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Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection

The Pharmacogenomics Journal volume 21, pages 673–681 (2021)Cite this article

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Abstract

One in every ten drug candidates fail in clinical trials mainly due to efficacy and safety related issues, despite in-depth preclinical testing. Even some of the approved drugs such as chemotherapeutics are notorious for their side effects that are burdensome on patients. In order to pave the way for new therapeutics with more tolerable side effects, the mechanisms underlying side effects need to be fully elucidated. In this work, we addressed the common side effects of chemotherapeutics, namely alopecia, diarrhea and edema. A strategy based on Random Forest algorithm unveiled an expression signature involving 40 genes that predicted these side effects with an accuracy of 89%. We further characterized the resulting signature and its association with the side effects using functional enrichment analysis and protein-protein interaction networks. This work contributes to the ongoing efforts in drug development for early identification of side effects to use the resources more effectively.

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Fig. 1: Model development with iterative feature selection.
Fig. 2: Performance metrics varying with dimension of the feature space.
Fig. 3: Heatmap displaying standardized expression level of the signature genes and association with the studied side effects.
Fig. 4: Average local variable importance for the signature genes computed for each side effect sample set.
Fig. 5: Functional enrichment analysis of the signature genes.

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Acknowledgements

This study has been supported by TÜBİTAK (2209-A)-1919B011902354.

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  1. These authors contributed equally: Arzu Cakir, Melisa Tuncer.

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  1. Department of Genetics and Bioengineering, Istanbul Bilgi University, Istanbul, Eyupsultan, Turkey

    Arzu Cakir, Melisa Tuncer, Hilal Taymaz-Nikerel & Ozlem Ulucan

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  1. Arzu Cakir
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Cakir, A., Tuncer, M., Taymaz-Nikerel, H. et al. Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection. Pharmacogenomics J 21, 673–681 (2021). https://doi.org/10.1038/s41397-021-00246-4

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