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Seong D, Jung S, Bae S, Chung J, Son DS, Yi BK. Fast Healthcare Interoperability Resources (FHIR)-Based Quality Information Exchange for Clinical Next-Generation Sequencing Genomic Testing: Implementation Study. J Med Internet Res 2021; 23:e26261. [PMID: 33908889 PMCID: PMC8116992 DOI: 10.2196/26261] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/10/2021] [Accepted: 04/03/2021] [Indexed: 01/23/2023] Open
Abstract
Background Next-generation sequencing (NGS) technology has been rapidly adopted in clinical practice, with the scope extended to early diagnosis, disease classification, and treatment planning. As the number of requests for NGS genomic testing increases, substantial efforts have been made to deliver the testing results clearly and unambiguously. For the legitimacy of clinical NGS genomic testing, quality information from the process of producing genomic data should be included within the results. However, most reports provide insufficient quality information to confirm the reliability of genomic testing owing to the complexity of the NGS process. Objective The goal of this study was to develop a Fast Healthcare Interoperability Resources (FHIR)–based web app, NGS Quality Reporting (NGS-QR), to report and manage the quality of the information obtained from clinical NGS genomic tests. Methods We defined data elements for the exchange of quality information from clinical NGS genomic tests, and profiled a FHIR genomic resource to enable information exchange in a standardized format. We then developed the FHIR-based web app and FHIR server to exchange quality information, along with statistical analysis tools implemented with the R Shiny server. Results Approximately 1000 experimental data entries collected from the targeted sequencing pipeline CancerSCAN designed by Samsung Medical Center were used to validate implementation of the NGS-QR app using real-world data. The user can share the quality information of NGS genomic testing and verify the quality status of individual samples in the overall distribution. Conclusions This study successfully demonstrated how quality information of clinical NGS genomic testing can be exchanged in a standardized format. As the demand for NGS genomic testing in clinical settings increases and genomic data accumulate, quality information can be used as reference material to improve the quality of testing. This app could also motivate laboratories to perform diagnostic tests to provide high-quality genomic data.
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Affiliation(s)
- Donghyeong Seong
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Sungwon Jung
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Sungchul Bae
- Smart Healthcare Research Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Jongsuk Chung
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Dae-Soon Son
- School of Big Data Science, Data Science Convergence Research Center, Hallym University, Chuncheon, Republic of Korea
| | - Byoung-Kee Yi
- Smart Healthcare Research Institute, Samsung Medical Center, Seoul, Republic of Korea.,Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
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Ayati N, Afzali M, Hasanzad M, Kebriaeezadeh A, Rajabzadeh A, Nikfar S. Pharmacogenomics Implementation and Hurdles to Overcome; In the Context of a Developing Country. IRANIAN JOURNAL OF PHARMACEUTICAL RESEARCH : IJPR 2021; 20:92-106. [PMID: 35194431 PMCID: PMC8842599 DOI: 10.22037/ijpr.2021.114899.15091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Having multiple dimensions, uncertainties and several stakeholders, the costly pharmacogenomics (PGx) is associated with dynamic implementation complexities. Identification of these challenges is critical to harness its full potential, especially in developing countries with fragile healthcare systems and scarce resources. This is the first study aimed to identify most salient challenges related to PGx implementation, with respect to the experiences of early-adopters and local experts' prospects, in the context of a developing country in the Middle East. To perform a comprehensive reconnaissance on PGx adoption challenges a scoping literature review was conducted based on national drug policy components: efficacy/safety, access, affordability and rational use of medicine (RUM). Strategic option development and analysis workshop method with cognitive mapping as the technique was used to evaluate challenges in the context of Iran. The cognitive maps were face-validated and analyzed via Decision Explorer XML. The findings indicated a complex network of issues relative to PGx adoption, categorized in national drug policy indicators. In the rational use of medicine category, ethics, education, bench -to- bedside strategies, guidelines, compliance, and health system issues were found. Clinical trial issues, test's utility, and biomarker validation were identified in the efficacy group. Affordability included pricing, reimbursement, and value assessment issues. Finally, access category included regulation, availability, and stakeholder management challenges. The current study identified the most significant challenges ahead of clinical implementation of PGx in a developing country. This could be the basis of a policy-note development in future work, which may consolidate vital communication among stakeholders and accelerate the efficient implementation in developing new-comer countries.
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Affiliation(s)
- Nayyereh Ayati
- Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
| | - Monireh Afzali
- Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
| | - Mandana Hasanzad
- Medical Genomics Research Center, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran. ,Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
| | - Abbas Kebriaeezadeh
- Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences (TUMS), Tehran, Iran. ,Department of Toxicology and Pharmacology, Faculty of Pharmacy, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
| | - Ali Rajabzadeh
- Department of Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
| | - Shekoufeh Nikfar
- Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences (TUMS), Tehran, Iran. ,Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran. ,Corresponding author: E-mail:
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3
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Ryu B, Shin SY, Baek RM, Kim JW, Heo E, Kang I, Yang JS, Yoo S. Clinical Genomic Sequencing Reports in Electronic Health Record Systems Based on International Standards: Implementation Study. J Med Internet Res 2020; 22:e15040. [PMID: 32773368 PMCID: PMC7445611 DOI: 10.2196/15040] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 10/30/2019] [Accepted: 06/03/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND To implement standardized machine-processable clinical sequencing reports in an electronic health record (EHR) system, the International Organization for Standardization Technical Specification (ISO/TS) 20428 international standard was proposed for a structured template. However, there are no standard implementation guidelines for data items from the proposed standard at the clinical site and no guidelines or references for implementing gene sequencing data results for clinical use. This is a significant challenge for implementation and application of these standards at individual sites. OBJECTIVE This study examines the field utilization of genetic test reports by designing the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) for genomic data elements based on the ISO/TS 20428 standard published as the standard for genomic test reports. The goal of this pilot is to facilitate the reporting and viewing of genomic data for clinical applications. FHIR Genomics resources predominantly focus on transmitting or representing sequencing data, which is of less clinical value. METHODS In this study, we describe the practical implementation of ISO/TS 20428 using HL7 FHIR Genomics implementation guidance to efficiently deliver the required genomic sequencing results to clinicians through an EHR system. RESULTS We successfully administered a structured genomic sequencing report in a tertiary hospital in Korea based on international standards. In total, 90 FHIR resources were used. Among 41 resources for the required fields, 26 were reused and 15 were extended. For the optional fields, 28 were reused and 21 were extended. CONCLUSIONS To share and apply genomic sequencing data in both clinical practice and translational research, it is essential to identify the applicability of the standard-based information system in a practical setting. This prototyping work shows that reporting data from clinical genomics sequencing can be effectively implemented into an EHR system using the existing ISO/TS 20428 standard and FHIR resources.
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Affiliation(s)
- Borim Ryu
- Office of eHealth and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Soo-Yong Shin
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
- Big Data Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Rong-Min Baek
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jeong-Whun Kim
- Department of Otorhinolaryngology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Eunyoung Heo
- Office of eHealth and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Inchul Kang
- Research and Development Center, ezCaretech, Seoul, Republic of Korea
| | | | - Sooyoung Yoo
- Office of eHealth and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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4
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FHIR Genomics: enabling standardization for precision medicine use cases. NPJ Genom Med 2020; 5:13. [PMID: 32194985 PMCID: PMC7080712 DOI: 10.1038/s41525-020-0115-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 01/02/2020] [Indexed: 01/16/2023] Open
Abstract
The development of Fast Healthcare Interoperability Resources (FHIR) Genomics, a feasible and efficient method for exchanging complex clinical genomic data and interpretations, is described. FHIR Genomics is a subset of the emerging Health Level 7 FHIR standard and targets data from increasingly available technologies such as next-generation sequencing. Much care and integration of feedback have been taken to ease implementation, facilitate wide-scale interoperability, and enable modern app development toward a complete precision medicine standard. A new use case, the integration of the Variant Interpretation for Cancer Consortium (VICC) “meta-knowledgebase” into a third-party application, is described.
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Gazzarata R, Monteverde ME, Ruggiero C, Maggi N, Palmieri D, Parruti G, Giacomini M. Healthcare Associated Infections: An Interoperable Infrastructure for Multidrug Resistant Organism Surveillance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E465. [PMID: 31936787 PMCID: PMC7013448 DOI: 10.3390/ijerph17020465] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/25/2019] [Accepted: 12/30/2019] [Indexed: 01/26/2023]
Abstract
Prevention and surveillance of healthcare associated infections caused by multidrug resistant organisms (MDROs) has been given increasing attention in recent years and is nowadays a major priority for health care systems. The creation of automated regional, national and international surveillance networks plays a key role in this respect. A surveillance system has been designed for the Abruzzo region in Italy, focusing on the monitoring of the MDROs prevalence in patients, on the appropriateness of antibiotic prescription in hospitalized patients and on foreseeable interactions with other networks at national and international level. The system has been designed according to the Service Oriented Architecture (SOA) principles, and Healthcare Service Specification (HSSP) standards and Clinical Document Architecture Release 2 (CDAR2) have been adopted. A description is given with special reference to implementation state, specific design and implementation choices and next foreseeable steps. The first release will be delivered at the Complex Operating Unit of Infectious Diseases of the Local Health Authority of Pescara (Italy).
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Affiliation(s)
| | | | - Carmelina Ruggiero
- Healthropy S.r.l., 17100 Savona, Italy (C.R.); (N.M.); (M.G.)
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genoa, Italy
| | - Norbert Maggi
- Healthropy S.r.l., 17100 Savona, Italy (C.R.); (N.M.); (M.G.)
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genoa, Italy
| | - Dalia Palmieri
- Epidemiology Office, Azienda Unità Sanitaria Locale (AUSL) di Pescara, 65124 Pescara, Italy;
| | - Giustino Parruti
- Department of Infectious Disease, Azienda Sanitaria Locale (AUSL) di Pescara, 65124 Pescara, Italy;
| | - Mauro Giacomini
- Healthropy S.r.l., 17100 Savona, Italy (C.R.); (N.M.); (M.G.)
