Tumor Patterns and Cancer Risk in Carriers of <i>TP53</i> exonic Germline Variants that alter mRNA Splicing
Bibliographic record
Abstract
Abstract Pathogenic germline variants in the TP53 gene cause Li-Fraumeni syndrome (LFS), a highly penetrant cancer predisposition disorder. Most of these variants arise from single-nucleotide variations (SNVs) in TP53 exons, causing missense mutations. However, some of these SNVs may also alter mRNA splicing, defining spliceogenic single nucleotide variants (SE-SNVs) of uncertain clinical significance. We reassessed previously classified TP53 missense variants for spliceogenic effects using SpliceAI predictions, in vitro minigene assays, and transcriptomic data from TCGA. Genotype-phenotype correlations were evaluated using clinical data from carriers of TP53 germline variants across multiple databases and registries. Among 58 identified SE-SNVs, 40 were missense and 18 synonymous. Experimental validation showed that most induce aberrant splicing events, frequently via cryptic splice site activation, leading to frameshift and premature stop codons. Several missense variants previously classified as having mild or low pathogenicity were found instead to have strong spliceogenic effects and were associated with early-onset cancers typical of LFS, suggesting that splicing alterations may override their protein-coding impact. The frequent SNV c.375G>A leading to the synonymous variant p.T125= shows intermediate severity, likely due to partial retention of normal splicing activity. Our study highlights the underestimated pathogenic potential of SE-SNVs affecting the TP53 gene. These findings underscore the importance of integrating splicing predictions, functional assays, and transcript-level analyses into TP53 variant interpretation to improve risk stratification in LFS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".