Friend DREAM 2012-11-14


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Stephen Friend, Nov 14, 2012. DREAM Conference, San Francisco, CA

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Friend DREAM 2012-11-14

  1. 1. Corruption of Denial
  2. 2. Now possible to generate massive amount of human “omic’s” data
  3. 3. Network Modeling Approaches for Diseases are emerging
  4. 4. IT Infrastructure and Cloud compute capacity allowsa generative open approach to solving problems
  5. 5. Nascent Movement for patients to Control Sensitive information allowing sharing
  6. 6. Open Social Media allows citizens and experts to use gaming to solve problems
  7. 7. 1- Now possible to generate massive amount of human “omic’s” data2-“Top Down” Network Modeling Approaches for Diseases areemerging3- IT Infrastructure and Cloud compute capacity allowsa generative open approach to biomedical problem solving4-Nascent Movement for patients to Control Sensitive informationallowing sharing5- Open Social Media allows citizens and experts to use gaming tosolve problems A HUGE OPPORTUNITY -- A HUGE RESPONSIBILITY
  8. 8. ENVIRONMENT Non-coding RNA network BRAIN HEART ENVIRONMENT GI TRACT protein network KIDNEYENVIRONMENT metabolite network IMMUNE SYSTEM VASCULATURE transcriptional network ENVIRONMENT
  9. 9. .
  11. 11. • alchemist
  12. 12. Iterative Networked ApproachesTo Generating Analyzing and Supporting New Models Data Biological System Analysis Uncouple the automatic linkage between the data generators, analyzers, and validators
  14. 14. “We Must Guard Againstthe acquisition of unwarranted influence, whether sought or unsought, by the Military Industrial Complex” - Dwight D. Eisenhower 1961
  15. 15. BUILDING PRECISION MEDICINE Extensions of current Institutions Proprietary Short term SolutionsOpen Systems of Sharing in a Commons
  16. 16. An Alternative Biomedicine Information CommonsCommons are resources that are owned in common or shared amongcommunities. -David Bollier
  17. 17. At Sage Bionetworks we believe medical research has to changeResearch needs to become more open.Research should be transparent to other scientists and shared with the patientsit involves.Data, tools, and methods that researchers develop should be shared in formatsthat can be used time and time again.Results should be commonly available online to all, in real-time.And negative or neutral outcomes should be published.Research needs to involve more relevant people in a collaborativeapproach.Patients or citizens, researchers and organizations need to work together betterto decide what questions should be answered, and how the answers should beobtained. This would weight priorities more towards more practical benefits.
  18. 18. 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 32
  19. 19. Networked Approaches BioMedicine Information Commons Patients/ Data Generators Citizens CURATED DATA Data TOOLS/ Analysts METHODS RAW DATA ANALYSES/ MODELS Clinicians SYNAPSE Experimentalists
  20. 20. Technology Platform GovernanceImpactful Models Better Models of Disease: INFORMATION COMMONS Challenges
  21. 21. RAS Model using primary tumor data to predict KRAS mutation status Justin Guinney 290 CRC samples: • KRAS12 or KRAS13 (n=115) vs WT (n=175) • Penalized regression model using ElasticNet and gene expression data 1.0 0.8 Robust External True positive rate 0.6 Validation In CRC TCGA CRC Khambata−Ford 0.4 Gaedcke data sets 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 False positive rate Model specific to CRC: does not generalized to other KRAS dependent cancersRAS signatures derived from CRC cohort can classify mutation status in CRC 35
  22. 22. For 11/12 compounds, the #1 predictive feature in an unbiased analysis corresponds to the known stratifier of sensitivity Adam Margolin #2 CML lineage CML lineage #1 EGFR mut EGFR mut #1 EGFR mut EGFR mut #1 CML lineage CML linage #1 EGFR mut EGFR mut #1 ERBB2 expr ERBB2 expr Can the approach make new mut #1 BRAF BRAF mut discoveries? #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
  23. 23. Tool: PORTABLE LEGAL CONSENT Control of Private information by Citizens allows sharing John WilbanksJohn Wilbanks • Online educational wizard • Tutorial video • Legal Informed Consent Document • Profile registration • Data upload
  24. 24. 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
  25. 25. “Synapse is a compute platform for transparent, reproducible, andmodular collaborative research.”
  26. 26. Data Analysis with SynapseRun Any ToolOn Any PlatformRecord in SynapseShare with Anyone
  27. 27. Synapse is GitHub for Biomedical Data • Every code change versioned • Every issue tracked • Every project the starting point for new work• Data and code versioned • Social/Interactive Coding• Analysis history captured in real time• Work anywhere, and share the results with anyone• Social/Interactive Science
  28. 28. Currently at 16K+ datasets and ~1M models
  29. 29. Specific Version
  30. 30. Pancancer collaborative subtype discovery
  31. 31. Download analysis and meta-analysisDownload another Cluster Result Download Evaluation and view more stats • Perform Model averaging • Compare/contrast models • Find consensus clusters
  32. 32. Objective assessment of factors influencing modelperformance (>1 million predictions evaluated) Sanger CCLECross validation prediction accuracy (R2) Prediction accuracy improved by… Not discretizing data Including expression data Elastic net regression 130 compounds In Sock Jang 24 compounds
  33. 33. Erich Huang, Brian Bot, Dave Burdick
  34. 34. Sage-DREAM Breast Cancer Prognosis Challenge one month of building better disease models together Caldos/Aparicio breast cancer data154 participants; 27 countries 334 participants; >35 countries Sep 26 StatusChallenge Launch: July 17 >500 models posted to Leaderboard
  35. 35. Bob Young Top Hat " #$$%&!Eric Topol UCSD myTodd Park gene CTOMichael Nielsen my dat aWadah Khanfar Al JazeeraEric Hershman m y paper UshhidiJennifer Pahlka Code for America 01""*) & ) &*!+,$-$. /!& ( & 2, % & " $) -5 " .6*& 7 7 "$*& $% **& :& <=; >?2, $&., $A4 & ) 34 0" 0" .) & 89.4 ; & @ *A" *-.) , 7 4 &( ) & 5 5 5 C % A"$% **C .%& $%:4 B& *, ) .) " & Keyn ot e Sp eak er s: Law r en ce Lessi g – author “The future of ideas” &“Remix” Jam i e Heyw ood – patients like me Lan ce Ar m st r on g – LiveStrong Davi d Hau ssl er - UCSC Sage Commons Congress – San Francisco April 19-20 Genome Browser Jam es Boyl e – Duke Law School Ad r i en Tr eu ille –Foldit ! " #$%$( Young Investigator Awards Ten ) *+% -" .*/& & , & Ear n one of t en t r i p s t o Com m ons Cong r ess i n SF! – t o ap p l y vi si t h t t p :/ / b i t .l y/ 2012YIA!
  36. 36. Now possible to generate massive amount of human “omic’s”dataNetwork Modeling for Diseases are emergingIT Infrastructure and Cloud compute capacity allowsa generative open approach to biomedical problem solvingNascent Movement for patients to Control Private informationallowing sharingOpen Social Media allowing citizens and experts to use gamingto solve problemsTHESE FIVE TRENDS CAN ENABLE AN OPEN COMMUNITY OFIMPATIENT CITIZENS-- AS PATIENTS/RESEARCHERS/FUNDERS
  37. 37. Upon this gifted age, in its dark hour,Rains from the sky a meteoric showerOf Facts…they lie unquestioned,uncombined.Wisdom enough to leech us of our illIs daily spun; but there exists no loomTo weave it into fabric. - Edna St. Vincent Millay