Efficacy of Daratumumab Containing Regimens Post Lenalidomide Maintenance in Transplant Eligible Patients: Real-World Experience from the Canadian Myeloma Research Group Database
Bibliographic record
Abstract
Introduction Lenalidomide maintenance following autologous stem cell transplant (ASCT) remains a standard of care among transplant eligible patients with newly diagnosed multiple myeloma (NDMM). Many previous clinical trials done in patients following one prior line of therapy either excluded patients progressing on lenalidomide or included a very small proportion of these patients. Given the paucity of data in this setting, the optimal management of patients progressing on lenalidomide maintenance remains unknown. Daratumumab-containing triplet regimens have recently been introduced for these patients, typically in combination with pomalidomide (DPd), lenalidomide (DRd), or bortezomib (DVd). To our knowledge, there is no prospective data to allow comparison of the efficacy of these three regimens in patients progressing on lenalidomide maintenance, which is an increasingly common clinical scenario. Understanding the comparative efficacy, tolerability and toxicity of these regimens in patients progressing on lenalidomide maintenance in the 'real-world' is needed in order to help clinicians make appropriate decisions and guide future studies. Methods The Canadian Myeloma Research Group Database (formerly known as the Myeloma Canada Research Network Database, MCRN-DB) is a prospectively maintained disease specific database with over 7000 patients enrolled from 14 academic sites across Canada with legacy data collected from 2007. The Munster Myeloma database collects myeloma specific information in a German academic center and currently contains data from 800 patients collected from 2005. All consecutive patients treated with daratumumab based regimens in second line following relapse on lenalidomide maintenance were included in the analysis from the two databases analyzed up to 30/06/2020. Results A total of 1380 NDMM patients on lenalidomide maintenance post autologous stem cell transplant were identified in the two databases. From them, 73 patients were included in this analysis as they were treated with daratumumab containing regimen in second line. Specifically, 18 (24.7%) of these patients were treated with DPd, 32 (43.8%) patients with DRd, and 23 (31.5%) patients with DVd. The baseline characteristics, maintenance details, post-maintenance response rates and toxicity for each regimen are shown in Table 1. The median follow-up for the cohort from the time of daratumumab initiation was 8.3 months (range 0.4 - 40.0). Although, a higher proportion of patients in the DPd arm obtained a CR/VGPR compared to DRd or DVd, it did not reach statistical significance (p-value 0.06). The median PFS of the entire cohort was 16.96 months (95% CI 11.47-23.44). The median PFS of the individual regimens was as follows: DPd 17.65 months, DRd not reached and DVd 11.47 months as demonstrated in Figure 1 (p-value =0.46). Conclusion In summary, our results show that daratumumab-based regimens are effective among patients progressing on lenalidomide maintenance in the real world. Despite the small sample size, the results presented here are in line with recent sub-analyses of phase III studies examining the common daratumumab-based regimens used in this setting (CASTOR with median PFS of DVd between 7.8 months in all lenalidomide refractory patients and 27 months in all patients in first relapse; MM014 with median PFS of DPd after lenalidomide refractoriness of 21.8 months). The efficacy of DRd, in which daratumumab is added to an increased dose of lenalidomide, is notable and warrants further evaluation to identify which patients are most likely to benefit. Additional studies with longer follow-up are required to assess the optimal daratumumab-based regimen to be used in this growing population of patients relapsing after lenalidomide maintenance. Disclosures Mian: Janssen: Consultancy, Honoraria; Celgene: Consultancy; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Sanofi: Consultancy. Venner:Celgene, Amgen: Research Funding; Janssen, BMS/Celgene, Sanofi, Takeda, Amgen: Honoraria. Jimenez-Zepeda:Janssen, Celgene, Amgen, Takeda: Honoraria. McCurdy:Sanofi: Honoraria; GSK: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Sebag:Celgene: Honoraria; Takeda: Honoraria; Amgen: Honoraria; Janssen: Honoraria, Research Funding. Song:Celgene: Research Funding; Celgene, Janssen, Amgen, Takeda: Honoraria. Leblanc:Celgene: Research Funding; Celgene Canada; Janssen Inc.; Amgen Canada; Takeda Canada: Membership on an entity's Board of Directors or advisory committees. White:Karyopharm: Honoraria; Antengene: Honoraria; GSK: Honoraria; Takeda: Honoraria; Sanofi: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Amgen: Honoraria. Stakiw:Roche: Research Funding; Celgene: Honoraria; Lundbeck: Honoraria; Amgen: Honoraria; Janssen: Honoraria, Research Funding; Novartis: Honoraria; BMS: Honoraria. Louzada:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Kotb:Karyopharm: Current equity holder in publicly-traded company; Celgene: Honoraria; Janssen: Honoraria; Amgen: Honoraria; Takeda: Honoraria; Merck: Honoraria, Research Funding; Sanofi: Research Funding. Reece:Janssen: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy; Amgen: Consultancy, Honoraria; Millenium: Research Funding; BMS: Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Merck: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Otsuka: Research Funding.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".