Universal Poor Survival in Children With Medulloblastoma Harboring Somatic <i>TP53</i> Mutations
Bibliographic record
Abstract
PURPOSE: Medulloblastoma is the prototype of treatment success in modern pediatric neuro-oncology. Unfortunately, 20% to 30% of tumors recur despite maximal resection and multimodal therapy. Multiple biologic prognostic markers have been investigated to predict recurrences, but controversy remains regarding their clinical utility. Because p53 immunopositivity is an adverse prognostic marker in pediatric medulloblastoma and TP53 mutations are associated with chemotherapy and radiation therapy resistance, we aimed to determine the extent and role of TP53 mutations in pediatric medulloblastoma treatment failure. PATIENTS AND METHODS: One hundred eight of 111 consecutive patients diagnosed with medulloblastoma in our institution from 1995 to 2007 were included. Median follow-up time was 5.3 years in survivors. All samples were immunostained for p53 and erbB-2. Histologic grade and immunostaining were scored by two blinded reviewers. For 49 patients, frozen material was available for TP53 sequencing. The main outcome measures were overall and progression-free survival. RESULTS: Sixteen percent of sequenced medulloblastomas harbored a TP53 mutation. As a screening test, p53 immunohistochemistry was 100% sensitive and 83% specific for a TP53 mutation. Strikingly, all mutated tumors recurred early, and 5-year survival for average-risk patients was 0% for TP53-mutated medulloblastoma compared with 74% +/- 8% for wild-type medulloblastoma (P < .0001). Furthermore, 75% of recurrences in average-risk patients were associated with TP53 mutations. On multivariate analysis, TP53 mutation status was the strongest adverse prognostic factor (hazard ratio = 10.4, P = .003). CONCLUSION: Lack of long-term survival in TP53-mutated medulloblastomas highlights the role of TP53 mutations in medulloblastoma resistance to conventional therapies and the need for alternative treatments, and prospective validation of these findings is needed.
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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 | 0.000 | 0.000 |
| 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".