Kenichi Shimada

The paper analyzes HER2+ breast cancer using multi-platform data to identify molecular predictors of response to neoadjuvant therapy and highlights intra-tumoral heterogeneity and immune exclusion mechanisms.

The paper shows BRCA-mutant breast cancers respond to PARP inhibitors with immune remodeling, but PD-L1 blockade is ineffective due to spatial separation of PD-L1+ cells and PD-1+ T cells.

StateMap integrates CRISPR fitness and gene expression data to map cancer pathway states, identifying therapeutic vulnerabilities in the Hippo pathway.

The paper identifies distinct immune and stromal features in HR+ HER2-low and HER2-0 breast cancers using molecular and multiplexed imaging.

This study identifies an estrogen receptor signaling program in HR+ breast cancer that suppresses immune responses by reducing T cell infiltration and HLA expression, suggesting therapeutic targets to enhance immunogenicity.

The paper uses machine learning to analyze toxicogenomic data and identifies toxin-induced disease states, tolerance mechanisms, and organ-to-whole-body communication in rats.