The neonatal blood spot metabolome in retinoblastoma.

Background: Retinoblastoma is rare but nevertheless the most common pediatric eye cancer that occurs in children under age 5. High-resolution metabolomics (HRM) is a powerful analytical approach to profile metabolic features and pathways or identify metabolite biomarkers. To date, no studies have used pre-diagnosis blood samples from retinoblastoma cases and compared them to healthy controls to elucidate early perturbations in tumor pathways. Objectives: Here, we report on metabolic profiles of neonatal blood comparing cases later in childhood diagnosed with retinoblastoma and controls. Methods: We employed untargeted metabolomics analysis using neonatal dried blood spots for 1327 children (474 retinoblastoma cases and 853 healthy controls) born in California from 1983 to 2011. Cases were selected from the California Cancer Registry and controls, frequency matched to cases by birth year, from California birth rolls. We performed high-resolution metabolomics to extract metabolic features, partial least squares discriminant analysis (PLS-DA) and logistic regression to identify features associated with disease, and Mummichog pathway analysis to characterize enriched biological pathways. Results: PLS-DA identified 1917 discriminative features associated with retinoblastoma and Mummichog identified 14 retinoblastoma-related enriched pathways including linoleate metabolism, pentose phosphate pathway, pyrimidine metabolism, fructose and mannose metabolism, vitamin A metabolism, as well as fatty acid and lipid metabolism. Interpretation: Our findings linked a retinoblastoma diagnosis in early life to newborn blood metabolome perturbations indicating alterations in inflammatory pathways and energy metabolism. Neonatal blood spots may provide a venue for early detection for this or potentially other childhood cancers.