Stephen Friend IMPAKT Breast Cancer Conference 2011-05-05

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Stephen Friend May 5, 2011 IMPAKT Breast Cancer Conference Brussels, BE

Stephen Friend May 5, 2011 IMPAKT Breast Cancer Conference Brussels, BE

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  • 1. Making use of genomic data mountainsPreparing for the Delivery of Personalized Medicine Integrated Network Maps of disease Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ San Francisco IMPAKT BREAST CANCER CONFERENCE May 5, 2011
  • 2. Academia
  • 3. Biotech
  • 4. Industry
  • 5. Sage Bionetworks Non-Profit FoundationSharing data and Building integrative disease models
  • 6. embrace complexitydevelop better maps of diseasechange how we sharebroaden whom we engage
  • 7. The Merck/Moffitt Strategy: Direct patient selection from database matching molecular signatures to clinical trialsBRCA ness#" Select Target LOF Profiles stored at hospital DNA Damage PI3K Ras Re contact Trial design Clinical trial Portfolio Trial sig A Disease Trial sig B recurrence and trial eligibility Trial sig C •  Validation of molecular hypotheses •  Patient selection on available profiling data Biomarker Driven Branched Subpopulation based Trials- Problem is in getting enough samples
  • 8. Personalized Medicine 101:Capturing Single bases pair mutations = ID of responders
  • 9. Cancer Complexity: Overlapping of EGFR and Her2 Pathways
  • 10. One Dimensional Technology Slices ENVIRONMENT Building an Altered Component List # /0/ $ "/ 40, 0 ! ( * # )5 2 + BRAIN HEART HEART E ENVIRONMENT GI TRACT TRACT 10) / 40, 24+ )5 2 / KIDNEYENVIRONMENT .)% 04 / 40, 4& -+ )5 2 ) IMMUNE SYSTEM SYSTEM YS Y VAS VASCULAT VASCULATURE A ATURE AT 4 / + + % / 40, 2 32 / - )5 2 % 10 4 ENVIRONMENT
  • 11. Familiar but Incomplete
  • 13. “Data Intensive Science” - Fourth Scientific Paradigm Equipment capable of generating massive amounts of data IT Interoperability Open Information System Evolving Models hosted in a Compute Space- Knowledge expert
  • 15. “Data Intensive Science”- “Fourth Scientific Paradigm”For building: “Better Maps of Human Disease” Equipment capable of generating massive amounts of data IT Interoperability Open Information System Evolving Models hosted in a Compute Space- Knowledge Expert
  • 16. It is now possible to carry out comprehensive monitoring of many traits at the population levelMonitor disease and molecular traits in populations Putative causal gene Disease trait
  • 17. The "Rosetta Integrative Genomics Experiment#Generation, assembly, and : integration of data to build models that predict clinical outcome Merck Inc. Co. 5 Year Program Based at Rosetta Driven by Eric Schadt Total Resources >$150M•  Generate data need to build•  bionetworks•  Assemble other available data useful for building networks•  Integrate and build models•  Test predictions•  Develop treatments•  Design Predictive Markers
  • 18. 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 29 Integrated# " Genetics Approaches
  • 19. Integration of Genotypic, Gene Expression & Trait Data Schadt et al. Nature Genetics 37: 710 (2005) Millstein et al. BMC Genetics 10: 23 (2009) Causal Inference “Global Coherent Datasets” •  population based •  100s-1000s individuals Chen et al. Nature 452:429 (2008) Zhu et al. Cytogenet Genome Res. 105:363 (2004) Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)
  • 20. Gene Co-expression Network AnalysisWeighted Gene Network Analysis Novel gene network causal for D&O   >140 citations (Nature, 2008)  >3400 full-text downloads 31 Novel pathways and gene targets Novel oncogene in brain cancer (PNAS, 2006) in Atherosclerosis (PNAS, 2006) ASPM ATF4 SREBP UPR XBP1
  • 21. 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
  • 22. Extensive Publications now Substantiating Scientific Approach Probabilistic Causal Bionetwork Models• >60 Publications from Rosetta Genetics Group (~30 scientists) over 5 years including high profile papers in PLoS Nature and Nature GeneticsMetabolic "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, etcCVD "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.GenomeBone "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.
