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Transcriptional Gene Interactions in Breast Cancer: Discovery and Analysis of Risk-Associated Regulatory Polymorphisms Combining Microarray and Linkage Analyses
Since only one percent of the human DNA comprises the exonic protein encoding regions of the genome it seems logical to identify mutations that influence disease susceptibility by mechanisms other than protein structure or function. One mechanism worthy of pursuit is gene transcription. The advent of microarray technology provides researchers with the quantitative tools to measure gene expression globally and allows one to begin to dissect aberrant transcription patterns in diseased tissue. Expression signatures are extremely powerful and are capable of distinguishing tumors from patients possessing either BRCA1 or BRCA2 mutations. Additional studies have shown that variation in expression can be allele specific. This suggests that heritable risk variants in cis-acting transcriptional control elements may be one possible explanation leading to the aberrant transcript levels observed in breast tumors. To identify these risk variants we hypothesize that germline mutations in regulatory regions of genes shown to be dysregulated in BrCa can be efficiently identified by an innovative combination of statistics, comparative phylogenetics and family-based linkage methodologies. We propose four specific aims. (1) We will perform meta-analysis of multiple, publicly available BrCa microarray datasets to identify candidate genes based on statistical measures of differential expression rather than actual expression measurements. (2) We will utilize bioinformatic and comparative phylogenetic analyses of our candidates in orthologous genes from primates and mammals to identify evolutionarily conserved transcriptional regulatory elements. (3) We will identify disease alleles using genetic enrichment stratagies in a previoulsy acquired BrCa affected sibling pair cohort. Allele-sharing enrichment and postulated gene-gene intractions will allow us to target the most likely cases for disease allele detection. (4) Finally, we will initiate experiments to characterize how these high-risk transcriptional alleles demonstrate abnormal interactions with elements of the transcriptional apparatus in biochemical assays designed to quantify transcription of the gene. Understanding the genetic basis of breast cancer will have a significant impact on public health and may aid both in the development of presymtomatic diagnostic tests and possibly provide avenues for therapeutic treatment of the disease. In addition, the use of gene-gene interactions can aid in elucidating the complex genetic pathways and networks underlying the biology of many diseases including breast cancer.
Screening for patients possessing BRCA1/2 mutations identifies only a very small fraction of women at-risk. To identify additional women at risk for breast cancer, researchers are employing experiments that measure gene expression patterns in breast tumors to identify new genes that, if included in screening, would allow many more women a better sense of their risks for disease. Expression arrays are powerful tools for the analysis of breast cancer. Arrays facilitate a more detailed characterization of tumor diversity compared to standard pathological procedures and have also been used to predict patient outcomes and response to therapy. Indeed, heritable differences in global gene expression patterns are supported by differences in gene expression patterns between BRCA1 and BRCA2 mutation carriers. We hypothesize that genes shown to be dysregulated in BrCa microarrays may contain heritable mutations in DNA elements that control their expression. We will integrate 4 distinct scientific methodologies to identify and study these mutant alleles in breast cancer families. First, we will determine a targeted set of aberrantly expressed candidate genes from the simultaneous statistical analysis of multiple publicly available breast cancer microarray datasets. Second, we will employ both bioinformatic and DNA sequence comparisons in the identical genes from both primate and mammalian species to identify DNA control elements that are conserved through evolution that may be involved in the transcriptional expression of our candidates. Third, we will search for disease alleles in these DNA elements by enriching for patients with a strong genetic etiology by using a previously recruited cohort of sisters affected with breast cancer (Affected Sibling Pairs) and genetic marker enrichment strategies. Fourth, we will initiate biochemical studies of these disease alleles to measure their influence on elements of gene transcription. Ultimately our goal is to identify new disease alleles that influence gene expression and, if possible, begin to understand how this mechanism takes place biochemically. Rewards of this research may include both improvements in risk screening and better treatments for this and possibly other cancers by virtue of a more detailed understanding of the mechanisms responsible for the transcriptional regulation of genes. Additionally, providing new information on inherited disease risk will better inform women about their personal risk and could reduce anxiety for those currently considered at risk.