Revisiting the MMTV Zoonotic Hypothesis to Account for Geographic Variation in Breast Cancer Incidence
Bibliographic record
Abstract
Human breast cancer incidence varies by geographic location. More than 20 years ago, we proposed that zoonotic transmission of the mouse mammary tumor virus (MMTV) from the western European house mouse, Mus musculus domesticus, might account for the regional differences in breast cancer incidence. In the intervening years, several developments provide additional support for this hypothesis, including the limited impact of genetic factors for breast cancer susceptibility revealed by genome-wide association studies and the strong effect of antiretroviral therapy to reduce breast cancer incidence. At the same time, economic globalization has further expanded the distribution of M. m. domesticus to Asia, leading to a significant increase in breast cancer incidence in this region. Here, we revisit this evidence and provide an update to the MMTV zoonotic hypothesis for human breast cancer at a time when the world is recovering from the global COVID-19 zoonotic pandemic. We present evidence that mouse population outbreaks are correlated with spikes in breast cancer incidence in Australia and New Zealand and that globalization has increased the range of M. m. domesticus and MMTV. Given the success of global vaccination campaigns for HPV to eradicate cervical cancer, a similar strategy for MMTV may be warranted. Until breast cancer incidence is reduced by such an approach, zoonotic transmission of MMTV from mice to humans as an etiologic factor for breast cancer will remain controversial.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".