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

期刊

ENDOCRINE-RELATED CANCER
卷 26, 期 5, 页码 539-550

出版社

BIOSCIENTIFICA LTD
DOI: 10.1530/ERC-19-0024

关键词

pheochromocytoma; paraganglioma; molecular genetics; driver mutations; meta-analysis

资金

  1. Akademiska Sjukhuset, Uppsala
  2. Paradifference foundation
  3. Lions Cancerforskningsfond, Uppsala
  4. National Cancer Institute
  5. Eunice Kennedy Shriver National Institute of Child Health and Human Development
  6. ASCO Conquer Cancer Foundation Young Investigator Award

向作者/读者索取更多资源

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 dopa mine (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.

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