Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas
Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary background inover one-third of patients. Mutations in succinate dehydrogenase (SDH) genes increase the risk for PPGLs and severalother tumours. Mutations in subunit B (SDHB) in particular are a risk factor for metastatic disease, further highlight-ing the importance of identifying SDHx mutations for patient management. Genetic variants of unknown signi-cance, where implications for the patient and family members are unclear, are a problem for interpretation. Forsuch 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 chromatogra-phy–mass spectrometry-based measurements of metabolite precursors and products of enzymatic reactions providean alternative method. Here, we compare SDHB-IHC with metabolite proling in 189 tumours from 187 PPGLpatients. Besides evaluating succinate:fumarate ratios (SFRs), machine learning algorithms were developed to estab-lish predictive models for interpreting metabolite data. Metabolite proling showed higher diagnostic specicitycompared to SDHB-IHC (99.2% versus 92.5%, p = 0.021), whereas sensitivity was comparable. Application of machine learning algorithms to metabolite proles improved predictive ability over that of the SFR, in particular forhard-to-interpret cases of head and neck paragangliomas (AUC 0.9821 versus 0.9613, p = 0.044). Importantly, thecombination of metabolite proling with SDHB-IHC has complementary utility, as SDHB-IHC correctly classied allbut one of the false negatives from metabolite proling strategies, while metabolite proling correctly classied allbut one of the false negatives/positives from SDHB-IHC. From 186 tumours with conrmed status of SDHx variantpathogenicity, the combination of the two methods resulted in 185 correct predictions, highlighting the benets ofboth strategies for patient management.
|Keywords||mass spectrometry, succinate to fumarate ratio, multi-observer, Krebs cycle metabolites, linear discriminant analysis, LC–MS/MS, diagnostics, variants of unknown signicance, metabolite proling, prediction models|
|Persistent URL||dx.doi.org/10.1002/path.5472, hdl.handle.net/1765/130950|
|Journal||Journal of Pathology|
Wallace, P.W., Conrad, C., Brückmann, S., Pang, Y., Caleiras, E., Murakami, M., … Richter, S. (2020). Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas. Journal of Pathology, 251(4), 378–387. doi:10.1002/path.5472