PHGDH heterogeneity potentiates cancer cell dissemination and metastasis
Bibliographic record
Abstract
Cancer metastasis requires the transient activation of cellular programs enabling dissemination and seeding in distant organs1. Genetic, transcriptional and translational heterogeneity contributes to this dynamic process2,3. Metabolic heterogeneity has also been observed4, yet its role in cancer progression is less explored. Here we find that the loss of phosphoglycerate dehydrogenase (PHGDH) potentiates metastatic dissemination. Specifically, we find that heterogeneous or low PHGDH expression in primary tumours of patients with breast cancer is associated with decreased metastasis-free survival time. In mice, circulating tumour cells and early metastatic lesions are enriched with Phgdhlow cancer cells, and silencing Phgdh in primary tumours increases metastasis formation. Mechanistically, Phgdh interacts with the glycolytic enzyme phosphofructokinase, and the loss of this interaction activates the hexosamine–sialic acid pathway, which provides precursors for protein glycosylation. As a consequence, aberrant protein glycosylation occurs, including increased sialylation of integrin αvβ3, which potentiates cell migration and invasion. Inhibition of sialylation counteracts the metastatic ability of Phgdhlow cancer cells. In conclusion, although the catalytic activity of PHGDH supports cancer cell proliferation, low PHGDH protein expression non-catalytically potentiates cancer dissemination and metastasis formation. Thus, the presence of PHDGH heterogeneity in primary tumours could be considered a sign of tumour aggressiveness. PHDGH heterogeneity in primary tumours could be a sign of tumour aggressiveness.
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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".