Breast Cancer Pathology Research Group, Division of Oncology, School of Medicine, Academic Unit of Clinical Oncology, University of Nottingham, Nottingham City Hospital, Hucknall Road, Nottingham NG5 1PB, UK.
Br J Cancer. 2013 Oct 1;109(7):1886-94. doi: 10.1038/bjc.2013.528. Epub 2013 Sep 5.
Breast cancer is a heterogeneous disease characterised by complex molecular alterations underlying the varied behaviour and response to therapy. However, translation of cancer genetic profiling for use in routine clinical practice remains elusive or prohibitively expensive. As an alternative, immunohistochemical analysis applied to routinely processed tissue samples could be used to identify distinct biological classes of breast cancer.
In this study, 1073 archival breast tumours previously assessed for 25 key breast cancer biomarkers using immunohistochemistry and classified using clustering algorithms were further refined using naïve Bayes classification performance. Criteria for class membership were defined using the expression of a reduced panel of 10 proteins able to identify key molecular classes. We examined the association between these breast cancer classes with clinicopathological factors and patient outcome.
We confirm patient classification similar to established genotypic biological classes of breast cancer in addition to novel sub-divisions of luminal and basal tumours. Correlations between classes and clinicopathological parameters were in line with expectations and showed highly significant association with patient outcome. Furthermore, our novel biological class stratification provides additional prognostic information to the Nottingham Prognostic Index.
This study confirms that distinct molecular phenotypes of breast cancer can be identified using robust and routinely available techniques and both the luminal and basal breast cancer phenotypes are heterogeneous and contain distinct subgroups.
乳腺癌是一种异质性疾病，其特征是复杂的分子改变，导致其表现和对治疗的反应各不相同。然而，将癌症基因谱分析转化为常规临床实践仍然难以实现或过于昂贵。作为替代方案，应用于常规处理的组织样本的免疫组织化学分析可用于识别不同的乳腺癌生物学类型。
在这项研究中，对 1073 例存档的乳腺癌肿瘤进行了分析，这些肿瘤先前使用免疫组织化学法评估了 25 种关键的乳腺癌标志物，并使用聚类算法进行了分类，然后进一步使用朴素贝叶斯分类性能进行了细化。类别成员的标准是使用能够识别关键分子类别的 10 种蛋白质的表达来定义的。我们研究了这些乳腺癌类型与临床病理因素和患者预后之间的关联。
我们确认了患者分类与乳腺癌的既定基因生物类型相似，此外还发现了 luminal 和基底肿瘤的新细分。类别的相关性与临床病理参数一致，与患者预后高度相关。此外，我们新的生物学分类分层为 Nottingham 预后指数提供了额外的预后信息。
这项研究证实，使用可靠且常规可用的技术可以识别乳腺癌的不同分子表型，并且 luminal 和基底乳腺癌表型是异质的，并且包含不同的亚群。
