Friend Gastein 2012-10-04

491 views
429 views

Published on

Stephen Friend, Oct 4, 2012. European Health Forum, Gastein, Austria

Published in: Health & Medicine
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
491
On SlideShare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Friend Gastein 2012-10-04

  1. 1. A new community based vision of open access innovation in personalized medicine Stephen H Friend MD PhD President Sage Bionetworks Non-Profit Organization Seattle/ Amsterdam/ Beijing Gastein Oct 4, 2012
  2. 2. NOTMISSION IMPOSSIBLE
  3. 3. NOT MISSION IMPOSSIBLE1. It’s going to be harder than you think but inevitable2. Without deep citizen activation it will be unaffordable3. Sharing data and models between researchers especially between and within Universities will need to fundamentally change
  4. 4. Seattle x Gastein
  5. 5. The value of appropriate representations/ maps
  6. 6. DIVERSE POWERFUL USE OF MODELS AND NETWORKS
  7. 7. Extensive Publications now Substantiating Scientific Approach Probabilistic Causal Bionetwork Models•>80 Publications from Reseach Metabolic "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, etc CVD "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.Genome Bone "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.
  8. 8. List of Influential Papers in Network Modeling  50 network papers  http://sagebase.org/research/resources.php
  9. 9. Background: Information Commons for Biological Functions INFORMATION COMMONS
  10. 10. Networked Approaches BioMedicine Information Commons Patients/ Data Generators Citizens CURATED DATA Data TOOLS/ Analysts METHODS RAW DATA ANALYSES/ MODELS Clinicians SYNAPSE Experimentalists 14
  11. 11. NOT MISSION IMPOSSIBLE1. It’s going to be harder than you think but inevitable2. Without deep citizen activation it will be unaffordable3. Sharing data and models between researchers especially between and within Universities will need to fundamentally change
  12. 12. We still consider much clinical research as if we were“hunter gathers”- not sharing .
  13. 13. TENURE FEUDAL STATES
  14. 14. Sage Mission Sage Bionetworks is a non-profit organization with a vision tocreate a “commons” where integrative bionetworks are evolved by contributor scientists and citizens
  15. 15. Networked Team Approaches 1 USABLE 2 DATA PRIVACY BARRIERS 5 BioMedical Information Commons EDUCATION Patients/ Data BIOINFORMATICS Generators Citizens CURATED DATA Data TOOLS/ Analysts METHODS RAW DATA ANALYSES/ MODELS 4 Clinicians REWARDS 3 FOR SYNAPSE HOW TO Experimentalists SHARING DISTRIBUTE TASKS 22
  16. 16. COMPONENTS NEEDED FOR NETWORKED APPROCHES TOBUILDING EVOLVING MODELS OF DISEASE: RESEARCH 2.0 GEEKS AND SCIENTISTS SANDBOX PLACE TO BUILD MODELS SYNAPSE OF DISEASE
  17. 17. Synapse Platform: a compute space for collaborative research• Development of Robust, Reproducible, and Reusable analytical methods• Integration of Data, Tools and Methods from across community• Development of a Disease Model Repository• Forum for New Collaborations between technically and geographically distinct scientific groups• Access to Cloud-Compute resources co- located with large-scale data synapse.sagebase.org 24
  18. 18. COMPONENTS NEEDED FOR NETWORKED APPROCHES TOBUILDING EVOLVING MODELS OF DISEASE: RESEARCH 2.0 ALLOWS PATIENT TO REQUEST DATA BACK PORTABLE GIVES CONTROL OF DATA TO PATIENT LEGAL WHO CAN THEN SAY I WANT TO SHARE IT CONSENT GEEKS AND SCIENTISTS SANDBOX SYNAPSE PLACE TO BUILD MODELS OF DISEASE
  19. 19. Tool: PORTABLE LEGAL CONSENT US- approvedweconsent.usJohn Wilbanks • Online educational wizard • Tutorial video • Legal Informed Consent Document • Profile registration • Data upload 26
  20. 20. Open and Networked Approaches-PRIVACYBARRIERS Regulatory issues and bottlenecks Is data anonymized? Yes- proceed No- Is data pseudonymized? Yes- Is it “sensitive” data No (health, genomic,..) Yes No- Will key to person’s ID be shared with 3rd party? Consent is required No- Proceed with appropriate safeguards for data access and safekeeping 27
  21. 21. COMPONENTS NEEDED FOR NETWORKED APPROCHES TOBUILDING EVOLVING MODELS OF DISEASE: RESEARCH 2.0INCLUDING CITIZENS: DEMOCRATIZATION OF MEDICINE GEEKS AND SCIENTISTS SANDBOX SYNAPSE PLACE TO BUILD MODELS OF DISEASE PORTABLE ALLOWS PATIENT TO REQUEST DATA BACK LEGAL GIVES CONTROL OF DATA TO PATIENT CONSENT WHO CAN THEN SAY I WANT TO SHARE IT ENGAGES CITIZENS AS PARTNERS PATIENTS, RESEARCHERS, FUNDERS BRIDGE
  22. 22. USE OF CO-OPETITIONSThe Sage/Bionetworks/DREAM Breast Cancer Prognosis Challenge Building Better Models of Diseases Together Goal: Assess the accuracy of computational models designed to predict breast cancer survival based on clinical information about the patients tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles. 29
  23. 23. 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 30
  24. 24. Targeted treatment and drug repositioning in type 2diabetes using molecular disease signaturesGoal: identify pathophysiological subgroups of type 2 diabetes (T2D) to enablespecific treatment targeted to the cellular disease mechanisms. Patient Physician Researcher 31 Community based vision of open access innovation in personalized medicine
  25. 25. Diabetes Monitoring and Research: BRIDGE Approach32
  26. 26. MELANOMA Education is derived from top-down experiential Best accuracy of clinical knowledge diagnosis = 64% (Grin, 1990)160k new cases/year48k deaths in 2012 in US HPI ABCDE Both intra- and inter- “ugly duckling” institutional data are siloed MD Dermoscopy Pathology Molecular ?Photos There is no standard screening program for skin lesions; seeing an MD is self directed
