Nearly Half of <i>TP53</i> Germline Variants Predicted To Be Pathogenic in Patients With Osteosarcoma Are De Novo: A Report From the Children’s Oncology Group
Bibliographic record
Abstract
PURPOSE To ascertain the prevalence of recurrent de novo variants among 240 pediatric patients with osteosarcoma (OS; age < 20 years) unselected for family history of cancer. METHODS The identification of de novo variants was implemented in 2 phases. In the first, we identified genes with a rare (minor allele frequency < 0.01) de novo variant in > 1 of the 95 case-parent trios examined by whole-exome sequencing (WES) who passed quality control measures. In phase 2, 145 additional patients with OS were evaluated by targeted sequencing to identify rare de novo variants in genes nominated from phase 1. Recurrent rare variants identified from phase 1 and 2 were verified as either de novo or inherited by Sanger sequencing of affected patients and their parents. Categorical and continuous data were analyzed using Fisher exact test and t tests, respectively. RESULTS Among 95 case-parent trios who underwent WES, we observed 61 de novo variants in 60 genes among 47 patients, with TP53 identified as the only gene with a pathogenic or likely pathogenic (P/LP) de novo variant in more than one case-parent trio. Among all 240 patients with OS, 13 (5.4%) harbored a P/LP TP53 germline variant, of which 6 (46.2%) were confirmed to be de novo. CONCLUSION Apart from TP53, we did not observe any other recurrent de novo P/LP variants in the case-parent trios, suggesting that new mutations in other genes are not a frequent cause of pediatric OS. That nearly half of P/LP TP53 variants in our sample were de novo suggests universal screening for germline TP53 P/LP variants among pediatric patients with OS should be considered.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".