Abstract B50: Elucidating the roles of Kaiso in the metastasis of triple-negative breast cancer cells
Bibliographic record
Abstract
Abstract The triple negative breast cancer (TNBC) subtype represent a subset of breast tumours that lack expression of the estrogen-receptor, progesterone-receptor and human epidermal growth factor receptor-2. Interestingly, TNBC is more prevalent in young women of African ancestry (WAA) than other ethnicities, and WAA have a high mortality despite a low BC incidence compared to Caucasian women. This high mortality rate is attributed to the highly aggressive and metastatic nature of triple-negative tumors and limited treatment options or targeted-therapies. The metastatic nature of TNBC suggests a deregulation of the epithelial-to-mesenchymal (EMT) pathway that is often implicated in tumor progression. During EMT, cells downregulate the tumor suppressor and cell adhesion protein E-cadherin and re-express mesenchymal proteins such as N-cadherin and vimentin, which endow the cells with increased motility. During the course of our characterization of the unique transcription factor Kaiso that was implicated as a potential regulator of E-cadherin, we found that increased nuclear expression of Kaiso is significantly correlated with basal/TNBCs. However, little is known about the role of Kaiso in EMT or TNBC metastasis. Thus, the goal of our research is to elucidate the function of Kaiso in TNBC metastasis. To gain insight into Kaiso's role in TNBC metastasis we generated stable Kaiso-depleted TNBC cells (MDA-MB-231) using a Kaiso specific shRNA. Stable Kaiso-depleted cells were then analyzed for the gene expression profile of key EMT markers and for their migratory and invasive capacities in vitro using wound healing and Matrigel invasion assays. We found that Kaiso is highly expressed at both the transcript and protein level in breast tumor cells and tissues. Kaiso-depletion reduced the migratory and invasive capacities of TNBC cells, and induced the down-regulation of potent E-cadherin regulators including Slug and ZEB1 at both the transcript and protein levels. Interestingly, we observed that Kaiso-depletion also resulted in the downregulation of TGFβR1 and TGFβR2; key effectors of the TGFβ signaling pathway. Since TGFβ signaling is a potent inducer of EMT in late stage breast cancer via up-regulation of Snail and/or Slug and ZEB1/2, our results suggests that Kaiso may be promoting TNBC metastasis via the TGFβ signaling pathway. Kaiso's expression might thus be a useful biomarker for TNBC prognosis. Citation Format: Blessing I. Bassey, Catherine Crawford-Brown, Robin Hallet, Juliet Daniel. Elucidating the roles of Kaiso in the metastasis of triple-negative breast cancer cells. [abstract]. In: Proceedings of the Seventh AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 9-12, 2014; San Antonio, TX. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2015;24(10 Suppl):Abstract nr B50.
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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".