Immunotherapy of pediatric brain tumor patients should include an immunoprevention strategy: a medical hypothesis paper
Bibliographic record
Abstract
Adults diagnosed with Glioblastoma multiforme (GBM) are frequently faced with a 7% chance of surviving 2 years compared with pediatric patients with GBM who have a 26% survival rate. Our recent screen of possible glioma-associated antigen precursor protein (TAPP) profiles displayed from different types of pediatric brain tumors showed that pediatric patients contained a subset of the tumor antigens displayed by adult GBM patients. Adult GBM possess at least 27 tumor antigens that can potentially stimulate T cell immune responses, suggesting that these tumors are quite antigenic. In contrast, pediatric brain tumors only expressed nine tumor antigens with mRNA levels that were equivalent to those displayed by adult GBM. These tumor-associated antigens could be used as possible targets of therapeutic immunization for pediatric brain cancer patients. Children have developing immune systems that peak at puberty. An immune response mounted by these pediatric patients might account for their extended life spans, even though the pediatric brain tumors express far fewer total tumor-associated antigens. Here we present a hypothesis that pediatric brain tumor patients might be the best patients to show that immunotherapy can be used to successfully treat established cancers. We speculate that immunotherapy should include a panel of tumor antigens that might prevent the out-growth of more malignant tumor cells and thereby prevent the brain tumor relapse. Thus, pediatric brain tumor patients might provide an opportunity to prove the concept of immunoprevention.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".