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Enregistrement W3094679970 · doi:10.1182/blood-2020-137650

Cite-Seq Profiling of T Cells in Multiple Myeloma Patients Undergoing BCMA Targeting CAR-T or Bites Immunotherapy

2020· article· en· W3094679970 sur OpenAlexaff
Noémie Leblay, Ranjan Maity, Elie Barakat, Sylvia McCulloch, Peter Duggan, Víctor H. Jiménez‐Zepeda, Nizar J. Bahlis, Paola Neri

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueCAR-T cell therapy research
Établissements canadiensInstitute of Cancer ResearchUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésBone marrowT cellImmunologyCD8Immune systemImmunotherapyAntigenPeripheral blood mononuclear cellCell therapyMedicineCD19BiologyCell

Résumé

récupéré en direct d'OpenAlex

Adaptive T cell therapy using chimeric antigen receptor (CAR) T cells and bispecific T cell engagers (BiTEs) have demonstrated encouraging responses in heavily pre-treated multiple myeloma (MM) patients. However, the cellular and molecular predictors of clinical response are not fully understood as well as the mediators of acquired resistance remain elusive. Local immune suppression and T cell exhaustion are important mediators of responses therefore, it is plausible to speculate that a tolerant tumor microenvironment and the expansion of specific T cell populations may dictate clinical responses. In this study, we performed at the single cell level a broad immunophenotypic and transcriptomic characterization of the blood and bone marrow (BM) T cells of sensitive and resistant MM patients treated with adaptive T cell therapies. Using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) we measured the expansion of variable T cell subsets, T cell specific activation and inhibitor markers and their functional states in order to identify cellular mediators of resistance to these adoptive immune therapies. Serial blood samples and BM aspirates (n=12) were collected from patients treated with anti-BCMA CAR-T or BCMA-CD3 BiTEs at variable time points, prior and post initiation of therapy and at relapse. Bone marrow mononuclear fractions were isolated through ficoll density gradients coupled with magnetic sorting of CD3pos T cells. Unbiased mRNA profiling coupled with feature barcoding technology for cell surface protein (TotalSeq-B) of BM CD3pos T cells was then performed by using the chromium single cell (10x Genomics). Paired-end sequencing was performed on Illumina platform. Cell Ranger and Seurat pipeline were used for sample de-multiplexing, barcode processing, single-cell 3′ gene counting, cell surface protein expression and data analysis. CAR-T cells were identified by the expression of the chimeric CAR-T cell transcript. The parallel measurement of transcripts and cell surface protein phenotypes of CD3pos T cells using a panel of 19 immune surface markers underlined the T cell repertoire diversity and identified different T cell subsets among the CD8pos and CD4pos T cells. Notably, the cell surface protein information overlaid on the transcript-generated UMA allowed accurate identification of all main immune clusters, in particular for the CD45RA and CD45RO positive cells. Comparison of CITE-Seq features revealed that the T cells composition of the blood and BM niches differed significantly between sensitive and resistant patients. As such an enrichment of CD4pos T cells with a higher CD4:CD8 ratio was noted in responding patients. Phenotypic (CD45RA, CD45RO, CD95, CCR7, CD62L, CD28, CD27) and transcriptional signatures (TCF7, LEF1, GATA3, EOMES, TBX21, PRDM1) also identified a higher proportion of memory like T cells (Tscm, Tcm) in responding patients. In contrast, T cells of resistant patients were enriched with terminally exhausted (Tex) and senescent cells with loss of CD28, high GMZHand GMZB, CD57pos, CD69pos and CD160pos as well as upregulation of TBX21. Expression of T cell checkpoint inhibitors such as LAG3, TIGIT and PD1 was high in these Tex cells as well as in some Tem. Of note, ex vivo T cell activation studies with TIGIT blockade demonstrated T cell activation in an autologous MM and T cell co-culture system with enhanced MM cells death. An expanded cluster of regulatory T cells (Treg) FOXP3pos,CD25pos was also observed in two resistant patients. Of note, no loss of BCMA transcript or surface expression was noted in MM cells at the time of acquired resistance. Single cell transcriptome of primary MM cells and chromatin accessibility (ATAC-seq) analyses of T cells of these patients are ongoing to investigate the transcriptional programs and epigenetic factors underlying the immune escape. Combined single cell features profiling of the transcriptome and surface protein expression of T cells from MM patients receiving BCMA targeted CAR-T or BiTEs therapies revealed potential mediators of resistance. In particular, T cells composition (low CD4:CD8 ratio and reduced population of Tscm, Tcm) along with an enrichment of terminally exhausted T cells are the main features observed in resistant patients. Delineating these mechanisms will guide future T cells engineering studies to enhance the efficacy and response durability of adoptive immunotherapy in MM. Disclosures McCulloch: Amgen: Honoraria; Sanofi: Honoraria; Celgene: Honoraria; Janssen: Honoraria. Duggan:Jannsen: Consultancy; Amgen: Consultancy; Novartis: Honoraria; Celgene: Consultancy; Astra Zeneca: Consultancy. Jimenez-Zepeda:Janssen, Celgene, Amgen, Takeda: Honoraria. Bahlis:AbbVie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Genentech: Consultancy, Honoraria; BMS/Celgene and Janssen: Consultancy, Honoraria, Other: Travel, Accomodations, Research Funding; Karyopharm Therapeutics: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria. Neri:Celgene/BMS: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria.

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)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,301
Score d'incertitude au seuil0,683

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
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 Opus0,031
Tête enseignante GPT0,276
Écart entre enseignants0,246 · 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'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

Citations71
Publié2020
Routes d'admission1
Résumé présentoui

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