Yevgeniy R. Semenov

This paper identifies 116 risk loci for melanoma through GWAS, links susceptibility to immunity and Wnt signaling, and develops a PRS improving risk prediction.

The paper identifies melanoma differentiation and immune exhaustion as predictors of tumor progression and therapy response in in-transit melanoma patients through multiomic profiling.

The paper maps spatial patterns of tumor cell dedifferentiation and plasticity in melanoma by analyzing RNA and protein expression across 300 histological domains.

SpatialCells automates the analysis of spatially resolved multiplexed single-cell data to characterize tumor microenvironments, enabling efficient feature extraction and downstream machine learning applications.

This study identifies three germline variants associated with immune-related adverse events in patients treated with immune checkpoint inhibitors.

The paper demonstrates that discarded tumor sequencing reads can be used to impute germline genotypes, enabling the construction of large germline research cohorts from existing clinical data.