Multi-omics insights into metastatic pheochromocytomas and paragangliomas: Mechanisms, signatures, and prediction...

Pheochromocytomas and paragangliomas (PPGLs), rare neuroendocrine tumors, have been generally considered, since the 2017 WHO classification, as malignant neoplasms with various metastatic potential. Approximately 10-25% of PPGLs will develop metastases, with a varying 5-year-overall-survial of 40%-77%. Previous studies have extensively elucidated the mechanisms by which SDHB mutations promote tumor metastasis through inducing the pseudohypoxic and hypermethylation phenotype. Recent multi-omics studies further profiled the additional landscape of metastatic PPGLs, involving ATRX/TERT alterations, higher tumor mutational burden, microsatellite instability score, and somatic copy-number alteration, distinct microRNA (miRNA) profiling, aberrant kynurenine metabolic pathway, and immunosuppressive microenvironment, advancing our understanding of metastatic mechanisms. Consequently, beyond conventional predictors (e.g., SDHB mutation status, tumor size and location, levels of dopamine and methoxytyramine, Ki-67 labeling index, and loss of S100), emerging biomarkers for metastasis prediction were discovered and evaluated, including levels of circulating miR-483-5p, plasma succinate, tissue xanthurenic acid, and CDK1 expression, etc. Several scoring systems integrating tumor clinicopathological characteristics have been constructed to predict metastatic PPGLs; however, their clinical utility may be limited by high observer variation and low positive predictive value. Recent prediction models integrating multi-omics signatures of metastatic tumors have also been established, but without validation. This review comprehensively synthesizes current and emerging multi-omics signatures of metastatic PPGLs to elucidate underlying metastatic mechanisms. A systematic review of existing scoring systems and emerging predictive models for metastasis prediction was also conducted to highlight their advantages and limitations in the clinical application.