Initiation of human mammary cell tumorigenesis by mutant KRAS requires YAP inactivation
Bibliographic record
Abstract
Abstract High YAP activity is associated with poor prognosis human breast cancers, but its role during the initial stage of mammary cell transformation is unknown. To address this question, we designed experiments that exploit the ability of KRASG12D-transduced subsets of freshly isolated normal human mammary cells to form invasive tumors rapidly and efficiently when transplanted into immunodeficient mice. Initial examination of the newly developing tumors thus generated revealed a consistent marked loss of nuclear YAP, independent of the initial primary human mammary cell type transduced. Conversely, co-transduction of the same subsets of primary human mammary cells with KRASG12D plus the constitutively active YAPS127A prevented tumor formation. These findings contrast with the enhanced display of transformed properties obtained when the immortalized, but non-tumorigenic MCF10A cells are transduced just with YAPS127A. In addition, we show that YAPS127A-transduction of the human MDA-MB-231 breast cancer cell line (that carry a similar KRAS mutation) enhances their metastatic activity in vivo. We also discover that the KRASG12D-induced early loss of YAP in primary human mammary cells is associated with their induced secretion of amphiregulin. Collectively, these findings suggest that YAP can differentially affect the acquisition of malignant properties by human mammary cells at different stages of their transformation.
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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".