In silico, in vitro and case-control analyses as an effective combination for analyzing BRCA1 and BRCA2 unclassified variants in a population-based sample

BRCA1/2 variants of unknown significance in hereditary breast and ovarian cancer (HBOC) syndrome: looking for the hidden meaning

Hereditary breast and ovarian cancer syndrome is caused by germline mutations in BRCA1/2 genes. These genes are very large and their mutations are heterogeneous and scattered throughout the coding sequence. In addition to the above-mentioned mutations, variants of uncertain/unknown significance (VUSs) have been identified in BRCA genes, which make more difficult the clinical management of the patient and risk assessment. In the last decades, several laboratories have developed different databases that contain more than 2000 variants for the two genes and integrated strategies which include multifactorial prediction models based on direct and indirect genetic evidence, to classify the VUS and attribute them a clinical significance associated with a deleterious, high-low or neutral risk. This review provides a comprehensive overview of literature studies concerning the VUSs, in order to assess their impact on the population and provide new insight for the appropriate patient management in clinical practice.

Prioritizing variants of uncertain significance for reclassification using a rule-based algorithm in inherited retinal dystrophies

Inherited retinal dystrophies (IRD) are a highly heterogeneous group of rare diseases with a molecular diagnostic rate of >50%. Reclassification of variants of uncertain significance (VUS) poses a challenge for IRD diagnosis. We collected 668 IRD cases analyzed by our geneticists using two different clinical exome-sequencing tests. We identified 114 unsolved cases pending reclassification of 125 VUS and studied their genomic, functional, and laboratory-specific features, comparing them to pathogenic and likely pathogenic variants from the same cohort (N = 390). While the clinical exome used did not show differences in diagnostic rate, the more IRD-experienced geneticist reported more VUS (p = 4.07e-04). Significantly fewer VUS were reported in recessive cases (p = 2.14e-04) compared to other inheritance patterns, and of all the genes analyzed, ABCA4 and IMPG2 had the lowest and highest VUS frequencies, respectively (p = 3.89e-04, p = 6.93e-03). Moreover, few frameshift and stop-gain variants were found to be informed VUS (p = 6.73e-08 and p = 2.93e-06). Last, we applied five pathogenicity predictors and found there is a significant proof of deleteriousness when all score for pathogenicity in missense variants. Altogether, these results provided input for a set of rules that correctly reclassified ~70% of VUS as pathogenic in validation datasets. Disease- and setting-specific features influence VUS reporting. Comparison with pathogenic and likely pathogenic variants can prioritize VUS more likely to be reclassified as causal.

A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity

Summary

Assessing the pathogenicity of genetic variants can be a complex and challenging task. Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode non-functional protein products, an important predictor of Mendelian disease risk. However, most variant annotation tools do not adequately assess spliceogenicity outside the native splice site and thus the disease-causing potential of variants in other intronic and exonic regions is often overlooked. Here, we present a plugin for the Ensembl Variant Effect Predictor that packages MaxEntScan and extends its functionality to provide splice site predictions using a maximum entropy model. The plugin incorporates a sliding window algorithm to predict splice site loss or gain for any variant that overlaps a transcript feature. We also demonstrate the utility of the plugin by comparing our predictions to two mRNA splicing datasets containing several cancer-susceptibility genes.

Availability and implementation

Source code is freely available under the Apache License, Version 2.0: https://github.com/Ensembl/VEP_plugins.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of germline pathogenic variants in DNA damage repair genes by a next-generation sequencing multigene panel in BRCAX patients

BACKGROUND

Approximately 5-10% of breast carcinomas have been related to hereditary conditions and are attributable to pathogenic variants in the BRCA1 and BRCA2 genes, which is referred to as hereditary breast and ovarian cancer (HBOC) syndrome. The inclusion of additional genes that can be related to HBOC syndrome is under intense evaluation due to the high proportion of patients with HBOC criteria who do not present pathogenic mutations in BRCA genes, named BRCAX, despite having high clinical suspicion of hereditary cancer. The main aim is to identify new potentially pathogenic gene variants that may contribute to HBOC to improve the efficiency of routine diagnostic tests in this hereditary condition.

