Nakagawa Taku, Huang Sharon K, Martinez Steve R, Tran Andy N, Elashoff David, Ye Xing, Turner Roderick R, Giuliano Armando E, Hoon Dave S B
Department of Molecular Oncology, Division of Biostatistics, and Joyce Eisenberg Keefer Breast Center, John Wayne Cancer Institute, Santa Monica, CA 90404, USA.
Cancer Res. 2006 Dec 15;66(24):11825-30. doi: 10.1158/0008-5472.CAN-06-2337.
To determine if protein expression in primary breast cancers can predict axillary lymph node (ALN) metastasis, we assessed differences in protein expression between primary breast cancers with and without ALN metastasis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Laser capture microdissection was performed on invasive breast cancer frozen sections from 65 patients undergoing resection with sentinel lymph node (SLN) or level I and II ALN dissection. Isolated proteins from these tumors were applied to immobilized metal affinity capture (IMAC-3) ProteinChip arrays and analyzed by SELDI-TOF-MS to generate unique protein profiles. Correlations between unique protein peaks and histologically confirmed ALN status and other known clinicopathologic factors were examined using ANOVA and multivariate logistic regression. Two metal-binding polypeptides at 4,871 and 8,596 Da were identified as significant risk factors for nodal metastasis (P = 0.034 and 0.015, respectively) in a multivariate analysis. Lymphovascular invasion (LVI) was the only clinicopathologic factor predictive of ALN metastasis (P = 0.0038). In a logistic regression model combining the 4,871 and 8,596 Da peaks with LVI, the area under the receiver operating characteristic curve was 0.87. Compared with patients with negative ALN, those with > or =2 positive ALN or non-SLN metastases were significantly more likely to have an increased peak at 4,871 Da (P = 0.016 and 0.0083, respectively). ProteinChip array analysis identified differential protein peaks in primary breast cancers that predict the presence and number of ALN metastases and non-SLN status.
为了确定原发性乳腺癌中的蛋白质表达是否能够预测腋窝淋巴结(ALN)转移,我们使用表面增强激光解吸/电离飞行时间质谱(SELDI-TOF-MS)评估了有或无ALN转移的原发性乳腺癌之间蛋白质表达的差异。对65例行前哨淋巴结(SLN)或Ⅰ、Ⅱ级ALN清扫术的患者的浸润性乳腺癌冰冻切片进行激光捕获显微切割。将这些肿瘤中分离出的蛋白质应用于固定化金属亲和捕获(IMAC-3)蛋白质芯片阵列,并通过SELDI-TOF-MS进行分析以生成独特的蛋白质谱。使用方差分析和多因素逻辑回归检查独特蛋白质峰与组织学证实的ALN状态及其他已知临床病理因素之间的相关性。在多因素分析中,鉴定出分子量为4871和8596道尔顿的两种金属结合多肽是淋巴结转移的显著危险因素(P分别为0.034和0.015)。淋巴管浸润(LVI)是唯一可预测ALN转移的临床病理因素(P = 0.0038)。在一个将分子量为4871和8596道尔顿的峰与LVI相结合的逻辑回归模型中,受试者工作特征曲线下面积为0.87。与ALN阴性的患者相比,有≥2个阳性ALN或非SLN转移的患者在4871道尔顿处出现峰升高的可能性显著更高(P分别为0.016和0.0083)。蛋白质芯片阵列分析确定了原发性乳腺癌中可预测ALN转移的存在和数量以及非SLN状态的差异蛋白质峰。