Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.

Andy R Eugene,Jolanta Masiak,Beata Eugene

doi:10.12688/f1000research.14451.2

Abstract

Background: We sought to test the hypothesis that transcriptiome-level genes signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatment responders. Methods: Gene expression study data was obtained from the Lithium Treatment-Moderate dose Use Study data accessed from the National Center for Biotechnology Information's Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis was conducted using the Linear Models for Microarray and RNA-Seq (limma) package and the Random Forests machine learning algorithm in R. Results: In pre-treatment lithium responders, the following genes were found having a greater than 0.5 fold-change, and differentially expressed indicating a male bias: RBPMS2, SIDT2, CDH23, LILRA5, and KIR2DS5; while the female-biased genes were: HLA-H, RPS23, FHL3, RPL10A, NBPF14, PSTPIP2, FAM117B, CHST7, and ABRACL. Conclusions: Using machine learning, we developed a pre-treatment gender- and gene-expression-based predictive model selective for lithium responders with an ROC AUC of 0.92 for men and an ROC AUC of 1 for women.

Highlights

Lithium is the most well-established mood-stabilizer in the practice of psychiatry (Jermain et al, 1991; Landersdorfer et al, 2017)
Data DNA microarray data analyzed in this study are originally referenced from the Lithium Treatment-Moderate dose Use Study placed in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) via accession number GSE45484 with the Illumina HumanHT12 V4.0 expression Beadchip GPL10558 platform file to associate gene names and descriptions
In our first analysis, which aimed to group patients based on gender alone and not based on clinical variables detailed in the original study, data-driven gene analytics identified four female-labeled patient samples with gene expression levels similar to that found in male patients for the following Y-chromosome genes: RPS4Y1, EIF1AY, KDM5D, RPS4Y2; and the XIST gene located on the X-chromosome

Summary

Introduction

Lithium is the most well-established mood-stabilizer in the practice of psychiatry (Jermain et al, 1991; Landersdorfer et al, 2017). Divergent clinical response rates have been reported among male and female patients diagnosed with bipolar disorder and treated with lithium (Viguera et al, 2000). In a 1986, Zetin and colleagues published the results of a study that evaluated four methods for predicting lithium daily dosages, and the final equation resulted in a 147.8mg/day increased dosage-adjustment for male patients (Zetin et al, 1986). A later study by Lobeck and colleagues corroborated the 147.8 mg/day male increase dose requirement for the lithium maintenance dose in bipolar patients (Lobeck et al, 1987). Conclusions: Using machine learning, we developed a pre-treatment gender- and gene-expression-based predictive model selective for lithium responders with an ROC AUC of 0.92 for men and an ROC AUC of 1 for women.

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