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Alsaffar MM, Hasan M, McStay GP, Sedky M. Digital DNA lifecycle security and privacy: an overview. Brief Bioinform 2022; 23:6518049. [PMID: 35106557 DOI: 10.1093/bib/bbab607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/29/2021] [Accepted: 12/30/2021] [Indexed: 11/14/2022] Open
Abstract
DNA sequencing technologies have advanced significantly in the last few years leading to advancements in biomedical research which has improved personalised medicine and the discovery of new treatments for diseases. Sequencing technology advancement has also reduced the cost of DNA sequencing, which has led to the rise of direct-to-consumer (DTC) sequencing, e.g. 23andme.com, ancestry.co.uk, etc. In the meantime, concerns have emerged over privacy and security in collecting, handling, analysing and sharing DNA and genomic data. DNA data are unique and can be used to identify individuals. Moreover, those data provide information on people's current disease status and disposition, e.g. mental health or susceptibility for developing cancer. DNA privacy violation does not only affect the owner but also affects their close consanguinity due to its hereditary nature. This article introduces and defines the term 'digital DNA life cycle' and presents an overview of privacy and security threats and their mitigation techniques for predigital DNA and throughout the digital DNA life cycle. It covers DNA sequencing hardware, software and DNA sequence pipeline in addition to common privacy attacks and their countermeasures when DNA digital data are stored, queried or shared. Likewise, the article examines DTC genomic sequencing privacy and security.
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Affiliation(s)
- Muhalb M Alsaffar
- Department of Computing, AI and Robotics, School of Digital, Technologies and Arts, Staffordshire University, College Road, ST4 2DE, Staffordshire, United Kingdom
| | | | - Gavin P McStay
- Department of Biological Sciences, School of Health, Science and Wellbeing, Staffordshire University, College Road, Stoke-on-Trent, Staffordshire, ST4 2DE, United Kingdom
| | - Mohamed Sedky
- Department of Computing, AI and Robotics, School of Digital, Technologies and Arts, Staffordshire University, College Road, ST4 2DE, Staffordshire, United Kingdom
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2
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Chad L, Szego MJ. Please give me a copy of my child's raw genomic data. NPJ Genom Med 2021; 6:15. [PMID: 33597540 PMCID: PMC7889911 DOI: 10.1038/s41525-021-00175-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 12/24/2020] [Indexed: 11/23/2022] Open
Abstract
In this work, we explore whether raw genetic data generated during sequencing ought to be returned to a pediatric patient and/or their parents/guardians. We identify the principles used by various professional societies in their guidelines on the return of secondary findings and apply them to this new context. We conclude that since each situation is unique, decisions should be made on a case-by-case basis according to the best interests of the child.
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Affiliation(s)
- Lauren Chad
- Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Bioethics, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | - Michael J Szego
- Centre for Clinical Ethics, Unity Health Toronto, Toronto, ON, Canada. .,The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada. .,Dalla Lana School of Public Health, Departments of Family and Community Medicine and Molecular Genetics, University of Toronto, Toronto, ON, Canada.
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3
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Narayanasamy S, Markina V, Thorogood A, Blazkova A, Shabani M, Knoppers BM, Prainsack B, Koesters R. Genomic Sequencing Capacity, Data Retention, and Personal Access to Raw Data in Europe. Front Genet 2020; 11:303. [PMID: 32435258 PMCID: PMC7218066 DOI: 10.3389/fgene.2020.00303] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 03/13/2020] [Indexed: 12/30/2022] Open
Abstract
Whole genome/exome sequencing (WGS/WES) has become widely adopted in research and, more recently, in clinical settings. Many hope that the information obtained from the interpretation of these data will have medical benefits for patients and—in some cases—also their biological relatives. Because of the manifold possibilities to reuse genomic data, enabling sequenced individuals to access their own raw (uninterpreted) genomic data is a highly debated issue. This paper reports some of the first empirical findings on personal genome access policies and practices. We interviewed 39 respondents, working at 33 institutions in 21 countries across Europe. These sequencing institutions generate massive amounts of WGS/WES data and represent varying organisational structures and operational models. Taken together, in total, these institutions have sequenced ∼317,259 genomes and exomes to date. Most of the sequencing institutions reported that they are able to store raw genomic data in compliance with various national regulations, although there was a lack of standardisation of storage formats. Interviewees from 12 of the 33 institutions included in our study reported that they had received requests for personal access to raw genomic data from sequenced individuals. In the absence of policies on how to process such requests, these were decided on an ad hoc basis; in the end, at least 28 requests were granted, while there were no reports of requests being rejected. Given the rights, interests, and liabilities at stake, it is essential that sequencing institutions adopt clear policies and processes for raw genomic data retention and personal access.
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Affiliation(s)
| | | | - Adrian Thorogood
- Centre of Genomics and Policy, McGill University, Montreal, QC, Canada
| | - Adriana Blazkova
- Megeno S.A., Esch-sur-Alzette, Luxembourg.,Faculty of Language and Literature, Humanities, Arts and Education, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Mahsa Shabani
- Metamedica, Faculty of Law and Criminology, Ghent University, Ghent, Belgium
| | - Bartha M Knoppers
- Centre of Genomics and Policy, McGill University, Montreal, QC, Canada
| | - Barbara Prainsack
- Department of Political Science, University of Vienna, Vienna, Austria.,Department of Global Health & Social Medicine, King's College London, London, United Kingdom
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4
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Zerka F, Barakat S, Walsh S, Bogowicz M, Leijenaar RTH, Jochems A, Miraglio B, Townend D, Lambin P. Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care. JCO Clin Cancer Inform 2020; 4:184-200. [PMID: 32134684 PMCID: PMC7113079 DOI: 10.1200/cci.19.00047] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/16/2020] [Indexed: 02/06/2023] Open
Abstract
Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns.Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives.Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes.Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. Our purpose is to review the major implementations of distributed learning in health care.
