The Tumor Associated Antigen PRAME Exhibits Dualistic Functions That Are Targetable in Diffuse Large B-Cell Lymphoma
Notice bibliographique
Résumé
Background: With the goal of translating biological discovery into clinical actionability, deciphering crosstalk in the cellular ecosystem of the tumor microenvironment (TME) has emerged as a research focus. Although comparatively little is known about the immune biology of diffuse large B-cell lymphoma (DLBCL), as reflected in clonal selection of specific somatic gene mutations in response to immune system pressure and the specific composition of the TME, PRAME has emerged as a prominent member of the cancer germline antigen/ tumor associated antigen (TAA) family of proteins. It is expressed in various types of cancers, but generally not in normal tissues, apart from male germinal cells, and triggers autologous T-cell mediated immune responses. PRAME is highlighted as a new cancer therapeutic target of T-cell or antibody-based immunotherapies with promising anti-tumor responses in early phase clinical trials and pre-clinical models for several types of cancers. In the context of developing new immunotherapies, targeting TAAs that are presented by major histocompatibility complexes on tumor cells is a promising therapeutic strategy for patients that experience treatment failure. Material and methods: We performed integrative genomic analysis of whole-transcriptome RNAseq, targeted genomic sequencing, and high-resolution copy number analysis in 347 de novo DLBCL tumors from patients uniformly treated with R-CHOP. Findings were correlated with pathological and clinical parameters, as well as TME composition. Using DLBCL-derived cell lines (7 EZH2 mutated and 5 wt) and CRISPR-Cas9 genome editing, we performed in vitro functional studies to characterize cell-intrinsic effects of PRAME knockout. Moreover, we studied EZH2 inhibition in a murine model of Ezh2 mutant lymphoma with a focus on mechanistic links between EZH2 activity and PRAME expression, as well as TME composition (cell-extrinsic effects). Results: We discovered recurrent, and highly focal deletions of 22q11.22 including the PRAME gene, which were associated with poor treatment outcome, independent of pathological and clinical risk factors. To explore PRAME-deletion-associated phenotypes and interaction with the tumor microenvironment (TME), we analyzed corresponding RNAseq, immunohistochemistry (IHC), and flow-cytometry data from our DLBCL cohort and utilized enzyme-linked immunospot (ELISPOT) assays using patient-derived peripheral blood mononuclear cells. PRAME deletions contributed to an immunologically "cold" TME, representing a somatically acquired mechanism to evade anti-tumor T-cell response (cell-extrinsic effect). Using PRAME knock out by CRISPR-Cas9 in vitro, TRAIL-mediated apoptotic signaling was impaired. In addition, PRAME down-modulation was strongly associated with somatic EZH2 Y641 mutations in DLBCL. Using proximity ligation assays and co-IP, we demonstrated that PRAME directly interacted with EZH2 as a negative regulator, establishing a link between PRAME deletions and EZH2 mutations with anti-apoptotic signaling in DLBCL (cell-intrinsic effect). An in vivo murine model of Ezh2 mutant lymphoma showed decreased T-cell infiltration in the TME and Ezh2 inhibition induced PRAME restoration as compared to vehicle controls. IHC for CD3, CD4, FOXP3, and GZMB revealed significant increases in various T-cell populations of the TME, including Tregs and cytotoxic T cells. EZH2 inhibition with EPZ-6438 abrogated these dualistic effects leading to increased immune cell infiltration in the tumor microenvironment and acceleration of apoptosis via PRAME restoration. Moreover, restoration of PRAME antigen presentation by EZH2 inhibition resulted in enhancement of PRAME binding using a T-cell receptor mimic PRAME antibody (Pr20), suggesting immunotherapeutic potential. Conclusion: Our findings highlight multiple functions of PRAME during lymphomagenesis. PRAME restoration by EZH2 inhibition provides a preclinical rationale for synergistic therapies combining epigenetic re-programming with PRAME-targeted therapies. Disclosures Dao: Eureka Therapeutics: Consultancy. Melnick:Jubilant: Consultancy; Epizyme: Consultancy; Constellation: Consultancy; Janssen: Research Funding; Daiichi Sankyo: Research Funding. Scheinberg:Lantheus: Current equity holder in private company; Eureka Therapeutics: Consultancy, Current equity holder in private company, Patents & Royalties: Eureka Therapuetics and MSKCC have filed patent on this ScFV and TCRm; Actinium: Consultancy, Current equity holder in private company; Contrafect: Current equity holder in private company; Sapience: Consultancy, Current equity holder in private company; Iovance: Current equity holder in private company; Enscyse: Current equity holder in private company; Arvenas: Current equity holder in private company; Pfizer: Consultancy, Current equity holder in private company; Sellas: Consultancy, Current equity holder in private company; Oncopep: Consultancy. Scott:Janssen: Consultancy, Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; Celgene: Consultancy; Roche/Genentech: Research Funding. Steidl:Curis Inc: Consultancy; Roche: Consultancy; AbbVie: Consultancy; Seattle Genetics: Consultancy; Bayer: Consultancy; Bristol-Myers Squibb: Research Funding; Juno Therapeutics: Consultancy.
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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 ».