Tumor heterogeneity and immune-evasive T follicular cell lymphoma phenotypes at single-cell resolution
Bibliographic record
Abstract
Abstract T follicular helper (T FH ) cell lymphomas (TFHLs) are characterized by T FH -like properties and accompanied by substantial immune-cell infiltration into tumor tissues. Nevertheless, the comprehensive understanding of tumor-cell heterogeneity and immune profiles of TFHL remains elusive. To address this, we conducted single-cell transcriptomic analysis on 9 lymph node (LN) and 16 peripheral blood (PB) samples from TFHL patients. Tumor cells were divided into 5 distinct subclusters, with significant heterogeneity observed in the expression levels of T FH markers. Copy number variation (CNV) and trajectory analyses indicated that the accumulation of CNVs, together with gene mutations, may drive the clonal evolution of tumor cells towards T FH -like and cell proliferation phenotypes. Additionally, we identified a novel tumor-cell-specific marker, PLS3. Notably, we found a significant increase in exhausted CD8 + T cells with oligoclonal expansion in TFHL LNs and PB, along with distinctive immune evasion characteristics exhibited by infiltrating regulatory T, myeloid, B, and natural killer cells. Finally, in-silico and spatial cell-cell interaction analyses revealed complex networking between tumor and immune cells, driving the formation of an immunosuppressive microenvironment. These findings highlight the remarkable tumor-cell heterogeneity and immunoevasion in TFHL beyond previous expectations, suggesting potential roles in treatment resistance.
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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 | 0.000 | 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".