Mass spectrometry imaging identifies metabolic patterns associated with malignant potential in pheochromocytoma and paraganglioma

in European Journal of Endocrinology
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  • 1 Medizinische Klinik und Poliklinik IV, Klinikum der Ludwig-Maximilians-Universität München, Munich, Germany
  • 2 Research Unit Analytical Pathology, German Research Center for Environmental Health (GmbH), Helmholtz Zentrum München, Neuherberg, Germany
  • 3 Institute for Pathology, University of Würzburg, Würzburg, Germany
  • 4 Department of Internal Medicine I, Division of Endocrinology and Diabetology, University Hospital Würzburg, University of Würzburg, Würzburg, Germany
  • 5 Medicover Oldenburg MVZ, Oldenburg, Germany
  • 6 Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany
  • 7 Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
  • 8 Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Carl Gustav Carus, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
  • 9 Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
  • 10 Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
  • 11 German Center for Diabetes Research (DZD), München-Neuherberg, Germany
  • 12 Department of Pathology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
  • 13 Institute for Pathology and Molecular Pathology, Universitätsspital Zürich, Zurich, Switzerland
  • 14 Université de Paris, PARCC, INSERM, Equipe labellisée par la Ligue contre le Cancer, Paris, France
  • 15 Genetics department, AP-HP, Hôpital européen Georges Pompidou, Paris, France
  • 16 Hereditary Endocrine Cancer Group, CNIO, Madrid, Spain and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
  • 17 Department of Medicine, Division of Endocrinology, Radboud University Medical Center, Nijmegen, the Netherlands
  • 18 Department of Experimental and Clinical Biomedical Sciences ‘Mario Serio’, University of Florence, Florence, Italy
  • 19 Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
  • 20 Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
  • 21 Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany
  • 22 Department of Endocrinology, Diabetology and Clinical Nutrition, Universitätsspital Zürich, Zurich, Switzerland

Correspondence should be addressed to Matthias Kroiss E-mail Kroiss_M@ukw.de

*M Murakami and N Sun contributed equally as first authors

M Kroiss and F Beuschlein contributed equally as senior authors

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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.

Supplementary Materials

    • Supplementary Methods
    • Supplementary Figure 1: MSI images of distinctive metabolites across the entire TMA. Zoomed images of patient 165 with triplicated tissue cores were represented as an example
    • Supplementary Figure 2: Clinical and immunohistochemistry markers of metastatic PPGLs.Kaplan-Meier plots for metastasis-free survival in relation to clinical parameters including maximal tumor size, tumor location, 24h urinary metanephrine and mutation status (A). Boxplots of staining intensities (SI) of SDHB and SDHA protein with respect to mutation status (B). Kruskal-Wallis test were used for statistical analysis. P <0.05, compared to *SDHB mutation, &#x2020;SDHC mutation, &#x2021;SDHD mutation, and &#x00A7;VHL mutation. Boxplots of staining intensities of SDHB and SDHA protein with respect to SDHx mutation status (C). Mann-Whitney U test was used for statistical analysis. **P <0.001. A ROC curve for prediction of SDHx mutation by SI of SDHB and area under curves (AUC) were also shown (D). Kaplan-Meier plots for metastasis-free survival in relation to staining intensities of SDHB protein (E). Cut off were shown as red and black lines. Log rank test was used to statistically compare the curves and P-values are provided.
    • Supplementary Figure 3: Metabolomic features of PPGLs. Unsupervised hierarchical clustering analysis of metabolome profiles of 184 PPGLs including cluster 1, cluster 2 and wildtype (A). Groups based on metabolites pattern, mutation status, tumor location and metastatic behavior are shown below heat map. Distribution of patient numbers with regard to mutation status is shown. Ortho-PLSDA analysis of metabolome profiles of 141 PPGLs in which sample age and institution were annotated (B). Comparisons between SDHx and VHL mutation status in the purine metabolism (C); adenosine monophosphate (AMP), adenosine diphosphate (ADP) and adenosine triphosphate (ATP). Mann-Whitney U test was used for statistical analysis and P-values are shown.
    • Supplementary Figure 4: In vitro models using PC12 cells. Knockdown of Sdhb (siSdhb) and Vhl gene (siVhl) in PC12 cells, confirmed by quantitative real time PCR (P = 1.63E-07 and 1.29E-05, respectively, A) and immunoblot with Sdhb and Vhl protein and corresponding control (B). Succinate concentration and succinate/fumarate ratio in scrambled siRNA and knockdown of Sdhb gene in PC12 cells (C). Significant differences of tryptophan in PC12 cells (P = 0.005, D). Student&#x2019;s t test or Kruskal-Wallis test were used for statistical analysis. *P <0.05, **P <0.001.
    • Supplementary Figure 5: Scoring system using maximal tumor size, staining intensity of SDHB and xanthurenic acid for prediction of metastatic behavior of PPGLs. Patients profiles of a scoring system for metastasis, using maximal tumor size, SDHB mutation status and xanthurenic acid (A). Kaplan-Meier plots for metastasis-free survival in relation to scoring system and the examination of scoring system by cox proportional hazards model were shown (B). Log rank test was used to statistically compare the curves and P-values are provided.
    • Supplementary Table 1: Multivariate analysis using Cox proportional hazard model for metastasis-free survival.
    • Supplementary Table 2: Results of pathway enrichment analysis between cluster 1 (-like status) and 2 (-like status).
    • Supplementary Table 3: A Clinical characteristic of PPGL patients for discovery cohort, stratified by evidence of malignancy.

 

     European Society of Endocrinology

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