Chanrion Maïa, Fontaine Hélène, Rodriguez Carmen, Negre Vincent, Bibeau Frédéric, Theillet Charles, Hénaut Alain, Darbon Jean-Marie
INSERM U868, Cancer Research Centre, CRLC Val d'Aurelle-Paul Lamarque, Montpellier, France.
BMC Cancer. 2007 Mar 5;7:39. doi: 10.1186/1471-2407-7-39.
Current histo-pathological prognostic factors are not very helpful in predicting the clinical outcome of breast cancer due to the disease's heterogeneity. Molecular profiling using a large panel of genes could help to classify breast tumours and to define signatures which are predictive of their clinical behaviour.
To this aim, quantitative RT-PCR amplification was used to study the RNA expression levels of 47 genes in 199 primary breast tumours and 6 normal breast tissues. Genes were selected on the basis of their potential implication in hormonal sensitivity of breast tumours. Normalized RT-PCR data were analysed in an unsupervised manner by pairwise hierarchical clustering, and the statistical relevance of the defined subclasses was assessed by Chi2 analysis. The robustness of the selected subgroups was evaluated by classifying an external and independent set of tumours using these Chi2-defined molecular signatures.
Hierarchical clustering of gene expression data allowed us to define a series of tumour subgroups that were either reminiscent of previously reported classifications, or represented putative new subtypes. The Chi2 analysis of these subgroups allowed us to define specific molecular signatures for some of them whose reliability was further demonstrated by using the validation data set. A new breast cancer subclass, called subgroup 7, that we defined in that way, was particularly interesting as it gathered tumours with specific bioclinical features including a low rate of recurrence during a 5 year follow-up.
The analysis of the expression of 47 genes in 199 primary breast tumours allowed classifying them into a series of molecular subgroups. The subgroup 7, which has been highlighted by our study, was remarkable as it gathered tumours with specific bioclinical features including a low rate of recurrence. Although this finding should be confirmed by using a larger tumour cohort, it suggests that gene expression profiling using a minimal set of genes may allow the discovery of new subclasses of breast cancer that are characterized by specific molecular signatures and exhibit specific bioclinical features.
由于乳腺癌的异质性，目前的组织病理学预后因素在预测乳腺癌的临床结局方面帮助不大。使用大量基因进行分子谱分析有助于对乳腺肿瘤进行分类，并定义可预测其临床行为的特征。
为此，采用定量逆转录聚合酶链反应（RT-PCR）扩增技术研究了199例原发性乳腺肿瘤和6例正常乳腺组织中47个基因的RNA表达水平。根据基因对乳腺肿瘤激素敏感性的潜在影响来选择基因。通过成对层次聚类以无监督方式分析标准化的RT-PCR数据，并通过卡方分析评估所定义亚类的统计相关性。使用这些由卡方定义的分子特征对一组外部独立肿瘤进行分类，以评估所选亚组的稳健性。
基因表达数据的层次聚类使我们能够定义一系列肿瘤亚组，这些亚组要么让人联想到先前报道的分类，要么代表假定的新亚型。对这些亚组的卡方分析使我们能够为其中一些亚组定义特定的分子特征，通过使用验证数据集进一步证明了其可靠性。我们以这种方式定义的一个新的乳腺癌亚类，称为亚组7，特别有趣，因为它聚集了具有特定生物临床特征的肿瘤，包括5年随访期间的低复发率。
对199例原发性乳腺肿瘤中47个基因表达的分析使它们被分类为一系列分子亚组。我们的研究突出了亚组7，它很显著，因为它聚集了具有特定生物临床特征（包括低复发率）的肿瘤。尽管这一发现应通过使用更大的肿瘤队列来证实，但它表明使用最少一组基因进行基因表达谱分析可能会发现以特定分子特征为特征并表现出特定生物临床特征的乳腺癌新亚类。
