Stoller Biomarker Discovery Centre, Institute of Cancer Sciences, Faculty of Medical and Human Sciences , University of Manchester , Manchester M13 9PL , United Kingdom.
Stem Cell and Leukaemia Proteomics Laboratory, Institute of Cancer Sciences, Faculty of Medical and Human Sciences , University of Manchester , Manchester M13 9PL , United Kingdom.
J Proteome Res. 2019 Sep 6;18(9):3369-3382. doi: 10.1021/acs.jproteome.9b00287. Epub 2019 Aug 21.
Lung cancer is the most common cause of cancer-related mortality worldwide, characterized by late clinical presentation (49-53% of patients are diagnosed at stage IV) and consequently poor outcomes. One challenge in identifying biomarkers of early disease is the collection of samples from patients prior to symptomatic presentation. We used blood collected during surgical resection of lung tumors in an iTRAQ isobaric tagging experiment to identify proteins effluxing from tumors into pulmonary veins. Forty proteins were identified as having an increased abundance in the vein draining from the tumor compared to "healthy" pulmonary veins. These protein markers were then assessed in a second cohort that utilized the mass spectrometry (MS) technique: Sequential window acquisition of all theoretical fragment ion spectra (SWATH) MS. SWATH-MS was used to measure proteins in serum samples taken from 25 patients <50 months prior to and at lung cancer diagnosis and 25 matched controls. The SWATH-MS analysis alone produced an 11 protein marker panel. A machine learning classification model was generated that could discriminate patient samples from patients within 12 months of lung cancer diagnosis and control samples. The model was evaluated as having a mean AUC of 0.89, with an accuracy of 0.89. This panel was combined with the SWATH-MS data from one of the markers from the first cohort to create a 12 protein panel. The proteome signature developed for lung cancer risk can now be developed on further cohorts.
肺癌是全球癌症相关死亡的最常见原因，其特征为晚期临床症状（49-53%的患者被诊断为 IV 期），因此预后较差。在识别早期疾病生物标志物方面的一个挑战是在出现症状之前从患者身上采集样本。我们使用在肺肿瘤切除手术期间收集的血液进行 iTRAQ 等压标记实验，以鉴定从肿瘤进入肺静脉的外溢蛋白。与“健康”肺静脉相比，有 40 种蛋白被鉴定为在肿瘤引流静脉中丰度增加。然后，我们在第二组中使用质谱（MS）技术：所有理论片段离子谱序贯窗口采集（SWATH）MS 来评估这些蛋白标志物。SWATH-MS 用于测量来自 25 名患者的血清样本中的蛋白，这些患者在肺癌诊断前 <50 个月和肺癌诊断时采集了样本，另外还包括 25 名匹配的对照者。SWATH-MS 分析单独产生了一个由 11 个蛋白标志物组成的标记物面板。生成了一个机器学习分类模型，可以区分肺癌诊断后 12 个月内的患者样本、患者样本和对照样本。该模型的评估平均 AUC 为 0.89，准确率为 0.89。该面板与来自第一个队列的一个标志物的 SWATH-MS 数据相结合，创建了一个由 12 个蛋白组成的标志物面板。用于肺癌风险的蛋白质组特征现在可以在进一步的队列中开发。
