JTO：10种生物标志物有助于肺癌检测

近日，《胸部肿瘤学杂志》(Journal of Thoracic Oncology)发的一项研究表明，对10种肺癌血清生物标志物进行检查可更准确地判断CT发现的结节，避免侵入性活检和X线随访。

在这项探索性研究中，匹兹堡大学的William L. Bigbee博士及其同事将来自匹兹堡大学癌症研究所乔治亚·库珀肺研究登记系统的“训练集”56例非小细胞肺癌(NSCLC)病例与来自匹兹堡肺筛查研究(PLuSS)的56名肺癌高危志愿对照者匹配。已知所有对照者均没有癌症。研究者随后对2组的血清样本进行分析，检查70种潜在癌症相关生物标志物的存在情况。

这些生物标志物除了包括有助于分析肺癌/宿主相互作用的许多宿主和肿瘤源性因子之外，还包括一些既往报道的上皮细胞癌相关血清标志物。这项研究的最初目的是从这些生物标志物中找出最能有效区分肺癌样本和匹配对照样本的标志物。

研究者采用规则学习算法，将这些潜在生物标志物减至8种：催乳素、转甲状腺素蛋白、血小板反应素-1、E-选择素、C-C基序趋化因子5、巨噬细胞迁移抑制因子、纤溶酶原激活物抑制物1和受体酪氨酸蛋白激酶erbB-2。

这8种生物标志物在区分训练集肺癌病例样本与对照样本方面的敏感性为9.2.9%，特异性为87.5%。

研究者然后又在原有8种生物标志物基础上，增加了细胞角蛋白片段19-9和血清淀粉样蛋白A 这2种生物标志物，并且对另外的盲法“验证集”中的病例和对照各30例进行了评价。

这10种生物标志物对验证集的整体分类性能较好：敏感性为73.3%，特异性为93.3%。在所进行的60次预测中，仅出现10次分类错误。另外，根据患者人口学因素对准确性进行分析发现，这10种生物标志物在男性和女性中区分病例和对照的性能均同样较好，并且吸烟状况和气道阻塞均不导致结果偏倚。

总体而言，年龄不是影响错误分类病例或对照的显著因素，但3例38~44岁病例中的2例被这一含有10种生物标志物的模型错误分类为对照。这种不准确性的原因可能在于训练集未纳入诊断时46岁以下的病例和50岁以下的对照者。

CT扫描发现的结节也不影响这些生物标志物的预测值。事实上，与无结节或具有良性结节的PLuSS患者相比，具有可疑结节的PLuSS患者更常被准确分类为对照。在对照者中发现的所有结节在最初检出后至少3年均维持非癌状态，后续CT扫描显示，这些结节要么消退要么未进一步生长。

研究者最后评价了这一含有10种生物标志物的模型在区分早期肿瘤和晚期肿瘤方面的准确性。在Ⅰ/Ⅱ期肺部肿瘤中，验证集中15%的Ⅰ/Ⅱ肿瘤被该模型错误分类，而50%的Ⅲ/Ⅳ期肿瘤被错误分类，表明该模型在区分早期肺癌方面的性能较好。该模型对Ⅰ/Ⅱ期肿瘤的特异性为93.3%，准确性为89.2%。

研究者表示，该生物标志物模型不能够满足对一般人群的筛查要求。然而，临床上对高危患者的这些生物标志物进行检查可有助于更好地解读筛查性CT扫描的结果，特别是在有烟草暴露的COPD或肺气肿患者中。这些初步结果尚需在更大规模的患者队列中进行正式验证。（生物谷Bioon.com）

doi:10.1097/JTO.0b013e31824ab6b0
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A Multiplexed Serum Biomarker Immunoassay Panel Discriminates Clinical Lung Cancer Patients from High-Risk Individuals Found to be Cancer-Free by CT Screening

Bigbee, William L. PhD, Gopalakrishnan, Vanathi PhD, Weissfeld, Joel L. MD, MPH, Wilson, David O. MD, MPH,; Dacic, Sanja MD, PhD, Lokshin, Anna E. PhD, Siegfried, Jill M. PhD

Introduction: Clinical decision making in the setting of computed tomography (CT) screening could benefit from accessible biomarkers that help predict the level of lung cancer risk in high-risk individuals with indeterminate pulmonary nodules.

Methods: To identify candidate serum biomarkers, we measured 70 cancer-related proteins by Luminex xMAP (Luminex Corporation) multiplexed immunoassays in a training set of sera from 56 patients with biopsy-proven primary non–small-cell lung cancer and 56 age-, sex-, and smoking-matched CT-screened controls.

Results: We identified a panel of 10 serum biomarkers—prolactin, transthyretin, thrombospondin-1, E-selectin, C-C motif chemokine 5, macrophage migration inhibitory factor, plasminogen activator inhibitor, receptor tyrosine-protein kinase, erbb-2, cytokeratin fragment 21.1, and serum amyloid A—that distinguished lung cancer patients from controls with an estimated balanced accuracy (average of sensitivity and specificity) of 76.0 ± 3.8% from 20-fold internal cross-validation. We then iteratively evaluated this model in an independent test and verification case/control studies confirming the initial classification performance of the panel. The classification performance of the 10-biomarker panel was also analytically validated using enzyme-linked immunosorbent assays in a second independent case/control population, further validating the robustness of the panel.

Conclusions: The performance of this 10-biomarker panel–based model was 77.1% sensitivity/76.2% specificity in cross-validation in the expanded training set, 73.3% sensitivity/93.3% specificity (balanced accuracy 83.3%) in the blinded verification set with the best discriminative performance in stage I/II cases: 85% sensitivity (balanced accuracy 89.2%). Importantly, the rate of misclassification of CT-screened controls was not different in most control subgroups with or without airflow obstruction or emphysema or pulmonary nodules. These biomarkers have potential to aid in the early detection of lung cancer and more accurate interpretation of indeterminate pulmonary nodules detected by CT screening.