Recent advances in PTEN signalling axes in cancer
Bibliographic record
Abstract
In over two decades since the discovery of phosphatase and tensin homologue deleted on chromosome 10 (PTEN), nearly 18,000 publications have attempted to elucidate its functions and roles in normal physiology and disease. The frequent disruption of PTEN in cancer cells was a strong indication that it had critical roles in tumour suppression. Germline PTEN mutations have been identified in patients with heterogeneous tumour syndromic diseases, known as PTEN hamartoma tumour syndrome (PHTS), and in some individuals with autism spectrum disorders (ASD). Today we know that by limiting oncogenic signalling through the phosphoinositide 3-kinase (PI3K) pathway, PTEN governs a number of processes including survival, proliferation, energy metabolism, and cellular architecture. Some of the most exciting recent advances in the understanding of PTEN biology and signalling have revisited its unappreciated roles as a protein phosphatase, identified non-enzymatic scaffold functions, and unravelled its nuclear function. These discoveries are certain to provide a new perspective on its full tumour suppressor potential, and knowledge from this work will lead to new anti-cancer strategies that exploit PTEN biology. In this review, we will highlight some outstanding questions and some of the very latest advances in the understanding of the tumour suppressor PTEN.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".