Use of DNA-damaging agents and RNA pooling to assess expression profiles associated with BRCA1 and BRCA2 mutation status in familial breast cancer patients

Abstract

A large number of rare sequence variants of unknown clinical significance have been identified in the breast cancer susceptibility genes, BRCA1 and BRCA2. Laboratory-based methods that can distinguish between carriers of pathogenic mutations and non-carriers are likely to have utility for the classification of these sequence variants. To identify predictors of pathogenic mutation status in familial breast cancer patients, we explored the use of gene expression arrays to assess the effect of two DNA-damaging agents (irradiation and mitomycin C) on cellular response in relation to BRCA1 and BRCA2 mutation status. A range of regimes was used to treat 27 lymphoblastoid cell-lines (LCLs) derived from affected women in high-risk breast cancer families (nine BRCA1, nine BRCA2, and nine non-BRCA1/2 or BRCAX individuals) and nine LCLs from healthy individuals. Using an RNA-pooling strategy, we found that treating LCLs with 1.2 microM mitomycin C and measuring the gene expression profiles 1 hour post-treatment had the greatest potential to discriminate BRCA1, BRCA2, and BRCAX mutation status. A classifier was built using the expression profile of nine QRT-PCR validated genes that were associated with BRCA1, BRCA2, and BRCAX status in RNA pools. These nine genes could distinguish BRCA1 from BRCA2 carriers with 83% accuracy in individual samples, but three-way analysis for BRCA1, BRCA2, and BRCAX had a maximum of 59% prediction accuracy. Our results suggest that, compared to BRCA1 and BRCA2 mutation carriers, non-BRCA1/2 (BRCAX) individuals are genetically heterogeneous. This study also demonstrates the effectiveness of RNA pools to compare the expression profiles of cell-lines from BRCA1, BRCA2, and BRCAX cases after treatment with irradiation and mitomycin C as a method to prioritize treatment regimes for detailed downstream expression analysis.

Figure 1. Number of genes differentially expressed among BRCA1, BRCA2, and BRCAX.

Differential expression was determined by (A) fold-change (geometric mean of the expression ratios >2), and (B) statistical correlation using the F-test and alpha levels 0.05 and 0.01.

Figure 2. Classifying BRCA1, BRCA2, and BRCAX subtype by MMC response genes.

(A) Venn diagram illustrating the number of genes identified from three analyses: 1) 3-way comparison of BRCA1, BRCA2 and BRCAX pools (F-test, P<0.05); 2) Pairwise comparison of 1.2 µM MMC-T₆₀ treated and non-treated BRCA1/2/X pools (<10% false discovery rate; 90% confidence level); and 3) 2-way comparison of 1.2 µM MMC-T₆₀ treated and non-treated healthy control pools (T-test, P<0.05). The extent of overlap between gene lists is shown. (B) List of 36 genes that are differentially expressed between BRCA1, BRCA2, and BRCAX, and are MMC responsive in affected carrier pools but not in healthy controls. (C) Supervised hierarchical clustering of treated (1.2 µM MMC-T₆₀) sample pools using the 36-gene list.

Figure 3. The coefficient of variation (i.e. standard deviation divided by the mean) of the expression values for the nine MMC responsive genes.

For each gene, microarray and/or QRT–PCR derived data are compared across RNA pools, virtual pools and individual samples.