Variants of Uncertain Significance (VUS) are genetic variants whose association with a disease phenotype has not been established. They are a common finding in sequencing-based genetic tests and pose a significant clinical challenge. The objective of this study was to assess the use of functional data to classify variants according to pathogenicity. We conduct functional analysis of a large set of BRCA1 VUS combining a validated functional assay with VarCall, a Bayesian hierarchical model to estimate the likelihood of pathogenicity given the functional data. The results from the functional assays were incorporated into a joint analysis of 214 BRCA1 VUS to predict their likelihood of pathogenicity (breast cancer). We show that applying the VarCall model (1.0 sensitivity; lower bound of 95% confidence interval (CI) = 0.75 and 1.0 specificity; lower bound of 95% CI = 0.83) to the current set of BRCA1 variants, use of the functional data would significantly reduce the number of VUS associated with the C-terminal region of the BRCA1 protein by ~ 87%. We extend this work developing yeast-based functional assays for two other genes coding for BRCT domain containing proteins, MCPH1 and MDC1. Analysis of missense variants in MCPH1 and MDC1 shows that structural inference based on the BRCA1 data set can aid in prioritising variants for further analysis. Taken together our results indicate that systematic functional assays can provide a robust tool to aid in clinical annotation of VUS. We propose that well-validated functional assays could be used for clinical annotation even in the absence of additional sources of evidence.

BackgroundInactivating germline mutations in the tumour suppressor gene BRCA1 are associated with a significantly increased risk of developing breast and ovarian cancer. A large number (>1500) of unique BRCA1 variants have been identified in the population and can be classified as pathogenic, non-pathogenic or as variants of unknown significance (VUS). Many VUS are rare missense variants leading to single amino acid changes. Their impact on protein function cannot be directly inferred from sequence information, precluding assessment of their pathogenicity. Thus, functional assays are critical to assess the impact of these VUS on protein activity. BRCA1 is a multifunctional protein and different assays have been used to assess the impact of variants on different biochemical activities and biological processes.Methods and resultsTo facilitate VUS analysis, we have developed a visualisation resource that compiles and displays functional data on all documented BRCA1 missense variants. BRCA1 Circos is a web-based visualisation tool based on the freely available Circos software package. The BRCA1 Circos web tool (http://research.nhgri.nih.gov/bic/circos/) aggregates data from all published BRCA1 missense variants for functional studies, harmonises their results and presents various functionalities to search and interpret individual-level functional information for each BRCA1 missense variant.ConclusionsThis research visualisation tool will serve as a quick one-stop publically available reference for all the BRCA1 missense variants that have been functionally assessed. It will facilitate meta-analysis of functional data and improve assessment of pathogenicity of VUS.

The DNA damage response (DDR) involves a complex network of signaling events mediated by modular protein domains such as the BRCT (BRCA1 C-terminal) domain. Thus, proteins that interact with BRCT domains and are a part of the DDR constitute potential targets for sensitization to DNA damaging chemotherapy agents. We performed a pharmacological screen to evaluate seventeen kinases, identified in a BRCT-mediated interaction network as targets to enhance platinum-based chemotherapy in lung cancer. Inhibition of mitotic kinase WEE1 was found to have the most effective response in combination with platinum compounds in lung cancer cell lines. In the BRCT-mediated interaction network, WEE1 was found in complex with PAXIP1, a protein containing six BRCT domains involved in transcription and in the cellular response to DNA damage. We show that PAXIP1 BRCT domains regulate WEE1-mediated phosphorylation of CDK1. Further, ectopic expression of PAXIP1 promotes enhanced caspase 3-mediated apoptosis in cells treated with WEE1 inhibitor AZD1775 (formerly, MK-1775) and cisplatin compared with cells treated with AZD1775 alone. Cell lines and patient-derived xenograft models expressing both PAXIP1 and WEE1 exhibited synergistic effects of AZD1775 and cisplatin. In summary, PAXIP1 is involved in sensitizing lung cancer cells to the WEE1 inhibitor AZD1775 in combination with platinum-based treatment. We propose that WEE1 and PAXIP1 levels may be used as mechanism-based biomarkers of response when WEE1 inhibitor AZD1775 is combined with DNA damaging agents.

Recent technological advances have transformed cancer genetics research. These advances have served as the basis for the generation of a number of richly annotated datasets relevant to the cancer geneticist. In addition, many of these technologies are now within reach of smaller laboratories to answer specific biological questions. Thus, one of the most pressing issues facing an experimental cancer biology research program in genetics is incorporating data from multiple sources to annotate, visualize, and analyze the system under study. Fortunately, there are several computational resources to aid in this process. However, a significant effort is required to adapt a molecular biology-based research program to take advantage of these datasets. Here, we discuss the lessons learned in our laboratory and share several recommendations to make this transition effectively. This article is not meant to be a comprehensive evaluation of all the available resources, but rather highlight those that we have incorporated into our laboratory and how to choose the most appropriate ones for your research program.

<p>Legends for Supplementary Figures and Tables</p>

<p>(A) Experimental design timeline for apoptosis assays. (B) Caspase-3 activity assay in H522 cells (left panel) which express both WEE1 and PAXIP1 and in H1395 cells (right panel) which express PAXIP1 but no WEE1. (C-E) Caspase-3 activity was measured after treatment with AZD1775 at the time points indicated in H322 cells overexpressing full length PAXIP1 (C), or with PAXIP1 knockdown (D), or overexpressing TAP-PAXIP1 tBRCT C2 (E).</p>

<p>(A-B) Lung cancer cell lines were treated with 2.5 μM AZD1775 alone for 72 h and linear correlation (R2) between percentage cell viability and WEE1 and pY15-CDK1 expression was determined. (C) Immunoprecipitation of BRCA1 using a monoclonal antibody. Note that BRCA1 is shown as two light bands (hypo- and hyper phosphorylated forms) and that the antibody preferentially immunoprecipitates the hypo-phosphorylated form (faster migrating band). (D) Control western blots for PAXIP1 antibodies (#370 and #369). (E) Immunoprecipitation of PAXIP1 using #369 antibody and blotted against the #370 antibody. Ectopically expressed TAP-tagged PAXIP1 is also recognized by the #370 antibody. (F) AZD1775 does not disrupt the interaction between PAXIP1 tBRCT C2 and WEE1. (biological replicate of Figure 2E). TAP-tBRCT C2 or a TAP-GFP were ectopically expressed in 293FT cells. Equal amounts of lysates expressing TAP-tBRCT C2 were used to incubate in the presence (1 µM) or absence of AZD1775 for 1 h. Lysates were pulled down with streptavidin beads and blotted against CBP to demonstrate equivalent pull down of the ectopic proteins (bottom panel). Incubation with AZD1775 slightly increased binding of WEE1 to PAXIP1 tBRCT C2 (right panel).</p>

<p>Flow cytometry gating information for Figure 4E.</p>