Department of Data Science, The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan.
Comput Math Methods Med. 2013;2013:865980. doi: 10.1155/2013/865980. Epub 2013 Apr 17.
The establishment of high-throughput technologies has brought substantial advances to our understanding of the biology of many diseases at the molecular level and increasing expectations on the development of innovative molecularly targeted treatments and molecular biomarkers or diagnostic tests in the context of clinical studies. In this review article, we position the two critical statistical analyses of high-dimensional genomic data, gene screening and prediction, in the framework of development and validation of genomic biomarkers or signatures, through taking into consideration the possible different strategies for developing genomic signatures. A wide variety of biomarker-based clinical trial designs to assess clinical utility of a biomarker or a new treatment with a companion biomarker are also discussed.
高通量技术的建立为我们在分子水平上对许多疾病的生物学特性有了更深入的了解,并对创新性的分子靶向治疗和分子生物标志物或诊断试验在临床研究中的发展寄予了越来越高的期望。在这篇综述文章中,我们通过考虑开发基因组特征所采用的不同策略,将高维基因组数据分析的两个关键统计分析,基因筛选和预测,置于基因组生物标志物或特征的开发和验证框架中。我们还讨论了各种基于生物标志物的临床试验设计,以评估生物标志物或新的伴随生物标志物治疗的临床效用。