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Horgan D, Borisch B, Cattaneo I, Caulfield M, Chiti A, Chomienne C, Cole A, Facey K, Hackshaw A, Hendolin M, Georges N, Kalra D, Tumienė B, von Meyenn M. Factors Affecting Citizen Trust and Public Engagement Relating to the Generation and Use of Real-World Evidence in Healthcare. Int J Environ Res Public Health 2022; 19:ijerph19031674. [PMID: 35162696 PMCID: PMC8835047 DOI: 10.3390/ijerph19031674] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/21/2022] [Accepted: 01/28/2022] [Indexed: 01/09/2023]
Abstract
The potential for the use of real-world data (RWD) to generate real-world evidence (RWE) that can inform clinical decision-making and health policy is increasingly recognized, albeit with hesitancy in some circles. If used appropriately, the rapidly expanding wealth of health data could improve healthcare research, delivery of care, and patient outcomes. However, this depends on two key factors: (1) building structures that increase the confidence and willingness of European Union (EU) citizens to permit the collection and use of their data, and (2) development of EU health policy to support and shape data collection infrastructures, methodologies, transmission, and use. The great potential for use of RWE in healthcare improvement merits careful exploration of the drivers of, and challenges preventing, efficient RWD curation. Literature-based research was performed to identify relevant themes and discussion topics for two sets of expert panels, organized by the European Alliance for Personalised Medicine. These expert panels discussed steps that would enable a gradual but steady growth in the quantity, quality, and beneficial deployment of RWE. Participants were selected to provide insight based on their professional medical, economic, patient, industry, or governmental experience. Here, we propose a framework that addresses public trust and access to data, cross-border governance, alignment of evidence frameworks, and demonstrable improvements in healthcare decisions. We also discuss key case studies that support these recommendations, in accordance with the discussions at the expert panels.
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Affiliation(s)
- Denis Horgan
- European Alliance for Personalised Medicine, 1040 Brussels, Belgium
- Correspondence: ; Tel.: +386-30-607-281
| | - Bettina Borisch
- Department of Histopathology, University of Geneva, 1202 Geneva, Switzerland;
| | | | - Mark Caulfield
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK;
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20090 Milan, Italy;
- IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Christine Chomienne
- Cell Biology-Hematology Department, Université de Paris INSERM, 75010 Paris, France;
| | - Amanda Cole
- Office of Health Economics, London SW1E 6QT, UK;
| | - Karen Facey
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK;
| | - Allan Hackshaw
- Faculty of Medical Sciences, University College London, London W1T 4TJ, UK;
| | - Minna Hendolin
- Sitra—The Finnish Innovation Fund, 00180 Helsinki, Finland;
| | | | - Dipak Kalra
- Faculty of Medicine and Health Sciences, University of Ghent, 9000 Ghent, Belgium;
| | - Birutė Tumienė
- Vilnius University Hospital Santariskiu Klinikos, 08661 Vilnius, Lithuania;
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Rao S, Beckman RA, Riazi S, Yabar CS, Boca SM, Marshall JL, Pishvaian MJ, Brody JR, Madhavan S. Quantification and expert evaluation of evidence for chemopredictive biomarkers to personalize cancer treatment. Oncotarget 2018; 8:37923-37934. [PMID: 27888622 PMCID: PMC5514962 DOI: 10.18632/oncotarget.13544] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Accepted: 11/12/2016] [Indexed: 02/06/2023] Open
Abstract
Predictive biomarkers have the potential to facilitate cancer precision medicine by guiding the optimal choice of therapies for patients. However, clinicians are faced with an enormous volume of often-contradictory evidence regarding the therapeutic context of chemopredictive biomarkers. We extensively surveyed public literature to systematically review the predictive effect of 7 biomarkers claimed to predict response to various chemotherapy drugs: ERCC1-platinums, RRM1-gemcitabine, TYMS-5-fluorouracil/Capecitabine, TUBB3-taxanes, MGMT-temozolomide, TOP1-irinotecan/topotecan, and TOP2A-anthracyclines. We focused on studies that investigated changes in gene or protein expression as predictors of drug sensitivity or resistance. We considered an evidence framework that ranked studies from high level I evidence for randomized controlled trials to low level IV evidence for pre-clinical studies and patient case studies. We found that further in-depth analysis will be required to explore methodological issues, inconsistencies between studies, and tumor specific effects present even within high evidence level studies. Some of these nuances will lend themselves to automation, others will require manual curation. However, the comprehensive cataloging and analysis of dispersed public data utilizing an evidence framework provides a high level perspective on clinical actionability of these protein biomarkers. This framework and perspective will ultimately facilitate clinical trial design as well as therapeutic decision-making for individual patients.
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Affiliation(s)
- Shruti Rao
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA
| | - Robert A Beckman
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA.,Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.,Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
| | - Shahla Riazi
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA
| | - Cinthya S Yabar
- Pancreas, Biliary and Related Cancer Center, Department of Surgery, Thomas Jefferson University, Philadelphia, PA, USA.,Department of Surgery, Albert Einstein Medical Center, Philadelphia, PA, USA
| | - Simina M Boca
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA.,Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.,Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
| | - John L Marshall
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.,Otto J. Ruesch Center for the Cure of Gastrointestinal Cancer, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Michael J Pishvaian
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.,Otto J. Ruesch Center for the Cure of Gastrointestinal Cancer, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Jonathan R Brody
- Pancreas, Biliary and Related Cancer Center, Department of Surgery, Thomas Jefferson University, Philadelphia, PA, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA.,Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
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Strande NT, Riggs ER, Buchanan AH, Ceyhan-Birsoy O, DiStefano M, Dwight SS, Goldstein J, Ghosh R, Seifert BA, Sneddon TP, Wright MW, Milko LV, Cherry JM, Giovanni MA, Murray MF, O’Daniel JM, Ramos EM, Santani AB, Scott AF, Plon SE, Rehm HL, Martin CL, Berg JS. Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource. Am J Hum Genet 2017; 100:895-906. [PMID: 28552198 DOI: 10.1016/j.ajhg.2017.04.015] [Citation(s) in RCA: 325] [Impact Index Per Article: 46.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 04/26/2017] [Indexed: 10/19/2022] Open
Abstract
With advances in genomic sequencing technology, the number of reported gene-disease relationships has rapidly expanded. However, the evidence supporting these claims varies widely, confounding accurate evaluation of genomic variation in a clinical setting. Despite the critical need to differentiate clinically valid relationships from less well-substantiated relationships, standard guidelines for such evaluation do not currently exist. The NIH-funded Clinical Genome Resource (ClinGen) has developed a framework to define and evaluate the clinical validity of gene-disease pairs across a variety of Mendelian disorders. In this manuscript we describe a proposed framework to evaluate relevant genetic and experimental evidence supporting or contradicting a gene-disease relationship and the subsequent validation of this framework using a set of representative gene-disease pairs. The framework provides a semiquantitative measurement for the strength of evidence of a gene-disease relationship that correlates to a qualitative classification: "Definitive," "Strong," "Moderate," "Limited," "No Reported Evidence," or "Conflicting Evidence." Within the ClinGen structure, classifications derived with this framework are reviewed and confirmed or adjusted based on clinical expertise of appropriate disease experts. Detailed guidance for utilizing this framework and access to the curation interface is available on our website. This evidence-based, systematic method to assess the strength of gene-disease relationships will facilitate more knowledgeable utilization of genomic variants in clinical and research settings.
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