The detection of disease-related plasma biomarkers has challenged the proteomic community for years. Attractive features for plasma proteomics includes the ease of collection and small volume needed for analysis, but on the other hand, the presence of highly abundant proteins complicates sample preparation procedures and reduces dynamic range. Data independent acquisition label free quantitation (DIA-LFQ) by mass spectrometry partly overcomes the dynamic range issue; however, generating the peptide spectral reference libraries that allow extensive analysis of the plasma proteome can be a slow and expensive task which is unattainable for many laboratories. We investigated the re-purposing of publically available plasma proteome datasets and the impact on peptide/protein detection for DIA-LFQ. We carried out these studies in the context of identifying putative biomarkers of response to neoadjuvant chemotherapy (NAC) for pancreatic ductal adenocarcinoma, as no useful plasma biomarkers have been clinically adopted. We demonstrated the benefit in searching DIA data against multiple spectral libraries to show that complement proteins were linked to NAC response in PDAC patients, confirming previous observations of the prognostic utility of complement following adjuvant chemotherapy. Our workflow demonstrates that DIA-LFQ can be readily applied in the oncology setting for the putative assignment of clinically relevant plasma biomarkers.

Statement of significance

The proteomic mass spectrometry analysis of undepleted, unfractionated human plasma has benefits for sample throughput but remains challenging to obtain deep coverage. This work evaluated the re-purposing of open source peptide mass spectrometry data from human plasma to create spectral reference libraries for use in Data independent acquisition (DIA). We showed how seeding in locally acquired data to integrate iRT peptides into spectral libraries increased identification confidence by facilitating querying of multiple libraries. This workflow was applied to the discovery of putative plasma biomarkers for response to neoadjuvant chemotherapy (NAC) in pancreatic ductal adenocarcinoma patients. There is a paucity of prior information in the literature on this topic and we show that good responder patients have reduced levels of complement proteins.

中文翻译：

---

胰腺导管腺癌新辅助化疗反应的血浆生物标志物数据独立获取

与疾病相关的血浆生物标志物的检测已经挑战了蛋白质组学多年。血浆蛋白质组学的吸引人的特点包括易于收集和分析所需的小体积，但另一方面，高度丰富的蛋白质的存在使样品制备过程复杂化并降低了动态范围。质谱法的独立于数据的无标记获取定量分析（DIA-LFQ）在一定程度上克服了动态范围问题；但是，生成允许对血浆蛋白质组进行广泛分析的肽谱参考库可能是​​一项缓慢而昂贵的任务，这对于许多实验室而言是无法实现的。我们调查了公众可获得的血浆蛋白质组数据集的重新用途以及对DIA-LFQ的肽/蛋白质检测的影响。我们在鉴定胰腺导管腺癌对新辅助化疗（NAC）反应的假定生物标志物的背景下进行了这些研究，因为尚未在临床上采用有用的血浆生物标志物。我们证明了针对多个光谱库搜索DIA数据的好处，表明补体蛋白与PDAC患者的NAC反应相关，从而证实了辅助化疗后补体对预后的实用性观察。我们的工作流程表明，DIA-LFQ可以很容易地应用于肿瘤学中，用于临床相关血浆生物标志物的推定分配。我们证明了针对多个光谱库搜索DIA数据的好处，表明补体蛋白与PDAC患者的NAC反应相关，从而证实了辅助化疗后补体对预后的实用性观察。我们的工作流程表明，DIA-LFQ可以很容易地应用于肿瘤学领域，用于临床相关血浆生物标志物的推定分配。我们证明了针对多个光谱库搜索DIA数据的好处，表明补体蛋白与PDAC患者的NAC反应相关，从而证实了辅助化疗后补体对预后的实用性观察。我们的工作流程表明，DIA-LFQ可以很容易地应用于肿瘤学领域，用于临床相关血浆生物标志物的推定分配。

重要声明

蛋白质组质谱分析对未耗尽，未分离的人体血浆的分析有助于提高样品通量，但仍然难以获得较深的覆盖率。这项工作评估了来自人血浆的开放源肽质谱数据的重新用途，以创建用于数据独立采集（DIA）的光谱参考库。我们展示了如何在本地获取的数据中进行播种以将iRT肽整合到光谱库中，如何通过促进对多个库的查询来提高识别信心。该工作流程应用于发现胰管腺癌患者对新辅助化疗（NAC）有反应的假定血浆生物标志物。关于该主题的文献缺乏足够的先验信息，我们表明，反应良好的患者补体蛋白水平降低。