ErbB2-driven downregulation of the transcription factor Irf6 in breast epithelial cells is required for their 3D growth
Bibliographic record
Abstract
BACKGROUND: The ability of solid tumor cells to resist anoikis, apoptosis triggered by cell detachment from the extracellular matrix (ECM), is thought to be critical for 3D tumor growth. ErbB2/Her2 oncoprotein is often overproduced by breast tumor cells and blocks their anoikis by partially understood mechanisms. In our effort to understand them better, we observed that detachment of nonmalignant human breast epithelial cells from the ECM upregulates the transcription factor Irf6. Irf6 is thought to play an important role in mammary gland homeostasis and causes apoptosis by unknown mechanisms. We noticed that ErbB2, when overproduced by detached breast epithelial cells, downregulates Irf6. METHODS: To test whether ErbB2 downregulates Irf6 in human ErbB2-positive breast cancer cells, we examined the effect of ErbB2 inhibitors, such as the anti-ErbB2 antibody trastuzumab or the ErbB2/epidermal growth factor receptor small-molecule inhibitor lapatinib, on Irf6 in these cells. Moreover, we performed Irf6 IHC analysis of tumor samples derived from the locally advanced ErbB2-positive breast cancers before and after neoadjuvant trastuzumab-based therapies. To examine the role of Irf6 in anoikis of nonmalignant and ErbB2-overproducing breast epithelial cells, we studied anoikis after knocking down Irf6 in the former cells by RNA interference and after overproducing Irf6 in the latter cells. To examine the mechanisms by which cell detachment and ErbB2 control Irf6 expression in breast epithelial cells, we tested the effects of genetic and pharmacological inhibitors of the known ErbB2-dependent signaling pathways on Irf6 in these cells. RESULTS: We observed that trastuzumab and lapatinib upregulate Irf6 in ErbB2-positive human breast tumor cells and that neoadjuvant trastuzumab-based therapies tend to upregulate Irf6 in human breast tumors. We found that detachment-induced Irf6 upregulation in nonmalignant breast epithelial cells requires the presence of the transcription factor ∆Np63α and that Irf6 mediates their anoikis. We showed that ErbB2 blocks Irf6 upregulation in ErbB2-overproducing cells by activating the mitogen-activated protein kinases that inhibit ∆Np63α-dependent signals required for Irf6 upregulation. Finally, we demonstrated that ErbB2-driven Irf6 downregulation in ErbB2-overproducing breast epithelial cells blocks their anoikis and promotes their anchorage-independent growth. CONCLUSIONS: We have demonstrated that ErbB2 blocks anoikis of breast epithelial cells by downregulating Irf6.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".