Changde Cheng, Ph.D.
Hosting: Information Session & Interview Room
Department: Medicine/Division of Hematology and Oncology
Description: We develop quantitative and AI-enabled approaches to understand disease evolution and to discover clinically actionable biomarkers and therapeutic strategies. Our research sits at the interface of cancer genomics, tumor immunology, and machine learning, with a strong emphasis on integrating single-cell and multi-omics data (scRNA-seq/CITE-seq, functional perturbation screens, chromatin/epigenetic profiling, and clinical bulk RNA-seq) using reproducible, mechanism-informed computational frameworks.
We focus on three interconnected areas: (1) tumor-immune ecosystem programs that predict clinical outcomes and immunotherapy response, with particular interest in innate-like T cells, NK cells, and myeloid regulation; (2) clonal evolution and progression risk in clonal hematopoiesis and myeloid malignancies (e.g., AML and CHIP), combining statistical genomics with clinically grounded risk modeling; and (3) epigenetic and combinatorial therapeutic mechanisms to identify actionable regulatory hubs and drug targets. We also pursue theory-driven projects in evolutionary genetics (e.g., chromosomal inversions, selection in structured populations) to build generalizable models of adaptation, aging, and constraints on complex traits.