Two distinct classes of thymic tumors in patients with MEN1 show LOH at the MEN1 locus.
Adel MandlJames M WelchGayathri KapoorVaishali I ParekhDavid S SchrumpR Taylor RipleyMary F WalterJaydira Del RiveroSmita JhaWilliam F SimondsRobert T JensenLee S WeinsteinJenny E BlauSunita K AgarwalPublished in: Endocrine-related cancer (2021)
Patients with the multiple endocrine neoplasia type 1 (MEN1) syndrome carry germline heterozygous loss-of-function mutations in the MEN1 gene which predisposes them to develop various endocrine and non-endocrine tumors. Over 90% of the tumors show loss of heterozygosity (LOH) at chromosome 11q13, the MEN1 locus, due to somatic loss of the wild-type MEN1 allele. Thymic neuroendocrine tumors (NETs) or thymic carcinoids are uncommon in MEN1 patients but are a major cause of mortality. LOH at the MEN1 locus has not been demonstrated in thymic tumors. The goal of this study was to investigate the molecular aspects of MEN1-associated thymic tumors including LOH at the MEN1 locus and RNA-sequencing (RNA-Seq) to identify genes associated with tumor development and potential targeted therapy. A retrospective chart review of 294 patients with MEN1 germline mutations identified 14 patients (4.8%) with thymic tumors (12 thymic NETs and 2 thymomas). LOH at the MEN1 locus was identified in 10 tumors including the 2 thymomas, demonstrating that somatic LOH at the MEN1 locus is also the mechanism for thymic tumor development. Unsupervised principal component analysis and hierarchical clustering of RNA-Seq data showed that thymic NETs formed a homogenous transcriptomic group separate from thymoma and normal thymus. KSR2 (kinase suppressor of Ras 2), that promotes Ras-mediated signaling, was abundantly expressed in thymic NETs, a potential therapeutic target. The molecular insights gained from our study about thymic tumors combined with similar data from other MEN1-associated tumors may lead to better surveillance and treatment of these rare tumors.
Keyphrases
- middle aged
- rna seq
- single cell
- public health
- wild type
- machine learning
- chronic kidney disease
- climate change
- dna methylation
- cardiovascular disease
- ejection fraction
- risk assessment
- newly diagnosed
- early onset
- genome wide association study
- cardiovascular events
- transcription factor
- deep learning
- genome wide
- replacement therapy
- patient reported