Enabling Single‐Cell Drug Response Annotations from Bulk RNA‐Seq Using SCAD

• DOI: 10.1002/advs.202204113
• Publication Date: 2023-02-10T10:00:08Z
• Authors: Zetian Zheng; Junyi Chen; Xingjian Chen; Lei Huang; Weidun Xie; Qiuzhen Lin; Xiangtao Li; Ka‐Chun Wong
• Keywords: Vorinostat; Pharmacogenomics; RNA-Seq

Abstract The single‐cell RNA sequencing (scRNA‐seq) quantifies the gene expression of individual cells, while bulk (bulk RNA‐seq) characterizes mixed transcriptome cells. inference drug sensitivities for cells can provide new insights to understand mechanism anti‐cancer response heterogeneity and resistance at cellular resolution. However, pharmacogenomic information related their corresponding scRNA‐Seq is often limited. Therefore, a transfer learning model proposed infer level. This framework learns profiles pharmacogenomics from population cell lines in large public dataset transfers knowledge efficacy results suggest that it suitable learn pre‐clinical pre‐existing subpopulations with different prior exposure. In addition, offers perspective on combinations. It observed drug‐resistant subpopulation be sensitive other drugs (e.g., subset JHU006 Vorinostat‐resistant Gefitinib‐sensitive); such finding corroborates previously reported combination (Gefitinib + Vorinostat) strategy several cancer types. identified sensitivity biomarkers reveal into tumor treatment