Diagnosis of Basal-Like Breast Cancer Using a FOXC1-Based Assay

利用基于FOXC1的检测方法诊断基底样乳腺癌

阅读:1

Abstract

BACKGROUND: Diagnosis of basal-like breast cancer (BLBC) remains a bottleneck to conducting effective clinical trials for this aggressive subtype. We postulated that elevated expression of Forkhead Box transcription factor C1 (FOXC1) is a simple and accurate diagnostic biomarker for BLBC. METHODS: Accuracy of FOXC1 expression in identifying BLBC was compared with the PAM50 gene expression panel in gene expression microarray (GEM) (n = 1992) and quantitative real-time polymerase chain reaction (qRT-PCR) (n = 349) datasets. A FOXC1-based immunohistochemical (IHC) assay was developed and assessed in 96 archival formalin-fixed, paraffin-embedded (FFPE) breast cancer samples that also underwent PAM50 profiling. All statistical tests were two-sided. RESULTS: A FOXC1-based two-tier assay (IHC +/- qRT-PCR) accurately identified BLBC (AUC = 0.88) in an independent cohort of FFPE samples, validating the accuracy of FOXC1-defined BLBC in GEM (AUC = 0.90) and qRT-PCR (AUC = 0.88) studies, when compared with platform-specific PAM50-defined BLBC. The hazard ratio (HR) for disease-specific survival in patients having FOXC1-defined BLBC was 1.71 (95% CI = 1.31 to 2.23, P < .001), comparable to PAM50 assay-defined BLBC (HR = 1.74, 95% CI = 1.40 to 2.17, P < .001). FOXC1 expression also predicted the development of brain metastasis. Importantly, unlike triple-negative or Core Basal IHC definitions, a FOXC1-based definition is able to identify BLBC in both ER+ and HER2+ patients. CONCLUSION: A FOXC1-based two-tier assay, by virtue of being rapid, simple, accurate, and cost-effective may emerge as the diagnostic assay of choice for BLBC. Such a test could substantially improve clinical trial enrichment of BLBC patients and accelerate the identification of effective chemotherapeutic options for this aggressive disease.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。