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

Minigene-based functional assessment of variants suspected to affect pre-mRNA splicing, using synthetic DNA and recombination-based cloning, without requiring patient-derived RNA.

Next-generation sequencing of cancer predisposition genes is routine, yet a substantial fraction of detected variants remains clinically unresolved. Using a customised 77-gene panel, the authors analysed 2,142 individuals and identified 384 pathogenic or likely pathogenic variants across 54 genes, a diagnostic yield of approximately 18%; 17% of cases carried a variant of uncertain significance, many suspected to affect splicing and hard to interpret given the limited reliability of in silico predictions and the lack of experimental evidence. They developed a streamlined minigene workflow for rapid functional evaluation of such variants, based on synthetic DNA and recombination-based cloning, which removes the need for patient-derived RNA and allows construct generation within a clinically compatible timeframe. Computational prioritisation using AlphaGenome was integrated into variant selection, with the assays providing direct evidence of splicing outcomes. Retrospective application of this strategy allowed reclassification of previously unresolved variants and clarified cases with discordant computational evidence.

Synthesis written by Geno'X. For the full original abstract, please refer to the source publication.

The strength of this setup is not the technique — minigenes are old — but its independence from patient RNA: no need to recall the patient or to work around a degraded transcript. Using AlphaGenome upstream to prioritise which variants to assay is the real operational novelty, and also the weakest point given the absence of sensitivity figures. Without the number of variants tested or the reclassification rate achieved, this remains a convincing feasibility demonstration rather than a measured performance.

Analysis by Dr Thibaut Benquey