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Enregistrement W4417017792 · doi:10.1182/blood-2025-133

Efficacy and safety of frail adults treated with ciltacabtagene autoleucel in the real-world: A CIBMTR analysis

2025· article· en· W4417017792 sur OpenAlexaff
Hira Mian, Muhammad Salman Faisal, Ruta Brazauskas, Temitope Oloyede, Nausheen Ahmed, Aimaz Afrough, Larry D. Anderson, Rahul Banerjee, Jesús G. Berdeja, Aram Bidikian, Jakob D. DeVos, Binod Dhakal, Ajoy Dias, Danai Dima, Yvonne A. Efebera, Lohith Gowda, Doris K. Hansen, Hamza Hashmi, Heather Landau, Lazaros J. Lekakis, Abu‐Sayeef Mirza, Ravi Narra, Krina K. Patel, Ashley Rosko, Mark A. Schroeder, Surbhi Sidana, Saad Z. Usmani, Marcelo C. Pasquini, Othman Salim Akhtar, Taiga Nishihori, Meera Mohan

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueCAR-T cell therapy research
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésCytopeniaComorbidityRetrospective cohort studyTransplantationCohortCohort studyMultiple myelomaMultivariate analysis

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Ciltacabtagene Autoleucel (Cilta-cel), an anti-BCMA CAR-T cell therapy, is approved for relapsed/refractory multiple myeloma (RRMM) based upon the results of several pivotal clinical trials. Frail adults are known to be under-represented in clinical trials. To ensure cilta-cel is effectively utilized in this subgroup, there is a need to understand both the efficacy and safety of cilta-cel among frail adults treated in the real-world. Methods: We conducted a retrospective cohort study of patients (pts) with RRMM treated with standard of care cilta-cel reported to the Center for International Blood and Marrow Transplantation Research (CIBMTR). Eligible pts had received ≥4 prior lines of therapy (LOT) between Mar 2022-Aug 2024 and completed a 100-day follow up form. Frailty was defined using the simplified frailty index (Facon et al., Leukemia, 2020), incorporating age, performance status, and comorbidities (hematopoietic cell transplantation specific comorbidity index score ≥2, 1 point). Pts with a frailty score ≥2 were classified as frail. Efficacy outcomes included overall response rate (ORR), progression-free survival (PFS), and overall survival (OS). Safety outcomes included CRS, ICANS, prolonged cytopenia (>3 months), clinically significant infections, and treatment-related mortality (TRM). We used multivariable regression to assess the independent impact of frailty on outcomes, adjusting for key patient- and disease-related variables. Results: Among 595 treated pts, frailty status was available for 541, of whom 183 (33.8%) were categorized as frail and 358 (66.2%) as non-frail. Median ages were 65.5 years (range, 38 – 84; ≥70 years, 35%) in the frail group and 63 years (range, 34 -80; ≥70 years, 15.6%) in the non-frail group. Overall, median prior LOT were 7 (range 4–24), 25.1% pts had high-risk cytogenetics, and 7.6% had prior BCMA exposure with no statistically significant differences between frail and non-frail pts. At a median follow up of 12 months, the best ORR in the frail group was 82.9% versus 88.5% in non-frail pts. The 12-month PFS in frail pts was 62.7% (95% CI, 53.6-71.3%) versus 75.9% (95% CI, 70.4-81.1%) in non-frail adults (log-rank p<0.01). Similarly, the 12-month OS was 72.8% (95% CI, 64.9-80.0%) in the frail group versus 90.4% (95% CI, 86.6-93.7%) in non-frail pts (log-rank p<0.01). The 12-month TRM was 6.8% (95% CI, 3.4-11.2%) in frail pts versus 3.6% (95% CI, 1.8-6.1%) in non-frail pts (p=0.11). A total of 82 pts (15.2%) died during the follow up period (frail, n=45; non-frail, n=37). Progression was the most common cause of death in both groups (57.8% and 62.2%), followed by infections (13.3% and 8.1%). Two pts died from ICANS, and one pt died from CRS in the frail group. There were no ICANS-related deaths in the non frail group; one pt died from CRS. In terms of toxicity, 434 (80.2%) pts developed CRS, with grade 2+ CRS in 22.4% frail versus 17.9% of non-frail pts. A total of 141 patients (26.1%) developed neurotoxicity with rates of grade 2+ neurotoxicity being higher in frail (n=21, 11.5%) versus non-frail (n=18, 5%). More specifically, any-grade ICANS was noted in 32.2% of frail versus 17.6% non-frail pts. Cranial nerve palsies and parkinsonism developed among 2.6% and 2.8% of frail and non-frail pts. Rates of prolonged cytopenia were 30.6% in frail versus 21.2% in non-frail pts. Other toxicities including macrophage activation syndrome/hemophagocytic lymphohistiocytosis (3.7%) and clinically significant infections (47.0%) did not differ between frail and non-frail adults. A total of 23 pts (4.5%) developed a secondary malignancy, with no differences between frail and non-frail pts. After adjusting for patient- and disease-related factors in a multivariable model, frailty was not significantly associated with response or risk for CRS grade 2+. However, frail pts experienced significantly worse PFS (HR 1.67, 95% CI 1.16-2.40, p=0.0059) and OS (HR 2.46, 95% CI 1.57-3.87, p <0.0001). Additionally, frailty doubled the odds of developing any-grade ICANS (OR 2.01, 95% CI 1.32-3.08, p=0.0012). Conclusion: This study represents the largest cohort to-date examining outcomes of frail adults treated with cilta-cel. Frail pts experienced inferior survival and increased risk for ICANS compared to their non-frail counterparts. These findings highlight the urgent need for tailored CAR-T strategies and prospective studies focused on this vulnerable population

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)0,004
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,019

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,013
Tête enseignante GPT0,302
Écart entre enseignants0,289 · 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'étudeObservationnel
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
Publié2025
Routes d'admission1
Résumé présentoui

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