Sandeep Singhal, assistant professor in the School of Medicine & Health Sciences Department of Pathology, basic research scholar of the DaCCoTA, and director of the IDeA Networks of Biomedical Research Excellence (INBRE) bioinformatics division, recently published a paper in Communications Biology – Nature, one of the world’s leading journals in the natural sciences, on a potential novel biomarker for cancer.
The paper, which explored the use of digital pathology to profile the subcellular distribution of the transcriptional regulator protein Kaiso (ZBTB33) in breast tumors from American women, found new connections between Kaiso and the autophagy-related proteins LC3A and LC3B, “that are associated with features of the tumor immune microenvironment, survival, and race.”
These findings make Kaiso a possible biomarker for breast cancer risk management and predictor of progression.
“Precision medicine is an emerging practice of medicine that combines genomics, big data analytics, and population health to produce targeted therapies and guide decisions made in regard to the prevention, diagnosis, and treatment of disease, for cancer in particular,” explained Singhal. “Racial diversity is tied to both our genetics and our environment, which is why we attempt to link genetics, ancestry, and disease, particularly when race is described in terms of continent of origin.”
This is why Singhal and his team sought to leverage “Artificial Intelligence-based automated image analysis algorithms” to the subcellular distribution of Kaiso in a racially diverse cohort of breast cancer patients.
“We found that both nuclear and cytoplasmic Kaiso proteins are associated with breast cancer outcomes, and each are independent predictors of overall breast cancer survival,” Singhal said. “Specifically, patients stratified by nuclear and cytoplasmic Kaiso are enriched in cell stress and immune response, which differentially predict survival based on genetic ancestry.”
The general findings presented in Singhal’s study highlight Kaiso’s potential as a predictive biomarker to guide future treatment decisions, particularly in the use of immunotherapy.
These results pave the way for future applications in prospective studies where profiles of nuclear and cytoplasmic Kaiso can be evaluated in clinical trials as both a predictive and prognostic breast cancer biomarker. And because the predictive value of Kaiso varies across racial groups, these findings further emphasize the need for the inclusion of diverse racial and ethnic groups in clinical trials.
“The future of this research is to identify the specific connections between pharmacogenetics and race/ethnicity and develop Kaiso as a predictive genetic biomarker of overall breast cancer survival and response to therapy,” Singhal concluded. “The next stage of this project is to validate the role of Kaiso as a unique predictive genetic biomarker, characterize its functional linkage and association with tumor progression, genomic properties, features of the tumor microenvironment, and patient exposures through a racially diverse population with collaboration of different institutions.”