文章：

精准医疗时代生物标志物的发展：以肺癌为例

Biomarker development in the precision medicine era: lung cancer as a case study

原文发布日期：2016-07-08

DOI: 10.1038/nrc.2016.56

类型: Review Article

开放获取: 否

要点：

1. Precision medicine seeks to identify and classify individual patients so that optimal treatment decisions can be made. Nations are recognizing this area of research by developing national cohorts from which to collect data and developing regulatory guidelines on biomarkers.
2. The cost of genetic sequencing and other 'omics' technologies has decreased while the quality of data they generate has increased. Thus, immense amounts of molecular data are being derived from cohort studies to begin developing new biomarkers to classify patients into subtypes.
3. The development of biomarkers is largely limited by the following factors: low statistical power in rare subtypes; risk of false-positive findings in studies that do not validate their findings in a separate cohort and/or conduct concomitant mechanistic experiments; and technical reproducibility concerns.
4. Genomics and protein immunohistochemistry have led the way for developing biomarkers. Other molecular measurements (for example, metabolomics and microbiomics) are still in preliminary stages and are often not validated in another cohort.
5. Integrating different types of molecules into a biomarker panel, along with other patient data, is the future of precision medicine; however, the sheer number of potential combinations of data types complicates the concerns about statistical power and reproducibility.
6. Currently, improved biomarkers are needed to differentiate lung nodules identified by new US national screening recommendations into non-cancer, cancer with poor survival probability and cancer with higher survival probability subtypes, to provide thousands of individuals with precise treatment decisions.

要点翻译：

1. 精准医疗旨在对个体患者进行识别与分类，从而制定最佳治疗方案。各国正通过组建国家队列以收集数据，并制定生物标志物监管指南来重视这一研究领域。
2. 基因测序及其他组学技术的成本持续下降，生成的数据质量却不断提升。因此，通过队列研究获取的海量分子数据正推动新型生物标志物的开发，以实现对患者亚型的精准划分。
3. 当前生物标志物的发展主要受以下因素制约：罕见亚型研究统计效能不足；未在独立队列中验证研究结果或未开展同步机制实验的研究存在假阳性风险；以及技术可重复性方面的隐忧。
4. 基因组学与蛋白质免疫组织化学技术一直是生物标志物开发的主导力量。其他分子检测技术（如代谢组学、微生物组学）仍处于起步阶段，且往往未经过独立队列验证。
5. 将不同类别分子数据与其他患者信息整合为生物标志物组合是精准医疗的未来方向，然而数据类型潜在组合的庞大体量使得统计效能与可重复性问题更趋复杂。
6. 当前亟需改进生物标志物技术，以根据美国最新筛查指南鉴定的肺结节区分为：非癌性结节、低生存概率癌症与高生存概率癌症亚型，从而为成千上万民众提供精准的治疗决策。

英文摘要：

Precision medicine relies on validated biomarkers with which to better classify patients by their probable disease risk, prognosis and/or response to treatment. Although affordable 'omics'-based technology has enabled faster identification of putative biomarkers, the validation of biomarkers is still stymied by low statistical power and poor reproducibility of results. This Review summarizes the successes and challenges of using different types of molecule as biomarkers, using lung cancer as a key illustrative example. Efforts at the national level of several countries to tie molecular measurement of samples to patient data via electronic medical records are the future of precision medicine research.

摘要翻译： 

精准医疗依赖于经过验证的生物标志物，以便更好地根据患者的疾病风险、预后和/或治疗反应对其进行分类。尽管价格合理的“组学”技术加快了候选生物标志物的识别，但生物标志物的验证仍因统计效能低和结果重现性差而受阻。本综述总结了使用不同类型分子作为生物标志物的成功与挑战，并以肺癌为主要例证。多个国家在国家层面将样本分子测量与患者数据通过电子病历关联的努力，是精准医学研究的未来方向。

原文链接：

Biomarker development in the precision medicine era: lung cancer as a case study