Our group develops computational methods to study the phenotypic diversity, differentiation hierarchies, and clinical significance of tumor cell subsets and their surrounding microenvironments. Projects in the lab routinely blend AI, data science, and biology with cutting-edge bulk, single-cell, and/or spatial genomic modalities. Key results are further explored experimentally, with the ultimate goal of translating promising findings into next-generation diagnostics and anti-cancer therapies.
As a member of the Institute for Stem Cell Biology and Regenerative Medicine, the Department of Biomedical Data Science, and as an affiliate of graduate programs in Biomedical Informatics, Cancer Biology, and Immunology, we are also interested in the development of impactful biomedical data science tools in areas beyond our immediate research focus, including developmental biology, regenerative medicine, and systems immunology.
Just as normal cell types are ultimately derived from stem cells, increasing evidence suggests that human tumors may also arise from cancer stem cells, which have been implicated in tumor development, maintenance, and metastasis. Despite their importance, our fundamental understanding of "tumor initiating cells" remains incomplete, and therapies that target these cells are lacking. To address this need, we are developing new cellular and genetic analysis methods to delineate single-cell developmental states (e.g., CytoTRACE). Our goal is to discover and better understand tumor initiating cells in primary human malignancies in order to improve cancer patient outcomes.
Tissue composition is a major determinant of phenotypic variation and a key factor influencing disease outcomes. Although single cell RNA sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, it is currently impractical for large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. To overcome these challenges, we have developed new platforms for digital cytometry, including CIBERSORTx, EcoTyper, and CytoSPACE. These approaches leverage single-cell RNA sequencing and expression deconvolution, enabling large-scale and spatially-informed cell profiling of complex tissue specimens. We are currently applying these frameworks to address multiple biological and clinical questions of interest across diverse tumor types.