Targeting Lymphoma Associated Myeloid-Monocytic Cells through CSF1R Blockade Enhances CAR-T Cell Response in Aggressive B Cell Lymphoma
Notice bibliographique
Résumé
Introduction: Chimeric antigen receptor (CAR) T cell therapy has substantially improved the outcome of patients suffering from relapsed and/or refractory (r/r) aggressive B cell lymphoma. However, around 60% of patients do not show long-term remissions after CAR-T cell therapy. Recent studies have indicated a relevant role of the lymphoma microenvironment (LME) in response and resistance to CAR-T cell therapy. However, targeting the LME in aggressive B cell lymphoma to boost CAR-T cell efficacy has not yet been sufficiently explored. We therefore aimed to unravel the immunosuppressive capacity of the LME and its myelo-monocytic cell compartment with the ultimate goal to identify potential therapeutic targets and enhance CAR-T cell response. Methods: To elucidate hallmarks associated with an immunosuppressive LME and CAR-T cell resistance in patients with r/r B cell lymphoma, we applied multi-dimensional analyses to pre- and post-CAR-T cell-treated human lymphoma specimens (n = 41), including bulk RNA sequencing, single-cell RNA sequencing of 47,078 live cells and Imaging Mass Cytometry (IMC). To validate our findings and explore the potential of new therapeutic targets, we utilized ex vivo co-culture experiments, a fully murine CD19 CAR-T cell therapy platform in an immunocompetent, autochthonous DLBCL mouse model and performed bulk RNA sequencing and IMC of diseased spleens. Results: In our cohort of CAR-T cell treated patients (n = 104) durable response, defined as complete remission six months after CAR-T cell therapy, resulted in prolonged progression-free and overall survival. In CAR-T cell non-durable responding lymphoma patients, we identified a prognostically relevant lymphoma-associated myelo-monocytic (LAMM) signature including genes such as CD14, CD68, MARCO, ITGAM, IL1B, IL10 and S100A9. Furthermore, non-durable response was characterized by increased hypoxia and reduced (CD8+) T cell infiltration. In particular, in-depth profiling using single-cell RNA sequencing and IMC revealed a distinct CSF1R+CD14+CD68+ LAMM cell population associated with non-durable response and poor clinical outcome in CAR-T cell-treated patients with r/r B cell lymphoma. Importantly, high LAMM and low CD8+ T cell infiltration prior to CAR-T cell therapy showed a reduced progression-free survival when compared to low LAMM and high CD8+ T cell infiltration in r/r B cell lymphoma samples. Next, in ex vivo co-culture experiments we demonstrated that CSF1R+ LAMM cells strongly inhibit the proliferation and the cytotoxic capacity of CAR-T cells. To elaborate on LAMM-T cell interaction at a molecular level, we performed inference analysis of cell-cell communication in our single-cell RNA sequencing dataset using CellphoneDB which revealed that LAMM cells exert their immunosuppressive function by direct interaction with T cells via prostaglandin E2 (PGE2) and EP2/EP4 receptor signaling. Most strikingly, applying a fully autochthonous DLBCL CAR-T cell mouse model, we demonstrated that the combination of CD19 CAR-T cell therapy with CSF1R blockade switches an immunosuppressive LME into a T cell-enriched LME, which was accompanied by a follicular architecture and blood vessel normalization of diseased spleens indicated by IMC analysis. Finally, we showed that the combination of CSF1R inhibition and CD19 CAR-T cell therapy displayed synergistic treatment effects and prolonged survival with long-lasting, complete remissions. Conclusion: Our multiomic data and preclinical models provide strong evidence that CSF1R+ LAMM cells contribute to CAR-T cell failure in r/r aggressive B cell lymphoma and that CSF1R inhibition synergistically improves CD19 CAR-T cell response, promotes an immunosupportive microenvironment and restores anti-lymphoma immunity. Given that CSF1R inhibitors have already been clinically evaluated and FDA-approved in other malignancies, this therapeutic combination has the potential for rapid clinical translation. Based on our findings, we propose to test the combination of CAR-T cell therapy and CSF1R inhibitors in patients with r/r aggressive B cell lymphoma within prospective clinical trials.
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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 | 0,000 | 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) | 0,001 | 0,000 |
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.
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 ».