Large-scale analysis of human gene expression variability associates highly variable drug targets with lower drug effectiveness and safety.

Eyal Simonovsky,Ronen Schuster,Esti Yeger-Lotem,Alfonso Valencia

doi:10.1093/bioinformatics/btz023

Eyal Simonovsky, Ronen Schuster + Show 2 more

Open Access

https://doi.org/10.1093/bioinformatics/btz023

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Abstract

MotivationThe effectiveness of drugs tends to vary between patients. One of the well-known reasons for this phenomenon is genetic polymorphisms in drug target genes among patients. Here, we propose that differences in expression levels of drug target genes across individuals can also contribute to this phenomenon.ResultsTo explore this hypothesis, we analyzed the expression variability of protein-coding genes, and particularly drug target genes, across individuals. For this, we developed a novel variability measure, termed local coefficient of variation (LCV), which ranks the expression variability of each gene relative to genes with similar expression levels. Unlike commonly used methods, LCV neutralizes expression levels biases without imposing any distribution over the variation and is robust to data incompleteness. Application of LCV to RNA-sequencing profiles of 19 human tissues and to target genes of 1076 approved drugs revealed that drug target genes were significantly more variable than protein-coding genes. Analysis of 113 drugs with available effectiveness scores showed that drugs targeting highly variable genes tended to be less effective in the population. Furthermore, comparison of approved drugs to drugs that were withdrawn from the market showed that withdrawn drugs targeted significantly more variable genes than approved drugs. Last, upon analyzing gender differences we found that the variability of drug target genes was similar between men and women. Altogether, our results suggest that expression variability of drug target genes could contribute to the variable responsiveness and effectiveness of drugs, and is worth considering during drug treatment and development.Availability and implementationLCV is available as a python script in GitHub (https://github.com/eyalsim/LCV).Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

The recognition that patients are a heterogeneous population is one of the pillars of the rising paradigm of precision medicine (Ashley, 2016)
General measure for expression variability, termed local coefficient of variation (LCV), which is highly correlated with DM and EV yet is more robust and does not assume a predefined variability distribution
We hypothesized that the variability in the responses of patients to drugs could be related to differences in expression levels of drug target genes across individuals

Summary

Introduction

The recognition that patients are a heterogeneous population is one of the pillars of the rising paradigm of precision medicine (Ashley, 2016). Due to this heterogeneity, patients that show a similar phenotype might respond differently to the same treatment regimen (Evans and Relling, 2004; Relling and Evans, 2015). Certain treatments might not be beneficial, or might even be harmful, to some patients (Schork, 2015) (Fig. 1A). The variability in the response to drugs may result from several factors including genetic polymorphisms or mutations, differences in the physiological state of patients, disease severity, and other external factors (Eichler et al, 2011; Evans and McLeod, 2003).

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