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Old 07-10-2010, 03:03 AM   #1
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Default PubMed: Detection of inherited mutations for breast and ovarian cancer using genomic

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Detection of inherited mutations for breast and ovarian cancer using genomic capture and massively parallel sequencing.

Proc Natl Acad Sci U S A. 2010 Jun 28;

Authors: Walsh T, Lee MK, Casadei S, Thornton AM, Stray SM, Pennil C, Nord AS, Mandell JB, Swisher EM, King MC

Inherited loss-of-function mutations in the tumor suppressor genes BRCA1, BRCA2, and multiple other genes predispose to high risks of breast and/or ovarian cancer. Cancer-associated inherited mutations in these genes are collectively quite common, but individually rare or even private. Genetic testing for BRCA1 and BRCA2 mutations has become an integral part of clinical practice, but testing is generally limited to these two genes and to women with severe family histories of breast or ovarian cancer. To determine whether massively parallel, "next-generation" sequencing would enable accurate, thorough, and cost-effective identification of inherited mutations for breast and ovarian cancer, we developed a genomic assay to capture, sequence, and detect all mutations in 21 genes, including BRCA1 and BRCA2, with inherited mutations that predispose to breast or ovarian cancer. Constitutional genomic DNA from subjects with known inherited mutations, ranging in size from 1 to >100,000 bp, was hybridized to custom oligonucleotides and then sequenced using a genome analyzer. Analysis was carried out blind to the mutation in each sample. Average coverage was >1200 reads per base pair. After filtering sequences for quality and number of reads, all single-nucleotide substitutions, small insertion and deletion mutations, and large genomic duplications and deletions were detected. There were zero false-positive calls of nonsense mutations, frameshift mutations, or genomic rearrangements for any gene in any of the test samples. This approach enables widespread genetic testing and personalized risk assessment for breast and ovarian cancer.

PMID: 20616022 [PubMed - as supplied by publisher]

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