Salvage Autologous Stem Cell Transplant in Relapsed Myeloma Patients in the Era of Modern Treatment: Is There a Role?
Notice bibliographique
Résumé
Background: Clinical paradigms for the management of relapsed multiple myeloma (MM) following upfront ASCT are evolving rapidly. Traditionally, patients (pts) relapsing after first autologous transplant (ASCT 1) have been considered potential candidates for salvage transplant (ASCT 2), especially if achieving a prolonged remission post-ASCT 1. However, much of the data supporting the utility of ASCT 2 precedes routine use of novel agent-based induction and immunomodulatory agent (IMID) based-maintenance strategies. In our institution we had previously reported results in ASCT 2 pts who had received re-induction containing bortezomib (btz) at relapse, and had noted a median PFS of 19.1 months. (Farshchi-Zarabi, et al, Blood 2019, 128(: abstract 5821). However, the role of maintenance therapy was not assessed at that time. Here, we report on outcomes of 49 pts receiving novel agent based-induction and re-induction therapy before both ASCT 1 and 2, and assess for differences based on whether or not IMID maintenance was given after ASCT 1. Methods: Pts receiving proteasome inhibitor (PI) and/or lenalidomide (IMID)-based regimens before both ASCT 1 and 2 from January 2006-July 2019 were identified using the Princess Margaret Cancer Centre Myeoma Database. Pts were eligible for ASCT 2 if they maintained ≥stable disease with salvage chemotherapy and had achieved a PFS approaching 2 yrs after ASCT 1. A retrospective chart review was performed to investigate the PFS, transplant-related mortality (TRM) and overall survival (OS) outcomes of these pts. Results: Between September 2012 and December 2017, 71 pts underwent ASCT 2 at our institution: 49 pts followed at Princess Margaret post-ASCT 2 were eligible for inclusion; 22 patients did not have detailed F/U data at our institution and were excluded from the analysis. Baseline characteristics are summarized in Table 1. The majority of pts were female; (65%) had ISS stage 1 or 2 (68%). Median age at diagnosis was 60 yrs (range 35-67). FISH testing was available in 31 of 49 (63%) pts, with 22% characterized as high-risk. Induction regimens for ASCT 1 are summarized in Table 1. Most (84%) had received Btz-based induction initially, and 69% had achieved ≥VGPR after induction and 86% at day 100 post-ASCT 1. Median time to progression was 38.8 mos (range 16.77-66.38) post-ASCT 1; 32 patients (65%) had received IMID-based maintenance post-ASCT 1, 26 with lenalidomide and 6 with thalidomide. Re-induction regimens for ASCT 2 were variable (Table 1). Post-ASCT 2, the overall response rate was 95% with 62% achieving ≥ VGPR at day 100. TRM occurred in 2 pts within 3 months of transplant: 1 died from sepsis/multi-organ failure and 1 from progressive decline/failure to thrive without definitive disease progression. 39 of the 46 (87%) patients alive and responding after ASCT 2 received maintenance (Table 1). The median PFS was 24 months (95% CI 12.6-37.6) for all pts at a median follow-up of 21 months. The median PFS was 16 months for pts who received IMID maintenance after ASCT 1 versus 32 months for those not receiving maintenance. Conclusion: In the era of novel-agent-based induction and re-induction regimens, the PFS after salvage ASCT is 2 years. Although our results are limited by sample size, the median PFS after salvage transplant in the subset of patients relapsing after lenalidomide maintenance appears shorter than that observed without maintenance. Nevertheless, the PFS after ASCT 2 in these lenalidomide-refractory pts is 16 months. Given that many of the recent phase 3 trials evaluating triplet regimens excluded such individuals, our data may help guide the choice of therapy when pts relapse on lenalidomide maintenance after ASCT 1. Disclosures Chen: Celgene: Honoraria, Research Funding; Amgen: Honoraria; Janssen: Honoraria, Research Funding. Prica:Celgene: Honoraria; Janssen: Honoraria. Reece:Otsuka: Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Merck: Research Funding. Tiedemann:Takeda: Honoraria; Novartis: Honoraria; Amgen: Honoraria; BMS: Honoraria; Janssen: Honoraria; Celgene: Honoraria. Trudel:Astellas: Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria; Genentech: Research Funding; Sanofi: Honoraria; Pfizer: Honoraria; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Research Funding. Kukreti:Celgene: Honoraria; Amgen: Honoraria; Takeda: 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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| 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 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 ».