Goodison Steve, Sun Yijun, Urquidi Virginia
M. D. Anderson Cancer Center Orlando, Cancer Research Institute, 6900 Lake Nona Blvd, Orlando, FL 32827, USA.
Bioanalysis. 2010 May;2(5):855-62. doi: 10.4155/bio.10.35.
The ability to compare genome-wide expression profiles in human tissue samples has the potential to add an invaluable molecular pathology aspect to the detection and evaluation of multiple diseases. Applications include initial diagnosis, evaluation of disease subtype, monitoring of response to therapy and the prediction of disease recurrence. The derivation of molecular signatures that can predict tumor recurrence in breast cancer has been a particularly intense area of investigation and a number of studies have shown that molecular signatures can outperform currently used clinicopathologic factors in predicting relapse in this disease. However, many of these predictive models have been derived using relatively simple computational algorithms and whether these models are at a stage of development worthy of large-cohort clinical trial validation is currently a subject of debate. In this review, we focus on the derivation of optimal molecular signatures from high-dimensional data and discuss some of the expected future developments in the field.
在人体组织样本中比较全基因组表达谱的能力，有可能为多种疾病的检测和评估增添极其重要的分子病理学内容。其应用包括初始诊断、疾病亚型评估、治疗反应监测以及疾病复发预测。能够预测乳腺癌肿瘤复发的分子特征的推导，一直是一个特别热门的研究领域，许多研究表明，在预测该疾病的复发方面，分子特征比目前使用的临床病理因素表现更优。然而，这些预测模型大多是使用相对简单的计算算法推导出来的，这些模型是否已发展到值得进行大规模队列临床试验验证的阶段，目前仍是一个有争议的问题。在这篇综述中，我们专注于从高维数据中推导最优分子特征，并讨论该领域一些预期的未来发展。
