Oncogenic Viruses and Breast Cancer: Mouse Mammary Tumor Virus (MMTV), Bovine Leukemia Virus (BLV), Human Papilloma Virus (HPV), and Epstein–Barr Virus (EBV)
Bibliographic record
Abstract
BACKGROUND: Although the risk factors for breast cancer are well established, namely female gender, early menarche and late menopause plus the protective influence of early pregnancy, the underlying causes of breast cancer remain unknown. The development of substantial recent evidence indicates that a handful of viruses may have a role in breast cancer. These viruses are mouse mammary tumor virus (MMTV), bovine leukemia virus (BLV), human papilloma viruses (HPVs), and Epstein-Barr virus (EBV-also known as human herpes virus type 4). Each of these viruses has documented oncogenic potential. The aim of this review is to inform the scientific and general community about this recent evidence. THE EVIDENCE: MMTV and human breast cancer-the evidence is detailed and comprehensive but cannot be regarded as conclusive. BLV and human breast cancer-the evidence is limited. However, in view of the emerging information about BLV in human breast cancer, it is prudent to encourage the elimination of BLV in cattle, particularly in the dairy industry. HPVs and breast cancer-the evidence is substantial but not conclusive. The availability of effective preventive vaccines is a major advantage and their use should be encouraged. EBV and breast cancer-the evidence is also substantial but not conclusive. Currently, there are no practical means of either prevention or treatment. Although there is evidence of genetic predisposition, and cancer in general is a culmination of events, there is no evidence that inherited genetic traits are causal. CONCLUSION: The influence of oncogenic viruses is currently the major plausible hypothesis for a direct cause of human 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".