中国学者发现HR阳性HER2阴性乳腺癌预后新标志
2016年8月17日，美国《细胞》与英国《柳叶刀》旗下《EBioMedicine》在线发表中山大学孙逸仙纪念医院乳腺肿瘤中心、广东省恶性肿瘤表观遗传与基因调控重点实验室、中山大学数学与计算科学学院、华南统计科学研究中心、中山大学肿瘤防治中心、华南肿瘤学国家重点实验室、肿瘤医学协同创新中心、南昌市第三医院乳腺疾病防治中心乳腺疾病重点实验室、香港中文大学威尔斯亲王医院的研究报告（通讯作者为宋尔卫教授和苏逢锡教授），开发出一种基于乳腺癌干细胞（BCSC）相关微核糖核酸（miRNA）的分类法，将激素受体（HR）阳性、HER2阴性乳腺癌患者分类为高风险和低风险组。
HR阳性、HER2阴性乳腺癌约占所有原发性乳腺癌病例的60%，并且大约20%的早期雌激素受体（ER）阳性患者可能在治疗后出现局部或远端复发。HR阳性、HER2阴性的乳腺癌患者通常肿瘤复发的风险较低，因此，其中的一些人不能受益于细胞毒性化疗，从而导致患者过度治疗。因此，在临床实践中，就需要HR阳性、HER2阴性乳腺癌的预后和预测模型。
为了达到这个目的，基因检测，如OncotypeDx和PAM50，已被开发并在多个临床试验中得以验证。然而，这些模型采用计算机算法，选择候选基因用于调查，而不考虑其生物学基础。一种基于假设的方法——涉及DNA修复途径中的因素，已用于顺铂化疗的卵巢癌患者的评分系统，可有效地预测其临床结果。因此，选择预后和治疗预测的分子标记的靶基因——通过衡量其生物学原理，已经成为一种经济有效的方法。
癌症干细胞（CSC）是一个肿瘤细胞亚群，具有恶性肿瘤中出现的干细胞样特征，其在癌症复发和转移中起着重要的作用。肿瘤干细胞比例升高的乳腺癌患者，可通过BCSC标志物免疫染色而得以鉴定，包括ALDH1和CD44高CD24低，并表现出不良临床疗效。然而，缺乏特异性标记和BCSC标记的数量有限，限制了其在临床实践中作为有效生物标志物的应用。
此外，大多数这些标记实际上并不反映乳腺癌干细胞的功能特征。另一方面，该研究小组以往的研究表明，BCSC有一个独特的miRNA表达谱，异常的miRNA对于调节BCSC生物学发挥了至关重要的作用。这些BCSC相关的miRNA可能作为致癌基因或肿瘤抑制基因，调节其自我更新、抗细胞凋亡、侵袭、分化转移为血管内皮细胞和乳腺癌干细胞的化疗耐药性，从而有助于肿瘤的发展和复发。事实上，BCSC相关miRNA的异常表达，与患者预后和疗效相关。因此，研究BCSC相关的miRNA表达谱，提供了一种基于假设的方法，构建一种分子标记，具有明确的功能原理，预测乳腺癌患者的预后和治疗益处。
该研究开发了一种基于BCSC相关miRNA的分类法，将HR阳性、HER2阴性乳腺癌患者分类成高风险组和低风险组，通过多中心研究，从内部和外部验证这种分类法是否可作为HR阳性HER阴性患者远端无复发生存率的预后标志物。此外，该研究将其预后疗效与每个miRNA、临床病理危险因素和IHC4评分，进行了比较分析。
总之，这项研究表明，BCSC相关miRNA预测模型可有效地将具有低复发风险的HR阳性、HER2阴性乳腺癌患者，与高复发风险患者区分，而不用考虑淋巴结或绝经状态和诊断时的年龄。
与传统的临床病理预测因子相比，这种基于miRNA的分类法可以给HR阳性HER2阴性乳腺癌患者提供更好的预测价值，因此其有助于我们将低风险的HR阳性、HER2阴性乳腺癌患者与高风险患者区分。在这种情况下，临床医生可能会建议对低风险患者采取侵袭性更小的疗法，以指导个性化治疗。因此，这种模型可以有助于HR阳性HER2阴性乳腺癌患者的个性化临床决策制定。
EBioMedicine. 2016 Aug 17. [Epub ahead of print]
Prognostic Value of a BCSC-associated MicroRNA Signature in Hormone Receptor-Positive HER2-Negative Breast Cancer.
