Multiome Single Cell Analyses Identify a Distinct CD8+ T Cell Phenotype Linked to Treatment Response in PTLD
Notice bibliographique
Résumé
INTRODUCTION: Post-transplant lymphoproliferative disorders (PTLD) represent a frequent complication in solid organ transplant recipients, primarily caused by immune suppression. While reduction of immunosuppression and administration of rituximab can benefit some patients, those with severe symptoms or with incomplete response to rituximab require chemoimmunotherapy, frequently poorly tolerated. Although immune suppression is a critical factor in the pathogenesis of PTLD, our understanding of the role and composition of the tumor microenvironment (TME) in PTLD remains very limited. Here, we sought to characterize the biological diversity of infiltrating immune cells and gene regulation mechanisms at single cell resolution. METHODS: In this study, we performed single cell nuclei multiome RNA and Assay for Transposase-Accessible Chromatin sequencing (snRNA/ATACseq) assay on fresh frozen tissue collected from lymph nodes from: i) 10 pre-treatment PTLD patients (all monomorphic diffuse large B-cell lymphoma [DLBCL]); ii) 8 de novo DLBCL patients; iii) 3 reactive lymph nodes (RLN) serving as normal controls. We integrated the expression data from all cells and performed batch correction and normalization. RESULTS: To describe the PTLD-specific TME, we first conducted a systematic comparative analysis of the microenvironment between PTLD and RLN. Unsupervised clustering using PhenoGraph and visualization in the UMAP space identified 33 unique cell clusters. The CD8+ T-cell clusters with cytotoxic marker expression were predominantly derived from PTLD samples. In contrast, the regulatory T cell population was significantly lower in PTLD compared to RLN. Newt, we investigated cellular profile differences based on EBV status, noting that EBV-positive tumors (N = 4) formed distinct clusters from EBV-negative tumors (N =6). Differential gene expression analysis of CD8+ T cells revealed that cytotoxic markers such as GZMK and NKG7 were upregulated in EBV-positive PTLD, while naïve T-cell quiescence markers like FOXO1 were upregulated in EBV-negative cases. Cell-to-cell communication analysis using CellChat identified unique interactions in EBV-positive PTLD (e.g. CXCL9-CXCR3) and EBV-negative PTLD (e.g. CCL5-CCR1). We then examined T cell profiles of pre-treatment biopsies in patients where the PTLD had responded to immune-suppression reduction or rituximab alone (“immune responders”) (N = 5), and in those requiring chemoimmunotherapy (N = 5). To investigate T-cell subsets in detail, we performed unsupervised sub-clustering. A CD8+ T-cell sub-cluster (C1) was identified as most enriched in immune responders and characterized by high TCF-1 expression along with other naïve T-cell markers such as CCR7 and IL7R. However, C1 lacked memory and/or activated T-cell markers such as CD244 and PDCD1, suggesting a precursor-exhausted T-cell (TPEX)-like phenotype. TPEX cells are known to retain proliferative capacity, despite exhaustion, and are distinguished from terminally exhausted T-cell phenotypes. In contrast, TOX, a transcription factor (TF), a key regulator of the exhaustion program, was upregulated in patients requiring chemoimmunotherapy. We further confirmed increased chromatin accessibility of TCF-1, along with high TCF-1 RNA transcript levels, in the nuclei of CD8 T cell within C1 cluster. Motif analysis identified that multiple candidates in the top 10 enriched TF binding motifs belonged to the KLF TF family, including KLF4 and KLF5, which have established roles in maintaining naïve T-cell quiescence. Additionally, differential-TF binding motif analysis revealed that the open chromatin regions of CD8+ T cells in immune responders were enriched for the motif of EOMES, a known critical regulator for T-cell expansion and anti-tumor response. Finally, we confirmed that TPEX-like CD8+ T cells were significantly more abundant in PTLD than in de novo DLBCL, supporting a distinct role of TPEX-like CD8+ T cells in the PTLD TME, linked to treatment response. CONCLUSIONS: Our analysis provides novel insights into the immune contexture of PTLD. Importantly, our findings enhance our understanding of treatment response mechanisms related to a reduction of immunosuppression reduction and rituximab monotherapy. These findings may contribute to the development of novel biomarkers and treatment strategies.
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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 | 0,001 | 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 ».