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

19 “policy challenges” identified Data-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 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 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 Access 1.Personal right to access in interoperable format 2.Scientific replication and verification 3.Clinical interpretation Transparent Analysis 1.Independent verification in science 2.Evidence-based decisions in medicine 3.Not just data, but also algorithms 4.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