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genoa, Italy
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Aronson S, Babb L, Ames D, Gibbs RA, Venner E, Connelly JJ, Marsolo K, Weng C, Williams MS, Hartzler AL, Liang WH, Ralston JD, Devine EB, Murphy S, Chute CG, Caraballo PJ, Kullo IJ, Freimuth RR, Rasmussen LV, Wehbe FH, Peterson JF, Robinson JR, Wiley K, Overby Taylor C. Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example. J Am Med Inform Assoc 2019; 25:1375-1381. [PMID: 29860405 DOI: 10.1093/jamia/ocy051] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 04/18/2018] [Indexed: 11/14/2022] Open
Abstract
The eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.
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Affiliation(s)
- Samuel Aronson
- Research Information Science and Computing, Partners HealthCare, Boston, Massachusetts, USA.,Partners Personalized Medicine, Partners HealthCare, Boston Massachusetts, USA
| | - Lawrence Babb
- Mitogen-GeneInsight, Sunquest Information Systems, Boston, Massachusetts, USA
| | - Darren Ames
- DNAnexus, Inc., Mountain View, California, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
| | - Eric Venner
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
| | - John J Connelly
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Keith Marsolo
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, USA
| | - Andrea L Hartzler
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.,Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Wayne H Liang
- Informatics Institute, University of Alabama at Birmingham, Birmingham, Alabama, USA.,Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - James D Ralston
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.,Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Emily Beth Devine
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Shawn Murphy
- Research Information Science and Computing, Partners HealthCare, Boston, Massachusetts, USA
| | | | | | - Iftikhar J Kullo
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA
| | - Robert R Freimuth
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University, Chicago, Illinois, USA
| | - Firas H Wehbe
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University, Chicago, Illinois, USA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jamie R Robinson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ken Wiley
- National Human Genome Research Institute, Bethesda, Maryland, USA
| | - Casey Overby Taylor
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, USA.,Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
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7
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Gray SW, Gagan J, Cerami E, Cronin AM, Uno H, Oliver N, Lowenstein C, Lederman R, Revette A, Suarez A, Lee C, Bryan J, Sholl L, Van Allen EM. Interactive or static reports to guide clinical interpretation of cancer genomics. J Am Med Inform Assoc 2019; 25:458-464. [PMID: 29315417 PMCID: PMC6018970 DOI: 10.1093/jamia/ocx150] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 12/01/2017] [Indexed: 12/20/2022] Open
Abstract
Objective Misinterpretation of complex genomic data presents a major challenge in the implementation of precision oncology. We sought to determine whether interactive genomic reports with embedded clinician education and optimized data visualization improved genomic data interpretation. Materials and Methods We conducted a randomized, vignette-based survey study to determine whether exposure to interactive reports for a somatic gene panel, as compared to static reports, improves physicians’ genomic comprehension and report-related satisfaction (overall scores calculated across 3 vignettes, range 0–18 and 1–4, respectively, higher score corresponding with improved endpoints). Results One hundred and five physicians at a tertiary cancer center participated (29% participation rate): 67% medical, 20% pediatric, 7% radiation, and 7% surgical oncology; 37% female. Prior to viewing the case-based vignettes, 34% of the physicians reported difficulty making treatment recommendations based on the standard static report. After vignette/report exposure, physicians’ overall comprehension scores did not differ by report type (mean score: interactive 11.6 vs static 10.5, difference = 1.1, 95% CI, −0.3, 2.5, P = .13). However, physicians exposed to the interactive report were more likely to correctly assess sequencing quality (P < .001) and understand when reports needed to be interpreted with caution (eg, low tumor purity; P = .02). Overall satisfaction scores were higher in the interactive group (mean score 2.5 vs 2.1, difference = 0.4, 95% CI, 0.2-0.7, P = .001). Discussion and Conclusion Interactive genomic reports may improve physicians’ ability to accurately assess genomic data and increase report-related satisfaction. Additional research in users’ genomic needs and efforts to integrate interactive reports into electronic health records may facilitate the implementation of precision oncology.
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Affiliation(s)
- Stacy W Gray
- City of Hope Comprehensive Cancer Center, Duarte, CA, USA.,Beckman Research Institute, Duarte, CA, USA
| | - Jeffrey Gagan
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Ethan Cerami
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Angel M Cronin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Hajime Uno
- Harvard Medical School, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nelly Oliver
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Carol Lowenstein
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ruth Lederman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Anna Revette
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | | | - Lynette Sholl
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Eliezer M Van Allen
- Harvard Medical School, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,The Broad Institute, Cambridge, MA, USA
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9
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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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10
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Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2016:3617572. [PMID: 27195526 PMCID: PMC4955563 DOI: 10.1155/2016/3617572] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Accepted: 03/17/2016] [Indexed: 12/30/2022]
Abstract
Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.
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11
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Crump JK, Del Fiol G, Williams MS, Freimuth RR. Prototype of a Standards-Based EHR and Genetic Test Reporting Tool Coupled with HL7-Compliant Infobuttons. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2018; 2017:330-339. [PMID: 29888091 PMCID: PMC5961781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Integration of genetic information is becoming increasingly important in clinical practice. However, genetic information is often ambiguous and difficult to understand, and clinicians have reported low-self-efficacy in integrating genetics into their care routine. The Health Level Seven (HL7) Infobutton standard helps to integrate online knowledge resources within Electronic Health Records (EHRs) and is required for EHR certification in the US. We implemented a prototype of a standards-based genetic reporting application coupled with infobuttons leveraging the Infobutton and Fast Healthcare Interoperability Resources (FHIR) Standards. Infobutton capabilities were provided by Open Infobutton, an open source package compliant with the HL7 Infobutton Standard. The resulting prototype demonstrates how standards-based reporting of genetic results, coupled with curated knowledge resources, can provide dynamic access to clinical knowledge on demand at the point of care. The proposed functionality can be enabled within any EHR system that has been certified through the US Meaningful Use program.
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Affiliation(s)
- Jacob K Crump
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | - Robert R Freimuth
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
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12
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Best M, Newson AJ, Meiser B, Juraskova I, Goldstein D, Tucker K, Ballinger ML, Hess D, Schlub TE, Biesecker B, Vines R, Vines K, Thomas D, Young MA, Savard J, Jacobs C, Butow P. The PiGeOn project: protocol of a longitudinal study examining psychosocial and ethical issues and outcomes in germline genomic sequencing for cancer. BMC Cancer 2018; 18:454. [PMID: 29685123 PMCID: PMC5914013 DOI: 10.1186/s12885-018-4366-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 04/12/2018] [Indexed: 12/23/2022] Open
Abstract
Background Advances in genomics offer promise for earlier detection or prevention of cancer, by personalisation of medical care tailored to an individual’s genomic risk status. However genome sequencing can generate an unprecedented volume of results for the patient to process with potential implications for their families and reproductive choices. This paper describes a protocol for a study (PiGeOn) that aims to explore how patients and their blood relatives experience germline genomic sequencing, to help guide the appropriate future implementation of genome sequencing into routine clinical practice. Methods We have designed a mixed-methods, prospective, cohort sub-study of a germline genomic sequencing study that targets adults with cancer suggestive of a genetic aetiology. One thousand probands and 2000 of their blood relatives will undergo germline genomic sequencing as part of the parent study in Sydney, Australia between 2016 and 2020. Test results are expected within12–15 months of recruitment. For the PiGeOn sub-study, participants will be invited to complete surveys at baseline, three months and twelve months after baseline using self-administered questionnaires, to assess the experience of long waits for results (despite being informed that results may not be returned) and expectations of receiving them. Subsets of both probands and blood relatives will be purposively sampled and invited to participate in three semi-structured qualitative interviews (at baseline and each follow-up) to triangulate the data. Ethical themes identified in the data will be used to inform critical revisions of normative ethical concepts or frameworks. Discussion This will be one of the first studies internationally to follow the psychosocial impact on probands and their blood relatives who undergo germline genome sequencing, over time. Study results will inform ongoing ethical debates on issues such as informed consent for genomic sequencing, and informing participants and their relatives of specific results. The study will also provide important outcome data concerning the psychological impact of prolonged waiting for germline genomic sequencing. These data are needed to ensure that when germline genomic sequencing is introduced into standard clinical settings, ethical concepts are embedded, and patients and their relatives are adequately prepared and supported during and after the testing process.
