Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning [version 3; peer review: 1 approved, 2 approved with reservations]

• Published in: F1000 research 2018, Vol.7, p.474-474
• Main Authors: Eugene, Andy R, Masiak, Jolanta, Eugene, Beata
• Format: Article
• Language: English
• Subjects: Adult; Biomarkers - metabolism; Biotechnology; Bipolar disorder; Bipolar Disorder - drug therapy; Cohort Studies; DNA microarrays; Female; Gene Expression; Humans; Learning algorithms; Lithium; Lithium Compounds - therapeutic use; Machine Learning; Male; Medical records; Middle Aged; Patients; Prediction models; Ribonucleic acid; RNA; Studies
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.14451.3

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.