Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population
Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population
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DOI：
10.1158/1078-0432.ccr-16-1153
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发表时间：
2017-03-15
影响因子：
11.5
通讯作者：
Di Leo, Angelo
中科院分区：
文献类型：
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作者：
Hart, Christopher D.;Vignoli, Alessia;Di Leo, Angelo
目的：检测早期乳腺癌 (EBC) 患者的微转移性疾病信号可以改善风险分层，并更好地调整辅助治疗。我们之前表明，术后血清代谢组学特征可以预测雌激素受体 (ER) 阴性 EBC 患者单中心队列的复发。在这里，我们使用参加国际 III 期试验的 ER 阳性绝经前 EBC 女性的术前血清样本对此进行了进一步研究。实验设计：对 590 个 EBC 样本（319 个复发或≥ 6 年临床随访）和 109 个转移性乳腺癌 (MBC) 样本进行质子核磁共振 (NMR) 光谱分析。使用 85 个 EBC 和所有 MBC 样本的训练集构建了随机森林 (RF) 分类模型。然后将该模型应用于 234 个 EBC 样本的测试集，并根据样本被错误分类为转移的可能性生成复发风险评分。结果：在训练集中，RF 模型以 84.9% 的辨别准确度将 EBC 与 MBC 区分开。在测试集中，RF 复发风险评分与复发相关，ROC 分析中的 AUC 为 0.747。准确度最大为 71.3%（敏感性，70.8%；特异性，71.4%）。该模型的表现与年龄、肿瘤大小、分级、HER2 状态和淋巴结状态以及佐剂无关！在线复发风险评分。结论：在多中心 EBC 患者组中，我们开发了一个基于术前血清代谢组学特征的模型，该模型可预测疾病复发，独立于传统的临床病理学危险因素。 (C) 2016 年 AACR。
Purpose: Detecting signals of micrometastatic disease in patients with early breast cancer (EBC) could improve risk stratification and allow better tailoring of adjuvant therapies. We previously showed that postoperative serum metabolomic profiles were predictive of relapse in a single-center cohort of estrogen receptor (ER)-negative EBC patients. Here, we investigated this further using preoperative serum samples from ER-positive, premenopausal women with EBC who were enrolled in an international phase III trial.Experimental Design: Proton nuclear magnetic resonance (NMR) spectroscopy of 590 EBC samples (319 with relapse or >= 6 years clinical follow-up) and 109 metastatic breast cancer (MBC) samples was performed. A Random Forest (RF) classification model was built using a training set of 85 EBC and all MBC samples. The model was then applied to a test set of 234 EBC samples, and a risk of recurrence score was generated on the basis of the likelihood of the sample being misclassified as metastatic.Results: In the training set, the RF model separated EBC from MBC with a discrimination accuracy of 84.9%. In the test set, the RF recurrence risk score correlated with relapse, with an AUC of 0.747 in ROC analysis. Accuracy was maximized at 71.3% (sensitivity, 70.8%; specificity, 71.4%). The model performed independently of age, tumor size, grade, HER2 status and nodal status, and also of Adjuvant! Online risk of relapse score.Conclusions: In a multicenter group of EBC patients, we developed a model based on preoperative serum metabolomic profiles that was prognostic for disease recurrence, independent of traditional clinicopathologic risk factors. (C) 2016 AACR.