DrugFormer: Graph‐Enhanced Language Model to Predict Drug Sensitivity
Abstract Drug resistance poses a crucial challenge in healthcare, with response rates to chemotherapy and targeted therapy remaining low. Individual patient's resistance is exacerbated by the intricate heterogeneity of tumor cells, presenting significant obstacles to effective treatment. To add...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202405861 |
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| _version_ | 1850201497072041984 |
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| author | Xiaona Liu Qing Wang Minghao Zhou Yanfei Wang Xuefeng Wang Xiaobo Zhou Qianqian Song |
| author_facet | Xiaona Liu Qing Wang Minghao Zhou Yanfei Wang Xuefeng Wang Xiaobo Zhou Qianqian Song |
| author_sort | Xiaona Liu |
| collection | DOAJ |
| description | Abstract Drug resistance poses a crucial challenge in healthcare, with response rates to chemotherapy and targeted therapy remaining low. Individual patient's resistance is exacerbated by the intricate heterogeneity of tumor cells, presenting significant obstacles to effective treatment. To address this challenge, DrugFormer, a novel graph‐augmented large language model designed to predict drug resistance at single‐cell level is proposed. DrugFormer integrates both serialized gene tokens and gene‐based knowledge graphs for the accurate predictions of drug response. After training on comprehensive single‐cell data with drug response information, DrugFormer model presents outperformance, with higher F1, precision, and recall in predicting drug response. Based on the scRNA‐seq data from refractory multiple myeloma (MM) and acute myeloid leukemia (AML) patients, DrugFormer demonstrates high efficacy in identifying resistant cells and uncovering underlying molecular mechanisms. Through pseudotime trajectory analysisunique drug‐resistant cellular states associated with poor patient outcomes are revealed. Furthermore, DrugFormer identifies potential therapeutic targets, such as COX8A, for overcoming drug resistance across different cancer types. In conclusion, DrugFormer represents a significant advancement in the field of drug resistance prediction, offering a powerful tool for unraveling the heterogeneity of cellular response to drugs and guiding personalized treatment strategies. |
| format | Article |
| id | doaj-art-544eceeee3f7410e949d627f244d4388 |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-544eceeee3f7410e949d627f244d43882025-08-20T02:11:59ZengWileyAdvanced Science2198-38442024-10-011140n/an/a10.1002/advs.202405861DrugFormer: Graph‐Enhanced Language Model to Predict Drug SensitivityXiaona Liu0Qing Wang1Minghao Zhou2Yanfei Wang3Xuefeng Wang4Xiaobo Zhou5Qianqian Song6Center for Computational Systems Medicine McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX 77030 USADepartment of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville FL 32611 USADepartment of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville FL 32611 USADepartment of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville FL 32611 USABiostatistics and Bioinformatics H. Lee Moffitt Cancer Center and Research Institute Tampa FL USACenter for Computational Systems Medicine McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX 77030 USADepartment of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville FL 32611 USAAbstract Drug resistance poses a crucial challenge in healthcare, with response rates to chemotherapy and targeted therapy remaining low. Individual patient's resistance is exacerbated by the intricate heterogeneity of tumor cells, presenting significant obstacles to effective treatment. To address this challenge, DrugFormer, a novel graph‐augmented large language model designed to predict drug resistance at single‐cell level is proposed. DrugFormer integrates both serialized gene tokens and gene‐based knowledge graphs for the accurate predictions of drug response. After training on comprehensive single‐cell data with drug response information, DrugFormer model presents outperformance, with higher F1, precision, and recall in predicting drug response. Based on the scRNA‐seq data from refractory multiple myeloma (MM) and acute myeloid leukemia (AML) patients, DrugFormer demonstrates high efficacy in identifying resistant cells and uncovering underlying molecular mechanisms. Through pseudotime trajectory analysisunique drug‐resistant cellular states associated with poor patient outcomes are revealed. Furthermore, DrugFormer identifies potential therapeutic targets, such as COX8A, for overcoming drug resistance across different cancer types. In conclusion, DrugFormer represents a significant advancement in the field of drug resistance prediction, offering a powerful tool for unraveling the heterogeneity of cellular response to drugs and guiding personalized treatment strategies.https://doi.org/10.1002/advs.202405861drug resistanceknowledge graphlanguage modelsingle‐cell RNA sequencing |
| spellingShingle | Xiaona Liu Qing Wang Minghao Zhou Yanfei Wang Xuefeng Wang Xiaobo Zhou Qianqian Song DrugFormer: Graph‐Enhanced Language Model to Predict Drug Sensitivity Advanced Science drug resistance knowledge graph language model single‐cell RNA sequencing |
| title | DrugFormer: Graph‐Enhanced Language Model to Predict Drug Sensitivity |
| title_full | DrugFormer: Graph‐Enhanced Language Model to Predict Drug Sensitivity |
| title_fullStr | DrugFormer: Graph‐Enhanced Language Model to Predict Drug Sensitivity |
| title_full_unstemmed | DrugFormer: Graph‐Enhanced Language Model to Predict Drug Sensitivity |
| title_short | DrugFormer: Graph‐Enhanced Language Model to Predict Drug Sensitivity |
| title_sort | drugformer graph enhanced language model to predict drug sensitivity |
| topic | drug resistance knowledge graph language model single‐cell RNA sequencing |
| url | https://doi.org/10.1002/advs.202405861 |
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