Dienstmann Rodrigo, Dong Fei, Borger Darrell, Dias-Santagata Dora, Ellisen Leif W, Le Long P, Iafrate A John
Massachusetts General Hospital and Harvard Medical School, Molecular Pathology Lab, USA.
Massachusetts General Hospital and Harvard Medical School, Molecular Pathology Lab, USA.
Mol Oncol. 2014 Jul;8(5):859-73. doi: 10.1016/j.molonc.2014.03.021. Epub 2014 Apr 4.
Of hundreds to thousands of somatic mutations that exist in each cancer genome, a large number are unique and non-recurrent variants. Prioritizing genetic variants identified via next generation sequencing technologies remains a major challenge. Many such variants occur in tumor genes that have well-established biological and clinical relevance and are putative targets of molecular therapy, however, most variants are still of unknown significance. With large amounts of data being generated as high throughput sequencing assays enter the clinical realm, there is a growing need to better communicate relevant findings in a timely manner while remaining cognizant of the potential consequences of misuse or overinterpretation of genomic information. Herein we describe a systematic framework for variant annotation and prioritization, and we propose a structured molecular pathology report using standardized terminology in order to best inform oncology clinical practice. We hope that our experience developing a comprehensive knowledge database of emerging predictive markers matched to targeted therapies will help other institutions implement similar programs.
在每个癌症基因组中存在的成百上千种体细胞突变中,大量是独特的、非复发性变异。对通过下一代测序技术鉴定出的基因变异进行优先级排序仍然是一项重大挑战。许多此类变异发生在具有明确生物学和临床相关性且是分子治疗假定靶点的肿瘤基因中,然而,大多数变异的意义仍然未知。随着高通量测序检测进入临床领域并产生大量数据,越来越需要及时更好地传达相关发现,同时要意识到基因组信息滥用或过度解读的潜在后果。在此,我们描述了一个变异注释和优先级排序的系统框架,并提出了一份使用标准化术语的结构化分子病理学报告,以便为肿瘤学临床实践提供最佳信息。我们希望我们开发与靶向治疗相匹配的新兴预测标志物综合知识数据库的经验将有助于其他机构实施类似项目。