Matching-adjusted indirect comparison (MAIC) of teclistamab (tec) versus selinexor-dexamethasone (sel-dex) for the treatment of patients (pts) with triple-class exposed (TCE) relapsed/refractory multiple myeloma (RRMM).
Bibliographic record
Abstract
e20028 Background: Tec is a B-cell maturation antigen × CD3 bispecific antibody being evaluated in MajesTEC-1 (NCT04557098), a single-arm, phase 1/2 study in pts with RRMM who were exposed to ≥3 lines of therapy (LOT), including an immunomodulatory drug, a proteasome inhibitor, and an anti-CD38 monoclonal antibody. While there is currently no standard of care for treatment of pts with TCE RRMM, sel-dex is a recently approved, novel therapeutic option. Given the absence of a control arm in MajesTEC-1, we used an unanchored MAIC to compare efficacy outcomes of pts who received tec in MajesTEC-1 vs pts treated with sel-dex in the single-arm, phase 2b STORM Part 2 trial (NCT02336815). Methods: An unanchored MAIC was performed with individual pt-level data (IPD) from pts treated with tec (1.5 mg/kg weekly) in MajesTEC-1 at a clinical cutoff of Sep 7, 2021 (N = 150) and published summary-level data from pts who received sel-dex in STORM Part 2 (N = 122). After applying the STORM Part 2 eligibility criteria (penta-exposed, triple-class refractory, and refractory to last LOT), IPD from pts in MajesTEC-1 (N = 69) were weighted to match the aggregated baseline pt characteristics from STORM Part 2. Baseline characteristics of prognostic significance (refractory status, cytogenetic profile, revised International Staging System stage, presence of extramedullary disease, and number of prior LOT) were adjusted for in the analysis. Comparative efficacy of tec vs sel-dex was estimated for overall response rate (ORR), complete response or better (≥CR) rate, progression-free survival (PFS), duration of response (DOR), and overall survival (OS). For binary endpoints (ORR and ≥CR rate), the relative effects of tec vs sel-dex were estimated using an odds ratio (OR) and 95% CI derived from a weighted logistic regression. Time-to-event endpoints (PFS, OS, and DOR) were estimated using a weighted Cox proportional hazards model. Results: After adjustment, the effective sample size (ESS) of the MajesTEC-1 cohort was 37. Baseline characteristics were balanced between the 2 cohorts. Pts treated with tec had improved ORR (OR 3.14; 95% CI 1.48–6.69; P= 0.0029), ≥CR rate (OR 16.3; 95% CI 3.5–77.1; P= 0.0004), PFS (HR 0.58; 95% CI 0.30–1.11; P= 0.1007), DOR (hazard ratio [HR] 0.04; 95% CI 0.01–0.10; P< 0.0001), and OS (HR 0.52; 95% CI 0.28–0.95; P= 0.0344) compared with sel-dex. Despite a reduced ESS that reduced limited power to detect statistically significant differences, the majority of outcomes was statistically significant in favor of tec. Conclusions : In this MAIC, tec showed significantly improved efficacy over sel-dex for all outcomes except PFS, which was numerically in favor of tec, highlighting its potential as a highly effective treatment option for pts with TCE RRMM who received ≥3 prior LOT.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".