Abstract 1285: Deep mutagenesis analysis on BRCA1 variants in high throughput DNA repair assays

Abstract

Abstract Genetic screening of Hereditary Breast and Ovarian Cancer (HBOC)-associated genes can aid in prevention of the onset of cancer by increased surveillance or preventative surgery if the genetic information is clear. Often, the genetic information is ambiguous since missense substitutions may be common in aggregate but individually these missense variants occur too infrequently for genetic segregation to be conclusive and are known as Variants of Unknown Significance (VUS). One commonly screened HBOC gene is BRCA1, with mutations found in 40-45% of hereditary breast cancer cases. The BRCA1 protein has the critical tumor suppressive function mediating homology-directed repair (HDR), which is used for the repair of double-stranded breaks (DSB) in DNA. Clinical mutant databases, such as ClinVar, contain the clinical classification of BRCA1, but, unfortunately, in many of these databases, most of the variants are classified as VUS or have conflicting data. We suggest that laboratory-based functional analysis can augment the genetic and clinical data, enabling the understanding of whether a specific variant is benign or pathogenic and predisposing to breast and ovarian cancer. We have developed a high-throughput method for analyzing the function of individual missense variants of BRCA1 in the DNA repair process. The goal is to have functional analysis for every possible amino acid substitution in BRCA1, and this will be publicly available for geneticists to consider when evaluating the likelihood of cancer predisposition an individual with a VUS in BRCA1. We have completed analysis of the amino-terminal RING domain, and we will present new results for the carboxy-terminal BRCT domain. We develop a computational pipeline that starts with sequence data from DNA repair assays to obtain a score for each variant. This pipeline will include statistical programs to evaluate the confidence level for these results, which will then be compared to the information provided in the ClinVar database. In summary, this work describes a computational framework for deconvoluting sequencing results of a high-throughput approach to measure the functional impact of each possible missense variant in the BRCT domain of BRCA1. Citation Format: Mariame Diabate, Alexandrea Adamovich, Tapahsama Banerjee, Michael Freitas, Jeffrey Parvin. Deep mutagenesis analysis on BRCA1 variants in high throughput DNA repair assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1285.