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Affiliation(s)
- Megan Best
- Psycho-oncology Co-operative Research Group (PoCoG), Level 6 North, Lifehouse (C39Z), University of Sydney, Sydney, NSW, 2006, Australia. .,Sydney Health Ethics, Sydney School of Public Health, University of Sydney, Sydney, NSW, 2006, Australia.
| | - Ainsley J Newson
- Sydney Health Ethics, Sydney School of Public Health, University of Sydney, Sydney, NSW, 2006, Australia
| | - Bettina Meiser
- Prince of Wales Clinical School, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Ilona Juraskova
- Centre for Medical Psychology and Evidence-based Decision-making, School of Psychology (CeMPED - Psychology), University of Sydney, Sydney, NSW, 2006, Australia
| | - David Goldstein
- Prince of Wales Clinical School, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Kathy Tucker
- Hereditary Cancer Centre, Prince of Wales Hospital, Sydney, NSW, 2052, Australia
| | - Mandy L Ballinger
- Cancer Division, Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, NSW, 2021, Australia
| | - Dominique Hess
- Cancer Division, Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, NSW, 2021, Australia
| | - Timothy E Schlub
- Sydney School of Public Health, University of Sydney, Sydney, NSW, 2006, Australia
| | - Barbara Biesecker
- National Human Genome Research, National Institutes of Health, 31 Center Drive, MSC 2073, Bethesda, MD, 20892, USA
| | - Richard Vines
- Rare Cancers, PO Box 440, Bowral, NSW, 2576, Australia
| | - Kate Vines
- Rare Cancers, PO Box 440, Bowral, NSW, 2576, Australia
| | - David Thomas
- Cancer Division, Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, NSW, 2021, Australia
| | - Mary-Anne Young
- Genome One, Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, NSW, 2021, Australia
| | - Jacqueline Savard
- Sydney Health Ethics, Sydney School of Public Health, University of Sydney, Sydney, NSW, 2006, Australia
| | - Chris Jacobs
- Prince of Wales Clinical School, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Phyllis Butow
- Psycho-oncology Co-operative Research Group (PoCoG), Level 6 North, Lifehouse (C39Z), University of Sydney, Sydney, NSW, 2006, Australia.,Centre for Medical Psychology and Evidence-based Decision-making, School of Psychology (CeMPED - Psychology), University of Sydney, Sydney, NSW, 2006, Australia
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13
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Klein ME, Parvez MM, Shin JG. Clinical Implementation of Pharmacogenomics for Personalized Precision Medicine: Barriers and Solutions. J Pharm Sci 2017; 106:2368-2379. [DOI: 10.1016/j.xphs.2017.04.051] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 04/14/2017] [Accepted: 04/24/2017] [Indexed: 12/11/2022]
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14
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Heale BSE, Khalifa A, Stone BL, Nelson S, Del Fiol G. Physicians' pharmacogenomics information needs and seeking behavior: a study with case vignettes. BMC Med Inform Decis Mak 2017; 17:113. [PMID: 28764766 PMCID: PMC5540399 DOI: 10.1186/s12911-017-0510-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 07/19/2017] [Indexed: 11/10/2022] Open
Abstract
Background Genetic testing, especially in pharmacogenomics, can have a major impact on patient care. However, most physicians do not feel that they have sufficient knowledge to apply pharmacogenomics to patient care. Online information resources can help address this gap. We investigated physicians’ pharmacogenomics information needs and information-seeking behavior, in order to guide the design of pharmacogenomics information resources that effectively meet clinical information needs. Methods We performed a formative, mixed-method assessment of physicians’ information-seeking process in three pharmacogenomics case vignettes. Interactions of 6 physicians’ with online pharmacogenomics resources were recorded, transcribed, and analyzed for prominent themes. Quantitative data included information-seeking duration, page navigations, and number of searches entered. Results We found that participants searched an average of 8 min per case vignette, spent less than 30 s reviewing specific content, and rarely refined search terms. Participants’ information needs included a need for clinically meaningful descriptions of test interpretations, a molecular basis for the clinical effect of drug variation, information on the logistics of carrying out a genetic test (including questions related to cost, availability, test turn-around time, insurance coverage, and accessibility of expert support).Also, participants sought alternative therapies that would not require genetic testing. Conclusion This study of pharmacogenomics information-seeking behavior indicates that content to support their information needs is dispersed and hard to find. Our results reveal a set of themes that information resources can use to help physicians find and apply pharmacogenomics information to the care of their patients. Electronic supplementary material The online version of this article (doi:10.1186/s12911-017-0510-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bret S E Heale
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT, 84108, USA.,Intermountain Healthcare, West Valley, UT, USA
| | - Aly Khalifa
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT, 84108, USA
| | - Bryan L Stone
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Scott Nelson
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT, 84108, USA.
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15
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Soualmia LF, Lecroq T. Bioinformatics Methods and Tools to Advance Clinical Care. Findings from the Yearbook 2015 Section on Bioinformatics and Translational Informatics. Yearb Med Inform 2017; 10:170-3. [PMID: 26293864 DOI: 10.15265/iy-2015-026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain and clinical care. METHOD We provide a synopsis of the articles selected for the IMIA Yearbook 2015, from which we attempt to derive a synthetic overview of current and future activities in the field. As last year, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor has evaluated separately the set of 1,594 articles and the evaluation results were merged for retaining 15 articles for peer-review. RESULTS The selection and evaluation process of this Yearbook's section on Bioinformatics and Translational Informatics yielded four excellent articles regarding data management and genome medicine that are mainly tool-based papers. In the first article, the authors present PPISURV a tool for uncovering the role of specific genes in cancer survival outcome. The second article describes the classifier PredictSNP which combines six performing tools for predicting disease-related mutations. In the third article, by presenting a high-coverage map of the human proteome using high resolution mass spectrometry, the authors highlight the need for using mass spectrometry to complement genome annotation. The fourth article is also related to patient survival and decision support. The authors present datamining methods of large-scale datasets of past transplants. The objective is to identify chances of survival. CONCLUSIONS The current research activities still attest the continuous convergence of Bioinformatics and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care. Indeed, there is a need for powerful tools for managing and interpreting complex, large-scale genomic and biological datasets, but also a need for user-friendly tools developed for the clinicians in their daily practice. All the recent research and development efforts contribute to the challenge of impacting clinically the obtained results towards a personalized medicine.
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Affiliation(s)
- L F Soualmia
- Dr Lina F. Soualmia, Normandie Univ., Rouen University and Hospital, SIBM & LITIS EA 4108, Information Processing in Biology & Health, 1, rue de Germont, Cour Leschevin porte 21, 76031 Rouen Cedex, France, Tel : +33 232 885 869, E-mail:
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16
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Doig KD, Fellowes A, Bell AH, Seleznev A, Ma D, Ellul J, Li J, Doyle MA, Thompson ER, Kumar A, Lara L, Vedururu R, Reid G, Conway T, Papenfuss AT, Fox SB. PathOS: a decision support system for reporting high throughput sequencing of cancers in clinical diagnostic laboratories. Genome Med 2017; 9:38. [PMID: 28438193 PMCID: PMC5404673 DOI: 10.1186/s13073-017-0427-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 04/07/2017] [Indexed: 01/08/2023] Open
Abstract
Background The increasing affordability of DNA sequencing has allowed it to be widely deployed in pathology laboratories. However, this has exposed many issues with the analysis and reporting of variants for clinical diagnostic use. Implementing a high-throughput sequencing (NGS) clinical reporting system requires a diverse combination of capabilities, statistical methods to identify variants, global variant databases, a validated bioinformatics pipeline, an auditable laboratory workflow, reproducible clinical assays and quality control monitoring throughout. These capabilities must be packaged in software that integrates the disparate components into a useable system. Results To meet these needs, we developed a web-based application, PathOS, which takes variant data from a patient sample through to a clinical report. PathOS has been used operationally in the Peter MacCallum Cancer Centre for two years for the analysis, curation and reporting of genetic tests for cancer patients, as well as the curation of large-scale research studies. PathOS has also been deployed in cloud environments allowing multiple institutions to use separate, secure and customisable instances of the system. Increasingly, the bottleneck of variant curation is limiting the adoption of clinical sequencing for molecular diagnostics. PathOS is focused on providing clinical variant curators and pathology laboratories with a decision support system needed for personalised medicine. While the genesis of PathOS has been within cancer molecular diagnostics, the system is applicable to NGS clinical reporting generally. Conclusions The widespread availability of genomic sequencers has highlighted the limited availability of software to support clinical decision-making in molecular pathology. PathOS is a system that has been developed and refined in a hospital laboratory context to meet the needs of clinical diagnostics. The software is available as a set of Docker images and source code at https://github.com/PapenfussLab/PathOS. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0427-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kenneth D Doig
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia. .,Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia. .,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia. .,Peter MacCallum Cancer Centre, 305 Grattan Street, Parkville, VIC, 3000, Australia.
| | - Andrew Fellowes
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Anthony H Bell
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Andrei Seleznev
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - David Ma
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Jason Ellul
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Jason Li
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Maria A Doyle
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Ella R Thompson
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Amit Kumar
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Children's Cancer Institute, University of New South Wales, Sydney, NSW, Australia
| | - Luis Lara
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Ravikiran Vedururu
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Gareth Reid
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Thomas Conway
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Anthony T Papenfuss
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia.,Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Stephen B Fox
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.,Department of Pathology, University of Melbourne, Melbourne, VIC, Australia
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17
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He KY, Ge D, He MM. Big Data Analytics for Genomic Medicine. Int J Mol Sci 2017; 18:ijms18020412. [PMID: 28212287 PMCID: PMC5343946 DOI: 10.3390/ijms18020412] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 02/08/2017] [Accepted: 02/09/2017] [Indexed: 12/25/2022] Open
Abstract
Genomic medicine attempts to build individualized strategies for diagnostic or therapeutic decision-making by utilizing patients’ genomic information. Big Data analytics uncovers hidden patterns, unknown correlations, and other insights through examining large-scale various data sets. While integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a Big Data infrastructure exhibit challenges, they also provide a feasible opportunity to develop an efficient and effective approach to identify clinically actionable genetic variants for individualized diagnosis and therapy. In this paper, we review the challenges of manipulating large-scale next-generation sequencing (NGS) data and diverse clinical data derived from the EHRs for genomic medicine. We introduce possible solutions for different challenges in manipulating, managing, and analyzing genomic and clinical data to implement genomic medicine. Additionally, we also present a practical Big Data toolset for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.
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Affiliation(s)
- Karen Y He
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA.
| | | | - Max M He
- BioSciKin Co., Ltd., Nanjing 210042, China.
- Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA.
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18
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Abstract
The rise of genomically targeted therapies and immunotherapy has revolutionized the practice of oncology in the last 10–15 years. At the same time, new technologies and the electronic health record (EHR) in particular have permeated the oncology clinic. Initially designed as billing and clinical documentation systems, EHR systems have not anticipated the complexity and variety of genomic information that needs to be reviewed, interpreted, and acted upon on a daily basis. Improved integration of cancer genomic data with EHR systems will help guide clinician decision making, support secondary uses, and ultimately improve patient care within oncology clinics. Some of the key factors relating to the challenge of integrating cancer genomic data into EHRs include: the bioinformatics pipelines that translate raw genomic data into meaningful, actionable results; the role of human curation in the interpretation of variant calls; and the need for consistent standards with regard to genomic and clinical data. Several emerging paradigms for integration are discussed in this review, including: non-standardized efforts between individual institutions and genomic testing laboratories; “middleware” products that portray genomic information, albeit outside of the clinical workflow; and application programming interfaces that have the potential to work within clinical workflow. The critical need for clinical-genomic knowledge bases, which can be independent or integrated into the aforementioned solutions, is also discussed.
