Context Pheochromocytomas and paragangliomas (PPGLs) are characterized by distinct genotype-phenotype relationships according to studies largely restricted to Caucasian populations. Objective To assess for possible differences in genetic landscapes and genotype-phenotype relationships of PPGLs in Chinese versus European populations. Design Cross-sectional study. Setting Two tertiary-care centers in China and nine in Europe. Participants Patients with pathologically-confirmed diagnosis of PPGL, including 719 from China and 919 Europeans. Main Outcome Measures Next generation sequencing performed in tumor specimens with mutations confirmed by Sanger sequencing and tested in peripheral blood if available. Frequencies of mutations were examined according to tumor location and catecholamine biochemical phenotypes. Results Among all patients, higher frequencies of HRAS, FGFR1 and EPAS1 mutations were observed in Chinese than Europeans, whereas the reverse was observed for NF1, VHL, RET and SDHx. Among patients with apparently sporadic PPGLs, the most frequently mutated genes in Chinese were HRAS (16.5[13.6-19.3]% vs 9.8[7.6-12.1]%) and FGFR1 (9.8[7.6-12.1]% vs 2.2[1.1-3.3]%), whereas among Europeans the most frequently mutated genes were NF1 (15.9[13.2-18.6]% vs 6.6[4.7-8.5]%) and SDHx (10.7[8.4-13.0]% vs 4.2[2.6-5.7]%). Among Europeans, almost all paragangliomas lacked appreciable production of epinephrine and identified gene mutations were largely restricted to those leading to stabilization of hypoxia inducible factors. In contrast, among Chinese there was a larger proportion of epinephrine-producing paragangliomas, mostly due to HRAS and FGFR1 mutations. Conclusions This study establishes Sino-European differences in the genetic landscape and presentation of PPGLs, including ethnic differences in genotype-phenotype relationships indicating a paradigm shift in our understanding of the biology of these tumors.

Objective: To evaluate the feasibility and accuracy of machine learning based texture analysis of unenhanced CT images in differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in adrenal incidentaloma (AI).Methods: Seventy-nine patients with 80 LPA and 29 patients with 30 sPHEO were included in the study. Texture parameters were derived using imaging software (MaZda). Thirty texture features were selected and LPA was performed for the features selected. The number of positive features was used to predict results. Logistic multiple regression analysis was performed on the 30 texture features, and a predictive equation was created based on the coefficients obtained.Results: LPA yielded a misclassification rate of 19.39% in differentiating sPHEO from LPA. Our predictive model had an accuracy rate of 94.4% (102/108), with a sensitivity of 86.2% (25/29) and a specificity of 97.5% (77/79) for differentiation. When the number of positive features was greater than 8, the accuracy of prediction was 85.2% (92/108), with a sensitivity of 96.6% (28/29) and a specificity of 81% (64/79).Conclusions: Machine learning-based quantitative texture analysis of unenhanced CT may be a reliable quantitative method in differentiating sPHEO from LPA when AI is present.

Chromophobe renal cell carcinoma (chRCC) is the third most common subtype of kidney cancers. In the present study, we identified 58 treatment-naïve primary chRCC patients from The Cancer Genome Atlas dataset and analyzed the genome-wide microRNA (miRNA) expression profiles, with the aim to assess the relationship of miRNA expression with the progression and prognosis of chRCC. Overall, a total of 105 miRNAs were found to be differentially expressed between tumor and the adjacent normal tissues from 22 chRCC patients. In the unpaired condition (58 chRCC vs. 22 normal tissues), 77 (96.3%) samples were distinguished correctly by the signatures. In the progression-related profiles, 27 miRNAs were selected for pathologic T and 9 for lymph node involvement. In the survival analyses, the expression levels of mir-191, mir-19a, mir-210, and mir-425 were significantly associated with both recurrence-free survival (RFS) and overall survival, while mir-210 was proven as an independent prognostic factor in terms of RFS. In summary, miRNAs are expressed differentially in chRCC, and unique expression of miRNAs is associated with the progression and prognosis of chRCC.