Sequential Use of Carfilzomib and Pomalidomide in Relapsed Multiple Myeloma: A Multi-Institutional Report from the Canadian Myeloma Research Group (CMRG) Database
Bibliographic record
Abstract
Introduction: The treatment of multiple myeloma (MM) has dramatically improved due to the availability of immunotherapies such as daratumumab (Dara). However, in Canada, myeloma treatments now account for up to 20% of some provincial drug budgets. As Dara may be effective for a prolonged period and is quickly moving into first-line therapy, the Canadian body which provides guidelines to the provincial ministries of health recommended in 2019 against open sequencing of drugs in relapsed MM. Specifically, patients who receive Dara are now only eligible for public funding for either carfilzomib (CAR) or pomalidomide (POM)--but not both-- for relapsed MM. Given the known heterogeneity of myeloma, data gaps regarding the optimal sequencing of the available agents and uncertainly regarding the impact of this new restriction on patient outcomes, we utilized our Canadian national myeloma database to assess the sequencing of these two agents. The goal of our study was to understand the efficacy of these two commonly used treatments in the relapsed setting: 1) POM- after CAR-based therapy and 2) CAR- after POM-based therapy. Methods: We performed a retrospective observational study using the Canadian Myeloma Research Group Database (CMRG-DB), analyzed up to 30/06/2020. The CMRG-DB (formerly 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 and includes legacy data collected from 2007. All patients with MM who were treated for relapsed disease with approved regimens using POM after CAR, or CAR after POM were included. Our primary outcomes were overall response rates (ORR) in each respective cohort. Secondary outcomes were progression-free survival (PFS), overall survival (OS), and a landmark OS analysis from treatment initiation with the first of the two agents. Survival was estimated using Kaplan-Meier methods and compared between groups using log rank test. Results: A total of 121 patients were included: 49 treated with POM after CAR, and 72 with CAR after POM. In the POM after CAR group, the median line of treatment was 4th for POM and 3rd for CAR. In the CAR after POM group, the median line of treatment was 4th for POM and 5th for CAR. In 79/121 patients (65%), the two therapies were directly sequential, 40/49 (82%) for the POM after CAR group, and 38/72 (54%) in the CAR after POM group. Baseline characteristics and treatment details are shown in Table 1. The ORR was 51% for patients treated with POM after CAR, and 49% for patients treated with CAR after POM. The median PFS for POM after CAR was 4.93 months (95% CI, 2.76-7.07), and for CAR after POM was 5.36 months (95% CI, 3.75-6.94). The median OS for patients treated POM after CAR was 11.01 months (95% CI, 4.50-19.13), and for patients treated with CAR after POM the median OS was 10.98 months (95% CI, 8.98-19.17) (Figure 1). In a landmark analysis using the time of the treatment initiation with the first of the two agents, the median OS of patients treated with CAR after POM was 37.61 months (95% CI 26.66-46.52) and 25.32 months (95% CI 14.56-41.19) for patients treated with POM after CAR (p=0.1270) (Figure 2). Conclusion: In this real-world observational study we demonstrated that both CAR- and POM-based therapies were effective treatment options for patients with advanced relapsed MM as each produced responses in approximately 50% of patients with a median PFS of about 5 months and median OS of 11 months. These results are comparable to those noted in prospective clinical trials leading to the approval of these agents in this setting. Further, a landmark analysis showed that using both agents sequentially late in the disease course provided reasonable OS outcomes, regardless of the order in which they are sequenced. Finally, as the cost of MM therapy increases, the use of real-world data can help determine the impact of funding decisions on the outcome of patients treated in a publicly funded universal health care system such the one in Canada. Disclosures McCurdy: GSK: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Sanofi: Honoraria; Amgen: Consultancy, Honoraria. Venner:Janssen, BMS/Celgene, Sanofi, Takeda, Amgen: Honoraria; Celgene, Amgen: Research Funding. Louzada:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. LeBlanc:Celgene: Research Funding; Celgene Canada; Janssen Inc.; Amgen Canada; Takeda Canada: Membership on an entity's Board of Directors or advisory committees. Sebag:Takeda: Honoraria; Celgene: Honoraria; Janssen: Honoraria, Research Funding; Amgen: Honoraria. Song:Celgene: Research Funding; Celgene, Janssen, Amgen, Takeda: Honoraria. Jimenez-Zepeda:Janssen, Celgene, Amgen, Takeda: Honoraria. Kotb:Takeda: Honoraria; Merck: Honoraria, Research Funding; Celgene: Honoraria; Janssen: Honoraria; Sanofi: Research Funding; Karyopharm: Current equity holder in publicly-traded company; Amgen: Honoraria. Mian:Sanofi: Consultancy; Takeda: Consultancy, Honoraria; Celgene: Consultancy; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. White:Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Stakiw:Roche: Research Funding; Lundbeck: Honoraria; BMS: Honoraria; Novartis: Honoraria; Amgen: Honoraria; Celgene: Honoraria; Janssen: Honoraria, Research Funding. Reece:Janssen, Bristol-Myers Squibb, Amgen, Takeda: Consultancy, Honoraria; Janssen, Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Merck: 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".