Mass Spectrometry-Based Serum and Plasma Peptidome Profiling for Prediction of Treatment Outcome in Patients With Solid Malignancies
Mass Spectrometry-Based Serum and Plasma Peptidome Profiling for Prediction of Treatment Outcome in Patients With Solid Malignancies
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DOI：
10.1634/theoncologist.2014-0101
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发表时间：
2014-10-01
期刊：
影响因子：
5.8
通讯作者：
Verheul, Henk M. W.
中科院分区：
文献类型：
--
作者：
Labots, Mariette;Schutte, Lisette M.;Verheul, Henk M. W.
简介。实体恶性肿瘤患者需要治疗选择工具来提高靶向治疗的疗效。基于质谱的肽组学可提供异常信号通路和蛋白水解事件的读数，从而确定预测性生物标志物，而血清或血浆肽组可提供与治疗反应相关的易于获取的特征。在这篇系统性综述中，我们评估了基于 MS 的血液肽图谱分析，以便在临床上迅速实施。使用基于以下层次结构的语法对 PubMed 和 Embase 中的研究进行了检索：（a）基于血液的基质辅助或表面增强激光解吸/电离飞行时间 MS 肽谱分析；（b）实体恶性肿瘤患者；（c）开始任何治疗方式之前；（d）有结果数据。有 38 项研究符合审查条件；其中大部分研究是在非小细胞肺癌（NSCLC）患者中进行的。在 14 项报告了基于 MS 分类模型的研究中，11 个模型的中位分类预测准确率为 80%（范围：66%-93%）。对9项NSCLC研究的汇总分析显示，被分类为 "预后差 "和 "预后好 "的患者的中位无进展生存期分别为2.0 +/- 1.06个月和4.6 +/- 1.60个月，具有显著的临床意义；中位总生存期分别为4.01 +/- 1.60个月和10.52 +/- 3.49个月，也具有显著的临床意义。基于治疗前质谱的血清和血浆肽组学在预测实体瘤患者的治疗结果方面显示出良好的效果。但由于样本量有限以及许多研究缺乏特征验证，迄今为止还无法在临床上应用。我们的汇总分析和 PROSE 研究的最新结果表明，这种特征分析方法有助于选择治疗方案，但还需要进行更多的前瞻性研究。
Introduction. Treatment selection tools are needed to enhance the efficacy of targeted treatment in patients with solid malignancies. Providing a readout of aberrant signaling pathways and proteolytic events, mass spectrometry-based (MS-based) peptidomics enables identification of predictive biomarkers, whereas the serum or plasma peptidome may provide easily accessible signatures associated with response to treatment. In this systematic review, we evaluate MS-based peptide profiling in blood for prompt clinical implementation.Methods. PubMed and Embase were searched for studies using a syntax based on the following hierarchy: (a) blood-based matrix-assisted or surface-enhanced laser desorption/ionization time-of-flight MS peptide profiling (b) in patients with solid malignancies (c) prior to initiation of any treatment modality, (d) with availability of outcome data.Results. Thirty-eight studies were eligible for review; the majority were performed in patients with non-small cell lung cancer (NSCLC). Median classification prediction accuracy was 80% (range: 66%-93%) in 11 models from 14 studies reporting an MS-based classification model. A pooled analysis of 9 NSCLC studies revealed clinically significant median progression-free survival in patients classified as "poor outcome" and "good outcome" of 2.0 +/- 1.06 months and 4.6 +/- 1.60 months, respectively; median overall survival was also clinically significant at 4.01 +/- 1.60 months and 10.52 +/- 3.49 months, respectively.Conclusion. Pretreatment MS-based serum and plasma peptidomics have shown promising results for prediction of treatment outcome in patients with solid tumors. Limited sample sizes and absence of signature validation in many studies have prohibited clinical implementation thus far. Our pooled analysis and recent results from the PROSE study indicate that this profiling approach enables treatment selection, but additional prospective studies are warranted.