Friend p4c 2012-11-29


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Stephen Friend, Nov 29, 2012. Partnering for Cures 2012, New York, NY

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Friend p4c 2012-11-29

  1. 1. Sage Bionetworks:BRIDGE(are you making the right investments?)Stephen H FriendPresident Sage Bionetworks(Non-Profit Foundation)
  2. 2. Options as a Citizen
  3. 3. Options as a Foundation
  4. 4. What will it take to understand disease? Biobanks RNA, DNA and proteins Moving beyond altered components lists
  5. 5. What will it take to understand disease? Driver Mutations Modifier Genes Environmental factors Context dependencies Co-Medications Pharmacogenomic factors State of the Immune System
  6. 6. What will it cost to understand disease?
  7. 7. How can we afford to get there? Institutional Extensions Foundational Walled Gardens Academic Consortia New Proprietary Data Aggregators
  8. 8. Five Powerful Convergence BreakthroughsEnable some Alternative Paths 1- Now possible to generate massive amount of human “omic’s” data 2-“Top Down” Network Modeling for Diseases are emerging 3- IT Infrastructure and Cloud compute capacity allows a generative open approach to biomedical problem solving 4- Nascent Movement for patients to Control Private information allowing sharing5- Open Social Media allowing citizens and experts to use gaming to solve problemsTHESE FIVE TRENDS TOGETHER CAN ENABLE AN OPEN COMMUNITY OF IMPATIENT CITIZENS -- AS PATIENTS/RESEARCHERS/FUNDERS
  9. 9. The Biomedical Information Commons Alternative Collecting Storing Data DataBiomedicine Information Commons Processing Sharing Data Data Commons are resources that are owned in common or shared among communities. -David Bollier
  10. 10. Components of the Biomedical Commons Data Generators Patients/ Citizens CURATED DATA Data TOOLS/ Analysts METHODS RAW DATA Clinicians ANALYZES/ MODELS SYNAPSE Experimentalists
  11. 11. Why Sage Bionetworks? We believe in a world where biomedical research has changed. It will be conducted in an open, collaborative way where each of us can contribute to making better, faster, relevant discoveriesWe enable others We activate• Develop platforms for We perform research • Diverse collaborations with collaboration and • Leading computational individuals/researchers and engagement – Synapse, biology research institutions to grow the BRIDGE • Novel training and biomedical Commons together• Defining governance internship programs • Crowdsourcing approaches to approaches– PLC challenge the communities
  12. 12. So…What is BRIDGE? A place where patients, researchers and funders can collaborate to define and contribute to research in their, and other disease, communities An online platform we are defining with five disease communities and their launch projects
  13. 13. What will BRIDGE give us? Changing the research dialogue Sharing of data and Rich data from a research with a wider wide participant base audience A networked team to Crowdsourcing collaborate and method of research Really involving Citizen-Patients learn
  14. 14. TO CONSENT RESEARCH BRIDGE Education Surverys/Forums Data Use Tracking Games The six domains Learning From Adjacent Diseases BRIDGE’s main components and interactions Crowdsourcing14
  15. 15. Synapse is GitHub for Biomedical Data “Synapse is a compute platform for transparent, reproducible, and modular collaborative research.”• Data and code versioned • Every code change versioned• Analysis history captured in real time • Every issue tracked• Work anywhere, and share the results with anyone • Every project the starting point for new work• Social/Interactive Science • Social/Interactive Coding
  16. 16. Currently at 16K+ datasets and ~1M models
  17. 17. BRIDGE Seed Projects Fanconi Diabetes Melanoma Anemia Activated Hunt Community Project Breast Cancer Real Names Genomic Parkinson’s Research Project
  19. 19. BREAST CANCER GENOMIC RESEARCH: CURRENT APPROACHES 1. Isloated breast cancer cohorts 2. Many funders, many disparate Funded researchers 3. Data objectives 4. Clinical/genomic is siloed 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 consensus 19for breast cancer
  21. 21. BREAST CANCER PROGNOSIS “CO-OPETITIONS” TO BUILD BETTER DISEASE MODELS TOGETHER 2. Core/surgical biopsy Path lab Novel Data usage Clinical informatics1. Activated 8. Field-test best modelsbreast cancer in clinic and hospitalpatients 3. Aggregate 7. Give back education Com and risk assessment to Findin BC patient 5. Open community- citizens data via muni Citizen based “co-opetitions” gs BRIDGE portal Portal forge new computational ty models Foru 6. “Co-opetitions” leaderboard allows4. BC data curated, ms researchers to workopen and supported by togetheranalysis tools 21
