1] Roy Castle Lung Cancer Research programme, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, Liverpool, UK [2] BIOMICs Research Group, University of the Basque Country, Vitoria, Spain.
Br J Cancer. 2013 Oct 29;109(9):2404-11. doi: 10.1038/bjc.2013.623. Epub 2013 Oct 10.
Diagnosis is jeopardised when limited biopsy material is available or histological quality compromised. Here we developed and validated a prediction algorithm based on microRNA (miRNA) expression that can assist clinical diagnosis of lung cancer in minimal biopsy material to improve clinical management.
Discovery utilised Taqman Low Density Arrays (754 miRNAs) in 20 non-small cell lung cancer (NSCLC) tumour/normal pairs. In an independent set of 40 NSCLC patients, 28 miRNA targets were validated using qRT-PCR. A prediction algorithm based on eight miRNA targets was validated blindly in a third independent set of 47 NSCLC patients. The panel was also tested in formalin-fixed paraffin-embedded (FFPE) specimens from 20 NSCLC patients. The genomic methylation status of highly deregulated miRNAs was investigated by pyrosequencing.
In the final, frozen validation set the panel had very high sensitivity (97.5%), specificity (96.3%) and ROC-AUC (0.99, P=10(-15)). The panel provided 100% sensitivity and 95% specificity in FFPE tissue (ROC-AUC=0.97 (P=10(-6))). DNA methylation abnormalities contribute little to the deregulation of the miRNAs tested.
The developed prediction algorithm is a valuable potential biomarker for assisting lung cancer diagnosis in minimal biopsy material. A prospective validation is required to measure the enhancement of diagnostic accuracy of our current clinical practice.
当可获得的活检材料有限或组织学质量受损时，诊断就会受到影响。在这里，我们开发并验证了一种基于 microRNA（miRNA）表达的预测算法，该算法可在最小活检材料中辅助临床诊断肺癌，以改善临床管理。
在 20 对非小细胞肺癌（NSCLC）肿瘤/正常样本中利用 Taqman 低密度阵列（754 个 miRNA）进行了发现研究。在一个独立的 40 名 NSCLC 患者组中，使用 qRT-PCR 验证了 28 个 miRNA 靶标。基于 8 个 miRNA 靶标建立的预测算法在第三个独立的 47 名 NSCLC 患者组中进行了盲法验证。该面板还在 20 名 NSCLC 患者的福尔马林固定石蜡包埋（FFPE）标本中进行了测试。通过焦磷酸测序研究了高度失调 miRNA 的基因组甲基化状态。
在最终的冷冻验证组中，该面板具有非常高的灵敏度（97.5%）、特异性（96.3%）和 ROC-AUC（0.99，P=10(-15)）。该面板在 FFPE 组织中提供了 100%的灵敏度和 95%的特异性（ROC-AUC=0.97（P=10(-6)））。DNA 甲基化异常对所测试 miRNA 的失调贡献很小。
开发的预测算法是辅助最小活检材料中肺癌诊断的有价值的潜在生物标志物。需要前瞻性验证来衡量我们当前临床实践中诊断准确性的提高。
