SpadaHC is a collaborative initiative aimed at improving the diagnosis of hereditary cancer cases through data sharing within a secure environment. This platform enables the development of new research projects and fosters network-based collaboration. New research projects can be submitted at any time. To apply, please complete the application form and send it to our contact email. You can download the form here:
The proposal will be reviewed within a maximum of one month by the steering committee, which will make the final decision through a simple majority vote based on predefined selection criteria. Within a maximum of 7 working days after approval, the applicant will be notified of the decision via email. A 15-day period will be granted for appeals or revisions, which will be forwarded to the steering committee for resolution within one month.
A project supervisor will be assigned to each approved study, responsible for submitting progress reports to the steering committee every 6 or 12 months. Selected applicants must register as platform users to conduct their research projects. Additionally, upon approval of a new project, they must sign and accept the terms of the data transfer agreement (DTA) between the researcher and SpadaHC coordinators on behalf of the registry.
Beyond the mentioned application process, both registered and non-registered users, whether national or international, can develop independent projects based on SpadaHC data and publish resulting scientific communications following authorship regulations.
Below, you can explore all the projects, both completed and ongoing, that have been conducted using SpadaHC. If you would like more details about any project or are interested in contributing, feel free to reach out via email.
Title: National Multicenter Collaborative Strategy to Improve Variant Classification in DNA Mismatch Repair Genes
Abstract: The identification of germline pathogenic variants in DNA mismatch repair (MMR) genes enables the diagnosis of Lynch syndrome and constitutional mismatch repair deficiency (CMMRD). This project aims to establish a consensus on the classification of variants of uncertain significance (VUS) or discordantly classified variants in these genes, facilitating a more accurate diagnosis for these cases.
Coordinators: Dr. Marta Pineda, Dr. Clara Ruiz, Dr. Pilar Garre
Publications or Relevant Links: N/A
Status: Open (since 2024)
Title: Dataset for validation of ML model to predict genomic variant pathogenicity
Abstract: A key challenge in precision medicine is the identification of variants of unknown significance (VUS), which lack clinical applicability. IMPaCT-VUSCan was created to address this by integrating AI and in silico approaches to improve variant classification. A machine learning (ML) model was developed to predict variant pathogenicity, aiding genetic diagnosis and supporting precision oncology within the National Health System. The project aims to reclassify uncertain variants and improve hereditary cancer diagnosis by validating an ML model with new classified variants outside ClinVar.
Coordinators: Dr. Fátima Al-Shahrour
Publications or Relevant Links: N/A
Status: Open (since 2024)
Title: Resolving Variant Classification Discrepancies in Hereditary Cancer Genes
Abstract: This project addressed 84 classification discrepancies recorded in SpadaHC. Employing a systematic three-phase review process based on a two-tier classification model, our strategy achieved a cost-effective balance by minimizing the number of laboratories involved at each stage. At the conclusion of every phase, all participating laboratories resubmitted their updated classifications along with detailed rationale, allowing for the resolution of residual discrepancies and enhancing the overall consistency and clinical relevance of the database.
Coordinators: Dr. Conxi Lázaro, Dr. Gabriel Capellá, The SpadaHC Consortium
Publications or Relevant Links: https://doi.org/10.1093/database/baae055
Status: Finished (2022-2023)