Clinical and pre-clinical utility of genomics in medulloblastoma
Bibliographic record
Abstract
INTRODUCTION: Integrated genomics has significantly advanced our understanding of medulloblastoma heterogeneity. It is now clear that it actually comprises at least four distinct molecular subgroups termed Wnt/Wingless (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4 with stark clinical and biological differences. Areas covered: This paper reviews advances in the classification and risk stratification of medulloblastoma, specifically integrating subgroup with clinical and cytogenetic risk factors, with a summary of the potential to lead to more precise therapies. Moreover, the current state of preclinical modeling is summarized with respect to their utility in generating new treatments and correlation with genomic discoveries. Opportunities and challenges in developing new treatment paradigms are summarized and discussed, specifically new therapies for very high-risk metastatic/MYC-amplified Group 3 and TP53-mutant SHH and reductions in therapy for lower risk groups. Expert commentary: Survival across medulloblastoma has been stagnant for over 30 years, and new treatment paradigms are urgently required. Current therapy significantly over treats a high proportion of patients leaving them with lifelong side effects; while many patients still succumb to their disease. Applying biological advances could improve quality of life for a significant proportion of patients while offering new upfront approaches to the highest risk patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".