Prevalence and Functional Consequence of <i>TP53</i> Mutations in Pediatric Adrenocortical Carcinoma: A Children's Oncology Group Study
Bibliographic record
Abstract
PURPOSE: Adrenocortical carcinoma (ACC) is a rare pediatric malignancy. It occurs in excess among individuals with the Li-Fraumeni syndrome, which results primarily from germline mutations in the TP53 gene. Prior series exploring frequencies of germline TP53 mutation among children with ACC have been small, geographically limited, or subject to referral bias. The functional consequence of mutations has not been related to phenotype. We provide a genotype-phenotype analysis of TP53 mutations in pediatric ACC and propose a model for tissue-specific effects based on adrenocortical ontogeny. PATIENTS AND METHODS: Eighty-eight consecutive, unrelated children with ACC, unselected for family history, underwent germline TP53 sequencing. Rate and distribution of mutations were identified. Functional analysis was performed for novel TP53 variants. Correlation with the International Agency for Research on Cancer p53 database further delineated mutational distribution, association with family history, and risk for multiple primary malignancies (MPMs). RESULTS: Germline mutations were present in 50% of children. These mutations did not correspond to the conventional hotspot mutations. There was a wide range of mutant protein function. Patients bearing alleles encoding protein with higher functionality were less likely to have a strong family cancer history, whereas those with greater loss of function had MPMs and/or positive family history. In patients with MPMs, ACC was the most frequent initial malignancy. Finally, we demonstrated age-dependent rates of TP53 mutation positivity. CONCLUSION: TP53 mutations are prevalent in children with ACC but decline with age. Mutations result in a broad spectrum of functional loss. Effect of individual mutations may predict carrier and familial disease penetrance with potentially broad implications for clinical surveillance and counseling.
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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 | 0.002 | 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) | 0.001 | 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".