  • 23. The transcriptional network for mesenchymal transformation of brain tumoursMaria Stella Carro1*{, Wei Keat Lim2,3*{, Mariano Javier Alvarez3,4*, Robert J. Bollo8, Xudong Zhao1, Evan Y. Snyder9, Erik P. Sulman10, Sandrine L. Anne1{, Fiona Doetsch5, Howard Colman11, Anna Lasorella1,5,6, Ken Aldape12, Andrea Califano1,2,3,4 & Antonio Iavarone1,5,7 NATURE 463:318, 21 JANUARY 2010
  • 24. List of Influential Papers in Network Modeling   50 network papers 
  • 25. (Eric Schadt)
  • 26. What s needed to engage “data intensive” science for disease maps?•  Three critical components: –  Big databases, organized (connected) to facilitate integration and model building 1) Complex DB •  GE Health, IBM, Microsoft, Google, and so on EMR Results dbGAP KeggGO,etc , . GEO –  Data integration and construction of predictive models •  Computational, math/stat, high-performance computing, and biological expertise •  Significant high-performance computing resources 2) –  Tools and educational resources to translate complex material to a hierarchy of 3) of “users”and ways to cite model-publish
  • 27. Recognition that the benefits of bionetwork based molecularmodels of diseases are powerful but that they requiresignificant resourcesAppreciation that it will require decades of evolvingrepresentations as real complexity emerges and needs to beintegrated with therapeutic interventions
  • 28. 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 Repository Commons Pilots Discovery Platform
  • 29. Sage Bionetworks Strategy: Integrate with Communities of Interest Map Users- Disease Map and Tool Users- ( Scientists, Industry, Foundations, Regulators...) Platform Builders – Sage Platform and Infrastructure Builders- ( Academic Biotech and Industry IT Partners...) Barrier Breakers- Data Sharing Barrier Breakers- (Patients Advocates, Governance M APS and Policy Makers,  Funders...) FOR M Data Generators- PLAT NEW Data Tool and Disease Map Generators- (Global coherent data sets, Cytoscape, REPOSITORY Clinical Trialists, Industrial Trialists, CROs…) Commons Pilots- Data Sharing Commons Pilots- (Federation, CCSB, Inspire2Live....)
  • 30. Sage Bionetworks Functional Organization Platform Commons Research Cancer Neurological Disease Metabolic Disease Curation/Annotation Building Data Disease Repository Maps CTCAP Public Data Pfizer Merck Data Outposts Merck TCGA/ICGC Federation Takeda CCSB Astra Zeneca CHDI Commons Gates NIH Pilots LSDF-WPP Inspire2Live Hosting Data POC Hosting Tools Bayesian Models Co-expression Models Hosting Models Discovery Tools & Platform Methods KDA/GSVA LSDF 41
  • 31. Sage Bionetworks Collaborators  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca, Amgen  Foundations   CHDI, Gates Foundation  Government   NIH, LSDF  Academic   Levy (Framingham)   Rosengren (Lund)   Krauss (CHORI)  Federation   Ideker, Califarno, Butte, Schadt 42
  • 32. Building Integrated Models 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 43 “Integrated” Genetics Approaches
  • 33. Sage Bionetworks 22 publications in last year
  • 34. Bin ZhangIntegration of Multiple Networks for Jun ZhuPathway and Target Identification CNV Data Gene Expression Clinical Traits Co-Expression Bayesian Network Network Integration of Coexp. & Bayesian Networks 45 Key Driver Analysis 45
  • 35. Bin ZhangKey Driver Analysis Jun Zhu Justin Guinney 46
  • 36. Justin GuinneyGene Set Variation Analysis (GSVA) Sonja Haenzelmann ! *#$ %%"! ! ! ! % %! $! !%&+ %& & % ! ! Meta-pathways * * # %$ * "!%&$ & &$ * % # * ! & & % ( & "! " & $ ! " ) ! % & ! %&"$ % "$ % $" , $" ! % % % ! ! ! % % % ! % &% % !% % % Pathway CNV Cross-tissue Pathway Clustering Pathways 47
  • 37. Bin ZhangModel of Breast Cancer: Co-expression Xudong Dai Jun Zhu A) Miller 159 samples B) Christos 189 samplesNKI: N Engl J Med. 2002 Dec 19;347(25):1999.Wang: Lancet. 2005 Feb 19-25;365(9460):671.Miller: Breast Cancer Res. 2005;7(6):R953.Christos: J Natl Cancer Inst. 2006 15;98(4):262. C) NKI 295 samples E) Super modules Cell cycle Pre-mRNA ECM D) Wang 286 samples Blood vessel Immune response 48 Zhang B et al., Towards a global picture of breast cancer (manuscript).
