Toca-1 is suppressed by p53 to limit breast cancer cell invasion and tumor metastasis
Bibliographic record
Abstract
INTRODUCTION: Transducer of Cdc42-dependent actin assembly-1 (Toca-1) recruits actin regulatory proteins to invadopodia, and promotes breast tumor metastasis. Since metastatic breast tumors frequently harbor mutations in the tumor suppressor p53, we tested whether p53 regulates Toca-1 expression. METHODS: Normal mammary epithelial cells (HBL-100, MCF10A) and breast cancer cell lines expressing wild-type (WT) p53 (DU4475, MTLn3) were treated with camptothecin or Nutlin-3 to stabilize p53 to test effects on Toca-1 mRNA and protein levels. Chromatin immunoprecipitation (ChIP) assays were performed to identify p53 binding site in Toca-1 gene. Stable silencing of p53 and Toca-1 were performed in MTLn3 cells to test effects on invadopodia and cell invasion in vitro, and tumor metastasis in vivo. RESULTS: We observed that breast cancer cell lines with mutant p53 have high levels of Toca-1 compared to those with WT p53. Stabilization of WT p53 led to further reduction in Toca-1 mRNA and protein levels in normal breast epithelial cells and breast cancer cells. ChIP assays revealed p53 binding within intron 2 of toca1, and reduced histone acetylation within its promoter region upon p53 upregulation or activation. Stable silencing of WT p53 in MTLn3 cells led to increased extracellular matrix degradation and cell invasion compared to control cells. Interestingly, the combined silencing of p53 and Toca-1 led to a partial rescue of these effects of p53 silencing in vitro and reduced lung metastases in mice. In human breast tumors, Toca-1 levels were high in subtypes with frequent p53 mutations, and high Toca-1 transcript levels correlated with increased risk of relapse. CONCLUSIONS: Based on these findings, we conclude that loss of p53 tumor suppressor function in breast cancers leads to upregulation of Toca-1, and results in enhanced risk of developing metastatic disease.
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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) | 0.002 | 0.001 |
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".