Genetic Bases of Estrogen-Induced Tumorigenesis in the Rat: Mapping of Loci Controlling Susceptibility to Mammary Cancer in a Brown Norway × ACI Intercross
Bibliographic record
Abstract
Exposure to estrogens is associated with an increased risk of breast cancer. Our laboratory has shown that the ACI rat is uniquely susceptible to 17beta-estradiol (E2)-induced mammary cancer. We previously mapped two loci, Emca1 and Emca2 (estrogen-induced mammary cancer), that act independently to determine susceptibility to E2-induced mammary cancer in crosses between the susceptible ACI rat strain and the genetically related, but resistant, Copenhagen (COP) rat strain. In this study, we evaluate susceptibility to E2-induced mammary cancer in a cross between the ACI strain and the unrelated Brown Norway (BN) rat strain. Whereas nearly 100% of the ACI rats developed mammary cancer when treated continuously with E2, BN rats did not develop palpable mammary cancer during the 196-day course of E2 treatment. Susceptibility to E2-induced mammary cancer segregated as a dominant or incompletely dominant trait in a cross between BN females and ACI males. In a population of 251 female (BN x ACI)F(2) rats, we observed evidence for a total of five genetic determinants of susceptibility. Two loci, Emca4 and Emca5, were identified when mammary cancer status at sacrifice was evaluated as the phenotype, and three additional loci, Emca6, Emca7, and Emca8, were identified when mammary cancer number was evaluated as the phenotype. A total of three genetic interactions were identified. These data indicate that susceptibility to E2-induced mammary cancer in the BN x ACI cross behaves as a complex trait controlled by at least five loci and multiple gene-gene interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".