📝 摘要
The histopathologic classification of breast cancer stratifies tumors based on tumor grade, stage, and type. Despite an overall correlation with survival, this classification is poorly predictive and tumors with identical grade and stage can have markedly contrasting outcomes. Recently, breast carcinomas have been classified by their gene expression profiles on frozen material. The validation of such a classification on formalin-fixed paraffin-embedded tumor archives linked to clinical information in a high-throughput fashion would have a major impact on clinical practice. The authors tested the ability of tumor tissue microarrays (TMAs) to sub-classify breast cancers using a TMA containing 107 breast cancers. The pattern of expression of 13 different protein biomarkers was assessed by immunohistochemistry and the multidimensional data was analyzed using an unsupervised two-dimensional clustering algorithm. This revealed distinct tumor clusters which divided into two main groups correlating with tumor grade (P<0.001) and nodal status (P = 0.04). None of the protein biomarkers tested could individually identify these groups. The biological significance of this classification is supported by its similarity with one derived from gene expression microarray analysis. Thus, molecular profiling of breast cancer using a limited number of protein biomarkers in TMAs can sub-classify tumors into clinically and biologically relevant subgroups.
🧾 论文要素
| 作者 | Grace M. Callagy; Elena F. Cattaneo; Yataro Daigo; Lisa C. Happerfield; Lynda G. Bobrow; Paul D.P. Pharoah; Carlos H. Caldas |
|---|---|
| 期刊 | Diagnostic Molecular Pathology |
| 出版机构 | https://openalex.org/P4310315671 |
| 年份 | 2003 |
| 归属类 | 乳腺癌 |
| 平台 | OpenAlex |
| 被引数 | 171 |
| 阅读量 | 13 |
| DOI | 10.1097/00019606-200303000-00004 ↗ |
| 关键词 | Login; Computer science; Logo (programming language); World Wide Web; Personally identifiable information |