Bridging the treatment gap in infant medulloblastoma: molecularly informed outcomes of a globally feasible regimen
Bibliographic record
Abstract
BACKGROUND: Infant medulloblastoma represents an enormous challenge in neuro-oncology, due to their simultaneous high-risk of recurrence and high risk of severe neurodevelopmental sequelae with craniospinal irradiation. Currently infant medulloblastoma are treated with intensified protocols, either comprising intraventricular methotrexate or autologous transplant, both of which carry significant morbidity and are not feasible in the majority of the world. We sought to evaluate the molecular predictors of outcome in a cohort of infants homogeneously treated with induction chemotherapy, focal radiation and maintenance chemotherapy. METHODS: In a retrospective analysis, 29 young children treated with a craniospinal irradiation sparing strategy from Hospital Garrahan in Buenos Aires were profiled using Illumina HumanMethylationEPIC arrays, and correlated with survival. RESULTS: Twenty-nine children (range, 0.3-4.6 y) were identified, comprising 17 sonic hedgehog (SHH), 10 Group 3/4, and 2 non-medulloblastomas. Progression-free survival (PFS) across the entire cohort was 0.704 (95% CI: 0.551-0.899). Analysis by t-distributed stochastic neighbor embedding revealed 3 predominant groups, SHHβ, SHHγ, and Group 3. Survival by subtype was highly prognostic with SHHγ having an excellent 5-year PFS of 100% (95% CI: 0.633-1) and SHHβ having a PFS of 0.56 (95% CI: 0.42-1). Group 3 had a PFS of 0.50 (95% CI: 0.25-1). Assessment of neurocognitive outcome was performed in 11 patients; the majority of survivors fell within the low average to mild intellectual disability, with a median IQ of 73.5. CONCLUSIONS: We report a globally feasible and effective strategy avoiding craniospinal radiation in the treatment of infant medulloblastoma, including a robust molecular correlation along with neurocognitive outcomes.
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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 | 0.000 | 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".