Dataset Information

Beyond DNA: An Integrated and Functional Approach for Classifying Germline Variants in Breast Cancer Genes.

ABSTRACT: Genetic testing for hereditary breast cancer is an integral part of individualized care in the new era of precision medicine. The accuracy of an assay is reliant on not only the technology and bioinformatics analysis utilized but also the experience and infrastructure required to correctly classify genetic variants as disease-causing. Interpreting the clinical significance of germline variants identified by hereditary cancer testing is complex and has a significant impact on the management of patients who are at increased cancer risk. In this review we give an overview of our clinical laboratory's integrated approach to variant assessment. We discuss some of the nuances that should be considered in the assessment of genomic variants. In addition, we highlight lines of evidence such as functional assays and structural analysis that can be useful in the assessment of rare and complex variants.

SUBMITTER: Pesaran T

PROVIDER: S-EPMC5086358 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Beyond DNA: An Integrated and Functional Approach for Classifying Germline Variants in Breast Cancer Genes.

Pesaran T T Karam R R Huether R R Li S S Farber-Katz S S Chamberlin A A Chong H H LaDuca H H Elliott A A

International journal of breast cancer 20161016

Genetic testing for hereditary breast cancer is an integral part of individualized care in the new era of precision medicine. The accuracy of an assay is reliant on not only the technology and bioinformatics analysis utilized but also the experience and infrastructure required to correctly classify genetic variants as disease-causing. Interpreting the clinical significance of germline variants identified by hereditary cancer testing is complex and has a significant impact on the management of pa ...[more]

PMID: 27822389

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Project description:Breast cancer (BC) is the most prevalent cancer in women and a major public health problem in Morocco. Several Moroccan studies have focused on studying this disease, but more are needed, especially at the genetic and molecular levels. Therefore, we investigated the potential association of several functional germline variants in the genes commonly mutated in sporadic breast cancer.In this case-control study, we examined 36 single nucleotide polymorphisms (SNPs) in 13 genes (APOBEC3A, APOBEC3B, ARID1B, ATR, MAP3K1, MLL2, MLL3, NCOR1, RUNX1, SF3B1, SMAD4, TBX3, TTN), which were located in the core promoter, 5'-and 3'UTR or which were nonsynonymous SNPs to assess their potential association with inherited predisposition to breast cancer development. Additionally, we identified a ~29.5-kb deletion polymorphism between APOBEC3A and APOBEC3B and explored possible associations with BC. A total of 226 Moroccan breast cancer cases and 200 matched healthy controls were included in this study.The analysis showed that12 SNPs in 8 driver genes, 4 SNPs in APOBEC3B gene and 1 SNP in APOBEC3A gene were associated with BC risk and/or clinical outcome at P ? 0.05 level. RUNX1_rs8130963 (odds ratio (OR) = 2.25; 95 % CI 1.42-3.56; P = 0.0005; dominant model), TBX3_rs8853 (OR = 2.04; 95 % CI 1.38-3.01; P = 0.0003; dominant model), TBX3_rs1061651 (OR= 2.14; 95 % CI1.43-3.18; P = 0.0002; dominant model), TTN_rs12465459 (OR = 2.02; 95 % confidence interval 1.33-3.07; P = 0.0009; dominant model), were the most significantly associated SNPs with BC risk. A strong association with clinical outcome were detected for the genes SMAD4 _rs3819122 with tumor size (OR = 0.45; 95 % CI 0.25-0.82; P = 0.009) and TTN_rs2244492 with estrogen receptor (OR = 0.45; 95 % CI 0.25-0.82; P = 0.009).Our results suggest that genetic variations in driver and APOBEC3 genes were associated with the risk of BC and may have impact on clinical outcome. However, the reported association between the deletion polymorphism and BC risk was not confirmed in the Moroccan population. These preliminary findings require replication in larger studies.

Dataset Information

Beyond DNA: An Integrated and Functional Approach for Classifying Germline Variants in Breast Cancer Genes.

Publications

Beyond DNA: An Integrated and Functional Approach for Classifying Germline Variants in Breast Cancer Genes.

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