Anti-MKI67 monoclonal antibody, clone 0D23C3

Copy number variations (CNVs) are key contributors in breast cancer initiation and progression. However, to date, no CNV-based gene signature is developed for breast cancer. 21-gene Oncotype DX, a clinically validated signature, was identified using only RNA expression data in breast cancer patients. In this study, we evaluated whether CNVs of Oncotype DX genes can be used to predict the prognosis of breast cancer patients. Transcriptomic data of 547 and genomic data of 816 of breast cancer patients were downloaded from The Cancer Genome Atlas database. To establish the prognostic relevance between the CNVs of Oncotype DX genes and clinicopathological features, statistical analysis including Pearson Correlation, Fisher-exact, Chi square, Kaplan-Meier survival and Cox regression analyses were performed. 86% genes showed positive CNV-expression correlation. CNVs in 52% and 47.6% genes showed association with ER+ and PR+ status, respectively. 71% of the genes (including ERBB2, CTSV, CD68, GRB7, MKI67, MMP1, PGR, RPLP0, TFRC, BAG1, BCL2, BIRC5, FLNB, GSTM1 and SCUBE2) showed association with poor overall survival. 14% of the genes (including CTSV, RPLP0 and BIRC5) genes showed association with disease free survival. Cox regression analysis revealed ESR1, metastasis and node stage as independent prognostic factors for overall survival of breast cancer patients. The results suggested that CNV-based assay of Oncotype DX genes can be used to predict the survival of breast cancer patients. In future, identifying new gene signatures for better breast cancer prognosis using CNV level information will be worth investigating.

The aim of our study was to examine an association between gene expression assessed using a 23-gene microarray and receptor status of breast cancer samples categorized as ER positive, HER2 positive and triple negative subtypes. The ER positive cohort was subsequently divided into Luminal A, Luminal B HER2 negative and Luminal B HER2 positive subtypes. Core- needle biopsies were collected from 78 female patients with inoperable locally advanced breast cancer or resectable tumors suitable for downstaging, before any treatment. Expressions of 23 genes were determined by means of TagMan Low Density Arrays. Analysis of variance was used to select genes with discriminatory potential between receptor subtypes. We introduced a correction for false discovery rates (presented as q values) due to testing multiple hypothesis. Pairwise post-hoc comparisons of receptor subtypes were performed using Tukey 's HSD test. Five genes out of a 23-gene microarray differed significantly in relation to breast cancer receptor-based subtypes. Among these five genes, we identified: BCL2 (p=0.0002, q=0.0009), MKI67 (p=0.0037, q=0.0064), IGF1R (p=0.0040, q=0.0064), FOXC1 (p=0.0113, q=0.0135) and IRF1 (p=0.0435, q=0.0416) as ones showing ER positive, HER2 positive and triple negative -subtype specific expression profiles. When incorporating Luminal A, Luminal B HER2 negative, Luminal B HER2 positive subtypes into analysis, four genes: BCL2 (p=0.0006, q=0.0034), MKI67 (p=0.0078, q=0.0198), FOXC1 (p=0.0102, q=0.0198) and IGF1R (p=0.0174, q=0.0254) were selected. Elevated levels of IGF1R and BCL2 were significantly linked with Luminal A subtype. Triple negative breast cancer subtype was associated with higher expression of IRF1, FOXC1 and MKI67. In HER2 positive cohort lower expression of all five analyzed genes was noted.