Recent Advances in Pineoblastoma Research: Molecular Classification, Modelling and Targetable Vulnerabilities
Bibliographic record
Abstract
Pineoblastoma (PB) is a rare yet lethal pediatric brain cancer of the pineal gland, a small endocrine organ that secretes melatonin to regulate the circadian rhythm. For PB patients ≤5 years of age, the overall survival rate is approximately 15%; metastatic PB is incurable. Standard treatment, including surgical resection, radiation, and systemic chemotherapy, improves survival but compromises neurocognitive function. A better understanding of the disease and the generation of preclinical models may enable re-evaluation of previous clinical trials, development of precision therapeutic strategies and improve patient outcome. Over the past 5 years, PB has been recognized to include several major subtypes driven by (i) loss of microRNA processing factors DICER and DROSHA characterized by a relatively good prognosis; (ii) loss of the retinoblastoma tumor suppressor RB1; and (iii) amplification or induction of the cMYC protooncogene, with the latter two subtypes exhibiting exceedingly poor prognosis. Recently, mouse models for the major PB subtypes (RB1-, DICER1- and DROSHA-) except MYC- have been established. This progress, including better understanding of the disease, cell of origin, tumor progression, role of autophagy, and targetable vulnerabilities, holds promise for novel therapeutic strategies to combat each subtype of this lethal childhood malignancy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".