Mass spectral data from multiple samples are suitable for a hypothesis-free development of clinically useful multivariate tests using modern machine learning techniques. However, the transition from discovery to adoption of proteomic tests has proved challenging. Slow adoption of these tests in clinical practice is, in part, related to insufficient understanding of the biological mechanisms underlying multivariate tests developed based on correlative studies. While identification of individual proteins may provide important insights, elucidation of concerted relationships of sets of proteins with biological pathways can better reflect complex phenomena, such as cancerogenesis and response to treatment. Protein set enrichment analysis (PSEA) allows identification of associations of mass spectral features or test classifications with biological function by looking for consistent correlations across a group of proteins.

We evaluated the utility of PSEA for exploring the biological information content of mass spectra, using a sample set with mass spectral data and matched protein expression information. This made it possible to detect significant biological associations with mass spectral peaks without identifying their protein constituents. We demonstrated that the method produces reproducible associations and can be used for elucidation of the mechanisms of action associated with two previously developed multivariate mass spectrometry-based tests. Significant correlations with several host immune response-related processes were found on the level of individual mass spectral features and with test classifications. The results illustrate the utility of the PSEA approach applied to mass spectral data as a method for elucidating biological mechanisms underlying phenotypes related to different physiological states of the organism.

来自多个样本的质谱数据适用于使用现代机器学习技术无假设地开发临床上有用的多变量测试的情况。但是，从发现到采用蛋白质组学测试的过渡已证明具有挑战性。这些测试在临床实践中的缓慢采用，部分原因是对基于相关研究开发的多元测试所基于的生物学机制的了解不足。虽然鉴定单个蛋白质可能提供重要的见识，但阐明蛋白质与生物途径之间协调的关系可以更好地反映复杂的现象，例如致癌作用和对治疗的反应。

我们使用带有质谱数据和匹配的蛋白质表达信息的样本集，评估了PSEA用于探索质谱生物信息内容的效用。这使得可以检测与质谱峰的重要生物学关联，而无需确定其蛋白质成分。我们证明了该方法可产生可重现的关联，并可用于阐明与两个先前开发的基于多元质谱的测试相关的作用机理。在个别质谱特征水平和测试分类中发现与几种宿主免疫应答相关过程的显着相关性。