Department of Fourth General Surgery, Changhai Hospital, Shanghai 200433, China.
Breast Cancer Res Treat. 2013 Aug;141(1):23-32. doi: 10.1007/s10549-013-2664-1. Epub 2013 Aug 10.
The heterogeneity of breast cancer makes its diagnosis and treatment far from being optimal. Analysis of traditional pathological and prognostic markers based on immunohistochemistry (IHC) is inadequate in elucidating the inherent heterogeneity of breast cancer, especially basal-like breast carcinoma (BLBC) which displays complex and unique epidemiological, phenotypic, and molecular features with distinctive relapse patterns and poor clinical outcomes. Gene expression profiling opened an avenue in research as independent predictors by classifying breast cancers into discrete groups with prognostic references, but it is not cost-effective in clinical application. It is necessary to develop an effective predictive gene list from gene profiling to optimize the treatment with traditional markers. In this report, we analyzed the correlation between IHC and gene profiling of breast cancer with an emphasis on the BLBC, highlighting the potential discovery of diagnostic markers and cellular mechanisms that may guide the development of BLBC-targeted therapy. Random forest-based classification and PAM50 gene-sets were used in the comparison analysis of traditional prognostic markers including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and microarray profiles. An intrinsic 40-gene set was developed to classify breast cancer subtypes, and genes expression differentiations were used to explore the different mechanisms between the BLBC and non-BLBC subtypes based on the comparison of clinicopathological markers and microarray profiling. Pathways and DNA repairs were analyzed to evaluate the biological mechanisms in BLBC and other breast cancer subtypes. It is reasonable to define BLBC as those tumors that are negative for ER, PR, and HER2 by IHC for their accordance with gene expression profiles. Focal adhesion kinase, ERBB, and their signaling pathways may play crucial role in BLBC. The intrinsic 40-gene set can be used to classify breast cancer and help to optimize therapeutic management of BLBC.
乳腺癌的异质性使其诊断和治疗远非最佳。基于免疫组织化学(IHC)的传统病理和预后标志物分析不足以阐明乳腺癌的固有异质性,尤其是基底样乳腺癌(BLBC),其具有复杂而独特的流行病学、表型和分子特征,具有独特的复发模式和不良临床结局。基因表达谱分析为独立预测因子开辟了研究途径,通过将乳腺癌分为具有预后参考的离散组进行分类,但在临床应用中并不具有成本效益。有必要从基因谱中开发出有效的预测基因列表,以优化传统标志物的治疗效果。在本报告中,我们分析了乳腺癌的 IHC 和基因谱之间的相关性,重点是 BLBC,强调了诊断标志物和细胞机制的潜在发现,这些标志物和机制可能指导 BLBC 靶向治疗的发展。随机森林分类和 PAM50 基因集用于传统预后标志物(包括雌激素受体(ER)、孕激素受体(PR)、人表皮生长因子受体 2(HER2))和微阵列谱的比较分析。开发了一个内在的 40 基因集来分类乳腺癌亚型,并使用基因表达差异来探索 BLBC 和非 BLBC 亚型之间的不同机制,基于临床病理标志物和微阵列谱的比较。分析了途径和 DNA 修复,以评估 BLBC 和其他乳腺癌亚型中的生物学机制。根据 IHC 对 ER、PR 和 HER2 的阴性定义 BLBC 是合理的,因为它们与基因表达谱一致。粘着斑激酶、ERBB 及其信号通路可能在 BLBC 中发挥关键作用。内在的 40 基因集可用于分类乳腺癌,并有助于优化 BLBC 的治疗管理。