Friend NRNB 2012-12-13


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Stephen Friend, Dec 9, 2012. NRNB Symposium on Network Biology, San Francisco, CA

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Friend NRNB 2012-12-13

  1. 1. Corruption of Denial Stephen Friend Sage Bionetworks
  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-Network Modeling Approaches for Diseases are emerging3- 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. The value of appropriate representations/ maps
  13. 13. OR
  14. 14. BUILDING PRECISION MEDICINE Extensions of Current Institutions Proprietary Short term SolutionsOpen Systems of Sharing in a Commons
  15. 15. An Alternative Biomedicine Information CommonsCommons are resources that are owned in common or shared amongcommunities. -David Bollier
  16. 16. Why Sage Bionetworks? We believe in a world where biomedical research has changed. It will be conducted in an open, collaborative way where all parties can contribute to making better, faster, relevant discoveries We activate/We challenge We enable others• Diverse collaborations with • Developing platforms for individuals/researchers and collaboration and institutions to collectively engagement – Synapse, grow the biomedical BRIDGE Commons • Defining governance• Crowdsourcing approaches to approaches– PLC challenge the communities We research • Leading biomedical modeling research • Novel training doctoral and internship programs
  17. 17. 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, Vidal 27
  18. 18. Governance Technology PlatformImpactful Models Better Models of Disease: INFORMATION COMMONS Challenges
  19. 19. Two recurring problems in Alzheimer’s disease research Ambiguous pathology Are disease-associated molecular systems & genes destructive, adaptive, or both? Bottom line: We need to identify causal factors vs correlative or adaptive features of disease.Diverse mechanismsHow do diverse mutations and environmentalfactors combine into a core pathology?Bottom line: There is no rigorous / consistent globalframework that integrates diverse disease factors. 29
  20. 20. Identifying key disease systems and genes- Gaiteri et al.1.) Identify groups of genes that move together – coexpressed “modules” - correlated expression of multiple genes across many patients - coexpression calculated separately for Disease/healthy groups - these gene groups are often coherent cellular subsystems, enriched in one or more GO functions Example “modules” of coexpressed genes, color-coded
  21. 21. Identifying key disease systems and genes1.) Identify groups of genes that move together – coexpressed “modules”2.) Prioritize the disease-relevance of the modules by clinical and network measures Prioritize modules through expression synchrony with clinical measures or tendency too reconfigure themselves in disease vs
  22. 22. Identifying key disease systems and genes1.) Identify groups of genes that move together – coexpressed “modules”2.) Prioritize the disease-relevance of the modules by clinical and network measures3.) Incorporate genetic information to find directed relationships between genes Infer directed/causal relationships Prioritize modules through expression and clear hierarchical structure by synchrony with clinical measures or tendency too reconfigure themselves in disease incorporating eSNP information (no hair-balls here) vs
  23. 23. Example network finding: microglia activation in ADModule selection – what identifies these modules as relevant to Alzheimer’s disease?The eigengene of a module of ~400 probes correlates with Braak score, age, cognitive disease severityand cortical atrophy. Members of this module are on average differentially expressed (both up- anddown-regulated).Evidence these modules are related to microglia functionThe members of this module are enriched with GO categories (p<.001) such as “response to bioticstimulus” that are indicative of immunologic function for this module.The microglia markers CD68 and CD11b/ITGAM are contained in the module (this is rare – even when amodule appears to represent a specific cell-type, the histological markers may be lacking).Numerous key drivers (SYK, TREM2, DAP12, FC1R, TLR2) are important elements of microglia signaling . Alzgene hits found in co-regulated microglia module:
  24. 24. Figure key:Five main immunologic familiesfound in Alzheimer’s-associatedmoduleSquare nodes in surrounding networkdenote literature-supported nodes.Node size is proportional toconnectivity in the full module.Core family members are shaded.(Interior circle) Width ofconnections between 5immune families arelinearly scaled to thenumber of inter-familyconnections.Labeled nodes are either highlyconnected in the original network,implicated by at least 2 papers asassociated with Alzheimer’s disease,or core members of one of the 5immune families.
  25. 25. Transforming networks into biological hypotheses
  26. 26. Testing network-based hypotheses
