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Record W4405041550 · doi:10.1182/blood-2024-194926

Multiome Single Cell Analyses Identify a Distinct CD8+ T Cell Phenotype Linked to Treatment Response in PTLD

2024· article· en· W4405041550 on OpenAlexaff
Tomohiro Aoki, Davidson Zhao, Noémie Lang, Yifan Yin, Ting Liu, Michael Hong, Johanna Regala, Troy Ketela, Anca Prica, John Kuruvilla, Pedro Farinha, Jan Delabie, Federico Gaiti, Kerry J. Savage, David W. Scott, Robert Kridel

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreSpinal Cord Injury BCUniversity Health Network
Fundersnot available
KeywordsPhenotypeBiologyCD8CellImmunologyCytotoxic T cellGeneticsMolecular biologyImmune systemGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.040
GPT teacher head0.336
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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