Dataset Information

Aberrant Splicing of SDHC in Families With Unexplained Succinate Dehydrogenase-Deficient Paragangliomas.

ABSTRACT: Context: Germline mutations in the succinate dehydrogenase genes (SDHA/B/C/D, SDHAF2-collectively, "SDHx") have been implicated in paraganglioma (PGL), renal cell carcinoma (RCC), gastrointestinal stromal tumor (GIST), and pituitary adenoma (PA). Negative SDHB tumor staining is indicative of SDH-deficient tumors, usually reflecting an underlying germline SDHx mutation. However, approximately 20% of individuals with SDH-deficient tumors lack an identifiable germline SDHx mutation.

Methods: We performed whole-exome sequencing (WES) of germline and tumor DNA followed by Sanger sequencing validation, transcriptome analysis, metabolomic studies, and haplotype analysis in 2 Italian-Australian families with SDH-deficient PGLs and various neoplasms, including RCC, GIST, and PA.

Results: Germline WES revealed a novel SDHC intronic variant, which had been missed during previous routine testing, in 4 affected siblings of the index family. Transcriptome analysis demonstrated aberrant SDHC splicing, with the retained intronic segment introducing a premature stop codon. WES of available tumors in this family showed chromosome 1 deletion with loss of wild-type SDHC in a PGL and a somatic gain-of-function KIT mutation in a GIST. The SDHC intronic variant identified was subsequently detected in the second family, with haplotype analysis indicating a founder effect.

Conclusions: This is the deepest intronic variant to be reported among the SDHx genes. Intronic variants beyond the limits of standard gene sequencing analysis should be considered in patients with SDH-deficient tumors but negative genetic test results.

SUBMITTER: De Sousa SMC

PROVIDER: S-EPMC7646550 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Aberrant Splicing of SDHC in Families With Unexplained Succinate Dehydrogenase-Deficient Paragangliomas.

De Sousa Sunita M C SMC Toubia John J Hardy Tristan S E TSE Feng Jinghua J Wang Paul P Schreiber Andreas W AW Geoghegan Joel J Hall Rachel R Rawlings Lesley L Buckland Michael M Luxford Catherine C Novos Talia T Clifton-Bligh Roderick J RJ Poplawski Nicola K NK Scott Hamish S HS Torpy David J DJ

Journal of the Endocrine Society 20200616 12

<h4>Context</h4>Germline mutations in the succinate dehydrogenase genes (SDHA/B/C/D, SDHAF2-collectively, "SDHx") have been implicated in paraganglioma (PGL), renal cell carcinoma (RCC), gastrointestinal stromal tumor (GIST), and pituitary adenoma (PA). Negative SDHB tumor staining is indicative of SDH-deficient tumors, usually reflecting an underlying germline SDHx mutation. However, approximately 20% of individuals with SDH-deficient tumors lack ...[more]

PMID: 33195952

Similar Datasets

Project description:Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary background in over one-third of patients. Mutations in succinate dehydrogenase (SDH) genes increase the risk for PPGLs and several other tumours. Mutations in subunit B (SDHB) in particular are a risk factor for metastatic disease, further highlighting the importance of identifying SDHx mutations for patient management. Genetic variants of unknown significance, where implications for the patient and family members are unclear, are a problem for interpretation. For such cases, reliable methods for evaluating protein functionality are required. Immunohistochemistry for SDHB (SDHB-IHC) is the method of choice but does not assess functionality at the enzymatic level. Liquid chromatography-mass spectrometry-based measurements of metabolite precursors and products of enzymatic reactions provide an alternative method. Here, we compare SDHB-IHC with metabolite profiling in 189 tumours from 187 PPGL patients. Besides evaluating succinate:fumarate ratios (SFRs), machine learning algorithms were developed to establish predictive models for interpreting metabolite data. Metabolite profiling showed higher diagnostic specificity compared to SDHB-IHC (99.2% versus 92.5%, p = 0.021), whereas sensitivity was comparable. Application of machine learning algorithms to metabolite profiles improved predictive ability over that of the SFR, in particular for hard-to-interpret cases of head and neck paragangliomas (AUC 0.9821 versus 0.9613, p = 0.044). Importantly, the combination of metabolite profiling with SDHB-IHC has complementary utility, as SDHB-IHC correctly classified all but one of the false negatives from metabolite profiling strategies, while metabolite profiling correctly classified all but one of the false negatives/positives from SDHB-IHC. From 186 tumours with confirmed status of SDHx variant pathogenicity, the combination of the two methods resulted in 185 correct predictions, highlighting the benefits of both strategies for patient management. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Dataset Information

Aberrant Splicing of SDHC in Families With Unexplained Succinate Dehydrogenase-Deficient Paragangliomas.

Publications

Aberrant Splicing of <i>SDHC</i> in Families With Unexplained Succinate Dehydrogenase-Deficient Paragangliomas.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets