A prognostic model comprising pT stage, N status, and the chemokine receptors CXCR4 and CXCR7 powerfully predicts outcome in neoadjuvant resistant rectal cancer patients

包括 pT 分期、N 状态和趋化因子受体 CXCR4 和 CXCR7 的预后模型可有效预测新辅助耐药直肠癌患者的预后

阅读:7
作者:Crescenzo D'Alterio, Antonio Avallone, Fabiana Tatangelo, Paolo Delrio, Biagio Pecori, Laura Cella, Alessia Pelella, Francesco Paolo D'Armiento, Chiara Carlomagno, Franco Bianco, Lucrezia Silvestro, Roberto Pacelli, Maria Napolitano, Rosario Vincenzo Iaffaioli, Stefania Scala

Abstract

Despite the optimization of the local treatment of advanced rectal cancer (LARC), combination of preoperative chemoradiotherapy (CRT) and surgery, approximately one third of patients will develop distant metastases. Since the chemokine receptor CXCR4 has been implicated in metastasis development and prognosis in colorectal cancer, the role of the entire axis CXCR4-CXCL12-CXCR7 was evaluated to identify high relapse risk rectal cancer patients. Tumor specimens of 68 LARC patients undergoing surgery after neoadjuvant-CRT were evaluated for CXCR4, CXCR7, and CXCL12 expression through immunohistochemistry. Multivariable prognostic model was developed using classical prognostic factors along with chemokine receptor expression profiles. High CXCR4 correlated with a shorter relapse-free survival (RFS) (p = 0.0006) and cancer specific survival (CSS) (p = 0.0004). Concomitant high CXCR4-negative/low CXCR7 or high CXCR4-negative/low CXCL12 significantly impaired RFS (p = 0.0003 and p = 0.0043) and CSS (p = 0.0485 and p = 0.0026). High CXCR4/N+ identified the worst prognostic category for RFS (p < 0.0001) and CSS (p = 0.0003). The optimal multivariable predictive model for RFS was a five-variable model consisting of gender, pT stage, N status, CXCR4, and CXCR7 (AUC = 0.92, 95% CI = 0.77-0.98). The model is informative and supportive for adjuvant treatment and identifies CXCR4 as a new therapeutic target in rectal cancer.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。