A disentangled generative model for improved drug response prediction in patients via sample synthesis
Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer. Computational methods have been widely explored and have become increasingly accurate in recent years. However, the clinical application of prediction methods is still in it...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Journal of Pharmaceutical Analysis |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095177924002259 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849318367309070336 |
|---|---|
| author | Kunshi Li Bihan Shen Fangyoumin Feng Xueliang Li Yue Wang Na Feng Zhixuan Tang Liangxiao Ma Hong Li |
| author_facet | Kunshi Li Bihan Shen Fangyoumin Feng Xueliang Li Yue Wang Na Feng Zhixuan Tang Liangxiao Ma Hong Li |
| author_sort | Kunshi Li |
| collection | DOAJ |
| description | Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer. Computational methods have been widely explored and have become increasingly accurate in recent years. However, the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients. We present a novel disentangled synthesis transfer network (DiSyn) for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients. DiSyn uses a domain separation network (DSN) to disentangle drug response related features, employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement. DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets, The Cancer Genome Atlas (TCGA), Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2 (I-SPY2) and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia (NIBR PDXE), achieving competitive performance with the state-of-the-art methods on cancer patients and mice. Furthermore, the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction. |
| format | Article |
| id | doaj-art-03db1d5a7a2f4dc4ba3005cdf563a0a6 |
| institution | Kabale University |
| issn | 2095-1779 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pharmaceutical Analysis |
| spelling | doaj-art-03db1d5a7a2f4dc4ba3005cdf563a0a62025-08-20T03:50:50ZengElsevierJournal of Pharmaceutical Analysis2095-17792025-06-0115610112810.1016/j.jpha.2024.101128A disentangled generative model for improved drug response prediction in patients via sample synthesisKunshi Li0Bihan Shen1Fangyoumin Feng2Xueliang Li3Yue Wang4Na Feng5Zhixuan Tang6Liangxiao Ma7Hong Li8CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, ChinaCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, ChinaCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, ChinaCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, ChinaMolecular Pathology Laboratory, National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Shanghai, 201800, ChinaMolecular Pathology Laboratory, National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Shanghai, 201800, ChinaCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, ChinaCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, ChinaCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China; Corresponding author.Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer. Computational methods have been widely explored and have become increasingly accurate in recent years. However, the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients. We present a novel disentangled synthesis transfer network (DiSyn) for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients. DiSyn uses a domain separation network (DSN) to disentangle drug response related features, employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement. DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets, The Cancer Genome Atlas (TCGA), Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2 (I-SPY2) and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia (NIBR PDXE), achieving competitive performance with the state-of-the-art methods on cancer patients and mice. Furthermore, the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.http://www.sciencedirect.com/science/article/pii/S2095177924002259Precision medicineTransfer learningDrug response prediction |
| spellingShingle | Kunshi Li Bihan Shen Fangyoumin Feng Xueliang Li Yue Wang Na Feng Zhixuan Tang Liangxiao Ma Hong Li A disentangled generative model for improved drug response prediction in patients via sample synthesis Journal of Pharmaceutical Analysis Precision medicine Transfer learning Drug response prediction |
| title | A disentangled generative model for improved drug response prediction in patients via sample synthesis |
| title_full | A disentangled generative model for improved drug response prediction in patients via sample synthesis |
| title_fullStr | A disentangled generative model for improved drug response prediction in patients via sample synthesis |
| title_full_unstemmed | A disentangled generative model for improved drug response prediction in patients via sample synthesis |
| title_short | A disentangled generative model for improved drug response prediction in patients via sample synthesis |
| title_sort | disentangled generative model for improved drug response prediction in patients via sample synthesis |
| topic | Precision medicine Transfer learning Drug response prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2095177924002259 |
| work_keys_str_mv | AT kunshili adisentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT bihanshen adisentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT fangyouminfeng adisentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT xueliangli adisentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT yuewang adisentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT nafeng adisentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT zhixuantang adisentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT liangxiaoma adisentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT hongli adisentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT kunshili disentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT bihanshen disentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT fangyouminfeng disentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT xueliangli disentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT yuewang disentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT nafeng disentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT zhixuantang disentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT liangxiaoma disentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis AT hongli disentangledgenerativemodelforimproveddrugresponsepredictioninpatientsviasamplesynthesis |