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Laurijssen S, van der Graaf R, Schuit E, den Haan M, van Dijk W, Groenwold R, le Sessie S, Grobbee D, de Vries M. Learning healthcare systems in cardiology: A qualitative interview study on ethical dilemmas of a learning healthcare system. Learn Health Syst 2024; 8:e10379. [PMID: 38249849 PMCID: PMC10797564 DOI: 10.1002/lrh2.10379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/31/2023] [Accepted: 06/14/2023] [Indexed: 01/23/2024] Open
Abstract
Background Implementation of an LHS in cardiology departments presents itself with ethical challenges, including ethical review and informed consent. In this qualitative study, we investigated stakeholders' attitudes toward ethical issues regarding the implementation of an LHS in the cardiology department. Methods We conducted a qualitative study using 35 semi-structured interviews and 5 focus group interviews with 34 individuals. We interviewed cardiologists, research nurses, cardiovascular patients, ethicists, health lawyers, epidemiologists/statisticians and insurance spokespersons. Results Respondents identified different ethical obstacles for the implementation of an LHS within the cardiology department. These obstacles were mainly on ethical oversight in LHSs; in particular, informed con sent and data ownership were discussed. In addition, respondents reported on the role of patients in LHS. Respondents described the LHS as a possibility for patients to engage in both research and care. While the LHS can promote patient engagement, patients might also be reduced to their data and are therefore at risk, according to respondents. Conclusions Views on the ethical dilemmas of a LHSs within cardiology are diverse. Similar to the literary debate on oversight, there are different views on how ethical oversight should be regulated. This study adds to the literary debate on oversight by highlighting that patients wish to be informed about the learning activities within the LHS they participate in, and that they wish to actively contribute by sharing their data and identifying learning goals, provided that informed consent is obtained.
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Affiliation(s)
- Sara Laurijssen
- Department of HealthcareSaxion Applied UniversityDeventerNetherlands
| | | | | | | | | | | | | | | | - Martine de Vries
- Department of Medical Ethics and Health LawLeids Universitair Medisch CentrumLeidenNetherlands
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Coates A, Chung AQH, Lessard L, Grudniewicz A, Espadero C, Gheidar Y, Bemgal S, Da Silva E, Sauré A, King J, Fung-Kee-Fung M. The use and role of digital technology in learning health systems: A scoping review. Int J Med Inform 2023; 178:105196. [PMID: 37619395 DOI: 10.1016/j.ijmedinf.2023.105196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/12/2023] [Accepted: 08/12/2023] [Indexed: 08/26/2023]
Abstract
OBJECTIVE The review aimed to identify which digital technologies are proposed or used within learning health systems (LHS) and to analyze the extent to which they support learning processes in LHS. MATERIALS AND METHODS Multiple databases and grey literature were searched with terms related to LHS. Manual searches and backward searches of reference lists were also undertaken. The review considered publications from 2007 to 2022. Records focusing on LHS, referring to one or more digital technologies, and describing how at least one digital technology could be used in LHS were included. RESULTS 2046 records were screened for inclusion and 154 records were included in the analysis. Twenty categories of digital technology were identified. The two most common ones across records were data recording and processing and electronic health records. Digital technology was primarily leveraged to support data access and aggregation and data analysis, two of the seven recognized learning processes within LHS learning cycles. DISCUSSION The results of the review show that a wide array of digital technologies is being leveraged to support learning cycles within LHS. Nevertheless, an over-reliance on a narrow set of technologies supporting knowledge discovery, a lack of direct evaluation of digital technologies and ambiguity in technology descriptions are hindering the realization of the LHS vision. CONCLUSION Future LHS research and initiatives should aim to integrate digital technology to support practice change and impact evaluation. The use of recognized evaluation methods for health information technology and more detailed descriptions of proposed technologies are also recommended.
