Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.15.182071v1?rss=1 Authors: Meurs, J., Scurr, D. J., Storer, L. C. D., Grundy, R. G., Alexander, M. R., Rahman, R., Kim, D.-H. Abstract: Surgically-resected biopsies from childhood brain tumors are archived in a formalin-fixed paraffin-embedded (FFPE) state. Identified key tumor regions are conserved through the fabrication of tissue microarrays (TMA). The vast amount of TMA libraries available have the potential to be used for metabolite profiling; however, due to incompatibility of TMAs with current analysis strategies, the potential has been unexplored. The recent emergence of liquid extraction surface analysis tandem mass spectrometry (LESA-MS/MS) and Orbitrap secondary ion mass spectrometry (3D OrbiSIMS) allows for a novel untargeted metabolite profiling and imaging approach for characterization of tumor TMAs. This strategy employs serial mass spectrometry (MS) analysis, with the high mass resolving imaging power of 3D OrbiSIMS combining complementarity, with the detection of a greater number of metabolites using LESA-MS/MS. Using multivariate analysis on data generated from both techniques permits the identification of metabolites that group patients based on eventual tumor recurrence, leading to the discovery of novel affected metabolic pathways and the prediction of tumor recurrence with high confidence. Moreover, both techniques reveal a unique set of classifiers and metabolic pathways for predicting pediatric ependymoma relapse from a set of primary tumors. The serial MS strategy opens new opportunities to retrospectively link molecular data from any FFPE TMA library with clinical outcomes and to discover novel drug targets with the aim of developing stratified drug treatments. Statement of significanceValuable and rare tumor tissue, such as from pediatric brain, is typically conserved through the creation of tissue microarrays (TMA) as the same biopsy tissue can be cored repeatedly. The small amount of sample has been a considerable obstacle for many modes of metabolomic analyses, which routinely require larger amounts of tissue. Using serial direct mass spectrometry techniques and multivariate analysis, tumor recurrence was predicted from differences in ion intensity, and novel and unique metabolic pathways in tumor relapse were discovered. This ability to retrospectively acquire metabolomic information from TMA archives will have a huge impact on understanding tumor development, heterogeneity and relapse to facilitate the development of personalized therapies that target pediatric brain tumor metabolism. Copy rights belong to original authors. Visit the link for more info