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Enregistrement W2913302924 · doi:10.1182/blood-2018-99-117531

Immunome Single Cell Profiling Reveals T Cell Exhaustion with Upregulation of Checkpoint Inhibitors LAG3 and Tigit on Marrow Infiltrating T Lymphocytes in Daratumumab and IMiDs Resistant Patients

2018· article· en· W2913302924 sur OpenAlexaff
Paola Neri, Ranjan Maity, Inès Tagoug, Sylvia McCulloch, Peter Duggan, Víctor H. Jiménez‐Zepeda, Jason Tay, Anjan Thakurta, Nizar J. Bahlis

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensInstitute of Cancer ResearchUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésDaratumumabTIGITPomalidomideCD38Bone marrowImmunotherapyBiologyCancer researchImmunologyImmune systemAntibodyMultiple myelomaMonoclonal antibodyCD34Stem cellLenalidomide

Résumé

récupéré en direct d'OpenAlex

Abstract Background: The addition of immunomodulatory (IMiDs) drugs to monoclonal antibodies targeting the transmembrane glycoprotein CD38 has demonstrated very encouraging and durable responses in myeloma patients. It is believed that the synergistic effects observed with anti-CD38 antibodies and IMiDs are derived from their co-modulation of the host adaptive and innate immunity and therefore it is plausible to speculate that acquired resistance to daratumumab and IMiDs may be largely immune-mediated. The aim of the present study was to 1) interrogate at the single cell level the bone marrow immune repertoire of daratumumab sensitive and resistant patients, 2) identify cellular mediators of resistance to anti-CD38 antibodies and 3) define potential means to reinstate sensitivity to daratumumab and IMiDs. Methods and Results: Serial BM aspirates (n=44) were collected from patients treated with single agent daratumumab (MMY3012 trial) or daratumumab + pomalidomide (MM014 trial) prior to initiation of therapy, C3D1 and at relapse. Bone marrow mononuclear fractions were isolated through ficoll density gradients coupled with magnetic sorting of CD138pos and CD138neg cells. Unbiased mRNA profiling of BM CD138neg cells was performed by single-cell RNA-seq (scRNA-seq) using the GemCode system (10x Genomics). Paired-end sequencing was performed on Illumina NEXTseq and NOVAseq platforms. Cell Ranger Single and Seurat were used for sample de-multiplexing, barcode processing, single-cell 3′ gene counting and data analysis. Sequencing data were analyzed by principal component analysis (PCA), clustering with multi-sample batch correction and then visualized by t-distributed stochastic neighbor embedding (t-SNE) projection. Comparison of the single cell transcriptomes of CD138neg cells from responding patients pre- and post- treatment revealed that Daratumumab and Pomalidomide dramatically modify the immune cells composition (immunome) of the bone marrow niches leading to: 1) expansion of effector T cells (KLRG1high, GZMAhigh, CCL5high), 2) significant depletion of CD38high / FCGR3Ahigh NK cells with retained population of cytotoxic NK cells (CD27high, KLRB1high, NCR3high, GZMApos, PRF1pos), 3) depletion of FCGR3Ahigh / CD14low monocytes, 4) expansion of M1 inflammatory macrophages and depletion of plasmacytoid dendritic cells. Similar changes were seen in patients treated with single agent daratumumab (without IMiDs) however with a lesser expansion of effector T cells and in particular reduced marrow infiltrating inflammatory macrophages. In contrast, the immunome of daratumumab and pomalidomide resistant patients was characterized by a reduced central memory T cells (TCM), and a largely exhausted effector T cells populations that are CD28neg and expressing checkpoint inhibitors (LAG3and TIGIT significantly more than PDCD1) as well as high expression of TIM3 (HAVCR2) on marrow macrophages. Upregulation of LAG3 and TIGIT expression on T cells was also confirmed at the antigenic level by flow cytometry. Consistent with the non-bystander and suppressive effect of LAG3 and TIGIT on the function of effector T cells, activation (CD107a expression) and proliferation of LAGpos and/or TIGITpos sorted bone marrow T cells from resistant patients were significantly reduced in response to autologous myeloma cells stimulation or CD3/CD28 crosslinking. Lastly, a higher proportion and number of clonal T cell (through single cell TCR sequencing) was also observed in responding (≥ PR) vs non-responding (< PR) patients. Interrogation of the myeloma cells transcriptome, showed little to no loss of CD38 transcript at the time of acquired resistance with rather upregulation of complement inhibitory molecule CD59 and NFκB signature genes. Conclusion: A systematic unsupervised interrogation of the bone marrow immunome of daratumumab and IMiDs treated MM patients demonstrated a significant activation of adaptive and innate immunity in responding patients and revealed an expansion of exhausted T cells with upregulation of the checkpoint inhibitors LAG3 and TIGIT in resistant patients. Our findings warrant the exploration of LAG3- and/or TIGIT-blocking strategies as potential means to reinstate sensitivity to daratumumab and IMiDs in myeloma patients. Disclosures Neri: Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. McCulloch:Celgene: Honoraria; Takeda: Other: Travel expenses. Thakurta:Celgene Corporation: Employment, Equity Ownership. Bahlis:Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding.

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: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
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 savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,012
Tête enseignante GPT0,235
Écart entre enseignants0,223 · 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

Citations20
Publié2018
Routes d'admission1
Résumé présentoui

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