Long-Term Outcomes and Adverse Events of CAR T-19 Cell Therapy in Relapsed or Refractory B-Cell Acute Lymphoblastic Leukemia - a Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
Background: Acute lymphoblastic leukemia (ALL) is a prevalent cancer affecting both children and adults. Despite advances in chemotherapy and hematopoietic stem cell transplantation (HSCT), many patients with relapsed or refractory (r/r) B-cell acute lymphoblastic leukemia (B-ALL) fail to achieve long-term remission. Chimeric antigen receptor (CAR) T-cell therapy has emerged as a promising treatment modality. This systematic review and meta-analysis aim to evaluate the long-term outcomes and adverse events associated with CAR T-cell therapy in Relapsed or Refractory(r/r) B-ALL patients. Methods: This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in PubMed, Science Direct, and Cochrane Library up to June 2024, using keywords related to CAR T-cell therapy and B-ALL. Inclusion criteria included clinical trials on CAR T-cell therapy for r/r B-ALL, published in English, with at least three patients. Studies focusing on allogeneic CAR T-cells, combination therapies with HSCT or other treatments, and those not reporting on relevant outcomes were excluded. Data extraction and risk of bias assessment were independently performed by three authors using the Newcastle-Ottawa Scale (NOS). Meta-analyses were conducted using Comprehensive Meta-Analysis (CMA) V3 and Review Manager 5.4, with event rates and odds ratios as primary outcomes. Results: Out of 2,659 identified studies, 2,065 were excluded after title and abstract screening. After full-text screening and removal of duplicates, 10 studies were included in the qualitative and quantitative analysis. These studies collectively enrolled 1,367 participants, primarily pediatric and young adults, with a median age of 14.2 years (range 0-30.4). Male participants comprised 54.2% of the population. The most common CAR T-cell therapy targeted CD19, used in 8 studies (80%), followed by anti-CD22 in 1 study (10%) and a combination of CD19/CD22 in 1 study (10%). The pooled analysis demonstrated a high rate of minimal residual disease-negative complete remission (MRD-CR), with an overall event rate of 70% (95% CI: 61% to 78%, I² = 88.35%). Anti-CD19 CAR T-cell therapy showed the highest efficacy with an event rate of 74.75% (95% CI: 61% to 80%, I² = 89.84%). Combination therapies targeting both CD19 and CD22 had an event rate of 69% (95% CI: 53% to 83%, I² = 82.56%). Significant adverse effects were noted, including Cytokine Release Syndrome (CRS) with a mean incidence of 81.8% (95% CI: 76.7% to 86.9%), neurotoxicity at 33.2% (95% CI: 28.1% to 38.3%), and hematologic toxicities at 71.9% (95% CI: 66.4% to 77.4%). Discussion: CAR T-cell therapy, particularly anti-CD19, shows substantial efficacy in achieving MRD-CR in r/r B-ALL patients. The combination of CD19 and CD22 targeting CAR T-cells further enhances efficacy, addressing antigen escape. Despite high efficacy, significant adverse effects, such as CRS, neurotoxicity, and hematologic toxicities, necessitate effective management strategies. The variability in study designs, patient populations, and CAR T-cell constructs among included studies contributed to high heterogeneity. This underscores the need for standardized protocols and large-scale, multi-center trials to validate and extend these findings. Conclusion: CAR T-cell therapy represents a groundbreaking advancement in treating r/r B-ALL, offering high rates of durable remissions. However, the associated significant adverse effects require careful management. Future research should focus on optimizing CAR T-cell constructs, refining treatment protocols, and conducting large-scale, multi-center trials to improve patient outcomes. Continued advancements in CAR T-cell therapy have the potential to transform B-ALL treatment, providing durable remissions and improved survival for patients with limited treatment options.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,019 | 0,044 |
| Bibliométrie | 0,007 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».