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A machine learning model using SNPs obtained from a genome-wide association study predicts the onset of vincristine-induced peripheral neuropathy

The Pharmacogenomics Journal volume 22, pages 241–246 (2022)Cite this article

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Abstract

Vincristine treatment may cause peripheral neuropathy. In this study, we identified the genes associated with the development of peripheral neuropathy due to vincristine therapy using a genome-wide association study (GWAS) and constructed a predictive model for the development of peripheral neuropathy using genetic information-based machine learning. The study included 72 patients admitted to the Department of Hematology, Tokushima University Hospital, who received vincristine. Of these, 56 were genotyped using the Illumina Asian Screening Array-24 Kit, and a GWAS for the onset of peripheral neuropathy caused by vincristine was conducted. Using Sanger sequencing for 16 validation samples, the top three single nucleotide polymorphisms (SNPs) associated with the onset of peripheral neuropathy were determined. Machine learning was performed using the statistical software R package “caret”. The 56 GWAS and 16 validation samples were used as the training and test sets, respectively. Predictive models were constructed using random forest, support vector machine, naive Bayes, and neural network algorithms. According to the GWAS, rs2110179, rs7126100, and rs2076549 were associated with the development of peripheral neuropathy on vincristine administration. Machine learning was performed using these three SNPs to construct a prediction model. A high accuracy of 93.8% was obtained with the support vector machine and neural network using rs2110179 and rs2076549. Thus, peripheral neuropathy development due to vincristine therapy can be effectively predicted by a machine learning prediction model using SNPs associated with it.

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Fig. 1: Manhattan plot of associations from the GWAS of peripheral neuropathy.
Fig. 2: Regional association plot for a peripheral neuropathy-associated locus.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We are thankful to the Prof. Aiko Yamauchi of the Tokushima University for her useful suggestions and discussions. We would like to thank Editage for English language editing.

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Author notes

  1. These authors contributed equally: Hiroki Yamada, Rio Ohmori.

Authors and Affiliations

  1. Department of Pharmaceutical Information Science, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8505, Japan

    Hiroki Yamada, Rio Ohmori & Youichi Sato

  2. Department of Pharmacy, Tokushima University Hospital, Tokushima, 770-8503, Japan

    Naoto Okada & Keisuke Ishizawa

  3. Department of Community Medicine and Medical Science, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8503, Japan

    Shingen Nakamura

  4. Department of Hematology, Endocrinology and Metabolism, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8503, Japan

    Kumiko Kagawa, Shiro Fujii & Masahiro Abe

  5. Division of Transfusion Medicine and Cell Therapy, Tokushima University Hospital, Tokushima, 770-8503, Japan

    Hirokazu Miki

  6. Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8503, Japan

    Keisuke Ishizawa

  7. Clinical Research Center for Developmental Therapeutics, Tokushima University Hospital, Tokushima, 770-8503, Japan

    Keisuke Ishizawa

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Contributions

YS conceived and designed the study; HY, RO, and YS performed the experiments and data acquisition; HY and YS analyzed the data; NO, SN, KK, SF, HM, KI, and MA collected information on patient diagnoses and side effects; YS collected the samples; HY, RO, and YS wrote the paper. All authors read and approved the manuscript.

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Correspondence to Youichi Sato.

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Yamada, H., Ohmori, R., Okada, N. et al. A machine learning model using SNPs obtained from a genome-wide association study predicts the onset of vincristine-induced peripheral neuropathy. Pharmacogenomics J 22, 241–246 (2022). https://doi.org/10.1038/s41397-022-00282-8

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