Molecular characterization of cancer reveals interactions between ionizing radiation and chemicals on rat mammary carcinogenesis
Bibliographic record
Abstract
Although various mechanisms have been inferred for combinatorial actions of multiple carcinogens, these mechanisms have not been well demonstrated in experimental carcinogenesis models. We evaluated mammary carcinogenesis initiated by combined exposure to various doses of radiation and chemical carcinogens. Female rats at 7 weeks of age were γ-irradiated (0.2-2 Gy) and/or exposed to 1-methyl-1-nitrosourea (MNU) (20 or 40 mg/kg, single intraperitoneal injection) or 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) (40 mg/kg/day by gavage for 10 days) and were observed until 50 weeks of age. The incidence of mammary carcinoma increased steadily as a function of radiation dose in the absence of chemicals; mathematical analysis supported an additive increase when radiation was combined with a chemical carcinogen, irrespective of the chemical species and its dose. Hras mutations were characteristic of carcinomas that developed after chemical carcinogen treatments and were overrepresented in carcinomas induced by the combination of radiation and MNU (but not PhIP), indicating an interaction of radiation and MNU at the level of initiation. The expression profiles of seven classifier genes, previously shown to distinguish two classes of rat mammary carcinomas, categorized almost all examined carcinomas that developed after individual or combined treatments with radiation (1 Gy) and chemicals as belonging to a single class; more comprehensive screening using microarrays and a separate test sample set failed to identify differences in gene expression profiles among these carcinomas. These results suggest that a complex, multilevel interaction underlies the combinatorial action of radiation and chemical carcinogens in the experimental model.
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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 | 0.001 | 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) | 0.001 | 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".