Real-world outcomes of teclistamab for multiple myeloma in Canada: Multi-institutional report from the Canadian myeloma research group (CMRG) database
Bibliographic record
Abstract
Abstract Introduction: Teclistamab is a highly effective treatment for relapsed multiple myeloma (MM), with an overall response rate (ORR) of 63% and progression-free survival (PFS) of 11.4 months in the MajesTEC-1 trial. real-world data are needed to understand outcomes in a broader patient population. In this study, we examined real-world outcomes of MM pts receiving teclistamab in Canada. Methods: This is a retrospective observational study utilizing the Canadian Myeloma Research Group Database (CMRG-DB), which is a prospectively maintained disease-specific database with > 10,000 patients enrolled from 21 academic centres across Canada. All MM pts who received teclistamab outside of clinical trials from May 2023-March 2025 were included in this study. Primary outcomes were the overall response rate (ORR), duration of response (DoR), progression-free survival (PFS) and overall survival (OS) calculated from the start date of teclistamab. Time-to-event outcomes were estimated using the Kaplan-Meier method. Secondary objectives included efficacy outcomes in pts achieving at least a VGPR. Results: We identified 52 pts who received at least one dose of teclistamab, of which 46% were female, median age was 68 (42-90) and 44% had high-risk cytogenetics. Pts had received a median of 4 prior lines (range, 3-10), with 75% of pts having received a prior autologous stem cell transplant, 77% being triple class- refractory, 12% penta-drug refractory, and 8% BCMA-refractory. Median follow up time for all pts was 9.4 months. Among all treated pts, the ORR was 58%, with 50% of all pts achieving at least a VGPR (95% CI, 49%-94%). The median PFS was 10.4 months (95% CI, 3.3-NE)and median OS not reached, with 12-month OS estimate of 67% (95% CI, 54%-83%). Among the pts achieving, median DOR, PFS and OS were not reached. The 9-month was 70% (95% CI, 51%-97%), 12-month PFS 78% (95% CI, 59%-100%) and 12-month OS 95% (95% CI, 86%-100%). Conclusions: In this real-world study, Canadian myeloma pts had similar outcomes to those in the MajesTEC-1 trial. In particular, pts achieving a deep response (at least VGPR) had very favorable PFS and OS at one year, compared to historical outcomes from the LocoMMotion, Mammoth, and CMRG real-world studies. . Given the short follow-up in the current study, efficacy data will be updated at the meeting.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".