Real-world treatment landscape after anti-BCMA CAR T-cell therapy in relapsed/refractory multiple myeloma: An international Study
Notice bibliographique
Résumé
Abstract Introduction: Despite significant advances, multiple myeloma (MM) remains largely incurable. The introduction of CD38-targeting agents has dramatically improved 5-year overall survival from 27% to 60%. Most patients now receive more than three lines of therapy and eventually become refractory to proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), and anti-CD38 antibodies. Novel BCMA-targeted therapies, such as CAR T-cells (cilta-cel from 2nd line, ide-cel from 3rd line) and bispecific T-cell engagers (e.g., teclistamab, elranatamab from 4th line), have become new standards of care. However, the median progression-free survival (PFS) post-CAR T therapy is often less than three years, highlighting an urgent need for defined subsequent treatment strategies. Aims: This real-world study aimed to identify the primary combinations and regimens used after anti-BCMA CAR T-cell therapy in relapsed/refractory MM across EU5 countries (France, Germany, Italy, Spain, UK), the US, and Japan. Methods: Anonymous patient charts (N=100) from onco-hematologists in the EU5, US, and Japan were analyzed (October-December 2022, October-December 2023, January-March 2025). The study focused on patients who had previously received ide-cel or cilta-cel between their 3rd and 5th lines of therapy and subsequently initiated a new treatment. Abbreviations: K=carfilzomib; E=elotuzumab; Pom=pomalidomide; d=dexamethasone; Isa=isatuximab; F=panobinostat; R=lenalidomide; Tec=teclistamab; X=selinexor; Elra=elranatamab; Belamaf=belantamab mafodotin; Ixa=ixazomib. Results: The overall cohort (N=100) had a median age of 63.5 years, with 50% of patients under 65, 43% between 65 and 75, and 7% over 75 years old. Seven patients were in the 3rd line setting (median age 58.9 years), 28 were in the 4th line (median age 64.3 years), and 65 were in the 5th line (median age 63.6 years). Over half of the patients (52%) were lenalidomide-refractory, with the majority in the 5th line (n=41) compared to only 10 in the 4th line and one in the 3rd line. The largest number of patients were treated in France (n=34) and the US (n=34), followed by Germany (n=19). The cohort showed a good performance status (ECOG 0-1: 56%) and varied cytogenetic risk (high: 31%, intermediate: 39%). Patients were categorized as “fit” (41%) or “intermediate-fit” (44%). Frequent comorbidities (77%) included mild renal failure (18%), peripheral neuropathy (32%), hypertension (40%), dyslipidemia (26%), and diabetes (15%). Therapies at Relapse Post-CAR T (N=100): 3rd line (9%, n=7):The most frequent regimens included elotuzumab-based treatments (17%), Kd (14%), isatuximab-based combinations (14%), panobinostat-based therapies (14%), and selinexor-based therapy (14%). 4th line (29%, n=28):Typical regimens were teclistamab (24%), elotuzumab-based combinations (11%), Rd (11%), Xd (8%), Elra (3%), and belamaf (3%). 5th line (63%, n=65):The main options were Tec (23%), Elra (11%), belamaf-based treatments (12%), Ixa-dex (8%), Xd (5%), and Isa-dex (4%). A small percentage of patients (2% for ide-cel, 2% for cilta-cel) were re-challenged with CAR T in the 5th line, although the time interval for sequential CAR T could not be analyzed. Discrepancies emerged between the main countries in the 4th and 5th lines (representing 91% of the cohort): in France and Germany, anti-BCMA agents (especially T-cell engagers) were the most frequent, while treatment strategies were more varied in the US (elotuzumab-based treatments, Rd, teclistamab, Ixa-dex, and Xd). Conclusion: This real-world study of 100 MM patients relapsing after anti-BCMA CAR T-cell treatment shows that 91% were in their 4th or 5th line of therapy. A large majority were triple-class exposed, and 52% were lenalidomide-refractory. In the absence of a clear standard of care, common treatments were identified: elotuzumab- and isatuximab-based regimens in the 3rd line; teclistamab and elotuzumab-based options in the 4th line; and teclistamab, elranatamab, and belantamab mafodotin in the 5th line. Geographic discrepancies reflect variations in drug availability, reimbursement policies, and clinical development.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».