Unravelling genetic predisposition to familial breast and ovarian cancer: new susceptibility genes and variant interpretation by in silico approaches

Programa de Doctorat en Biomedicina / Tesi realitzada a l'Institut d'Oncologia Vall d’Hebron (VHIO)Patients with hereditary breast and ovarian cancer (HBOC) in whom a causative pathogenic variant is not identified after genetic analysis may not benefit from prevention, early detection, or precision treatment measures. This negative or inconclusive results are due, among other causes, to the detection of variants of uncertain significance (VUS).The main objective of this thesis is to increase the capacity of genetic diagnosis of patients with HBOC, by focusing on i) the optimisation in the interpretation of exonic and intronic variants that might affect RNA quality or quantity but remain as variants of uncertain significance (VUS) and ii) the identification of new susceptibility genes for HBOC. The article included in this thesis, Moles-Fernández et al., 2018 (DOI: 10.3389/fgene.2018.00366) explains an optimization in the identification of potentially spliceogenic variants located near to splicing sites, and provides recommendations to use for analysing donor and acceptor sites. Moreover, the creation or activation of cryptic sites along deep intronic regions could alter splicing causing the inclusion of intronic sequences in RNA. In the article, Moles-Fernández et al., 2021 (DOI: 10.3390/cancers13133341), a framework for the identification of deep intronic spliceogenic is provided, after the performance analysis of SpliceAI in silico tool in a dataset of spliceogenic and non-spliceogenic deep intronic variants. In addition, the importance of the splicing regulatory elements balance in the pseudoexon creation is described. The American College of Medical Genetics (ACMG) variant interpretation guidelines provide general recommendations to classify variants. In the included article Feliubadalò et al., 2021 (DOI: 10.1093/clinchem/hvaa250), ACMG guidelines were adapted to ATM gene. We focused on in silico splicing evidence (PP3/BP4). After reclassification of variants following the adapted guidelines, a reduction of VUS was obtained. On the other hand, in patients without pathogenic variants identified in HBOC related genes, the phenotype could be due to deleterious variants in genes still not known associated with the disease. For this reason, in Moles-Fernández et al., (article in preparation), the aim was to identify candidate genes through exomes and extended panel analysis and validate their risk association by performing a case-control study. The significant identification of loss-of-function variants in ALKBH3, BLM, CAMKK1, FANCD2, FANCM, NEIL3, PER1, RBL1, RECQL4, WRN and XRCC4 genes in patients with HBOC suggests that they might be breast/ovarian cancer susceptibility genes

A New Set of in Silico Tools to Support the Interpretation of ATM Missense Variants Using Graphical Analysis

Establishing the pathogenic nature of variants in ATM, a gene associated with breast cancer and other hereditary cancers, is crucial for providing patients with adequate care. Unfortunately, achieving good variant classification is still difficult. To address this challenge, we extended the range of in silico tools with a series of graphical tools devised for the analysis of computational evidence by health care professionals. We propose a family of fast and easy-to-use graphical representations in which the impact of a variant is considered relative to other pathogenic and benign variants. To illustrate their value, the representations are applied to three problems in variant interpretation. The assessment of computational pathogenicity predictions showed that the graphics provide an intuitive view of pre-diction reliability, complementing and extending conventional numerical reliability indexes. When applied to variant of unknown significance populations, the representations shed light on the nature of these variants and can be used to prioritize variants of unknown significance for further studies. In a third application, the graphics were used to compare the two versions of the ATM-adapted American College of Medical Genetics and Genomics and Association for Molecular Pathology guidelines, obtaining valuable information on their relative virtues and weaknesses. Finally, a server [ATMision (ATM missense in silico interpretation online)] was generated for users to apply these representations in their variant interpretation problems, to check the ATM-adapted guidelines' criteria for computational evidence on their variant(s) and access different sources of information. (J Mol Diagn 2024, 26: 17-28; https://doi.org/10.1016/j.jmoldx.2023.09.009

A Collaborative Effort to Define Classification Criteria for ATM Variants in Hereditary Cancer Patients

Background Gene panel testing by massive parallel sequencing has increased the diagnostic yield but also the number of variants of uncertain significance. Clinical interpretation of genomic data requires expertise for each gene and disease. Heterozygous ATM pathogenic variants increase the risk of cancer, particularly breast cancer. For this reason, ATM is included in most hereditary cancer panels. It is a large gene, showing a high number of variants, most of them of uncertain significance. Hence, we initiated a collaborative effort to improve and standardize variant classification for the ATM gene. Methods Six independent laboratories collected information from 766 ATM variant carriers harboring 283 different variants. Data were submitted in a consensus template form, variant nomenclature and clinical information were curated, and monthly team conferences were established to review and adapt American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) criteria to ATM, which were used to classify 50 representative variants. Results Amid 283 different variants, 99 appeared more than once, 35 had differences in classification among laboratories. Refinement of ACMG/AMP criteria to ATM involved specification for twenty-one criteria and adjustment of strength for fourteen others. Afterwards, 50 variants carried by 254 index cases were classified with the established framework resulting in a consensus classification for all of them and a reduction in the number of variants of uncertain significance from 58% to 42%. Conclusions Our results highlight the relevance of data sharing and data curation by multidisciplinary experts to achieve improved variant classification that will eventually improve clinical management.FEDER funds-a way to build Europe PI19/00553 PI16/00563 PI16/01898 SAF2015-68016-RGeneralitat de Catalunya 2017SGR1282 2017SGR496CERCA Program: Government of CataloniaXunta de GaliciaInstituto de Salud Carlos III. AES PI19/00340Spanish Government SAF2016-80255-REuropean Commission EFA086/15Instituto de Salud Carlos III European Commissio

