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. 2025 Jan 8;19(1):2.
doi: 10.1186/s40246-024-00714-5.

Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays

Cristina Fortuno  1 , Inés Llinares-Burguet  2 , Daffodil M Canson  1 , Miguel de la Hoya  3 , Elena Bueno-Martínez  2 , Lara Sanoguera-Miralles  2 , Sonsoles Caldes  1 , Paul A James  4   5 , Eladio A Velasco-Sampedro  2 , Amanda B Spurdle  6   7
Affiliations

Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays

Cristina Fortuno et al. Hum Genomics. .

Abstract

Background: TP53 variant classification benefits from the availability of large-scale functional data for missense variants generated using cDNA-based assays. However, absence of comprehensive splicing assay data for TP53 confounds the classification of the subset of predicted missense and synonymous variants that are also predicted to alter splicing. Our study aimed to generate and apply splicing assay data for a prioritised group of 59 TP53 predicted missense or synonymous variants that are also predicted to affect splicing by either SpliceAI or MaxEntScan.

Methods: We conducted splicing analyses using a minigene construct containing TP53 exons 2 to 9 transfected into human breast cancer SKBR3 cells, and compared results against different splice prediction methods, including correlation with the SpliceAI-10k calculator. We additionally applied the splicing results for TP53 variant classification using an approach consistent with the ClinGen Sequence Variant Interpretation Splicing Subgroup recommendations.

Results: Aberrant transcript profile consistent with loss of function, and for which a PVS1 (RNA) code would be assigned, was observed for 42 (71%) of prioritised variants, of which aberrant transcript expression was over 50% for 26 variants, and over 80% for 15 variants. Data supported the use of SpliceAI ≥ 0.2 cutoff for predicted splicing impact of TP53 variants. Prediction of aberration types using SpliceAI-10k calculator generally aligned with the corresponding assay results, though maximum SpliceAI score did not accurately predict level of aberrant expression. Application of the observed splicing results was used to reclassify 27/59 (46%) test variants as (likely) pathogenic or (likely) benign.

Conclusions: In conclusion, this study enhances the integration of splicing predictions and provides splicing assay data for exonic variants to support TP53 germline classification.

Keywords: PVS1; SpliceAI; Splicing; TP53; VCEP specifications.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This project has been approved by the QIMR Berghofer Medical Research Institute Human Research Ethics Committee (HREC) under QIMR HREC Approval P1051. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Correlation between SpliceAI maximum scores and the expression level of the corresponding PVS1-assigned aberrant transcripts, using individual scores (A) and score ranges (B)
Fig. 2
Fig. 2
Predictive performance of SpliceAI-10k calculator inside or outside the splice region and at different percentage aberrant transcript cutoffs. Data presented for variants inside the splice region includes the splice site dinucleotide variants. A variant having a predicted aberrant transcript that matches with at least one variant-induced transcript in the assay is counted as a concordant observation
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References

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