CAR T Cell Therapy for Neuroblastoma
Bibliographic record
Abstract
Patients with high risk neuroblastoma have a poor prognosis and survivors are often left with debilitating long term sequelae from treatment. Even after integration of anti-GD2 monoclonal antibody therapy into standard, upftont protocols, 5-year overall survival rates are only about 50%. The success of anti-GD2 therapy has proven that immunotherapy can be effective in neuroblastoma. Adoptive transfer of chimeric antigen receptor (CAR) T cells has the potential to build on this success. In early phase clinical trials, CAR T cell therapy for neuroblastoma has proven safe and feasible, but significant barriers to efficacy remain. These include lack of T cell persistence and potency, difficulty in target identification, and an immunosuppressive tumor microenvironment. With recent advances in CAR T cell engineering, many of these issues are being addressed in the laboratory. In this review, we summarize the clinical trials that have been completed or are underway for CAR T cell therapy in neuroblastoma, discuss the conclusions and open questions derived from these trials, and consider potential strategies to improve CAR T cell therapy for patients with neuroblastoma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".