Computational tools for splicing defect prediction in breast/ovarian cancer genes: how efficient are they at predicting RNA alterations?

In silico tools for splicing defect prediction have a key role to assess the impact of variants of uncertain significance. Our aim was to evaluate the performance of a set of commonly used splicing in silico tools comparing the predictions against RNA in vitro results. This was done for natural splice sites of clinically relevant genes in hereditary breast/ovarian cancer (HBOC) and Lynch syndrome. A study divided into two stages was used to evaluate SSF-like, MaxEntScan, NNSplice, HSF, SPANR, and dbscSNV tools. A discovery dataset of 99 variants with unequivocal results of RNA in vitro studies, located in the 10 exonic and 20 intronic nucleotides adjacent to exon-intron boundaries of BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, ATM, BRIP1, CDH1, PALB2, PTEN, RAD51D, STK11, and TP53, was collected from four Spanish cancer genetic laboratories. The best stand-alone predictors or combinations were validated with a set of 346 variants in the same genes with clear splicing outcomes reported in the literature. Sensitivity, specificity, accuracy, negative predictive value (NPV) and Mathews Coefficient Correlation (MCC) scores were used to measure the performance. The discovery stage showed that HSF and SSF-like were the most accurate for variants at the donor and acceptor region, respectively. The further combination analysis revealed that HSF, HSF+SSF-like or HSF+SSF-like+MES achieved a high performance for predicting the disruption of donor sites, and SSF-like or a sequential combination of MES and SSF-like for predicting disruption of acceptor sites. The performance confirmation of these last results with the validation dataset, indicated that the highest sensitivity, accuracy, and NPV (99.44%, 99.44%, and 96.88, respectively) were attained with HSF+SSF-like or HSF+SSF-like+MES for donor sites and SSF-like (92.63%, 92.65%, and 84.44, respectively) for acceptor sites. We provide recommendations for combining algorithms to conduct in silico splicing analysis that achieved a high performance. The high NPV obtained allows to select the variants in which the study by in vitro RNA analysis is mandatory against those with a negligible probability of being spliceogenic. Our study also shows that the performance of each specific predictor varies depending on whether the natural splicing sites are donors or acceptors

Evaluation of the association of heterozygous germline variants in NTHL1 with breast cancer predisposition: an international multi-center study of 47,180 subjects.

Bi-allelic loss-of-function (LoF) variants in the base excision repair (BER) gene NTHL1 cause a high-risk hereditary multi-tumor syndrome that includes breast cancer, but the contribution of heterozygous variants to hereditary breast cancer is unknown. An analysis of 4985 women with breast cancer, enriched for familial features, and 4786 cancer-free women revealed significant enrichment for NTHL1 LoF variants. Immunohistochemistry confirmed reduced NTHL1 expression in tumors from heterozygous carriers but the NTHL1 bi-allelic loss characteristic mutational signature (SBS 30) was not present. The analysis was extended to 27,421 breast cancer cases and 19,759 controls from 10 international studies revealing 138 cases and 93 controls with a heterozygous LoF variant (OR 1.06, 95% CI: 0.82-1.39) and 316 cases and 179 controls with a missense variant (OR 1.31, 95% CI: 1.09-1.57). Missense variants selected for deleterious features by a number of in silico bioinformatic prediction tools or located within the endonuclease III functional domain showed a stronger association with breast cancer. Somatic sequencing of breast cancers from carriers indicated that the risk associated with NTHL1 appears to operate through haploinsufficiency, consistent with other described low-penetrance breast cancer genes. Data from this very large international multicenter study suggests that heterozygous pathogenic germline coding variants in NTHL1 may be associated with low- to moderate- increased risk of breast cancer

BRCA1 and BRCA2 5′ noncoding region variants identified in breast cancer patients alter promoter activity and protein binding

