Preclinical pediatric brain tumor models for immunotherapy: Hurdles and a way forward
Bibliographic record
Abstract
Brain tumors are the most common solid tumor in children and the leading cause of cancer-related deaths. Over the last few years, improvements have been made in the diagnosis and treatment of children with Central Nervous System tumors. Unfortunately, for many patients with high-grade tumors, the overall prognosis remains poor. Lower survival rates are partly attributed to the lack of efficacious therapies. The advent and success of immune checkpoint inhibitors (ICIs) in adults have sparked interest in investigating the utility of these therapies alone or in combination with other drug treatments in pediatric patients. However, to achieve improved clinical outcomes, the establishment and selection of relevant and robust preclinical pediatric high-grade brain tumor models is imperative. Here, we review the information that influenced our model selection as we embarked on an international collaborative study to test ICIs in combination with epigenetic modifying agents to enhance adaptive immunity to treat pediatric brain tumors. We also share challenges that we faced and potential solutions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".