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 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 genes, in individuals who met guidelines for hereditary cancer risk evaluation. We performed whole exome sequencing in 404 individuals in 257 families and classified 1640 variants from these genes. Potentially clinically actionable (likely-pathogenic/pathogenic, LP/P) versus nonactionable (VUS/likely-benign/benign) calls were 92% and 88% concordant with locus specific databases and Clinvar, respectively. LP/P mutations were identified in 11 of 25 breast cancer susceptibility genes in 27 BRCA1/2 negative families (11%). Evaluation of 84 additional autosomal dominant cancer susceptibility genes identified LP/P mutations in only four additional families (1.7%), suggesting they do not influence risk in this cohort. However, individuals from nine of 257 families (3.5%) had incidental LP/P mutations in 32 non-cancer disease genes, and 7% of individuals were monoallelic carriers of an LP/P mutation in 39 autosomal recessive cancer syndrome genes. Furthermore, 90% of individuals had at least one VUS. In summary, these data support the clinical utility of ACMG variant classification guidelines. In addition, evaluation of extended panels of cancer genes in breast/ovarian cancer families leads to only an incremental clinical benefit but substantially increases the complexity of the results. Citation Format: Kara N. Maxwell, Steven N. Hart, Joseph Vijai, Kasmintan A. Schrader, Tinu Thomas, Bradley Wubbenhorst, Vignesh Ravichandran, Raymond M. Moore, Chunling Hu, Lucia Guidugli, Brandon Wenz, Thomas P. Slavin, Susan M. Domchek, Mark E. Robson, Csilla Szabo, Susan L. Neuhausen, Jeffrey N. Weitzel, Kenneth Offit, Fergus J. Couch, Katherine L. Nathanson. Evaluation of ACMG guideline classified variants in 180 cancer and incidental non-cancer genes in families with breast/ovarian cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5220.
Abstract 2597: Breast and ovarian cancer risks associated with cancer predisposition gene mutations identified by multigene panel testing
Abstract Multigene panel testing (MGPT) for hereditary cancer is increasing in popularity in the USA. Many panels include genes identified as hereditary breast and/or ovarian cancer (HBOC) genes despite limited data regarding the precise cancer risks associated with mutations in these genes. Here we report on results from BreastNext and OvaNext panel testing of 20 genes (ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, MLH1, MRE11A, MSH2, MSH6, NBN, NF1, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, TP53) in a cohort of 15,083 individuals. The majority of individuals were from high-risk breast and/or ovarian (Br/Ov) cancer families, with 92.4% of all probands meeting National Comprehensive Cancer Network HBOC testing criteria. Pathogenic mutations were identified in 9.4% of the overall cohort. To estimate gene-specific breast and ovarian cancer risks, case-control analyses were performed comparing the frequencies of pathogenic mutations from Caucasian breast or ovarian cancer cases from BreastNext and OvaNext with frequencies from Caucasian, non-Finnish, non-TCGA controls from the Exome Aggregation Consortium (ExAC) database. Mutations in the well studied ATM and CHEK2 genes were associated with moderate risks (OR>2) of breast cancer and mutations in PALB2 were associated with high-risks (OR>5) of breast cancer, consistent with previous reports. In addition, the study suggested that pathogenic mutations in MSH6, RAD51D, CDH1, and NF1 are associated with moderate to high risks of breast cancer. In contrast, RAD51C, RAD51D, and BRIP1 mutations were associated with high risks of ovarian cancer, PALB2 mutations were associated with moderate risks, but ATM and CHEK2 mutations were not associated with increased ovarian cancer risk. In addition, modeling of missense mutations in the predisposition genes using in silico prediction algorithms suggested that missense mutations in CDH1, CHEK2, MSH2, and MSH6 are associated with moderate risks of breast cancer and missense mutations in RAD51C increase risks of ovarian cancer. This large breast and ovarian cancer case-control analysis provides useful data for many predisposition genes previously lacking risk estimates, and should prove useful for clinical risk management of patients after clinical panel testing. Citation Format: Fergus J. Couch, David E. Goldgar, Steven N. Hart, Emily Hallberg, Raymond Moore, Huong Meeks, Robert Huether, Holly LaDuca, Elizabeth Chao, Jill Dolinsky. Breast and ovarian cancer risks associated with cancer predisposition gene mutations identified by multigene panel testing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2597.
Prevalence of Pathogenic Mutations in Cancer Predisposition Genes among Pancreatic Cancer Patients
Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms | February 2, 2015 | Volume: No volume available | Issue: No issue available | Pages: 163-170
Abstract Motivation: The Biological Reference Repository (BioR) is a toolkit for annotating variants. BioR stores public and user-specific annotation sources in indexed JSON-encoded flat files (catalogs). The BioR toolkit provides the functionality to combine and retrieve annotation from these catalogs via the command-line interface. Several catalogs from commonly used annotation sources and instructions for creating user-specific catalogs are provided. Commands from the toolkit can be combined with other UNIX commands for advanced annotation processing. We also provide instructions for the development of custom annotation pipelines. Availability and implementation: The package is implemented in Java and makes use of external tools written in Java and Perl. The toolkit can be executed on Mac OS X 10.5 and above or any Linux distribution. The BioR application, quickstart, and user guide documents and many biological examples are available at http://bioinformaticstools.mayo.edu . Contact: Kocher.JeanPierre@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.