Lung Cancer Serum Biomarker Discovery Using Glycoprotein Capture and Liquid Chromatography Mass Spectrometry

Targeted glycoproteomics represents an attractive approach for conducting peripheral blood based cancer biomarker discovery due to the well-known altered pattern of protein glycosylation in cancer and the reduced complexity of the resultant glycoproteome. Here we report its application to a set of pooled nonsmall cell lung cancer (NSCLC) case sera (9 adenocarcinoma and 6 squamous cell carcinoma pools from 54 patients) and matched controls pools, including 8 clinical control pools with computed tomography detected nodules but being nonmalignant as determined by biopsy from 54 patients, and 8 matched healthy control pools from 106 cancer-free subjects. The goal of the study is to discover biomarkers that may enable improved early detection and diagnosis of lung cancer. Immunoaffinity subtraction was used to first deplete the topmost abundant serum proteins; the remaining serum proteins were then subjected to hydrazide chemistry based glycoprotein capture and enrichment. Hydrazide resin in situ trypsin digestion was used to release nonglycosylated peptides. Formerly N-linked glycosylated peptides were released by peptide-N-glycosidase F (PNGase F) treatment and were subsequently analyzed by liquid chromatography (LC)−tandem mass spectrometry (MS/MS). A MATLAB based in-house tool was developed to facilitate retention time alignment across different LC−MS/MS runs, determination of precursor ion m/z values and elution profiles, and the integration of mass chromatograms based on determined parameters for identified peptides. A total of 38 glycopeptides from 22 different proteins were significantly differentially abundant across the case/control pools (P < 0.01, Student’s t test) and their abundances led to a near complete separation of case and control pools based on hierarchical clustering. The differential abundances of three of these candidate proteins were verified by commercially available ELISAs applied in the pools. Strong positive correlations between glycopeptide mass chromatograms and ELISA-measured protein abundance was observed for all of the selected glycoproteins.

靶向糖蛋白组学（Targeted glycoproteomics）是一种颇具应用前景的基于外周血的癌症生物标志物发现策略，这得益于癌症中蛋白质糖基化模式改变这一公认现象，以及所得糖蛋白组的复杂度显著降低。本研究将该策略应用于一组合并的非小细胞肺癌（nonsmall cell lung cancer, NSCLC）血清样本池：包含来自54名患者的9个腺癌样本池与6个鳞状细胞癌样本池，以及匹配的对照样本池，其中包括8个经计算机断层扫描（computed tomography, CT）检测发现结节但经活检证实为良性的临床对照样本池（来自54名患者），另有8个匹配的健康对照样本池（来自106名无癌受试者）。本研究的目标是发掘可助力肺癌早期检测与诊断优化的生物标志物。实验流程如下：首先采用免疫亲和减法去除血清中丰度最高的蛋白质；随后将剩余血清蛋白通过基于酰肼化学的糖蛋白捕获与富集方法进行处理。利用酰肼树脂原位胰蛋白酶消化释放非糖基化肽段，经肽-N-糖基化酶F（peptide-N-glycosidase F, PNGase F）处理后释放原本的N-连接糖基化肽段，再通过液相色谱-串联质谱（liquid chromatography−tandem mass spectrometry, LC-MS/MS）进行分析。本研究开发了一款基于MATLAB的自研工具，用于实现不同LC-MS/MS分析批次间的保留时间对齐、前体离子质荷比（m/z）与洗脱曲线的确定，以及基于预设参数对已鉴定肽段的质谱色谱图进行积分。最终在病例与对照样本池中鉴定到来自22种不同蛋白质的共38个糖肽，其丰度在组间存在显著差异（P < 0.01，学生t检验（Student’s t test））；基于层次聚类分析，这些糖肽的丰度可实现病例组与对照组样本池的近乎完全分离。其中3种候选蛋白质的丰度差异通过商业化酶联免疫吸附测定（enzyme-linked immunosorbent assay, ELISA）试剂盒在样本池中得到验证。所有选定糖蛋白的糖肽质谱色谱图信号与ELISA检测得到的蛋白质丰度均呈现显著正相关。