  • 38. Bin ZhangModel of Breast Cancer: Integration Xudong Dai Jun Zhu Conserved Super-modules = predictive Breast Cancer Bayesian Network mRNA proc. Chromatin of survival Extract gene:gene relationships for selected super-modules from BN and define Key Drivers Pathways & Regulators (Key drivers=yellow; key drivers validated in siRNA screen=green) Cell Cycle (Blue) Chromatin Modification (Black) Pre-mRNA proc. (Brown) mRNA proc. (red) 49 Zhang B et al., Key Driver Analysis in Gene Networks (manuscript)
  • 39. Bin ZhangModel of Breast Cancer: Mining Xudong Dai Jun Zhu Co-expression sub-networks predict survival; KDA identifies drivers Co-expression modules Map to Bayesian Define Key Drivers correlate with survival Network 50
  • 40. Brig MechamValidating Prostate Cancer Models Xudong Dai Pete Nelson Rich Klingoffer classification Gene Expression Data on >1000 prostate cancer samples (GEO) CNV Gene Expression & CNV Data Data ~200 prostate cancers Gene Expression (Taylor et al) Clinical Traits Co-Expression Bayesian Network Network Integration of Coexp. & Gene Expression & CNV Bayesian Networks 51 Data ~120 rapid autopsy Mets (Nelson) Key Driver Analysis Integrated network analysis Gene Expression & CNV Data ~30 prostate Key Drivers Matched to xenografts Xenografts for validations (Nelson) with Presage Technology siRNA Screen Data (Nelson) 51
  • 41. Developing predictive models of genotype specific sensitivityto Perturbations- Margolin Predictive model
  • 42. Developing predictive models of genotype specific sensitivity to Perturbations- MargolinMarkov random field feature priors identify pathway-level alterations conferring compound sensitivity Transfer learning allows information flow between related prediction tasks
  • 43. Examples: The Sage Non-Responder Project in Cancer •  To identify Non-Responders to approved drug regimens so Purpose: we can improve outcomes, spare patients unnecessary toxicities from treatments that have no benefit to them, and reduce healthcare costs Leadership: •  Co-Chairs Stephen Friend, Todd Golub, Charles Sawyers & Rich Schilsky Initial •  AML (at first relapse) Studies: •  Non-Small Cell Lung Cancer •  Ovarian Cancer (at first relapse) •  Breast Cancer •  Renal Cell •  Multiple MyelomaSage Bionetworks • Non-Responder Project
  • 44. 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.
  • 45. CTCAP Workstreams Uncurated GCD Curated GCD •  Single common identifier to link datatypes •  Gender mismatches removed Public Sage Domain GCDs Curated & QC! GCD d  Curated GCD •  Gene expression data corrected for batch Uncurated effects, etc GCD  Curated & QC’d GCDCollaborators Database GCDs (Sage)  Network Models •  Public • Collaboration •  Internal Co- Private expressio Domain n Public Databases GCDs Network  dbGAP Analysis Integrate d Network Bayesian Analysis Network Analysis
  • 46. Examples: The Sage Federation •  Founding Lab Groups –  Seattle- Sage Bionetworks –  New York- Columbia: Andrea Califano –  Palo Alto- Stanford: Atul Butte –  San Diego- UCSD: Trey Ideker –  San Francisco: UCSF/Sage: Eric Schadt •  Initial Projects –  Aging –  Diabetes –  Warburg •  Goals: Share all datasets, tools, models Develop interoperability for human data
  • 47. Warburg Effect Studied by the Federation s Genome-wide Network and Modeling Approach Warburg effect: the association of aerobic glycolysis, an inefficient way for ATP generation, with cancer cell and their progression. Linked with rapidly dividing cells. Two Key Questions: 1.  Are cancer cells genetically decoupled from the altered metabolism that is seen in rapidly dividing cells? 2.  Is there evidence that cancer outcomes are associated with altered metabolic circuits?
