Adoptive Cellular Therapies in Pediatric Leukemia Patients After Allogeneic-Hematopoietic Stem Cell Transplants
Bibliographic record
Abstract
Allogeneic hematopoietic stem cell transplantation (allo-HSCT) provides a curative potential for high-risk patients with leukemia following first-line therapies, driven by potent immune cell-dependent anti-tumour activities. Although deep remission can be achieved, many patients relapse after allo-HSCT, and further treatment options are scarce. Given the potent immune cell-mediated anti-leukemic effects of allo-HSCT, adoptive cellular therapies (ACTs) have been explored as an adjunctive therapy to enhance the efficacy of allo-HSCT or to treat patients who relapse after allo-HSCT. Interestingly, evidence suggests a stratified therapeutic approach is warranted between pediatric and adult leukemic cases, due to differences in genetic mutations and treatment tolerability. However, pediatric-specific investigations are limited, especially in the cellular therapeutic landscape to treat relapse after allo-HSCT. Known severe toxicities attributed to ACTs need to be addressed for this younger population to ensure prolonged quality of life. This review summarizes the current landscape of ACTs, including donor lymphocyte infusion, chimeric Ag receptor-T cell, NK cell, and double-negative T cell therapies, for treating pediatric leukemia post allo-HSCT, highlighting efficacy, safety, and gaps in pediatric-specific data to guide future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".