Whitney Duncan H, Elashoff Michael R, Porta-Smith Kate, Gower Adam C, Vachani Anil, Ferguson J Scott, Silvestri Gerard A, Brody Jerome S, Lenburg Marc E, Spira Avrum
Allegro Diagnostics, Corp, Maynard, MA, USA.
Present affiliation: Veracyte, Inc, South San Francisco, CA, USA.
BMC Med Genomics. 2015 May 6;8:18. doi: 10.1186/s12920-015-0091-3.
The gene expression profile of cytologically-normal bronchial airway epithelial cells has previously been shown to be altered in patients with lung cancer. Although bronchoscopy is often used for the diagnosis of lung cancer, its sensitivity is imperfect, especially for small and peripheral suspicious lesions. In this study, we derived a gene expression classifier from airway epithelial cells that detects the presence of cancer in current and former smokers undergoing bronchoscopy for suspect lung cancer and evaluated its sensitivity to detect lung cancer among patients from an independent cohort.
We collected bronchial epithelial cells (BECs) from the mainstem bronchus of 299 current or former smokers (223 cancer-positive and 76 cancer-free subjects) undergoing bronchoscopy for suspected lung cancer in a prospective, multi-center study. RNA from these samples was run on gene expression microarrays for training a gene-expression classifier. A logistic regression model was built to predict cancer status, and the finalized classifier was validated in an independent cohort from a previous study.
We found 232 genes whose expression levels in the bronchial airway are associated with lung cancer. We then built a classifier based on the combination of 17 cancer genes, gene expression predictors of smoking status, smoking history, and gender, plus patient age. This classifier had a ROC curve AUC of 0.78 (95% CI, 0.70-0.86) in patients whose bronchoscopy did not lead to a diagnosis of lung cancer (n = 134). In the validation cohort, the classifier had a similar AUC of 0.81 (95% CI, 0.73-0.88) in this same subgroup (n = 118). The classifier performed similarly across a range of mass sizes, cancer histologies and stages. The negative predictive value was 94% (95% CI, 83-99%) in subjects with a non-diagnostic bronchoscopy.
We developed a gene expression classifier measured in bronchial airway epithelial cells that is able to detect lung cancer in current and former smokers who have undergone bronchoscopy for suspicion of lung cancer. Due to the high NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing in patients whose bronchoscopy is non diagnostic.
先前研究表明，肺癌患者细胞学正常的支气管气道上皮细胞的基因表达谱会发生改变。虽然支气管镜检查常用于肺癌诊断，但其敏感性并不理想，尤其是对于小的和外周可疑病变。在本研究中，我们从气道上皮细胞中推导了一种基因表达分类器，用于检测接受支气管镜检查以排查肺癌的现吸烟者和既往吸烟者中是否存在癌症，并评估其在独立队列患者中检测肺癌的敏感性。
在一项前瞻性多中心研究中，我们收集了299例接受支气管镜检查以排查肺癌的现吸烟者或既往吸烟者（223例癌症阳性和76例无癌受试者）主支气管的支气管上皮细胞（BEC）。对这些样本的RNA进行基因表达微阵列检测，以训练基因表达分类器。构建逻辑回归模型来预测癌症状态，并在先前研究的独立队列中对最终确定的分类器进行验证。
我们发现232个基因，其在支气管气道中的表达水平与肺癌相关。然后，我们基于17个癌症基因、吸烟状态的基因表达预测因子、吸烟史、性别以及患者年龄的组合构建了一个分类器。在支气管镜检查未确诊肺癌的患者（n = 134）中，该分类器的ROC曲线AUC为0.78（95%CI，0.70 - 0.86）。在验证队列中，同一亚组（n = 118）的分类器AUC相似，为0.81（95%CI，0.73 - 0.88）。该分类器在一系列肿块大小、癌症组织学类型和分期中的表现相似。在支气管镜检查未确诊的受试者中，阴性预测值为94%（95%CI，83 - 99%）。
我们开发了一种在支气管气道上皮细胞中测量的基因表达分类器，能够在接受支气管镜检查以排查肺癌的现吸烟者和既往吸烟者中检测肺癌。由于该分类器的高阴性预测值，它可能为支气管镜检查未确诊的患者是否需要进一步侵入性检查的临床决策提供参考。
