Real-world evaluation of teclistamab for the treatment of relapsed/refractory multiple myeloma (RRMM): an International Myeloma Working Group Study
Bibliographic record
Abstract
Teclistamab, a BCMAxCD3-directed bispecific antibody, has shown high response rates and durable remissions in triple-class-exposed patients with relapsed/refractory multiple myeloma. We performed a retrospective study evaluating the efficacy and safety of teclistamab in 210 patients treated at 9 academic centers from five countries within the IMWG Immunotherapy Working Group Committee. Patients were heavily pretreated, with 83% having triple-class refractory disease and 44% with prior BCMA-targeted therapy. With a median follow-up of 5.3 months, the overall response rate (ORR) was 67% in 188 response-evaluable patients, including 55% with a very good partial response or better. The 6-month progression-free survival (PFS) and overall survival rates were 53% (95% CI, 46-61%) and 73% (67-80%), respectively. Patients who received prior BCMA-directed therapy compared to BCMA-treatment-naïve patients had a lower ORR (58.3 vs 74.0%; P = 0.03) and PFS (6-month PFS 43% [95% CI, 33-55%] vs 63% [54-73%]; logrank P = 0.004). Step-up dosing occurred in an outpatient setting for 23% of patients. CRS occurred in 54% of patients, and infections were reported in 56.2% of patients, with 22% having grade ≥3 infections. In this multicenter real-world study, we found that teclistamab can lead to rapid responses in heavily pretreated myeloma patients with comparable efficacy and safety profiles, as demonstrated in MajesTEC-1.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".