Biomarker Tests for Molecularly Targeted Therapies Key to Unlocking Precision Medicine (2016) / Chapter Skim
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4 Supporting Data Infrastructure for Biomarker Tests for Molecularly Targeted Therapies
4 Supporting Data Infrastructure for Biomarker Tests for Molecularly Targeted Therapies
Pages 143-182
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. As discussed in Chapter 2, numerous experts have described an unmet need for a robust data infrastructure and the associated challenges with implementing a rapid learning health care system (Chute and Kohane, 2013; Etheredge, 2014; French and Kampfrath, 2014; Friedman et al., 2015; 143
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. This chapter explores the ways in which overcoming current obstacles in health information technology and data sharing will provide the necessary infrastructure to support a rapid learning system for the clinical use of biomarker tests for molecularly targeted therapies.
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However, a complementary role exists for decision support related to well-established clinical uses of biomarker tests and targeted therapies. This section of the chapter explores the data infrastructure needed for the use of such systems to reinforce evidence-based precision medicine within a rapid learning system.
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More relevant to the implementation of precision medicine is the broader AMIA term biomedical informatics, defined as "the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, driven by efforts to improve human health" (Kulikowski et al., 2012, p.
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. The intersection of novel methods in translational biomedical informatics has the potential to greatly enhance clinical decision support.
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These examples illustrate the complex, interdisciplinary process of operationalizing CDSSs for guiding molecularly targeted therapy (see Box 4-1)
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or at defined points within the clinical workflow (e.g., computerized order entry for a targeted therapy that requires a biomarker test result) (Williams, 2015)
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. In addition to ensuring routine testing for well-established clinical uses, CDSSs within a rapid learning system for biomarker tests for molecularly targeted therapies would also facilitate the matching of patients to clinical trials, where appropriate.
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, further research is necessary to ensure optimal support of clinical care within rapid learning systems. The recent Improving Diagnosis in Health Care report from the National Academies of Sciences, Engineering, and Medicine emphasized that CDSSs, while promising, must be used strategically in order to assist clinicians during their existing workflows and environments, and most importantly to ensure the CDSSs themselves do not introduce novel sources M-PACTd The National Cancer Institute (NCI)
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. Clinically Relevant Reporting Certain considerations to ensure clinical relevance must be taken into account for biomarker test results to be used effectively to guide selection of molecularly targeted therapy; structured data fields should be reported for variants with an established effect on genes or protein variants associated with phenotypes, and implications for treatment (IOM, 2012)
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. the purposes of directing molecularly targeted therapy will be a continuous research endeavor.
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Eisenberg Center This center translates comparative effectiveness for Clinical Decisions research findings into plain language that patients can and Communications understand. It creates a variety of products, ranging Science from research summaries to decision aids and other materials, for use by patients, clinicians, and policy makers.
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. EHR functionality will need to go beyond the support of clinical decision making, and enable rapid learning based on continuously aggregated data from "real-world" clinical practice and patient experience to facilitate the evaluation of outcomes associated with the use of biomarker tests and molecularly targeted therapies.
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. For molecularly targeted therapies in particular, generating evidence to support the use of a biomarker or associated therapy will require structured data fields
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. Therefore, to support the data infrastructure needs of a rapid learning system, the committee recommends that EHR and LIS vendors and relevant software developers should enable the capture and linkage of biomarker tests, molecularly targeted therapies, and longitudinal clinical patient data in the EHR (Recommendation 6a)
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Because each practice will need to customize the EHR for their specific use, the customization of point-of-care decision support should align with available evidence-based clinical practice guidelines. Finally, given the increasing educational role provided by CDSSs in a rapid learning system for biomarker tests and targeted therapies, the committee recommends licensing and specialty boards should recognize Continuing Medical Education (CME)
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. Data Sharing For large-scale data analysis to generate knowledge effectively, it will be important to aggregate evidence on promising biomarker tests and targeted therapies.
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A critical prerequisite for successful data sharing is overcoming the institutional, organizational, or other opposition to making data available. Sharing Clinical Trial Data featured a prominent discussion on the disincentives to sharing data, particularly the potential for researchers unrelated from the initial data gathering to misuse data or use the data to undermine the primary research (IOM, 2015c)
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. Thus, CER in parallel with broader clinical data sharing may enable valuable assessments related to the clinical utility of biomarker tests for molecularly targeted therapies.
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Inventory to mutations found in lung cancer, mycancergenome.org/ Refine and namely EGFR mutations. Program about/direct Enhance Cancer has goals to expand this to all Treatment known mutations.
