Gene expression signatures predict response to therapy with growth hormone

Abstract

AbstractRecombinant human growth hormone (r-hGH) is used as a therapeutic agent for disorders of growth including growth hormone deficiency (GHD) and Turner syndrome (TS). Treatment is costly and current methods to model response can only account for up to 60% of the variance. The aim of this work was to take a novel genomic approach to growth prediction. GHD (n=71) and TS patients (n=43) were recruited in a study on the long term response to r-hGH over five years of therapy. Pharmacogenomic analysis was performed using 1219 genetic markers and baseline blood transcriptome. Random forest was used to determine predictive value of transcriptomic data associated with growth response. No genetic marker passed the stringency criteria required for predictive value. However, we demonstrated that transcriptomic data can be used to predict growth with a high accuracy (AUC > 0.9) for short and long term therapeutic response in GHD and TS. Network models identified an identical core set of genes in both GHD and TS at each year of therapy whose expression can be used to classify therapeutic response to r-hGH. Combining transcriptomic markers with clinical phenotype was shown to significantly reduce predictive error. We have characterised the utility of baseline transcriptome for the prediction of growth response including the identification of a set of common genes in GHD and TS. This work could be translated into a single genomic test linked to a prediction algorithm to improve clinical management.One Sentence SummaryA blood transcriptome signature predicts response to recombinant human growth hormone in both growth hormone deficient and Turner syndrome childrenTrial registration numbers:NCT00256126&NCT00699855