Phenotypic Characterization and Prognostic Impact of CD103+ Tissue-Resident Memory T Cells in Diffuse Large B-cell Lymphoma
Bibliographic record
Abstract
Tissue-resident memory T (TRM) cells stably occupy tissues and contribute to immunosurveillance, where they are associated with favorable survival outcomes in solid tumors. Although TRM cells have been observed in lymph nodes, their phenotype and prognostic significance in diffuse large B-cell lymphoma (DLBCL) remains uncharacterized. In this study, CD103+ T cells were quantified in DLBCL samples by immunofluorescence staining of tissue biopsies and flow cytometry of cell disaggregates and linked with clinical outcomes. Across two patient cohorts, CD103+ T cells were identified in both nodal and extranodal DLBCL, and higher CD103+ T-cell levels correlated with superior clinical outcomes. Single-cell RNA sequencing revealed ITGAE-expressing T cells in both malignant and reactive lymph node samples. However, transcriptional profiles differed as a canonical TRM population was exclusively observed in the malignant setting. This TRM cluster was enriched for genes associated with cytotoxicity and activation and was validated in an external cellular indexing of transcriptomes and epitopes by sequencing (CITEseq) dataset. Flow cytometry additionally confirmed protein expression of TRM markers (CXCR6, CD39, and PD-1) on CD69+CD103+ T cells. We assessed functional activity in coculture experiments of CD103+ versus CD103- T cells with autologous CD20+ B cells, in which CD103+ T cells displayed enhanced killing. CD103+ TRM cells in DLBCL represent a prognostically favorable population with an activated/cytotoxic T-cell phenotype.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".