Multiome Single Cell Analyses Identify a Distinct CD8+ T Cell Phenotype Linked to Treatment Response in PTLD
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".