Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Cancer. 2012 Mar 15;118(6):1607-18. doi: 10.1002/cncr.26450. Epub 2011 Aug 25.
The importance of definitive histological subclassification has increased as drug trials have shown benefit associated with histology in nonsmall-cell lung cancer (NSCLC). The acuity of this problem is further exacerbated by the use of minimally invasive cytology samples. Here we describe the development and validation of a 4-protein classifier that differentiates primary lung adenocarcinomas (AC) from squamous cell carcinomas (SCC).
Quantitative immunofluorescence (AQUA) was employed to measure proteins differentially expressed between AC and SCC followed by logistic regression analysis. An objective 4-protein classifier was generated to define likelihood of AC in a training set of 343 patients followed by validation in 2 independent cohorts (n = 197 and n = 235). The assay was then tested on 11 cytology specimens.
Statistical modeling selected thyroid transcription factor 1 (TTF1), CK5, CK13, and epidermal growth factor receptor (EGFR) to generate a weighted classifier and to identify the optimal cutpoint for differentiating AC from SCC. Using the pathologist's final diagnosis as the criterion standard, the molecular test showed a sensitivity of 96% and specificity of 93%. Blinded analysis of the validation sets yielded sensitivity and specificity of 96% and 97%, respectively. Our assay classified the cytology specimens with a specificity of 100% and sensitivity of 87.5%.
Molecular classification of NSCLC using an objective quantitative test can be highly accurate and could be translated into a diagnostic platform for broad clinical application.
随着药物试验表明非小细胞肺癌（NSCLC）的组织学与获益相关，明确的组织学分类的重要性增加了。微创细胞学样本的使用进一步加剧了这个问题的紧迫性。在这里，我们描述了一种 4 蛋白分类器的开发和验证，该分类器可区分原发性肺腺癌（AC）和鳞状细胞癌（SCC）。
采用定量免疫荧光（AQUA）测量 AC 和 SCC 之间差异表达的蛋白质，然后进行逻辑回归分析。在 343 例患者的训练集中生成一个客观的 4 蛋白分类器，以定义 AC 的可能性，然后在 2 个独立队列（n=197 和 n=235）中进行验证。然后在 11 个细胞学标本上测试该检测。
统计建模选择甲状腺转录因子 1（TTF1）、CK5、CK13 和表皮生长因子受体（EGFR）来生成加权分类器，并确定区分 AC 和 SCC 的最佳切点。使用病理学家的最终诊断作为标准，分子检测的敏感性为 96%，特异性为 93%。验证集的盲法分析分别产生了 96%和 97%的敏感性和特异性。我们的检测方法对细胞学标本的特异性为 100%，敏感性为 87.5%。
使用客观的定量检测对 NSCLC 进行分子分类可以非常准确，并可转化为广泛临床应用的诊断平台。
