Loss of the 14-3-3σ Tumor Suppressor Is a Critical Event in ErbB2-Mediated Tumor Progression
Bibliographic record
Abstract
UNLABELLED: 14-3-3σ is a putative tumor suppressor involved in cell-cycle progression and epithelial polarity. We demonstrate that loss of one or both copies of the conditional 14-3-3σ allele results in accelerated mammary and salivary tumorigenesis in mice expressing an activated erbB2 oncogene under the endogenous erbB2 promoter. Significantly, the majority of tumors bearing a single conditional 14-3-3σ allele lose expression of the remaining 14-3-3σ allele, which is associated with epigenetic methylation of the 14-3-3σ locus. In addition to accelerated tumor onset, in a mouse mammary tumor virus-driven ErbB2 tumor model, loss of 14-3-3σ results in enhanced metastatic phenotype that is correlated with loss of cellular junctions. Taken together, these results provide compelling evidence that 14-3-3σ is a potent tumor suppressor involved in ErbB2-driven breast cancer initiation and metastasis. SIGNIFICANCE: 14-3-3σ has been identified as a normal mammary epithelial cell marker frequently downregulated during neoplastic development. Consistent with its potential role as a tumor suppressor, we demonstrate that targeted disruption of 14-3-3σ in a number of epithelial tissues can profoundly impact both the initiation and metastatic phases of ErbB2-mediated tumor progression through modulation of a number of distinct signaling networks.
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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".