Real-world sequencing of anti-BCMA CAR T-cells and bispecific T-cell engagers in Relapsed/Refractory multiple myeloma: A multi-country analysis
Notice bibliographique
Résumé
Abstract Background Multiple myeloma (MM) is the second most common hematologic malignancy, driven by uncontrolled plasma cell proliferation. While recent advances with combinations of proteasome inhibitors, IMiDs, corticosteroids, and anti-CD38 monoclonal antibodies have improved 5-year survival rates to 60%, most patients eventually relapse and become triple-class refractory. B-cell maturation antigen (BCMA) has emerged as a key therapeutic target in this setting. Novel BCMA-targeted therapies, including CAR T-cells and bispecific T-cell engagers, offer new hope. Despite targeting the same antigen, their mechanisms, efficacy, and safety profiles vary, and head-to-head comparisons are lacking. As a result, the optimal sequencing of these powerful new therapies in clinical practice remains a significant challenge.Aims This real-world study aimed to identify the typical treatment sequencing and key drivers for selecting anti-BCMA CAR T-cells versus bispecific T-cell engagers in triple-class exposed patients with relapsed/refractory MM. The analysis focused on treatments administered outside of clinical trials across a broad international cohort.Methods We conducted a retrospective analysis of anonymous patient charts based on data reported by onco-hematologists making treatment decisions for MM patients in the EU5 countries (France, Germany, Italy, Spain, UK), the US, Japan, and China. The dataset included 341 unique patient charts for patients treated with an anti-BCMA CAR T-cell product (including ide-cel and cilta-cel) and 570 unique patient charts for patients treated with a bispecific antibody (including teclistamab and elranatamab). Data collection took place over three distinct periods: October–December 2022, October–December 2023, and January–March 2025. The analysis focused on comparing typical lines of therapy, disease characteristics (ISS score, cytogenetic profile), and patient profiles (mean age, ECOG status, and comorbidities) between the two treatment groups.Results The analysis included a total of 911 patient charts. The CAR T-cell population (n=341) had a mean age of 62.8 years, while the bispecific T-cell engager population (n=570) had a significantly higher mean age of 68.9 years (p<0.05). CAR T-cell therapy was used in earlier lines of therapy for a greater proportion of patients (18% in 2nd/3rd lines) compared to bispecifics (11%). When stratified by line of therapy, the mean age for CAR T-cell patients was 57.0 years in the 2nd line, 60.3 years in the 3rd line, 63.1 years in the 4th line, and 63.9 years in the 5th line+. In the bispecific cohort, the mean age was 58.4 years in the 2nd line, 66.3 years in the 3rd line, 69.9 years in the 4th line, and 68.6 years in the 5th line+. Disease characteristics were generally similar between the two groups, though the CAR T-cell group showed a slight trend toward a higher proportion of high-risk cytogenetics. Patient fitness, as assessed by ECOG status, revealed a distinct pattern: 82% of CAR T-cell patients in the 4th line and 81% in the 5th line+ had an ECOG of 0-1, compared to 71% of bispecific patients in both the 4th and 5th lines. This suggests a selection for fitter patients for CAR T-cell therapy, even in later lines. A subgroup of 31 patients received a BCMA bispecific antibody after a previous CAR T-cell exposure, primarily in the 4th (n=8) and 5th (n=23) lines of therapy. Their mean age was 65.4 years, and their general fitness and comorbidity status were comparable to the overall CAR T-cell population (66% ECOG 0-1, 77% with at least one comorbidity).Conclusion This real-world study suggests distinct treatment selection patterns for anti-BCMA therapies. CAR T-cells are preferentially used in younger, more fit patients in earlier lines of therapy. In contrast, bispecific antibodies are more often used in older, less fit patients, including those who have previously been treated with a CAR T-cell agent. These data provide valuable insights into current clinical practice for sequencing these novel therapies outside of clinical trials.
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 | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».