The evolution to hepta-refractory myeloma involves sequential loss of CD38, BCMA and GPRC5D
Notice bibliographique
Résumé
Abstract Introduction: No other hematologic malignancy has seen more drug approvals than multiple myeloma (MM). Yet, MM remains largely incurable, even in the era of T-cell engaging therapies. Patients who have exhausted all treatment options can be considered “hepta-refractory” (i.e., resistant to 2 immunomodulatory drug (IMiD) classes, 2 proteasome inhibitor classes, CD38 antibodies, and both BCMA- and GPRC5D-targeted therapies). These patients are increasingly encountered in clinical practice. Often in good general condition, they actively seek further treatment. Understanding the mechanisms of relapse and resistance is key to guiding future therapy. Here, we investigated the clinical outcomes and underlying genomic landscape of hepta-refractory MM using whole genome sequencing (WGS). Methods: We included 33 patients meeting the criteria for hepta-refractory MM. WGS was performed in 17 patients, including 3 with sequential samples and 8 with extramedullary biopsies. WGS (depth 107x) was performed with purified CD138+ cells on NovaSeq instruments. Variants were called with Strelka2, Manta, GATK4 and HadoopCNV. Immunohistochemical (IHC) staining for BCMA was performed in 12 patients using an anti-BCMA antibody (E6D7B, Cell Signaling; 1:100) . Results: Median progression-free survival (PFS) in subsequent therapy lines and overall survival (OS) in hepta-refractory MM were 3.0 and 11.9 months, respectively. Despite this poor outcome, eight patients experienced a PFS of more than 6 months following various salvage therapies, which suggests underlying biological differences. WGS revealed frequent inactivation of tumor suppressor genes, including biallelic events affecting TP53 (n=6/17, 35%) and CDKN2C (n=6), consistent with a strong enrichment of high-risk disease. According to the latest IMWG consensus, 13/17 (77%) patients met criteria for high-risk MM. Next, we examined drug resistance mechanisms. Alterations in the CRBN pathway (comprising CRBN, CUL4A/B, DDB1, IKZF1/3, and COP9 signalosome) were detected in 12/17 (71%) patients, highlighting a major mechanism of IMiD resistance. CD38, the target of daratumumab or isatuximab, was affected by 4p deletions in 5/17 (29%) patients, including 2 patients with focal biallelic loss. The BCMA-encoding gene TNFRSF17 was altered in 9/17 (53%) patients: monoallelic loss in 2, and biallelic events in 7 patients. GPRC5D loss was also frequent: monoallelic in 2, and biallelic in 6/17 (35%). Notably, 5 patients (29%) showed biallelic loss of both target genes, TNFRSF17 and GPRC5D, severely limiting further immunotherapy options. In all patients with biallelic TNFRSF17 loss, BCMA protein expression was absent by IHC. Conversely, BCMA expression in TNFRSF17 wildtype cases varied from negative to strong, suggesting epigenetic modulation beyond WGS-detectable events. To gain insight into the evolution of ultra-refractory disease, we reconstructed phylogenetic trees for 3 patients with longitudinal samples during T-cell–engaging therapies. All showed branching evolution, consistent with initial deep responses. Of two patients treated with talquetamab, one acquired biallelic GPRC5D events at relapse, having already harbored a TNFRSF17 biallelic event after BCMA-directed therapies. The second developed monoallelic loss of GPRC5D. The third patient, treated sequentially with talquetamab, teclistamab and CAR-T, exhibited pronounced parallel evolution with the emergence of four distinct biallelic GPRC5D events and two independent TNFRSF17 deletions, underscoring the adaptive capacity of late-stage MM. We also explored the utility of WGS in guiding clinical decisions. In 6 patients retreated with BCMA-targeted therapies, 4 had no TNFRSF17 alterations and all responded. Vice versa, two patients with biallelic TNFRSF17 deletions were refractory to salvage BCMA-directed CAR-T therapy. Conclusion: Hepta-refractory MM is marked by extensive genetic alterations associated with high-risk disease and multidrug resistance. WGS proved helpful in selecting further therapies for this ultra-refractory population.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 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) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».