Publication: Gene expression signatures predict response to therapy with growth hormone.
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Identifiers
Date
2021-05-27
Authors
Stevens, Adam
Murray, Philip
De-Leonibus, Chiara
Garner, Terence
Koledova, Ekaterina
Ambler, Geoffrey
Kapelari, Klaus
Binder, Gerhard
Maghnie, Mohamad
Zucchini, Stefano
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
Nature Publishing Group
Abstract
Recombinant 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 are inexact. GHD (n = 71) and TS patients (n = 43) were recruited to study response to r-hGH over 5 years. Analysis was performed using 1219 genetic markers and baseline (pre-treatment) blood transcriptome. Random forest was used to determine predictive value of transcriptomic data associated with growth response. No genetic marker passed the stringency criteria for prediction. However, we identified an identical set of genes in both GHD and TS whose expression could be used to classify therapeutic response to r-hGH with a high accuracy (AUC > 0.9). Combining transcriptomic markers with clinical phenotype was shown to significantly reduce predictive error. This work could be translated into a single genomic test linked to a prediction algorithm to improve clinical management.
Description
MeSH Terms
Child
Female
Humans
Male
Prospective Studies
Transcriptome
Treatment Outcome
Turner Syndrome
Female
Humans
Male
Prospective Studies
Transcriptome
Treatment Outcome
Turner Syndrome
DeCS Terms
Marcadores Genéticos
Hormona del Crecimiento
Bosques Aleatorios
Transcriptoma
Síndrome de Turner
Hormona del Crecimiento
Bosques Aleatorios
Transcriptoma
Síndrome de Turner
CIE Terms
Keywords
Gene Expression Profiling, Genetic Markers, Growth Disorders, Human Growth Hormone
Citation
Stevens A, Murray P, De Leonibus C, Garner T, Koledova E, Ambler G, et al. Gene expression signatures predict response to therapy with growth hormone. Pharmacogenomics J. 2021 Oct;21(5):594-607