Rate My Professor Ruping Sun

Dr. Sun is a computational geneticist whose research centers on the translational genomics of solid tumors. He has developed expertise in algorithm design and statistical analysis of (epi)genetic sequencing data, as well as computational modeling of cancer, including gene regulatory circuits and cellular automata models. His pioneering work links intra-tumor heterogeneity with tumor growth dynamics using multi-region sequencing of solid tumors and introduces regional-assembly into fusion transcript prediction, identifying CD74-NRG1 as a potential therapeutic target for the invasive mucinous subtype of lung cancer. The SunPath Lab focuses on quantifying and modeling tumor heterogeneity from next-generation sequencing data to infer cancer kinetics, developing methods for high-fidelity quantification of (epi)genomic heterogeneity, and visualizing patterns to reveal principles of genomic heterogeneity, clonal dynamics, and somatic variant detectability. Key publications include 'Evolving copy number gains promote tumor expansion and bolster mutational diversification' (Nature Communications, 2024), 'Oncogene-like addiction to aneuploidy in human cancers' (Science, 2023), 'Elements and evolutionary determinants of genomic divergence between paired primary and metastatic tumors' (PLoS Computational Biology, 2021), 'Quantitative evidence for early metastatic seeding in colorectal cancer' (Nature Genetics, 2019), 'Promoter of lncRNA gene PVT1 is a tumor-suppressor DNA boundary element' (Cell, 2018), 'Between-region genetic divergence reflects the mode and tempo of tumor evolution' (Nature Genetics, 2017), and 'A population genetics perspective on the determinants of intra-tumor heterogeneity' (Biochimica et Biophysica Acta - Reviews on Cancer, 2017). His contributions advance mechanistic understanding of tumor evolution and its clinical implications through collaborative, interdisciplinary approaches.