Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum.
Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum.
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DOI：
10.1016/s0140-6736(06)69342-2
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发表时间：
2006-09-16
期刊：
影响因子：
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通讯作者：
Krishna S
中科院分区：
文献类型：
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作者：
Agranoff D;Fernandez-Reyes D;Papadopoulos MC;Rojas SA;Herbster M;Loosemore A;Tarelli E;Sheldon J;Schwenk A;Pollok R;Rayner CF;Krishna S
我们研究了蛋白质组指纹图谱与质谱血清分析的潜力，再加上模式识别方法，以确定生物标志物，可以提高结核病的诊断。我们通过表面增强激光解吸电离飞行时间质谱法获得了活动性肺结核患者和对照组的血清蛋白质组学图谱。一种基于支持向量机（SVM）的监督机器学习方法被用来获得一个分类器，区分两个独立的测试集之间的组。我们使用k折交叉验证和随机抽样的SVM分类器，以评估分类器进一步。通过相关性分析选择相关质量峰，并使用SVM进行评估。我们测试了候选生物标志物的诊断潜力，通过肽质量指纹图谱，通过常规免疫测定和SVM分类器在这些数据上训练。我们的支持向量机分类器区分与重叠的临床特征的控制与活动性结核病患者的蛋白质组学谱。对结核病患者的诊断准确率为94%（敏感性93.5%，特异性94.9%），且不受HIV状态的影响。在20个信息量最大的峰上训练的分类器实现了90%的诊断准确率。从这些峰中，两种肽（血清淀粉样蛋白A蛋白和甲状腺素运载蛋白）进行了鉴定和定量的免疫测定。因为这些肽反映炎症状态，我们还定量了新蝶呤和C反应蛋白。使用这些值的组合的SVM分类器的应用给出了高达84%的结核病的诊断准确率。在第二个前瞻性收集的测试集上的验证使用整个蛋白质组特征和20个选定的峰给出了类似的准确度。使用四种生物标志物的组合，我们实现了高达78%的诊断准确率。我们通过蛋白质组指纹图谱和模式识别确定的结核病潜在生物标志物与疾病有着合理的生物学联系，可用于开发新的诊断测试。
We investigated the potential of proteomic fingerprinting with mass spectrometric serum profiling, coupled with pattern recognition methods, to identify biomarkers that could improve diagnosis of tuberculosis. We obtained serum proteomic profiles from patients with active tuberculosis and controls by surface-enhanced laser desorption ionisation time of flight mass spectrometry. A supervised machine-learning approach based on the support vector machine (SVM) was used to obtain a classifier that distinguished between the groups in two independent test sets. We used k-fold cross validation and random sampling of the SVM classifier to assess the classifier further. Relevant mass peaks were selected by correlational analysis and assessed with SVM. We tested the diagnostic potential of candidate biomarkers, identified by peptide mass fingerprinting, by conventional immunoassays and SVM classifiers trained on these data. Our SVM classifier discriminated the proteomic profile of patients with active tuberculosis from that of controls with overlapping clinical features. Diagnostic accuracy was 94% (sensitivity 93·5%, specificity 94·9%) for patients with tuberculosis and was unaffected by HIV status. A classifier trained on the 20 most informative peaks achieved diagnostic accuracy of 90%. From these peaks, two peptides (serum amyloid A protein and transthyretin) were identified and quantitated by immunoassay. Because these peptides reflect inflammatory states, we also quantitated neopterin and C reactive protein. Application of an SVM classifier using combinations of these values gave diagnostic accuracies of up to 84% for tuberculosis. Validation on a second, prospectively collected testing set gave similar accuracies using the whole proteomic signature and the 20 selected peaks. Using combinations of the four biomarkers, we achieved diagnostic accuracies of up to 78%. The potential biomarkers for tuberculosis that we identified through proteomic fingerprinting and pattern recognition have a plausible biological connection with the disease and could be used to develop new diagnostic tests.