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
Background: Targeting tumor-associated macrophages (TAMs) is a promising immunotherapy for cancers, but current strategies are limited due to strategic caveats. PU.1 is a transcription factor required for macrophage generation and differentiation. To date, the effect of PU.1 inhibition on solid tumors is unknown. Methods: This study examines the anti-tumor effect of PU.1 inhibition and its mechanism using the small-molecule DB2313 in mouse melanoma and breast tumor models. Results: We found that inhibition of PU.1 by DB2313 suppresses B16-OVA melanoma and 4T1 breast tumor growth in mice. In the melanoma tumor model, DB2313 enhanced tumor recruitment of CD4+ T helper 1 (Th1) and cytotoxic T/natural killer (NK) cells by targeting TAMs. Transcriptome and targeted gene expression analyses revealed that PU.1 inhibition by DB2313 and small-interference RNAs enhances CXCL9 expression in bulk tumors, TAMs, and bone marrow-derived macrophages. The anti-tumor effects of DB2313 were abolished by depleting macrophages with clodronate or inhibiting the CXCL9-CXCR3 chemokine axis using CXCL9- or CXCR3-neutralizing antibodies. Conclusions: These results suggest that pharmacological inhibition of PU.1 suppresses tumor growth by at least promoting the infiltration of lymphocytes into tumors 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 | 0.000 |
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".