A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer
Cisplatin, a platinum-based chemotherapeutic agent, is widely used as a front-line treatment for several malignancies. However, treatment outcomes vary widely due to intrinsic and acquired resistance. In this study, cisplatin-perturbed gene expression and pathway enrichment were used to define a gen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S200103702300020X |
| _version_ | 1827577710468136960 |
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| author | Ahmad Nasimian Mehreen Ahmed Ingrid Hedenfalk Julhash U. Kazi |
| author_facet | Ahmad Nasimian Mehreen Ahmed Ingrid Hedenfalk Julhash U. Kazi |
| author_sort | Ahmad Nasimian |
| collection | DOAJ |
| description | Cisplatin, a platinum-based chemotherapeutic agent, is widely used as a front-line treatment for several malignancies. However, treatment outcomes vary widely due to intrinsic and acquired resistance. In this study, cisplatin-perturbed gene expression and pathway enrichment were used to define a gene signature, which was further utilized to develop a cisplatin sensitivity prediction model using the TabNet algorithm. The TabNet model performed better (>80 % accuracy) than all other machine learning models when compared to a wide range of machine learning algorithms. Moreover, by using feature importance and comparing predicted ovarian cancer patient samples, BCL2L1 was identified as an important gene contributing to cisplatin resistance. Furthermore, the pharmacological inhibition of BCL2L1 was found to synergistically increase cisplatin efficacy. Collectively, this study developed a tool to predict cisplatin sensitivity using cisplatin-perturbed gene expression and pathway enrichment knowledge and identified BCL2L1 as an important gene in this setting. |
| first_indexed | 2024-03-08T21:31:45Z |
| format | Article |
| id | doaj.art-f4c116a148a04ab89c53dc1eeeed0673 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:31:45Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-f4c116a148a04ab89c53dc1eeeed06732023-12-21T07:30:47ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-0121956964A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancerAhmad Nasimian0Mehreen Ahmed1Ingrid Hedenfalk2Julhash U. Kazi3Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden; Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, SwedenDivision of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden; Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, SwedenDivision of Oncology, Department of Clinical Sciences Lund, Lund University and Skåne University Hospital, 223 81 Lund, SwedenDivision of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden; Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, Sweden; Correspondence to: Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Medicon village Building 404:C3, Scheelevägen 8, 22363 Lund, Sweden.Cisplatin, a platinum-based chemotherapeutic agent, is widely used as a front-line treatment for several malignancies. However, treatment outcomes vary widely due to intrinsic and acquired resistance. In this study, cisplatin-perturbed gene expression and pathway enrichment were used to define a gene signature, which was further utilized to develop a cisplatin sensitivity prediction model using the TabNet algorithm. The TabNet model performed better (>80 % accuracy) than all other machine learning models when compared to a wide range of machine learning algorithms. Moreover, by using feature importance and comparing predicted ovarian cancer patient samples, BCL2L1 was identified as an important gene contributing to cisplatin resistance. Furthermore, the pharmacological inhibition of BCL2L1 was found to synergistically increase cisplatin efficacy. Collectively, this study developed a tool to predict cisplatin sensitivity using cisplatin-perturbed gene expression and pathway enrichment knowledge and identified BCL2L1 as an important gene in this setting.http://www.sciencedirect.com/science/article/pii/S200103702300020XWNT/β-cateninXGBoostRandom ForestElastic netOvarian cancerBCL-XL |
| spellingShingle | Ahmad Nasimian Mehreen Ahmed Ingrid Hedenfalk Julhash U. Kazi A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer Computational and Structural Biotechnology Journal WNT/β-catenin XGBoost Random Forest Elastic net Ovarian cancer BCL-XL |
| title | A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer |
| title_full | A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer |
| title_fullStr | A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer |
| title_full_unstemmed | A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer |
| title_short | A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer |
| title_sort | deep tabular data learning model predicting cisplatin sensitivity identifies bcl2l1 dependency in cancer |
| topic | WNT/β-catenin XGBoost Random Forest Elastic net Ovarian cancer BCL-XL |
| url | http://www.sciencedirect.com/science/article/pii/S200103702300020X |
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