Tungsten Targets the Tumor Microenvironment to Enhance Breast Cancer Metastasis
Bibliographic record
Abstract
The number of individuals exposed to high levels of tungsten is increasing, yet there is limited knowledge of the potential human health risks. Recently, a cohort of breast cancer patients was left with tungsten in their breasts following testing of a tungsten-based shield during intraoperative radiotherapy. While monitoring tungsten levels in the blood and urine of these patients, we utilized the 66Cl4 cell model, in vitro and in mice to study the effects of tungsten exposure on mammary tumor growth and metastasis. We still detect tungsten in the urine of patients' years after surgery (mean urinary tungsten concentration at least 20 months post-surgery = 1.76 ng/ml), even in those who have opted for mastectomy, indicating that tungsten does not remain in the breast. In addition, standard chelation therapy was ineffective at mobilizing tungsten. In the mouse model, tungsten slightly delayed primary tumor growth, but significantly enhanced lung metastasis. In vitro, tungsten did not enhance 66Cl4 proliferation or invasion, suggesting that tungsten was not directly acting on 66Cl4 primary tumor cells to enhance invasion. In contrast, tungsten changed the tumor microenvironment, enhancing parameters known to be important for cell invasion and metastasis including activated fibroblasts, matrix metalloproteinases, and myeloid-derived suppressor cells. We show, for the first time, that tungsten enhances metastasis in an animal model of breast cancer by targeting the microenvironment. Importantly, all these tumor microenvironmental changes are associated with a poor prognosis in humans.
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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 | 0.000 | 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".