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Affiliation(s)
- Alison Coates
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | | | - Lysanne Lessard
- Telfer School of Management, University of Ottawa, Ottawa, Canada, Institut du Savoir Montfort - Research, Ottawa, Canada, LIFE Research Institute, University of Ottawa, Ottawa, Canada.
| | - Agnes Grudniewicz
- Telfer School of Management, University of Ottawa, Ottawa, Canada, Institut du Savoir Monfort - Research, Ottawa, Canada
| | - Cathryn Espadero
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | - Yasaman Gheidar
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | - Sampath Bemgal
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | | | - Antoine Sauré
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | - James King
- Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Michael Fung-Kee-Fung
- Departments of Obstetrics-Gynaecology and Surgery, Faculty of Medicine, University of Ottawa, Ottawa, Canada, The Ottawa Hospital - General Campus, University of Ottawa/Ottawa Regional Cancer Centre, Ottawa, Canada
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3
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McDonald PL, Phillips J, Harwood K, Maring J, van der Wees PJ. Identifying requisite learning health system competencies: a scoping review. BMJ Open 2022; 12:e061124. [PMID: 35998963 PMCID: PMC9403130 DOI: 10.1136/bmjopen-2022-061124] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVES Learning health systems (LHS) integrate knowledge and practice through cycles of continuous quality improvement and learning to increase healthcare quality. LHS have been conceptualised through multiple frameworks and models. Our aim is to identify and describe the requisite individual competencies (knowledge, skills and attitudes) and system competencies (capacities, characteristics and capabilities) described in existing literature in relation to operationalising LHS. METHODS A scoping review was conducted with descriptive and thematic analysis to identify and map competencies of LHS for individuals/patients, health system workers and systems. Articles until April 2020 were included based on a systematic literature search and selection process. Themes were developed using a consensus process until agreement was reached among team members. RESULTS Eighty-nine articles were included with most studies conducted in the USA (68 articles). The largest number of publications represented competencies at the system level, followed by health system worker competencies. Themes identified at the individual/patient level were knowledge and skills to understand and share information with an established system and the ability to interact with the technology used to collect data. Themes at the health system worker level were skills in evidence-based practice, leadership and teamwork skills, analytical and technological skills required to use a 'digital ecosystem', data-science knowledge and skill and self-reflective capacity. Researchers embedded within LHS require a specific set of competencies. Themes identified at the system level were data, infrastructure and standardisation; integration of data and workflow; and culture and climate supporting ongoing learning. CONCLUSION The identified individual stakeholder competencies within LHS and the system capabilities of LHS provide a solid base for the further development and evaluation of LHS. International collaboration for stimulating LHS will assist in further establishing the knowledge base for LHS.
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Affiliation(s)
- Paige L McDonald
- Department of Clinical Research and Leadership, The George Washington University, Washington, District of Columbia, USA
| | - Jessica Phillips
- Department of Clinical Research and Leadership, The George Washington University, Washington, District of Columbia, USA
| | - Kenneth Harwood
- College of Health and Education, Marymount University, Arlington, Virginia, USA
| | - Joyce Maring
- Department of Health, Human Function, The George Washington University, Washington, District of Columbia, USA
| | - Philip J van der Wees
- Department of Clinical Research and Leadership, The George Washington University, Washington, District of Columbia, USA
- Rehabilitation and IQ Healthcare, Radboudumc, Nijmegen, The Netherlands
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An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. NATURE CANCER 2022; 2:709-722. [PMID: 35121948 DOI: 10.1038/s43018-021-00236-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 06/14/2021] [Indexed: 12/11/2022]
Abstract
Despite widespread adoption of electronic health records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here, we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for individual prognosis. Using this framework, currently composed of thousands of individuals with cancer and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology. As proof of concept, we report an analysis using this infrastructure, which identified the Framingham risk score to be robustly associated with mortality among individuals with early-stage and advanced-stage cancer, a potentially actionable finding from a real-world cohort of individuals with cancer. Finally, we show how natural language processing (NLP) of medical notes could be used to continuously update estimates of prognosis as a given individual's disease course unfolds.
