Differential Capture of Serum Proteins for Expression Profiling and Biomarker Discovery in Pre- and Posttreatment Head and Neck Cancer Samples
Differential Capture of Serum Proteins for Expression Profiling and Biomarker Discovery in Pre- and Posttreatment Head and Neck Cancer Samples
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DOI：
10.1097/mlg.0b013e31814cf389
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发表时间：
2008-01-01
期刊：
影响因子：
2.6
通讯作者：
Drake, Richard R.
中科院分区：
文献类型：
--
作者：
Freed, Gary L.;Cazares, Lisa H.;Drake, Richard R.
简介：我们小组的长期目标是开发基于蛋白质组学的方法来检测和使用蛋白质生物标志物，以改善头颈鳞状细胞癌 (HNSCC) 的诊断、预后和定制治疗。我们之前已经证明，血清的蛋白质表达谱可以识别多种蛋白质生物标志物事件，这些事件可以作为评估 HNSCC 疾病状态和预后的分子指纹。 方法：使用自动化 Bruker Daltonics (Billerica, MA) ClinProt 基质辅助激光解吸/电离飞行时间 (MALDI-TOF) 质谱仪。在 MALDI-TOF 分析之前，使用磁性化学亲和珠来差异捕获血清蛋白。使用后处理软件和模式识别遗传算法（ClinProt 2.0）对所得光谱进行分析。 HNSCC 队列包含来自 24 名患者的 48 份血清样本，其中包括匹配的治疗前样本和治疗后 6 至 12 个月的样本，用于进一步分析。使用 MALDI-TOF/TOF 鉴定低质量差异表达肽。结果：在 1,000 至 10,000 m/z 的工作质量范围内，离子珠捕获方法解析了大约 200 个峰。对于弱阳离子珠捕获生成的光谱，k-近邻遗传算法能够正确分类治疗前 HNSCC 样本中 94% 的正常样本、治疗后样本中的 80% 的预处理样本以及治疗后样本中的 87% 的正常样本。然后通过 MALDI-TOF/TOF 质谱分析这些肽，以便直接从用相同磁珠化学处理的血清中或在捕获的蛋白质的凝胶电泳分离后进行序列鉴定。我们能够将其与使用表面增强激光解吸电离 (SELDI)-TOF 的类似研究进行比较，以表明该方法作为该过程的有效工具，并在我们组的识别方面有所改进。结论：这项初步研究使用了新的高分辨率 MALDI-TOF 质谱法。珠子分级分离适用于自动化蛋白质分析，并且能够同时识别 HNSCC 的潜在生物标志物蛋白质。此外，与我们之前使用 SELDI-TOF 获得的数据相比，我们能够证明 MALDI-TOF 在识别 HNSCC 组方面有所改进。使用这种 MALDI-TOF 技术作为发现平台，我们预计会生成生物标志物组，用于更准确地预测 HNSCC 的预后和治疗效果。
Introduction: A long-term goal of our group is to develop proteomic-based approaches to the detection and use of protein biomarkers for improvement in diagnosis, prognosis, and tailoring of treatment for head and neck squamous cell cancer (HNSCC). We have previously demonstrated that protein expression profiling of serum can identify multiple protein biomarker events that can serve as molecular fingerprints for the assessment of HNSCC disease state and prognosis.Methods: An automated Bruker Daltonics (Billerica, MA) ClinProt matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometer was used. Magnetic chemical affinity beads were used to differentially capture serum proteins prior to MALDI-TOF analysis. The resulting spectra were analyzed using postprocessing software and a pattern recognition genetic algorithm (ClinProt 2.0). An HNSCC cohort of 48 sera samples from 24 patients consisting of matched pretreatment and 6 to 12 month posttreatment samples was used for further analysis. Low-mass differentially expressed peptides were identified using MALDI-TOF/TOF.Results: In the working mass range of 1,000 to 10,000 m/z, approximately 200 peaks were resolved for ionic bead capture approaches. For spectra generated from weak cation bead capture, a k-nearest neighbor genetic algorithm was able to correctly classify 94% normal from pretreatment HNSCC samples, 80% of pretreatment from posttreatment samples, and 87% of normal from posttreatment samples. These peptides were then analyzed by MALDI-TOF/TOF mass spectometry for sequence identification directly from serum processed with the same magnetic bead chemistry or alternatively after gel electrophoresis separation of the captured proteins. We were able to compare this with similar studies using surface-enhanced laser desorption ionization (SELDI)-TOF to show this method as a valid tool for this process with some improvement in the identification of our groups.Conclusions: This initial study using new high-resolution MALDI-TOF mass spectrometry coupled with. bead fractionation is suitable for automated protein profiling and has the capability to simultaneously identify potential biomarker proteins for HNSCC. In addition, we were able to show improvement with the MALDI-TOF in identifying groups with HNSCC when compared with our prior data using SELDI-TOF. Using this MALDI-TOF technology as a discovery platform, we anticipate generating biomarker panels for use in more accurate prediction of prognosis and treatment efficacies for HNSCC.