  27. 27. Design-stage AD projects at Sage Fusing our expertise in… Gene regulatory networks Diffusion Spectrum Imaging Feedback Microcircuits & neuronal diversityJoin us in uniting genes, circuits and regionsto build multi-scale biophysical disease models.Contact
  28. 28. Tool: PORTABLE LEGAL CONSENT Control of Private information by Citizens allows sharing John WilbanksJohn Wilbanks • Online educational wizardTED Talk • Tutorial video • Legal Informed Consent Document“Let’s pool our medical data” • Profile • Data upload
  29. 29. 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
  30. 30. 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
  31. 31. Data Analysis with SynapseRun Any ToolOn Any PlatformRecord in SynapseShare with Anyone
  32. 32. “Synapse is a compute platform for transparent, reproducible, andmodular collaborative research.”
  33. 33. Currently at 16K+ datasets and ~1M models
  34. 34. Download analysis and meta-analysisDownload another Cluster Result Download Evaluation and view more stats • Perform Model averaging • Compare/contrast models • Find consensus clusters • Visualize in Cytoscape
  35. 35. Pancancer collaborative subtype discovery
  36. 36. 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
  37. 37. Erich Huang, Brian Bot, Dave Burdick
  38. 38. 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 Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge 
Phase 2 Best Performing Te
  39. 39. Sage Bionetworks-DREAM Breast Cancer PrognosisChallenge 
Phase 2 Best Performing Team: Attractor Metagenes
Team Members: Wei-Yi Cheng, Tai-Hsien Ou Yang, andDimitris Anastassiou 
Affiliation: Center for Computational Biology andBioinformatics and Department of Electrical Engineering,Columbia University
  40. 40. How to disrupt the System?Build a way for the patients actively to engage theirinsights in real-time around what is happening tothem ( their state of wellness or disease) where theirnarratives, samples, data, insights, and funds areshown to enable decision making in what they shoulddo, what treatments they need
  41. 41. BRIDGE Seed ProjectsFanconi Diabetes MelanomaAnemia Activated Hunt CommunityProject Breast Real Names Cancer Parkinson’s Genomic Project Research 54
  42. 42. BREAST CANCER GENOMIC RESEARCH: CURRENT APPROACHES 1. Isloated breast cancer cohorts 2. Many funders, many disparate objectives Funded researchers 3. Data is siloed 4. Clinical/genomic data are accessible but minimally useable 5. Little incentive toannotate data and curatefor other scientists6. Limited impact of 7. Many publishedtoday’s fragmented breast cancerdata on standard-of- prognosis modelscare improvements but little consensusfor breast cancerpatients 55
  43. 43. BRIDGE APPROACH: BREAST CANCER PROGNOSIS “CO-OPETITIONS” TO BUILD BETTER DISEASE MODELS TOGETHER 2. Core/surgical biopsy Path lab Clinical informatics1. Activated 8. Field-test best modelsbreast cancer in clinic and hospitalpatients 3. Aggregate BC patient Com 7. Give back education Findings data via and risk assessment to muni Citizen citizens BRIDGE portal ty Portal 5. Open community- Foru based “co-opetitions” ms forge new computational models 4. BC data 6. “Cco-opetitions” curated, open leaderboard allows and supported researchers to work by analysis tools together 56
  44. 44. MELANOMA Screening – Could it be better? Education is derived Best accuracy of from top-down clinical diagnosis = experiential 64% knowledge (Grin, 1990) 160k new cases/year 48k deaths in 2012 in US HPI ABCDE Both intra- and “ugly duckling” inter- institutional MD Dermoscopy Pathology data are siloed Molecular ?Photos There is no standard screening program for skin lesions; seeing an MD is self directed 57
  45. 45. Initial focus on building the data neededNovel Data collection 4. Give back risk- + Usage assessment & education to the citizens 1.Activated citizens take skin pictures virtual cycle: continuous 2. Store aggregation of data tons of data! enriching the model 3. Run algorithmic cChallenges in the compute space 59
  46. 46. 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
  48. 48. Navigating between states Normal State Disease StateRui Chang et al. PLoS Computational Biology
  50. 50. CORRUPTION OF DENIAL Complexity of systems Proximity of SolutionsSufficiency of current phenotypic data and appropriateness of role of patientsEffectiveness of how we work with big Data
  51. 51. " #$$%&! Bob Young Top Hat my Jane McConigal gene IoF my Wadah Khanfar Al Jazeera dat a Patrick Meier m y paper Ushhidi Jennifer 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 .%& $%:4 B& *, ) .) " & Keyn ot e Sp eak er s: Law r en ce Lessi g – author “The future of ideas” &“Remix” Jam i e Heyw ood – patients like me Lan ce Ar m st r on g – LiveStrong Davi d Hau ssl er - UCSC Genome Browser Jam es Boyl e – Duke Law School Ad r i en Tr eu ille –FolditSage Commons Congress – San Francisco April 19-20 ! " #$%$( ) *+% -" .*/& & , & TenCong r ess i n SF! Ear n one of t en t r i p s t o Com m ons Young Investigator Awards – t o ap p l y vi si t h t t p :/ / b i t .l y/ 2012YIA!