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多乳腺癌干细胞模型预测 T、N 期乳腺癌复发。

A multiple breast cancer stem cell model to predict recurrence of T, N breast cancer.

机构信息

Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, China.

Key Laboratory of Transplant Engineering and Immunology, Ministry of Health, West China Hospital, Sichuan University, Chengdu, China.

出版信息

BMC Cancer. 2019 Jul 24;19(1):729. doi: 10.1186/s12885-019-5941-5.

Abstract

BACKGROUND

Local or distant relapse is the key event for the overall survival of early-stage breast cancer after initial surgery. A small subset of breast cancer cells, which share similar properties with normal stem cells, has been proven to resist to clinical therapy contributing to recurrence.

METHODS

In this study, we aimed to develop a prognostic model to predict recurrence based on the prevalence of breast cancer stem cells (BCSCs) in breast cancer. Immunohistochemistry and dual-immunohistochemistry were performed to quantify the stem cells of the breast cancer patients. The performance of Cox proportional hazard regression model was assessed using the holdout methods, where the dataset was randomly split into two exclusive sets (70% training and 30% testing sets). Additionally, we performed bootstrapping to overcome a possible biased error estimate and obtain confidence intervals (CI).

RESULTS

Four groups of BCSCs (ALDH1A3, CD44/CD24, integrin alpha 6 (ITGA6), and protein C receptor (PROCR)) were identified as associated with relapse-free survival (RFS). The correlated biomarkers were integrated as a prognostic panel to calculate a relapse risk score (RRS) and to classify the patients into different risk groups (high-risk or low-risk). According to RRS, 67.81 and 32.19% of patients were categorized into low-risk and high-risk groups respectively. The relapse rate at 5 years in the low-risk group (2.67, 95% CI: 0.72-4.63%) by Kaplan-Meier method was significantly lower than that of the high-risk group (19.30, 95% CI: 12.34-26.27%) (p <  0.001). In the multiple Cox model, the RRS was proven to be a powerful classifier independent of age at diagnosis or tumour size (p <  0.001). In addition, we found that high RRS score ER-positive patients do not benefit from hormonal therapy treatment (RFS, p = 0.860).

CONCLUSION

The RRS model can be applied to predict the relapse risk in early stage breast cancer. As such, high RRS score ER-positive patients do not benefit from hormonal therapy treatment.

摘要

背景

局部或远处复发是早期乳腺癌初始手术后总生存的关键事件。一小部分乳腺癌细胞与正常干细胞具有相似的特性,已被证明能够抵抗临床治疗,从而导致复发。

方法

在这项研究中,我们旨在开发一种基于乳腺癌中乳腺癌干细胞(BCSCs)流行率的预测复发的预后模型。通过免疫组织化学和双重免疫组织化学来量化乳腺癌患者的干细胞。使用保留方法评估 Cox 比例风险回归模型的性能,其中数据集被随机分为两个独立的集合(70%的训练集和 30%的测试集)。此外,我们进行了自举法以克服可能存在的有偏误差估计并获得置信区间(CI)。

结果

鉴定出四组 BCSC(ALDH1A3、CD44/CD24、整合素 alpha 6(ITGA6)和蛋白 C 受体(PROCR))与无复发生存(RFS)相关。相关生物标志物被整合为一个预后面板,以计算复发风险评分(RRS)并将患者分为不同的风险组(高风险或低风险)。根据 RRS,分别有 67.81%和 32.19%的患者被归类为低风险和高风险组。Kaplan-Meier 法显示,低风险组(2.67,95%CI:0.72-4.63%)在 5 年内的复发率明显低于高风险组(19.30,95%CI:12.34-26.27%)(p<0.001)。在多 Cox 模型中,RRS 被证明是一种独立于诊断时年龄或肿瘤大小的强大分类器(p<0.001)。此外,我们发现高 RRS 评分的 ER 阳性患者不能从激素治疗中获益(RFS,p=0.860)。

结论

RRS 模型可用于预测早期乳腺癌的复发风险。因此,高 RRS 评分的 ER 阳性患者不能从激素治疗中获益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1853/6657050/fbd6dee27b52/12885_2019_5941_Fig1_HTML.jpg

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