Multivariate analysis enables resource efficient spatial omics for drug discovery

Thomas M. Menino Convention & Exhibition Center 

In GSK's work developing oncology therapies, comprehensive characterization of the tumor microenvironment, particularly the interplay of target heterogeneity and expression, is informative of clinical potential, precise treatment strategies, novel biomarkers, and bispecific pairings. Traditional tumor characterization methods like immunohistochemistry are widely accessible due to their affordability and speed but are often restricted to analyzing a limited number of markers per sample. High-parameter spatial proteomic technologies address this challenge but are typically more costly and time-intensive.
This talk will present our approach to selecting regions of interest for high-plex spatial proteomics studies from higher-throughput, lower-plex studies. We used multivariate analysis to guide experimental design by implementing dimensionality reduction and clustering methods to select representative regions from a broad, lower-resolution imaging study. We demonstrate that exploratory studies can be leveraged for identification of diverse and characteristic samples to optimize learnings from spatial omics studies, reducing the cost and time needed for impact on drug discovery.

spatial omics

experimental design

drug discovery 

Section on Statistics in Genomics and Genetics