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Gene expression signatures predict response to therapy with growth hormone

Authors :
Pierre Chatelain
Eirik Vangsøy-Hansen
Mohamad Maghnie
Geoffrey Ambler
Stefano Zucchini
Ekaterina Koledova
Alicia Belgorosky
Terence Garner
Cheri Deal
Gerhard Binder
Peter E. Clayton
Régis Coutant
Adam Stevens
Klaus Kapelari
Jovanna Dahlgren
Philip Murray
Chiara De Leonibus
Juan-Pedro Lopez Siguero
Diego Yeste
Julia Skorodok
Elena Bashnina
Lars Hagenäs
Institut Català de la Salut
[Stevens A, Murray P, De Leonibus C, Garner T] Faculty of Biology, Medicine and Health, Division of Developmental Biology and Medicine, University of Manchester and Manchester Academic Health Science Centre, Royal Manchester Children’s Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester, UK. [Koledova E] Merck Healthcare KGaA, Darmstadt, Germany. [Ambler G] The Children’s Hospital, Westmead, Sydney, NSW, Australia. [Yeste D] Vall d’Hebron Hospital Universitari, Barcelona, Spain
Vall d'Hebron Barcelona Hospital Campus
Source :
The Pharmacogenomics Journal, Scientia
Publication Year :
2021

Abstract

Xarxes reguladores de gens; Marcadors predictius Redes reguladoras de genes; Marcadores predictivos Gene regulatory networks; Predictive markers 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. Trial registration numbers: NCT00256126 and NCT00699855. This work was supported by Merck KGaA, Darmstadt, Germany.

Details

Language :
English
Database :
OpenAIRE
Journal :
The Pharmacogenomics Journal, Scientia
Accession number :
edsair.doi.dedup.....edd084ba7e057b23110887a87808815d