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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876747/ https://www.ncbi.nlm.nih.gov/pubmed/36733702 http://dx.doi.org/10.1016/j.csbj.2023.01.020 |
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author | Nasimian, Ahmad Ahmed, Mehreen Hedenfalk, Ingrid Kazi, Julhash U. |
author_facet | Nasimian, Ahmad Ahmed, Mehreen Hedenfalk, Ingrid Kazi, Julhash U. |
author_sort | Nasimian, Ahmad |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-9876747 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-98767472023-02-01 A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer Nasimian, Ahmad Ahmed, Mehreen Hedenfalk, Ingrid Kazi, Julhash U. Comput Struct Biotechnol J Research Article 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. Research Network of Computational and Structural Biotechnology 2023-01-16 /pmc/articles/PMC9876747/ /pubmed/36733702 http://dx.doi.org/10.1016/j.csbj.2023.01.020 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Nasimian, Ahmad Ahmed, Mehreen Hedenfalk, Ingrid Kazi, Julhash U. A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer |
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 | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876747/ https://www.ncbi.nlm.nih.gov/pubmed/36733702 http://dx.doi.org/10.1016/j.csbj.2023.01.020 |
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