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一个用于确定胸腺瘤转移行为的基因特征。

A gene signature to determine metastatic behavior in thymomas.

机构信息

Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.

出版信息

PLoS One. 2013 Jul 24;8(7):e66047. doi: 10.1371/journal.pone.0066047. Print 2013.

Abstract

PURPOSE

Thymoma represents one of the rarest of all malignancies. Stage and completeness of resection have been used to ascertain postoperative therapeutic strategies albeit with limited prognostic accuracy. A molecular classifier would be useful to improve the assessment of metastatic behaviour and optimize patient management.

METHODS

qRT-PCR assay for 23 genes (19 test and four reference genes) was performed on multi-institutional archival primary thymomas (n = 36). Gene expression levels were used to compute a signature, classifying tumors into classes 1 and 2, corresponding to low or high likelihood for metastases. The signature was validated in an independent multi-institutional cohort of patients (n = 75).

RESULTS

A nine-gene signature that can predict metastatic behavior of thymomas was developed and validated. Using radial basis machine modeling in the training set, 5-year and 10-year metastasis-free survival rates were 77% and 26% for predicted low (class 1) and high (class 2) risk of metastasis (P = 0.0047, log-rank), respectively. For the validation set, 5-year metastasis-free survival rates were 97% and 30% for predicted low- and high-risk patients (P = 0.0004, log-rank), respectively. The 5-year metastasis-free survival rates for the validation set were 49% and 41% for Masaoka stages I/II and III/IV (P = 0.0537, log-rank), respectively. In univariate and multivariate Cox models evaluating common prognostic factors for thymoma metastasis, the nine-gene signature was the only independent indicator of metastases (P = 0.036).

CONCLUSION

A nine-gene signature was established and validated which predicts the likelihood of metastasis more accurately than traditional staging. This further underscores the biologic determinants of the clinical course of thymoma and may improve patient management.

摘要

目的

胸腺瘤是所有恶性肿瘤中最罕见的一种。尽管分期和切除的完整性用于确定术后治疗策略,但预测准确性有限。如果能有一种分子分类器,将有助于改善对转移行为的评估,并优化患者管理。

方法

对多机构存档的原发性胸腺瘤(n=36)进行了 23 个基因(19 个检测和 4 个参考基因)的 qRT-PCR 检测。根据基因表达水平计算出一个特征,将肿瘤分为低转移风险(1 类)和高转移风险(2 类)。该特征在一个独立的多机构患者队列(n=75)中进行了验证。

结果

开发并验证了一种可预测胸腺瘤转移行为的九基因特征。在训练集中,使用径向基机器建模,预测低(1 类)和高(2 类)转移风险的患者 5 年和 10 年无转移生存率分别为 77%和 26%(P=0.0047,对数秩)。对于验证集,预测低风险和高风险患者的 5 年无转移生存率分别为 97%和 30%(P=0.0004,对数秩)。对于验证集,Masaoka 分期 I/II 期和 III/IV 期患者的 5 年无转移生存率分别为 49%和 41%(P=0.0537,对数秩)。在评估胸腺瘤转移的常见预后因素的单变量和多变量 Cox 模型中,九基因特征是唯一独立的转移指标(P=0.036)。

结论

建立并验证了一个九基因特征,它比传统分期更准确地预测转移的可能性。这进一步强调了胸腺瘤临床病程的生物学决定因素,并可能改善患者管理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3110/3722217/841e41e08df9/pone.0066047.g001.jpg

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