Ranitidine modifies myeloid cell populations and inhibits breast tumor development and spread in mice
Bibliographic record
Abstract
Histamine receptor 2 (H2) antagonists are widely used clinically for the control of gastrointestinal symptoms, but also impact immune function. They have been reported to reduce tumor growth in established colon and lung cancer models. Histamine has also been reported to modify populations of myeloid-derived suppressor cells (MDSCs). We have examined the impact of the widely used H2 antagonist ranitidine, on both myeloid cell populations and tumor development and spread, in three distinct models of breast cancer that highlight different stages of cancer progression. Oral ranitidine treatment significantly decreased the monocytic MDSC population in the spleen and bone marrow both alone and in the context of an orthotopic breast tumor model. H2 antagonists ranitidine and famotidine, but not H1 or H4 antagonists, significantly inhibited lung metastasis in the 4T1 model. In the E0771 model, ranitidine decreased primary tumor growth while omeprazole treatment had no impact on tumor development. Gemcitabine treatment prevented the tumor growth inhibition associated with ranitidine treatment. In keeping with ranitidine-induced changes in myeloid cell populations in non-tumor-bearing mice, ranitidine also delayed the onset of spontaneous tumor development, and decreased the number of tumors that developed in LKB1(-/-)/NIC mice. These results indicate that ranitidine alters monocyte populations associated with MDSC activity, and subsequently impacts breast tumor development and outcome. Ranitidine has potential as an adjuvant therapy or preventative agent in breast cancer and provides a novel and safe approach to the long-term reduction of tumor-associated immune suppression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".