Texas Children's Cancer Center, Houston, TX, USA.
Proteomics Clin Appl. 2011 Oct;5(9-10):532-41. doi: 10.1002/prca.201000089. Epub 2011 Sep 7.
Despite intensive treatment regimens, overall survival for high-risk neuroblastoma (HRNB) is still poor. This is in part due to an inability to cure the disease once a patient has reached clinical relapse. Identifying plasma biomarkers of active disease may provide a way of relapse monitoring in HRNB.
In this study, we developed an integrated proteomic approach to identify plasma biomarkers for HRNB.
We identified seven candidate biomarkers (SAA, APOA1, IL-6, EGF, MDC, sCD40L and Eotaxin) for HRNB. These biomarkers were then used to create a multivariate classifier of HRNB, which showed a specificity of 90% (95% confidence interval (CI), 73%, 98%), and a sensitivity of 81% (95%CI, 54%, 96%) for classifying HRNB in a training set. When evaluated on independent test samples, the classifier exhibited 86% accuracy (95% CI, 42%, 100%) of identifying diagnostic samples, and 86% accuracy (95% CI, 70%, 100%) of detecting post-diagnosis longitudinal samples that having active disease.
Further validation of these biomarkers may improve patients' outcomes by developing a simple blood test for the detection of relapse prior to the development of clinically evident disease. Understanding the role of these biomarkers in immune surveillance of neuroblastoma may also provide a new direction of therapeutic strategies.
尽管采用了强化治疗方案，高危神经母细胞瘤（HRNB）患者的总体生存率仍然较差。这在一定程度上是由于一旦患者出现临床复发，就无法治愈该疾病。确定疾病活跃期的血浆生物标志物可能为 HRNB 的复发监测提供一种方法。
在这项研究中，我们开发了一种综合蛋白质组学方法来鉴定 HRNB 的血浆生物标志物。
我们鉴定出了七个候选生物标志物（SAA、APOA1、IL-6、EGF、MDC、sCD40L 和 Eotaxin）用于 HRNB。然后，这些生物标志物被用于创建 HRNB 的多变量分类器，该分类器在训练集中对 HRNB 进行分类的特异性为 90%（95%置信区间（CI），73%，98%），敏感性为 81%（95%CI，54%，96%）。当在独立的测试样本上进行评估时，该分类器在识别诊断样本方面的准确率为 86%（95%CI，42%，100%），在检测具有活动性疾病的诊断后纵向样本方面的准确率为 86%（95%CI，70%，100%）。
进一步验证这些生物标志物可能通过开发一种简单的血液检测方法，在出现临床明显疾病之前检测复发，从而改善患者的预后。了解这些生物标志物在神经母细胞瘤免疫监测中的作用也可能为治疗策略提供新的方向。
