Combination of SELDI-TOF-MS and data mining provides early-stage response prediction for rectal tumors undergoing multimodal neoadjuvant therapy
Combination of SELDI-TOF-MS and data mining provides early-stage response prediction for rectal tumors undergoing multimodal neoadjuvant therapy
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DOI：
10.1097/01.sla.0000245577.68151.bd
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发表时间：
2007-02-01
影响因子：
9
通讯作者：
Reynolds, John V.
中科院分区：
文献类型：
--
作者：
Smith, Fraser M.;Gallagher, William M.;Reynolds, John V.
目的：我们研究了血清蛋白质组的低分子量区域的蛋白质组学分析是否可以预测局部晚期直肠癌对新辅助放化疗（RCT）的组织学反应。摘要背景资料：血清蛋白质组学分析正在成为一种强大的新的癌症模式，在筛选和监测治疗反应方面。没有研究尚未评估其预测和监测直肠癌的反应RCT.Methods：前瞻性收集20例接受RCT的患者的连续血清样本。采样时间点如下：治疗前、24/48小时、1周、2周、3周、5周（RCT最后一天）和术前。使用基于残留肿瘤至纤维化的程度的5分肿瘤消退等级（TRG）来测量对治疗的响应。使用表面增强激光解吸/电离-飞行时间质谱法（SELDI-TOF-MS）对所有血清样品进行一式两份分析。使用光谱的支持向量机（SVM）分析，基于良好和不良反应者之间最大差异表达的蛋白质，为每个时间点生成预测算法。结果：总共产生了230个光谱，代表来自9个良好应答者（TRG 1+2）和11个不良应答者（TRG 3-5）的所有可用时间点。SVM分析表明，在进入治疗的24/48小时时间点的血清蛋白质组内的变化提供了最佳的分类准确度。更详细地说，一个队列的14个蛋白质峰，共同区分良好和不良反应者，87.5%的灵敏度和80%specific.Conclusions：血清蛋白质组学分析可能是一个早期反应预测直肠癌的多模式治疗方案。这些数据表明，这种新型的微创方式可能是一个有用的辅助治疗直肠癌的多模式管理，并在未来的临床试验设计。
Objective: We investigated whether proteomic analysis of the low molecular weight region of the serum proteome could predict histologic response of locally advanced rectal cancer to neoadjuvant radiochemotherapy (RCT).Summary Background Data: Proteomic analysis of serum is emerging as a powerful new modality in cancer, in terms of both screening and monitoring response to treatment. No study has yet assessed its ability to predict and monitor the response of rectal cancer to RCT.Methods: Sequential serum samples from 20 patients undergoing RCT were prospectively collected. Time points sampled were as follows: pretreatment, 24/48 hours, 1 week, 2 weeks, 3 weeks, 5 weeks (last day of RCT), and presurgery. Response to treatment was measured using a 5-point tumor regression grade (TRG) based on the degree of residual tumor to fibrosis. All serum samples were analyzed in duplicate using surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS). Support vector machine (SVM) analysis of spectra was used to generate a predictive algorithm for each time point based on proteins that were maximally differentially expressed between good and poor responders. This algorithm was then tested using leave-one-out cross validation.Results: In total, 230 spectra were generated representing all available time points from 9 good responders (TRG 1+2) and 11 poor responders (TRG 3-5). SVM analysis indicated that changes within the serum proteome at the 24/48 hours time point into treatment provided optimal classification accuracy. In more detail, a cohort of 14 protein peaks were identified that collectively differentiated between good and poor responders, with 87.5% sensitivity and 80% specificity.Conclusions: Serum proteomic analysis may represent an early response predictor in multimodal treatment regimens of rectal cancer. These data suggest that this novel, minimally invasive modality may be a useful adjunct in the multimodal management of rectal cancer, and in the design of future clinical trials.