P906: GENOMIC ANALYSIS TO IDENTIFY DETERMINANTS OF INHERENT RESPONSE AND RESISTANCE TO ELRANATAMAB IN MAGNETISMM-3 COHORT A
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».