Latent Cytomegalovirus Infection in Female Mice Increases Breast Cancer Metastasis
Bibliographic record
Abstract
Cytomegalovirus (CMV) infects 40⁻70% of women, but infection has been reported in >95% of breast cancer patients. We investigated the consequences of these observations by infecting mice with mCMV or a negative control medium for 4 days, 11 days or 10 weeks to establish active, intermediate or latent infections, respectively. Syngeneic 4T1 or E0771 breast cancer cells were then injected into a mammary fat pad of BALB/c or C57BL/6 mice, respectively. Infection did not affect tumor growth in these conditions, but latently infected BALB/c mice developed more lung metastases. The latent mCMV infection of MMTV-PyVT mice, which develop spontaneous breast tumors, also did not affect the number or sizes of breast tumors. However, there were more tumors that were multilobed with greater blood content, which had enhanced vasculature and decreased collagen content. Most significantly, mCMV infection also increased the number and size of lung metastases, which showed a higher cell proliferation. Viral DNA was detected in breast tumors and lung nodules although viral mRNA was not. These novel results have important clinical implications since an increased metastasis is prognostic of decreased survival. This work provides evidence that treating or preventing HCMV infections may increase the life expectancy of breast cancer patients by decreasing metastasis.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".