Department of General Surgery, Rush University Medical Center, Chicago, Illinois 60612, USA.
Clin Cancer Res. 2010 Jul 1;16(13):3452-62. doi: 10.1158/1078-0432.CCR-09-3192. Epub 2010 Jun 22.
Non-small cell lung cancer (NSCLC) has an overall 5-year survival of <15%; however, the 5-year survival for stage I disease is over 50%. Unfortunately, 75% of NSCLC is diagnosed at an advanced stage not amenable to surgery. A convenient serum assay capable of unambiguously identifying patients with NSCLC may provide an ideal diagnostic measure to complement computed tomography-based screening protocols.
Standard immunoproteomic method was used to assess differences in circulating autoantibodies among lung adenocarcinoma patients relative to cancer-free controls. Candidate autoantibodies identified by these discovery phase studies were translated into Luminex-based "direct-capture" immunobead assays along with 10 autoantigens with previously reported diagnostic value. These assays were then used to evaluate a second patient cohort composed of four discrete populations, including: 117 NSCLC (81 T(1-2)N(0)M(0) and 36 T(1-2)N(1-2)M(0)), 30 chronic obstructive pulmonary disorder (COPD)/asthma, 13 nonmalignant lung nodule, and 31 "normal" controls. Multivariate statistical methods were then used to identify the optimal combination of biomarkers for classifying patient disease status and develop a convenient algorithm for this purpose.
Our immunoproteomic-based biomarker discovery efforts yielded 16 autoantibodies differentially expressed in NSCLC versus control serum. Thirteen of the 25 analytes tested showed statistical significance (Mann-Whitney P < 0.05 and a receiver operator characteristic "area under the curve" over 0.65) when evaluated against a second patient cohort. Multivariate statistical analyses identified a six-biomarker panel with only a 7% misclassification rate.
We developed a six-autoantibody algorithm for detecting cases of NSCLC among several high-risk populations. Population-based validation studies are now required to assign the true value of this tool for identifying early-stage NSCLC.
非小细胞肺癌（NSCLC）的整体 5 年生存率<15%；然而，I 期疾病的 5 年生存率超过 50%。不幸的是，75%的 NSCLC 被诊断为晚期，无法手术。一种方便的血清检测方法，能够明确识别 NSCLC 患者，可能为补充基于计算机断层扫描的筛查方案提供理想的诊断措施。
采用标准免疫蛋白质组学方法评估肺腺癌患者与无癌症对照者之间循环自身抗体的差异。通过这些发现阶段的研究确定候选自身抗体，并将其转化为基于 Luminex 的“直接捕获”免疫珠测定，以及 10 种具有先前报道的诊断价值的自身抗原。然后，使用这些测定评估由四个不同人群组成的第二个患者队列，包括：117 例 NSCLC（81 例 T（1-2）N（0）M（0）和 36 例 T（1-2）N（1-2）M（0））、30 例慢性阻塞性肺疾病（COPD）/哮喘、13 例非恶性肺结节和 31 例“正常”对照。然后使用多变量统计方法来识别用于分类患者疾病状态的最佳生物标志物组合，并为此目的开发一种方便的算法。
我们基于免疫蛋白质组学的生物标志物发现工作产生了 16 种在 NSCLC 与对照血清中差异表达的自身抗体。在对第二个患者队列进行评估时，测试的 25 种分析物中的 13 种显示出统计学意义（Mann-Whitney P<0.05 和受试者工作特征“曲线下面积”超过 0.65）。多变量统计分析确定了一个具有仅 7%错误分类率的六标志物面板。
我们开发了一种用于在多个高危人群中检测 NSCLC 病例的六自身抗体算法。现在需要进行基于人群的验证研究，以确定该工具用于识别早期 NSCLC 的真正价值。
