Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany.
German Cancer Consortium (DKTK), Partner Site Munich, 80336 Munich, Germany.
Int J Mol Sci. 2023 Feb 2;24(3):2860. doi: 10.3390/ijms24032860.
Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.
免疫组织化学评估雌激素受体、孕激素受体和人表皮生长因子受体 2 的状态可对不同亚型的乳腺癌进行分层，并确定治疗方案。三阴性乳腺癌（TNBC）不表达受体过表达，常与患者预后较差相关。质谱成像转录组织标本的分子含量，而无需额外的标记或对样品进行初步分析，因此是一种用于无偏测定组织成分（特别是肿瘤标志物）的极好方法。在这项研究中，通过质谱成像对 1191 个人类乳腺癌样本的蛋白质组内容进行了特征描述，并利用上皮区域来训练和测试机器学习模型，以对个体受体状态进行分类，并对 TNBC 进行分类。所提出的分类模型对雌激素和孕激素受体具有很高的准确性，对 TNBC 的分类准确率超过 95%。分子特征分析表明，波形蛋白的过表达与 TNBC 相关，免疫组织化学验证也支持这一结果，这为诊断和治疗提供了一个新的潜在靶点。
