MétaCan
Menu
Back to cohort
Record W4313816321 · doi:10.1158/1538-7755.disp22-c036

Abstract C036: Correlation of Kaiso and androgen receptor expression in women of African ancestry with triple-negative breast cancer

2023· article· en· W4313816321 on OpenAlexaff
Stephanie Ali Fairbairn, Robert W. Cowan, Juliet M. Daniel, Shawn M. Hercules

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2023
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsPrincess Margaret Cancer CentreMcMaster University
Fundersnot available
KeywordsTriple-negative breast cancerAndrogen receptorBreast cancerTissue microarrayCancer researchOncologyCancerImmunohistochemistryMetastasisTranscription factorMedicineInternal medicineBiologyProstate cancerGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Breast cancer (BCa) is the most frequently diagnosed female cancer and a leading cause of female deaths worldwide, regardless of increased awareness and improved therapies. Triple-negative breast cancer (TNBC) is one of the most challenging BCa subtypes as it is highly heterogeneous, metastatic in nature and has limited targeted therapies. TNBC is most prevalent in young women of African ancestry (WAA), who despite having lower BCa rates, have a disproportionately higher mortality rate compared to women of European ancestry (WEA). However, the cause for this racial disparity is currently unknown, thus highlighting the importance of unraveling the genetic and molecular factors that contribute to TNBC in WAA. Our lab previously showed that the transcription factor Kaiso may be linked to this disparity as it contributes to increased metastasis and mortality in WAA TNBC patients. Interestingly, multiple studies have indicated that WAA TNBC tissues also express less Androgen Receptor (AR) than WEA TNBC tissues, supporting the existence of a novel BCa subtype – quadruple-negative breast cancer (QNBC). Notably, in silico analysis revealed several Kaiso binding sites in the AR promoter region, and that high Kaiso and low AR expression correlated with poorer overall survival in BCa patients. Thus, we hypothesized that high Kaiso and low AR expression could be contributing to the increased mortality in WAA with TNBC. This study seeks to examine the relationship between Kaiso and AR, the clinical significance of this relationship and its role, if any, in TNBC racial disparities. Using tissue microarrays (TMAs) and immunohistochemistry (IHC), we found reduced and cytoplasmic AR expression in WAA compared to WEA. Moreover, preliminary findings from western blot analyses showed an increase in AR expression in response to Kaiso depletion in TNBC cells. These findings suggest that AR could be a bona fide Kaiso target gene and that there may be clinical relevance of high Kaiso, and low AR expression in BCa survival, especially in patients with an African heritage. Ongoing experiments are focused on determining if Kaiso directly associates with the endogenous AR promoter region and discerning the link between high Kaiso and low AR expression in TNBC racial disparities. A significant correlation between Kaiso and AR expression will help to validate the existence of QNBC in WAA and support Kaiso and AR as clinically relevant prognostic markers for TNBC, especially in WAA. Citation Format: Stephanie Ali Fairbairn, Robert Cowan, Juliet M. Daniel, Shawn M. Hercules. Correlation of Kaiso and androgen receptor expression in women of African ancestry with triple-negative breast cancer [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C036.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.059
GPT teacher head0.384
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCancer Epidemiology Biomarkers & PreventionSame topicProstate Cancer Treatment and ResearchFrench-language works237,207