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Objective Within the past decade, important genetic drivers of pheochromocytoma and paraganglioma (PPGLs) development have been identified. The pathophysiological mechanism that translates these alterations into functional autonomy and potentially malignant behavior has not been elucidated in detail. Here we used MALDI-mass spectrometry imaging (MALDI-MSI) of formalin-fixed paraffin-embedded tissue specimens to comprehensively characterize the metabolic profiles of PPGLs. Design and methods MALDI-MSI was conducted in 344 PPGLs and results correlated with genetic and phenotypic information. We experimentally silenced genetic drivers by siRNA in PC12 cells to confirm their metabolic impact in vitro. Results Tissue abundance of kynurenine pathway metabolites such as xanthurenic acid was significantly lower (P = 2.35E−09) in the pseudohypoxia pathway cluster 1 compared to PPGLs of the kinase-driven PPGLs cluster 2. Lower abundance of xanthurenic acid was associated with shorter metastasis-free survival (log-rank tests P = 7.96E−06) and identified as a risk factor for metastasis independent of the genetic status (hazard ratio, 32.6, P = 0.002). Knockdown of Sdhb and Vhl in an in vitro model demonstrated that inositol metabolism and sialic acids were similarly modulated as in tumors of the respective cluster. Conclusions The present study has identified distinct tissue metabolomic profiles of PPGLs in relation to tumor genotypes. In addition, we revealed significantly altered metabolites in the kynurenine pathway in metastatic PPGLs, which can aid in the prediction of its malignant potential. However, further validation studies will be required to confirm our findings.

Contains fulltext : 225895.pdf (Publisher’s version ) (Open Access) Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary background in over one-third of patients. Mutations in succinate dehydrogenase (SDH) genes increase the risk for PPGLs and several other tumours. Mutations in subunit B (SDHB) in particular are a risk factor for metastatic disease, further highlighting the importance of identifying SDHx mutations for patient management. Genetic variants of unknown significance, where implications for the patient and family members are unclear, are a problem for interpretation. For such cases, reliable methods for evaluating protein functionality are required. Immunohistochemistry for SDHB (SDHB-IHC) is the method of choice but does not assess functionality at the enzymatic level. Liquid chromatography-mass spectrometry-based measurements of metabolite precursors and products of enzymatic reactions provide an alternative method. Here, we compare SDHB-IHC with metabolite profiling in 189 tumours from 187 PPGL patients. Besides evaluating succinate:fumarate ratios (SFRs), machine learning algorithms were developed to establish predictive models for interpreting metabolite data. Metabolite profiling showed higher diagnostic specificity compared to SDHB-IHC (99.2% versus 92.5%, p = 0.021), whereas sensitivity was comparable. Application of machine learning algorithms to metabolite profiles improved predictive ability over that of the SFR, in particular for hard-to-interpret cases of head and neck paragangliomas (AUC 0.9821 versus 0.9613, p = 0.044). Importantly, the combination of metabolite profiling with SDHB-IHC has complementary utility, as SDHB-IHC correctly classified all but one of the false negatives from metabolite profiling strategies, while metabolite profiling correctly classified all but one of the false negatives/positives from SDHB-IHC. From 186 tumours with confirmed status of SDHx variant pathogenicity, the combination of the two methods resulted in 185 correct predictions, highlighting the benefits of both strategies for patient management. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.