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Abstract
Between the years 2010 and 2012, the lifetime probability of developing female breast cancer was 12.3%, or approximately 1 in 8. Worldwide, breast cancer is the most common cancer in women. Survival is increasing. Between 2005 and 2011, the 5-year relative survival was found to be 89%. This is thought to be due to both the increase in utilization of population-wide screening, as well as advances in treatment. Less than 10% of breast cancers can be attributed to an inherited genetic mutation. Breast cancer is more commonly associated with environmental, reproductive, and lifestyle factors, some of which are potentially modifiable.
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Maxwell K, Hart S, Vijai J, Schrader K, Slavin T, Thomas T, Wubbenhorst B, Ravichandran V, Moore R, Hu C, Guidugli L, Wenz B, Domchek S, Robson M, Szabo C, Neuhausen S, Weitzel J, Offit K, Couch F, Nathanson K. Evaluation of ACMG-Guideline-Based Variant Classification of Cancer Susceptibility and Non-Cancer-Associated Genes in Families Affected by Breast Cancer. Am J Hum Genet 2016; 98:801-817. [PMID: 27153395 DOI: 10.1016/j.ajhg.2016.02.024] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Accepted: 02/26/2016] [Indexed: 01/24/2023] Open
Abstract
Sequencing tests assaying panels of genes or whole exomes are widely available for cancer risk evaluation. However, methods for classification of variants resulting from this testing are not well studied. We evaluated the ability of a variant-classification methodology based on American College of Medical Genetics and Genomics (ACMG) guidelines to define the rate of mutations and variants of uncertain significance (VUS) in 180 medically relevant genes, including all ACMG-designated reportable cancer and non-cancer-associated genes, in individuals who met guidelines for hereditary cancer risk evaluation. We performed whole-exome sequencing in 404 individuals in 253 families and classified 1,640 variants. Potentially clinically actionable (likely pathogenic [LP] or pathogenic [P]) versus nonactionable (VUS, likely benign, or benign) calls were 95% concordant with locus-specific databases and Clinvar. LP or P mutations were identified in 12 of 25 breast cancer susceptibility genes in 26 families without identified BRCA1/2 mutations (11%). Evaluation of 84 additional genes associated with autosomal-dominant cancer susceptibility identified LP or P mutations in only two additional families (0.8%). However, individuals from 10 of 253 families (3.9%) had incidental LP or P mutations in 32 non-cancer-associated genes, and 9% of individuals were monoallelic carriers of a rare LP or P mutation in 39 genes associated with autosomal-recessive cancer susceptibility. Furthermore, 95% of individuals had at least one VUS. In summary, these data support the clinical utility of ACMG variant-classification guidelines. Additionally, evaluation of extended panels of cancer-associated genes in breast/ovarian cancer families leads to only an incremental clinical benefit but substantially increases the complexity of the results.
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Caminsky NG, Mucaki EJ, Perri AM, Lu R, Knoll JHM, Rogan PK. Prioritizing Variants in Complete Hereditary Breast and Ovarian Cancer Genes in Patients Lacking Known BRCA Mutations. Hum Mutat 2016; 37:640-52. [PMID: 26898890 DOI: 10.1002/humu.22972] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 01/22/2016] [Accepted: 02/16/2016] [Indexed: 12/11/2022]
Abstract
BRCA1 and BRCA2 testing for hereditary breast and ovarian cancer (HBOC) does not identify all pathogenic variants. Sequencing of 20 complete genes in HBOC patients with uninformative test results (N = 287), including noncoding and flanking sequences of ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, EPCAM, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD51B, STK11, TP53, and XRCC2, identified 38,372 unique variants. We apply information theory (IT) to predict and prioritize noncoding variants of uncertain significance in regulatory, coding, and intronic regions based on changes in binding sites in these genes. Besides mRNA splicing, IT provides a common framework to evaluate potential affinity changes in transcription factor (TFBSs), splicing regulatory (SRBSs), and RNA-binding protein (RBBSs) binding sites following mutation. We prioritized variants affecting the strengths of 10 splice sites (four natural, six cryptic), 148 SRBS, 36 TFBS, and 31 RBBS. Three variants were also prioritized based on their predicted effects on mRNA secondary (2°) structure and 17 for pseudoexon activation. Additionally, four frameshift, two in-frame deletions, and five stop-gain mutations were identified. When combined with pedigree information, complete gene sequence analysis can focus attention on a limited set of variants in a wide spectrum of functional mutation types for downstream functional and co-segregation analysis.
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Affiliation(s)
- Natasha G Caminsky
- Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Eliseos J Mucaki
- Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Ami M Perri
- Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Ruipeng Lu
- Department of Computer Science, Faculty of Science, Western University, London, Ontario, Canada
| | - Joan H M Knoll
- Department of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Cytognomix Inc, London, Ontario, Canada
| | - Peter K Rogan
- Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Computer Science, Faculty of Science, Western University, London, Ontario, Canada.,Cytognomix Inc, London, Ontario, Canada.,Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
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Ochs-Balcom HM, Sun X, Chen Y, Barnholtz-Sloan J, Erwin DO, Jandorf L, Sucheston-Campbell L, Elston RC. Putative linkage signals identified for breast cancer in African American families. Cancer Epidemiol Biomarkers Prev 2015; 24:442-7. [PMID: 25477366 PMCID: PMC4323921 DOI: 10.1158/1055-9965.epi-14-1131] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Genome-wide association studies have identified polymorphisms associated with breast cancer subtypes and across multiple population subgroups; however, few studies to date have applied linkage analysis to other population groups. METHODS We performed the first genome-wide breast cancer linkage analysis in 106 African American families (comprising 179 affected and 79 unaffected members) not known to be segregating BRCA mutations to search for novel breast cancer loci. We performed regression-based model-free multipoint linkage analyses of the sibling pairs using SIBPAL, and two-level Haseman-Elston linkage analyses of affected relative pairs using RELPAL. RESULTS We identified -log10 P values that exceed 4 on chromosomes 3q and 12q, as well as a region near BRCA1 on chromosome 17 (-log10 P values in the range of 3.0-3.2) using both sibling-based and relative-based methods; the latter observation may suggest that undetected BRCA1 mutations or other mutations nearby such as HOXB13 may be segregating in our sample. CONCLUSIONS In summary, these results suggest novel putative regions harboring risk alleles in African Americans that deserve further study. IMPACT We hope that our study will spur further family-based investigation into specific mechanisms for breast cancer disparities.
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Affiliation(s)
- Heather M Ochs-Balcom
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, New York.
| | - Xiangqing Sun
- Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Yanwen Chen
- Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, Ohio. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Jill Barnholtz-Sloan
- Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, Ohio. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Deborah O Erwin
- Department of Cancer Prevention and Population Sciences, Roswell Park Cancer Institute, Buffalo, New York
| | - Lina Jandorf
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lara Sucheston-Campbell
- Department of Cancer Prevention and Population Sciences, Roswell Park Cancer Institute, Buffalo, New York
| | - Robert C Elston
- Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, Ohio. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
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