Genomic and Metabolic Hallmarks of SDH- and FH-deficient Renal Cell Carcinomas

Yoo, Angela; Tang, Christopher S.; Zucker, Mark; Fitzgerald, Kelly; DiNatale, Renzo G.; Rappold, Phillip M.; Weiss, Kate; Freeman, Benjamin; Lee, Chung-Han; Schultz, Nikolaus; Motzer, Robert J.; Russo, Paul; Coleman, Jonathan; Reuter, Victor E.; Chen, Ying-Bei; Carlo, Maria I.; Gill, Anthony J.; Kotecha, Ritesh; Hakimi, A. Ari; Reznik, Ed

doi:10.1016/j.euf.2021.12.002

Cited by 15 publications

(8 citation statements)

References 42 publications

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“…They also predispose to malignant PPGL and cause DNA hypermethylation similar to SDHx PV (Castro-Vega et al, 2014, Letouze et al, 2013. Metabolic profiling of FH and SDHx-mutated renal cell carcinoma identified elevated guanine pools in both tumor types, but changes in urea cycle metabolites were only present in tumors harboring FH PV (Yoo et al, 2022). Similar results were obtained in FH-mutated leiomyomas (Heinonen et al, 2017) and are most likely similar in PPGL.…”

Section: Ppgl Genetics and Tumorigenic Effects Of Oncometabolitessupporting

confidence: 57%

“…Metabolites that may help to better classify SDHx and FH mutated PPGLs are guanine, acylcarnitines for SDHx and urea cycle metabolites like argininosuccinate for FH mutant tumors. Although data have so far been presented only for renal cell carcinomas, it is highly likely that these features are also present in PPGLs (Yoo et al, 2022). Omics setups that recognize patterns rather than isolated alterations could further improve patient stratification, e.g.…”

Section: Metabolome-guided Diagnostics and Future Directionsmentioning

confidence: 99%

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Metabolomics in paraganglioma: applications and perspectives from genetics to therapy

Richter

Garrett

Bechmann

et al. 2023

Endocrine-Related Cancer

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Metabolites represent the highest layer of biological information. Their diverse chemical nature enables networks of chemical reactions that are critical for maintaining life by providing energy and building blocks. Quantification by targeted and untargeted analytical methods using either mass spectrometry or nuclear magnetic resonance spectroscopy has been applied to pheochromocytoma/paraganglioma (PPGL) with the long-term goal to improve diagnosis and therapy. PPGLs have unique features that provide useful biomarkers and clues for targeted treatments. Firstly, high production rates of catecholamines and metanephrines allow for specific and sensitive detection of the disease in plasma or urine. Secondly, PPGLs are associated with heritable pathogenic variants (PV) in around 40% of cases, many of which occur in genes encoding enzymes, such as succinate dehydrogenase (SDH) and fumarate hydratase (FH). These genetic aberrations lead to overproduction of oncometabolites succinate or fumarate, respectively, and are detectable in tumors and blood. Such metabolic dysregulation can be exploited diagnostically, with the aim to ensure appropriate interpretation of gene variants, especially those with unknown significance, and facilitate early tumor detection through regular patient follow-up. Furthermore, SDHx and FH PV alter cellular pathways, including DNA hypermethylation, hypoxia signaling, redox homeostasis, DNA repair, calcium signaling, kinase cascades, and central carbon metabolism. Pharmacological interventions targeted towards such features have the potential to uncover treatments against metastatic PPGL, around 50% of which are associated with germline PV in SDHx. With the availability of omics technologies for all layers of biological information personalized diagnostics and treatment is in close reach.

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Section: Ppgl Genetics and Tumorigenic Effects Of Oncometabolitessupporting

confidence: 57%

Section: Metabolome-guided Diagnostics and Future Directionsmentioning

confidence: 99%

Metabolomics in paraganglioma: applications and perspectives from genetics to therapy

Richter

Garrett

Bechmann

et al. 2023

Endocrine-Related Cancer

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“…Patients with VHL disease are particularly prone to renal tumors when a stochastic secondary inactivation of the other VHL allele occurs (27). Similar to those with VHL disease, patients with TSC1/2, FH and SDHB/C/D germline mutations are more likely to have RCC (28)(29)(30). Germline mutation in MET can promote hereditary pRCC initiation and progression (31,32).…”

Section: Discussionmentioning

confidence: 99%

Prevalence of germline mutations in cancer susceptibility genes in Chinese patients with renal cell carcinoma

Feng¹,

Cao²,

Ouyang³

et al. 2023

Transl Androl Urol

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Background: Germline pathogenic variants are estimated to affect 3-5% of patients with renal cell carcinoma (RCC). The identification of patients with hereditary RCC is important for cancer screening and treatment guidance.Methods: Whole-exome sequencing (WES) (n=69) or gene panel sequencing containing 139 genes (n=54) related to germline cancer predisposition was used to analyze germline mutations in 123 patients with

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“…The second challenge relates to the very likely possibility that some correlations between metabolites will be specific to a particular tissue, disease, or other biological context. For example, certain metabolites may accumulate to a large extent in the context of mutations to genes coding for metabolic enzymes [29]. Further generalizations of NMF, including those which leverage additional information about the tissue source or disease of interest, prior information on the relationship of metabolites to one another in the metabolic network, or a secondary dataset (e.g., genomics, gene expression) may improve the predictive performance of MIRTH.…”

Section: Discussionmentioning

confidence: 99%

MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization

et al. 2022

Self Cite

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Out of the thousands of metabolites in a given specimen, most metabolomics experiments measure only hundreds, with poor overlap across experimental platforms. Here, we describe Metabolite Imputation via Rank-Transformation and Harmonization (MIRTH), a method to impute unmeasured metabolite abundances by jointly modeling metabolite covariation across datasets which have heterogeneous coverage of metabolite features. MIRTH successfully recovers masked metabolite abundances both within single datasets and across multiple, independently-profiled datasets. MIRTH demonstrates that latent information about otherwise unmeasured metabolites is embedded within existing metabolomics data, and can be used to generate novel hypotheses and simplify existing metabolomic workflows.

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Genomic and Metabolic Hallmarks of SDH- and FH-deficient Renal Cell Carcinomas

Cited by 15 publications

References 42 publications

Metabolomics in paraganglioma: applications and perspectives from genetics to therapy

Metabolomics in paraganglioma: applications and perspectives from genetics to therapy

Prevalence of germline mutations in cancer susceptibility genes in Chinese patients with renal cell carcinoma

MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization

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