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下一代测序技术的实施与精准肿瘤学的决策支持

Operationalization of Next-Generation Sequencing and Decision Support for Precision Oncology.

作者信息

Zeng Jia, Johnson Amber, Shufean Md Abu, Kahle Michael, Yang Dong, Woodman Scott E, Vu Thuy, Moorthy Shhyam, Holla Vijaykumar, Meric-Bernstam Funda

机构信息

The University of Texas MD Anderson Cancer Center, Houston, TX.

出版信息

JCO Clin Cancer Inform. 2019 Sep;3:1-12. doi: 10.1200/CCI.19.00089.

Abstract

Genomic testing has become a part of routine oncology care and plays critical roles in diagnosis, prognostic assessment, and treatment selection. Thus, in parallel, the variety of genomic testing providers and sequencing platforms has grown exponentially. Selection of the best-fit panel for each case can be daunting, with many factors to consider. Among them is whether alteration interpretation and therapy/clinical trial matching are included and/or sufficient. In this article, we review some common commercially available sequencing platforms for the genes and types of alterations tested, samples needed, and reporting content provided. We review publicly available resources for a do-it-yourself approach to alteration interpretation when it is not provided or when supplemental research is needed, along with resources to identify genomically matched treatment options that are approved and/or investigational. However, with both commercially provided interpretation and publicly available resources, there are still caveats and limitations that can stem from insufficient or ambiguous nomenclature as well as from the presentation of information. Use cases in which clinical decision making was affected are discussed. After treatment options are identified, it is important to assess the level of evidence for use within the patient's tumor type and molecular profile. However, numerous level-of-evidence scales have been published in recent years, so we provide a publicly available tool to facilitate interoperability. The level of evidence, along with other factors, such as allelic frequency and copy number, can be used to prioritize treatment options when multiple are identified.

摘要

基因组检测已成为肿瘤常规治疗的一部分,在诊断、预后评估和治疗选择中发挥着关键作用。因此,与此同时,基因组检测供应商和测序平台的种类呈指数级增长。为每个病例选择最合适的检测组合可能具有挑战性,需要考虑许多因素。其中包括是否包含变异解读以及治疗/临床试验匹配情况是否充分。在本文中,我们回顾了一些常见的商业可用测序平台,包括所检测基因和变异类型、所需样本以及提供的报告内容。当未提供变异解读或需要补充研究时,我们回顾了可用于自行解读变异的公开可用资源,以及用于识别已获批和/或正在研究的基因组匹配治疗方案的资源。然而,无论是商业提供的解读还是公开可用资源,都仍然存在一些警告和限制,这些可能源于命名不充分或不明确以及信息呈现方式。我们讨论了影响临床决策的实际案例。在确定治疗方案后,评估在患者肿瘤类型和分子特征中使用该方案的证据水平很重要。然而,近年来已经发表了许多证据水平量表,因此我们提供了一个公开可用的工具以促进互操作性。当确定了多种治疗方案时,证据水平以及其他因素,如等位基因频率和拷贝数,可用于对治疗方案进行优先排序。

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