<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.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".