Chen Yu, Yan Wenqing, Xie Zhi, Guo Weibang, Lu Danxia, Lv Zhiyi, Zhang Xuchao
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.
Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China.
Mol Clin Oncol. 2021 Feb;14(2):36. doi: 10.3892/mco.2020.2198. Epub 2020 Dec 24.
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 , , and . 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.
下一代测序（NGS）技术在治疗决策中日益成为重要的临床工具。然而，由于在理解基因变异的临床重要性以及将结果有效转化为临床医生可采取行动的信息方面存在局限性，NGS数据的解读在医疗现场面临挑战。本研究比较了两种对肿瘤样本中可采取行动的基因和基因突变进行注释和报告的方法：一种是由经验丰富的分子肿瘤生物信息专家进行手动整理、注释和报告的传统方法；另一种是基于云的认知技术，目的是检测中国肺癌患者中具有潜在意义的基因突变。由广东肺癌研究所（GLCI）研究团队对2014年至2016年间在115份患者组织样本上先前进行的285个基因靶向外显子测序数据进行了手动注释和评估，然后通过Watson for Genomics（WfG）认知基因组技术进行分析。对两种方法的注释结果进行了比较分析，以确定所产生突变在数量和质量上的差异。WfG分析与GLCI生物信息专家的注释结果完全一致率为43.48%。在65份（56.52%）样本中，WfG分析平均在每个样本中比GLCI手动审查多识别和解读1.54个突变位点。这些突变位点位于27个基因上，包括……（此处原文未完整列出基因名称）。EP300基因中的突变最为普遍，存在于30.77%的样本中。WfG分析解读的肿瘤突变负荷（TMB）（1.82）显著高于GLCI审查解读的TMB（0.73）。与生物信息专家的手动整理相比，WfG分析提供了全面的见解和额外的基因改变信息，为肺癌患者的临床治疗策略提供依据。这些发现表明认知计算在提高基因改变的综合检测和解读效率方面具有重要作用，这可能为靶向癌症治疗提供机会。