Chang Gong, Weige Tan, Kai Chen, Na You, Shan Zhu, Gehao Liang, Xinhua Xie, Qian Li, Yunjie Zeng, Nengtai Ouyang, Zhihua Li, Musheng Zeng, ShiMei Zhuang, Wan-Yee Lau, Qiang Liu, Dong Yin, Xueqin Wang, Fengxi Su, Erwei Song.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Prevention and Cure Center of Breast Disease, Key Laboratory of Breast Disease, the Third Hospital of Nanchang City, Nanchang, Jiangxi, China; Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China.
Highlights
Biology-driven strategy can be used in development of prognostic model.
The BCSC-associated miRNA classifier can predict prognosis for HR + HER2 - breast cancer.
The BCSC-associated miRNA classifier outperforms IHC4 scoring and 21-gene RS.
Chemotherapy can improve DRFS in patients predicted as high-risk.
Breast cancer patients with high proportion of cancer stem cells (BCSCs) have poor clinical outcomes. MiRNAs regulate key features of BCSCs as oncogenes or tumor suppressors. Although hormone receptor (HR)-positive, HER2-negative breast cancers are the most common subtype, current methods are inadequate to predict its clinical outcome. In this multicenter study, we identified and validated a 10 BCSC-associated-miRNA classifier that can predict survival for HR + HER2 - patients. Retrospective analysis showed that this classifier outperformed IHC4 scoring and 21-gene Recurrence Score (RS), and chemotherapy could improve survival in high-risk patients determined by this classifier. This model may facilitate personalized clinical decision for HR + HER2 - individuals.
PURPOSE: Breast cancer patients with high proportion of cancer stem cells (BCSCs) have unfavorable clinical outcomes. MicroRNAs (miRNAs) regulate key features of BCSCs. We hypothesized that a biology-driven model based on BCSC-associated miRNAs could predict prognosis for the most common subtype, hormone receptor (HR)-positive, HER2-negative breast cancer patients.
PATIENTS AND METHODS: After screening candidate miRNAs based on literature review and a pilot study, we built a miRNA-based classifier using LASSO Cox regression method in the training group (n = 202) and validated its prognostic accuracy in an internal (n = 101) and two external validation groups (n = 308).
RESULTS: In this multicenter study, a 10-miRNA classifier incorporating miR-30c, miR-21, miR-181a, miR-181c, miR-125b, miR-7, miR-200a, miR-135b, miR-22 and miR-200c was developed to predict distant relapse free survival (DRFS). With this classifier, HR + HER2 - patients were scored and classified into high-risk and low-risk disease recurrence, which was significantly associated with 5-year DRFS of the patients. Moreover, this classifier outperformed traditional clinicopathological risk factors, IHC4 scoring and 21-gene risk score (RS). The patients with high-risk recurrence determined by this classifier benefit more from chemotherapy.
CONCLUSIONS: Our 10-miRNA-based classifier provides a reliable prognostic model for disease recurrence in HR + HER2 - breast cancer patients. This model may facilitate personalized therapy-decision making for HR + HER2 - individuals.
ABBREVIATIONS: BCSCs, Breast cancer stem cells; miRNAs, MicroRNAs; DRFS, Distant relapse free survival; IHC, Immunohistochemistry; ER, Estrogen receptor; HR, Hormone receptor; CSCs, Cancer stem cells; RS, Recurrence score; FFPE, Formalin-fixed paraffin-embedded; SYSMH, Sun Yat-sen Memorial Hospital; IRB, Institutional review board; LASSO, Least Absolute Shrinkage and Selection Operator; ROC, Receiver operating characteristic; AUC, Area under curve; EMT, Epithelial-mesenchymal transition; PR, Progesterone receptor; HER2, Human epidermal growth factor receptor 2; BCS, Breast conserving surgery; ET, Endocrine therapy; TAM → AI, Tamoxifen followed by aromatase inhibitor
KEYWORDS: Breast cancer stem cell; miRNA; Biology-driven approach; Classifier; Prognosis
DOI: 10.1016/j.ebiom.2016.08.016