We seek to understand tumor ecosystems at single-cell resolution during the development and evolution of human cancer. To this end, we use computational approaches to analyze, integrate, and interpret large-scale genomic data — with an emphasis on single-cell RNA-sequencing data and emerging genomic technologies. We participate in long-term, data-driven collaborations with clinicians and other labs at the Washington University School of Medicine, in which quantitative analysis is closely integrated with clinical studies and molecular biology. Areas of focus include:
Intratumoral expression heterogeneity. Virtually all tumors are genetically heterogeneous, containing subclonal populations of cells that are defined by distinct mutations. Subclones can have unique phenotypes that influence tumor evolution and disease progression, but these phenotypes are difficult to characterize, because it is often impossible to purify individual subclones. Using single-cell RNA-sequencing, however, it is possible to assay gene expression in every cell in every subclone without physically purifying the subclones. By combining this data with whole genome sequence data obtained from paired tumor/normal samples, we hope to better understand the biological differences among subclones.
Tumor evolution. We are studying the biological basis of relapse – and its relationship to intratumoral expression heterogeneity — by combining scRNA-seq and whole genome sequencing of tumor samples obtained at presentation and relapse.
Spatial, genetic, epigenetic, and immunological heterogeneity in solid tumors. By integrating exome sequencing, scRNA-seq, and single cell T-cell receptor sequencing, we are building multidimensional portraits of solid tumors aimed at understanding the relationships among transcriptional heterogeneity, genetic heterogeneity, neoantigens, infiltrating immune cells, and clonotypic diversity of T-cell receptors.