Histamine receptor 2 blockade reduces breast tumor development and metastasis (TUM9P.1010)
Bibliographic record
Abstract
Abstract Histamine has multiple immunomodulatory roles that impact innate and acquired immunity. Histamine receptor antagonists are widely used clinically to treat allergic disease and gastrointestinal disorders, but their ability to alter effective immune function to breast tumors is unknown. We hypothesized that histamine receptor antagonist treatment could alter immune function in mouse models of breast cancer, therefore altering the growth and spread of breast cancer. Two injectable breast tumor models (the 4T1-BALB/c and E0771-C57BL/6 model) and a spontaneous breast cancer model (STK-/-/NIC model) were utilized. Histamine receptor antagonists were administered in the drinking water prior to tumor cell injection or at time of weaning, respectively. The immune cell populations in the spleen and bone marrow of tumor-bearing mice were analyzed. Ranitidine and famotidine (Hrh2 antagonists) but not selective Hrh1 or Hrh4 antagonists significantly inhibited metastasis in the 4T1 model. Ranitidine treatment also caused significant primary tumor regression in the E0771 breast cancer model. In the STK-/-/NIC model, ranitidine increased latency and decreased the number of tumors per mouse at week 26. Bone marrow and spleen analysis revealed that oral ranitidine treatment significantly and selectively decreased monocyte populations. These studies suggest that HRH2 antagonists may have potential as a beneficial immunomodulatory agent in the prevention or treatment of breast cancer.
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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) | 0.003 | 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".