Kaiso (ZBTB33) subcellular partitioning functionally links LC3A/B, the tumor microenvironment, and breast cancer survival

The use of digital pathology for the histomorphologic profiling of pathological specimens is expanding the precision and specificity of quantitative tissue analysis at an unprecedented scale; thus, enabling the discovery of new and functionally relevant histological features of both predictive and prognostic significance. In this study, we apply quantitative automated image processing and computational methods to profile the subcellular distribution of the multi-functional transcriptional regulator, Kaiso (ZBTB33), in the tumors of a large racially diverse breast cancer cohort from a designated health disparities region in the United States. Multiplex multivariate analysis of the association of Kaiso's subcellular distribution with other breast cancer biomarkers reveals novel functional and predictive linkages between Kaiso and the autophagy-related proteins, LC3A/B, that are associated with features of the tumor immune microenvironment, survival, and race. These findings identify effective modalities of Kaiso biomarker assessment and uncover unanticipated insights into Kaiso's role in breast cancer progression. Through automated image analysis, Singhal et al quantify nuclear versus cytoplasmic distribution of the Kaiso transcription factor in breast cancer patient tissue. They find that Kaiso distribution correlates with breast cancer subtype and overall survival, and discover a link between cytoplasmic Kaiso and autophagy marker LC3.

Automated summary

The study applies digital pathology to analyze the subcellular distribution of the transcription factor Kaiso in breast cancer tumors, revealing novel associations with other biomarkers and providing insights into Kaiso's role in breast cancer progression. Automated image analysis quantifies the distribution of Kaiso, showing correlations with breast cancer subtype, overall survival, and a link with the autophagy marker LC3.