Abstract
Background: We sought to test the hypothesis that transcriptome-level gene 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 Decision Tree and Random Forest machine learning algorithms in R. Results: Using quantitative gene expression values reported from patient blood samples, the RBPMS2 and LILRA5 genes classify male lithium responders with an area under the receiver operator characteristic curve (AUROC) of 0.92 and the ABRACL, FHL3, and NBPF14 genes classify female lithium responders AUROC of 1. A Decision Tree rule for establishing male versus female samples, using gene expression values were found to be: if RPS4Y1 ≥ 9.643, patient is a male and if RPS4Y1 < 9.643, patient is female with a probability=100%. Conclusions: We developed a pre-treatment gender- and gene-expression-based predictive model selective for classifying male lithium responders with a sensitivity of 96% using 2-genes and female lithium responders with sensitivity=92% using 3-genes.
Highlights
Lithium is the most well-established mood-stabilizer in the practice of psychiatry (Jermain et al, 1991; Landersdorfer et al, 2017)
Duan Liu, Mayo Clinic, Rochester, USA Any reports and responses or comments on the Keywords lithium, treatment response, gene expression, machine learning, microarray, transcriptome, precision medicine, pharmacogenomics, psychiatry, genomic medicine article can be found at the end of the article
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
Summary
Lithium is the most well-established mood-stabilizer in the practice of psychiatry (Jermain et al, 1991; Landersdorfer et al, 2017). If TDM is applied broadly among medical specialties, pharmacogenomic reports that focus on pharmacokinetic-based gene-drug interactions (e.g. CYP2D6Paroxetine or CYP2C19-Clopidogrel) may not be necessary in all cases and insurance reimbursement would not be a ratelimiting step in advancing genomic medicine. This approach alone would not account for the hypersensitivity-type pharmacogenomic reactions; a TDM pharmacogenomichypersensitivity reaction hybrid approach may be an option when concerns about the electronic medical record costs, genotyping and/or sequencing machine costs, and data server infrastructure costs are prohibitive factors causing hospital systems and primary care clinics not to implement pharmacogenomic testing. A Decision Tree rule for establishing male versus female samples, using gene expression values were found to be: if RPS4Y1 ≥ 9.643, patient is a male and if RPS4Y1 < 9.643, patient is female with a probability=100%
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