  27. 27. MELANOMA 4. give back risk-assessment & education to the citizens1.activated citizenstake skin pictures virtual cycle: continuous aggregation 2. store of data tons of data! enriching the model 3. run algorithmic challenges in the compute space
  28. 28. The challenge of Open science Regulatory issues and bottlenecks• Cultural barriers• Lack of leadership• Privacy barriers• Complex, country-specific regulations try to codify ethical principals Common areas of Concern with Genomic Data •Privacy •Research Oversight •Informed Consent •Data Stewardship 35
  29. 29. Enabling Cooperative Discovery Common Concerns with use of genomic data • Privacy • Research Oversight • Informed Consent • Data Stewardship Common Concerns with sharing scientific data • Being scooped • Loss of funding • Tenure denied • Publication record • Loss of potential profit • Lack of recognition • Loss of control 36
  30. 30. Consent must be a freely given, unambiguous and specific.CONSENT Consent may involve clicking an icon, sending an email or subscribing to a service. Consent can be withdrawn at anytime (research exemption).Potential Issues:• Single study focus: Use of existing data is often difficult due to consent language either too vague or obsolete.• Re-consenting isn’t always feasible: Use of archival data and/or specimen collected from deceased individuals prior to genomics era.• Consent conditional on guarantee of anonymity, privacy and confidentialityQuestions:• Is the DNA data of a deceased 50 years old male, smoker, codename XY12ZS, identifiable data subject to consent requirement?• How can we ensure optimal use of data expected by participants?• How will standard information notice and consent keep up with new technologies?Potential Opportunities:• Promote continuous interaction between subject and researchers- educate• Roll-out Portable Legal Consent within Europe 37
  31. 31. SAFEGUARDS Appropriate technical and organizational measures shall be taken against unauthorized or unlawful processing of personal data and against accidental loss or destruction of, or damage to, personal data. Privacy by default and by design. Data controller is liable and accountable for data processor Synapse safeguards: Multiple solutions to address compliance Potential Issues: • Guaranteed anonymity and privacy is a myth: Unintentional misuse of the data, accidental data breach or intentional violation of terms may still occur whether the data is handled electronically or not. • Enforcement challenges: Cannot police each activity from all users or assess the adequacy of data protection by each user in a open collaborative space. • Obtaining written contracts with each users is a bottleneck- Questions: • Shouldn’t we focus on education rather than on unrealistic guarantees of privacy? • Will we introduce legislations that prevent discrimination based on personal data: Anti- discrimination by default? Anticipated actions: • Engage fines, exclusion, public shame as possible responses to breach or violations
  32. 32. TRANSFER Personal data shall not be transferred to a country or territory outside the European Economic Area unless that country or territory ensures an adequate level of protection for the rights and freedoms of data subjects in relation to the processing of personal data.Issue:• Web technology doesn’t tie to geographical boundaries• US Safe-harbor stamp from US department of commerce for e- commerce, not research.• Restrict feasibility of international Challenge/modeling competitions• Incompatible with Cloud computing for BIG DATA analysis Questions: • Will we need to restrict EU data to EU servers and • have them used by EU scientists only? • Will we need to split international datasets? Anticipated actions: • Discuss possibility to certify non-EU data repositories for inclusion and transfer of EU data. • Let subjects determine where their data can be used 39
  33. 33. CLOUD Cloud providers must provide information on How, Where and by Whom the data is being processed at all time. Cloud customers should perform a risk assessment related to cloud provider’s data protection practices. Rules on data transfer remain. Potential Issues: • Based on single data user for single dataset • Cloud providers will not accept to host sensitive data if they are liable for misuse of the data by their customers or sub-processors • Same resource for both data storage and data analysis • Data location: EU data on EU-CLOUD. What about non-EU data? • Roles and responsibilities for Synapse developer vs. Cloud provider and synapse users Opportunities: EU could develop certification for CLOUD providers with respect to data protection Cloud use and data transfer limitations Should not be incompatible 40
  34. 34. E-privacy Users must be informed of use of cookies or similar devices and be allowed to opt-out Potential issues: • The need for transparency and accountability of Synapse users implies renouncing to privacy by design – Synapse users must register – Actions are tracked – Access and Compliance Team can audit data use in Synapse – Opting out is not allowed.Anticipated actions:Explain full transparencyexisting in Synapse and otherInformation Commonsand refuse access to users who opt-out 41
  35. 35. NOT IF WE WORK TOGETHER MISSION IMPOSSIBLE1. It’s going to be much harder than you think - but inevitable2. Without deep citizen activation it will be unaffordable3. Sharing data and models between researchers especially between and within Universities will need to fundamentally change

×