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A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer

Nasimian, Ahmad LU ; Ahmed, Mehreen LU ; Hedenfalk, Ingrid LU orcid and Kazi, Julhash U. LU orcid (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.

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author
; ; and
organization
publishing date
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 (&gt;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}},
}