Privacy, Informed Consent, Data Access and Transparent Analysis: Challenges ahead for breast cancer research Robert Cook-Deegan Research Professor, Duke University
Jan 21, 2016
Privacy, Informed Consent, Data Access and Transparent
Analysis: Challenges ahead for breast cancer
research
Robert Cook-DeeganResearch Professor, Duke
University
19 “policy challenges” identifiedData-sharing = #1 most important
#19 feasible to fix, i.e., least tractable
Rounds 1 & 2 of a Delphi study on introducing next-generation sequencing into clinical practice
Among policy options, ‘do nothing’ the leastfavored
Reasons for not sharing data
• It’s a pain (time and effort)• Interface glitches • “They’re using research data
for clinical interpretation”• Liability?• Precluded by privacy rules or
informed consent agreement• The data are really valuable
– Prospect of commercial value– and they “belong to us”
• Institutional stupidity, inertia, arrogance or combinations
Governing the Commons
Infrastructure = Databases, linkages, standardsData and knowledge are non-rivalrous
TRAGEDY OF THE COMMONS?
• the main issue facing research commons is under-use
• the value of a research commons is enhanced as more people use the resource - “network effect”
• global rather than local in scope
a global research commons must be managed to facilitate not only use, but also re-contribution from the user community,
creating a feedback loop between withdrawal, value-added research, and
deposit
(Schofield, Bubela et al. 2010: Nature)
REQUIREMENTS FOR A ROBUST COMMONS
• Rules that match the structure of the community and desired outcomes
• Active participation of community (ground up!)
• Some autonomy in rule making• System for self-monitoring of
behavior• Graduated system of sanctions• Incentive structures• Access to resolution mechanisms
Data Access-Transparent Analysis (DA-TA)
Data Access1. Personal right to access in interoperable format2. Scientific replication and verification3. Clinical interpretationTransparent Analysis4. Independent verification in science5. Evidence-based decisions in medicine6. Not just data, but also algorithms7. Disease models, interpretive frameworks
“Your genome belongs to you”
• Make patient/consumer access a design principle 1. A “right to my genomic data”2. Interoperable standards
Science 343: 373-4, 24 Jan 2014
Terry & Cook-Deegan, Health Affairs blog, 8 June 2012
Restatements of Independent Verification in Genomics
• Cech report (Sharing Publication-Related Data and Materials) 2002, National Research Council
• Precision Medicine report (Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease) 2011, Institute of Medicine
• Omics (Omenn) report (Evolution of Translational Omics) 2012, Institute of Medicine
Policies on data-sharing in genomics
• Bermuda Principles for human DNA sequence data (1996)• Ft Lauderdale (other organisms)• NHGRI data-sharing policy (1997); NIH (2003; 2014)• GWAS (2006-7)• Toronto (2009)• Wellcome Trust (2011)• Sage Bionetworks Principles (2011)• One Mind Consortium “open science principles”• Global Alliance for Genomics and Health (2014, 2015)
– Framework, International Charter, specific policy documents on informed consent, data security, etc.
Incentives
• Get payers to demand independent verification as condition of reimbursement
• Accreditation of labs and tests: DA-TA• Pay for sharing, create CPT code• Consumer demand: don’t order tests from
labs that perpetuate secrecy• Shaming strategies (judged likely to be
ineffective)
Toto, we’re not in Bermuda anymore!
• Geographic diversity• Diversity of Data• Linkage to other data• Privacy and informed consent (data are about people)
• Intensity and diversity of commercial interests
International challenges
• Bermuda = US, UK (90%), France, Germany, Japan (in 1999, added China)– Data-sharing was hard to achieve, and met
resistance in Japan and Germany• Genomics today: – China, S Korea, Singapore major players– Europe, Canada, Australia: OECD +– Major projects in Middle East, E Europe, N Europe,
Africa
Data diversity challenges
• Sequence data in many layers– Raw, assembly, variant call, clinically relevant
variants• Biological data to guide clinical inference– Animal models (knock-in, knock-out, genomic
editing)– Bioinformatics– Experimental data
• Proteomic, metabolomic, etc.
Data source diversity
• Most data will flow from clinical testing, not research laboratories
• Infrastructure just getting established• Diverse and conflicting business models– Open science (GeneDx, Invitae)– Intermediate (Quest, LabCorp)– Proprietary (Myriad, others?)– Academic institutions span this full range too
Data linkage challenges
• Confusing state of electronic health records– Incentives of major players to make data sticky– Massive technical complications in sharing– Legal flux
• Genealogical data• Exposure data• Demographic data• Self-reported data
Privacy and informed consent challenges
• Data are about people• IRB and Ethics Review Boards
– Atomized and institution-based – National differences
• Informed consent for clinical samples & data– Legacy problem– Prospective studies require multiple approvals– Need opt-out and special provisions from “broad consent”
• National laws about export of genetic data and resources• The most useful data cannot be delinked from identifiable people• Indeed, an individual-centered data infrastructure is the central
aspiration of the 2011 IOM report on Precision Medicine
Building out from BRCA
• Sharing Clinical Reports Project (R Nussbaum)• Free the Data (Genetic Alliance)• BRCA Challenge (Global Alliance for Genomics and
Health, Variome, UNESCO)• BRCA Share (Quest/LabCorp + UMD)• ARUP/Utah/Huntsman database• ENIGMA and CIMBA• Cancer research consortia (PROMPT, etc.)• Myriad proprietary model
• CIMBA consortium: over a decade• 263 authors!• Over 70 institutions• Global• Pooled data• Shared methods
Journal of the American Medical Association (JAMA): 7 April 2015
It can be done
New tools and powers
• New IRB rules (Common Rule revision underway)• Removal of CLIA lab exemption, so individuals can
now get their own data (Oct 2014)• Precision Medicine Initiative– Assembling the cohort requires solving problems– Cancer front and center, NCI leadership– Office of the National Coordinator and DHHS Office of Civil
Rights directly engaged– Global Alliance for Genomics and Health (frameworks, policies)
– Partnership framework
Despair or Optimism?
© Ignorant Fisherman blog