Biostatistics Center, Massachusetts General Hospital Cancer Center , Boston, Massachusetts 02114, United States.
J Proteome Res. 2013 Dec 6;12(12):5383-94. doi: 10.1021/pr400132j. Epub 2013 Oct 28.
Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.
蛋白质生物标志物对于深入了解癌症生物学以及提高癌症的诊断、监测和治疗能力至关重要。为了让最有前途的蛋白质生物标志物候选物能够进入临床验证研究，必须克服重要的分析和临床障碍。尽管当代蛋白质组学技术支持对单个临床标本中大量蛋白质进行测量，但样本通量仍然相对较低。在典型的临床蛋白质组学研究中，这个问题更加突出，这些研究通常缺乏适当的实验设计，导致在生物标志物管道的每个阶段分析的生物样本太少，无法达到足够的统计效力。为了解决这一关键缺陷，美国国立癌症研究所（NCI）、美国国立心肺血液研究所（NHLBI）和美国临床化学协会（AACC）与美国食品和药物管理局（FDA）联合举办了一次研讨会。研讨会的一个重要成果是制定了用于设计生物标志物发现和验证研究的统计框架。本文描述了使用定量临床判断来为临床相关性设定统计标准的方法，以及在临床验证阶段之前的发现和验证阶段计算蛋白质组学研究生物样本量的方法。这是就蛋白质组学生物标志物发现和验证研究的统计设计建立定量标准达成共识的第一步。