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Price G, Mackay R, Aznar M, McWilliam A, Johnson-Hart C, van Herk M, Faivre-Finn C. Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going? Radiother Oncol 2021; 164:183-195. [PMID: 34619237 DOI: 10.1016/j.radonc.2021.09.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 09/02/2021] [Accepted: 09/26/2021] [Indexed: 01/31/2023]
Abstract
Learning health systems and rapid-learning are well developed at the conceptual level. The promise of rapidly generating and applying evidence where conventional clinical trials would not usually be practical is attractive in principle. The connectivity of modern digital healthcare information systems and the increasing volumes of data accrued through patients' care pathways offer an ideal platform for the concepts. This is particularly true in radiotherapy where modern treatment planning and image guidance offers a precise digital record of the treatment planned and delivered. The vision is of real-world data, accrued by patients during their routine care, being used to drive programmes of continuous clinical improvement as part of standard practice. This vision, however, is not yet a reality in radiotherapy departments. In this article we review the literature to explore why this is not the case, identify barriers to its implementation, and suggest how wider clinical application might be achieved.
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Affiliation(s)
- Gareth Price
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom.
| | - Ranald Mackay
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
| | - Marianne Aznar
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
| | - Alan McWilliam
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
| | - Corinne Johnson-Hart
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
| | - Marcel van Herk
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
| | - Corinne Faivre-Finn
- The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
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Zayas-Cabán T, Abernethy AP, Brennan PF, Devaney S, Kerlavage AR, Ramoni R, White PJ. Leveraging the health information technology infrastructure to advance federal research priorities. J Am Med Inform Assoc 2021; 27:647-651. [PMID: 32090259 DOI: 10.1093/jamia/ocaa011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 01/10/2020] [Accepted: 01/18/2020] [Indexed: 11/12/2022] Open
Abstract
Ensuring that federally funded health research keeps pace with the explosion of health data depends on better information technology (IT), access to high-quality electronic health data, and supportive policies. Because it prominently funds and conducts health research, the U.S. federal government needs health IT to rapidly evolve and has the ability to drive that evolution. The Office of the National Coordinator for Health Information Technology developed the National Health IT Priorities for Research: A Policy and Development Agenda (the Agenda) that identifies health IT priorities for research in consultation with relevant federal agencies. This article describes support for the Agenda from the Food and Drug Administration, the National Institutes of Health, and the Veterans Health Administration. Advancing the Agenda will benefit these agencies and support their missions as well as the entire ecosystem leveraging the health IT infrastructure or using data from health IT systems for research.
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Affiliation(s)
- Teresa Zayas-Cabán
- Office of the National Coordinator for Health Information Technology, U.S. Department of Health and Human Services, Washington, DC, USA
| | | | | | - Stephanie Devaney
- All of Us Research Program, National Institutes of Health, Rockville, Maryland, USA
| | - Anthony R Kerlavage
- National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Rachel Ramoni
- Office of Research and Development, Veterans Health Administration, Washington, DC, USA
| | - P Jon White
- Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA
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A Theory-Informed Approach to Locally Managed Learning School Systems: Integrating Treatment Integrity and Youth Mental Health Outcome Data to Promote Youth Mental Health. SCHOOL MENTAL HEALTH 2021. [DOI: 10.1007/s12310-021-09413-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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9
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Rubinstein SM, Warner JL. CancerLinQ: Origins, Implementation, and Future Directions. JCO Clin Cancer Inform 2018; 2:1-7. [DOI: 10.1200/cci.17.00060] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Rapid-learning health systems have been proposed as a potential solution to the problem of quality in medicine, by leveraging data generated from electronic health systems in near-real time to improve quality and reduce cost. Given the complex, dynamic nature of cancer care, a rapid-learning health system offers large potential benefits to oncology practice. In this article, we review the rationale for developing a rapid-learning health system for oncology and describe the sequence of events that led to the development of ASCO’s CancerLinQ (Cancer Learning Intelligence Network for Quality) initiative, as well as the current state of CancerLinQ, including its importance to efforts such as the Beau Biden Cancer Moonshot. We then review the considerable challenges facing optimal implementation of a rapid-learning health system such as CancerLinQ, including integration of rapidly expanding multiomic data, capturing big data from a variety of sources, an evolving competitive landscape, and implementing a rapid-learning health system in a way that satisfies many stakeholders, including patients, providers, researchers, and administrators.
