R. Stephanie Huang

This study uses single-cell and spatial transcriptomics to identify distinct tumor states in uterine leiomyosarcomas and proposes drug targets based on these states.

The paper shows that therapy-resistant cancer cell clones have distinct drug sensitivity profiles, and combinations targeting these subpopulations can effectively eliminate resistant populations.

This paper introduces scIDUC, a computational framework that predicts drug response at the single-cell level by integrating single-cell transcriptomics with pan-cancer cell line data, showing high accuracy in identifying therapeutic vulnerabilities.

Simplicity provides a web-based tool for visualizing and analyzing high-throughput cancer cell line screens, enabling recalculation of summary statistics at user-defined drug concentrations.

This study develops human iPSC-derived tumor models to investigate the cooperative effects of H3.3K27M and PDGFRA mutations in DMG, identifying therapeutic vulnerabilities.

The paper identifies lncRNAs as significant predictors of anticancer drug sensitivity, showing they can enhance drug response predictions beyond protein-coding genes.

The paper identifies cancer-specific eQTLs by modeling genotype-tumor purity interactions in heterogeneous tumor gene expression data, showing most risk variants affect tumor microenvironment cells.