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New Strategy Broadens Detection of Breast & Ovarian Cancer Gene Variants

By LabMedica International staff writers
Posted on 09 May 2016
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Image: Predicted alteration in untranslated region (UTR) structure using mFOLD for variants flagged by SNPfold. Wild-type (a,c,e) and variant (b,d,f) structure pairs are displayed, with the variant location indicated by a red arrow. SHAPE analysis revealed differences in reactivity between mutant and variant mRNAs, confirming alterations to 2° structure (Image courtesy of Mucaki EJ et al, 2016, BMC Medical Genomics.)
Image: Predicted alteration in untranslated region (UTR) structure using mFOLD for variants flagged by SNPfold. Wild-type (a,c,e) and variant (b,d,f) structure pairs are displayed, with the variant location indicated by a red arrow. SHAPE analysis revealed differences in reactivity between mutant and variant mRNAs, confirming alterations to 2° structure (Image courtesy of Mucaki EJ et al, 2016, BMC Medical Genomics.)
Using next generation sequencing (NGS) technology and information theory (IT), researchers have developed a new method for identifying mutations and prioritizing sequence variants related to hereditary breast and ovarian cancer (HBOC). This could not only reduce the number of possible variants to test for, but also increase the number of patients that are properly diagnosed.

The potentially game-changing approach, developed by Peter Rogan, PhD, with his students and collaborators from the Schulich School of Medicine & Dentistry, University of Western Ontario (London, Ontario, Canada), reveals gene variants missed by conventional genetic testing. Their method was first applied to 102 women at risk or with a diagnosis of inherited breast cancer. They also studied 287 women with no known mutations. Aside from protein coding and copy number changes, IT-based sequence analysis was used to identify and prioritize pathogenic non-coding variants that occurred within sequence elements predicted to be involved in mRNA splicing, transcription, and untranslated region (UTR) binding and structure. This approach was supplemented by in silico and laboratory analysis of UTR structure.

Of women tested for HBOC risk, some carry disease-causing variants that are well understood. If a woman tests positive for high breast cancer risk variant of the BRCA1 or BRCA2 genes, doctors can make a decision about proceeding with specific treatments and genetic counseling. However, for the majority of women the standard BRCA test does not provide clear indication of how to proceed. This is due lack of standard procedure for diagnosis of patients who carry gene variants of uncertain significance (VUS).

The newly developed strategy consists of complete gene sequence analysis followed by a unified framework for interpreting non-coding variants that may affect gene expression. This approach distills large numbers of variants detected by NGS to a limited set of variants prioritized as potential deleterious changes. In the study, the team analyzed genes known to harbor mutations that increase HBOC risk (genes ATM, BRCA1, BRCA2, CDH1, CHEK2, PALB2,and TP53) by analyzing complete gene sequences: coding, non-coding, and intergenic regions 10 kb up- and downstream. Their study of the 102 women identified 15,311 unique sequence variants, of which only 245 occurred in coding regions.

“When a woman with a family history of breast cancer sees her physician, they want to know if they have a mutation in breast/ovarian cancer genes,” says Dr. Rogan, “All of the patients that we studied had been sequenced for BRCA1 or BRCA2. The causative cancer gene variants are hiding in plain sight in these and other cancer genes, but the original testing laboratory didn’t recognize them. Our approach can reveal gene variants that might explain their increased risk for cancer.” To increase the number of women who will benefit from genetic testing for breast cancer, doctors and government policy makers should include additional genes as well as approaches that can interpret complete gene sequences.

The work was published in two reports: by Mucaki EJ et al published April 11, 2016 in the journal BMC Medical Genomics; and by Caminsky NG et al published March 18, 2016, in the journal Human Mutation.

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