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

Abstract

Background: Next-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.

Methods: To 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.

Results: Application 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.

Conclusion: This 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.