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Affiliation(s)
- Jeremy L Warner
- Department of Medicine, Division of Hematology/Oncology, Vanderbilt University, Nashville, TN, USA. .,Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37232, USA. .,Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Sandeep K Jain
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37232, USA.,Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
| | - Mia A Levy
- Department of Medicine, Division of Hematology/Oncology, Vanderbilt University, Nashville, TN, USA.,Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37232, USA.,Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
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19
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Rasmussen LV, Overby CL, Connolly J, Chute CG, Denny JC, Freimuth R, Hartzler AL, Holm IA, Manzi S, Pathak J, Peissig PL, Smith M, Williams MS, Shirts BH, Stoffel EM, Tarczy-Hornoch P, Rohrer Vitek CR, Wolf WA, Starren J. Practical considerations for implementing genomic information resources. Experiences from eMERGE and CSER. Appl Clin Inform 2016; 7:870-82. [PMID: 27652374 DOI: 10.4338/aci-2016-04-ra-0060] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 08/12/2016] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES To understand opinions and perceptions on the state of information resources specifically targeted to genomics, and approaches to delivery in clinical practice. METHODS We conducted a survey of genomic content use and its clinical delivery from representatives across eight institutions in the electronic Medical Records and Genomics (eMERGE) network and two institutions in the Clinical Sequencing Exploratory Research (CSER) consortium in 2014. RESULTS Eleven responses representing distinct projects across ten sites showed heterogeneity in how content is being delivered, with provider-facing content primarily delivered via the electronic health record (EHR) (n=10), and paper/pamphlets as the leading mode for patient-facing content (n=9). There was general agreement (91%) that new content is needed for patients and providers specific to genomics, and that while aspects of this content could be shared across institutions there remain site-specific needs (73% in agreement). CONCLUSION This work identifies a need for the improved access to and expansion of information resources to support genomic medicine, and opportunities for content developers and EHR vendors to partner with institutions to develop needed resources, and streamline their use - such as a central content site in multiple modalities while implementing approaches to allow for site-specific customization.
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Affiliation(s)
- Luke V Rasmussen
- Luke Rasmussen, Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 750 North Lake Shore Drive, 11th Floor, Rubloff Building, Chicago, IL 60611, Phone: 312-503-2823
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20
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Mathias PC, Tarczy-Hornoch P, Shirts BH. Modeling the costs of clinical decision support for genomic precision medicine. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2016; 2016:60-4. [PMID: 27570652 PMCID: PMC5001767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Clinical decision support (CDS) within the electronic health record represents a promising mechanism to provide important genomic findings within clinical workflows. To better understand the current and possible future costs of genomic CDS, we leveraged our local CDS experience to assemble a simple model with inputs such as initial cost and numbers of patients, rules, and institutions. Our model assumed efficiencies of scale and allowed us to perform a one-way sensitivity analysis of the impact of each model input. The number of patients with genomic results per institution was the only single variable that could decrease the cost of CDS per useful alert below projected genomic sequencing costs. Because of the prohibitive upfront cost of sequencing large numbers of individuals, increasing the number of institutions using genomic CDS and improving the efficiency of sharing CDS infrastructure represent the most promising paths to making genomic CDS cost-effective.
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Affiliation(s)
| | - Peter Tarczy-Hornoch
- Departments of Biomedical Informatics and Medical Education, Pediatrics, and Computer Science and Engineering; University of Washington, Seattle, WA
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21
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Abstract
This article provides surgical pathologists an overview of health information systems (HISs): what they are, what they do, and how such systems relate to the practice of surgical pathology. Much of this article is dedicated to the electronic medical record. Information, in how it is captured, transmitted, and conveyed, drives the effectiveness of such electronic medical record functionalities. So critical is information from pathology in integrated clinical care that surgical pathologists are becoming gatekeepers of not only tissue but also information. Better understanding of HISs can empower surgical pathologists to become stakeholders who have an impact on the future direction of quality integrated clinical care.
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Affiliation(s)
- S Joseph Sirintrapun
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| | - David R Artz
- Memorial Sloan Kettering Cancer Center, 633 3rd Avenue, New York, NY 10017, USA
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22
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Green RC, Goddard KAB, Jarvik GP, Amendola LM, Appelbaum PS, Berg JS, Bernhardt BA, Biesecker LG, Biswas S, Blout CL, Bowling KM, Brothers KB, Burke W, Caga-Anan CF, Chinnaiyan AM, Chung WK, Clayton EW, Cooper GM, East K, Evans JP, Fullerton SM, Garraway LA, Garrett JR, Gray SW, Henderson GE, Hindorff LA, Holm IA, Lewis MH, Hutter CM, Janne PA, Joffe S, Kaufman D, Knoppers BM, Koenig BA, Krantz ID, Manolio TA, McCullough L, McEwen J, McGuire A, Muzny D, Myers RM, Nickerson DA, Ou J, Parsons DW, Petersen GM, Plon SE, Rehm HL, Roberts JS, Robinson D, Salama JS, Scollon S, Sharp RR, Shirts B, Spinner NB, Tabor HK, Tarczy-Hornoch P, Veenstra DL, Wagle N, Weck K, Wilfond BS, Wilhelmsen K, Wolf SM, Wynn J, Yu JH. Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine. Am J Hum Genet 2016; 98:1051-1066. [PMID: 27181682 DOI: 10.1016/j.ajhg.2016.04.011] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 04/14/2016] [Indexed: 12/11/2022] Open
Abstract
Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine.
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Affiliation(s)
- Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Partners Personalized Medicine, Boston, MA 02139, USA.
| | - Katrina A B Goddard
- Center for Health Research, Kaiser Permanente Northwest, Portland, OR 97227, USA
| | - Gail P Jarvik
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; Clinical Sequencing Exploratory Research Coordinating Center, University of Washington, Seattle, WA 98195, USA
| | - Laura M Amendola
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; Clinical Sequencing Exploratory Research Coordinating Center, University of Washington, Seattle, WA 98195, USA
| | - Paul S Appelbaum
- Department of Psychiatry, Columbia University Medical Center and New York State Psychiatric Institute, New York, NY 10032, USA
| | - Jonathan S Berg
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Barbara A Bernhardt
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Leslie G Biesecker
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Sawona Biswas
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Carrie L Blout
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Kevin M Bowling
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Kyle B Brothers
- Department of Pediatrics, University of Louisville, Louisville, KY 40202, USA
| | - Wylie Burke
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; Clinical Sequencing Exploratory Research Coordinating Center, University of Washington, Seattle, WA 98195, USA; Department of Bioethics and Humanities, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | | | - Arul M Chinnaiyan
- Michigan Center for Translational Pathology, Ann Arbor, MI 48109, USA; Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA; Departments of Pathology and Urology, University of Michigan, Ann Arbor, MI 48109, USA; Howard Hughes Medical Institute, Ann Arbor, MI 48109, USA
| | - Wendy K Chung
- Department of Pediatrics, Columbia University, New York, NY 10029, USA; Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA
| | - Ellen W Clayton
- Center for Biomedical Ethics and Society, Vanderbilt University, Nashville, TN 37203, USA
| | - Gregory M Cooper
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Kelly East
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - James P Evans
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Stephanie M Fullerton
- Department of Bioethics and Humanities, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Levi A Garraway
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Oncology and Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Jeremy R Garrett
- Children's Mercy Bioethics Center, Children's Mercy Hospital, Kansas City, MO 64108, USA; Departments of Pediatrics and Philosophy, University of Missouri - Kansas City, Kansas City, MO 64110, USA
| | - Stacy W Gray
- Harvard Medical School, Boston, MA 02115, USA; Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Gail E Henderson
- Department of Social Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Lucia A Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Ingrid A Holm
- Harvard Medical School, Boston, MA 02115, USA; Division of Genetics and Genomics and the Manton Center for Orphan Diseases Research, Boston Children's Hospital, Boston, MA 02115, USA
| | | | - Carolyn M Hutter
- Division of Genomic Medicine, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Pasi A Janne
- Harvard Medical School, Boston, MA 02115, USA; Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Steven Joffe
- Department of Medical Ethics & Health Policy, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - David Kaufman
- Division of Genomics and Society, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Bartha M Knoppers
- Centre of Genomics and Policy, Faculty of Medicine, Department of Human Genetics, McGill University, Montreal, QC H3A 1B1, Canada
| | - Barbara A Koenig
- Institute for Health and Aging, University of California, San Francisco, San Francisco, CA 94118, USA
| | - Ian D Krantz
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Teri A Manolio
- Division of Genomic Medicine, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Laurence McCullough
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jean McEwen
- Division of Genomics and Society, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Amy McGuire
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Donna Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Clinical Sequencing Exploratory Research Coordinating Center, University of Washington, Seattle, WA 98195, USA
| | - Jeffrey Ou
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; Clinical Sequencing Exploratory Research Coordinating Center, University of Washington, Seattle, WA 98195, USA
| | - Donald W Parsons
- Baylor College of Medicine and Texas Children's Cancer Center, Houston, TX 77030, USA
| | - Gloria M Petersen
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Sharon E Plon
- Baylor College of Medicine and Texas Children's Cancer Center, Houston, TX 77030, USA
| | - Heidi L Rehm
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Partners Personalized Medicine, Boston, MA 02139, USA; Laboratory for Molecular Medicine, Partners HealthCare, Cambridge, MA 02139, USA
| | - J Scott Roberts
- Department of Health Behavior & Health Education, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Dan Robinson
- Michigan Center for Translational Pathology, Ann Arbor, MI 48109, USA
| | - Joseph S Salama
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; Clinical Sequencing Exploratory Research Coordinating Center, University of Washington, Seattle, WA 98195, USA
| | - Sarah Scollon
- Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Richard R Sharp
- Biomedical Ethics Research Program, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Brian Shirts
- Department of Laboratory Medicine, University of Washington, Seattle, WA 98195, USA
| | - Nancy B Spinner
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Holly K Tabor
- Department of Pediatrics and Seattle Children's Research Institute, University of Washington, Seattle, WA, USA
| | - Peter Tarczy-Hornoch
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; University of Washington, Seattle, WA 98105, USA
| | - David L Veenstra
- Department of Pharmacy, University of Washington, Seattle, WA 98195, USA
| | - Nikhil Wagle
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Oncology and Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Karen Weck
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Benjamin S Wilfond
- Department of Pediatrics and Seattle Children's Research Institute, University of Washington, Seattle, WA, USA
| | - Kirk Wilhelmsen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Susan M Wolf
- Law School, Medical School, and Consortium on Law and Values in Health, Environment, & the Life Sciences, Minneapolis, University of Minnesota, MN 55455, USA
| | - Julia Wynn
- Department of Pediatrics, Columbia University, New York, NY 10029, USA
| | - Joon-Ho Yu
- Department of Pediatrics, University of Washington, Seattle, WA 98195, USA
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Abstract
OBJECTIVES Describe the state of Electronic Health Records (EHRs) in 1992 and their evolution by 2015 and where EHRs are expected to be in 25 years. Further to discuss the expectations for EHRs in 1992 and explore which of them were realized and what events accelerated or disrupted/derailed how EHRs evolved. METHODS Literature search based on "Electronic Health Record", "Medical Record", and "Medical Chart" using Medline, Google, Wikipedia Medical, and Cochrane Libraries resulted in an initial review of 2,356 abstracts and other information in papers and books. Additional papers and books were identified through the review of references cited in the initial review. RESULTS By 1992, hardware had become more affordable, powerful, and compact and the use of personal computers, local area networks, and the Internet provided faster and easier access to medical information. EHRs were initially developed and used at academic medical facilities but since most have been replaced by large vendor EHRs. While EHR use has increased and clinicians are being prepared to practice in an EHR-mediated world, technical issues have been overshadowed by procedural, professional, social, political, and especially ethical issues as well as the need for compliance with standards and information security. There have been enormous advancements that have taken place, but many of the early expectations for EHRs have not been realized and current EHRs still do not meet the needs of today's rapidly changing healthcare environment. CONCLUSION The current use of EHRs initiated by new technology would have been hard to foresee. Current and new EHR technology will help to provide international standards for interoperable applications that use health, social, economic, behavioral, and environmental data to communicate, interpret, and act intelligently upon complex healthcare information to foster precision medicine and a learning health system.
