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首页> 外文期刊>BMC Medical Genomics >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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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

机译：发现预后分子生物标志物的研究设计和数据分析注意事项：晚期浆液性卵巢癌无进展生存的“案例研究”

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Background 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. Methods 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. Results 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. Conclusions 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.
机译：背景技术准确发现可预后的分子生物学标志物是一项重要而又具有挑战性的任务，部分原因是用于临床结果的通常较弱的基因组信号与由于微阵列处理作用而经常产生的强噪声相结合。迫切需要有效的策略来解决这一挑战。方法我们着手评估仔细研究设计和数据标准化在发现预后分子生物标志物中的应用。以晚期浆液性卵巢癌的无进展生存期为例，我们对同一组肿瘤样品的两组microRNA阵列进行了实证分析：使用仔细的研究设计（即统一处理和随机阵列）收集了一组阵列样本分配），而另一组中的数组则没有。结果我们发现（1）即使随机化，处理效果也可能由于偶然的结果而混淆研究中的临床结果;（2）数据归一化可以降低混淆处理效果的水平;（3）好的研究设计不能替换为事后归一化。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，以检测处理效果并评估归一化方法的性能。结论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，说明了仔细研究设计和数据标准化的好处，并提供了确定处理效果并选择有益的标准化方法的实用方法。我们的工作要求仔细的研究设计和数据分析，以发现健壮且可翻译的分子生物标记。

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