12423 Pre-treatment Blood Transcriptome Accurately Predicts Growth Response To Daily And Weekly GH Treatment In Children Born Small For Gestational Age

Abstract

Abstract Disclosure: T. Garner: Grant Recipient; Self; Novo Nordisk. P. Murray: Grant Recipient; Self; Novo Nordisk. M. Hojby: Employee; Self; Novo Nordisk. Stock Owner; Self; Novo Nordisk. R. Ard: Employee; Self; Novo Nordisk. Stock Owner; Self; Novo Nordisk. P.E. Clayton: Grant Recipient; Self; Novo Nordisk. A. Stevens: Grant Recipient; Self; Novo Nordisk. Background: Predicting GH therapy response from gene expression has the potential to improve clinical management of short stature. Treatment in children born small for gestational age (SGA) requires daily GH injections. Although no long-acting GHs (LAGHs) are currently approved for SGA, results from REAL5 (randomised, multinational, open-label, controlled, phase 2 trial; NCT03878446) indicate that the LAGH somapacitan has an efficacy, safety, and tolerability profile similar to daily GH (1). Here, we investigate growth response prediction based on the baseline blood transcriptome with and without clinical variables in daily GH- or somapacitan-treated children in REAL5. Methods: REAL5 tested three somapacitan doses (0.16, 0.20, & 0.24 mg/kg/week; n=12, n=13, & n=12, respectively) versus two daily GH doses (0.035 & 0.067 mg/kg/day; n=12 & n=13, respectively). GH response was explored using four metrics calculated over 52-weeks: height velocity (HV; cm/year), HV standard deviation score (SDS), change in height SDS, and change in IGF-I SDS. Due to small sample sizes, dose groups were combined. Participants were split into tertiles to define fastest/slowest responders to each treatment which were compared to remaining tertiles. Differentially expressed genes (DEGs) were defined to distinguish one tertile from the remaining two. Random forest (RF) models were generated using the top 100 DEGs as well as in combination with a set of clinical variables. Area under the curve (AUC) describes RF prediction accuracy: for somapacitan sufficient samples were available for both training and validation while for daily growth hormone samples were only sufficient for training generating out of bag (OOB) AUCs, accompanied by error rates (ER). Accuracies were calculated for both fast & slow responders across all response metrics in each treatment. Results: We demonstrate excellent predictive ability of the transcriptome to identify SGA children who respond slowest in HV following treatment with either daily GH or somapacitan (OOB AUC (ER)= 1.0 (5.0%) & 0.99 (6.9%), respectively; validation AUC=0.812 for somapacitan), while prediction of other response measures was highly variable, particularly in validation (AUCs=0.56-1.0). The clinical variables combined with the transcriptomic data resulted in reduced accuracies and increased error rates (OOBs AUC(ER)=0.93(25%) [daily] & 0.90(19%) [somapacitan]). Conclusions: In the first use of transcriptomics for growth response prediction in children born SGA we show good performance for those treated with both daily and weekly GH. However, when the transcriptome is combined with clinical data performance is reduced, indicating the importance of gene expression signatures for GH-response prediction. These findings may be promising for personalizing dose in GH-treated SGA patients. (1) Juul et al. EJE 2023:188,19-30. Presentation: 6/3/2024