Abstract C23: High Kaiso expression correlates with increased TGFβ signaling and aggressive TNBC in women of African Ancestry (WAA)
Bibliographic record
Abstract
Abstract Despite increased awareness, early detection and improved therapies in the past 20 years, breast cancer (BC) remains the most frequent female cancer and a leading cause of female deaths worldwide. One of the most clinically challenging BC is the Triple Negative Breast Cancer (TNBC) subtype that is estrogen receptor (ER), progesterone receptor (PR) & human epidermal growth factor receptor-2 (HER2) negative. TNBC tumors are aggressive and metastatic, and have a poor prognosis since they cannot be treated with standard therapies (Tamoxifen or Herceptin). 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. The propensity of TNBC to metastasize suggests a deregulation of the epithelial-to-mesenchymal transition (EMT) pathway that endows cells with increased motility, and is implicated in tumor progression. Our lab and others recently found a significant correlation between high expression of the POZ-ZF transcription factor Kaiso, EMT, estrogen receptor (ER) negativity in WAA, and shorter metastasis-free survival. Notably when Kaiso is depleted in TNBC cell lines, the cells exhibited decreased TGFβ signaling (a known promoter of EMT), become less motile and invasive in vitro, and did not metastasize to lungs or liver in mouse xenograft models. These findings led us to hypothesize that Kaiso plays a role in the spread of aggressive BC possibly via up-regulation of TGFβ signaling. To gain insight into Kaiso's role in aggressive breast cancers and the racial disparity associated with BC outcomes, we generated a tissue microarray (TMA) comprised of TNBC tissues from Barbadian and Nigerian WAA, and performed expression profiling of Kaiso and various TGFβ signaling proteins (TGFβR1, TGFβR2 and p-Smad2) on the WAA-TMA. We found that high Kaiso expression correlated positively with increased TGFβ signalling in WAA TNBC tissues, thus supporting our hypothesis that Kaiso plays a role in TGFβ signaling and TNBC aggressiveness. Notably, we also observed that Kaiso depletion resulted in delayed onset of mouse xenograft tumors derived from MDA-231 TNBC cells. Mechanistic in vitro studies further revealed that Kaiso depletion reduced cell proliferation and increased apoptosis of TNBC cells, especially in response to the DNA-damaging drug Etoposide. Our findings linking high Kaiso expression to TNBC cell survival and active TGFβ signalling in aggressive TNBCs from WAA, raises the exciting possibility that Kaiso may be developed as a diagnostic and prognostic marker for TNBC patients regardless of race. Ongoing experiments are focused on elucidating Kaiso's exact role in aggressive breast tumors and the racial disparities in breast cancer outcomes. Citation Format: Blessing I. Bassey-Archibong, Lyndsay GA Rayner, Shawn Hercules, Sinthu Pathmanapan, Juliet M. Daniel. High Kaiso expression correlates with increased TGFβ signaling and aggressive TNBC in women of African Ancestry (WAA). [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr C23.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".