Faculty Opinions recommendation of Pten in stromal fibroblasts suppresses mammary epithelial tumours.
Bibliographic record
Abstract
The tumour stroma is believed to contribute to some of the most malignant characteristics of epithelial tumours. However, signalling between stromal and tumour cells is complex and remains poorly understood. Here we show that the genetic inactivation of Pten in stromal fibroblasts of mouse mammary glands accelerated the initiation, progression and malignant transformation of mammary epithelial tumours. This was associated with the massive remodelling of the extracellular matrix (ECM), innate immune cell infiltration and increased angiogenesis. Loss of Pten in stromal fibroblasts led to increased expression, phosphorylation (T72) and recruitment of Ets2 to target promoters known to be involved in these processes. Remarkably, Ets2 inactivation in Pten stroma-deleted tumours ameliorated disruption of the tumour microenvironment and was sufficient to decrease tumour growth and progression. Global gene expression profiling of mammary stromal cells identified a Pten-specific signature that was highly represented in the tumour stroma of patients with breast cancer. These findings identify the Pten-Ets2 axis as a critical stroma-specific signalling pathway that suppresses mammary epithelial tumours. PMID: 19847259 Funding information This work was supported by: NICHD NIH HHS, United States Grant ID: R01 HD047470-05 NICHD NIH HHS, United States Grant ID: R01 HD047470 NCI NIH HHS, United States Grant ID: R01 CA053271 NCI NIH HHS, United States Grant ID: R01 CA085619-05 NCI NIH HHS, United States Grant ID: P01 CA097189-050002 NCI NIH HHS, United States Grant ID: R01 CA121275 NCI NIH HHS, United States Grant ID: P01CA097189 NCI NIH HHS, United States Grant ID: R01CA85619 NCI NIH HHS, United States Grant ID: R01 CA121275-02 NICHD NIH HHS, United States Grant ID: R01HD47470 NCI NIH HHS, United States Grant ID: R01 CA085619 NCI NIH HHS, United States Grant ID: P01 CA097189 NCI NIH HHS, United States Grant ID: R01CA053271 More Less keyboard_arrow_down
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.025 |
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".