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非小细胞肺癌当前及未来的分子检测,我们能从新测序技术中期待什么?

Current and Future Molecular Testing in NSCLC, What Can We Expect from New Sequencing Technologies?

作者信息

Garinet Simon, Laurent-Puig Pierre, Blons Hélène, Oudart Jean-Baptiste

机构信息

INSERM UMR-S1147, Paris Sorbonne Cite University, 75270 Paris Cedex 06, France.

Department of Biochemistry, Unit of Pharmacogenetics and Molecular Oncology, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, 75015 Paris, France.

出版信息

J Clin Med. 2018 Jun 9;7(6):144. doi: 10.3390/jcm7060144.

Abstract

Recent changes in lung cancer care, including new approvals in first line and the introduction of high-throughput molecular technologies in routine testing led us to question ourselves on how deeper molecular testing may be helpful for the optimal use of targeted drugs. In this article, we review recent results in the scope of personalized medicine in lung cancer. We discuss biomarkers that have a therapeutic predictive value in lung cancer with a focus on recent changes and on the clinical value of large scale sequencing strategies. We review the use of second- and third-generation EGFR and ALK inhibitors with a focus on secondary resistance alterations. We discuss anti-BRAF and anti-MEK combo, emerging biomarkers as NRG1 and NTRKs fusions and immunotherapy. Finally, we discuss the different technical issues of comprehensive molecular profiling and show how large screenings might refine the prediction value of individual markers. Based on a review of recent publications (2012⁻2018), we address promising approaches for the treatment of patients with lung cancers and the technical challenges associated with the identification of new predictive markers.

摘要

肺癌治疗领域最近的变化,包括一线治疗的新批准以及常规检测中高通量分子技术的引入,促使我们思考更深入的分子检测如何有助于靶向药物的优化使用。在本文中,我们回顾了肺癌个性化医疗领域的最新成果。我们讨论了在肺癌中具有治疗预测价值的生物标志物,重点关注近期的变化以及大规模测序策略的临床价值。我们回顾了第二代和第三代EGFR和ALK抑制剂的使用,重点关注继发性耐药改变。我们讨论了抗BRAF和抗MEK联合用药、新兴生物标志物如NRG1和NTRK融合以及免疫疗法。最后,我们讨论了全面分子谱分析的不同技术问题,并展示了大规模筛查如何提高单个标志物的预测价值。基于对近期出版物(2012 - 2018年)的综述,我们探讨了肺癌患者治疗的有前景的方法以及与新预测标志物识别相关的技术挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31f4/6024886/df4ceb7853c4/jcm-07-00144-g001.jpg

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