Abstract 3138: FOXC1 represses estrogen receptor-α expression via increasing NF-κB activity in breast cancer: A novel mechanism to explain antiestrogen resistance
Bibliographic record
Abstract
Abstract The Forkhead-box transcription factor FOXC1 has recently been identified as a critical marker for human basal-like breast cancer, which lacks or under-expresses estrogen receptor-α (ERα). Here we show a consistent inverse correlation between FOXC1 expression and ERα expression in multiple cDNA microarray data sets of human breast cancer. Overexpression of FOXC1 in ERα-positive breast cancer cells induces cell growth, migration, and invasion, but downregulates ERα mRNA and protein levels and consequently reduces cellular responses to estradiol and tamoxifen. This could explain why tamoxifen-refractory breast cancer cells exhibit higher FOXC1 levels as compared with tamoxifen-sensitive parental cells. We also found that FOXC1 induces NF-κB activation, which is known to be associated with ER-negative breast cancer, by increasing Pin1-mediated p65 protein stability and thus p65 protein levels. NF-κB mediates, at least in part, the regulation of ERα by FOXC1, because inhibition of NF-κB by an IκBα super-repressor and small-molecule inhibitors attenuated the suppression of ERα expression by FOXC1. Furthermore, the importance of the NF-κB pathway in FOXC1-stimulated cell growth was demonstrated by increased cell sensitivity to pharmacologic inhibition of NF-κB. Taken together, these results reveal a novel ERα-regulating mechanism that may explain the loss or low expression of ERα in basal-like breast cancer and also may provide new insight into mechanisms for antiestrogen resistance in breast cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3138. doi:10.1158/1538-7445.AM2011-3138
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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) | 0.003 | 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".