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Affiliation(s)
- R S Evans
- R. Scott Evans, MS, PhD, FACMI, Department of Medical Informatics, LDS Hospital, 8th Ave & C Street, Salt Lake City, Utah 84143, USA, Tel: +1 801 408-3029, Fax: +1 801 408-5802, E-mail:
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Kruglyak KM, Lin E, Ong FS. Next-Generation Sequencing and Applications to the Diagnosis and Treatment of Lung Cancer. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 890:123-36. [PMID: 26703802 DOI: 10.1007/978-3-319-24932-2_7] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Cancer is a genetic disease characterized by uncontrolled growth of abnormal cells. Over time, somatic mutations accumulate in the cells of an individual due to replication errors, chromosome segregation errors, or DNA damage. When not caught by traditional mechanisms, these somatic mutations can lead to cellular proliferation, the hallmark of cancer. Lung cancer is the leading cause of cancer-related mortality in the United States, accounting for approximately 160,000 deaths annually. Five year survival rates for lung cancer remain low (<50 %) for all stages, with even worse prognosis (<15 %) in late stage cases. Technological advances, including advances in next-generation sequencing (NGS), offer the vision of personalized medicine or precision oncology, wherein an individual's treatment can be based on his or her individual molecular profile, rather than on historical population-based medicine. Towards this end, NGS has already been used to identify new biomarker candidates for the early diagnosis of lung cancer and is increasingly used to guide personalized treatment decisions. In this review we will provide a high-level overview of NGS technology and summarize its application to the diagnosis and treatment of lung cancer. We will also describe how NGS can drive advances that bring us closer to precision oncology and discuss some of the technical challenges that will need to be overcome in order to realize this ultimate goal.
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Affiliation(s)
| | - Erick Lin
- Medical Affairs, Ambry Genetics, Inc., Aliso Viejo, CA, USA
| | - Frank S Ong
- Medical Affairs and Clinical Development, NantHealth, LLC, Culver City, CA, USA.
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25
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Aziz A, Kawamoto K, Eilbeck K, Williams MS, Freimuth RR, Hoffman MA, Rasmussen LV, Overby CL, Shirts BH, Hoffman JM, Welch BM. The genomic CDS sandbox: An assessment among domain experts. J Biomed Inform 2016; 60:84-94. [PMID: 26778834 DOI: 10.1016/j.jbi.2015.12.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 12/11/2015] [Accepted: 12/29/2015] [Indexed: 01/17/2023]
Abstract
Genomics is a promising tool that is becoming more widely available to improve the care and treatment of individuals. While there is much assertion, genomics will most certainly require the use of clinical decision support (CDS) to be fully realized in the routine clinical setting. The National Human Genome Research Institute (NHGRI) of the National Institutes of Health recently convened an in-person, multi-day meeting on this topic. It was widely recognized that there is a need to promote the innovation and development of resources for genomic CDS such as a CDS sandbox. The purpose of this study was to evaluate a proposed approach for such a genomic CDS sandbox among domain experts and potential users. Survey results indicate a significant interest and desire for a genomic CDS sandbox environment among domain experts. These results will be used to guide the development of a genomic CDS sandbox.
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Affiliation(s)
- Ayesha Aziz
- Medical University of South Carolina, Charleston, SC, United States.
| | | | - Karen Eilbeck
- University of Utah, Salt Lake City, UT, United States.
| | | | | | | | | | | | | | - James M Hoffman
- St. Jude Children's Research Hospital, Memphis, TN, United States.
| | - Brandon M Welch
- Medical University of South Carolina, Charleston, SC, United States.
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26
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Jacquez GM, Sabel CE, Shi C. Genetic GIScience: Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome. ANNALS OF THE ASSOCIATION OF AMERICAN GEOGRAPHERS. ASSOCIATION OF AMERICAN GEOGRAPHERS 2015; 105:454-472. [PMID: 26339073 PMCID: PMC4554694 DOI: 10.1080/00045608.2015.1018777] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The exposome, defined as the totality of an individual's exposures over the life course, is a seminal concept in the environmental health sciences. Although inherently geographic, the exposome as yet is unfamiliar to many geographers. This article proposes a place-based synthesis, genetic geographic information science (Genetic GISc) that is founded on the exposome, genome+ and behavome. It provides an improved understanding of human health in relation to biology (the genome+), environmental exposures (the exposome), and their social, societal and behavioral determinants (the behavome). Genetic GISc poses three key needs: First, a mathematical foundation for emergent theory; Second, process-based models that bridge biological and geographic scales; Third, biologically plausible estimates of space-time disease lags. Compartmental models are a possible solution; this article develops two models using pancreatic cancer as an exemplar. The first models carcinogenesis based on the cascade of mutations and cellular changes that lead to metastatic cancer. The second models cancer stages by diagnostic criteria. These provide empirical estimates of the distribution of latencies in cellular states and disease stages, and maps of the burden of yet to be diagnosed disease. This approach links our emerging knowledge of genomics to cancer progression at the cellular level, to individuals and their cancer stage at diagnosis, to geographic distributions of cancer in extant populations. These methodological developments and exemplar provide the basis for a new synthesis in health geography: genetic geographic information science.
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Affiliation(s)
- Geoffrey M Jacquez
- Department of Geography, University at Buffalo-State University of New York ; BioMedware
| | - Clive E Sabel
- School of Geographical Sciences, University of Bristol
| | - Chen Shi
- Department of Geography, University at Buffalo-State University of New York
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Regan K, Payne PRO. From Molecules to Patients: The Clinical Applications of Translational Bioinformatics. Yearb Med Inform 2015; 10:164-9. [PMID: 26293863 PMCID: PMC4587059 DOI: 10.15265/iy-2015-005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE In order to realize the promise of personalized medicine, Translational Bioinformatics (TBI) research will need to continue to address implementation issues across the clinical spectrum. In this review, we aim to evaluate the expanding field of TBI towards clinical applications, and define common themes and current gaps in order to motivate future research. METHODS Here we present the state-of-the-art of clinical implementation of TBI-based tools and resources. Our thematic analyses of a targeted literature search of recent TBI-related articles ranged across topics in genomics, data management, hypothesis generation, molecular epidemiology, diagnostics, therapeutics and personalized medicine. RESULTS Open areas of clinically-relevant TBI research identified in this review include developing data standards and best practices, publicly available resources, integrative systemslevel approaches, user-friendly tools for clinical support, cloud computing solutions, emerging technologies and means to address pressing legal, ethical and social issues. CONCLUSIONS There is a need for further research bridging the gap from foundational TBI-based theories and methodologies to clinical implementation. We have organized the topic themes presented in this review into four conceptual foci - domain analyses, knowledge engineering, computational architectures and computation methods alongside three stages of knowledge development in order to orient future TBI efforts to accelerate the goals of personalized medicine.
