A new bioinformatics tool to help assess the significance of BRCA1 variants

HSP90 buffers deleterious genetic variations in BRCA1

Protein-folding chaperone HSP90 buffers genetic variation in diverse organisms, but the clinical significance of HSP90 buffering in disease remains unclear. Here, we show that HSP90 buffers mutations in the BRCT domain of BRCA1. HSP90-buffered BRCA1 mutations encode protein variants that retain interactions with partner proteins and rely on HSP90 for protein stability and function in cell survival. Moreover, HSP90-buffered BRCA1 variants confer PARP inhibitor resistance in cancer cell lines. Low-level HSP90 inhibition alleviates this resistance, revealing a cryptic and mutant-specific HSP90-contingent synthetic lethality. Hence, by stabilizing metastable variants across the entirety of the BRCT domain, HSP90 reduces the clinical severity of BRCA1 mutations allowing them to accumulate in populations. We estimate that HSP90 buffers 11% to 28% of known human BRCA1- BRCT missense mutations. Our work extends the clinical significance of HSP90 buffering to a prevalent class of variations in BRCA1 , pioneering its importance in cancer predisposition and therapy resistance.

Protein-folding chaperones predict structure-function relationships and cancer risk in BRCA1 mutation carriers

Predicting the risk of cancer mutations is critical for early detection and prevention, but differences in allelic severity of human carriers confound risk predictions. Here, we elucidate protein folding as a cellular mechanism driving differences in mutation severity of tumor suppressor BRCA1. Using a high-throughput protein-protein interaction assay, we show that protein-folding chaperone binding patterns predict the pathogenicity of variants in the BRCA1 C-terminal (BRCT) domain. HSP70 selectively binds 94% of pathogenic BRCA1-BRCT variants, most of which engage HSP70 more than HSP90. Remarkably, the magnitude of HSP70 binding linearly correlates with loss of folding and function. We identify a prevalent class of human hypomorphic BRCA1 variants that bind moderately to chaperones and retain partial folding and function. Furthermore, chaperone binding signifies greater mutation penetrance and earlier cancer onset in the clinic. Our findings demonstrate the utility of chaperones as quantitative cellular biosensors of variant folding, phenotypic severity, and cancer risk.

Protein-Folding Chaperones Predict Structure-Function Relationships and Cancer Risk in BRCA1 Mutation Carriers

Identifying pathogenic mutations and predicting their impact on protein structure, function and phenotype remain major challenges in genome sciences. Protein-folding chaperones participate in structure-function relationships by facilitating the folding of protein variants encoded by mutant genes. Here, we utilize a high-throughput protein-protein interaction assay to test HSP70 and HSP90 chaperone interactions as predictors of pathogenicity for variants in the tumor suppressor BRCA1. Chaperones bind 77% of pathogenic BRCA1-BRCT variants, most of which engaged HSP70 more than HSP90. Remarkably, the magnitude of chaperone binding to variants is proportional to the degree of structural and phenotypic defect induced by BRCA1 mutation. Quantitative chaperone interactions identified BRCA1-BRCT separation-of-function variants and hypomorphic alleles missed by pathogenicity prediction algorithms. Furthermore, increased chaperone binding signified greater cancer risk in human BRCA1 carriers. Altogether, our study showcases the utility of chaperones as quantitative cellular biosensors of variant folding and phenotypic severity.

HIGHLIGHTS

Chaperones detect an abundance of pathogenic folding variants of BRCA1-BRCT.Degree of chaperone binding reflects severity of structural and phenotypic defect.Chaperones identify separation-of-function and hypomorphic variants. Chaperone interactions indicate penetrance and expressivity of BRCA1 alleles.

HTAADVar: Aggregation and fully automated clinical interpretation of genetic variants in heritable thoracic aortic aneurysm and dissection

PURPOSE

Early detection and pathogenicity interpretation of disease-associated variants are crucial but challenging in molecular diagnosis, especially for insidious and life-threatening diseases, such as heritable thoracic aortic aneurysm and dissection (HTAAD). In this study, we developed HTAADVar, an unbiased and fully automated system for the molecular diagnosis of HTAAD.

METHODS

We developed HTAADVar (http://htaadvar.fwgenetics.org) under the American College of Medical Genetics and Genomics/Association for Molecular Pathology framework, with optimizations based on disease- and gene-specific knowledge, expert panel recommendations, and variant observations. HTAADVar provides variant interpretation with a self-built database through the web server and the stand-alone programs.

RESULTS

We constructed an expert-reviewed database by integrating 4373 variants in HTAAD genes, with comprehensive metadata curated from 697 publications and an in-house study of 790 patients. We further developed an interpretation system to assess variants automatically. Notably, HTAADVar showed a multifold increase in performance compared with public tools, reaching a sensitivity of 92.64% and specificity of 70.83%. The molecular diagnostic yield of HTAADVar among 790 patients (42.03%) also matched the clinical data, independently demonstrating its good performance in clinical application.

CONCLUSION

HTAADVar represents the first fully automated system for accurate variant interpretation for HTAAD. The framework of HTAADVar could also be generalized for the molecular diagnosis of other genetic diseases.

Fundamentals to function: Quantitative and scalable approaches for measuring protein stability

Folding a linear chain of amino acids into a three-dimensional protein is a complex physical process that ultimately confers an impressive range of diverse functions. Although recent advances have driven significant progress in predicting three-dimensional protein structures from sequence, proteins are not static molecules. Rather, they exist as complex conformational ensembles defined by energy landscapes spanning the space of sequence and conditions. Quantitatively mapping the physical parameters that dictate these landscapes and protein stability is therefore critical to develop models that are capable of predicting how mutations alter function of proteins in disease and informing the design of proteins with desired functions. Here, we review the approaches that are used to quantify protein stability at a variety of scales, from returning multiple thermodynamic and kinetic measurements for a single protein sequence to yielding indirect insights into folding across a vast sequence space. The physical parameters derived from these approaches will provide a foundation for models that extend beyond the structural prediction to capture the complexity of conformational ensembles and, ultimately, their function.

