元码多基因检测试剂盒获批上市全面覆盖非小细胞肺癌用药基因

 收藏

关键词：上市获批

资讯来源：小桔灯网

发布时间： 2021-07-21

来源：小桔灯网

2021年7月19日，由元码基因科技（苏州）有限公司（以下简称元码基因）自主研发的“人EGFR/KRAS/BRAF/PIK3CA/ALK/ROS1基因突变检测试剂盒（可逆末端终止测序法）”获得国家药品监督管理局（NMPA）三类医疗器械注册证（国械注准20213400525），正式获批上市。

元码多基因检测试剂盒获批截图

该试剂盒可用于体外定性检测非小细胞肺癌患者FFPE样本中EGFR、KRAS、BRAF、PIK3CA、ALK、ROS1基因的变异状态，为临床医生选择吉非替尼片、盐酸埃克替尼片、甲磺酸奥希替尼片、克唑替尼胶囊等靶向药物的非小细胞肺癌个体化治疗提供辅助性参考依据。

全面覆盖非小细胞肺癌用药基因

该试剂盒可一次检测涵盖非小细胞肺癌NCCN指南推荐的重点基因和NMPA批准已上市的所有非小细胞肺癌靶向药物治疗靶点，并覆盖肿瘤相关基因的点突变、缺失、融合（重排）等多种变异类型，是目前国内非小细胞肺癌检测基因数和位点数最为全面的产品之一，可为患者提供最为经济、有效的肺癌靶向用药解决方案。

大规模临床试验验证总符合率高

本试剂盒的临床试验历经一年多时间，在包括中国医学科学院北京协和医院、首都医科大学附属北京胸科医院、潍坊市人民医院三家临床试验中心完成，共1200多例临床样本。

在与已上市产品（荧光PCR法）的比较研究中，总符合率大于98%，在与金标准（Sanger测序法）的比较研究中，总符合率大于96%。在与EGFR、ALK、ROS1已上市的伴随诊断试剂进行比较研究，总符合率大于99%，体现了检测结果的高度一致性。

回顾性靶向药物药效研究共100多例有效样本，各靶点人群接受相应靶向药物治疗的客观缓解率与既往药物临床试验客观缓解率范围基本相符，均达到了临床预期水平。

周期快，通量灵活，满足用户多样化需求

该多基因检测试剂盒基于Illumina测序平台开发，精确度高，实验快捷，便于操作，可满足临床用户的多样化需求。

报告周期快

配套元码基因自主研发的自动化生信分析和报告系统，从样本接收到出具检测报告最快可在3天内完成，第一时间交付检测结果，为患者提供精准用药指导。

通量灵活

一次MiseqDx测序通量适合医院小批量多批次操作，无需凑样本烦恼。

临床获益全面

还可同时检测RET、MET、NRAS、HER2、CYP2C8、ERCC1、ERCC2、XRCC1、GSTP1、MDR1等肺癌靶向化疗用药等相关基因，以及信号通路中的关键基因的热点突变，帮助临床医生更好的挖掘肿瘤靶向化疗治疗的全面有用信息！

多年来，元码一直致力于将先进的基因组学技术应用于临床实践，此多基因检测试剂盒的获批，是公司努力为用户提供更精准的靶向治疗方案的见证，是元码布局肿瘤NGS领域的重要里程碑。此外，元码基因科研团队一直致力于肿瘤的精准治疗研究，在肺癌研究领域已发表多项有重要临床医学意义的学术成果[1]-[20]。

未来，元码基因将结合最新技术成果和临床数据积累，围绕NGS测序平台继续探索和开发新技术、新产品，推动肿瘤精准诊疗的临床发展，加强基因检测在中国肿瘤临床的普及应用。

参考文献（向下滑动查看更多）

[1]．Li, C., Wang, Y., Su, K., Liu, Y. and He, J. (2021) Presentation of EGFR mutations in 162 family probands with multiple primary lung cancer. Translational Lung Cancer Research, 10, 1734-1746.