  22. 22. Crowdsourced Research in ActionSage Bionetworks- DREAM Breast Cancer Prognosis Challenge | The Dream Project 26/ 11/ 2012 11:39 Home Challenges Team Ranking Conferences Discussion Literature Reverse Engineering News Contact us Login / Register. DREAM is a Dialogue for Reverse Engineering Assessments and Methods. The main objective is to catalyze the interaction between experiment and theory in the area of cellular network inference and quantitative model building in systems biology. A Model Challenge 26/ 11/ 2012 11:40 Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge Click here to get started with the Sage Bionetworks - DREAM Breast Cancer Prognosis Challenge NEW: Final phase of the challenge has started! Science Translational Medicine Enter Search Term ADVANCED AAAS.ORG FEEDBACK HELP LIBRARIANS Announcement 1. To remind you, we have set a deadline of October 15 to receive all of your submitted models for scoring and for determining Challenge winners (using the METABRIC data and then a little later this fall, using the Oslo-Val data). To make sure that none of you misses this crucial deadline, we will receive your models up to 11:59 pm Pacific on October 15. Please dont miss this deadline!! Sci TM Home Current Issue Rapid Publication Issue Archive Multimedia Sci TM Collections My Sci TM About Sci TM 2. To select the top model as assessed using METABRIC data, we will choose no more than 5 models from each individual or team.>Shortly Journals > Science Translational Medicine Hom e > 12 Septem ber 2012 > Home Science after the October 15 LaMarco, 4:(151): 151ec162 deadline, we will send out a message letting you know that unless we receive a note from you to the alternative, we will submit your 5 top-scoring models for the final METABRIC model assessment (as listed on the October 15 leader board). Science Translational Medicine Prev | Table of Contents | Next stm Sci Transl Med 12 Septem ber 2012: 3. Please note that a key aspect of our judging procedure will be to confirm that your model code is readable and reusable (i.e., such that others could use it or combine it151ec162 Vol. 4, Issue 151, p. Sci. Transl. Med. DOI: 10.1126/ scitranslmed.3004863 with their own code to build a new and potentially better model). EDITORS CHOICE 46 teams (or individuals) Synopsis COMPUTATIONAL BIOLOGY A Model Challenge The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical >1700 models submitted information about the patients tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles. Kelly LaMarco + Author Affiliations Background Many outperformed clinical co-variance What’ s first on the list in Robert Fulghum’ s book, All I Really Need to Know I Learned in Kindergarten? Molecular diagnostics for cancer therapeutic decision-making are among the most promising applications of genomic technology. Several diagnostic tests have gained Second? “Play f air.” Designers of the open- science Sage/ DREAM Breast Cancer “Share everything.” regulatory approval in recent years. Molecular profiles have proved particularly powerful in adding prognosis information to standard clinical practice in breast cancer, Prognosis Challenge learned these lessons well, and there is still tim e for other com putational m odelers to join in the show- and- tell. This open com putational challenge to identify predictors of predictions using gene-expression-based diagnostic tests such as MammaPrint [1] and Oncotype Dx [2]. breast cancer progression is accepting subm issions of m odels until 15 October 2012. Based on initial promising clinical results, computational approaches to infer molecular predictors of cancer clinical phenotypes are one of the most active areas of Breast cancer is the second leading cause of cancer death am ong wom en in the United States. Despite research in both industrial and academic institutions, leading to a flood of published reports of signatures predictive of cancer phenotypes. Several trends have that billions of dollars are spent each year on research and treatm ent, biom edical scientists the fact emerged through these numerous studies: 1) genes defining predictive signatures of the same phenotype often do not overlap across multiple studies; 2) predictive signaturesplete understanding of prognosis and survival rates, which vary greatly am ong patients. have an incom reported by one group may not prove robust in other studies; 3) there is no consensus regarding the most accurate signatures or computational methods for inferring Challenge is to use crowdsourcing to m old a com putational m odel that accurately The goal of the predicts breast cancer survival. Challenge participants are invited to use genom ic and clinical predictive signatures; 4) there is no consensus regarding the added value of incorporating molecular data in addition to or instead of traditionally used clinical covariates.
  24. 24. 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 24
  26. 26. 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 tons of data! aggregation of data enriching the model 3. Run algorithmic cChallenges in the compute 26 space
  27. 27. Data handling and governance Data collection and storage Participant Consent Genetic and other test results Electronic medical records Journals – history and progressions Structured Surveys Self-generated images
  28. 28. Next steps to Distributed Decoding of Diseases Make the benefit Borrowing apparent Finding Adjacent Next Gen Reward Foundations Shifting from Structures Finite to Infinite Finding Challenges Activated Communities BRIDGE
  29. 29. Sage Bionetworks:BRIDGE(are you making the right investments?)How are you activating citizens?How are you shifting rewards andincentives?Stephen H FriendPresident Sage Bionetworks(Non-Profit Foundation)