Structure-based functional analysis of BRCA1 RING domain variants: Concordance of computational mutagenesis, experimental assay, and clinical data

A significant impediment to the improvement of clinical outcomes in treating breast and ovarian cancers rests with the lack of available interpretations for BRCA1 variants of unknown significance. Two research groups recently implemented large-scale functional assays for quantifying effects of single missense mutations on homology-directed DNA repair activity of BRCA1 variants, which is critical for tumor suppression and strongly correlates with cancer risk, and their results are significantly concordant with each other as well as with known pathogenic and benign variant clinical data. In this work, we implemented an established computational mutagenesis procedure to characterize structural impacts of single residue replacements to the BRCA1 RING domain. The computational data showed similarly strong concordance with known clinical data as well as with experimental data from both functional assays. Predictions made by models trained on our computational data offer a complementary and orthogonal approach for classifying all remaining unexplored BRCA1 RING domain variants.

The neXtProt knowledgebase in 2020: data, tools and usability improvements

The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describe the incorporation of three new data sets that provide expression, function, protein-protein binary interaction, post-translational modifications (PTM) and variant information. New SPARQL query examples illustrating uses of the new data were added. neXtProt has continued to develop tools for proteomics. We have improved the peptide uniqueness checker and have implemented a new protein digestion tool. Together, these tools make it possible to determine which proteases can be used to identify trypsin-resistant proteins by mass spectrometry. In terms of usability, we have finished revamping our web interface and completely rewritten our API. Our SPARQL endpoint now supports federated queries. All the neXtProt data are available via our user interface, API, SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally, the data on our FTP site is now CC BY 4.0 to promote its reuse.

Determination of Pathogenicity of Breast Cancer 1 Gene Variants using the American College of Medical Genetics and Genomics and the Association for Molecular Pathology Guidelines

Objectives

Molecular diagnostic laboratories screen for mutations in disease-causing genes in order to confirm a clinical diagnosis. The classification of DNA variants as 'pathogenic' or 'likely pathogenic' mutations creates a workflow bottleneck, which becomes increasingly challenging as greater number of genes are screened. The classification challenge is also acute if there are conflicting reports regarding pathogenicity and differing classification criteria between laboratories. This study aimed to compare two procedures for the classification of variants in the breast cancer (BRCA)1 gene.

Methods

This bioinformatic study was conducted at LabPLUS, Auckland, New Zealand, from February to June 2017. DNA was extracted from peripheral blood samples of 30 patients and gene library construction was carried out using a commercially available targeted panel for the BRCA1 and BRCA2 genes. The genes were subsequently sequenced and the sequence data analysed. The guidelines published by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) provides a comprehensive framework for the interpretation of variants in genes that are associated with Mendelian disorders. The use of these guidelines were compared to the variant classifications that were achieved by reference to those reported in the BRCA Exchange database.

Results

The results showed concordance between the two classification protocols for a panel of 30 BRCA1 gene variants, although the transparency in following the ACMG/AMP guidelines provides a diagnostic laboratory with a generalisable approach that allows laboratory-directed revisions to be undertaken in light of new information.

Conclusion

The ACMG/AMP-based guidelines were applied to a cohort of patients with BRCA1 gene variants. The use of these guidelines provides a system which creates consistency in variant interpretation and supports subsequent clinical management.

Dealing With BRCA1/2 Unclassified Variants in a Cancer Genetics Clinic: Does Cosegregation Analysis Help?

Background: Detection of variants of uncertain significance (VUSs) in BRCA1 and BRCA2 genes poses relevant challenges for counseling and managing patients. VUS carriers should be managed similarly to probands with no BRCA1/2 variants detected, and predictive genetic testing in relatives is discouraged. However, miscomprehension of VUSs is common and can lead to inaccurate risk perception and biased decisions about prophylactic surgery. Therefore, efforts are needed to improve VUS evaluation and communication at an individual level.

Aims: We aimed at investigating whether cosegregation analysis, integrated with a careful review of available functional data and in silico predictions, may improve VUSs interpretation and counseling in individual families.

Methods: Patients with Breast Cancer (BC) and/or Ovarian Cancer (OC) fulfilling established criteria were offered genetic counseling and BRCA1/2 testing; VUSs identified in index cases were checked in other relatives affected by BC/OC whenever possible. As an alternative, if BC/OC clustered only in one branch of the family, the parental origin of the VUS was investigated. Public prediction tools and databases were used to collect additional information on the variants analyzed.

Results: Out of 1045 patients undergoing BRCA1/2 testing in the period October 2011–April 2018, 66 (6.3%) carried class 3 VUSs. Cosegregation analysis was performed for 13 VUSs in 11 kindreds. Seven VUSs (53.8%) did not cosegregate with breast/ovarian cancer in the family, which provided evidence against their role in cancer clustering in those families. Among the 6 cosegregating VUSs, for two (BRCA1 c.5152+2T>G and BRCA2 c.7975A>G) additional evidence exists from databases and in silico tools supporting their pathogenicity, which reinforces the hypothesis they may have had a predisposing effect in respective families. For the remaining four VUSs (31%), cosegregation analysis failed to provide relevant information.

Conclusion: Our findings suggest that cosegregation analysis in a clinical context may be helpful to improve test result interpretation in the specific family and, therefore, should be offered whenever possible. Besides, obtaining and sharing cosegregation data helps gathering evidence that may eventually contribute to VUS classification.