Metabolic rewiring of cancer cells induces metastasis via ERK5 but triggers recognition by NK cells
Bibliographic record
Abstract
Abstract Metastasis is largely controlled by Natural Killer (NK) cell-mediated immune surveillance. To colonize new environments, cancer cells undergo epithelial to mesenchymal transition (EMT), which allows them to detach and migrate. EMT is fueled by fatty acid oxidation (FAO), which partially replaces glycolytic-based tumor metabolism. Whether metabolic rewiring affects the targeting of cancer cells by NK cells remains unknown. Here, we show that forcing solid cancer cells to perform FAO by inhibiting pyruvate dehydrogenase kinase 1 with dichloroacetate (DCA) activates extracellular signal-regulated kinase-5 (ERK5), triggering EMT and tumor cell migration and invasion. Concomitantly, FAO induces the expression of ligands that mediate NK cell recognition. Consequently, NK cells better infiltrated DCA-treated 3D tumor spheroids, where they exerted their cytotoxic effects. DCA-treated cells showed increased migration in a zebrafish model, whereas metastasis from mammary cancer cells grafted into immune-deficient mice was enhanced by DCA. These migrating/metastatic cells are preferentially killed by NK cells, which strongly limit their invasive potential. Hence, FAO promotes both metastasis and NK-mediated tumor surveillance, highlighting the Achilles’ heel of metastatic cells, which may offer new therapeutic opportunities. Teaser Metastasis recognition and killing by immune cells, such as NK cells, requires a metabolic shift that relies on lipid metabolism.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".