About the PI

Ruping Sun
Assistant Professor
Advanced Cancer Genomics and Bioinformatics
Department of Laboratory Medicine and Pathology
Member of Masonic Comprehensive Cancer Center
University of Minnesota Twin Cities

@Sun_Ruping — Google Scholar

ruping at umn dot edu *

As a computational cancer biologist, my research focuses on computational innovations to detect somatic genomic variants from massive parallel sequencing data, and mathematical approaches to identify oncogenic events by leveraging the evolutionary dynamics of cancer. My rationale is that one can quantify intra tumor heterogeneity (ITH) from patient tumor sequencing data (such as multi-region, longitudinal and single-cell sampling data) and in turn leverage the patterns of ITH to infer the mode (or the way) of clonal evolution in vivo. This approach will delineate the roles of the genomic aberrations on the development of individual patient tumors. Leading a research group at the University of Minnesota, I am driven to bridge ITH with mathematical modeling and interpretability. We prototyped a framework to infer the timing of SCNAs during the somatic evolution toward the founder of clinical samples (Wang et al., bioRxiv, 2022). In addition, we obtained results expressing the genomic divergence between metastatic and primary tumors in terms of the time and mode of metastatic seeding (Sun and Nikolakopoulos, PLoS Comput Biol 2021). My long term goal is to create broadly applicable computational methods that advance a mechanistic understanding of patient tumor evolution and identify cancer genes for early detection or as drug targets.