Background: Sentinel lymph node biopsy is the standard surgical staging approach for operable triple-negative breast cancer (TNBC) with clinically negative axillae. In this study, we sought to develop a model to predict TNBC patients with negative nodal involvement, who would benefit from the exemption of the axillary staging surgery.
Materials and methods: We evaluated 3' untranslated region (3'UTR) profiles using microarray data of TNBC from two Gene Expression Omnibus datasets. Samples from GSE31519 were divided into training set (n = 164) and validation set (n = 163), and GSE76275 was used to construct testing set (n = 164). We built a six-member 3'UTR panel (ADD2, COL1A1, APOL2, IL21R, PKP2, and EIF4G3) using an elastic net model to estimate the risk of lymph node metastasis (LNM). Receiver operating characteristic and logistic analyses were used to assess the association between the panel and LNM status.
Results: The six-member 3'UTR-panel showed a high distinguishing power with an area under the curve of 0.712, 0.729, and 0.708 in the training, validation, and testing sets, respectively. After adjustment by tumor size, the 3'UTR panel retained significant predictive power in the training, validation, and testing sets (odds ratio = 4.93, 4.58, and 3.59, respectively; p < .05 for all). A combinatorial analysis of the 3'UTR panel and tumor size yielded an accuracy of 97.2%, 100%, and 100% in training, validation, and testing set, respectively.
Conclusion: This study established an integrative 3'UTR-based model as a promising predictor for nodal negativity in operable TNBC. Although a prospective study is needed to validate the model, our results may permit a no axillary surgery option for selected patients.
Implications for practice: Currently, sentinel lymph node biopsy is the standard approach for surgical staging in breast cancer patients with negative axillae. Prediction estimation for lymph node metastasis of breast cancer relies on clinicopathological characteristics, which is unreliable, especially in triple-negative breast cancer (TNBC)-a highly heterogeneous disease. The authors developed and validated an effective prediction model for the lymph node status of patients with TNBC, which integrates 3'UTR markers and tumor size. This is the first 3'UTR-based model that will help identify TNBC patients with low risk of nodal involvement who are most likely to benefit from exemption axillary surgery.
摘要
背景。前哨淋巴结活检是针对可手术的三阴性乳腺癌 (TNBC)(临床显示腋窝淋巴结阴性)的标准手术分期方法。在本研究中,我们致力于研发一种模型,以预测阴性淋巴转移的 TNBC 患者,使他们从免除腋窝分期手术中获益。
材料和方法。我们使用来自 2 个基因表达综合数据库的 TNBC 微阵列数据对 3' 非翻译区 (3'UTR) 概况进行了评估。我们将 GSE31519 中的样本划分为训练集 (n = 164) 和验证集 (n =163) 并将 GSE76275 中的样本用于构建测试集 (n =164)。我们利用弹性网络模型组建了一个 6 成员 3’UTR 小组(ADD2、COL1A1、APOL2、IL21R、PKP2和 EIF4G3),以评估淋巴结转移 (LNM) 的风险。受试者手术特征和逻辑分析用于评估小组和 LNM 状态之间的关系。
结果。6 成员 3'UTR 小组显示出高辨别能力,在训练集、验证集和测试集中的曲线下面积分别为 0.712、0.729 和 0.708。在根据肿瘤大小进行校正之后,3’UTR 小组在训练集、验证集和测试集中仍保留了显著的预测能力( 比值比分别为 4.93、4.58 和 3.59;所有p < 0.05)。针对 3'UTR 小组和肿瘤大小的组合分析在训练集、验证集和测试集中分别达到了 97.2%、100% 和 100% 的准确度。
结论。本研究建立了一个基于 3'UTR 的整合模型,该模型是可手术的 TNBC 淋巴结阴性的有希望的预测因素。尽管需要实施前瞻性研究来验证此模型,但是,我们的研究结果可能允许选定的患者不选择腋窝手术。
对临床实践的提示
目前,前哨淋巴结活检是针对腋窝淋巴结阴性的乳腺癌患者的标准手术分期方法。乳腺癌淋巴结转移的预测估计依赖于临床病理特征,该特征具有不可靠性,特别是在三阴性乳腺癌(TNBC,高度异质性疾病)中更是如此。作者开发并验证了一种针对 TNBC 患者淋巴结状态的有效预测模型,该模型将 3'UTR 标记物和肿瘤大小整合在一起。这是第一个基于 3'UTR 的模型,有助于识别淋巴结转移低风险的 TNBC 患者,此类患者最有可能从免除腋窝手术中获益。
Keywords: 3′ untranslated region; Alternative polyadenylation; Lymph node; Prediction modeling; Triple‐negative breast cancer.
© AlphaMed Press 2018.