Machine learning cellular dynamics in the tumor microenvironment

Music City Center 

Studying how intra-tumoral immune populations coordinate to generate anti-tumor responses can guide precise treatment prioritization. Recent genomic technologies that measure cell features at the resolution of single cells or in a spatially-resolved manner, present exciting opportunities to study the heterogeneity of cells and characterize complex interactions in the tumor microenvironment (TME). However, analyzing and integrating these data types in particular in complex patient specimens involves significant statistical and computational challenges. I will present a set of statistical machine learning methods developed to infer temporal and spatial dynamics of cells in the TME and tumor-immune interactions. I will show their application in the characterization of coordinated immune cell networks in an established adoptive cellular therapy, donor lymphocyte infusion (DLI) in relapsed leukemia, as well as checkpoint therapy in melanoma.

computational biology

spatiotemporal dynamics

single-cell genomics

spatial transcriptomics

cancer immunology