A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer
(2023) In Computational and Structural Biotechnology Journal 21. p.956-964- Abstract
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... (More)
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.
(Less)
- author
- Nasimian, Ahmad LU ; Ahmed, Mehreen LU ; Hedenfalk, Ingrid LU and Kazi, Julhash U. LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- BCL-XL, Elastic net, Ovarian cancer, Random Forest, WNT/β-catenin, XGBoost
- in
- Computational and Structural Biotechnology Journal
- volume
- 21
- pages
- 9 pages
- publisher
- Research Network of Computational and Structural Biotechnology
- external identifiers
-
- pmid:36733702
- scopus:85146653549
- ISSN
- 2001-0370
- DOI
- 10.1016/j.csbj.2023.01.020
- language
- English
- LU publication?
- yes
- id
- 0aa73ee6-43b0-4a7b-8b22-c629b686df36
- date added to LUP
- 2023-02-13 14:54:27
- date last changed
- 2024-03-21 17:55:50
@article{0aa73ee6-43b0-4a7b-8b22-c629b686df36, abstract = {{<p>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.</p>}}, author = {{Nasimian, Ahmad and Ahmed, Mehreen and Hedenfalk, Ingrid and Kazi, Julhash U.}}, issn = {{2001-0370}}, keywords = {{BCL-XL; Elastic net; Ovarian cancer; Random Forest; WNT/β-catenin; XGBoost}}, language = {{eng}}, pages = {{956--964}}, publisher = {{Research Network of Computational and Structural Biotechnology}}, series = {{Computational and Structural Biotechnology Journal}}, title = {{A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer}}, url = {{http://dx.doi.org/10.1016/j.csbj.2023.01.020}}, doi = {{10.1016/j.csbj.2023.01.020}}, volume = {{21}}, year = {{2023}}, }