P898: COMPARISON OF TECLISTAMAB WITH BELANTAMAB MAFODOTIN IN PATIENTS WITH TRIPLE-CLASS EXPOSED RELAPSED/REFRACTORY MULTIPLE MYELOMA USING MATCHING-ADJUSTED INDIRECT TREATMENT COMPARISON
Bibliographic record
Abstract
Background: Treatment options are limited for patients with relapsed/refractory multiple myeloma (RRMM) who are triple-class exposed (TCE) to immunomodulatory drugs, proteasome inhibitors, and anti-CD38 antibodies. While there is no standard of care for treatment of patients with TCE RRMM, belantamab mafodotin (belamaf) is a recently approved, novel therapeutic option. Teclistamab (tec; JNJ-64007957) is a B-cell maturation antigen × CD3 bispecific antibody currently being evaluated in the single-arm, phase 1/2 MajesTEC-1 study (NCT04557098) in patients with TCE RRMM who received ≥3 prior lines of therapy. Aims: Given the absence of a control arm in MajesTEC-1, the efficacy outcomes of patients treated with tec at the recommended phase 2 dose in MajesTEC-1 were compared with those treated with belamaf in the phase 2 DREAMM-2 study (NCT03525678). Methods: An unanchored matching-adjusted indirect comparison was conducted using individual patient-level data (IPD) from 150 patients treated with tec 1.5 mg/kg weekly in MajesTEC-1 (clinical cutoff of Sep 7, 2021), and published summary-level data from 97 patients treated with the approved dose of belamaf (2.5 mg/kg every 3 weeks) in DREAMM-2. After applying the DREAMM-2 eligibility criteria to patients from the intent-to-treat population of MajesTEC-1, IPD from MajesTEC-1 were weighted to match the aggregated DREAMM-2 baseline patient characteristics. Baseline characteristics of prognostic significance (such as cytogenetic profile, International Staging System stage, presence of extramedullary disease, number of prior lines of therapy, and refractory status) were adjusted for the analysis. Comparative efficacy of tec vs belamaf was estimated for overall response rate (ORR), complete response or better (≥CR) rate, duration of response (DOR), overall survival (OS), and progression-free survival (PFS). An odds ratio (OR) and 95% confidence interval (CI) derived from a weighted logistic regression analysis was used to compare the relative effects of tec vs belamaf for binary outcomes (ORR and ≥CR rate). A weighted Cox proportional hazards model was used to estimate time-to-event endpoints (DOR, OS, and PFS). Results: After adjustment, the effective sample size of the MajesTEC-1 cohort was 33. Baseline characteristics were balanced between MajesTEC-1 and DREAMM-2 after reweighting the MajesTEC-1 cohort. Tec-treated patients had improved outcomes compared with patients treated with belamaf: ORR (OR 2.05; 95% CI 0.92–4.57; P=0.0786), ≥CR rate (OR 2.13; 95% CI 0.80–5.65; P=0.1283), DOR (hazard ratio [HR] 0.19; 95% CI 0.05–0.73; P=0.0149), OS (HR 0.95; 95% CI 0.47–1.92; P=0.8897), and PFS (HR 0.63; 95% CI 0.34–1.15; P=0.1338). For most outcomes, the lack of statistical significance may be due to the reduced effective sample size following adjustment. Summary/Conclusion: In this comparative analysis, tec showed statistically improved DOR when compared with belamaf and numerically favorable results for other outcomes. This highlights the potential of tec as a treatment option for patients with TCE RRMM who received ≥3 prior lines of therapy.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".