Dr. Paul Geeleher focuses on developing and validating computational methods/approaches aimed at improving outcomes in pediatric cancers. Our lab has strong computational and wet-bench components.
The Geeleher Lab is associated with the Department of Computational Biology, the Graduate School of Biomedical Sciences and the Comprehensive Cancer Center at St. Jude Children’s Research Hospital in Memphis Tennessee.
More information available on our labs St Jude Homepage!
The poor outcomes in pediatric solid tumors are explained at least in part by striking intratumoral cellular plasticity, where isogenic cells have been shown to interconvert between different drug-resistant cell states. In the last 5 years, vast strides have been made in our ability to study patient tumor heterogeneity using high-throughput genomics methods like single cell and spatial transcriptomics. In parallel with these developments, we have gained the ability to drug and CRISPR screen very large numbers of preclinical disease models, for example, enormous panels of cancer cell lines. However, our ability to jointly leverage these new classes of data is lacking. For example, while screening in cancer cell lines is highly scalable and has yielded clinically impactful findings across many diseases, the fidelity of these models is often questionable and the computational infrastructure to determine which (if any) components of primary tumors are recapitulated by various preclinical models has not been developed. The overall goal of our lab is to develop the computational infrastructure that allows the results from preclinical screening to be interpreted in the context of a detailed understanding of patient tumor biology, and in understanding this relationship to nominate new opportunities for drug repurposing and the development of new drug targets. The primary computational tools we use for this purpose are that of unsupervised and supervised machine learning. While the computational work is highly generalizable, we specifically use these unique computational skills to push forward our own clinical translational and wet-lab mechanistic work focused on high-risk neuroblastoma.