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Enregistrement W4405044949 · doi:10.1182/blood-2024-201288

Genomic Determinants of Resistance to Anti-BCMA Chimeric Antigen Receptor T-Cell (CART) Therapies in Patients with Relapsed/Refractory Multiple Myeloma

2024· article· en· W4405044949 sur OpenAlexaff
Francesco Maura, Ciara L. Freeman, Holly Lee, Kylee Maclachlan, Michael Durante, Bachisio Ziccheddu, Meghan Menges, Benjamin Diamond, Marios Papadimitriou, Ariosto Siqueira Silva, Praneeth Sudalagunta, Noémie Leblay, Sungwoo Ahn, Etta Rozen Füller, Edward L. Briercheck, Phaedra Agius, Doris K. Hansen, Xiaofei Song, Xiaohong Zhao, Mark B. Meads, Jamie K. Teer, Ross Firestone, Juan‐José Garcés, Tomas Jelinek, Rachid Baz, Melissa Alsina, Eric L. Smith, Sergio Giralt, Sham Mailankody, Paola Neri, Saad Z. Usmani, Frederick L. Locke, Nizar J. Bahlis, Ola Landgren, Kenneth H. Shain

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésChimeric antigen receptorMedicineCartMultiple myelomaRefractory (planetary science)OncologyLenalidomideInternal medicineCancer researchImmunologyImmunotherapyBiologyCancer

Résumé

récupéré en direct d'OpenAlex

The introduction of chimeric antigen receptor T cells (CART) and bispecific T-cell engagers (TCE) has revolutionized the treatment landscape in patients with relapsed/ refractory multiple myeloma (RRMM). However, despite impressive responses reported to date, the mechanisms responsible for resistance or treatment failure remain inadequately determined. To investigate the genomic mechanisms involved in primary refractoriness and resistance to anti-BCMA immunotherapies we interrogated 122 whole genomes (WGS; 80X median coverage) and 10 whole exomes (WES) generated from a total of 96 patients treated with either CART (n=74) or T-cell engagers (TCE, n=22). 74 and 13 patients had samples collected before treatment with CART (idecel n=58; ciltacel n=16) and TCE, respectively. Patients treated with CART had a median progression-free survival (PFS) of 394 days, with 19 (25%) patients progressing within the first 100 days (i.e. refractory). The presence of pre-treatment extramedullary disease (EMD, 12%) and prior anti-BCMA exposure (20%) was associated with inferior progression free survival (PFS) (both p<0.0001). The MyCARe score high-risk patients (n=3, 4%) in this cohort had poor outcomes ; however, it failed to discriminate between low (n=21, 37.5%) and intermediate risk (n=32, 57%) (p=0.10). Loss of TNFRSF17 was observed in 5/96 (5%) patients, 4 of whom were treated with CART. Of these, 3 had previously been exposed to anti-BCMA therapies, such as belantamab mafodotin (n=2), and these genomic events were present before CART treatment, causing complete refractoriness to CART. Interestingly, all patients with biallelic loss of BCMA were also noted to have CYLD or TRAF3 biallelic loss, key regulator of NFkB signaling. We hypothesize that as BCMA is a driver of NFkB activation in MM cells and that only in the presence of genomic alterations involving NFkB, can this absence of BCMA be tolerated by the tumor cell, promoting resistance to CART. Next, we investigated what other alternations in pre-CART samples associate with inferior PFS and treatment refractory disease. Among known high-risk features 1q gain was significantly associated with inferior PFS. In investigating a large catalogue of driver genes, we identified multiple genomic drivers involved in resistance and primary refractoriness to anti-BCMA CAR-T. These drivers can be categorized into five major groups: one associated with favorable PFS and four associated with unfavorable PFS. The favorable group included patients with RPL10 mutations (84% patients in remission at 1 year). The second group included loss of genes involved in genomic instability and complexity such as RPL5, TP53, CDKN2C and presence of hyper-APOBEC. The third group included genes involved in the NFkB signaling (CYLD, TRAF3, NFKB2, MAP3K14). The fourth group included loss of function events involving transcription factors and regulators (e.g. SP140, KMT2C, DIS3). The last group had genomic events known to be involved in plasma cell differentiation (e.g. IKFZ3, CD38, XBP1, TNFRSF17). Overall, patients with genomic events from any two of the unfavorable groups (n=32) had significantly worse outcomes compared with the other patients (median PFS 75 vs 763 days, p<0.0001), accounting for 84% of all refractory patients. By employing a Cox proportional-hazards model, we demonstrated that these genomic features independently and more accurately predict refractoriness to anti-BCMA CAR-T therapy [p<0.0001; Hazard ratio (HR): 5.5497] compared to traditional risk scores like EMD (p=0.59, HR: 0.5945) and MyCARe (p=0.03, HR: 0.1694). Comparing WGS data from samples collected at the time of progression after CART (n=12) and post-TCE (n=9) patients, only one BCMA mutation (P33S) was observed after CART, and its impact on CART binding was not confirmed in functional studies. This is different from TCE where these mutations and antigen escape account for >50% of relapse (5/9; Lee et al. Nat Med 2023). Furthermore, it supports the hypothesis that the high prevalence of BCMA mutations seen post-TCE is a consequence of continuous selective pressure by TCE-based therapies. Overall, these data suggest that comprehensive genomic profiling can accurately predict clinical outcomes in MM patients treated with anti-BCMA CART outperforming current clinical predictors of risk and potentially serving as tool to select different treatment strategies.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,250
Écart entre enseignants0,239 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2024
Routes d'admission1
Résumé présentoui

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