Department of Medical Oncology, INSERM U981, Gustave Roussy Cancer Campus, Villejuif, France.
The Genome Institute, Washington University School of Medicine, St Louis, USA.
Ann Oncol. 2014 Dec;25(12):2295-2303. doi: 10.1093/annonc/mdu478. Epub 2014 Oct 24.
The implementation of cancer genomic testing into the clinical setting has brought major opportunities. However, as our understanding of cancer initiation, maintenance and progression improves through detailed cancer genomic studies, the challenges associated with driver identification and target classification in the clinical setting become clearer. Here, we review recent insights into cancer genomic testing in the clinical setting, and suggest a target classification approach that considers the levels of evidence supporting the prioritization of tumour drivers for therapeutic targeting in light of complex cancer clonal and sub-clonal structures and clinical successes and failures in the field. We argue that such classification approaches, together with transparent reporting of both positive and negative clinical data and continued research to identify the sub-clonal dynamics of driver events during the disease course, will facilitate inter-trial comparisons, optimize patient informed consent and provide a critically balanced evaluation of genomic testing in clinical practice.
癌症基因组检测在临床实践中的应用带来了重大机遇。然而，随着我们对癌症发生、维持和进展的理解通过详细的癌症基因组研究得到提高，与临床环境中驱动因素识别和靶标分类相关的挑战变得更加清晰。在这里，我们回顾了癌症基因组检测在临床环境中的最新研究进展，并提出了一种靶标分类方法，该方法考虑了支持根据肿瘤驱动因素进行治疗靶向的优先级的证据水平，同时考虑了复杂的癌症克隆和亚克隆结构以及该领域的临床成功和失败。我们认为，此类分类方法，以及阳性和阴性临床数据的透明报告，以及继续研究在疾病过程中识别驱动事件的亚克隆动态，将有助于临床试验之间的比较，优化患者知情同意，并对临床实践中的基因组检测进行批判性平衡评估。
