Department of Chemistry, Stanford University, Stanford, CA 94305.
Center for Excellence in Pulmonary Biology, Stanford University School of Medicine, Stanford, CA 94305.
Proc Natl Acad Sci U S A. 2019 Dec 3;116(49):24408-24412. doi: 10.1073/pnas.1909630116. Epub 2019 Nov 18.
The gold standard for cystic fibrosis (CF) diagnosis is the determination of chloride concentration in sweat. Current testing methodology takes up to 3 h to complete and has recognized shortcomings on its diagnostic accuracy. We present an alternative method for the identification of CF by combining desorption electrospray ionization mass spectrometry and a machine-learning algorithm based on gradient boosted decision trees to analyze perspiration samples. This process takes as little as 2 min, and we determined its accuracy to be 98 ± 2% by cross-validation on analyzing 277 perspiration samples. With the introduction of statistical bootstrap, our method can provide a confidence estimate of our prediction, which helps diagnosis decision-making. We also identified important peaks by the feature selection algorithm and assigned the chemical structure of the metabolites by high-resolution and/or tandem mass spectrometry. We inspected the correlation between mild and severe CFTR gene mutation types and lipid profiles, suggesting a possible way to realize personalized medicine with this noninvasive, fast, and accurate method.
囊性纤维化 (CF) 的金标准诊断方法是汗液中氯离子浓度的测定。目前的检测方法需要长达 3 小时才能完成，并且其诊断准确性存在公认的缺陷。我们提出了一种替代方法，通过结合解吸电喷雾电离质谱和基于梯度提升决策树的机器学习算法来分析汗液样本，从而识别 CF。这个过程只需 2 分钟，我们通过对 277 个汗液样本进行交叉验证，确定其准确率为 98 ± 2%。通过引入统计自举法，我们的方法可以为预测提供置信度估计，有助于诊断决策。我们还通过特征选择算法识别了重要的峰，并通过高分辨率和/或串联质谱确定了代谢物的化学结构。我们检查了轻度和重度 CFTR 基因突变类型与脂质谱之间的相关性，这表明可以通过这种非侵入性、快速和准确的方法实现个性化医疗。
