Bcl-2 expression predicts local relapse for early-stage breast cancer receiving conserving surgery and radiotherapy
Bcl-2 expression predicts local relapse for early-stage breast cancer receiving conserving surgery and radiotherapy
复制标题
DOI：
10.1007/s10549-008-0068-4
复制
发表时间：
2009-05-01
影响因子：
3.8
通讯作者：
Haffty, Bruce G.
中科院分区：
文献类型：
--
作者：
Yang, Qifeng;Moran, Meena S.;Haffty, Bruce G.
背景 本研究旨在评估 Bcl-2（一种凋亡相关蛋白）对于接受保乳治疗 (BCT) 的早期乳腺癌患者的预后意义。方法获得IRB批准后，504例接受BCT治疗的早期乳腺癌患者纳入本研究。将石蜡样本构建成具有两倍冗余的组织微阵列，并进行 Bcl-2 抗体处理和染色。 Bcl-2 表达与其他临床病理参数和临床结果相关。结果 116例（28%）检出Bcl-2表达。 Bcl-2 表达与雌激素受体和孕激素受体状态呈正相关（分别 P < 0.0001）。 Bcl-2 表达与同侧乳腺肿瘤复发相关（P = 0.0322，通过时序检验）。使用Cox比例风险模型的多变量分析表明，Bcl-2表达作为乳腺复发的独立预后因素仍然具有重要意义（P = 0.0348）。结论 Bcl-2 表达与早期乳腺癌患者局部复发风险增加高度相关。原发肿瘤的 Bcl-2 免疫组织化学染色可能有助于为这些患者选择最佳治疗方案。
Background This study was undertaken to evaluate the prognostic significance of Bcl-2, a apoptosis-related protein, in patients with early stage breast cancer treated with breast conservation treatment (BCT). Methods After obtaining IRB approval, 504 patients with early stage breast cancer treated with BCT were entered in this study. The paraffin specimens were constructed into tissue micro-arrays with two-fold redundancy, processed and stained for Bcl-2 antibody. Bcl-2 expression was correlated with other clinicopathological parameters and clinical outcomes. Results Bcl-2 expression was identified in 116 cases (28%). Bcl-2 expression was positively correlated with both estrogen receptor and progesterone receptor status (P < 0.0001, respectively). Bcl-2 expression was associated with ipsilateral breast tumor recurrence (P = 0.0322, by log-rank test). Multivariate analysis using Cox proportional hazard model showed that Bcl-2 expression still retained significance as an independent prognostic factor for breast recurrence (P = 0.0348). Conclusions Bcl-2 expression is highly associated with an increased risk of local recurrence in patients with early stage breast cancer. Immunohistochemical staining of the primary tumor for Bcl-2 might be useful to select optimal treatment options for these patients.