Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer.
Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer.
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研究设计和数据分析考虑了发现预后分子生物标志物的考虑：晚期浆液卵巢癌中无进展生存的案例研究。
DOI：
10.1186/s12920-016-0187-4
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发表时间：
2016-06-10
影响因子：
2.7
通讯作者：
Levine DA
中科院分区：
文献类型：
--
作者：
Qin LX;Levine DA
准确发现作为临床结果预后的分子生物标志物是一项重要但具有挑战性的任务，部分原因是临床结果的典型弱基因组信号和由于微阵列处理效应而导致的频繁强噪声的组合。迫切需要有效的战略来应对这一挑战。我们开始评估使用仔细的研究设计和数据标准化的预后分子生物标志物的发现。以晚期浆液性卵巢癌的无进展生存率为例，我们对同一组肿瘤样本的两组microRNA阵列进行了实证分析：一组中的阵列是使用仔细的研究设计（即，统一处理和随机阵列到样本分配）收集的，另一组中的阵列不是。我们发现（1）即使在随机化的情况下，由于偶然性，处理效应也可能混淆研究中的临床结局，（2）混淆处理效应的水平可以通过数据标准化来降低，（3）良好的研究设计不能被事后标准化所取代。此外，我们提供了一种实用的方法来定义阳性和阴性对照标记物，用于检测处理效果和评估归一化方法的性能。我们的工作展示了为弱基因组信号的临床结果寻找预后生物标志物的困难，说明了仔细的研究设计和数据标准化的好处，并提供了一种实用的方法来识别处理效果和选择有益的标准化方法。我们的工作需要仔细的研究设计和数据分析，以发现稳健和可翻译的分子生物标志物。本文的在线版本（doi：10.1186/s12920-016-0187-4）包含补充材料，可供授权用户使用。
Accurate discovery of molecular biomarkers that are prognostic of a clinical outcome is an important yet challenging task, partly due to the combination of the typically weak genomic signal for a clinical outcome and the frequently strong noise due to microarray handling effects. Effective strategies to resolve this challenge are in dire need. We set out to assess the use of careful study design and data normalization for the discovery of prognostic molecular biomarkers. Taking progression free survival in advanced serous ovarian cancer as an example, we conducted empirical analysis on two sets of microRNA arrays for the same set of tumor samples: arrays in one set were collected using careful study design (that is, uniform handling and randomized array-to-sample assignment) and arrays in the other set were not. We found that (1) handling effects can confound the clinical outcome under study as a result of chance even with randomization, (2) the level of confounding handling effects can be reduced by data normalization, and (3) good study design cannot be replaced by post-hoc normalization. In addition, we provided a practical approach to define positive and negative control markers for detecting handling effects and assessing the performance of a normalization method. Our work showcased the difficulty of finding prognostic biomarkers for a clinical outcome of weak genomic signals, illustrated the benefits of careful study design and data normalization, and provided a practical approach to identify handling effects and select a beneficial normalization method. Our work calls for careful study design and data analysis for the discovery of robust and translatable molecular biomarkers. The online version of this article (doi:10.1186/s12920-016-0187-4) contains supplementary material, which is available to authorized users.