# The linkage between Race, Kaiso and the tumor microenvironment in breast cancer health disparities

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $70,311

## Abstract

Women of African heritage suffer a higher breast cancer mortality compared to their European counterparts.
Though the biologic basis for these disparities remains poorly defined, recent studies suggest definitive roles
for biological variation in the gene expression pathways governing tumor behavior and alterations in the tumor
microenvironment. The transcription factor Kaiso (ZBTB33) is a gene regulatory factor, found in both the
nucleus and cytoplasm of breast cancer cells, that has been functionally linked to racial differences in survival
outcome in several epithelial cancers. In this study we leverage machine learning and artificial intelligence to
define functional linkages between Kaiso, autophagy and the immmune tumor microenvironment that
contribute to racial differences in breast cancer survival. We accomplish this through application of machine
learning and artificial intelligence to characterize the Kaiso dependent differences in spatial and topological
features of the tumor microenvironment using multiplex immunofluorescent technologies to profile a unique
breast cancer health disparities cohort (Specific Aim One). We then apply this technology to examine the
impact of Kaiso disruption on autophagy and the immune tumor microenvironment using a murine orthotopic
allograft model for Kaiso depletion in the presence and absence of pharmacologic blockade of autophagy
(Specific Aim Two). We then perform a large-scale application of artificial intelligence and deep learning to
profile the spatial and topological features of the tumor microenvironment in 901 racially diverse breast cancer
specimens by multiplex immunohistochemistry to define the detailed role of Kaiso, autophagy and the tumor
microenvironment in population-specific differences in breast cancer outcome (Specific Aim Three). Together
with a closely integrated multi-disciplinary team of breast cancer pathologists, cancer biologists, computer
scientists, biostatisticians, bioinformaticians and data scientists, we will define new prognostic and predictive
biomarkers that link Kaiso to tumor progression, the immune tumor microenvironment, breast cancer outcome
and how their association differs by race.

## Key facts

- **NIH application ID:** 10599488
- **Project number:** 3R01CA253368-03S1
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** KEVIN L. GARDNER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $70,311
- **Award type:** 3
- **Project period:** 2020-09-24 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10599488

## Citation

> US National Institutes of Health, RePORTER application 10599488, The linkage between Race, Kaiso and the tumor microenvironment in breast cancer health disparities (3R01CA253368-03S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10599488. Licensed CC0.

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