Efficacy and Safety of CAR T-cell Therapy in Relapsed/Refractory B-cell Acute Lymphoblastic Leukemia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Introduction Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, with rising global incidence. The prognosis for patients with relapsed or refractory B-cell ALL (r/r B-ALL) remains poor, necessitating novel therapies. Chimeric Antigen Receptor T-cell (CAR T-cell) therapy has shown promise in treating r/r B-ALL, offering significant improvements in remission rates. Methods A comprehensive literature search was conducted across PubMed, Embase, and Cochrane Library for studies evaluating CAR T-cell therapy in r/r B-ALL. Randomized controlled trials, cohort, and case-control studies were included, focusing on efficacy and safety outcomes. Data were extracted and pooled using random-effects models. The risk of bias was assessed with the New Ottawa Scale. Results A total of 32 studies were included, involving 1,55,365 patients. The pooled relapse rate was 0.39 (95% CI: 0.29–0.49), with no significant difference between CD19 and CD22 CAR T-cell therapies (p = 0.88). Co-stimulatory agents like 4-1BB showed the most favorable relapse rate of 0.38 (95% CI: 0.27–0.49). The overall Cytokine Release Syndrome (CRS) rate was 0.63 (95% CI: 0.53–0.73), and neurotoxicity occurred at a rate of 0.32 (95% CI: 0.24–0.41). Conclusion CAR T-cell therapy is effective in treating r/r B-ALL, with high remission rates and manageable adverse events. The choice of co-stimulatory agent and antigen target influences relapse outcomes. Further research is needed to refine CAR T-cell constructs and optimize patient-specific treatments.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".