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Affiliation(s)
| | - P R O Payne
- Philip R.O. Payne, PhD, FACMI, The Ohio State University, Department of Biomedical Informatics, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH 43210, USA, Tel: +1 614 292 4778, E-mail:
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28
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Shirts BH, Salama JS, Aronson SJ, Chung WK, Gray SW, Hindorff LA, Jarvik GP, Plon SE, Stoffel EM, Tarczy-Hornoch PZ, Van Allen EM, Weck KE, Chute CG, Freimuth RR, Grundmeier RW, Hartzler AL, Li R, Peissig PL, Peterson JF, Rasmussen LV, Starren JB, Williams MS, Overby CL. CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record. J Am Med Inform Assoc 2015; 22:1231-42. [PMID: 26142422 DOI: 10.1093/jamia/ocv065] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Accepted: 05/12/2015] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Clinicians' ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS). MATERIALS AND METHODS The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement. RESULTS There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information. CONCLUSION Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.
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Affiliation(s)
- Brian H Shirts
- Department of Laboratory Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Joseph S Salama
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | | | - Wendy K Chung
- Department of Pediatrics, Columbia University Medical Center, New York, NY, USA
| | - Stacy W Gray
- Department of Medicine, Harvard Medical School, Boston, MA, USA Dana-Farber Cancer Institute, Boston, MA, USA
| | - Lucia A Hindorff
- National Human Genome Research Institute, NIH, Rockville, MD, USA
| | - Gail P Jarvik
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Sharon E Plon
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Elena M Stoffel
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Peter Z Tarczy-Hornoch
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Eliezer M Van Allen
- Dana-Farber Cancer Institute, Boston, MA, USA The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Karen E Weck
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christopher G Chute
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Robert R Freimuth
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Robert W Grundmeier
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrea L Hartzler
- Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
| | - Rongling Li
- National Human Genome Research Institute, NIH, Rockville, MD, USA
| | - Peggy L Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt, Nashville, TN, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Justin B Starren
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Marc S Williams
- Genome Medicine Institute, Geisinger Medical Center, Danville, PA, USA
| | - Casey L Overby
- Genome Medicine Institute, Geisinger Medical Center, Danville, PA, USA Department of Medicine, Program for Personalized and Genomic Medicine and Center for Health-Related Informatics and Bioimaging, University of Maryland School of Medicine, Baltimore, MD, USA
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29
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Meric-Bernstam F, Johnson A, Holla V, Bailey AM, Brusco L, Chen K, Routbort M, Patel KP, Zeng J, Kopetz S, Davies MA, Piha-Paul SA, Hong DS, Eterovic AK, Tsimberidou AM, Broaddus R, Bernstam EV, Shaw KR, Mendelsohn J, Mills GB. A decision support framework for genomically informed investigational cancer therapy. J Natl Cancer Inst 2015; 107:djv098. [PMID: 25863335 DOI: 10.1093/jnci/djv098] [Citation(s) in RCA: 145] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Rapidly improving understanding of molecular oncology, emerging novel therapeutics, and increasingly available and affordable next-generation sequencing have created an opportunity for delivering genomically informed personalized cancer therapy. However, to implement genomically informed therapy requires that a clinician interpret the patient's molecular profile, including molecular characterization of the tumor and the patient's germline DNA. In this Commentary, we review existing data and tools for precision oncology and present a framework for reviewing the available biomedical literature on therapeutic implications of genomic alterations. Genomic alterations, including mutations, insertions/deletions, fusions, and copy number changes, need to be curated in terms of the likelihood that they alter the function of a "cancer gene" at the level of a specific variant in order to discriminate so-called "drivers" from "passengers." Alterations that are targetable either directly or indirectly with approved or investigational therapies are potentially "actionable." At this time, evidence linking predictive biomarkers to therapies is strong for only a few genomic markers in the context of specific cancer types. For these genomic alterations in other diseases and for other genomic alterations, the clinical data are either absent or insufficient to support routine clinical implementation of biomarker-based therapy. However, there is great interest in optimally matching patients to early-phase clinical trials. Thus, we need accessible, comprehensive, and frequently updated knowledge bases that describe genomic changes and their clinical implications, as well as continued education of clinicians and patients.
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Affiliation(s)
- Funda Meric-Bernstam
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB).
| | - Amber Johnson
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Vijaykumar Holla
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Ann Marie Bailey
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Lauren Brusco
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Ken Chen
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Mark Routbort
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Keyur P Patel
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Jia Zeng
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Scott Kopetz
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Michael A Davies
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Sarina A Piha-Paul
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - David S Hong
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Agda Karina Eterovic
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Apostolia M Tsimberidou
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Russell Broaddus
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Elmer V Bernstam
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Kenna R Shaw
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - John Mendelsohn
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
| | - Gordon B Mills
- Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB)
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Abstract
This article provides surgical pathologists an overview of health information systems (HISs): what they are, what they do, and how such systems relate to the practice of surgical pathology. Much of this article is dedicated to the electronic medical record. Information, in how it is captured, transmitted, and conveyed, drives the effectiveness of such electronic medical record functionalities. So critical is information from pathology in integrated clinical care that surgical pathologists are becoming gatekeepers of not only tissue but also information. Better understanding of HISs can empower surgical pathologists to become stakeholders who have an impact on the future direction of quality integrated clinical care.
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Affiliation(s)
- S Joseph Sirintrapun
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| | - David R Artz
- Memorial Sloan Kettering Cancer Center, 633 3rd Avenue, New York, NY 10017, USA
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Welch BM, Kawamoto K. The need for clinical decision support integrated with the electronic health record for the clinical application of whole genome sequencing information. J Pers Med 2015; 3:306-25. [PMID: 25411643 PMCID: PMC4234059 DOI: 10.3390/jpm3040306] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Whole genome sequencing (WGS) is rapidly approaching widespread clinical application. Technology advancements over the past decade, since the first human genome was decoded, have made it feasible to use WGS for clinical care. Future advancements will likely drive down the price to the point wherein WGS is routinely available for care. However, were this to happen today, most of the genetic information available to guide clinical care would go unused due to the complexity of genetics, limited physician proficiency in genetics, and lack of genetics professionals in the clinical workforce. Furthermore, these limitations are unlikely to change in the future. As such, the use of clinical decision support (CDS) to guide genome-guided clinical decision-making is imperative. In this manuscript, we describe the barriers to widespread clinical application of WGS information, describe how CDS can be an important tool for overcoming these barriers, and provide clinical examples of how genome-enabled CDS can be used in the clinical setting.
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Affiliation(s)
- Brandon M. Welch
- Program in Personalized Health Care, University of Utah, 15 North 2030 East, EIHG Room 2110, Salt Lake City, UT 84112, USA
- Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112, USA; E-Mail:
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +1-585-455-0461
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112, USA; E-Mail:
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Welch BM, Rodriguez Loya S, Eilbeck K, Kawamoto K. A proposed clinical decision support architecture capable of supporting whole genome sequence information. J Pers Med 2015; 4:176-99. [PMID: 25411644 PMCID: PMC4234046 DOI: 10.3390/jpm4020176] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Whole genome sequence (WGS) information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS) offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR). A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1) each component of the architecture; (2) the interaction of the components; and (3) how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.
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Affiliation(s)
- Brandon M. Welch
- Program in Personalized Health Care, University of Utah, 15 North 2030 East, EIHG Room 2110, Salt Lake City, UT 84112, USA
- Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112, USA; E-Mails: (K.E.); (K.K.)
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +1-585-455-0461
| | - Salvador Rodriguez Loya
- School of Engineering and Informatics, University of Sussex, Shawcross Building, Room Gc4, Falmer, Brighton, East Sussex, BN1 9QT, UK; E-Mail:
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112, USA; E-Mails: (K.E.); (K.K.)
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112, USA; E-Mails: (K.E.); (K.K.)
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Prince AER, Conley JM, Davis AM, Lázaro-Muñoz G, Cadigan RJ. Automatic Placement of Genomic Research Results in Medical Records: Do Researchers Have a Duty? Should Participants Have a Choice? THE JOURNAL OF LAW, MEDICINE & ETHICS : A JOURNAL OF THE AMERICAN SOCIETY OF LAW, MEDICINE & ETHICS 2015; 43:827-42. [PMID: 26711421 PMCID: PMC4780406 DOI: 10.1111/jlme.12323] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In genomics research, it is becoming common practice to return individualized primary and incidental findings to participants and several ongoing major studies have begun to automatically transfer these results to a participant's clinical medical record. This paper explores who should decide whether to place genomic research findings into a clinical medical record. Should participants make this decision, or does a researcher's duty to place this information in a medical record override the participant's autonomy? We argue that there are no clear ethical, legal, professional, or regulatory duties that mandate placement without the consent of the participant. We conclude that informing participants of results, together with a clear explanation, relevant recommendations and referral sources, and the option to consent to placement in the medical records will best discharge researchers' ethical and legal duties towards participants.
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Affiliation(s)
- Anya E R Prince
- Postdoctoral Research Associate at the Center for Genomics and Society at the University of North Carolina at Chapel Hill School of Medicine. Ms. Prince received her Juris Doctor and Masters of Public Policy from Georgetown University in Washington, D.C
| | - John M Conley
- William Rand Kenan, Jr. Professor of Law at the University of North Carolina, and an investigator in the university's Center for Genomics and Society. He received his A.B. from Harvard University in Cambridge, MA, and J.D. and Ph.D. (Anthropology) from Duke University in Durham, NC
| | - Arlene M Davis
- Research Associate Professor in the Department of Social Medicine at the University of North Carolina, core faculty in its Center for Bioethics, and Adjunct Associate Professor at the University of North Carolina School of Law. She received her Juris Doctor from the University of Washington School of Law, Seattle
| | - Gabriel Lázaro-Muñoz
- Postdoctoral Research Associate at the Center for Genomics and Society at the University of North Carolina School of Medicine. Dr. Lázaro-Muñoz received his Ph.D. in Neuroscience from New York University; his J.D. from the University of Pennsylvania School of Law; his Master of Bioethics degree from the Perelman School of Medicine at the University of Pennsylvania; and his B.A. from the University of Puerto Rico, Río Piedras
| | - R Jean Cadigan
- Research Assistant Professor in the Department of Social Medicine at the University of North Carolina. She received her Ph.D. in anthropology from the University of California, Los Angeles
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Davis MF, Haines JL. The intelligent use and clinical benefits of electronic medical records in multiple sclerosis. Expert Rev Clin Immunol 2014; 11:205-11. [PMID: 25495075 DOI: 10.1586/1744666x.2015.991314] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Electronic medical records (EMRs) are being quickly adopted in clinics around the world. This advancement can greatly enhance the clinical care of patients with multiple sclerosis (MS) by providing formats that allow easier review of medical documents and more structured avenues to store relevant information. MS clinicians should be involved with implementing and updating EMRs at their institutions to ensure EMR formats that benefit MS clinics. EMRs also provide opportunities for research studies of MS to access detailed, longitudinal data of MS disease course that would otherwise be difficult to collect.
