Lectures and seminars OnkPat Friday Seminar: Deciphering tumor evolution with spatiotemporal lineage tracing

07-11-2025 12:30 pm - 1:30 pm Add to iCal
Air & Fire, SciLifeLab

Welcome to OnkPat Friday Seminar, this week with Zheng Hu, professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.

Host: Kasper Karlsson, Department of Oncology-Pathology

Abstract

Elucidating the spatiotemporal evolution of cancer cells during growth and drug treatment is of great significance for achieving early diagnosis and precision therapy. We recently developed a base editor-based lineage tracing technology, enabling recording of high-resolution lineage information at single-cell level. Using a mouse model of intestinal tumor, we systematically mapped the multi-stage lineage architecture of tumor evolution, revealing the dynamic transition from polyclonal to monoclonal states and uncovering intercellular communication mechanisms that indicate the cooperative roles of multiple lineages in early tumor formation. Furthermore, by applying this approach to an immunotherapy mouse model, we uncovered the phenotypic plasticity, evolutionary trajectories, and spatial interaction patterns underlying tumor immune evasion, providing new insights into the prediction and intervention of tumor immune escape.

Biography

Dr. Zheng Hu is a Professor at Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences. He also serves as the Director of Center for Synthetic Biology and Evolution. Dr. Hu received his B.S. in Biomedical Engineering from Huazhong University of Science and Technology in 2010. He received his Ph.D in Evolutionary Genetics from Beijing Institute of Genomics, Chinese Academy of Sciences in 2015. From 2015 to 2020, he was a IGI Postdoctoral Fellow in Dr Christina Curtis’s lab at Stanford University School of Medicine. Dr Hu’s research interests span from cancer genomics, cancer evolution, lineage tracing to computational biology. His research on measuring cancer evolutionary dynamics has yielded novel insights into cancer formation and metastasis, facilitating biomarker discovery for risk prediction and treatment decision making.