<scp>JAK–STAT</scp> signalling shapes the <scp>NF‐κB</scp> response in <scp>CLL</scp> towards venetoclax sensitivity or resistance via <scp>Bcl‐XL</scp>
Bibliographic record
Abstract
Preventing or overcoming resistance to the Bcl-2 inhibitor venetoclax is an emerging unmet clinical need in patients with chronic lymphocytic leukaemia (CLL). The upregulation of anti-apoptotic Bcl-2 members through signalling pathways within the tumor microenvironment appears as a major factor leading to resistance to venetoclax. Previously, we reported that T cells can drive resistance through CD40 and non-canonical NF-κB activation and subsequent Bcl-XL induction. Moreover, the T cell-derived cytokines IL-21 and IL-4 differentially affect Bcl-XL expression and sensitivity to venetoclax via unknown mechanisms. Here, we mechanistically dissected how Bcl-XL is regulated in the context of JAK-STAT signalling in primary CLL. First, we demonstrated a clear antagonistic role of IL-21/STAT3 signalling in the NF-κB-mediated expression of Bcl-XL, whereas IL-4/STAT6 further promoted the expression of Bcl-XL. In comparison, Bfl-1, another NF-κB target, was not differentially affected by either cytokine. Second, STAT3 and STAT6 affected Bcl-XL transcription by binding to its promoter without disrupting the DNA-binding activity of NF-κB. Third, in situ proximity ligation assays (isPLAs) indicated crosstalk between JAK-STAT signalling and NF-κB, in which STAT3 inhibited canonical NF-κB by accelerating nuclear export, and STAT6 promoted non-canonical NF-κB. Finally, NF-κB inducing kinase (NIK) inhibition interrupted the NF-κB/STAT crosstalk and resensitized CLL cells to venetoclax. In conclusion, we uncovered distinct crosstalk mechanisms that shape the NF-κB response in CLL towards venetoclax sensitivity or resistance via Bcl-XL, thereby revealing new potential therapeutic targets.
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 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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".