Computational Biology and Cancer Genomics
Dr. Changde Cheng aims to improve cancer patient outcomes by using computational and analytical approaches to better understand the contribution of intratumoral cellular heterogeneity to therapeutic resistance.
Our laboratory is dedicated to developing innovative computational methods and machine-learning tools to gain a comprehensive understanding of cancer biology in patients. To investigate cancer initiation, progression, and therapeutic resistance, we leverage high-throughput techniques such as single-cell RNA-seq, spatial, multiomics, and perturbation sequencing. Our research focuses on identifying tumor cell subpopulations responsible for relapse and understanding how microenvironments influence their response to chemotherapy. We have developed high-performance machine-learning tools for single-cell analysis, which enable accurate subpopulation estimation and clustering. This allows us to define disease-causing cell states, predict their behavior under different conditions, and understand cell interactions.
Our ultimate goal is to translate these insights into clinical practice, identifying patients at risk of disease recurrence and improving treatment outcomes for the benefit of patients.