Inhibition of the transcription factor PU.1 suppresses tumor growth in mice by promoting the recruitment of cytotoxic lymphocytes through the CXCL9-CXCR3 axis
Bibliographic record
Abstract
Abstract Tumor-associated macrophages (TAMs) are among the most abundant immune cells associated with tumors, which often exhibit immune regulatory phenotypes that promote tumor growth and confer resistance to anti-tumor immune therapies. Despite extensive efforts in developing immunotherapeutic strategies aimed at controlling the recruitment or reprogramming of TAMs, success has been limited due to strategic caveats, underscoring the need for a novel approach targeting the TAMs. PU.1, a lineage-dependent transcription factor, is highly expressed throughout the lifespan of macrophages. We have found that inhibition of PU.1 by the small molecule DB2313 suppresses melanoma tumor growth in mice through enhanced tumor recruitment of CD4+ T helper cells and cytotoxic T/NK cells mediated by TAMs. Whole transcriptome and targeted gene expression analyses revealed that DB2313 upregulates CXCL9 expression in bone marrow-derived macrophages (BMDMs) and TAMs. The anti-tumor effects of DB2313 were abolished by depleting macrophages with clodronate or inhibiting the CXCL9-CXCR3 chemokine axis using neutralizing antibodies against CXCL9 or CXCR3. Collectively, these results suggest that pharmacological inhibition of PU.1 suppresses tumor growth by promoting tumor infiltrating lymphocytes through the CXCL9-CXCR3 chemokine axis. Our study establishes a framework for developing TAM-modulating immunotherapies by targeting the transcriptional factor PU.1.
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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) | 0.001 | 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 | 0.001 |
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".