José M Moreno-Cabrera, Lidia Feliubadaló, Marta Pineda, Patricia Prada-Dacasa, Mireia Ramos-Muntada, Jesús Del Valle, Joan Brunet, Bernat Gel, María Currás-Freixes, Bruna Calsina, Milton E Salazar-Hidalgo, Marta Rodríguez-Balada, Bàrbara Roig, Sara Fernández-Castillejo, Mercedes Durán Domínguez, Mónica Arranz Ledo, Mar Infante Sanz, Adela Castillejo, Estela Dámaso, José L Soto, Montserrat de Miguel, Beatriz Hidalgo Calero, José M Sánchez-Zapardiel, Teresa Ramon Y Cajal, Adriana Lasa, Alexandra Gisbert-Beamud, Anael López-Novo, Clara Ruiz-Ponte, Miriam Potrony, María I Álvarez-Mora, Ana Osorio, Isabel Lorda-Sánchez, Mercedes Robledo, Alberto Cascón, Anna Ruiz, Nino Spataro, Imma Hernan, Emma Borràs, Alejandro Moles-Fernández, Julie Earl, Juan Cadiñanos, Ana B Sánchez-Heras, Anna Bigas, Gabriel Capellá, Conxi Lázaro
{"title":"SpadaHC:改进西班牙人口遗传性癌症基因变异分类的数据库。","authors":"José M Moreno-Cabrera, Lidia Feliubadaló, Marta Pineda, Patricia Prada-Dacasa, Mireia Ramos-Muntada, Jesús Del Valle, Joan Brunet, Bernat Gel, María Currás-Freixes, Bruna Calsina, Milton E Salazar-Hidalgo, Marta Rodríguez-Balada, Bàrbara Roig, Sara Fernández-Castillejo, Mercedes Durán Domínguez, Mónica Arranz Ledo, Mar Infante Sanz, Adela Castillejo, Estela Dámaso, José L Soto, Montserrat de Miguel, Beatriz Hidalgo Calero, José M Sánchez-Zapardiel, Teresa Ramon Y Cajal, Adriana Lasa, Alexandra Gisbert-Beamud, Anael López-Novo, Clara Ruiz-Ponte, Miriam Potrony, María I Álvarez-Mora, Ana Osorio, Isabel Lorda-Sánchez, Mercedes Robledo, Alberto Cascón, Anna Ruiz, Nino Spataro, Imma Hernan, Emma Borràs, Alejandro Moles-Fernández, Julie Earl, Juan Cadiñanos, Ana B Sánchez-Heras, Anna Bigas, Gabriel Capellá, Conxi Lázaro","doi":"10.1093/database/baae055","DOIUrl":null,"url":null,"abstract":"<p><p>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. 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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/.