P898: COMPARISON OF TECLISTAMAB WITH BELANTAMAB MAFODOTIN IN PATIENTS WITH TRIPLE-CLASS EXPOSED RELAPSED/REFRACTORY MULTIPLE MYELOMA USING MATCHING-ADJUSTED INDIRECT TREATMENT COMPARISON
Notice bibliographique
Résumé
Background: Treatment options are limited for patients with relapsed/refractory multiple myeloma (RRMM) who are triple-class exposed (TCE) to immunomodulatory drugs, proteasome inhibitors, and anti-CD38 antibodies. While there is no standard of care for treatment of patients with TCE RRMM, belantamab mafodotin (belamaf) is a recently approved, novel therapeutic option. Teclistamab (tec; JNJ-64007957) is a B-cell maturation antigen × CD3 bispecific antibody currently being evaluated in the single-arm, phase 1/2 MajesTEC-1 study (NCT04557098) in patients with TCE RRMM who received ≥3 prior lines of therapy. Aims: Given the absence of a control arm in MajesTEC-1, the efficacy outcomes of patients treated with tec at the recommended phase 2 dose in MajesTEC-1 were compared with those treated with belamaf in the phase 2 DREAMM-2 study (NCT03525678). Methods: An unanchored matching-adjusted indirect comparison was conducted using individual patient-level data (IPD) from 150 patients treated with tec 1.5 mg/kg weekly in MajesTEC-1 (clinical cutoff of Sep 7, 2021), and published summary-level data from 97 patients treated with the approved dose of belamaf (2.5 mg/kg every 3 weeks) in DREAMM-2. After applying the DREAMM-2 eligibility criteria to patients from the intent-to-treat population of MajesTEC-1, IPD from MajesTEC-1 were weighted to match the aggregated DREAMM-2 baseline patient characteristics. Baseline characteristics of prognostic significance (such as cytogenetic profile, International Staging System stage, presence of extramedullary disease, number of prior lines of therapy, and refractory status) were adjusted for the analysis. Comparative efficacy of tec vs belamaf was estimated for overall response rate (ORR), complete response or better (≥CR) rate, duration of response (DOR), overall survival (OS), and progression-free survival (PFS). An odds ratio (OR) and 95% confidence interval (CI) derived from a weighted logistic regression analysis was used to compare the relative effects of tec vs belamaf for binary outcomes (ORR and ≥CR rate). A weighted Cox proportional hazards model was used to estimate time-to-event endpoints (DOR, OS, and PFS). Results: After adjustment, the effective sample size of the MajesTEC-1 cohort was 33. Baseline characteristics were balanced between MajesTEC-1 and DREAMM-2 after reweighting the MajesTEC-1 cohort. Tec-treated patients had improved outcomes compared with patients treated with belamaf: ORR (OR 2.05; 95% CI 0.92–4.57; P=0.0786), ≥CR rate (OR 2.13; 95% CI 0.80–5.65; P=0.1283), DOR (hazard ratio [HR] 0.19; 95% CI 0.05–0.73; P=0.0149), OS (HR 0.95; 95% CI 0.47–1.92; P=0.8897), and PFS (HR 0.63; 95% CI 0.34–1.15; P=0.1338). For most outcomes, the lack of statistical significance may be due to the reduced effective sample size following adjustment. Summary/Conclusion: In this comparative analysis, tec showed statistically improved DOR when compared with belamaf and numerically favorable results for other outcomes. This highlights the potential of tec as a treatment option for patients with TCE RRMM who received ≥3 prior lines of therapy.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 tête enseignante, 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 ».