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Determination of molecular markers for BRCA1 and BRCA2 heterozygosity using gene expression profiling

ABSTRACT: Approximately 5% of all breast cancers can be attributed to an inherited mutation in one of two cancer susceptibility genes, BRCA1 and BRCA2. We searched for genes that have the potential to distinguish healthy BRCA1 and BRCA2 mutation carriers from non-carriers based on differences in expression profiling. Using expression microarrays we compared gene expression of irradiated lymphocytes from BRCA1 and BRCA2 mutation carriers versus control non-carriers. We identified 137 probe sets in BRCA1 carriers and 1345 in BRCA2 carriers with differential gene expression. Gene Ontology analysis revealed that most of these genes relate to regulation pathways of DNA repair processes, cell cycle regulation and apoptosis. Real-time PCR was performed on the 36 genes which were most prominently differentially expressed in the microarray assay; 21 genes were shown to be significantly differentially expressed in BRCA1 or BRCA2 mutation carriers as compared to controls (p<0.05). Based on a validation study with 40 mutation carriers and 17 non-carriers, a multiplex model that included six or more coincidental genes of 18 selected genes was constructed in order to predict the risk of carrying a mutation. The results using this model showed sensitivity 95% and specificity 88%. In summary, our study provides insight into the biological effect of heterozygous mutations in BRCA1 and BRCA2 genes in response to ionizing irradiation induced DNA damage. We also suggest a set of 18 genes that can be used as a prediction and screening tool for BRCA1 or BRCA2 mutational carriers by using easily obtained lymphocytes. Using expression microarrays we compared gene expression of irradiated lymphocytes from BRCA1 and BRCA2 mutation carriers versus control non-carriers

ORGANISM(S): Homo sapiens

PROVIDER: GSE39976 | GEO | 2013/01/10

SECONDARY ACCESSION(S): PRJNA172190

REPOSITORIES: GEO

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Project description:Approximately 5% of all breast cancers can be attributed to an inherited mutation in one of two cancer susceptibility genes, BRCA1 and BRCA2. We searched for genes that have the potential to distinguish healthy BRCA1 and BRCA2 mutation carriers from non-carriers based on differences in expression profiling. Using expression microarrays we compared gene expression of irradiated lymphocytes from BRCA1 and BRCA2 mutation carriers versus control non-carriers. We identified 137 probe sets in BRCA1 carriers and 1345 in BRCA2 carriers with differential gene expression. Gene Ontology analysis revealed that most of these genes relate to regulation pathways of DNA repair processes, cell cycle regulation and apoptosis. Real-time PCR was performed on the 36 genes which were most prominently differentially expressed in the microarray assay; 21 genes were shown to be significantly differentially expressed in BRCA1 or BRCA2 mutation carriers as compared to controls (p<0.05). Based on a validation study with 40 mutation carriers and 17 non-carriers, a multiplex model that included six or more coincidental genes of 18 selected genes was constructed in order to predict the risk of carrying a mutation. The results using this model showed sensitivity 95% and specificity 88%. In summary, our study provides insight into the biological effect of heterozygous mutations in BRCA1 and BRCA2 genes in response to ionizing irradiation induced DNA damage. We also suggest a set of 18 genes that can be used as a prediction and screening tool for BRCA1 or BRCA2 mutational carriers by using easily obtained lymphocytes. Using expression microarrays we compared gene expression of irradiated lymphocytes from BRCA1 and BRCA2 mutation carriers versus control non-carriers Fresh blood samples were obtained from 9 BRCA1 and 8 BRCA2 mutation carriers and 9 mutation-negative women. Lymphocytes were collected from fresh blood samples, and RNA was extracted one hour after γ-irradiation

Project description:Use of DNA damaging agents and RNA pooling to assess expression profiles associated with BRCA1 and BRCA2 mutation status in familial breast cancer patients Background: A large number of rare sequence variants of unknown clinical significance have been identified in the breast cancer susceptibility genes, BRCA1 and BRCA2. Determining the functional effect of these variants as well as their role in breast cancer susceptibility can be challenging using current classification methods. Methodology/Principal Findings: 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 were 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 μM 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. Conclusions/Significance: 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.

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Determination of molecular markers for BRCA1 and BRCA2 heterozygosity using gene expression profiling

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