Meric-Bernstam Funda, Johnson Amber, Holla Vijaykumar, Bailey Ann Marie, Brusco Lauren, Chen Ken, Routbort Mark, Patel Keyur P, Zeng Jia, Kopetz Scott, Davies Michael A, Piha-Paul Sarina A, Hong David S, Eterovic Agda Karina, Tsimberidou Apostolia M, Broaddus Russell, Bernstam Elmer V, Shaw Kenna R, Mendelsohn John, Mills Gordon B
Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy , the University of Texas MD Anderson Cancer Center, Houston, TX (FMB, AJ, VH, AMB, JZ, KRS, JM, GBM); Departments of Investigational Cancer Therapeutics (FMB, LB, SAPP, DSH, AMT), Surgical Oncology (FMB), Hematopathology (MR, KPP), Bioinformatics & Computational Biology (KC), GI Medical Oncology (SK), Melanoma Medical Oncology (MAD), Experimental Therapeutics (RB), Systems Biology (AKE, GBM), the University of Texas MD Anderson Cancer Center, Houston, TX; School of Biomedical Informatics, the University of Texas Health Science Center, Houston, TX (EVB).
J Natl Cancer Inst. 2015 Apr 11;107(7). doi: 10.1093/jnci/djv098. Print 2015 Jul.
Rapidly improving understanding of molecular oncology, emerging novel therapeutics, and increasingly available and affordable next-generation sequencing have created an opportunity for delivering genomically informed personalized cancer therapy. However, to implement genomically informed therapy requires that a clinician interpret the patient's molecular profile, including molecular characterization of the tumor and the patient's germline DNA. In this Commentary, we review existing data and tools for precision oncology and present a framework for reviewing the available biomedical literature on therapeutic implications of genomic alterations. Genomic alterations, including mutations, insertions/deletions, fusions, and copy number changes, need to be curated in terms of the likelihood that they alter the function of a "cancer gene" at the level of a specific variant in order to discriminate so-called "drivers" from "passengers." Alterations that are targetable either directly or indirectly with approved or investigational therapies are potentially "actionable." At this time, evidence linking predictive biomarkers to therapies is strong for only a few genomic markers in the context of specific cancer types. For these genomic alterations in other diseases and for other genomic alterations, the clinical data are either absent or insufficient to support routine clinical implementation of biomarker-based therapy. However, there is great interest in optimally matching patients to early-phase clinical trials. Thus, we need accessible, comprehensive, and frequently updated knowledge bases that describe genomic changes and their clinical implications, as well as continued education of clinicians and patients.
对分子肿瘤学的快速深入理解、新兴的新型疗法以及越来越容易获得且价格合理的下一代测序技术,为提供基于基因组信息的个性化癌症治疗创造了机会。然而,要实施基于基因组信息的治疗,临床医生需要解读患者的分子特征,包括肿瘤的分子特征和患者的种系DNA。在本评论中,我们回顾了精准肿瘤学的现有数据和工具,并提出了一个框架,用于回顾关于基因组改变的治疗意义的现有生物医学文献。基因组改变,包括突变、插入/缺失、融合和拷贝数变化,需要根据它们在特定变异水平上改变“癌症基因”功能的可能性进行整理,以便区分所谓的“驱动因素”和“乘客”。可通过批准或研究性疗法直接或间接靶向的改变可能是“可操作的”。目前,在特定癌症类型的背景下,将预测性生物标志物与疗法联系起来的证据仅对少数基因组标志物有力。对于其他疾病中的这些基因组改变以及其他基因组改变,临床数据要么缺乏,要么不足以支持基于生物标志物的疗法的常规临床应用。然而,人们非常希望能让患者最佳地匹配早期临床试验。因此,我们需要可访问、全面且经常更新的知识库,以描述基因组变化及其临床意义,同时还需要对临床医生和患者进行持续教育。