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Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer

  • Authors:
    • Yu Chen
    • Wenqing Yan
    • Zhi Xie
    • Weibang Guo
    • Danxia  Lu
    • Zhiyi Lv
    • Xuchao Zhang
  • View Affiliations / Copyright

    Affiliations: Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510080, P.R. China
    Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 36
    |
    Published online on: December 24, 2020
       https://doi.org/10.3892/mco.2020.2198
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Abstract

Next generation sequencing (NGS) technology is an increasingly important clinical tool for therapeutic decision‑making. However, interpretation of NGS data presents challenges at the point of care, due to limitations in understanding the clinical importance of gene variants and efficiently translating results into actionable information for the clinician. The present study compared two approaches for annotating and reporting actionable genes and gene mutations from tumor samples: The traditional approach of manual curation, annotation and reporting using an experienced molecular tumor bioinformationist; and a cloud‑based cognitive technology, with the goal to detect gene mutations of potential significance in Chinese patients with lung cancer. Data from 285 gene‑targeted exon sequencing previously conducted on 115 patient tissue samples between 2014 and 2016 and subsequently manually annotated and evaluated by the Guangdong Lung Cancer Institute (GLCI) research team were analyzed by the Watson for Genomics (WfG) cognitive genomics technology. A comparative analysis of the annotation results of the two methods was conducted to identify quantitative and qualitative differences in the mutations generated. The complete congruence rate of annotation results between WfG analysis and the GLCI bioinformatician was 43.48%. In 65 (56.52%) samples, WfG analysis identified and interpreted, on average, 1.54 more mutation sites in each sample than the manual GLCI review. These mutation sites were located on 27 genes, including EP300, ARID1A, STK11 and DNMT3A. Mutations in the EP300 gene were most prevalent, and present in 30.77% samples. The Tumor Mutation Burden (TMB) interpreted by WfG analysis (1.82) was significantly higher than the TMB (0.73) interpreted by GLCI review. Compared with manual curation by a bioinformatician, WfG analysis provided comprehensive insights and additional genetic alterations to inform clinical therapeutic strategies for patients with lung cancer. These findings suggest the valuable role of cognitive computing to increase efficiency in the comprehensive detection and interpretation of genetic alterations which may inform opportunities for targeted cancer therapies.
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Copy and paste a formatted citation
Spandidos Publications style
Chen Y, Yan W, Xie Z, Guo W, Lu D, Lv Z and Zhang X: Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer. Mol Clin Oncol 14: 36, 2021.
APA
Chen, Y., Yan, W., Xie, Z., Guo, W., Lu, D., Lv, Z., & Zhang, X. (2021). Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer. Molecular and Clinical Oncology, 14, 36. https://doi.org/10.3892/mco.2020.2198
MLA
Chen, Y., Yan, W., Xie, Z., Guo, W., Lu, D., Lv, Z., Zhang, X."Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer". Molecular and Clinical Oncology 14.2 (2021): 36.
Chicago
Chen, Y., Yan, W., Xie, Z., Guo, W., Lu, D., Lv, Z., Zhang, X."Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer". Molecular and Clinical Oncology 14, no. 2 (2021): 36. https://doi.org/10.3892/mco.2020.2198
Copy and paste a formatted citation
x
Spandidos Publications style
Chen Y, Yan W, Xie Z, Guo W, Lu D, Lv Z and Zhang X: Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer. Mol Clin Oncol 14: 36, 2021.
APA
Chen, Y., Yan, W., Xie, Z., Guo, W., Lu, D., Lv, Z., & Zhang, X. (2021). Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer. Molecular and Clinical Oncology, 14, 36. https://doi.org/10.3892/mco.2020.2198
MLA
Chen, Y., Yan, W., Xie, Z., Guo, W., Lu, D., Lv, Z., Zhang, X."Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer". Molecular and Clinical Oncology 14.2 (2021): 36.
Chicago
Chen, Y., Yan, W., Xie, Z., Guo, W., Lu, D., Lv, Z., Zhang, X."Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer". Molecular and Clinical Oncology 14, no. 2 (2021): 36. https://doi.org/10.3892/mco.2020.2198
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