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Affiliation(s)
- Samuel M. Rubinstein
- Samuel M. Rubinstein, Vanderbilt University Medical Center; and Jeremy L. Warner, Vanderbilt University Medical Center; Vanderbilt University, Nashville, TN
| | - Jeremy L. Warner
- Samuel M. Rubinstein, Vanderbilt University Medical Center; and Jeremy L. Warner, Vanderbilt University Medical Center; Vanderbilt University, Nashville, TN
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10
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Kasichayanula S, Venkatakrishnan K. Reverse Translation: The Art of Cyclical Learning. Clin Pharmacol Ther 2018; 103:152-159. [DOI: 10.1002/cpt.952] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 11/27/2017] [Indexed: 12/18/2022]
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Abstract
BACKGROUND Cancer genetics and genomics are now an integral component of oncology care. Genetics and genomics guide recommendations not only for cancer prevention and early detection, but also for cancer treatment.
. OBJECTIVES This article documents the personal experiences of an oncology nurse who has worked in cancer prevention and early detection since the 1990s and describes the many changes that have occurred in cancer-related genetic and genomic care during that time.
. METHODS This is a personal account of genetic practice in the past 30 years.
. FINDINGS Nurses can no longer ignore cancer genetics and genomics in oncology care. Some aspects of care have changed dramatically, including the number of genetic tests and potential uses for genomic information; however, some remain the same, particularly the human component of care. Patients and families need comprehensive education and support to understand the role that genetics and genomics play in cancer care. Oncology nurses are well suited to provide this care.
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Hughes KS, Ambinder EP, Hess GP, Yu PP, Bernstam EV, Routbort MJ, Clemenceau JR, Hamm JT, Febbo PG, Domchek SM, Chen JL, Warner JL. Identifying Health Information Technology Needs of Oncologists to Facilitate the Adoption of Genomic Medicine: Recommendations From the 2016 American Society of Clinical Oncology Omics and Precision Oncology Workshop. J Clin Oncol 2017; 35:3153-3159. [DOI: 10.1200/jco.2017.74.1744] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
At the ASCO Data Standards and Interoperability Summit held in May 2016, it was unanimously decided that four areas of current oncology clinical practice have serious, unmet health information technology needs. The following areas of need were identified: 1) omics and precision oncology, 2) advancing interoperability, 3) patient engagement, and 4) value-based oncology. To begin to address these issues, ASCO convened two complementary workshops: the Omics and Precision Oncology Workshop in October 2016 and the Advancing Interoperability Workshop in December 2016. A common goal was to address the complexity, enormity, and rapidly changing nature of genomic information, which existing electronic health records are ill equipped to manage. The subject matter experts invited to the Omics and Precision Oncology Workgroup were tasked with the responsibility of determining a specific, limited need that could be addressed by a software application (app) in the short-term future, using currently available genomic knowledge bases. Hence, the scope of this workshop was to determine the basic functionality of one app that could serve as a test case for app development. The goal of the second workshop, described separately, was to identify the specifications for such an app. This approach was chosen both to facilitate the development of a useful app and to help ASCO and oncologists better understand the mechanics, difficulties, and gaps in genomic clinical decision support tool development. In this article, we discuss the key challenges and recommendations identified by the workshop participants. Our hope is to narrow the gap between the practicing oncologist and ongoing national efforts to provide precision oncology and value-based care to cancer patients.