© 2018 The Authors. Human Mutation published by Wiley Periodicals, Inc. The widespread use of next generation sequencing for clinical testing is detecting an escalating number of variants in noncoding regions of the genome. The clinical significance of the majority of these variants is currently unknown, which presents a significant clinical challenge. We have screened over 6,000 early-onset and/or familial breast cancer (BC) cases collected by the ENIGMA consortium for sequence variants in the 5′ noncoding regions of BC susceptibility genes BRCA1 and BRCA2, and identified 141 rare variants with global minor allele frequency \u3c 0.01, 76 of which have not been reported previously. Bioinformatic analysis identified a set of 21 variants most likely to impact transcriptional regulation, and luciferase reporter assays detected altered promoter activity for four of these variants. Electrophoretic mobility shift assays demonstrated that three of these altered the binding of proteins to the respective BRCA1 or BRCA2 promoter regions, including NFYA binding to BRCA1:c.-287C\u3eT and PAX5 binding to BRCA2:c.-296C\u3eT. Clinical classification of variants affecting promoter activity, using existing prediction models, found no evidence to suggest that these variants confer a high risk of disease. Further studies are required to determine if such variation may be associated with a moderate or low risk of BC

SpadaHC: a database to improve the classification of variants in hereditary cancer genes in the Spanish population

Accurate classification of genetic variants is crucial for clinical decision-making in hereditary cancer. In Spain, genetic diagnostic laboratories have traditionally approached this task independently due to the lack of a dedicated resource. Here we present SpadaHC, a web-based database for sharing variants in hereditary cancer genes in the Spanish population. SpadaHC is implemented using a three-tier architecture consisting of a relational database, a web tool and a bioinformatics pipeline. Contributing laboratories can share variant classifications and variants from individuals in Variant Calling Format (VCF) format. The platform supports open and restricted access, flexible dataset submissions, automatic pseudo-anonymization, VCF quality control, variant normalization and liftover between genome builds. Users can flexibly explore and search data, receive automatic discrepancy notifications and access SpadaHC population frequencies based on many criteria. In February 2024, SpadaHC included 18 laboratory members, storing 1.17 million variants from 4306 patients and 16 343 laboratory classifications. In the first analysis of the shared data, we identified 84 genetic variants with clinically relevant discrepancies in their classifications and addressed them through a three-phase resolution strategy. This work highlights the importance of data sharing to promote consistency in variant classifications among laboratories, so patients and family members can benefit from more accurate clinical management.Database URL: https://spadahc.ciberisciii.es/ Overview of SpadaHC and its main views. (A) List of existing variants in SpadaHC (in the image, search for the ATM gene). The 'Expert Cl.' column shows the classification made by a group of experts; the 'Lab Cl.' column shows a summary of the classifications made by the laboratories. (B) Allele frequency of a variant in the SpadaHC population according to clinical suspicion and sex. (C) Classifications provided by the laboratories for a variant. (D) List of patients carrying a variant. (E) Histogram showing the coverage and frequency (allele balance) with which the variant was detected in carrier patients. Alt text: SpadaHC overview; laboratories can share datasets of variant classifications (Excel) and variants from individuals (VCFs + Excel). The datasets undergo quality control, bioinformatics pipeline annotation and database integration before being displayed in SpadaHC. The graphical abstract also shows five views of SpadaHC

Computational Tools for Splicing Defect Prediction in Breast/Ovarian Cancer Genes: How Efficient Are They at Predicting RNA Alterations?

In silico tools for splicing defect prediction have a key role to assess the impact of variants of uncertain significance. Our aim was to evaluate the performance of a set of commonly used splicing in silico tools comparing the predictions against RNA in vitro results. This was done for natural splice sites of clinically relevant genes in hereditary breast/ovarian cancer (HBOC) and Lynch syndrome. A study divided into two stages was used to evaluate SSF-like, MaxEntScan, NNSplice, HSF, SPANR, and dbscSNV tools. A discovery dataset of 99 variants with unequivocal results of RNA in vitro studies, located in the 10 exonic and 20 intronic nucleotides adjacent to exon–intron boundaries of BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, ATM, BRIP1, CDH1, PALB2, PTEN, RAD51D, STK11, and TP53, was collected from four Spanish cancer genetic laboratories. The best stand-alone predictors or combinations were validated with a set of 346 variants in the same genes with clear splicing outcomes reported in the literature. Sensitivity, specificity, accuracy, negative predictive value (NPV) and Mathews Coefficient Correlation (MCC) scores were used to measure the performance. The discovery stage showed that HSF and SSF-like were the most accurate for variants at the donor and acceptor region, respectively. The further combination analysis revealed that HSF, HSF+SSF-like or HSF+SSF-like+MES achieved a high performance for predicting the disruption of donor sites, and SSF-like or a sequential combination of MES and SSF-like for predicting disruption of acceptor sites. The performance confirmation of these last results with the validation dataset, indicated that the highest sensitivity, accuracy, and NPV (99.44%, 99.44%, and 96.88, respectively) were attained with HSF+SSF-like or HSF+SSF-like+MES for donor sites and SSF-like (92.63%, 92.65%, and 84.44, respectively) for acceptor sites.We provide recommendations for combining algorithms to conduct in silico splicing analysis that achieved a high performance. The high NPV obtained allows to select the variants in which the study by in vitro RNA analysis is mandatory against those with a negligible probability of being spliceogenic. Our study also shows that the performance of each specific predictor varies depending on whether the natural splicing sites are donors or acceptors

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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