Integración de ómicas para la identificación de nuevos genes de susceptibilidad hereditaria a cáncer colorrectal

El cáncer colorrectal es una de las neoplasias con mayor incidencia y mortalidad a nivel mundial. La heredabilidad de esta enfermedad se estima en hasta un 35%; sin embargo, las estrategias empleadas hasta el momento solo han conseguido explicar un pequeño porcentaje de esta. En este trabajo de Tesis Doctoral se integran datos de ADN, ARN y epigenoma, a nivel germinal y tumoral, de pacientes enriquecidos genéticamente por un diagnóstico temprano de cáncer colorrectal. La identificación de nuevos genes candidatos de susceptibilidad a esta neoplasia contribuirá a explicar parte del riesgo heredado a desarrollar la enfermedad, aumentando el conocimiento sobre las bases hereditarias de cáncer colorrectal

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

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