  • 48. Federation! Genome-wide Network and s Modeling ApproachCalifano group at Columbia Sage Bionetworks Butte group at Stanford
  • 49. Characterizing Pattern of Glycolysis Genes in both Tumor and Normal Tissues of the Matched OriginAndy Beck! approach (Butte Lab) s Reannotated Expression Database from GEO and other depositary Chen R.. et al (2007) Nature Method 4:879 Thirty-three glycolysis Query expression of 33 genesgenes from KEGG Pathway from 11 tumor and normal and literatures tissues From matched origin Frequency of 27 glycolysis genes differentially expressed in normal and tumor profiles among the 11 selected tissues are compared
  • 50. Deriving Master Regulators from Transcription FactorsRegulatory Networks Glycolysis & Glycogenesis Metabolism Pathway
  • 51. Inferring Prostate Cancer Regulatory Modules for Glycolysis &Glycogenesis Metabolism Pathway Sage bionetworks approach Prostate cancer global coherent data set (GSE21032) Taylor BS. et al (2010) Cancer Cell 18(1):11-22 Integrated Bayesian Approach Zhu J. et al (2008) Nature Genetics 40(7): 854-61 Glycolysis and Inferred Transcriptional Glycogenesis Metablism Regulatory Network in Prostate Gene Set (GGMSE) Cancer Cox Proportional-Hazards Prostate Cancer Regulatory Regression model based on Modules for GGMSE and Other individual gene for recurrence free Metabolism Pathways survivalDuarte N. et al (2006) PNAS 107(6):1777-1782 Metabolism pathways with regulatory modules enriched by poor prognosis genes for prostate cancer
  • 52. Genes Associated with Poor Prognosis are disproportionallyfound among the networks regulating the "glycolysis# Genes P-Value<0.005 Size of the node proportional to -log10 P value for recurrence free survival. Inferred regulatory module for GGMSE Inferred regulatory module for Oxidative Phosphorylation and Sphingolipid >5 fold enrichment of recurrence free prognostic genes with Metabolism genes the Glycolysis BN module than random selection (p<1e-100)
  • 53. THE FEDERATIONButte Califano Friend Ideker Schadt vs
  • 54. Federated Aging Project : Combining analysis + narrative =Sweave Vignette Sage Lab R code + PDF(plots + text + code snippets) narrative HTML Data objectsCalifano Lab Ideker Lab Submitted PaperShared Data JIRA: Source code repository & wikiRepository
  • 55. Evolution of a Software Project
  • 56. Evolution of a Biology Project
  • 57. Potential Supporting TechnologiesAddama Taverna tranSMART
  • 58. SYNAPSE: Platform Node for Modelling
  • 60.
  • 62. We still consider much clinical research as if we were"hunter gathers# not sharing soon enough - .
  • 63. Assumption that genetic alterationsin human conditions should be owned
  • 64. Patients and control of their own data still not engaged often enough
  • 65. Publication Bias- Where can we find the negative clinical data?
  • 66. The Current Pharma Model is Broken: High costs, redundant failures, risk aversion and looming patent cliffs plague the industry•  In 2010, the pharmaceutical industry spent ~$100B for R&D•  Half of the 2010 R&D spend ($50B) covered pre-PH III activities•  Half of the pre-PH III costs ($25B) were for program targets that at least one other pharmaceutical company was actively pursuing•  Only 8% of pharma company small molecule PCCs make it to PH III•  In 2010, only 21 new medical entities were approved by FDA –  15 small molecules; 7 received “priority” reviews (3 of which were orphan) –  6 biologics; 3 received priority review – all recombinant enzymes for orphan diseases April  16-­‐17,  2011   79 San  Francisco  
  • 67. The time is ripe to provide an improved business model toensure that more high risk/high opportunity targets gain proof of clinical mechanism (POCM) April  16-­‐17,  2011   80 San  Francisco  
  • 68. Arch2POCM
  • 69. Arch2POCM MissionTo establish a pre-competitive stream of drug development data and POCM candidates that: 1.  Will focus on high risk/high opportunity targets and be powered by failed Ph II compounds 2.  Will inform the industry regarding those targets that are validated for clinical proof of concept mechanism (POCM) and those that are not 3.  Will drive down redundant efforts in discovery and early development 4.  Will allow clinicians and academics “BIG” to work on novel compounds and share their data with EMEA and FDA joining the dialog on the targets as not about a product to be approved 5.  5. Will allow Industry to generate proprietary compounds with IP for filing benefiting from the POCR in the common open stream April  16-­‐17,  2011   82 San  Francisco  
  • 70. Arch2POCM Can Serve As a Pilot For The Entire Pharmaceutical Industry•  Arch2POCM is a new drug development model that is intended to allow participants to: –  Eliminate redundant discovery and early development activities –  Leverage all information generated by the Arch2POCM to reduce the cost of failure –  Take advantage of validated POCM s –  Obtain value for existing assets that are not currently getting developed April  16-­‐17,  2011   83 San  Francisco  
  • 71. Entry points Pioneer targets - genomic/ genetic - disease networks - academic partners - private partners Lead Lead Preclinical Phase I Phase II identification optimisationAssay in vitro probe Lead Clinical Phase I Phase II candidate asset assetEarly Discovery
  • 72. Reagents and publications will facilitate collaboration, moreleveraged funds, improved disease maps and target discovery Clinical Phase I Phase II Lead candidate asset asset POCM in vitro in vivo probe probe Lead Lead Preclinical Phase I Phase II identification optimisation Efficacy Efficacy ADME, Human Efficacy in cellular in in vivo toxicology safety & in patients assays “disease” tolerability models
  • 73. 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 disease Building Disease Maps Data RepositoryIdentify subpopulations Cell Line With common Genomic Network Annotation Project Sensitivities Comparator Arms ofAlign Non-Responder Clinical Trials Indications Commons Pilots Discovery Platform Federation Align interface for data Arch2POCM Tools and models
  • 74. embrace complexitydevelop better maps of diseasechange how we sharebroaden whom we engage