METHODS

A retrospective cohort of 77 HBOC BRCAX patients was analyzed by next-generation sequencing using a targeted multigene panel composed of 25 genes related to hereditary cancer and deficiencies in DNA repair pathways.

RESULTS

We found 9 variants in 7 different genes, which were confirmed by automated sequencing. Six variants were classified as pathogenic or likely pathogenic. Three of them were located in the PALB2 gene, one in the BRIP1 gene, one in the BARD1 gene and 1 in the RAD50 gene. In addition, three variants of uncertain significance (VUS) were detected in the TP53, CHEK2, and CDH1 genes.

CONCLUSIONS

We identified that 8% of BRCAX patients were carriers of pathogenic variants in genes other than BRCA1 and BRCA2. Therefore, wide gene panels, including clinically actionable genes, should be routinely used in the screening of HBOC in our population. We observed differences from other studies in the prevalence of mutated genes, most likely due to differences in the selection criteria of the probands and in the population analyzed. The high incidence of deleterious variant detection in PALB2 supports its significant role in breast cancer susceptibility and reinforces its inclusion in the HBOC genetic diagnostic process.

Incorporation of semi-quantitative analysis of splicing alterations for the clinical interpretation of variants in BRCA1 and BRCA2 genes

BRCA1 and BRCA2 (BRCA1/2) genetic variants that disrupt messenger RNA splicing are commonly associated with increased risks of developing breast/ovarian cancer. The majority of splicing studies published to date rely on qualitative methodologies (i.e., Sanger sequencing), but it is necessary to incorporate semi-quantitative or quantitative approaches to accurately interpret the clinical significance of spliceogenic variants. Here, we characterize the splicing impact of 31 BRCA1/2 variants using semi-quantitative capillary electrophoresis of fluorescent amplicons (CE), Sanger sequencing and allele-specific assays. A total of 14 variants were found to disrupt splicing. Allelic-specific assays could be performed for BRCA1 c.302-1G>A and BRCA2 c.516+2T>A, c.1909+1G>A, c.8332-13T>G, c.8332-2A>G, c.8954-2A>T variants, showing a monoallelic contribution to full-length transcript expression that was concordant with semi-quantitative data. The splicing fraction of alternative and aberrant transcripts was also measured by CE, facilitating variant interpretation. Following Evidence-based Network for the Interpretation of Germline Mutant Alleles criteria, we successfully classified eight variants as pathogenic (Class 5), five variants as likely pathogenic (Class 4), and 14 variants as benign (Class 1). We also provide splicing data for four variants classified as uncertain (Class 3), which produced a "leaky" splicing effect or introduced a missense change in the protein sequence, that will require further assessment to determine their clinical significance.

BRCA1/2 missense mutations and the value of in-silico analyses

INTRODUCTION

The clinical implications of genetic variants in BRCA1/2 in healthy and affected individuals are considerable. Variant interpretation, however, is especially challenging for missense variants. The majority of them are classified as variants of unknown clinical significance (VUS). Computational (in-silico) predictive programs are easy to access, but represent only one tool out of a wide range of complemental approaches to classify VUS. With this single-center study, we aimed to evaluate the impact of in-silico analyses in a spectrum of different BRCA1/2 missense variants.

METHODS

We conducted mutation analysis of BRCA1/2 in 523 index patients with suspected hereditary breast and ovarian cancer (HBOC). Classification of the genetic variants was performed according to the German Consortium (GC)-HBOC database. Additionally, all missense variants were classified by the following three in-silico prediction tools: SIFT, Mutation Taster (MT2) and PolyPhen2 (PPH2).

RESULTS

Overall 201 different variants, 68 of which constituted missense variants were ranked as pathogenic, neutral, or unknown. The classification of missense variants by in-silico tools resulted in a higher amount of pathogenic mutations (25% vs. 13.2%) compared to the GC-HBOC-classification. Altogether, more than fifty percent (38/68, 55.9%) of missense variants were ranked differently. Sensitivity of in-silico-tools for mutation prediction was 88.9% (PPH2), 100% (SIFT) and 100% (MT2).

CONCLUSION

We found a relevant discrepancy in variant classification by using in-silico prediction tools, resulting in potential overestimation and/or underestimation of cancer risk. More reliable, notably gene-specific, prediction tools and functional tests are needed to improve clinical counseling.