近日,，《胸部腫瘤學雜志》(Journal of Thoracic Oncology)發(fā)的一項研究表明,，對10種肺癌血清生物標志物進行檢查可更準確地判斷CT發(fā)現的結節(jié)，避免侵入性活檢和X線隨訪,。
在這項探索性研究中,，匹茲堡大學的William L. Bigbee博士及其同事將來自匹茲堡大學癌癥研究所喬治亞·庫珀肺研究登記系統的“訓練集”56例非小細胞肺癌(NSCLC)病例與來自匹茲堡肺篩查研究(PLuSS)的56名肺癌高危志愿對照者匹配,。已知所有對照者均沒有癌癥。研究者隨后對2組的血清樣本進行分析,，檢查70種潛在癌癥相關生物標志物的存在情況,。
這些生物標志物除了包括有助于分析肺癌/宿主相互作用的許多宿主和腫瘤源性因子之外，還包括一些既往報道的上皮細胞癌相關血清標志物,。這項研究的最初目的是從這些生物標志物中找出最能有效區(qū)分肺癌樣本和匹配對照樣本的標志物,。
研究者采用規(guī)則學習算法，將這些潛在生物標志物減至8種：催乳素,、轉甲狀腺素蛋白,、血小板反應素-1、E-選擇素,、C-C基序趨化因子5,、巨噬細胞遷移抑制因子、纖溶酶原激活物抑制物1和受體酪氨酸蛋白激酶erbB-2,。
這8種生物標志物在區(qū)分訓練集肺癌病例樣本與對照樣本方面的敏感性為9.2.9%,，特異性為87.5%。
研究者然后又在原有8種生物標志物基礎上,，增加了細胞角蛋白片段19-9和血清淀粉樣蛋白A 這2種生物標志物,，并且對另外的盲法“驗證集”中的病例和對照各30例進行了評價。
這10種生物標志物對驗證集的整體分類性能較好：敏感性為73.3%,，特異性為93.3%,。在所進行的60次預測中,，僅出現10次分類錯誤,。另外，根據患者人口學因素對準確性進行分析發(fā)現,，這10種生物標志物在男性和女性中區(qū)分病例和對照的性能均同樣較好,，并且吸煙狀況和氣道阻塞均不導致結果偏倚。
總體而言,，年齡不是影響錯誤分類病例或對照的顯著因素,，但3例38~44歲病例中的2例被這一含有10種生物標志物的模型錯誤分類為對照。這種不準確性的原因可能在于訓練集未納入診斷時46歲以下的病例和50歲以下的對照者,。
CT掃描發(fā)現的結節(jié)也不影響這些生物標志物的預測值,。事實上，與無結節(jié)或具有良性結節(jié)的PLuSS患者相比,，具有可疑結節(jié)的PLuSS患者更常被準確分類為對照,。在對照者中發(fā)現的所有結節(jié)在最初檢出后至少3年均維持非癌狀態(tài)，后續(xù)CT掃描顯示,，這些結節(jié)要么消退要么未進一步生長,。
研究者最后評價了這一含有10種生物標志物的模型在區(qū)分早期腫瘤和晚期腫瘤方面的準確性。在Ⅰ/Ⅱ期肺部腫瘤中，驗證集中15%的Ⅰ/Ⅱ腫瘤被該模型錯誤分類,，而50%的Ⅲ/Ⅳ期腫瘤被錯誤分類,，表明該模型在區(qū)分早期肺癌方面的性能較好。該模型對Ⅰ/Ⅱ期腫瘤的特異性為93.3%,，準確性為89.2%,。
研究者表示，該生物標志物模型不能夠滿足對一般人群的篩查要求,。然而,，臨床上對高危患者的這些生物標志物進行檢查可有助于更好地解讀篩查性CT掃描的結果,，特別是在有煙草暴露的COPD或肺氣腫患者中,。這些初步結果尚需在更大規(guī)模的患者隊列中進行正式驗證。（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.
