Hart Christopher D, Vignoli Alessia, Tenori Leonardo, Uy Gemma Leonora, Van To Ta, Adebamowo Clement, Hossain Syed Mozammel, Biganzoli Laura, Risi Emanuela, Love Richard R, Luchinat Claudio, Di Leo Angelo
"Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, Istituto Toscano Tumori, Prato, Italy.
Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy.
Clin Cancer Res. 2017 Mar 15;23(6):1422-1431. doi: 10.1158/1078-0432.CCR-16-1153. Epub 2017 Jan 12.
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. 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. 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. 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. .
检测早期乳腺癌（EBC）患者的微转移疾病信号可改善风险分层，并有助于更精准地定制辅助治疗方案。我们之前表明，术后血清代谢组学谱可预测雌激素受体（ER）阴性EBC患者单中心队列中的复发情况。在此，我们使用来自参与一项国际III期试验的ER阳性、绝经前EBC女性的术前血清样本进一步对此进行研究。对590份EBC样本（319份复发或有≥6年临床随访）和109份转移性乳腺癌（MBC）样本进行了质子核磁共振（NMR）光谱分析。使用85份EBC样本和所有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份EBC样本的测试集，并根据样本被误分类为转移性的可能性生成复发风险评分。在训练集中，RF模型区分EBC和MBC的判别准确率为84.9%。在测试集中，RF复发风险评分与复发相关，在ROC分析中的AUC为0.747。准确率最高为71.3%（敏感性为70.8%；特异性为71.4%）。该模型的表现独立于年龄、肿瘤大小、分级、HER2状态和淋巴结状态，也独立于Adjuvant! Online复发风险评分。在一组多中心EBC患者中，我们基于术前血清代谢组学谱开发了一个模型，该模型可预测疾病复发，独立于传统临床病理风险因素。
