bioRxivCancer BiologyPreprint
Single-cell Pharmacogenomic Landscapes of Epigenetic Drug Resistance Revealed by Gastric Cancer Perturb-seq
Chang XuiD, Haoran MaiD, Taotao ShengiD, Ming Hui Lee, Siti Aishah Binte Abdul GhaniiD, Laura Perlaza-JiméneziD, Kie Kyon HuangiD, Shen Kiat Lim, Supriya SrivastavaiD, Xuewen OngiD, Su Ting TayiD, Shamaine Wei Ting HoiD, Zhi Xuan Ong, Angie Lay Keng TaniD, Feng Zhu, Hassan AshktorabiD, Alfred Sze‐Lok ChengiD, Anand Devaprasath D. JeyasekharaniD, Shang LiiD, Ming TehiD, Raghav SundariD, David R. PowelliD, Joseph RosenbluhiD, Wei Peng YongiD, Jimmy Bok Yan SoiD, Patrick B. O. TaniD
Abstract
Gene expression signatures ("molecular phenotypes") are extensively utilized in cancer research. To study how gastric cancer (GC) molecular phenotypes are shaped by cell-intrinsic genetic alterations interacting with cell-extrinsic therapeutic pressures, we performed direct capture Perturb-seq (dcPerturb-seq) to interrogate >200 GC-related genes across 4 distinct epigenetic drug classes in multiple gastric lines. We captured 17.7 million pharmocogenomic expression interactions in 625,866 cells representing baseline and post-therapeutic molecular phenotypes. This single-cell pharmacogenomic compendium confirmed previously known gene-driven molecular phenotypes, elucidated poorly characterized genes, and uncovered novel gene dosage-molecular phenotype relationships. Molecular phenotypes in post-therapeutic surviving cells revealed diverse gene perturbation-associated pathways causing convergent drug resistance (EMT plasticity, cell cycle alterations, metabolic reprogramming), highlighting combinatorial strategies for restoring sensitivity. Mapping of in vitro molecular phenotypes to primary human GCs imparted prognostic information and insights into spatial heterogeneity. Comparative analysis of gene perturbations across therapies and lines revealed both conserved and context-specific molecular alterations. Our results illustrate how Perturb-seq approaches can systematically map diverse cancer-associated molecular phenotypes across multiple gene/drug/cell line interactions, yielding translational insights.
- organism: human
- organism: mouse
- assay: scRNA-seq
- research focus: drug response / drug discovery
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: experimental assay
- Citations
- 0
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