Altered T Follicular Helper Cell Subsets and Function in Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
Follicular helper T cells (TFH) have specialized properties in promoting normal B cell activation but their role in chronic lymphocytic leukemia (CLL) is unknown. We find that TFH cells are elevated in CLL patients and are phenotypically abnormal, expressing higher levels of PD-1, TIGIT, CD40L, IFNγ and IL-21, and exhibiting abnormal composition of TFH1, TFH2 and TFH17 subsets. Frequencies of CD4-positive T cells expressing TFH1 markers and IL-21 were positively correlated with patient lymphocyte counts and RAI stage, suggesting that accumulation of abnormal TFH cells is concomitant with expansion of the leukemic B cell clone. Treatment with ibrutinib led to normalization of TFH frequencies and phenotype. TFH cells identified in CLL bone marrow display elevated expression of several functional markers compared to blood TFH cells. CLL T cell-B cell co-culture experiments revealed a correlation of patient TFH frequencies with functional ability of their CD4-positive T cells to promote CLL proliferation. Conversely, CLL cells can preferentially activate the TFH cell subset in co-culture. Together our results indicate that CLL development is associated with expansion of abnormal TFH populations that produce elevated levels of cytokines and costimulatory molecules which may help support CLL proliferation.
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 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".