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 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 con-tributing to cisplatin resistance. Furthermore, the pharmacological inhibition of BCL2L1 was found to sy-nergistically 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.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creative-commons.org/licenses/by/4.0/).

Automated summary

In this study, a gene signature was defined through the analysis of cisplatin-perturbed gene expression and pathway enrichment, and a cisplatin sensitivity prediction model was developed using the TabNet algorithm. The TabNet model outperformed other machine learning models with an accuracy of over 80%. Furthermore, BCL2L1 was identified as an important gene contributing to cisplatin resistance, and its pharmacological inhibition was found to enhance cisplatin efficacy. This study developed a tool to predict cisplatin sensitivity and identified BCL2L1 as a significant gene in this context.