High-resolution oligonucleotide array-CGH applied to the detection and characterization of large rearrangements in the hereditary breast cancer gene BRCA1.
ABSTRACT We have developed a new method for detecting and characterizing large rearrangements in the BRCA1 gene based on high-resolution oligonucleotide array-CGH technology. We designed a specific CGH array for the BRCA1 gene and its flanking regions. We then used this approach to analyze nine DNA samples known to contain large deletions and large duplications. When possible, the deleted or duplicated region was sequenced to identify the break point. All the large rearrangements were detected by the new method, and their size was estimated to be within 1--2 kb. This enabled us to develop a simple polymerase chain reaction screening test for other family members. A refined choice of oligonucleotides should improve the precision of the breakpoint determination. Finally, the high resolution of oligonucleotide array-CGH should help to detect new large rearrangements missed by other current methods.
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ABSTRACT: The homeodomain protein TGIF (TG-interacting factor) restricts TGF-β/Smad cytostatic signaling by interfering with the nucleocytoplasmic transit of the tumor suppressor cPML. Here, we identify PHRF1 as a ubiquitin ligase that enforces TGIF decay by driving its ubiquitination at lysine 130. In so doing, PHRF1 ensures redistribution of cPML into the cytoplasm, where it associates with SARA and coordinates activation of Smad2 by the TGF-β receptor. The PHRF1 gene resides within the tumor suppressor locus 11p15.5, which displays frequent loss in a wide variety of malignancies, including breast cancer. Remarkably, we found that the PHRF1 gene is deleted or silenced in a high proportion of human breast cancer samples and cancer cell lines. Reconstitution of PHRF1 into deficient cells impeded their propensity to form tumors in vivo, most likely because of the reemergence of TGF-β responsiveness. These findings unveil a paradigm behind inactivation of the cPML tumor suppressor network in human malignancies.Cell Reports 07/2013; · 7.21 Impact Factor
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ABSTRACT: Background Conventional Sanger sequencing reliably detects the majority of genetic mutations associated with hereditary cancers, such as single-base changes and small insertions or deletions. However, detection of genomic rearrangements, such as large deletions and duplications, requires special technologies. Microarray analysis has been successfully used to detect large rearrangements (LRs) in genetic disorders.Methods We designed and validated a high-density oligonucleotide microarray for the detection of gene-level genomic rearrangements associated with hereditary breast and ovarian cancer (HBOC), Lynch, and polyposis syndromes. The microarray consisted of probes corresponding to the exons and flanking introns of BRCA1 and BRCA2 (¿1,700) and Lynch syndrome/polyposis genes MLH1, MSH2, MSH6, APC, MUTYH, and EPCAM (¿2,200). We validated the microarray with 990 samples previously tested for LR status in BRCA1, BRCA2, MLH1, MSH2, MSH6, APC, MUTYH, or EPCAM. Microarray results were 100% concordant with previous results in the validation studies. Subsequently, clinical microarray analysis was performed on samples from patients with a high likelihood of HBOC mutations (13,124), Lynch syndrome mutations (18,498), and polyposis syndrome mutations (2,739) to determine the proportion of LRs.ResultsOur results demonstrate that LRs constitute a substantial proportion of genetic mutations found in patients referred for hereditary cancer genetic testing.Conclusion The use of microarray comparative genomic hybridization (CGH) for the detection of LRs is well-suited as an adjunct technology for both single syndrome (by Sanger sequencing analysis) and extended gene panel testing by next generation sequencing analysis. Genetic testing strategies using microarray analysis will help identify additional patients carrying LRs, who are predisposed to various hereditary cancers.Journal of Experimental & Clinical Cancer Research 09/2014; 33(1):74. · 3.27 Impact Factor