Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays
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 s...
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BMC
2025-01-01
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| Series: | Human Genomics |
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| Online Access: | https://doi.org/10.1186/s40246-024-00714-5 |
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| author | Cristina Fortuno Inés Llinares-Burguet Daffodil M. Canson Miguel de la Hoya Elena Bueno-Martínez Lara Sanoguera-Miralles Sonsoles Caldes Paul A. James Eladio A. Velasco-Sampedro Amanda B. Spurdle |
| author_facet | Cristina Fortuno Inés Llinares-Burguet Daffodil M. Canson Miguel de la Hoya Elena Bueno-Martínez Lara Sanoguera-Miralles Sonsoles Caldes Paul A. James Eladio A. Velasco-Sampedro Amanda B. Spurdle |
| author_sort | Cristina Fortuno |
| collection | DOAJ |
| description | 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. Graphical abstract |
| format | Article |
| id | doaj-art-ec06ea635fa04ad19f00e30e662d0526 |
| institution | Kabale University |
| issn | 1479-7364 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Human Genomics |
| spelling | doaj-art-ec06ea635fa04ad19f00e30e662d05262025-01-12T12:32:09ZengBMCHuman Genomics1479-73642025-01-0119111510.1186/s40246-024-00714-5Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assaysCristina Fortuno0Inés Llinares-Burguet1Daffodil M. Canson2Miguel de la Hoya3Elena Bueno-Martínez4Lara Sanoguera-Miralles5Sonsoles Caldes6Paul A. James7Eladio A. Velasco-Sampedro8Amanda B. Spurdle9Population Health Program, QIMR Berghofer Medical Research InstituteSplicing and Genetic Susceptibility to Cancer, Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Consejo Superior de Investigaciones Científicas - Universidad de Valladolid (CSIC-UVa)Population Health Program, QIMR Berghofer Medical Research InstituteMolecular Oncology Laboratory, Hospital Clínico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos)Splicing and Genetic Susceptibility to Cancer, Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Consejo Superior de Investigaciones Científicas - Universidad de Valladolid (CSIC-UVa)Splicing and Genetic Susceptibility to Cancer, Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Consejo Superior de Investigaciones Científicas - Universidad de Valladolid (CSIC-UVa)Population Health Program, QIMR Berghofer Medical Research InstituteParkville Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne HospitalSplicing and Genetic Susceptibility to Cancer, Instituto de Biomedicina y Genética Molecular de Valladolid (IBGM), Consejo Superior de Investigaciones Científicas - Universidad de Valladolid (CSIC-UVa)Population Health Program, QIMR Berghofer Medical Research InstituteAbstract 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. Graphical abstracthttps://doi.org/10.1186/s40246-024-00714-5TP53SplicingSpliceAIPVS1VCEP specifications |
| spellingShingle | Cristina Fortuno Inés Llinares-Burguet Daffodil M. Canson Miguel de la Hoya Elena Bueno-Martínez Lara Sanoguera-Miralles Sonsoles Caldes Paul A. James Eladio A. Velasco-Sampedro Amanda B. Spurdle Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays Human Genomics TP53 Splicing SpliceAI PVS1 VCEP specifications |
| title | Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays |
| title_full | Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays |
| title_fullStr | Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays |
| title_full_unstemmed | Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays |
| title_short | Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays |
| title_sort | exploring the role of splicing in tp53 variant pathogenicity through predictions and minigene assays |
| topic | TP53 Splicing SpliceAI PVS1 VCEP specifications |
| url | https://doi.org/10.1186/s40246-024-00714-5 |
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