Beane Jennifer, Sebastiani Paola, Whitfield Theodore H, Steiling Katrina, Dumas Yves-Martine, Lenburg Marc E, Spira Avrum
The Pulmonary Center, Boston University Medical Center, Boston, MA 02118, USA.
Cancer Prev Res (Phila). 2008 Jun;1(1):56-64. doi: 10.1158/1940-6207.CAPR-08-0011. Epub 2008 Mar 31.
Lung cancer is the leading cause of cancer death due, in part, to lack of early diagnostic tools. Bronchoscopy represents a relatively noninvasive initial diagnostic test in smokers with suspect disease, but it has low sensitivity. We have reported a gene expression profile in cytologically normal large airway epithelium obtained via bronchoscopic brushings, which is a sensitive and specific biomarker for lung cancer. Here, we evaluate the independence of the biomarker from other clinical risk factors and determine the performance of a clinicogenomic model that combines clinical factors and gene expression. Training (n = 76) and test (n = 62) sets consisted of smokers undergoing bronchoscopy for suspicion of lung cancer at five medical centers. Logistic regression models describing the likelihood of having lung cancer using the biomarker, clinical factors, and these data combined were tested using the independent set of patients with nondiagnostic bronchoscopies. The model predictions were also compared with physicians' clinical assessment. The gene expression biomarker is associated with cancer status in the combined clinicogenomic model (P < 0.005). There is a significant difference in performance of the clinicogenomic relative to the clinical model (P < 0.05). In the test set, the clinicogenomic model increases sensitivity and negative predictive value to 100% and results in higher specificity (91%) and positive predictive value (81%) compared with other models. The clinicogenomic model has high accuracy where physician assessment is most uncertain. The airway gene expression biomarker provides information about the likelihood of lung cancer not captured by clinical factors, and the clinicogenomic model has the highest prediction accuracy. These findings suggest that use of the clinicogenomic model may expedite more invasive testing and definitive therapy for smokers with lung cancer and reduce invasive diagnostic procedures for individuals without lung cancer.
肺癌是癌症死亡的主要原因,部分原因是缺乏早期诊断工具。支气管镜检查是对疑似疾病的吸烟者进行的一种相对无创的初步诊断测试,但它的敏感性较低。我们报告了通过支气管镜刷检获得的细胞学正常的大气道上皮细胞中的基因表达谱,这是一种用于肺癌的敏感且特异的生物标志物。在此,我们评估该生物标志物相对于其他临床风险因素的独立性,并确定结合临床因素和基因表达的临床基因组模型的性能。训练集(n = 76)和测试集(n = 62)由在五个医疗中心因怀疑肺癌而接受支气管镜检查的吸烟者组成。使用独立的非诊断性支气管镜检查患者数据集,测试了使用该生物标志物、临床因素以及两者结合来描述患肺癌可能性的逻辑回归模型。还将模型预测结果与医生的临床评估进行了比较。在联合临床基因组模型中,基因表达生物标志物与癌症状态相关(P < 0.005)。临床基因组模型与临床模型在性能上存在显著差异(P < 0.05)。在测试集中,临床基因组模型将敏感性和阴性预测值提高到100%,与其他模型相比,特异性更高(91%),阳性预测值更高(81%)。在医生评估最不确定的情况下,临床基因组模型具有较高的准确性。气道基因表达生物标志物提供了临床因素未捕捉到的肺癌可能性信息,并且临床基因组模型具有最高的预测准确性。这些发现表明,使用临床基因组模型可能会加快对肺癌吸烟者进行更具侵入性的检测和确定性治疗,并减少对无肺癌个体的侵入性诊断程序。