Privacy Informed Consent Data Access and Transparent Analysis

Privacy, Informed Consent, Data Access and Transparent Analysis: Challenges ahead for breast cancer research Robert Cook-Deegan Research Professor, Duke University

Rounds 1 & 2 of a Delphi study on introducing next-generation sequencing in 19 “policy challenges” identified Data-sharing = #1 most important #19 feasible to fix, i. e. , least tractable Among policy options, ‘do nothing’ the least favored

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, standards Data and knowledge are non-rivalrous

TRAGEDY OF THE COMMONS? the main issue facing a global research • commons must be research commons is managed to facilitate not only use, but also under-use re-contribution from • the user of community, the value a research commons is enhanced as creating a feedback loop between more people use the withdrawal, value-added research, and resource - “network deposit effect” rather than local in (Schofield, Bubela • etglobal al. 2010: Nature) scope

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 Access 1. Personal right to access in interoperable format 2. Scientific replication and verification 3. Clinical interpretation Transparent Analysis 1. 2. 3. 4. Independent verification in science Evidence-based decisions in medicine Not just data, but also algorithms Disease models, interpretive frameworks

“Your genome belongs to you” Terry & Cook-Deegan, Health Affairs blog, 8 June 2012 • Make patient/consumer access a design principle 1. A “right to my genomic data” 2. Interoperable standards Science 343: 373 -4, 24 Jan 2014

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 (Gene. Dx, Invitae) – Intermediate (Quest, Lab. Corp) – 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/Lab. Corp + 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 It can be done Journal of the American Medical Association (JAMA): 7 April 2015

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