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Affiliation(s)
- Kevin S. Hughes
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - Edward P. Ambinder
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - Gregory P. Hess
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - Peter Paul Yu
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - Elmer V. Bernstam
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - Mark J. Routbort
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - Jean Rene Clemenceau
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - John T. Hamm
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - Phillip G. Febbo
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - Susan M. Domchek
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - James L. Chen
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
| | - Jeremy L. Warner
- Kevin S. Hughes, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Edward P. Ambinder, Icahn School of Medicine at Mount Sinai; Peter Paul Yu, Memorial Sloan Kettering Cancer Center, New York, NY; Gregory P. Hess, Symphony Health, Conshohocken; Gregory P. Hess and Susan M. Domchek, University of Pennsylvania, Philadelphia, PA; Peter Paul Yu, Hartford HealthCare Cancer Institute, Hartford, CT; Elmer V. Bernstam, The University of Texas Health Sciences Center at Houston; Mark J. Routbort,
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Yoo BC, Kim KH, Woo SM, Myung JK. Clinical multi-omics strategies for the effective cancer management. J Proteomics 2017; 188:97-106. [PMID: 28821459 DOI: 10.1016/j.jprot.2017.08.010] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 08/10/2017] [Accepted: 08/14/2017] [Indexed: 02/06/2023]
Abstract
Cancer is a global health issue as a multi-factorial complex disease, and early detection and novel therapeutic strategies are required for more effective cancer management. With the development of systemic analytical -omics strategies, the therapeutic approach and study of the molecular mechanisms of carcinogenesis and cancer progression have moved from hypothesis-driven targeted investigations to data-driven untargeted investigations focusing on the integrated diagnosis, treatment, and prevention of cancer in individual patients. Predictive, preventive, and personalized medicine (PPPM) is a promising new approach to reduce the burden of cancer and facilitate more accurate prognosis, diagnosis, as well as effective treatment. Here we review the fundamentals of, and new developments in, -omics technologies, together with the key role of a variety of practical -omics strategies in PPPM for cancer treatment and diagnosis. BIOLOGICAL SIGNIFICANCE In this review, a comprehensive and critical overview of the systematic strategy for predictive, preventive, and personalized medicine (PPPM) for cancer disease was described in a view of cancer prognostic prediction, diagnostics, and prevention as well as cancer therapy and drug responses. We have discussed multi-dimensional data obtained from various resources and integration of multisciplinary -omics strategies with computational method which could contribute the more effective PPPM for cancer. This review has provided the novel insights of the current applications of each and combined -omics technologies, which showed their powerful potential for the establishment of PPPM for cancer.
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Affiliation(s)
- Byong Chul Yoo
- Biomarker Branch, Research Institute, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Kyung-Hee Kim
- Biomarker Branch, Research Institute, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea; Omics Core Laboratory, Research Institute, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Sang Myung Woo
- Biomarker Branch, Research Institute, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea; Center for Liver Cancer, Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Jae Kyung Myung
- Department of Cancer Biomedical System, National Cancer Centre Graduate School of Cancer Science and Policy, Goyang-si, Gyeonggi-do, Republic of Korea.
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14
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Lee J, Blumenthal GM, Hohl RJ, Huang SM. Cancer Therapy: Shooting for the Moon. Clin Pharmacol Ther 2017; 101:552-558. [PMID: 28418166 PMCID: PMC5525193 DOI: 10.1002/cpt.655] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 02/06/2017] [Indexed: 11/15/2022]
Affiliation(s)
- Jsh Lee
- Office of the Director, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - G M Blumenthal
- Office of Hematology & Oncology Products, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
| | - R J Hohl
- Penn State Cancer Institute, Pennsylvania State University, Hershey, Pennsylvania, USA
| | - S-M Huang
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
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