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      Next-Generation Sequencing–Based Cancer Panel Data Conversion Using International Standards to Implement a Clinical Next-Generation Sequencing Research System: Single-Institution Study

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          Abstract

          Background

          The analytical capacity and speed of next-generation sequencing (NGS) technology have been improved. Many genetic variants associated with various diseases have been discovered using NGS. Therefore, applying NGS to clinical practice results in precision or personalized medicine. However, as clinical sequencing reports in electronic health records (EHRs) are not structured according to recommended standards, clinical decision support systems have not been fully utilized. In addition, integrating genomic data with clinical data for translational research remains a great challenge.

          Objective

          To apply international standards to clinical sequencing reports and to develop a clinical research information system to integrate standardized genomic data with clinical data.

          Methods

          We applied the recently published ISO/TS 20428 standard to 367 clinical sequencing reports generated by panel (91 genes) sequencing in EHRs and implemented a clinical NGS research system by extending the clinical data warehouse to integrate the necessary clinical data for each patient. We also developed a user interface with a clinical research portal and an NGS result viewer.

          Results

          A single clinical sequencing report with 28 items was restructured into four database tables and 49 entities. As a result, 367 patients’ clinical sequencing data were connected with clinical data in EHRs, such as diagnosis, surgery, and death information. This system can support the development of cohort or case-control datasets as well.

          Conclusions

          The standardized clinical sequencing data are not only for clinical practice and could be further applied to translational research.

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          Most cited references8

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          Implementing personalized cancer genomics in clinical trials.

          The recent surge in high-throughput sequencing of cancer genomes has supported an expanding molecular classification of cancer. These studies have identified putative predictive biomarkers signifying aberrant oncogene pathway activation and may provide a rationale for matching patients with molecularly targeted therapies in clinical trials. Here, we discuss some of the challenges of adapting these data for rare cancers or molecular subsets of certain cancers, which will require aligning the availability of investigational agents, rapid turnaround of clinical grade sequencing, molecular eligibility and reconsidering clinical trial design and end points.
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            Clinical next-generation sequencing in patients with non-small cell lung cancer.

            A clinical assay was implemented to perform next-generation sequencing (NGS) of genes commonly mutated in multiple cancer types. This report describes the feasibility and diagnostic yield of this assay in 381 consecutive patients with non-small cell lung cancer (NSCLC).
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              An extracellular matrix-related prognostic and predictive indicator for early-stage non-small cell lung cancer

              The prognosis and prediction of adjuvant chemotherapy (ACT) response in early-stage non-small cell lung cancer (NSCLC) patients remain poor in this era of personalized medicine. We hypothesize that extracellular matrix (ECM)-associated components could be potential markers for better diagnosis and prognosis due to their differential expression in 1,943 primary NSCLC tumors as compared to 303 normal lung tissues. Here we develop a 29-gene ECM-related prognostic and predictive indicator (EPPI). We validate a robust performance of the EPPI risk scoring system in multiple independent data sets, comprising a total of 2,071 early-stage NSCLC tumors. Patients are stratified according to the universal cutoff score based on the EPPI when applied in the clinical setting; the low-risk group has significantly better survival outcome. The functional EPPI gene set represents a potential genomic tool to improve patient selection in early-stage NSCLC to further derive the best benefits of ACT and prevent unnecessary treatment or ACT-associated morbidity.
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                Author and article information

                Contributors
                Journal
                JMIR Med Inform
                JMIR Med Inform
                JMI
                JMIR Medical Informatics
                JMIR Publications (Toronto, Canada )
                2291-9694
                April 2020
                24 April 2020
                : 8
                : 4
                : e14710
                Affiliations
                [1 ] Cancer Data Center National Cancer Center Goyang Republic of Korea
                [2 ] Department of Digital Health Samsung Advanced Institute for Health Sciences and Technology Sungkyunkwan University Seoul Republic of Korea
                [3 ] Big Data Research Center Samsung Medical Center Seoul Republic of Korea
                [4 ] Department of Pathology National Cancer Center Goyang Republic of Korea
                Author notes
                Corresponding Author: Hyo Soung Cha kkido@ 123456ncc.re.kr
                Author information
                https://orcid.org/0000-0002-8769-6383
                https://orcid.org/0000-0002-2410-6120
                https://orcid.org/0000-0002-5704-646X
                https://orcid.org/0000-0003-3192-6044
                https://orcid.org/0000-0002-7131-5902
                https://orcid.org/0000-0002-2901-9333
                https://orcid.org/0000-0001-5336-3874
                https://orcid.org/0000-0003-0537-8355
                Article
                v8i4e14710
                10.2196/14710
                7210491
                32329738
                fbbd5335-77ac-47e6-970b-439f2aff21bd
                ©Phillip Park, Soo-Yong Shin, Seog Yun Park, Jeonghee Yun, Chulmin Shin, Jipmin Jung, Kui Son Choi, Hyo Soung Cha. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 24.04.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

                History
                : 15 May 2019
                : 3 October 2019
                : 19 November 2019
                : 7 February 2020
                Categories
                Original Paper
                Original Paper

                data standardization,clinical sequencing data,next-generation sequencing,translational research information system

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