The role of metabolic reprogramming in breast cancer progression and metastasis
Bibliographic record
Abstract
Breast cancer is the most commonly diagnosed cancer in woman and the emergence of metastasis is the most deadly aspect of the disease. Major bioenergetic and biosynthetic demands are associated with proliferation in order to sustain the exponential growth of a primary tumor and metabolic pathways must be reprogrammed to meet these demands. However, the metabolic challenges of cells during tumor initiation will differ from those that occur during tumor progression and dissemination. Despite progress in understanding the underlying mechanisms of how altered metabolism fuels the growth of primary tumors, the role that metabolic reprogramming plays in the metastatic process remains poorly characterized. This work focused on identifying the regulators of metabolic reprogramming and defining their roles in mediating breast cancer growth and metastasis. Using transgenic mouse models, we showed that loss of LKB1 cooperates with ErbB2 to promote breast cancer initiation and progression at early stages. Loss of LKB1 resulted in the activation of mTOR signaling, conferring a pro-growth metabolic advantage to the tumors. However, LKB1-deficient tumor cells displayed greater sensitivity to glucose limitation compared to their LKB1-proficient counterparts, suggesting a lack of metabolic flexibility that was rescued by rapamycin-mediated suppression of mTOR signaling.To investigate the metabolic reprogramming associated with breast cancer progression we compared the metabolic profiles of breast cancer cells that originated from a single primary tumor; however, which display different abilities to metastasize. Our results reveal an overall increase in metabolic activity (glycolysis and OXPHOS) that correlates with an increase in metastatic potential. However, we demonstrated that upon dissemination, metastatic breast cancer cells engage distinct metabolic programs depending on the site of metastasis. Using breast cancer explants isolated from bone, lung or liver metastases, we demonstrate a bifurcation in the way these cells utilize available carbon sources. Mitochondrial metabolism is elevated in bone- and lung-metastatic cells while liver-metastatic breast cancer cells preferentially engage glycolysis. We next determined the molecular mechanisms responsible for the glycolytic switch observed in the liver-metastatic breast cancer cells. The transcription factor HIF-1α is activated in liver-metastatic breast cancer cells under normoxic conditions and partially responsible for the observed metabolic reprogramming. Downstream of HIF-1α, PDK1 (pyruvate dehydrogenase kinase 1) was identified as an important driver of metabolic adaptation to energetic stress and was required for efficient liver metastasis. Our work demonstrates that, while loss of metabolic regulators may be advantageous for cancer initiation and early progression, retaining these key checkpoints is critical for metabolic adaptation to stress and successful metastatic dissemination.
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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".