Evidence map›Paper›PMID 42642213›Full record

ArticleJournal of medical genetics2026

Rapid minigene workflow for functional reclassification of splicing variants in hereditary cancer diagnostics.

Noemi Calandra, Elisabetta Mereu, Paola Ogliara, Guido Casalis Cavalchini, Daniela Francesca Giachino, Mirko Parasiliti Caprino, Giorgia Gai, Alessandro Mussa, Stefano Vallero, Enrico Grosso and 5 more

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Article in Journal of medical genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Noemi CalandraDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center 'Guido Tarone', University of Turin, Turin, Italy.
Elisabetta MereuDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center 'Guido Tarone', University of Turin, Turin, Italy.
Paola OgliaraDivision of Medical Genetics, Città Della Salute e della Scienza University Hospital, Turin, Italy.
Guido Casalis CavalchiniDivision of Medical Genetics, Città Della Salute e della Scienza University Hospital, Turin, Italy.ORCID http://orcid.org/0000-0001-8519-1923
Daniela Francesca GiachinoUnit of Medical Genetics, San Luigi Gonzaga University Hospital, Orbassano, Italy.
Mirko Parasiliti CaprinoDepartment of Medical Sciences, University of Turin, Turin, Italy.
Giorgia GaiDivision of Medical Genetics, Città Della Salute e della Scienza University Hospital, Turin, Italy.
Alessandro MussaDepartment of Paediatric and Public Health Sciences, University of Turin, Turin, Italy.ORCID http://orcid.org/0000-0003-2795-6013
Stefano ValleroPaediatric Onco-Hematology, Stem Cell Transplantation and Cellular Therapy Division, Regina Margherita Children's Hospital, Turin, Italy.
Enrico GrossoDivision of Medical Genetics, Città Della Salute e della Scienza University Hospital, Turin, Italy.
Andrea ZontaDivision of Medical Genetics, Città Della Salute e della Scienza University Hospital, Turin, Italy.
Franca FagioliDepartment of Paediatric and Public Health Sciences, University of Turin, Turin, Italy.
Matteo RuggiuDepartment of Biological Sciences, Laboratory of RNA Biology & Molecular Neuroscience, St John's University, Queens, New York, USA.
Barbara PasiniDivision of Medical Genetics, Città Della Salute e della Scienza University Hospital, Turin, Italy.
Roberto PivaDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center 'Guido Tarone', University of Turin, Turin, Italy roberto.piva@unito.it.ORCID http://orcid.org/0000-0002-2273-3470

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNext-generation sequencing of cancer predisposition genes is routinely used in hereditary cancer diagnostics. However, a substantial fraction of detected variants remains clinically unresolved. Using a customised 77-gene panel, we analysed 2142 individuals and identified 384 pathogenic or likely pathogenic variants across 54 genes, corresponding to a diagnostic yield of approximately 18%. Despite this, 17% of cases carried variants of uncertain significance, many of which were suspected to affect pre-mRNA splicing and are particularly challenging to interpret due to the limited reliability of in silico predictions and lack of experimental evidence.

methodsTo address this diagnostic gap, we developed a streamlined minigene-based workflow for rapid functional evaluation of splicing variants and applied it retrospectively. The approach relies on synthetic DNA and recombination-based cloning, eliminating the need for patient-derived RNA and enabling efficient construct generation within a clinically compatible timeframe. Computational prioritisation using AlphaGenome was integrated to support variant selection, while experimental assays provided direct evidence of splicing outcomes.

resultsApplication of this strategy allowed the reclassification of previously unresolved variants and clarified cases with discordant computational evidence. Importantly, the workflow is designed for implementation in routine diagnostic settings, with a turnaround time aligned with clinical reporting requirements.

conclusionThis approach provides a robust and scalable framework for functional interpretation of splicing variants, improving diagnostic resolution and supporting more informed clinical decision-making in hereditary cancer genetics.

Indexed as

Genetic Predisposition to DiseaseNeoplasmsRNA SplicingGenetic TestingHigh-Throughput Nucleotide SequencingHumansWorkflowGenetic Predisposition to DiseaseGenetic TechniquesGenetic VariationHuman Genetics

Identifiers

PMID42642213
PMCPMC13638327

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