<i>Pten</i> Loss and RAS/MAPK Activation Cooperate to Promote EMT and Metastasis Initiated from Prostate Cancer Stem/Progenitor Cells
Bibliographic record
Abstract
PTEN loss or PI3K/AKT signaling pathway activation correlates with human prostate cancer progression and metastasis. However, in preclinical murine models, deletion of Pten alone fails to mimic the significant metastatic burden that frequently accompanies the end stage of human disease. To identify additional pathway alterations that cooperate with PTEN loss in prostate cancer progression, we surveyed human prostate cancer tissue microarrays and found that the RAS/MAPK pathway is significantly elevated in both primary and metastatic lesions. In an attempt to model this event, we crossed conditional activatable K-ras(G12D/WT) mice with the prostate conditional Pten deletion model. Although RAS activation alone cannot initiate prostate cancer development, it significantly accelerated progression caused by PTEN loss, accompanied by epithelial-to-mesenchymal transition (EMT) and macrometastasis with 100% penetrance. A novel stem/progenitor subpopulation with mesenchymal characteristics was isolated from the compound mutant prostates, which was highly metastatic upon orthotopic transplantation. Importantly, inhibition of RAS/MAPK signaling by PD325901, a mitogen-activated protein (MAP)-extracellular signal-regulated (ER) kinase (MEK) inhibitor, significantly reduced the metastatic progression initiated from transplanted stem/progenitor cells. Collectively, our findings indicate that activation of RAS/MAPK signaling serves as a potentiating second hit to alteration of the PTEN/PI3K/AKT axis, and cotargeting both the pathways is highly effective in preventing the development of metastatic prostate cancers.
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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) | 0.001 | 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".