ChREBP-Mediated Choline Deprivation and Chemokine Secretion Shape Tumor-Associated Macrophages to Promote Immune Evasion
Bibliographic record
Abstract
Tumor metabolic reprogramming has been recognized as a critical determinant in tumor development and cancer immunotherapy response. Aberrant choline metabolism is emerging as a defining hallmark of cancer. In this study, we found that carbohydrate-responsive element-binding protein (ChREBP)-mediated choline deprivation induced tumor-associated macrophage (TAM) reprogramming and maintained an immunosuppressive tumor microenvironment. Mechanistically, ChREBP interacted with SP1 to increase the expression of immunosuppressive chemokines CCL2 and CCL7 and choline transporter SLC44A1. As such, high CCL2 and CCL7 expression promoted recruitment of TAMs. Tumor cells with high SLC44A1 levels competed with M1-like TAMs for choline, inhibiting cGAS/STING signaling and promoting the repolarization of M1-like to M2-like macrophages. Clinically, ChREBP-SP1-choline metabolism axis expression was associated with poor clinical outcome in colorectal cancer. Thus, the study identified the interplay between tumors and TAMs via choline competition as a previously unknown immune evasion mechanism in the tumor microenvironment and proposes ChREBP as a potential immunotherapeutic target in cancer. SIGNIFICANCE: ChREBP induces a choline-deprived tumor microenvironment and promotes chemokine secretion to facilitate immune evasion, suggesting targeting ChREBP as a therapeutic approach to improve the efficacy of immunotherapy.
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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 | 0.000 |
| 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".