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.
Original language | English (US) |
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Journal | Journal of the National Cancer Institute |
Volume | 107 |
Issue number | 8 |
DOIs | |
State | Published - Aug 1 2015 |
ASJC Scopus subject areas
- Oncology
- Cancer Research