PHGDH heterogeneity potentiates cancer cell dissemination and metastasis
Bibliographic record
Abstract
Abstract Cancer metastasis requires the transient activation of cellular programs enabling dissemination and seeding in distant organs. Genetic, transcriptional and translational intra-tumor heterogeneity contributes to this dynamic process. Beyond this, metabolic intra-tumor heterogeneity has also been observed, yet its role for cancer progression remains largely elusive. Here, we discovered that intra-tumor heterogeneity in phosphoglycerate dehydrogenase (PHGDH) protein expression drives breast cancer cell dissemination and metastasis formation. Specifically, we observed intra-tumor heterogeneous PHGDH expression in primary breast tumors, with low PHGDH expression being indicative of metastasis in patients. In mice, Phgdh protein, but not mRNA, expression is low in circulating tumor cells and early metastatic lesions, leading to increased dissemination and metastasis formation. Mechanistically, low PHGDH protein expression induces an imbalance in glycolysis that can activate sialic acid synthesis. Consequently, cancer cells undergo a partial EMT and show increased p38 as well as SRC phosphorylation, which activate cellular programs of dissemination. In turn, inhibition of sialic acid synthesis through knock-out of cytidine monophosphate N-acetylneuraminic acid synthetase (CMAS) counteracts the increased cancer cell dissemination and metastasis induced by low PHGDH expression. In conclusion, we find that heterogeneity in PHGDH protein expression promotes cancer cell dissemination and metastasis formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".