Department of Immunology, Genetics and Pathology, Uppsala University, 751 85, Uppsala, Sweden.
Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
BMC Cancer. 2019 Jul 29;19(1):741. doi: 10.1186/s12885-019-5943-3.
The overall prognosis of non-small cell lung cancer (NSCLC) is poor, and currently only patients with localized disease are potentially curable. Therefore, preferably non-invasively determined biomarkers that detect NSCLC patients at early stages of the disease are of high clinical relevance. The aim of this study was to identify and validate novel protein markers in plasma using the highly sensitive DNA-assisted multiplex proximity extension assay (PEA) to discriminate NSCLC from other lung diseases.
Plasma samples were collected from a total of 343 patients who underwent surgical resection for different lung diseases, including 144 patients with lung adenocarcinoma (LAC), 68 patients with non-malignant lung disease, 83 patients with lung metastasis of colorectal cancers and 48 patients with typical carcinoid. One microliter of plasma was analyzed using PEA, allowing detection and quantification of 92 established cancer related proteins. The concentrations of the plasma proteins were compared between disease groups.
The comparison between LAC and benign samples revealed significantly different plasma levels for four proteins; CXCL17, CEACAM5, VEGFR2 and ERBB3 (adjusted p-value < 0.05). A multi-parameter classifier was developed to discriminate between samples from LAC patients and from patients with non-malignant lung conditions. With a bootstrap aggregated decision tree algorithm (TreeBagger), a sensitivity of 93% and specificity of 64% was achieved to detect LAC in this risk population.
By applying the highly sensitive PEA, reliable protein profiles could be determined in microliter amounts of plasma. We further identified proteins that demonstrated different plasma concentration in defined disease groups and developed a signature that holds potential to be included in a screening assay for early lung cancer detection.
非小细胞肺癌(NSCLC)的总体预后较差,目前只有局限性疾病的患者有潜在的治愈可能。因此,最好是非侵入性地确定生物标志物,以便在疾病的早期阶段检测 NSCLC 患者,这具有很高的临床相关性。本研究旨在使用高度敏感的 DNA 辅助多重邻近延伸分析(PEA)来识别和验证血浆中的新型蛋白质标志物,以区分 NSCLC 与其他肺部疾病。
共收集了 343 例因不同肺部疾病接受手术切除的患者的血浆样本,包括 144 例肺腺癌(LAC)患者、68 例非恶性肺部疾病患者、83 例结直肠癌肺转移患者和 48 例典型类癌患者。使用 PEA 分析 1 微升血浆,允许检测和定量 92 种已建立的癌症相关蛋白质。比较疾病组之间的血浆蛋白浓度。
LAC 与良性样本的比较显示,四种蛋白质的血浆水平有显著差异;CXCL17、CEACAM5、VEGFR2 和 ERBB3(调整后的 p 值 < 0.05)。开发了一种多参数分类器来区分 LAC 患者和非恶性肺部疾病患者的样本。使用引导聚合决策树算法(TreeBagger),在该风险人群中检测 LAC 的灵敏度为 93%,特异性为 64%。
通过应用高度敏感的 PEA,可以在微升量的血浆中确定可靠的蛋白质谱。我们进一步确定了在定义的疾病组中显示不同血浆浓度的蛋白质,并开发了一种具有在早期肺癌检测筛选试验中纳入潜力的特征签名。