Abnormal T follicular helper cell subsets in Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
Abstract The B cell malignancy Chronic Lymphocytic Leukemia (CLL) is a slow progressing disease of monoclonal B cell expansion and immune dysfunction associated with recurrent infections and second malignancies. CD4+ CXCR5+ T cells, known as follicular helper T cells (Tfh), have specialized properties in promoting B cell activation but their role in CLL is unknown. Here we examine the phenotype, activation status, clinical relevance and potential function of circulating Tfh cell subsets in CLL patients. We find that the percentage of Tfh cells is significantly increased in CLL patients compared to healthy age-matched controls. Tfh frequencies are significantly increased in advanced stage CLL patients compared to early stage disease. CLL patients also exhibited different composition of Tfh1, Tfh2 and Th17 subsets and show differential expression of activation markers compared to healthy controls. Our results indicate that the Tfh1 subset is particularly activated and expanded in CLL patients with more progressive disease. Tfh subset expansion and activation correlated with specific cytokine and chemokine levels and with decline in serum immunoglobulin levels. A large proportion of CD4+ T cells in CLL bone marrow express activated Tfh1 phenotype, suggesting that these cells are present within lymphoid tissues where CLL cells proliferate. Lastly, we found that treatment of CLL patients with the Btk inhibitor Ibrutinib lead to normalization of Tfh populations and reduced Tfh activation markers. Our results indicate that Tfh subset profiles could serve as useful biomarker to evaluate the progression of CLL and suggest selective expansion of Tfh1 cells may help drive CLL proliferation and immune dysfunction.
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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".