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Dealing with prognostic signature instability: a strategy illustrated for cardiovascular events in patients with end-stage renal disease

机译：处理预后标志性不稳定性：末期肾病患者心血管事件的策略说明

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Background Identification of prognostic gene expression markers from clinical cohorts might help to better understand disease etiology. A set of potentially important markers can be automatically selected when linking gene expression covariates to a clinical endpoint by multivariable regression models and regularized parameter estimation. However, this is hampered by instability due to selection from many measurements. Stability can be assessed by resampling techniques, which might guide modeling decisions, such as choice of the model class or the specific endpoint definition. Methods We specifically propose a strategy for judging the impact of different endpoint definitions, endpoint updates, different approaches for marker selection, and exclusion of outliers. This strategy is illustrated for a study with end-stage renal disease patients, who experience a yearly mortality of more than 20 %, with almost 50 % sudden cardiac death or myocardial infarction. The underlying etiology is poorly understood, and we specifically point out how our strategy can help to identify novel prognostic markers and targets for therapeutic interventions. Results For markers such as the potentially prognostic platelet glycoprotein IIb, the endpoint definition, in combination with the signature building approach is seen to have the largest impact. Removal of outliers, as identified by the proposed strategy, is also seen to considerably improve stability. Conclusions As the proposed strategy allowed us to precisely quantify the impact of modeling choices on the stability of marker identification, we suggest routine use also in other applications to prevent analysis-specific results, which are unstable, i.e. not reproducible.
机译：背景从临床队列中鉴定预后基因表达标记可能有助于更好地了解疾病病因。通过多变量回归模型和正则化参数估计将基因表达协变量与临床终点关联时，可以自动选择一组潜在的重要标记。但是，由于从许多测量中进行选择而导致的不稳定性阻碍了这一点。可以通过重采样技术来评估稳定性，该技术可以指导建模决策，例如选择模型类或特定端点定义。方法我们专门提出了一种策略，用于判断不同的端点定义，端点更新，不同的标记选择方法以及排除异常值的影响。该策略在一项针对终末期肾脏疾病患者的研究中得到了说明，该患者的年死亡率超过20％，而心源性猝死或心肌梗塞的发生率则接近50％。对潜在病因了解甚少，我们特别指出了我们的策略如何帮助确定新的预后指标和治疗干预目标。结果对于标记物（如可能预后的血小板糖蛋白IIb），终点定义与特征构建方法相结合被认为具有最大的影响。如建议的策略所指出的，离群值的去除也被认为可以大大提高稳定性。结论由于所提出的策略使我们能够精确地量化建模选择对标记物识别稳定性的影响，因此我们建议在其他应用中也常规使用该方法以防止特定于分析的结果（不稳定，即不可重现）。

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