T-cell dysfunction in chronic lymphocytic leukemia from an epigenetic perspective
Bibliographic record
Abstract
Cellular immunotherapeutic approaches such as chimeric antigen receptor (CAR) T-cell therapy in chronic lymphocytic leukemia (CLL) thus far have not met the high expectations. Therefore it is essential to better understand the molecular mechanisms of CLLinduced T-cell dysfunction. Even though a significant number of studies are available on T-cell function and dysfunction in CLL patients, none examine dysfunction at the epigenomic level. In non-malignant T-cell research, epigenomics is widely employed to define the differentiation pathway into T-cell exhaustion. Additionally, metabolic restrictions in the tumor microenvironment that cause T-cell dysfunction are often mediated by epigenetic changes. With this review paper we argue that understanding the epigenetic (dys)regulation in T cells of CLL patients should be leveled to the knowledge we currently have of the neoplastic B cells themselves. This will permit a complete understanding of how these immune cell interactions regulate T- and B-cell function. Here we relate the cellular and phenotypic characteristics of CLL-induced T-cell dysfunction to epigenetic studies of T-cell regulation emerging from chronic viral infection and tumor models. This paper proposes a framework for future studies into the epigenetic regulation of CLL-induced Tcell dysfunction, knowledge that will help to guide improvements in the utility of autologous T-cell based therapies in CLL.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".