236 Visualizing somatic alterations in spatial transcriptomics data of skin cancer

Tools to visualize genetic alterations within tissues remain underdeveloped despite the growth of spatial transcriptomic technologies, which measure gene expression in different regions of tissues. When a genetic alteration occurs in an expressed gene, it can be detected in RNA-sequencing data. Therefore, we explored the feasibility of observing somatic alterations in spatial transcriptomics data of cutaneous squamous cell carcinoma (reanalyzed from Ji et. al. Cell, 2020). Skin cancer is an ideal “poster child” for these studies because of its high mutation burden. Recent work demonstrates that copy number alterations can be inferred from spatial transcriptomics data (Erickson et. al. Nature, 2022). Here, we describe new software to dramatically improve the inference of copy number from spatial transcriptomics data. Moreover, we demonstrate, for the first time, that single nucleotide variants are also detectable in spatial transcriptomic data. We applied these approaches to comprehensively map the location of point mutations, copy number alterations, and allelic imbalances in spatial transcriptomic data of skin cancer. We show that the tumors, analyzed here, are each dominated by a single clone of cells, suggesting that their regional variations in gene expression are likely driven by non-genetic factors. Furthermore, we observe mutant cells in histologically normal tissue surrounding one tumor, suggesting the tumor arose from a larger “field” of mutant cells. Finally, we detect mono-allelic expression of immunoglobulin heavy chains in B-cells, revealing clonal populations of plasma cells surrounding one tumor. In summary, we put forward solutions to add the genetic dimension to spatial transcriptomic datasets, augmenting the potential of this already promising new technology. Completion of these analyses revealed new insights into the tumor and microenvironmental ecosystem of skin cancer.