Department of Clinical Biochemistry and Immunology, Statens Serum Institut, DK-2300 Copenhagen S, Denmark.
Anal Chem. 2009 Mar 1;81(5):1907-13. doi: 10.1021/ac802293g.
The emergence of new biological disease markers from mass spectrometric studies of serum proteomes has been quite limited. There are challenges regarding the analytical and statistical procedures, preanalytical variability, and study designs. In this serological study of ovarian cancer, we apply classification methods in a strictly designed study with standardized sample collection procedures. A total of 265 sera from women admitted with symptoms of a pelvic mass were used for model building. We developed a rigorous approach for building classification models suitable for the highly multivariate data and illustrate how to evaluate and ensure data quality and optimize data preprocessing and data reduction. We document time dependent changes in peak profiles up to 15 months after sampling even when storing samples at -20 degrees C. The developed classification model was validated using completely independent samples, and a cross validation procedure which we call cross model validation was applied to get realistic performance values. The best models were able to classify with 79% specificity and 56% sensitivity, i.e., an analytical accuracy of 68%. However, the existing serum marker (CA-125) alone gave a better analytical accuracy (81%) in the same sample set. Also, the combination of mass spectrometric data and levels of CA-125 data did not improve the predictive performance of models. In conclusion, proteomic approaches to biomarker discovery are not necessarily yielding straightforward diagnostic leads but lay the foundation for more work.
从血清蛋白质组的质谱研究中出现的新的生物疾病标志物相当有限。在分析和统计程序、分析前变异性和研究设计方面存在挑战。在这项卵巢癌的血清学研究中，我们在一个具有标准化样本采集程序的严格设计的研究中应用分类方法。总共使用了 265 名因盆腔肿块症状入院的妇女的血清来建立模型。我们开发了一种严格的方法来建立适合高度多变量数据的分类模型，并说明如何评估和确保数据质量以及优化数据预处理和数据减少。我们记录了在采样后长达 15 个月时的峰谱的时间依赖性变化，即使在 -20°C 下储存样本也是如此。使用完全独立的样本和我们称为交叉模型验证的交叉验证过程验证了开发的分类模型，并获得了现实的性能值。最好的模型能够以 79%的特异性和 56%的灵敏度进行分类，即分析准确性为 68%。然而，在同一样本集中，现有的血清标志物（CA-125）单独使用时具有更好的分析准确性（81%）。此外，质谱数据和 CA-125 数据的组合并没有提高模型的预测性能。总之，蛋白质组学方法在生物标志物发现方面不一定会产生直接的诊断线索，但为进一步研究奠定了基础。