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Affiliation(s)
- Mary F Davis
- Brigham Young University, Microbiology and Molecular Biology, 4007 LSB, Provo, UT 84602, USA
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McLaughlin HM, Ceyhan-Birsoy O, Christensen KD, Kohane IS, Krier J, Lane WJ, Lautenbach D, Lebo MS, Machini K, MacRae CA, Azzariti DR, Murray MF, Seidman CE, Vassy JL, Green RC, Rehm HL. A systematic approach to the reporting of medically relevant findings from whole genome sequencing. BMC MEDICAL GENETICS 2014; 15:134. [PMID: 25714468 PMCID: PMC4342199 DOI: 10.1186/s12881-014-0134-1] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Accepted: 12/03/2014] [Indexed: 01/22/2023]
Abstract
Background The MedSeq Project is a randomized clinical trial developing approaches to assess the impact of integrating genome sequencing into clinical medicine. To facilitate the return of results of potential medical relevance to physicians and patients participating in the MedSeq Project, we sought to develop a reporting approach for the effective communication of such findings. Methods Genome sequencing was performed on the Illumina HiSeq platform. Variants were filtered, interpreted, and validated according to methods developed by the Laboratory for Molecular Medicine and consistent with current professional guidelines. The GeneInsight software suite, which is integrated with the Partners HealthCare electronic health record, was used for variant curation, report drafting, and delivery. Results We developed a concise 5–6 page Genome Report (GR) featuring a single-page summary of results of potential medical relevance with additional pages containing structured variant, gene, and disease information along with supporting evidence for reported variants and brief descriptions of associated diseases and clinical implications. The GR is formatted to provide a succinct summary of genomic findings, enabling physicians to take appropriate steps for disease diagnosis, prevention, and management in their patients. Conclusions Our experience highlights important considerations for the reporting of results of potential medical relevance and provides a framework for interpretation and reporting practices in clinical genome sequencing. Electronic supplementary material The online version of this article (doi:10.1186/s12881-014-0134-1) contains supplementary material, which is available to authorized users.
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Bennett NC, Farah CS. Next-generation sequencing in clinical oncology: next steps towards clinical validation. Cancers (Basel) 2014; 6:2296-312. [PMID: 25412366 PMCID: PMC4276967 DOI: 10.3390/cancers6042296] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2014] [Revised: 11/07/2014] [Accepted: 11/10/2014] [Indexed: 02/05/2023] Open
Abstract
Compelling evidence supports the transition of next generation sequencing (NGS) technology from a research environment into clinical practice. Before NGS technologies are fully adopted in the clinic, they should be thoroughly scrutinised for their potential as powerful diagnostic and prognostic tools. The importance placed on generating accurate NGS data, and consequently appropriate clinical interpretation, has stimulated much international discussion regarding the creation and implementation of strict guidelines and regulations for NGS clinical use. In the context of clinical oncology, NGS technologies are currently transitioning from a clinical research background into a setting where they will contribute significantly to individual patient cancer management. This paper explores the steps that have been taken, and those still required, for the transition of NGS into the clinical area, with particular emphasis placed on validation in the setting of clinical oncology.
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Affiliation(s)
- Nigel C Bennett
- The University of Queensland, UQ Centre for Clinical Research, Herston Qld 4029, Australia.
| | - Camile S Farah
- The University of Queensland, UQ Centre for Clinical Research, Herston Qld 4029, Australia.
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Overby CL, Rasmussen LV, Hartzler A, Connolly JJ, Peterson JF, Hedberg RE, Freimuth RR, Shirts BH, Denny JC, Larson EB, Chute CG, Jarvik GP, Ralston JD, Shuldiner AR, Starren J, Kullo IJ, Tarczy-Hornoch P, Williams MS. A Template for Authoring and Adapting Genomic Medicine Content in the eMERGE Infobutton Project. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2014; 2014:944-953. [PMID: 25954402 PMCID: PMC4419923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The Electronic Medical Records and Genomics (eMERGE) Network is a national consortium that is developing methods and best practices for using the electronic health record (EHR) for genomic medicine and research. We conducted a multi-site survey of information resources to support integration of pharmacogenomics into clinical care. This work aimed to: (a) characterize the diversity of information resource implementation strategies among eMERGE institutions; (b) develop a master template containing content topics of important for genomic medicine (as identified by the DISCERN-Genetics tool); and (c) assess the coverage of content topics among information resources developed by eMERGE institutions. Given that a standard implementation does not exist and sites relied on a diversity of information resources, we identified a need for a national effort to efficiently produce sharable genomic medicine resources capable of being accessed from the EHR. We discuss future areas of work to prepare institutions to use infobuttons for distributing standardized genomic content.
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Affiliation(s)
- Casey L Overby
- Program for Personalized and Genomic Medicine and Department of Medicine, University of Maryland, Baltimore, MD ; Center for Health-related Informatics and Bioimaging, University of Maryland, Baltimore, MD
| | - Luke V Rasmussen
- Department of Preventive Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA
| | - Andrea Hartzler
- The Information School, The Children's Hospital of Philadelphia, Philadelphia, PA
| | - John J Connolly
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN ; Department of Medicine, Vanderbilt University, Nashville, TN
| | - RoseMary E Hedberg
- Department of Preventive Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA
| | | | | | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN ; Department of Medicine, Vanderbilt University, Nashville, TN
| | | | | | - Gail P Jarvik
- Department of Medical Genetics, University of Washington, Seattle, WA
| | | | - Alan R Shuldiner
- Program for Personalized and Genomic Medicine and Department of Medicine, University of Maryland, Baltimore, MD
| | - Justin Starren
- Department of Preventive Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA ; Medical Social Sciences, Northwestern University, Chicago, IL
| | | | | | - Marc S Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, PA
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Welch BM, Rodriguez-Loya S, Eilbeck K, Kawamoto K. Clinical decision support for whole genome sequence information leveraging a service-oriented architecture: a prototype. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2014; 2014:1188-1197. [PMID: 25954430 PMCID: PMC4419907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Whole genome sequence (WGS) information could soon be routinely available to clinicians to support the personalized care of their patients. At such time, clinical decision support (CDS) integrated into the clinical workflow will likely be necessary to support genome-guided clinical care. Nevertheless, developing CDS capabilities for WGS information presents many unique challenges that need to be overcome for such approaches to be effective. In this manuscript, we describe the development of a prototype CDS system that is capable of providing genome-guided CDS at the point of care and within the clinical workflow. To demonstrate the functionality of this prototype, we implemented a clinical scenario of a hypothetical patient at high risk for Lynch Syndrome based on his genomic information. We demonstrate that this system can effectively use service-oriented architecture principles and standards-based components to deliver point of care CDS for WGS information in real-time.
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Affiliation(s)
- Brandon M Welch
- Medical University of South Carolina, Charleston, SC ; University of Utah, Salt Lake City, UT
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Fang H, Wu Y, Narzisi G, O'Rawe JA, Barrón LTJ, Rosenbaum J, Ronemus M, Iossifov I, Schatz MC, Lyon GJ. Reducing INDEL calling errors in whole genome and exome sequencing data. Genome Med 2014; 6:89. [PMID: 25426171 PMCID: PMC4240813 DOI: 10.1186/s13073-014-0089-z] [Citation(s) in RCA: 120] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2014] [Accepted: 10/16/2014] [Indexed: 12/30/2022] Open
Abstract
Background INDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts. Methods We characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low-quality INDELs (7% vs. 51%). Results Simulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (53%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (84% vs. 57%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data. Conclusions Overall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (for example, capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing. Electronic supplementary material The online version of this article (doi:10.1186/s13073-014-0089-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Han Fang
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA ; Stony Brook University, 100 Nicolls Rd, Stony Brook, NY USA ; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA
| | - Yiyang Wu
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA ; Stony Brook University, 100 Nicolls Rd, Stony Brook, NY USA
| | - Giuseppe Narzisi
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA ; New York Genome Center, New York, NY USA
| | - Jason A O'Rawe
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA ; Stony Brook University, 100 Nicolls Rd, Stony Brook, NY USA
| | - Laura T Jimenez Barrón
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA ; Centro de Ciencias Genomicas, Universidad Nacional Autonoma de Mexico, Cuernavaca, Morelos Mexico
| | - Julie Rosenbaum
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA
| | - Michael Ronemus
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA
| | - Ivan Iossifov
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA
| | - Michael C Schatz
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA
| | - Gholson J Lyon
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY USA ; Stony Brook University, 100 Nicolls Rd, Stony Brook, NY USA
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Lin E, Chien J, Ong FS, Fan JB. Challenges and opportunities for next-generation sequencing in companion diagnostics. Expert Rev Mol Diagn 2014; 15:193-209. [DOI: 10.1586/14737159.2015.961916] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Huser V, Sincan M, Cimino JJ. Developing genomic knowledge bases and databases to support clinical management: current perspectives. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2014; 7:275-83. [PMID: 25276091 PMCID: PMC4175027 DOI: 10.2147/pgpm.s49904] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Personalized medicine, the ability to tailor diagnostic and treatment decisions for individual patients, is seen as the evolution of modern medicine. We characterize here the informatics resources available today or envisioned in the near future that can support clinical interpretation of genomic test results. We assume a clinical sequencing scenario (germline whole-exome sequencing) in which a clinical specialist, such as an endocrinologist, needs to tailor patient management decisions within his or her specialty (targeted findings) but relies on a genetic counselor to interpret off-target incidental findings. We characterize the genomic input data and list various types of knowledge bases that provide genomic knowledge for generating clinical decision support. We highlight the need for patient-level databases with detailed lifelong phenotype content in addition to genotype data and provide a list of recommendations for personalized medicine knowledge bases and databases. We conclude that no single knowledge base can currently support all aspects of personalized recommendations and that consolidation of several current resources into larger, more dynamic and collaborative knowledge bases may offer a future path forward.
