Salvage Autologous Stem Cell Transplant in Relapsed Myeloma Patients in the Era of Modern Treatment: Is There a Role?
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".