Metastatic breast cancer cells are metabolically reprogrammed to maintain redox homeostasis during metastasis
Bibliographic record
Abstract
Metabolic rewiring is essential for tumor growth and progression to metastatic disease, yet little is known regarding how cancer cells modify their acquired metabolic programs in response to different metastatic microenvironments. We have previously shown that liver-metastatic breast cancer cells adopt an intrinsic metabolic program characterized by increased HIF-1α activity and dependence on glycolysis. Here, we confirm by in vivo stable isotope tracing analysis (SITA) that liver-metastatic breast cancer cells retain a glycolytic profile when grown as mammary tumors or liver metastases. However, hepatic metastases exhibit unique metabolic adaptations including elevated expression of genes involved in glutathione (GSH) biosynthesis and reactive oxygen species (ROS) detoxification when compared to mammary tumors. Accordingly, breast-cancer-liver-metastases exhibited enhanced de novo GSH synthesis. Confirming their increased capacity to mitigate ROS-mediated damage, liver metastases display reduced levels of 8-Oxo-2'-deoxyguanosine. Depletion of the catalytic subunit of the rate-limiting enzyme in glutathione biosynthesis, glutamate-cysteine ligase (GCLC), strongly reduced the capacity of breast cancer cells to form liver metastases, supporting the importance of these distinct metabolic adaptations. Loss of GCLC also affected the early steps of the metastatic cascade, leading to decreased numbers of circulating tumor cells (CTCs) and impaired metastasis to the liver and the lungs. Altogether, our results indicate that GSH metabolism could be targeted to prevent the dissemination of breast cancer cells.
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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".