Pharmacodynamic activity and clinical benefits of mezigdomide in high-risk biomarker subgroups from the CC-92480-MM-001 study
Bibliographic record
Abstract
Abstract Introduction: Mezigdomide (MEZI) is an oral CELMoD™ agent that induces rapid and potent degradation of lkaros and Aiolos. Reduction in these transcription factors results in direct tumoricidal and immunomodulatory effects in multiple myeloma (MM). MEZI showed promising efficacy and safety when combined with dexamethasone (DEX) in the phase 1/2 CC-92480-MM-001 trial (NCT03374085) in patients with relapsed/refractory MM (RRMM) (Richardson PG, et al. NEJM 2023;389(11):1009–1022); however, the effects of MEZI in high-risk molecular subgroups and/or plasmacytomas refractory to multiple prior therapies are unclear. Here we report biomarker analyses from blood and bone marrow samples to better understand pharmacodynamic (PD) activities in high-risk subgroups, as well as the potential associations of early biomarker changes with long-term clinical benefit. Methods: Clinical PD biomarker analyses focused on cohorts of patients with RRMM receiving MEZI plus DEX (MEZI-d) with doses ranging from 0.1 to 2.0 mg across multiple dosing schedules. Biomarker analyses included peripheral blood samples collected on treatment cycle (C)1 day1 pre- and post-dose to assess Aiolos expression in T cells. Serum free light chain (sFLC) and soluble B-cell maturation antigen (sBCMA) were analyzed as tumor burden biomarkers from C1 to C6. Bone marrow samples were collected for immunohistochemistry at screening through mid-C3 and for genomics analyses at screening. Biomarker analyses were evaluated in the escalation (n=77) and expansion (n=101) cohorts. Results: MEZI-d was pharmacodynamically active across all doses tested in the peripheral blood and tumors of patients with prior exposure and/or refractoriness to pomalidomide in their last prior line of therapy. No apparent association between baseline cereblon (CRBN)/Aiolos/Ikaros protein levels and response was observed in the tumors. A decreasing trend in Aiolos and Ikaros protein levels was observed in both responders (≥ partial response; median Aiolos degradation: −43.9%, n=7) and non-responders (median Aiolos degradation: −25.6%, n=13). Substrate degradation was also observed in patients with CRBN defects (defined as CRBN LOH; COPS7Bdel; COPS8del; Del10; CRBN mutation) and in those with high-risk molecular features [Del17p, TP53, t(4:14), t(14:16), Amp1q] as assessed by whole-genome sequencing (WGS). WGS analyses indicated that MEZI-d induced clinical response in patients with CRBN defects and ultra-high-risk molecular features (≥1 CRBN defect + ≥2 high-risk chromosomal abnormalities). In patients treated with MEZI-d, reductions in tumor burden biomarkers (sFLC and sBCMA) were observed in cytogenetic high-risk patients and in those with plasmacytomas. In the MEZI-d expansion cohort, analyses of Aiolos degradation in tumor, but not blood, samples were significantly associated with improved progression-free survival (PFS) in patients treated with MEZI-d by Cox proportional hazards regression model (CoxPH) (P=0.006). Kaplan Meier (KM) survival analysis using the median as the cut point showed that there was a significant improvement in PFS for patients who had Aiolos degradation ≥−47.1% (median PFS [mPFS] 9.9 months) compared with those with Aiolos degradation <−47.1% (mPFS, 2.8 months) (log rank, P=0.007; hazard ratio [HR], 0.17; 95% CI, 0.043–0.699). Moreover, patients who had a greater decrease in their involved light chain during the first 3 cycles of treatment also had a significant improvement in PFS (CoxPH, P<0.000001). KM analysis showed that PFS was significantly longer for patients achieving a nadir sFLC reduction of ≥−78.6% (mPFS, 7.1 months) compared with those with sFLC <−78.6% (mPFS, 2.3 months) (log rank, P<0.001; HR, 0.43; 95% CI, 0.270–0.700). Conclusions: Our data show that MEZI can induce antitumor activities in patients with RRMM with high-risk molecular features and/or plasmacytomas. Aiolos degradation and reductions in involved light chain as tumor burden biomarkers within the first 3 cycles of MEZI-d treatment was associated with improved PFS, indicating that these early changes and the potent, deep substrate degradation related to the mechanism of action of MEZI are associated with long-term clinical benefit and support dose selection in combination strategies for MEZI. Validation of these biomarker analyses in high-risk molecular subgroups and association of early changes with response will be further investigated in the phase 3 SUCCESSOR-1 and -2 trials.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".