total Targets Histone Lysine Histone/Protein K/R(me)n/ (meCpG) 30   demethylase Bromodomain Histone/Protein K(ac) 57   R Tudor domain Histone Kme2/3 - Rme2s 59   O Chromodomain Histone/Protein K(me)3 34   Y A MBT repeat Histone K(me)3 9   L PHD finger Histone K(me)n 97   Acetyltransferase Histone/Protein K 17   Methyltransferase Histone/Protein K&R 60   PARP/ADPRT Histone/Protein R&E 17   MACRO Histone/Protein (p)-ADPribose 15   Histone deacetylases Histone/Protein KAc 11   395  Now known to be amenable to small molecule inhibition 48
    • Why is Arch2POCM a “smart bet” for Pharma investment?Arch2POCM:  an  external  epigene/c  think  tank  from  which  Pharma  can  load  the  most  likely  to  succeed  targets  as  proprietary  programs  or  leverage  Arch2POCM  results  for  its  other  internal  efforts  •  A  front  row  seat  on  the  progression  of  6-­‐  8  epigene/c  targets  means  that:   •  Pharma  can  select  the  epigene/c  targets  that  best  compliment  their  internal  pormolio  and  for   which  there  is  the  greatest  interest   •  Pharma  can  structure  Arch2POCM’s  projects  so  that  key  objec/ves  line  up  with  internal  go/no-­‐ go  decisions   •  Pharma  can  use  Arch2POCM  data  to  trigger  its  internal  level  of  investment  on  a  par/cular   target   •  Pharma  can  use  Arch2POCM  resources  to  enrich  their  internal  epigene/cs  effort:  ac/ve   chemotypes,  assays,  pre-­‐clinical  models,  biomarkers,  gene/c  and  phenotypic  data  for  pa/ent   stra/fica/on,  rela/onships  to  epigene/c  experts  •   Pharma  can  use  Arch2POCM’s  lead  compound  chemotypes  to:   •   inform  their  proprietary  medicinal  chemistry  efforts  on  the  target   •   iden/fy  chemical  scaffolds  that  impact  epigene/c  pathways:  a  proprietary  combina/on   therapy  opportunity  •   Toxicity  screening  of  Arch2POCM  compounds  with  FDA  tools  can  be  used  to  guide   internal  proprietary  chemistry  efforts  in  oncology,  inflamma/on  and  beyond    •  Arch2POCM’s  crowd  of  scien/sts  and  clinicians  provides  its  Pharma  partners  with   parallel  shots  on  goal  at  the  best  context  for  Arch2POCM’s  compounds/targets   49
    • How will Arch2POCM provide “line of sight” to new medicines? Arch2POCM will partner with scientists, clinicians and CROs that: •  use “Omics” approaches to construct predictive models of disease networks (genomic, proteomic, signaling and metabolic) •  have strategies available to identify those disease network gene(s) which when perturbed, impact the overall functioning of the network •  already have epigenetic assays in place to identify chemotype structures (from discovery and/or pharma’s re-purposed un-used clinical assets) that impact the target and disease-correlated molecular phenotypes •  already have biomarker tools available that can be tested for correlation to Arch2POCM’s targets •  already have access to patient data and/or patient groups to mine for genetic and phenotypic signatures that may represent best responders for Arch2POCM clinical trials 50
    • How will Arch2POCM provide “line of sight” to new medicines? •  Arch2POCM’s Ph II validation of high risk high opportunity targets focuses Pharma’s NME efforts •  Positive POCM data: De-risked validated targets for Pharma development •  Negative POCM data: public release of this data minimizes the amount of time and money that Pharma and the industry place on failed targets •  Arch2POCM’s clinical candidate compounds provide Pharma with multiple paths to new medicines •  Arch2POCM compounds that achieve POCM can be advanced into Ph 3 by Arch2POCM Members •  The purchaser of Arch2POCM’s IND database obtains a significant time advantage over competitors to generate Phase III data and proceed to market •  NMEs that derive from Arch2POCM will launch with database exclusivity protections: 5-8 years to garner a return on investment •  The crowd’s testing of Arch2POCM compounds may identify alternative/better contexts for agonizing/antagonizing the disease biology target •  indications •  patient stratification •  combination therapy options 51
    • Arch2POCM: current partnering status•  Pharmaceutical Funding Partners –  Three companies are considering a potential role as industry anchors for Arch2POCM –  Two companies have demonstrated interest in Arch2POCM and their company leadership wants to go to next step- awaiting face to face discussions to go over agreement•  Public Funding Partners –  Good progress is being made to obtain financial backing for Arch2POCM from public funders in a number of countries (Canada, United Kingdom and Sweden) for both epigenetics and for CNS –  Ontario Brain Institute, Canada has allocated $3M to the development of an autism clinical network that is committed to work with Arch2POCM•  Philanthropic Funding Partners: awaiting designation of anchor partners•  In kind partners –  GE Healthcare (imaging): lead diagnostics partner and willing to share its experimental oncology biomarkers –  Cancer Research UK: through some of its drug discovery and development resources considering participating in Arch2POCM through “in kind efforts”•  Academic partners –  Institutions that have indicated willingness to let their scientists participate without patent filing: UCSF, Massachusetts General Hospital, University of North Carolina, University of Toronto, Oxford University, Karolinska Institute –  Academic community of epigenetic experts/resources already identified•  Regulatory partners: Because the objective of the Arch2POCM PPP is to probe and elucidate disease biology as opposed to develop new proprietary products, FDA and EMEA are ready to play an active role (toxicity screens, and legacy clinical trial data)•  Patient group partners: leaders from Genetic Alliance, Inspire2Live and the Love Avon Army of Women are actively engaged 52
    • STRATEGIC INFLECTION: FORCES AFFECTING A BUSINESS Society’s Needs Customers Academia Businesses Government Suppliers New Competitors New TechnologiesMDAndersonCC02272012 Confidential | © 2012 Third Rock Ventures PAGE 53
    • Networking  Disease  Model  Building