Impact of Maintenance Therapy after Salvage Autologous Stem Cell Transplantation in Relapsed Multiple Myeloma
Bibliographic record
Abstract
Background: Salvage autologous stem cell transplantations (ASCT) in the setting of relapsed multiple myeloma have historically been an important therapeutic option. There have not been any comparative studies looking at this approach in the era of novel therapeutic agents such as monoclonal antibodies and emerging immunotherapies. It is important to have a real world benchmark when understanding the landscape of potential treatments in this space. Maintenance therapy post frontline autologous stem cell transplantation has become standard of care due to the important improvement in progression free survival (PFS) as well as overall survival (OS). However, little is known about the impact of maintenance post-salvage transplant. The objectives of this study are to define PFS and OS for salvage transplants with or without maintenance and describe the outcomes by types of maintenance utilized in this setting. Secondly, to redefine the optimal duration of remission post-first transplant in the maintenance era that would justify a second autologous transplant. Historically, in the pre-maintenance era, 24 months was demonstrated as an optimal remission post first transplant to gain benefit from a second salvage transplant. Methods: This is a Canadian multicentre retrospective study utilizing the Canadian Myeloma Research Group Database, a national database with input from 16 Canadian centres hosting over 8700 patients. All patients included in the study had undergone a salvage ASCT between Jan 2012 to Dec 2021 at any line of treatment. Results: Three hundred and fifty-two patients met eligibility for inclusion in this analysis. Baseline characteristics are portrayed in table 1. The median PFS (mPFS) for patients undergoing salvage transplant with (n= 179) and without (n=173) maintenance were 42.1 (34.8-53.6) and 24.2 (20.7-28.1) months respectively. The mOS was 101m (97.6-NYR) in the maintenance group and NYR (53.7-NYR) in the no maintenance group. In patients who received any type of maintenance post ASCT1 (n=169) and had a duration of response greater than 36m to the first transplant, the salvage transplant without maintenance (n=54) provided a mPFS of 17.3m (15.2-35). In a similar group that had greater than 36m response and received maintenance after ASCT1 (n=92), the addition of maintenance after the salvage transplants significantly improved the mPFS to 34.8m (26.5-51, p=<0.01). Patients that received maintenance post ASCT1 and had less than 36m PFS represent a higher risk group. In these patients, a salvage transplant without post salvage maintenance (n=10), yielded a mPFS of 9.9m (8.5-NYR). The addition of post salvage maintenance, however, significantly improved outcomes for this group as well (n=13) to a mPFS of 29.1(14.6-NYR). The most common type of maintenance post-salvage was imid based (55.9%), followed by PI based (30.2%) and then PI+imid (7.8%). Overall response rates for salvage transplants with or without maintenance therapy were 94.1% and 89.9% respectively and > VGPR were 75.3% and 67.6% respectively. The mPFS based on type of maintenance therapy are portrayed in figure 1. There was no statistically significant difference in OS based on types of maintenance therapy. Further analyses are pending and will be presented. Conclusion: Salvage transplants followed by maintenance therapy in the first relapse space provide a meaningful duration of remission and this remains a good treatment option particularly in those with a long remission after their first ASCT. As novel immunotherapies such as CAR-T and bispecific antibodies move into earlier lines of treatment, this data could serve as an important real world benchmark when evaluating the landscape for these therapies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".