Munro Nicholas P, Cairns David A, Clarke Paul, Rogers Mark, Stanley Anthea J, Barrett Jennifer H, Harnden Patricia, Thompson Douglas, Eardley Ian, Banks Rosamonde E, Knowles Margaret A
Cancer Research UK Clinical Centre, St. James's University Hospital, Beckett Street, Leeds, United Kingdom.
Int J Cancer. 2006 Dec 1;119(11):2642-50. doi: 10.1002/ijc.22238.
Urinary biomarkers or profiles that allow noninvasive detection of recurrent transitional cell carcinoma (TCC) of the bladder are urgently needed. We obtained duplicate proteomic (SELDI) profiles from 227 subjects (118 TCC, 77 healthy controls and 32 controls with benign urological conditions) and used linear mixed effects models to identify peaks that are differentially expressed between TCC and controls and within TCC subgroups. A Random Forest classifier was trained on 130 profiles to develop an algorithm to predict the presence of TCC in a randomly selected initial test set (n = 54) and an independent validation set (n = 43) several months later. Twenty two peaks were differentially expressed between all TCC and controls (p < 10(-7)). However potential confounding effects of age, sex and analytical run were identified. In an age-matched sub-set, 23 peaks were differentially expressed between TCC and combined benign and healthy controls at the 0.005 significance level. Using the Random Forest classifier, TCC was predicted with 71.7% sensitivity and 62.5% specificity in the initial set and with 78.3% sensitivity and 65.0% specificity in the validation set after 6 months, compared with controls. Several peaks of importance were also identified in the linear mixed effects model. We conclude that SELDI profiling of urine samples can identify patients with TCC with comparable sensitivities and specificities to current tumor marker tests. This is the first time that reproducibility has been demonstrated on an independent test set analyzed several months later. Identification of the relevant peaks may facilitate multiplex marker assay development for detection of recurrent disease.
迫切需要能够无创检测膀胱复发性移行细胞癌（TCC）的尿液生物标志物或图谱。我们从227名受试者（118例TCC患者、77名健康对照者和32名患有良性泌尿系统疾病的对照者）获取了重复的蛋白质组学（SELDI）图谱，并使用线性混合效应模型来识别在TCC与对照之间以及TCC亚组内部差异表达的峰。在130个图谱上训练了一个随机森林分类器，以开发一种算法，用于预测在随机选择的初始测试集（n = 54）以及几个月后的独立验证集（n = 43）中TCC的存在情况。所有TCC与对照之间有22个峰差异表达（p < 10^(-7)）。然而，发现了年龄、性别和分析批次的潜在混杂效应。在年龄匹配的子集中，在0.005的显著性水平下，TCC与合并的良性和健康对照之间有23个峰差异表达。使用随机森林分类器，与对照相比，在初始集中预测TCC的灵敏度为71.7%，特异性为62.5%；在6个月后的验证集中，灵敏度为78.3%，特异性为65.0%。在线性混合效应模型中还识别出了几个重要的峰。我们得出结论，尿液样本的SELDI图谱分析能够识别TCC患者，其灵敏度和特异性与当前的肿瘤标志物检测相当。这是首次在几个月后分析的独立测试集上证明了可重复性。识别相关峰可能有助于开发用于检测复发性疾病的多重标志物检测方法。
