Bergamaschi A, Tagliabue E, Sørlie T, Naume B, Triulzi T, Orlandi R, Russnes H G, Nesland J M, Tammi R, Auvinen P, Kosma V-M, Ménard S, Børresen-Dale A-L
Department of Genetics, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical Centre, Montebello, Oslo, Norway.
J Pathol. 2008 Feb;214(3):357-67. doi: 10.1002/path.2278.
Prediction of the clinical outcome of breast cancer is multi-faceted and challenging. There is growing evidence that the complexity of the tumour micro-environment, consisting of several cell types and a complex mixture of proteins, plays an important role in development, progression, and response to therapy. In the current study, we investigated whether invasive breast tumours can be classified on the basis of the expression of extracellular matrix (ECM) components and whether such classification is representative of different clinical outcomes. We first examined the matrix composition of 28 primary breast carcinomas by morphology and gene expression profiling using 22K oligonucleotide Agilent microarrays. Hierarchical clustering of the gene expression profile of 278 ECM-related genes derived from the literature divided the tumours into four main groups (ECM1-4). A set of selected differentially expressed genes was validated by immunohistochemistry. The robustness of the ECM classification was confirmed by studying the four ECM groups in a previously published gene expression data set of 114 early-stage primary breast carcinomas profiled using cDNA arrays. Univariate survival analysis showed significant differences in clinical outcome among the various ECM subclasses. One set of tumours, designated ECM4, had a favourable outcome and was defined by the overexpression of a set of protease inhibitors belonging to the serpin family, while tumours with an ECM1 signature had a poorer prognosis and showed high expression of integrins and metallopeptidases, and low expression of several laminin chains. Furthermore, we identified three surrogate markers of ECM1 tumours: MARCO, PUNC, and SPARC, whose expression levels were associated with breast cancer survival and risk of recurrence. Our findings suggest that primary breast tumours can be classified based upon ECM composition and that this classification provides relevant information on the biology of breast carcinomas, further supporting the hypothesis that clinical outcome is strongly related to stromal characteristics.
乳腺癌临床结果的预测是多方面且具有挑战性的。越来越多的证据表明，由多种细胞类型和复杂蛋白质混合物组成的肿瘤微环境的复杂性在肿瘤的发生、发展及对治疗的反应中起着重要作用。在本研究中，我们调查了浸润性乳腺癌是否可以根据细胞外基质（ECM）成分的表达进行分类，以及这种分类是否代表不同的临床结果。我们首先通过形态学和使用22K寡核苷酸安捷伦微阵列的基因表达谱分析，检查了28例原发性乳腺癌的基质组成。从文献中获取的278个与ECM相关基因的基因表达谱的层次聚类将肿瘤分为四个主要组（ECM1 - 4）。一组选定的差异表达基因通过免疫组织化学进行了验证。通过在先前发表的使用cDNA阵列分析的114例早期原发性乳腺癌基因表达数据集中研究这四个ECM组，证实了ECM分类的稳健性。单因素生存分析显示，不同ECM亚类之间的临床结果存在显著差异。一组被指定为ECM4的肿瘤预后良好，其特征是一组属于丝氨酸蛋白酶抑制剂家族的蛋白酶抑制剂过表达，而具有ECM1特征的肿瘤预后较差，表现为整合素和金属肽酶高表达，以及几种层粘连蛋白链低表达。此外，我们确定了ECM1肿瘤的三个替代标志物：MARCO、PUNC和SPARC，它们的表达水平与乳腺癌的生存和复发风险相关。我们的研究结果表明，原发性乳腺癌可以根据ECM组成进行分类，并且这种分类提供了有关乳腺癌生物学的相关信息，进一步支持了临床结果与基质特征密切相关的假设。
