Our team based in Seattle conducted a comprehensive review including evolving trends in the management of Merkel cell carcinoma (MCC). This summary covers key decision points, including recommended work-up during initial diagnosis, treatment options for MCC when it’s in one place or has spread, management of recurrent MCC, and new treatments that are showing promise with fewer side effects and good results. This review gives valuable information on how to handle MCC overall and emphasizes new methods that are effective and less toxic on patients.
Recent spatial gene expression technologies enable comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing analysis methods do not address the limited resolution of the technology or use the spatial information efficiently. Here, we introduce BayesSpace, a fully Bayesian statistical method that uses the information from spatial neighborhoods for resolution enhancement of spatial transcriptomic data and for clustering analysis. We benchmark BayesSpace against current methods for spatial and non-spatial clustering and show that it improves identification of distinct intra-tissue transcriptional profiles from samples of the brain, melanoma, invasive ductal carcinoma and ovarian adenocarcinoma. Using immunohistochemistry and an in silico dataset constructed from scRNA-seq data, we show that BayesSpace resolves tissue structure that is not detectable at the original resolution and identifies transcriptional heterogeneity inaccessible to histological analysis. Our results illustrate BayesSpace’s utility in facilitating the discovery of biological insights from spatial transcriptomic datasets.