Minsoo Kim, PhD

Cells in healthy individuals were thought to carry the same number of chromosomes, but it was recently discovered that in the breast tissue of healthy women there are rare cell populations that acquire extra or lose copies, a condition called aneuploidy. These abnormal cells mirror the characteristics of breast cancers and may represent early precursors of the disease years before clinical diagnosis. Dr. Kim focuses on finding and characterizing these rare abnormal cells in healthy breast tissues. He hopes to build computational tools to understand what biomarkers set these cells apart from their healthy neighbors and how their surrounding microenvironment may influence their behavior, opening new avenues for early cancer detection and risk stratification. To test whether these signs truly predict cancer development, he plans to apply this approach to breast tissue samples from patients who were monitored over many years, some of whom later developed cancer, with the goal of giving clinicians better ways to detect breast cancer earlier and to identify at-risk patients.

Dr. Kim will develop a heterogeneous graph neural network (GNN) that jointly models single cell copy number alterations and gene expression with genes, cells, and chromosome segments as nodes. This will separate transcriptional changes driven by chromosomal gains and losses from other sources of variation. He will extend the model to spatial transcriptomic data to further isolate microenvironmental influences on gene expression and understand how these rare aneuploid cells interact with their local environment.