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Enregistrement W2983600820 · doi:10.1182/blood-2019-126222

Reduced Antigen Presentation May Contribute to Immunomodulatory Drug Resistance in Multiple Myeloma

2019· article· en· W2983600820 sur OpenAlexaff
David G. Coffey, Ola Landgren, Yuexin Xu, Dickran Kazandjian, Trevor J. Pugh, Sacha Gnjatic, Edus H. Warren, Damian J. Green

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensPrincess Margaret Cancer CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésLenalidomideMultiple myelomaMinimal residual diseaseImmunologyBone marrowMedicinePeripheral blood mononuclear cellCancerCancer researchImmune systemBiologyOncologyInternal medicineIn vitro

Résumé

récupéré en direct d'OpenAlex

Introduction: Maintenance therapy with the immunomodulatory drug (IMiD) lenalidomide improves progression-free survival (PFS) and overall survival among patients with multiple myeloma (MM). It has been suggested that IMiDs enhance immune-mediated anti-tumor responses through increased activation and proliferation of T cells and natural killer (NK) cells as well as inhibition of regulatory T cells. Despite the effectiveness of this therapy, nearly all patients relapse for reasons that remain uncharacterized. We sought to elucidate mechanisms of resistance to lenalidomide maintenance through investigation of mutations in cell-free DNA (cfDNA) and identification of changes in the innate and adaptive immune system by single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs). While MM is a cancer of plasma cells that primarily reside in the bone marrow, we focused on the peripheral blood which offers a comprehensive view of multiple sites of disease including extramedullary locations commonly found at the time of relapse. Methods: Twenty-four patients with MM and no minimal residual disease by 10-color flow cytometry and IGHVnext-generation sequencing (Invivoscribe) after induction therapy with or without an autologous stem cell transplant were selected for analysis. All patients were enrolled in NCT02538198 at Memorial Sloan Kettering Cancer Center. Peripheral blood was collected serially including timepoints prior to lenalidomide maintenance and after disease progression. Plasma and PBMCs were isolated by Ficoll-Paque density gradient centrifugation. cfDNA was extracted from plasma and targeted next-generation sequencing using a 70-gene panel of recurrently mutated genes in myeloma was performed using the QIAseq Targeted DNA kit (QIAGEN). DNA sequencing was also performed using the same gene panel on granulocytes to exclude germline variants. On PBMC samples, 5' gene expression and T and B cell receptor single-cell sequencing were performed using the Chromium Single Cell V(D)J kit (10X Genomics). Analysis of single-cell sequencing was performed on the top 100 principal components and visualized by Uniform Manifold Approximation and Projection (UMAP). A regression-based classifier was trained to automatically assign clustered cells into 26 immune cell phenotypes using lists of genes reported to be uniquely expressed by each cell type. Results: cfDNA sequencing was performed on 12 patients characterized as durable responders (PFS > 57 months) and 12 early progressors (median PFS 25 months). Prior to maintenance therapy, no significant difference was observed in the number of cfDNA mutations between these groups. The number of non-synonymous somatic mutations in cfDNA tended to increase over time and was significantly higher among patients taking lenalidomide for more than 2 years compared to patients at the time of relapsed disease (mean number of mutations was 7 in responders versus 3 in progressors).ZNF292was among the genes most significantly mutated in responders compared to progressors. ZNF292encodes a zinc finger protein involved in RNA binding and has previously been identified as a potential source of tumor-associated antigens in patients with monoclonal gammopathy of undetermined significance. To identify changes in immune cell phenotype and function that associate with disease progression, we performed single-cell RNA sequencing on PBMCs from the 12 progressors before maintenance and at the time of relapse. The median number of cells sequenced per PBMC sample was 3,132. Over 90% of cells could automatically be classified into a specific immune cell type by their gene expression. We observed the frequency of NK cells and plasmablasts were significantly increased at the time progression on lenalidomide while monocytes, dendritic cells, and basophils were decreased. Conclusion: Mechanisms of resistance among patients receiving treatment with IMiDs is not well known. Using targeted sequencing of cfDNA, we identify patients responding to maintenance tended to have a higher mutational load in cfDNA including genes previously reported to encode tumor-associated antigens. Additionally, we show that a decline in antigen-presenting dendritic cells is more common at the time of disease progression. Taken together, these results suggest reduced neoantigen production and a decline in antigen presentation may contribute to IMiD resistance. Disclosures Landgren: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Other: IDMC; Theradex: Other: IDMC; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees. Green:Celgene: Consultancy; Cellectar Biosciences: Research Funding; Seattle Genetics: Research Funding; Juno Therapeutics: Consultancy, Patents & Royalties, Research Funding; GSK: Consultancy.

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)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

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)0,0030,000

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 Opus0,016
Tête enseignante GPT0,290
Écart entre enseignants0,273 · 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'étudeExpérimental (laboratoire)
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
Publié2019
Routes d'admission1
Résumé présentoui

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