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

Ahmad Nasimian; Mehreen Ahmed; Ingrid Hedenfalk; Julhash U Kazi

doi:10.1016/j.csbj.2023.01.020

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

Comput Struct Biotechnol J. 2023 Jan 16:21:956-964. doi: 10.1016/j.csbj.2023.01.020. eCollection 2023.

Authors

Ahmad Nasimian^{1

2}, Mehreen Ahmed^{1

2}, Ingrid Hedenfalk³, Julhash U Kazi^{1

2}

Affiliations

¹ Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden.
² Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, Sweden.
³ Division of Oncology, Department of Clinical Sciences Lund, Lund University and Skåne University Hospital, 223 81 Lund, Sweden.

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 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.

Keywords: BCL-XL; Elastic net; Ovarian cancer; Random Forest; WNT/β-catenin; XGBoost.