Cheng Cheng Group

The Cheng Group specializes in cutting edge statistical methods to support both clinical and translational studies in pediatric oncology, primarily in Acute Lymphoblastic Leukemia. In clinical trials, the Cheng Group provides statistical design, implementation, and data analysis spanning all areas of clinical investigations, including efficacy, toxicology, survival, and long-term sequelae. In translational studies, the Cheng Group supports analyses in multi-omics studies, interfacing with bioinformatics and computational biology. As treatment regimens and biotechnologies rapidly evolve, the Cheng Group looks forward to applying statistical principles to the development of machine learning and artificial intelligence tools, with the overarching goal of supporting curative therapeutic practices for childhood cancers. 

Dr. Cheng joined St. Jude as an Assistant Member in 2000 to lead Biostatistics collaboration and support in the Hematologic Malignancies Program (HMP) of the St. Jude Childhood Cancer Center (SJCCC), and departments of Oncology, Pharmaceutical Science, and Pathology. Dr. Cheng has been the Biostatistics lead for clinical and translational research in Acute Lymphoblastic Leukemia (ALL) and has overseen statistical designs in front-line and relapsed ALL clinical trial protocols, analyses and publication of trial results. In collaboration in translational research, Dr. Cheng has led the analyses for several large-scale omics studies of ALL that revealed various biomarkers and causal variants for treatment response, toxicity, outcome, or development of ALL. Dr. Cheng has coauthored more than 220 peer-reviewed publications in areas of or closely related to treatment efficacy, adverse effects, biology, and long-term sequelae of childhood leukemias. Building upon his works in the theory and methods of quantile (percentile) and quantile-density function estimation and experience in high-dimensional cheminformatics data, his statistical research has focused on developing statistical inference methods to tackle challenges (big-p small-n, complex traits, etc.) in large-scale multi-omics studies, as well as in analysis of complex survival processes after cancer therapy. He is on the faculty of St. Jude Graduate School and teaches Biostatistics and Data Science.