[2]．Yang, Y., Yang, J., Liang, Y., Liao, B. and Huang, K. (2021) Identification and Validation of Efficacy of Immunological Therapy for Lung Cancer From Histopathological Images Based on Deep Learning. Frontiers in Genetics, 12.

[3]．Wu, Z., Wang, L., Li, C., Cai, Y. and Liu, Y. (2020) DeepLRHE: A Deep Convolutional Neural Network Framework to Evaluate the Risk of Lung Cancer Recurrence and Metastasis From Histopathology Images. Frontiers in Genetics, 11, 768.

[4]．Huang, X., Zhang, J., Jiang, Y., Liao, X. and Yin, T. (2020) IGF2BP3 May Contributes to Lung Tumorigenesis by Regulating the Alternative Splicing of PKM. Frontiers in Bioengineering and Biotechnology, 8, 679.

[5]．Song, Z., Chen, X., Shi, Y., Huang, R. and Chen, G. (2020) Evaluating the Potential of T-Cell Receptor Repertoires in Predicting the Prognosis of Resectable Non-Small Cell Lung Cancers. Molecular Therapy — Methods & Clinical Development.

[6]．He, B., Zhu, R., Yang, H., Lu, Q. and Lang, J. (2020) Assessing the Impact of Data Preprocessing on Analyzing Next Generation Sequencing Data. Frontiers in Bioengineering and Biotechnology, 8, 817.

[7]．Meng, H., Guo, X., Sun, D., Liang, Y. and Geng, J. (2019) Genomic Profiling of Driver Gene Mutations in Chinese Patients With Non-Small Cell Lung Cancer. Frontiers in Genetics, 10, 1008-.

[8]．(2019) DL-CNV: A deep learning method for identifying copy number variations based on next generation target sequencing. Mathematical biosciences and engineering : MBE, 17, 202-215.

[9]．Chen, S., Zhou, W., Tu, J., Li, J. and Huang, Z. (2021) A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data. Frontiers in Genetics, 12, 632761.

[10]．Gao, W., Yang, J., Zhuo, C., Yang, J. and Lin, J. (2020) TOOme: A novel computational framework to infer cancer tissue-of-origin by integrating both gene mutation and expression. Journal of Clinical Oncology, 38, 3591-3591.

[11]．He, B., Zhang, Y., Zhou, Z., Wang, B. and Tian, G. (2020) A Neural Network Framework for Predicting the Tissue-of-Origin of 15 Common Cancer Types Based on RNA-Seq Data. Frontiers in Bioengineering and Biotechnology, 8.

[12]．He, B., Dai, C., Lang, J., Bing, P. and Yang, J. (2020) A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 1866, 165916.

[13]．He, B., Lu, Q., Lang, J., Yu, H. and Tian, G. (2020) A New Method for CTC Images Recognition Based on Machine Learning. Frontiers in Bioengineering and Biotechnology, 8, 897.

[14]．Zhang, Y., Feng, T., Wang, S., Dong, R. and Wang, B. (2020) A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number Variations. Frontiers in Genetics, 11.

[15]．Wang, F., Yang, J., Lin, H., Li, Q. and Tian, G. (2020) Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues. Frontiers in Genetics, 11.

[16]．He, B., Xu, J., Tian, Y., Liao, B. and Bing, P. (2020) Gene Coexpression Network and Module Analysis across 52 Human Tissues. BioMed Research International, 2020, 1-14.

[17]．Li, X., Lin, Y., Gu, C. and Yang, J. (2019) FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs. Bmc Systems Biology, 13.

[18]．Xu, J., Cai, L., Liao, B., Zhu, W. and Yang, J. (2019) Identifying Potential miRNAs–Disease Associations With Probability Matrix Factorization. Frontiers in Genetics, 10, 1234.

[19]．Yao, Y., Ji, B., Shi, S., Xu, J. and Yang, J. (2020) IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data. IEEE Access, 8, 16517-16527.

[20]．Yang, J., Qin, Y., Zhang, T., Wang, F. and Yun, F. (2018) Identification of human age-associated gene co-expressions in functional modules using liquid association. Oncotarget, 9, 1063-1074.