Rapid Mass Spectrometric Metabolic Profiling of Blood Sera Detects Ovarian Cancer with High Accuracy
Rapid Mass Spectrometric Metabolic Profiling of Blood Sera Detects Ovarian Cancer with High Accuracy
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DOI：
10.1158/1055-9965.epi-10-0126
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发表时间：
2010-09-01
影响因子：
3.8
通讯作者：
McDonald, John F.
中科院分区：
文献类型：
--
作者：
Zhou, Manshui;Guan, Wei;McDonald, John F.
背景：卵巢癌的诊断是有问题的，因为这种疾病通常没有症状，特别是在进展和/或复发的早期阶段。我们报道了一种新的质谱学技术与一种新的支持向量机计算方法在癌症诊断中的集成，并描述了该方法在卵巢癌诊断中的应用。方法：我们结合了高通量环境电离质谱学技术(实时质谱学中的直接分析)来分析44例浆液性乳头状卵巢癌(I-IV期)和50例健康女性或良性疾病女性的血清相对代谢物水平。这些特征被输入到一个定制的基于支持向量机的机器学习算法中，用于诊断分类。结果：该方法以前所未有的99%~100%的准确率区分肿瘤和对照组(-30分裂验证试验的灵敏度和特异度均为100%；留一法交叉验证的灵敏度和特异度分别为100%和98%)。结论：该方法具有良好的临床应用前景。由于卵巢癌在普通人群中的患病率极低(类似于0.04%)，将需要进行广泛的前瞻性测试，以评估该测试在一般筛查应用中的潜在效用。然而，更直接的应用可能是作为高危人群的诊断工具或监测治疗后的癌症复发。影响：准确而廉价地诊断卵巢癌的能力将对卵巢癌的治疗和预后产生显著的积极影响。癌症流行病学生物标志物；19(9)；2262-71。(C)2010年AACR。
Background: Ovarian cancer diagnosis is problematic because the disease is typically asymptomatic, especially at the early stages of progression and/or recurrence. We report here the integration of a new mass spectrometric technology with a novel support vector machine computational method for use in cancer diagnostics, and describe the application of the method to ovarian cancer.Methods: We coupled a high-throughput ambient ionization technique for mass spectrometry (direct analysis in real-time mass spectrometry) to profile relative metabolite levels in sera from 44 women diagnosed with serous papillary ovarian cancer (stages I-IV) and 50 healthy women or women with benign conditions. The profiles were input to a customized functional support vector machine-based machine-learning algorithm for diagnostic classification. Performance was evaluated through a 64-30 split validation test and with a stringent series of leave-one-out cross-validations.Results: The assay distinguished between the cancer and control groups with an unprecedented 99% to 100% accuracy (100% sensitivity and 100% specificity by the 64-30 split validation test; 100% sensitivity and 98% specificity by leave-one-out cross-validations).Conclusion: The method has significant clinical potential as a cancer diagnostic tool. Because of the extremely low prevalence of ovarian cancer in the general population (similar to 0.04%), extensive prospective testing will be required to evaluate the test's potential utility in general screening applications. However, more immediate applications might be as a diagnostic tool in higher-risk groups or to monitor cancer recurrence after therapeutic treatment.Impact: The ability to accurately and inexpensively diagnose ovarian cancer will have a significant positive effect on ovarian cancer treatment and outcome. Cancer Epidemiol Biomarkers Prev; 19(9); 2262-71. (C) 2010 AACR.