Matching-adjusted indirect comparison (MAIC) of teclistamab (tec) versus selinexor-dexamethasone (sel-dex) for the treatment of patients (pts) with triple-class exposed (TCE) relapsed/refractory multiple myeloma (RRMM).
Notice bibliographique
Résumé
e20028 Background: Tec is a B-cell maturation antigen × CD3 bispecific antibody being evaluated in MajesTEC-1 (NCT04557098), a single-arm, phase 1/2 study in pts with RRMM who were exposed to ≥3 lines of therapy (LOT), including an immunomodulatory drug, a proteasome inhibitor, and an anti-CD38 monoclonal antibody. While there is currently no standard of care for treatment of pts with TCE RRMM, sel-dex is a recently approved, novel therapeutic option. Given the absence of a control arm in MajesTEC-1, we used an unanchored MAIC to compare efficacy outcomes of pts who received tec in MajesTEC-1 vs pts treated with sel-dex in the single-arm, phase 2b STORM Part 2 trial (NCT02336815). Methods: An unanchored MAIC was performed with individual pt-level data (IPD) from pts treated with tec (1.5 mg/kg weekly) in MajesTEC-1 at a clinical cutoff of Sep 7, 2021 (N = 150) and published summary-level data from pts who received sel-dex in STORM Part 2 (N = 122). After applying the STORM Part 2 eligibility criteria (penta-exposed, triple-class refractory, and refractory to last LOT), IPD from pts in MajesTEC-1 (N = 69) were weighted to match the aggregated baseline pt characteristics from STORM Part 2. Baseline characteristics of prognostic significance (refractory status, cytogenetic profile, revised International Staging System stage, presence of extramedullary disease, and number of prior LOT) were adjusted for in the analysis. Comparative efficacy of tec vs sel-dex was estimated for overall response rate (ORR), complete response or better (≥CR) rate, progression-free survival (PFS), duration of response (DOR), and overall survival (OS). For binary endpoints (ORR and ≥CR rate), the relative effects of tec vs sel-dex were estimated using an odds ratio (OR) and 95% CI derived from a weighted logistic regression. Time-to-event endpoints (PFS, OS, and DOR) were estimated using a weighted Cox proportional hazards model. Results: After adjustment, the effective sample size (ESS) of the MajesTEC-1 cohort was 37. Baseline characteristics were balanced between the 2 cohorts. Pts treated with tec had improved ORR (OR 3.14; 95% CI 1.48–6.69; P= 0.0029), ≥CR rate (OR 16.3; 95% CI 3.5–77.1; P= 0.0004), PFS (HR 0.58; 95% CI 0.30–1.11; P= 0.1007), DOR (hazard ratio [HR] 0.04; 95% CI 0.01–0.10; P< 0.0001), and OS (HR 0.52; 95% CI 0.28–0.95; P= 0.0344) compared with sel-dex. Despite a reduced ESS that reduced limited power to detect statistically significant differences, the majority of outcomes was statistically significant in favor of tec. Conclusions : In this MAIC, tec showed significantly improved efficacy over sel-dex for all outcomes except PFS, which was numerically in favor of tec, highlighting its potential as a highly effective treatment option for pts with TCE RRMM who received ≥3 prior LOT.
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 | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,006 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».