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Affiliation(s)
- Fadila Zerka
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Oncoradiomics, Liège, Belgium
| | - Samir Barakat
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Oncoradiomics, Liège, Belgium
| | - Sean Walsh
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Oncoradiomics, Liège, Belgium
| | - Marta Bogowicz
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Ralph T. H. Leijenaar
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Oncoradiomics, Liège, Belgium
| | - Arthur Jochems
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | - David Townend
- Department of Health, Ethics, and Society, CAPHRI (Care and Public Health Research Institute), Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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5
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Transformation of the Doctor-Patient Relationship: Big Data, Accountable Care, and Predictive Health Analytics. HEC Forum 2019; 31:261-282. [PMID: 31209679 DOI: 10.1007/s10730-019-09377-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The medical profession is steeped in traditions that guide its practice. These traditions were developed to preserve the well-being of patients. Transformations in science, technology, and society, while maintaining a self-governance structure that drives the goal of care provision, have remained hallmarks of the profession. The purpose of this paper is to examine ethical challenges in health care as it relates to Big Data, Accountable Care Organizations, and Health Care Predictive Analytics using the principles of biomedical ethics laid out by Beauchamp and Childress (autonomy, beneficence, non-maleficence, and justice). Among these are the use of Electronic Health Records within stipulations of the Health Insurance Portability and Accountability Act. Clinicians are well-positioned to impact health policy development to address ethical issues associated with the use of Big Data, Accountable Care, and Health Care Predictive Analytics as we work to transform the doctor-patient relationship towards improving population health outcomes and creating a healthier society.
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6
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Krumm N, Shirts BH. Technical, Biological, and Systems Barriers for Molecular Clinical Decision Support. Clin Lab Med 2019; 39:281-294. [PMID: 31036281 DOI: 10.1016/j.cll.2019.01.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Genome-enabled or molecular clinical decision support (CDS) systems provide unique advantages for the clinical use of genomic data; however, their implementation is complicated by technical, biological, and systemic barriers. This article reviews the substantial technical progress that has been made in the past decade and finds that the underlying biological limitations of genomics as well as systemic barriers to adoption of molecular CDS have been comparatively underestimated. A hybrid consultative CDS system, which integrates a genomics consultant into an active CDS system, may provide an interim path forward.
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Affiliation(s)
- Niklas Krumm
- Department of Laboratory Medicine, University of Washington, Box 357110, 1959 Northeast Pacific Street, NW120, Seattle, WA 98195-7110, USA.
| | - Brian H Shirts
- Department of Laboratory Medicine, University of Washington, Box 357110, 1959 Northeast Pacific Street, NW120, Seattle, WA 98195-7110, USA
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7
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Shabani M. Blockchain-based platforms for genomic data sharing: a de-centralized approach in response to the governance problems? J Am Med Inform Assoc 2019; 26:76-80. [PMID: 30496430 PMCID: PMC7647160 DOI: 10.1093/jamia/ocy149] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 10/12/2018] [Accepted: 10/18/2018] [Indexed: 01/29/2023] Open
Abstract
Blockchain-based platforms are emerging to provide solutions for technical and governance challenges associated with genomic data sharing. Providing capabilities for distributed data stewardship and participatory access control along with effective ways for enforcement of the data access agreements and data ownership are among the major promises of these platforms.
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Affiliation(s)
- Mahsa Shabani
- Center for Biomedical Ethics and Law, Department of Public Health and Primary Care, University of Leuven, Leuven, Belgium
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8
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Swaminathan R, Huang Y, Miller K, Pastore M, Hashimoto S, Jacobson T, Mouhlas D, Lin S. Transferring Exome Sequencing Data from Clinical Laboratories to Healthcare Providers: Lessons Learned at a Pediatric Hospital. Front Genet 2018. [PMID: 29515625 PMCID: PMC5826334 DOI: 10.3389/fgene.2018.00054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The adoption rate of genome sequencing for clinical diagnostics has been steadily increasing leading to the possibility of improvement in diagnostic yields. Although laboratories generate a summary clinical report, sharing raw genomic data with healthcare providers is equally important, both for secondary research studies as well as for a deeper analysis of the data itself, as seen by the efforts from organizations such as American College of Medical Genetics and Genomics and Global Alliance for Genomics and Health. Here, we aim to describe the existing protocol of genomic data sharing between a certified clinical laboratory and a healthcare provider and highlight some of the lessons learned. This study tracked and subsequently evaluated the data transfer workflow for 19 patients, all of whom consented to be part of this research study and visited the genetics clinic at a tertiary pediatric hospital between April 2016 to December 2016. Two of the most noticeable elements observed through this study are the manual validation steps and the discrepancies in patient identifiers used by a clinical lab vs. healthcare provider. Both of these add complexity to the transfer process as well as make it more susceptible to errors. The results from this study highlight some of the critical changes that need to be made in order to improve genomic data sharing workflows between healthcare providers and clinical sequencing laboratories.
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Affiliation(s)
- Rajeswari Swaminathan
- Research Information Solutions and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, United States
| | - Yungui Huang
- Research Information Solutions and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, United States
| | - Katherine Miller
- Research Information Solutions and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, United States
| | - Matthew Pastore
- Division of Molecular and Human Genetics, Nationwide Children's Hospital, Columbus, OH, United States
| | - Sayaka Hashimoto
- Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
| | - Theodora Jacobson
- Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
| | - Danielle Mouhlas
- Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
| | - Simon Lin
- Research Information Solutions and Innovation, The Research Institute at Nationwide Children's Hospital, Columbus, OH, United States
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