MétaCan
Menu
Retour à la cohorte
Enregistrement W4405038423 · doi:10.1182/blood-2024-200937

Matching-Adjusted Indirect Comparison (MAIC) of Lisocabtagene Maraleucel (liso-cel) Versus Axicabtagene Ciloleucel (axi-cel) for Second-Line (2L) Treatment of Patients (pts) with Refractory/Early Relapsed (R/R) Large B-Cell Lymphoma (LBCL): Update with 34 Months of Liso-Cel Follow-up

2024· article· en· W4405038423 sur OpenAlexaff
Jeremy S. Abramson, Manali Kamdar, Fei Fei Liu, Alessandro Crotta, Alessandro Previtali, S. Klijn, Pearl Wang, Yixie Zhang, Ashley Bonner, Matthew A. Lunning

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueCAR-T cell therapy research
Établissements canadiensEVERSANA (Canada)
Organismes subventionnairesnon disponible
Mots-clésRefractory (planetary science)MedicineInternal medicineSecond line treatmentNuclear medicineGastroenterologyOncologyPhysicsChemotherapy

Résumé

récupéré en direct d'OpenAlex

Background: Two CAR T cell therapies, liso-cel and axi-cel, demonstrated superior efficacy over salvage chemotherapy and autologous transplant as 2L therapy in transplant-intended pts with high-risk R/R LBCL, yet no head-to-head comparisons have been performed. A previous MAIC in the 2L setting with a median follow-up of 17.5 mo for liso-cel and 24.9 mo for axi-cel showed comparable efficacy and more favorable safety outcomes for liso-cel with lower rates of all-grade and grade ≥ 3 cytokine release syndrome (CRS) and neurological events (NEs) (Abramson JS, et al. Blood 2022). Here, we present updated results with long-term follow-up for liso-cel and axi-cel. Methods: MAICs were used to estimate population-adjusted relative treatment effects associated with liso-cel for event-free survival (EFS), PFS, ORR, and CR rate (TRANSFORM; NCT03575351; N = 184; data cutoff date: October 2023) vs axi-cel (ZUMA-7; NCT03391466; N = 359; data cutoff date: January 2023) and safety (TRANSFORM, n = 183; ZUMA-7, n = 338). Pts were excluded from the TRANSFORM data set if they did not meet ZUMA-7 eligibility criteria (ie, matching). Individual pt data (IPD) for pts remaining in the TRANSFORM data set were weighted using a method-of-moments propensity score model to match the marginal distribution (ie, mean, variance) of clinical factors among pts from ZUMA-7 (ie, adjustment). Baseline characteristics and outcome measures were revised to align with those defined in ZUMA-7. Efficacy comparisons were anchored through the common comparator, standard of care (SOC; with similar protocol-defined salvage chemotherapy regimens in both trials, followed by high-dose chemotherapy and autologous transplant in responders). Hazard ratios (HRs) were used to compare time-to-event outcomes (EFS, PFS), and odds ratios were used to compare binary outcomes (ORR, CR rate, safety). Selection and rank ordering of the treatment effect modifiers were guided by analysis of the TRANSFORM IPD and clinical experts. Factors to match (ie, pts from TRANSFORM were removed) and adjust (ie, pts from TRANSFORM were reweighted) for efficacy and safety comparisons were reported previously (Abramson JS, et al. Blood 2022). Safety comparisons were unanchored due to the absence of CAR T cell-associated toxicities in the SOC arms. Bridging chemotherapy was allowed in TRANSFORM but not in ZUMA-7; it was not possible to adjust for this factor given sample size constraints. Results: Median study follow-up time was 33.9 mo for liso-cel and 47.2 mo for axi-cel. Efficacy outcomes were comparable between therapies in the unmatched/unadjusted comparison. For liso-cel vs axi-cel, respectively, median (95% CI) EFS was 29.5 mo (9.5‒not reached [NR]) vs 8.3 mo (4.5‒15.8) with HR (95% CI) of 0.94 (0.60‒1.46), and median (95% CI) PFS was 29.5 mo (10.3‒NR) vs 14.7 mo (5.4‒43.5) with HR of 0.90 (95% CI, 0.56‒1.47). Median ORR was 87% vs 83% with odds ratio (95% CI) of 1.41 (0.58‒3.40), and CR rate was 74% vs 65% with odds ratio (95% CI) of 0.95 (0.44‒2.03). After matching with ZUMA-7 for pt eligibility, the TRANSFORM sample size was 158; matching and adjusting for the selected effect modifiers resulted in an effective sample size of 80 for the primary efficacy scenario comparisons (sample size for ZUMA-7 and median efficacy values for axi-cel were unchanged). After matching and adjustment, efficacy outcomes remained comparable between therapies. Median (95% CI) EFS for liso-cel was NR (6.21‒NR) with HR (95% CI) of 0.75 (0.43‒1.33), and median (95% CI) PFS was NR (9.4-NR) with HR (95% CI) of 0.68 (0.37‒1.23). ORR was 85% with odds ratio (95% CI) of 1.63 (0.60‒4.44), and CR rate was 68% with odds ratio (95% CI) of 0.94 (0.40‒2.22). For safety, MAIC results demonstrated lower odds ratios (95% CI) of grade ≥ 3 serious treatment-emergent adverse events (TEAEs; 0.49 [0.27‒0.90]), CRS (any grade, 0.09 [0.04‒0.18]; grade ≥ 3, 0.09 [0.01‒0.75]), and NEs (any grade, 0.08 [0.03‒0.18]; grade ≥ 3, 0.21 [0.06‒0.68]) for liso-cel vs axi-cel. Conclusions: Results from this updated MAIC of liso-cel and axi-cel for the 2L treatment of R/R LBCL showed comparable efficacy, with more favorable safety outcomes for liso-cel. Liso-cel demonstrated a better safety profile with lower rates of grade ≥ 3 serious TEAEs and lower rates of all-grade and grade ≥ 3 CRS and NEs compared with axi-cel.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,034

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,011
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0030,007
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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.

Tête enseignante Opus0,027
Tête enseignante GPT0,281
Écart entre enseignants0,254 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBloodMême sujetCAR-T cell therapy researchTravaux en français237 207