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Affiliation(s)
- Vojtech Huser
- Laboratory for Informatics Development, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Murat Sincan
- Undiagnosed Diseases Program, National Institutes of Health, MD, USA ; Office of the Clinical Director, National Human Genome Research Institute, National Institutes of Health, MD, USA
| | - James J Cimino
- Laboratory for Informatics Development, National Institutes of Health Clinical Center, Bethesda, MD, USA ; National Library of Medicine, National Institutes of Health, MD, USA
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Angrist M, Jamal L. Living laboratory: whole-genome sequencing as a learning healthcare enterprise. Clin Genet 2014; 87:311-8. [PMID: 25045831 DOI: 10.1111/cge.12461] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 06/30/2014] [Accepted: 07/15/2014] [Indexed: 01/16/2023]
Abstract
With the proliferation of affordable large-scale human genomic data come profound and vexing questions about management of such data and their clinical uncertainty. These issues challenge the view that genomic research on human beings can (or should) be fully segregated from clinical genomics, either conceptually or practically. Here, we argue that the sharp distinction between clinical care and research is especially problematic in the context of large-scale genomic sequencing of people with suspected genetic conditions. Core goals of both enterprises (e.g. understanding genotype-phenotype relationships; generating an evidence base for genomic medicine) are more likely to be realized at a population scale if both those ordering and those undergoing sequencing for diagnostic reasons are routinely and longitudinally studied. Rather than relying on expensive and lengthy randomized clinical trials and meta-analyses, we propose leveraging nascent clinical-research hybrid frameworks into a broader, more permanent instantiation of exploratory medical sequencing. Such an investment could enlighten stakeholders about the real-life challenges posed by whole-genome sequencing, such as establishing the clinical actionability of genetic variants, returning 'off-target' results to families, developing effective service delivery models and monitoring long-term outcomes.
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Affiliation(s)
- M Angrist
- Science and Society, Social Science Research Institute and Sanford School of Public Policy, Duke University, Durham, NC, USA
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43
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Nishimura AA, Tarczy-Hornoch P, Shirts BH. Pragmatic and Ethical Challenges of Incorporating the Genome into the Electronic Medical Record. CURRENT GENETIC MEDICINE REPORTS 2014; 2:201-211. [PMID: 26146597 DOI: 10.1007/s40142-014-0051-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Recent successes in the use of gene sequencing for patient care highlight the potential of genomic medicine. For genomics to become a part of usual care, pertinent elements of a patient's genomic test must be communicated to the most appropriate care providers. Electronic medical records may serve as a useful tool for storing and disseminating genomic data. Yet, the structure of existing EMRs and the nature of genomic data pose a number of pragmatic and ethical challenges in their integration. Through a review of the recent genome-EMR integration literature, we explore concrete examples of these challenges, categorized under four key questions: What data will we store? How will we store it? How will we use it? How will we protect it? We conclude that genome-EMR integration requires a rigorous, multi-faceted and interdisciplinary approach of study. Problems facing the field are numerous, but few are intractable.
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Affiliation(s)
- Adam A Nishimura
- Department of Biomedical Informatics and Medical Education, University of Washington
| | - Peter Tarczy-Hornoch
- Department of Biomedical Informatics and Medical Education, University of Washington ; Department of Pediatrics, University of Washington ; Department of Computer Science and Engineering, University of Washington
| | - Brian H Shirts
- Department of Laboratory Medicine, University of Washington
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Savage SK, Ziniel SI, Stoler J, Margulies DM, Holm IA, Brownstein CA. An assessment of clinician and researcher needs for support in the era of genomic medicine. Per Med 2014; 11:569-579. [PMID: 29758800 DOI: 10.2217/pme.14.48] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
AIM To assess clinicians' and researchers' past, current and anticipated future use of next-generation sequencing (NGS) and anticipated needs for support. Materials & methods: A web-based survey was conducted at Boston Children's Hospital. RESULTS Many clinicians anticipate that they will use exome/genome sequencing (44.8%) and/or candidate gene panels (50%) within the next year. Researcher respondents anticipate the need for exome/genome sequencing (48.0%) and candidate gene panels (31.8%). Few respondents (13.6%) said that they felt 'Completely Ready' or 'Pretty Much Ready' to incorporate NGS into their clinical practice or research. CONCLUSION Researchers and clinicians anticipate increased utilization of NGS. Respondents indicated varying degrees of need for a diverse list of support services, ranking interpretation and clinical correlation support as the most needed services.
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Affiliation(s)
- Sarah K Savage
- Division of Genetics & Genomics, Boston Children's Hospital, Boston, MA 02115, USA
| | - Sonja I Ziniel
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.,Division of Adolescent/Young Adult Medicine, Boston Children's Hospital, Boston, MA 02115, USA.,Center for Patient Safety & Quality Research, Program for Patient Safety and Quality, Boston Children's Hospital, Boston, MA 02115, USA
| | - Joan Stoler
- Division of Genetics & Genomics, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - David M Margulies
- Division of Genetics & Genomics, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.,Division for Developmental Medicine, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Ingrid A Holm
- Division of Genetics & Genomics, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.,Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA 02115, USA
| | - Catherine A Brownstein
- Division of Genetics & Genomics, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.,Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA 02115, USA
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Solomon BD. Obstacles and opportunities for the future of genomic medicine. Mol Genet Genomic Med 2014; 2:205-9. [PMID: 24936509 PMCID: PMC4049360 DOI: 10.1002/mgg3.78] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 03/25/2014] [Indexed: 12/15/2022] Open
Affiliation(s)
- Benjamin D Solomon
- Division of Medical Genomics, Inova Translational Medicine Institute, Inova Health System Falls Church, Virginia ; Department of Pediatrics, Inova Children's Hospital, Inova Health System Falls Church, Virginia
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DORSCHNER MICHAELO, AMENDOLA LAURAM, SHIRTS BRIANH, KIEDROWSKI LESLI, SALAMA JOSEPH, GORDON ADAMS, FULLERTON STEPHANIEM, TARCZY-HORNOCH PETER, BYERS PETERH, JARVIK GAILP. Refining the structure and content of clinical genomic reports. AMERICAN JOURNAL OF MEDICAL GENETICS. PART C, SEMINARS IN MEDICAL GENETICS 2014; 166C:85-92. [PMID: 24616401 PMCID: PMC4077592 DOI: 10.1002/ajmg.c.31395] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
To effectively articulate the results of exome and genome sequencing we refined the structure and content of molecular test reports. To communicate results of a randomized control trial aimed at the evaluation of exome sequencing for clinical medicine, we developed a structured narrative report. With feedback from genetics and non-genetics professionals, we developed separate indication-specific and incidental findings reports. Standard test report elements were supplemented with research study-specific language, which highlighted the limitations of exome sequencing and provided detailed, structured results, and interpretations. The report format we developed to communicate research results can easily be transformed for clinical use by removal of research-specific statements and disclaimers. The development of clinical reports for exome sequencing has shown that accurate and open communication between the clinician and laboratory is ideally an ongoing process to address the increasing complexity of molecular genetic testing.
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Affiliation(s)
- MICHAEL O. DORSCHNER
- Correspondence to: Michael O. Dorschner, 1959 NE Pacific Street Box 356560 Seattle, WA 98195-6560.
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Kannry JM, Williams MS. Integration of genomics into the electronic health record: mapping terra incognita. Genet Med 2013; 15:757-60. [PMID: 24097178 PMCID: PMC4157459 DOI: 10.1038/gim.2013.102] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Accepted: 06/17/2013] [Indexed: 12/31/2022] Open
Abstract
Successfully realizing the vision of genomic medicine will require management of large amounts of complex data. The electronic health record (EHR) is destined to play a critical role in the translation of genomic information into clinical care. The papers in this special issue explore the challenges associated with the implementation of genomics in the EHR. The proposed solutions are meant to provide guidance for those responsible for moving genomics into the clinic.
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Affiliation(s)
- Joseph M. Kannry
- EMR Clinical Transformation Group, Mount Sinai Medical Center New York, NY, USA
| | - Marc S. Williams
- Genomic Medicine Institute, Geisinger Health System Danville, PA, USA
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The undiscovered country: the future of integrating genomic information into the EHR. Genet Med 2013; 15:842-5. [PMID: 24071799 PMCID: PMC4259267 DOI: 10.1038/gim.2013.130] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Accepted: 07/18/2013] [Indexed: 12/12/2022] Open
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Overby CL, Kohane I, Kannry JL, Williams MS, Starren J, Bottinger E, Gottesman O, Denny JC, Weng C, Tarczy-Hornoch P, Hripcsak G. Opportunities for genomic clinical decision support interventions. Genet Med 2013; 15:817-23. [PMID: 24051479 DOI: 10.1038/gim.2013.128] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Accepted: 07/15/2013] [Indexed: 12/12/2022] Open
Affiliation(s)
- Casey Lynnette Overby
- 1] Department of Biomedical Informatics, Columbia University, New York, New York, USA [2] Program for Personalized & Genomic Medicine and Center for Health-Related Informatics and Bioimaging, University of Maryland School of Medicine, Baltimore, Maryland, USA
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