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Meta-Analysis
. 2019 May;26(5):539-550.
doi: 10.1530/ERC-19-0024.

Genotype-phenotype correlations in pheochromocytoma and paraganglioma: a systematic review and individual patient meta-analysis

Joakim Crona  1   2 , Angela Lamarca  3 , Suman Ghosal  2 , Staffan Welin  1 , Britt Skogseid  1 , Karel Pacak  2
Affiliations
Meta-Analysis

Genotype-phenotype correlations in pheochromocytoma and paraganglioma: a systematic review and individual patient meta-analysis

Joakim Crona et al. Endocr Relat Cancer. 2019 May.

Abstract

Pheochromocytoma and paraganglioma (PPGL) can be divided into at least four molecular subgroups. Whether such categorizations are independent factors for prognosis or metastatic disease is unknown. We performed a systematic review and individual patient meta-analysis aiming to estimate if driver mutation status can predict metastatic disease and survival. Driver mutations were used to categorize patients according to three different molecular systems: two subgroups (SDHB mutated or wild type), three subgroups (pseudohypoxia, kinase signaling or Wnt/unknown) and four subgroups (tricarboxylic acid cycle, VHL/EPAS1, kinase signaling or Wnt/unknown). Twenty-one studies and 703 patients were analyzed. Multivariate models for association with metastasis showed correlation with SDHB mutation (OR 5.68 (95% CI 1.79-18.06)) as well as norepinephrine (OR 3.01 (95% CI 1.02-8.79)) and dopamine (OR 6.39 (95% CI 1.62-25.24)) but not to PPGL location. Other molecular systems were not associated with metastasis. In multivariate models for association with survival, age (HR 1.04 (95% CI 1.02-1.06)) and metastases (HR 6.13 (95% CI 2.86-13.13)) but neither paraganglioma nor SDHB mutation remained significant. Other molecular subgroups did not correlate with survival. We conclude that molecular categorization accordingly to SDHB provided independent information on the risk of metastasis. Driver mutations status did not correlate independently with survival. These data may ultimately be used to guide current and future risk stratification of PPGL.

Keywords: driver mutations; meta-analysis; molecular genetics; paraganglioma; pheochromocytoma.

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Figures

Figure 1:
Figure 1:
Clinical correlations to modified molecular subgroups from the Cancer Genome Atlas. Epi, Epinephrine; Kinase; Kinase signaling subgroup; Norepi, Norepinephrine; PCC, Pheochromocytoma; PGL, Paraganglioma; TCA, tricarboxylic acid; UK, unknown; VHL/EPAS1, Pseudohypoxia VHL/EPAS1 related. ***: chi-square test including all four molecular subgroups had a significance level <0.001
See this image and copyright information in PMC

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