Abstract B17: High expression of the transcription factor Kaiso correlates with aggressive breast cancers (BC) and racial disparity in BC outcomes in women of African ancestry
Bibliographic record
Abstract
Abstract The highly metastatic triple negative breast cancers (TNBC) lack expression of estrogen-receptor (ER), progesterone-receptor (PR) and human epidermal growth factor receptor-2 (HER2), and thus have limited patient treatment options and prognosis. Intriguingly TNBC is most prevalent in young women of African Ancestry (WAA) who, despite having lower BC incidence rates than Caucasian women, have higher mortality rates. This racial disparity appears unrelated to socio-economic status and increasing evidence hints at an ancestral genetic predisposition to TNBC development and high mortality rates. Recently, a significant correlation was found between nuclear expression of the transcription factor Kaiso, ERα negativity, basal/TNBCs and invasive ductal carcinoma in African American (AA) women. Emerging evidence from our lab also found that high Kaiso expression is associated with shorter metastasis-free survival in aggressive breast cancers. Based on these findings we hypothesized that Kaiso may play a crucial role in the metastatic spread of advanced breast cancers, and perhaps contribute to the racial disparity in TNBC prevalence and BC outcomes in WAA. To gain insight into Kaiso's role in aggressive breast cancers and the racial disparity associated with BC outcomes, we generated stable Kaiso-depleted TNBC cells (MDA-231) and then performed xenograft studies in immuno-compromised mice. Kaiso-depleted MDA-231 cells exhibited no or negligible metastases to the lungs and liver, compared to control MDA-231 cells, which formed extensive metastases in the lungs and liver of immuno-compromised mice. We also analysed Kaiso expression levels and subcellular localization in a tissue microarray (TMA) comprised of breast tumor tissues from ~100 Caucasian and ~100 AA women. Intriguingly, we observed a significant correlation between high nuclear Kaiso expression and ER(-) status in WAA, but surprisingly we found that high nuclear Kaiso expression is associated with ER(+) status and better overall survival in women of European descent. Collectively our findings implicate high Kaiso expression in breast cancer aggressiveness and suggest a context-dependent role for Kaiso in BC outcomes in women of different ethnicities. Ongoing studies are focused on clarifying the role of Kaiso in TNBC outcomes in WAA, and will incorporate analysis of a clinically annotated TMA comprised of ~200 TNBC tissues obtained from Barbadian and Nigerian women in conjunction with mechanistic studies to elucidate the signaling pathways involved in Kaiso-mediated tumor metastasis. Our findings may unravel a therapeutic role for Kaiso as a useful biomarker for the prognosis of TNBCs especially in WAA. Citation Format: Blessing I. Bassey-Archibong, Lyndsay G. A. Rayner, Kevin Gardner, Juliet Daniel. High expression of the transcription factor Kaiso correlates with aggressive breast cancers (BC) and racial disparity in BC outcomes in women of African ancestry. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr B17.
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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) | 0.004 | 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".