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
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».