Philip G Murray1,2, Adam Stevens1, Chiara De Leonibus1, Ekaterina Koledova3, Pierre Chatelain4, Peter E Clayton1,2. 1. Division of Developmental Biology and Medicine, Faculty of Biology, Medicine and Health, University of Manchester and Manchester Academic Health Science Centre, Manchester, United Kingdom. 2. Royal Manchester Children's Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom. 3. Global Medical Affairs Endocrinology, Global Medical, Safety & CMO Office, Merck KGaA, Darmstadt, Germany. 4. Department Pediatrie, Hôpital Mère-Enfant - Université Claude Bernard, Lyon, France.
Abstract
BACKGROUND: The effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and random forest analysis. METHODS: Prepubertal treatment-naive children with GHD (n = 98) were enrolled from the PREDICT study, and controls (n = 26) were acquired from online data sets. Whole blood gene expression was correlated with peak growth hormone (GH) using rank regression and a random forest algorithm tested for prediction of the presence of GHD and in classification of GHD as severe (peak GH <4 μg/l) and nonsevere (peak ≥4 μg/l). Performance was assessed using area under the receiver operating characteristic curve (AUC-ROC). RESULTS: Rank regression identified 347 probe sets in which gene expression correlated with peak GH concentrations (r = ± 0.28, P < 0.01). These 347 probe sets yielded an AUC-ROC of 0.95 for prediction of GHD status versus controls and an AUC-ROC of 0.93 for prediction of GHD severity. CONCLUSION: This study demonstrates highly accurate diagnosis and disease classification for GHD using a combination of transcriptomics and random forest analysis. TRIAL REGISTRATION: NCT00256126 and NCT00699855. FUNDING: Merck and the National Institute for Health Research (CL-2012-06-005).
BACKGROUND: The effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and random forest analysis. METHODS: Prepubertal treatment-naive children with GHD (n = 98) were enrolled from the PREDICT study, and controls (n = 26) were acquired from online data sets. Whole blood gene expression was correlated with peak growth hormone (GH) using rank regression and a random forest algorithm tested for prediction of the presence of GHD and in classification of GHD as severe (peak GH <4 μg/l) and nonsevere (peak ≥4 μg/l). Performance was assessed using area under the receiver operating characteristic curve (AUC-ROC). RESULTS: Rank regression identified 347 probe sets in which gene expression correlated with peak GH concentrations (r = ± 0.28, P < 0.01). These 347 probe sets yielded an AUC-ROC of 0.95 for prediction of GHD status versus controls and an AUC-ROC of 0.93 for prediction of GHD severity. CONCLUSION: This study demonstrates highly accurate diagnosis and disease classification for GHD using a combination of transcriptomics and random forest analysis. TRIAL REGISTRATION: NCT00256126 and NCT00699855. FUNDING: Merck and the National Institute for Health Research (CL-2012-06-005).
Authors: E Ghigo; J Bellone; G Aimaretti; S Bellone; S Loche; M Cappa; E Bartolotta; F Dammacco; F Camanni Journal: J Clin Endocrinol Metab Date: 1996-09 Impact factor: 5.958
Authors: A Stevens; P Clayton; L Tatò; H W Yoo; M D Rodriguez-Arnao; J Skorodok; G R Ambler; M Zignani; J Zieschang; G Della Corte; B Destenaves; A Champigneulle; J Raelson; P Chatelain Journal: Pharmacogenomics J Date: 2013-04-09 Impact factor: 3.550
Authors: Adam Stevens; Philip Murray; Chiara De Leonibus; Terence Garner; Ekaterina Koledova; Geoffrey Ambler; Klaus Kapelari; Gerhard Binder; Mohamad Maghnie; Stefano Zucchini; Elena Bashnina; Julia Skorodok; Diego Yeste; Alicia Belgorosky; Juan-Pedro Lopez Siguero; Regis Coutant; Eirik Vangsøy-Hansen; Lars Hagenäs; Jovanna Dahlgren; Cheri Deal; Pierre Chatelain; Peter Clayton Journal: Pharmacogenomics J Date: 2021-05-27 Impact factor: 3.550