Journal Article

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

Wong, Ka‐Chun, Zheng, Zetian, Huang, Lei, Xie, Weidun, Li, Xiangtao, Chen, Junyi, Chen, Xingjian, Lin, Qiuzhen

2023

Overview

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

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
• Subject: Bias / Biomarkers / Breast cancer / Cancer therapies / Cells / Datasets / Deep learning / drug response annotation / Gefitinib / Genomics / Kinases / Machine learning / Patients / RNA-Seq - methods / Sequence Analysis, RNA - methods / single‐cell sequencing / Transcriptome - genetics / transfer learning / Vorinostat