Stephen Friend Koo Foundation / Sun Yat-Sen Cancer Center 2012-03-12
Moving beyond linear investigations Both of the science and of how we workIntegrating layers of omics data models and building compute spaces capable of enabling models to be evolved by teams of teams Koo Foundation / Sun Yat-Sen Cancer Center March12, 2012 Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ Amsterdam
So what is the problem? Most approved therapies were assumed to be monotherapies for diseases represen4ng homogenous popula4ons Our exis4ng disease models o9en assume pathway knowledge suﬃcient to infer correct therapies
Explosion of Biological Genomic & Clinical Informa<on • Computa<onal methods for integra<ng massive molecular and clinical datasets obtained across sizable popula<ons into predic<ve disease models can recapitulate complex biological systems • Data should feed and reﬁne a set of models that inform our understanding of disease causality as well as generate new mechanisms, targets, diagnos<cs and knowledge.
“Data Intensive” Science- Fourth Scientific Paradigm Equipment capable of generating massive amounts of data IT Interoperability Open Information System Host evolving computational models in a “Compute Space”
WHY NOT USE “DATA INTENSIVE” SCIENCE TO BUILD BETTER DISEASE MAPS?
what will it take to understand disease? DNA RNA PROTEIN (dark maWer) MOVING BEYOND ALTERED COMPONENT LISTS
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)  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)  Lpl Lipoprotein lipase 46% ko Provided by Dr. Ira Goldberg (Columbia University, NY)  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
Building Realistic, Predictive Models of Disease: Can this lead us from gene to drug?
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.
List of Influential Papers in Network Modeling 50 network papers http://sagebase.org/research/resources.php
“Data Intensive” Science- Fourth Scientific Paradigm Score Card for Medical Sciences Equipment capable of generating massive amounts of data A- IT Interoperability D Open Information System D- Host evolving computational models in a “Compute Space F
We still consider much clinical research as if w hunter gathers - not sharing .
Clinical/genomic data are accessible but minimally usableLittle incentive to annotate and curate data for other scientists to use
Mathematicalmodels of disease are not built to be reproduced orversioned by others
Lack of standard forms for future rights and consents
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
Model of Breast Cancer: Co-expression 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 28 Zhang B et al., Towards a global picture of breast cancer (manuscript).
CHRIS GAITERI-‐ALZHEIMER’S What is this? Bayesian networks enriched in inﬂamma<on genes correlated with disease severity in pre-‐frontal cortex of 250 Alzheimer’s pa<ents. What does it mean? Inﬂamma<on in AD is an interac<ve mul<-‐pathway system. More broadly, network structure organizes complex disease eﬀects into coherent sub-‐systems and can priori<ze key genes. Are you joking? Gene valida<on shows novel key drivers increase Abeta uptake and decrease neurite length through an ROS burst. (highly relevant to AD pathology)
PLATFORM Sage Platform and Infrastructure Builders- ( Academic Biotech and Industry IT Partners...) PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots- (Federation, CCSB, Inspire2Live....) M S FOR MAP PLATNEW RULES GOVERN
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
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/
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
Four Pilots involving Sage Bionetworks CTCAP The Federa<on Portable Legal Consent M S Sage Congress Project FOR MAP PLAT NEW 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
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
Federated Aging Project : Combining analysis + narra<ve =Sweave Vignette Sage Lab R code + PDF(plots + text + code snippets) narrative HTML Data objectsCalifano Lab Ideker Lab Submitted Paper Shared Data JIRA: Source code repository & wiki Repository
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 48