P906: GENOMIC ANALYSIS TO IDENTIFY DETERMINANTS OF INHERENT RESPONSE AND RESISTANCE TO ELRANATAMAB IN MAGNETISMM-3 COHORT A
Bibliographic record
Abstract
Topic: 14. Myeloma and other monoclonal gammopathies - Clinical Background: MagnetisMM-3 (NCT04649359) is an open-label, multicenter, non-randomized phase 2 study of elranatamab monotherapy in patients with multiple myeloma refractory to at least 1 proteasome inhibitor, 1 immunomodulatory drug, and 1 anti-CD38 antibody. Aims: This analysis examined molecular correlates of elranatamab response and resistance in patients naïve to B-cell maturation antigen (BCMA)-directed therapy (Cohort A). Methods: Bone marrow aspirate (BMA) samples collected at screening were analyzed by whole exome and whole transcriptome sequencing. To investigate the contribution of the tumor microenvironment in elranatamab response, the abundance of cell types in the BMA samples collected at screening was estimated using single sample gene set enrichment analysis (ssGSEA) of LM22 cell type signatures. For this analysis, response was defined as a best overall response of very good partial response or better, and non-response was defined as partial response or worse. Results:TNFRSF17 (BCMA encoding gene) expression correlated with markers of disease burden: levels increased with disease stage (with progressively higher levels in R-ISS stages I, II, and III; p=0.014), were higher in patients with high-risk cytogenetics (p=0.002), and correlated with plasma cell content in BMA samples (ρ=0.80; p<10-10) (Figure). TNFRSF17 expression trended higher in non-responders (p=0.08), but this trend was diminished when adjusting for disease burden. These findings suggest that TNFRSF17 expression in bulk bone marrow samples is associated with higher disease burden and likely poorer response. According to ssGSEA of LM22 cell types, plasma cells in BMA were associated with non-responders (p=0.03). Further multivariable modeling (controlling for plasma cell content) revealed additional cell types associated with response, including macrophages and monocytes, which were associated with poor outcome. Patients with both low plasma and low myeloid cells were most likely to respond. Genome wide copy number analysis showed that TNFRSF17 locus amplification was associated with non-response (p=0.008). Chromosomal alterations associated with non-response were identified, including genomic loci known to define high-risk multiple myeloma (eg, 1q21+) and loci not known to be associated with high-risk multiple myeloma (eg, 17q21+ and 6p21+). Summary/Conclusion: Genomic analysis of BMA samples from MagnetisMM-3 identified an association between higher TNFRSF17 expression in the tumor microenvironment and unfavorable outcomes, likely due to its surrogacy with increased tumor burden. Features of high-risk disease were also associated with lack of response to elranatamab. Adjusting for tumor cell content revealed additional aspects of the tumor microenvironment associated with poor response, including increased myeloid cell populations. Lastly, alterations in specific genomic loci were also associated with response, consistent with tumor intrinsic features influencing elranatamab response.Keywords: Multiple myeloma, Myeloma, Clinical trial, Gene expression
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".