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. The recently announced GENIE project seeks to "fulfill an unmet need in oncology by providing the statistical power necessary to improve clinical decision-making, particularly in the case of rare cancers and rare variants in common cancers," and as such is a particularly relevant potential proof of concept for the committee's proposed rapid learning system for biomarker tests for molecularly targeted therapies (AACR, 2016)
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. Challenges for Data Sharing The Sharing Clinical Trial Data report concluded with observations on challenges to broad sharing of data, some of which are relevant for a rapid learning system to assess biomarker tests and molecularly targeted therapies.
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. There will be a need to ensure the quality of data, particularly genomic sequence data, that are submitted to a shared database for biomarker tests for molecularly targeted therapies.
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Given the expanding role of EHRs for both decision support and research as described above, and their potential to provide valuable clinical data to larger repositories, an additional mechanism to sustain the development and use of data sharing may take the form of payments to health care providers who submit data, similar to those provided by CMS to early adopters of EHRs. Data security and privacy may pose challenges to broad data sharing for the purpose of rapid learning for biomarker tests and molecularly targeted therapies.
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. Given these facts, and the proposed changes to the Common Rule that will require informed consent for all research involving biospecimens and related data, the committee believes that patient consent measures are a reasonable requirement for data sharing to facilitate rapid learning for biomarker tests and molecularly targeted therapies.
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. These recommendations are consistent with the role of supporting data infrastructure for biomarker tests for molecularly targeted therapies, as outlined in this chapter.
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others) on the optimal design of a clinical sequencing program, including the ability to leverage existing and future data infrastructure to support the integration of genomics into clinical care (NHGRI, 2015)
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, and to facilitate the continuous assessment of the evidence supporting the clinical use of biomarker tests and molecularly targeted therapies, the committee recommends the Secretary of HHS should charge FDA and NIH to convene a Task Force (comprising FDA, CMS, the Department of Veterans Affairs, NIH, the Department of Defense, the Patient-Centered Outcomes Research Institute, and other public and private partners) to develop a sustainable national repository of biomarker tests, molecularly targeted therapies, and longitudinal clinical patient data to facilitate rapid learning approaches.
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SOURCES: FDA, 2015a; Health Affairs, 2015. SUMMARY AND RECOMMENDATIONS The committee's vision for a rapid learning system to assess biomarker tests and corresponding molecularly targeted therapies depends on robust data infrastructure; many of the recommendations presented throughout this report relate to the capabilities outlined here for EHRs and a national repository for shared data.
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and laboratory information system (LIS) vendors and relevant software developers should enable the capture and linkage of biomarker tests, molecu larly targeted therapies, and longitudinal clinical patient data in the EHR to facilitate data transfer into one or more national databases (as described in Recommendation 7)
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to develop a sustain able national repository of biomarker tests, molecularly targeted therapies, and longitudinal clinical patient data to facilitate rapid learning approaches. • This prospective, integrated, and structured database should include biomarker test description, test results and interpreta tion, treatment decisions and outcomes, other relevant electronic health record data generated during clinical practice, clinical trial data, billing/reimbursement data, patient-reported outcomes, and longitudinal clinical patient data.
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Presentation to the Committee on Policy Issues in the Clini cal Development and Use of Biomarkers for Molecularly Targeted Therapies, April 1, 2015, Washington DC. ASCO (American Society of Clinical Oncology)
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2015. Electronic health records (EHR)
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Presentation to the Committee on Policy Issues in the Clinical Development and Use of Biomarkers for Molecularly Targeted Therapies, January 29, 2015, Washington, DC. Ginsberg, G
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Presentation to the Committee on Policy Issues in the Clinical Development and Use of Biomarkers for Molecularly Targeted Therapies, June 11, 2015, Washington, DC. Hudson, K
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2012. Mining electronic health records: Towards better research applications and clinical care.
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2012. Importance of health informa tion technology, electronic health records, and continuously aggregating data to com parative effectiveness research and learning health care.
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2014. Design and anticipated outcomes of the eMERGE-PGx project: A multicenter pilot for preemptive pharmacogenomics in electronic health record systems.
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Pre sentation to the Committee on Policy Issues in the Clinical Development and Use of Biomarkers for Molecularly Targeted Therapies, April 1, 2015, Washington, DC. Stead, W
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Presentation to the Committee on Policy Issues in the Clinical Development and Use of Biomarkers for Molecularly Targeted Thera pies, April 1, 2015, Washington, DC.
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