Post-Autologous Stem Cell Transplantation Therapy for Multiple Myeloma Patients: Impact on Clinical Outcomes
Bibliographic record
Abstract
Introduction Autologous stem cell transplantation (auto-SCT) has dramatically improved the outcomes for patients with MM. While the outcomes are better, still most patients will inevitably relapse. Two different approaches have been reported aiming to improve clinical outcomes: consolidation and maintenance. In the present study, we evaluate the impact of post-autoSCT therapy over survival outcomes. Methods All consecutive patients who underwent single auto-SCT at Tom Baker Cancer Center from 01/04 to 03/17 were evaluated. A p value of Results 302 consecutive patients with MM who underwent single auto-SCT at our Institution over the defined period were evaluated. Clinical characteristics are shown in Table 1 . 78 patients did not receive any form of post-autoSCT therapy, 93 patients had received consolidation followed by maintenance and 130 received some form of maintenance only. At the time of analysis, 201 patients are still alive and 161 have already progressed. A trend towards better median overall survival (OS) was observed for those patients receiving post-autoSCT therapy (95.9 vs 69.7 months, p=0.2) ( Fig 1a ). Furthermore, patients receiving consolidation followed by maintenance and maintenance only had a median PFS of 45 and 36.9 months, compared to 24.1 months for those with no post-autoSCT therapy (p=0.0001) ( Fig 1b ). In addition, high-risk cytogenetic (HRC) patients defined by FISH (t(4;14), t(14;16) and p53 del) had a better OS in the post-autoSCT therapy group (56 vs 26 months, p=0.04). Median PFS was also longer in the post-autoSCT therapy group for the HRC group (22.5 vs 9.1 months, p=0.001). In conclusion , post-autoSCT therapy is an important approach that has improved clinical outcomes for MM patients undergoing single autoSCT. Patients with HRC MM seemed to have better outcomes as a result of this strategy. However, survival still remains poor compared to standard risk myeloma patients. The effect of consolidation is currently controversial and requires further assessment with long-term follow-up and subset analysis to better estimate patients that might benefit from it. Disclosures Jimenez-Zepeda: Celgene: Honoraria; Janssen: Honoraria; Takeda: Honoraria; Amgen: Honoraria. Neri: Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding. Bahlis: Takeda: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".