Outcomes of Maintenance Treatment Post Autologous Stem Cell Transplantation in Newly Diagnosed Multiple Myeloma Patients: A Canadian Single Center Experience
Notice bibliographique
Résumé
Abstract Introduction: Despite the significant advances in treatment of patients with multiple myeloma, therapy of patients with high risk disease remains challenging. Maintenance treatment with Lenalidomide following autologous stem cell transplant (ASCT) is the standard of care in patients with multiple myeloma; however its efficacy in patients with high risk features is conflicting. The efficacy of proteasome inhibitors (PI) ± lenalidomide (Len) maintenance in high risk patients has been evaluated in other studies with discrepant results with respect to improvement in long term outcomes. In this study we sought to assess the impact of various maintenance strategies on outcomes of transplant eligible newly diagnosed myeloma patients in the real world setting. Methods: This is a retrospective, single center Canadian cohort study (London, Canada) with the primary objective to compare the PFS in the high risk transplant eligible (TE) newly diagnosed multiple myeloma (NDMM) patients receiving maintenance treatment versus no maintenance post ASCT, diagnosed from January 1,2007 to April 30,2021. Secondary objectives are comparison of PFS, OS and overall response rate (ORR) based on risk group and various maintenance strategies. ORR is defined as partial response or better. To investigate the effect of different variables on survival outcomes we used Kaplan-Meier curves, univariate and multivariate cox regression analysis. Results: In total, 155 TE- NDMM patients were included. 61 (39%) were high risk defined as: deletion 17p, t (4; 14), t (14; 16), 1q amp. One hundred and thirty (84%) patients received PI or Len-based induction therapy. Of the entire cohort, 53 (34%) patients received maintenance treatment post ASCT: 41 Len to progression, 12 Len+Pi. In the entire cohort, ORR was 71% (38 of 53) for patients who received maintenance and 53% (55 of 102) for those who did not receive maintenance (p=0.7). In the 61 high risk patients, 33 received maintenance: 11 Len+PI and 22 Len single agent. ORR was significantly better for maintenance, 63% (21 of 33) compared to 11% (3 of 27) for no maintenance (p=0.0054). Maintenance therapy also influenced PFS. In total, median PFS was significantly higher in patients who received maintenance; with 63% of patients alive and on therapy at 60 months, compared to no maintenance with median PFS of 40 months (HR 0.46,95% CI(025-0.85),p= 0.011). A subgroup analysis of the impact of maintenance between the standard and high risk groups suggested excellent PFS for both: 56.8% for standard and 67.1% for high risk at 60 months. On the other hand, median PFS was dismal for the high risk no maintenance group: only 19 months versus 45.5 months in the standard group ( HR=0.14,95% CI(0.05-0.38), p <0.0001). Dual versus single drug maintenance strategy had no impact on PFS. Median PFS was the same for the patients who received Len+PI or Len single agent, not yet reached (NYR) in both (p=0.47). In the entire cohort there was no difference in the median OS, NYR in both groups at 60 months; 51% in maintenance, 53% in no maintenance (p=0.25). Similarly in the high risk patients median OS was not different, 61 months in maintenance vs NYR in no maintenance (45% vs 52% at 60 months, p=0.38). OS was also not different in high risk patients who received Len+PI vs Len single agent maintenance, NYR vs 61 months (p= 0.68). In the standard risk group median OS was NYR at 60 months in both maintenance and no maintenance groups (p=0.57) Conclusion: In this Canadian single center retrospective cohort study we compared PFS and OS in patients with TE-NDMM who received maintenance compared to those with no maintenance treatment post ASCT. We found PFS was significantly improved in the high risk patients who received maintenance treatment but we found no impact on dual versus single maintenance strategy. This further validates the findings of large phase 3 clinical trials illustrating a positive impact of maintenance treatment post ASCT irrespective of the cytogenetics risk in the real world setting. Although the maintenance treatment could not entirely overcome the worse outcome in the high risk group, more than 40% of the high risk patients who received maintenance were still alive at 60 months. The question of the best maintenance strategy for high risk myeloma patients remains open and further studies are warranted Figure 1 Figure 1. Disclosures Lam: Beigene: Honoraria, Membership on an entity's Board of Directors or advisory committees; SeaGen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Hoffmann-La Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Johnson & Johnson: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Meyers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees. Phua: Amgen: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; NovoNordisk: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria. Louzada: Pfizer: Honoraria; Janssen: Honoraria; Amgen: Honoraria; Celgene: Honoraria.
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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 | 0,002 |
| 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 | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».