A lung cancer risk classifier comprising genome maintenance genes measured in normal bronchial epithelial cells.
A lung cancer risk classifier comprising genome maintenance genes measured in normal bronchial epithelial cells.
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DOI：
10.1186/s12885-017-3287-4
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发表时间：
2017-05-02
期刊：
影响因子：
3.8
通讯作者：
Willey JC
中科院分区：
文献类型：
--
作者：
Yeo J;Crawford EL;Zhang X;Khuder S;Chen T;Levin A;Blomquist TM;Willey JC
每年对高危人群进行低剂量CT （LDCT）筛查可使肺癌死亡率降低20%以上。然而，根据人口统计学标准选择进行筛查的受试者一生中患肺癌的风险通常低于10%。因此，需要一种生物标志物来更好地对LDCT筛查的受试者进行分层。为了实现这一目标，我们之前报道了一种肺癌风险测试（LCRT）生物标志物，该标志物包括在正常支气管上皮细胞（NBEC）中测量的14个基因组维持（GM）途径基因，可准确区分癌症（CA）和非癌症（NC）受试者。这里报道的研究的主要目标是优化LCRT生物标志物，使其具有高特异性和易于临床实施。我们建立了有针对性的竞争性多重PCR扩增子文库，用于下一代测序（NGS）分析从120名受试者（包括61例CA病例和59例NC对照）中收集的NBEC标本中33个GM靶基因中68个位点的转录本丰度。根据对先前报道的LCRT生物标志物的贡献和/或与肺癌风险相关的先前证据选择基因进行分析。采用线性判别分析确定最准确的分类器，适合对受试者进行分层筛选。经交叉验证，包含12个基因（CDKN1A、E2F1、ERCC1、ERCC4、ERCC5、GPX1、GSTP1、KEAP1、RB1、TP53、TP63、XRCC1）表达值和年龄、性别、吸烟包年等人口学因素的模型，曲线下接收者操作特征面积（ROC AUC）为0.975 （95% CI: 0.96-0.99）。总体分类准确率为93% (95% CI 88% ~ 98%)，敏感性93.1%，特异性92.9%，阳性预测值93.1%，阴性预测值93%。该分类器的ROC AUC明显优于仅包含人口统计学特征的最佳模型（p < 0.0001）。本文报道的LCRT生物标志物在高通量、高质量控制的靶向NGS平台上显示出高精度和易于实施。因此，它在正在进行的LCRT盲法前瞻性队列研究的标本中进行了临床验证。在验证之后，与目前单独的人口统计学标准相比，该生物标志物有望通过更好地分层受试者进行年度肺癌筛查而具有临床效用。本文的在线版本（doi:10.1186/s12885-017-3287-4）包含补充材料，仅供授权用户使用。
Annual low dose CT (LDCT) screening of individuals at high demographic risk reduces lung cancer mortality by more than 20%. However, subjects selected for screening based on demographic criteria typically have less than a 10% lifetime risk for lung cancer. Thus, there is need for a biomarker that better stratifies subjects for LDCT screening. Toward this goal, we previously reported a lung cancer risk test (LCRT) biomarker comprising 14 genome-maintenance (GM) pathway genes measured in normal bronchial epithelial cells (NBEC) that accurately classified cancer (CA) from non-cancer (NC) subjects. The primary goal of the studies reported here was to optimize the LCRT biomarker for high specificity and ease of clinical implementation. Targeted competitive multiplex PCR amplicon libraries were prepared for next generation sequencing (NGS) analysis of transcript abundance at 68 sites among 33 GM target genes in NBEC specimens collected from a retrospective cohort of 120 subjects, including 61 CA cases and 59 NC controls. Genes were selected for analysis based on contribution to the previously reported LCRT biomarker and/or prior evidence for association with lung cancer risk. Linear discriminant analysis was used to identify the most accurate classifier suitable to stratify subjects for screening. After cross-validation, a model comprising expression values from 12 genes (CDKN1A, E2F1, ERCC1, ERCC4, ERCC5, GPX1, GSTP1, KEAP1, RB1, TP53, TP63, and XRCC1) and demographic factors age, gender, and pack-years smoking, had Receiver Operator Characteristic area under the curve (ROC AUC) of 0.975 (95% CI: 0.96–0.99). The overall classification accuracy was 93% (95% CI 88%–98%) with sensitivity 93.1%, specificity 92.9%, positive predictive value 93.1% and negative predictive value 93%. The ROC AUC for this classifier was significantly better (p < 0.0001) than the best model comprising demographic features alone. The LCRT biomarker reported here displayed high accuracy and ease of implementation on a high throughput, quality-controlled targeted NGS platform. As such, it is optimized for clinical validation in specimens from the ongoing LCRT blinded prospective cohort study. Following validation, the biomarker is expected to have clinical utility by better stratifying subjects for annual lung cancer screening compared to current demographic criteria alone. The online version of this article (doi:10.1186/s12885-017-3287-4) contains supplementary material, which is available to authorized users.