Decoding Drug Response With Structurized Gridding Map-Based Cell Representation

A thorough understanding of cell-line drug response mechanisms is crucial for drug development, repurposing, and resistance reversal. While targeted anticancer therapies have shown promise, not all cancers have well-established biomarkers to stratify drug response. Single-gene associations only explain a small fraction of the observed drug sensitivity, so a more comprehensive method is needed. However, while deep learning models have shown promise in predicting drug response in cell lines, they still face significant challenges when it comes to their application in clinical applications. Therefore, this study proposed a new strategy called DD-Response for cell-line drug response prediction. First, a limitation of narrow modeling horizons was overcome to expand the model training domain by integrating multiple datasets through source-specific label binarization. Second, a modified representation based on a two-dimensional structurized gridding map (SGM) was developed for cell lines & drugs, avoiding feature correlation neglect and potential information loss. Third, a dual-branch, multi-channel convolutional neural network-based model for pairwise response prediction was constructed, enabling accurate outcomes and improved exploration of underlying mechanisms. As a result, the DD-Response demonstrated superior performance, captured cell-line characteristic variations, and provided insights into key factors impacting cell-line drug response. In addition, DD-Response exhibited scalability in predicting clinical patient responses to drug therapy. Overall, because of DD-response's excellent ability to predict drug response and capture key molecules behind them, DD-response is expected to greatly facilitate drug discovery, repurposing, resistance reversal, and therapeutic optimization.

Authors

• Jiayi Yin
• Hanyu Zhang Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS Key Lab of AI Safety of Chinese Academy of Sciences (CAS) University of Chinese Academy of Sciences, CAS
• Xiuna Sun
• Nanxin You
• Minjie Mou
• Mingkun Lu
• Ziqi Pan Shanghai Jiao Tong University
• Fengcheng Li
• Honglin Li Innovation Center for Artificial Intelligence and Drug Discovery, East China Normal University, Shanghai, China
• Su Zeng
• Feng Zhu

Keywords

• Drugs
• Cancer
• Predictive models
• Databases
• Data models
• Correlation
• Immune system
• Drug Response
• Gridding
• Drug Discovery
• Drug Resistance
• Deep Learning
• Drug Development
• Clinical Patient
• Deep Learning Models
• Prediction Of Response
• Drug Sensitivity
• Cell Lines In Response
• Training Domain
• Reverse Drug Resistance
• Neural Network
• Cancer Types
• Cancer Cell Lines
• Prediction Accuracy
• Convolutional Neural Network
• Gene Set Enrichment Analysis
• Cancer Drug
• Latent Vector
• Drug Repurposing
• Cancer Cell Line Encyclopedia
• Original Gene Expression
• Gene Expression In Cell Lines
• Accurate Prediction Of Response
• Grid Map
• Original Representation
• Precise Characterization
• Extreme Gradient Boosting
• Drug response prediction
• cell lines representation
• precision medicine
• structurized gridding map
• Humans
• Antineoplastic Agents
• Neural Networks, Computer
• Cell Line, Tumor
• Computational Biology
• Neoplasms