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

• Published in: The pharmacogenomics journal Vol. 22; no. 4; pp. 241 - 246
• Main Authors: Yamada, Hiroki, Ohmori, Rio, Okada, Naoto, Nakamura, Shingen, Kagawa, Kumiko, Fujii, Shiro, Miki, Hirokazu, Ishizawa, Keisuke, Abe, Masahiro, Sato, Youichi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2022; Nature Publishing Group
• Subjects: 45; 45/43; 631/208/205; 692/53/2423; Bayesian analysis; Biomedical and Life Sciences; Biomedicine; Gene Expression; Genome-wide association studies; Genomes; Hematology; Human Genetics; Learning algorithms; Machine learning; Neural networks; Oncology; Peripheral neuropathy; Pharmacotherapy; Prediction models; Psychopharmacology; Single-nucleotide polymorphism; Support vector machines; Vincristine
• ISSN: 1470-269X; 1473-1150; 1473-1150
• DOI: 10.1038/s41397-022-00282-8

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.