The CCL17-CCR4 axis is critical for mutant STAT6-mediated microenvironmental remodelling and therapeutic resistance in Relapsed/Refractory Diffuse Large B Cell Lymphoma
Bibliographic record
Abstract
Abstract Relapsed and refractory Diffuse Large B Cell Lymphoma (rrDLBCL) presents a significant challenge in hematology-oncology, with approximately 30-40% of DLBCL patients experiencing relapse or resistance to treatment. This underscores the urgent need to better understand the molecular mechanisms governing therapeutic resistance. Signal Transducer and Activator of Transcription 6 (STAT6) has been previously identified as a gene with recurrent D419 gain-of-function mutations in rrDLCBL. When STAT6 D419 mutations are present in DLBCL tumour cells, we have demonstrated that transcription of the chemokine CCL17 (aka TARC) is increased, and tumours have increased infiltration of CD4+ T cells. However, the significance of increased T cell infiltration had not been determined. In the present study, we developed a mouse model of STAT6 D419N mutant DLBCL, that recapitulates the critical features of human STAT6 D419 mutant DLBCL, including increased expression of phospho-STAT6, increased CD4+ T cell invasion, and resistance to doxorubicin treatment. With this model, we found CD4+ T cells in STAT6 D419N tumours have higher expression of the receptor for CCL17, CCR4. Using ex vivo functional assays we demonstrate that STAT6 D419N tumour cells are directly chemoattractive to CCR4+ CD4+ T cells, and when CCR4 is inhibited using a small molecule antagonist, CD4+ T cells in STAT6 D419N tumours are reduced and STAT6 D419N tumours regain therapeutic sensitivity to doxorubicin. Using PhenoCycler imaging of human rrDLBCL samples, we find that STAT6 D419 tumours indeed have increased expression of phospho-STAT6+ and increased cellular interactions between phospho-STAT6+ tumour cells and CD4+/ CCR4+ CD4+ T cells. Thus, our data identify CCR4 as an attractive therapeutic target in STAT6 D419 mutant rrDLBCL.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".