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Enregistrement W4392296400 · doi:10.1097/01.cot.0001009844.75140.19

Tactics of Multiple Myeloma in Evading Targeted Immunotherapies

2024· article· en· W4392296400 sur OpenAlexaboutno aff
Dibash Kumar Das

Notice bibliographique

RevueOncology Times · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMultiple myelomaComputational biologyCancer researchComputer scienceMedicineInternal medicineBiology

Résumé

récupéré en direct d'OpenAlex

In a recent study featured in Nature Medicine, scientists unraveled the complex strategies employed by multiple myeloma to evade targeted immunotherapies, shedding light on how the disease adapts to resist treatments designed to eradicate it (2023; https://doi.org/10.1038/s41591-023-02491-5). This investigation focused on better understanding the mechanisms of antigen escape from B-cell maturation antigen (BCMA) and G protein-coupled receptor family C group 5 member D (GPRC5D) targeted immunotherapies in multiple myeloma. This knowledge promises to enhance future therapeutic approaches and bolster our understanding of multiple myeloma's adaptive resilience against cutting-edge treatments. Researchers focused on investigating tumor-intrinsic factors driving multiple myeloma antigen escape post-targeted therapies. Utilizing advanced genomic techniques, including whole genome sequencing and copy number variation analysis, the researchers analyzed samples from 40 patients who had relapsed refractory multiple myeloma and underwent anti-BCMA and/or anti-GPRC5D chimeric antigen receptor (CAR) T-cell or bispecific T-cell engager (TCE) therapy, indicating resistance to various classes of treatments. Their findings unearthed previously unknown mechanisms contributing to immune evasion. 1. TNFRSF17 Loss: The TNFRSF17 gene, also known as BCMA, plays a vital role in the survival and growth of malignant plasma cells in multiple myeloma. In this study, the loss of TNFRSF17 emerged as a significant factor in treatment resistance. Specifically, the research identified two distinct patterns: Post-Therapy Genomic Events: Nearly half of the patients experiencing disease progression after anti-BCMA CAR-T therapy exhibited genomic events affecting the TNFRSF17 locus. This finding suggested a pivotal role for TNFRSF17 loss in the development of treatment resistance. Anti-BCMA TCE Therapy: Patients who relapsed after anti-BCMA TCE therapy also showed alterations in the TNFRSF17 gene, albeit through different mechanisms. About 42.8 percent of these patients demonstrated either TNFRSF17 biallelic loss or extracellular domain mutation events. Such alterations compromised the effectiveness of anti-BCMA therapies, contributing to disease progression. Detailed case studies within the research highlighted how TNFRSF17 loss led to treatment resistance. For instance, one patient displayed a BCMA-negative clone resulting from the loss of TNFRSF17. This loss was observed in a significant proportion of post-relapse tumor cells, underlining its role in therapy evasion. 2. BCMA Extracellular Domain Mutations: Mutations in the BCMA gene's extracellular domain emerged as another critical factor in rendering anti-BCMA therapies ineffective. The study identified specific alterations in the BCMA gene that altered the binding sites targeted by anti-BCMA therapies. Unique Mutations: Six patients demonstrated nontruncating mutations in the BCMA extracellular domain, a phenomenon not previously reported in anti-BCMA therapy resistance. These mutations affected the interactions between the BCMA protein and various anti-BCMA therapies. Impact on Therapeutic Efficacy: These mutations led to compromised efficacy of anti-BCMA therapies. Detailed investigation revealed how certain mutations disrupted the binding of specific anti-BCMA therapies, resulting in treatment resistance. Tumor Dynamics Additionally, the study found instances of GPRC5D loss after anti-GPRC5D therapy in four patients. Interestingly, some patients exhibited pre-existing gene alterations that predisposed them to evade certain treatments. The analysis of large datasets further supported these findings, confirming the prevalence of TNFRSF17 and GPRC5D mutations in multiple myeloma. Further analysis revealed the prevalence of these evasion mechanisms before therapy initiation and at the time of relapse. Surprisingly, even before treatment, some patients exhibited subclonal mutations predisposing them to evade targeted therapies, challenging the conventional understanding of treatment-naive multiple myeloma. For additional information into their research, Oncology Times chatted with study author, Paola Neri, MD, PhD, Associate Professor of Medicine, Attending Physician in the Hematology Division at University of Calgary, and a member of the Arnie Charbonneau Cancer Institute. In addition, she is the Scientific Director of the Precision Oncology Hub in the Translational Research Laboratory at the Tom Baker Cancer Centre in Calgary, Canada. Oncology Times: Were there distinctive differences in the genetic landscape or clonal evolution between BCMA-negative relapses post-anti-BCMA therapies and GPRC5D-negative relapses after anti-GPRC5D therapies? Neri: “Antigenic escape is the predominant mechanism of acquired resistance to anti-BCMA and GPRC5D-targeted immunotherapies in multiple myeloma. The main difference observed in our study is the higher incidence of GPRC5D antigen loss when compared to BCMA. As such, monoallelic copy number losses in TNFRSF17 (BCMA) are present in 4-6 percent of T-cell immunotherapy-naïve multiple myeloma patients, while GPRC5D monoallelic loss is observed in 15 percent of naïve patients. “Furthermore, BCMA antigenic loss due to biallelic or monoallelic deletions coupled with BCMA extracellular domain mutations is observed in about 40 percent of relapse cases post anti-BCMA, especially TCE. However, the convergent evolution leading to biallelic antigenic loss of GPRC5D was observed in nearly all patients progressing on talquetamab (anti-GPRC5D TCE). This higher incidence of GPRC5D antigenic loss compared to BCMA is consistent with the oncogenic dependency of plasma cells that require BCMA rather than GPRC5D signaling to survive and proliferate.” Oncology Times: Could you explain the clinical implications of the observed convergent evolution leading to GPRC5D loss in multiple myeloma patients? Neri: “Clinically, the emergence of these mutant clones highlights the need for dynamic monitoring of antigenic loss with adapted interventions or use of multi-antigenic targeting modalities to minimize the risk of clonal escape. Ongoing strategies are currently evaluating the dual targeting of GPRC5D and BCMA by investigating the combination of talquetamab alternated to teclistamab or the use of anti-GPRC5D TCE as consolidation or maintenance approach following BCMA-targeting CAR-T cells. We expect eradication of multiple myeloma clones expressing low or no antigens and higher efficacy. Furthermore, exploration of alternative targets beyond BCMA or GPRC5D, such as FCRL5, is ongoing and has showed promising results in patients with relapsed/refractory multiple myeloma.” Oncology Times: How might this information influence treatment selection or design of future targeted immunotherapies in multiple myeloma? Neri: “The discovery of mutations in BCMA and GPRC5D that differentially attenuate CAR-T/TCE activity, leading to multiple myeloma disease relapse, highlights the critical relevance of screening for these variants. Therefore, their recognition is key for the selection of the right target and guarantees the efficacy of targeted immunotherapies in multiple myeloma. Similarly, identifying the immune-mediated causes of resistance and critical determinants of response to these therapies can lead to the identification of potential means to reverse tolerance, helping propagate an anti-tumor response and long-lasting remission in multiple myeloma patients. Ongoing studies to identify the optimal dosing schedule, duration of therapy, and combinations with other anti-multiple myeloma agents will enhance their efficacy and improve multiple myeloma patients' survival.” Dibash Kumar Das is a contributing writer.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,363
Score d'incertitude au seuil0,867

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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 Opus0,028
Tête enseignante GPT0,352
Écart entre enseignants0,324 · 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 tête enseignante, 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

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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