Longitudinal Correlative Profiles of Responders, Nonresponders, and Those with Relapse on Treatment with Teclistamab in the Phase 1/2 MajesTEC-1 Study of Patients with Relapsed/Refractory Multiple Myeloma
Bibliographic record
Abstract
Introduction: Teclistamab (tec) is the only approved B-cell maturation antigen (BCMA) × CD3 bispecific antibody with a personalized, weight-based dosing schedule for the treatment of triple-class exposed relapsed/refractory multiple myeloma (RRMM). In the phase 1/2 MajesTEC-1 study (NCT03145181/NCT04557098), tec demonstrated deep and durable responses. To better understand mechanisms of resistance and relapse in MajesTEC-1, we assessed longitudinal BCMA expression and immune profiles. Methods: Patients (N=165) with RRMM received subcutaneous tec 1.5 mg/kg weekly after step-up doses of 0.06 and 0.3 mg/kg, with the option to switch to every-other-week dosing if they achieved at least a partial response after ≥4 cycles of therapy in phase 1 or a complete response or better for ≥6 months in phase 2. Bone marrow and peripheral blood samples at baseline, on treatment, and at disease progression (PD) were analyzed by flow cytometry for BCMA expression and immune cell populations. Bone marrow aspirates were analyzed by cytometry by time of flight (CyTOF) for immune cell populations. Soluble BCMA (sBCMA) was analyzed in serum samples by electrochemiluminescence ligand binding. Results: Patients who responded to tec had a greater recovery of CD3+ T cells in the periphery and bone marrow during the first treatment cycles, which was sustained over time compared with nonresponders. Greater activation during the first cycle was observed in responders, indicated by a transient increase in CD38 on CD8+ T cells. In contrast, an exhausted T-cell phenotype in the blood and bone marrow was observed longitudinally in nonresponders vs responders, indicated by increasing and sustained levels of activation and exhaustion markers including persistence of CD38+ T cells and expression of LAG-3, PD-1 (Figure 1A), PD-1/LAG-3, and PD-1/TIM-3 on CD4+ or CD8+ T cells. Higher levels of immunosuppressive regulatory T cells (Tregs), including CD38+ Tregs, were also sustained in the periphery in nonresponders compared with responders. BCMA expression and sBCMA levels were assessed in a subset of patients who initially responded then relapsed on tec, who had matched baseline and end-of-treatment/PD samples. There were no significant changes in the frequency of BCMA+ plasma cells, BCMA receptor density, or sBCMA levels at PD relative to baseline, suggesting BCMA loss was not a mechanism of relapse in these patients. In contrast, higher proportions of peripheral CD4+ and/or CD8+ T cells expressing CD38, TIM-3, PD-1, and PD-1/TIM-3 were observed at relapse vs baseline. Analysis of bone marrow progression samples using CyTOF in patients who relapsed showed significantly lower CD28 expression on CD8+ T cells, significantly higher expression of exhaustion markers (CD38, PD-1, TIM-3, EOMES, and TOX) and perforin on CD4+ and/or CD8+ T cells, a higher frequency of CD4+ and CD8+ T cells expressing TIM-3 and co-expressing CD38/TOX, PD-1/TIM-3, and TOX/TIM-3 (Figure 1B), and significantly higher CD38 expression on T-cell receptor gamma delta T cells, at PD than at baseline. PD-1 and TIGIT expression was also significantly higher at PD than at baseline across CD25hiCD127dim and/or CD25hiCD127dimFoxP3+ Tregs in patients who relapsed. Conclusions: Patients responding to tec exhibited a differential immune profile in early cycles compared with nonresponders, with greater transient T-cell activation. In contrast, an exhausted T-cell phenotype was sustained in nonresponders during the first 2 treatment cycles. While mutations in BCMA cannot be excluded, we did not detect BCMA loss as a mechanism of relapse, but observed a dysfunctional immune phenotype at PD, with increased T-cell exhaustion and higher frequency of gamma delta T cells and immunosuppressive Tregs. These results suggest the importance of immune fitness and T-cell function in achieving and maintaining a response to tec. Ongoing studies will evaluate these correlatives in earlier treatment lines, where patients may have more favorable immune profiles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".