Subtyping atypical eosinophilic renal cell tumours through integrated morphological, immunohistochemical, and mutational characters

Introducing molecular features into diagnostic criteria in the WHO Classification of Renal Tumours, Fifth Edition (2022), significantly improves the discrimination between morphologically similar neoplasms. Renal cell tumours with eosinophilic cytoplasm often exhibit overlapping histological features, making the differential diagnoses challenging. In this retrospective study, we investigated 16 cases of atypical eosinophilic renal cell tumours (ATERCT), with four renal oncocytoma (RO) and three classic chromophobe renal cell carcinomas (chRCC) as controls. Morphology, immunohistochemistry (IHC), special staining and whole-exome sequencing (WES) were employed to facilitate the subtyping of these cases. Based on histomorphological features (tumour growth pattern and nuclear morphology), IHC markers (CK7, CK20, CD117, and mTOR) and special colloidal iron staining, 7 cases were classified including 2 eosinophilic chRCC, 2 RO, 1 eosinophilic vacuolated tumour (EVT), 1 eosinophilic solid and cystic renal cell carcinoma (ESC RCC) and 1 low-grade eosinophilic tumour (LOT).The gene mutational profiles by WES confirmed 5 of the 7 cases with marker gene mutations, and suggested the pathological types of 6 cases in the remaining 9 cases, which left 3 cases unresolved. The landscape of mutational profiling demonstrated an individual-specific pattern in the 23 patients that no single mutation definitively distinguished among RO, eosinophilic chRCC, LOT, EVT, or ESC RCC. A panel covering 69 mutations from 34 genes could segregate the 23 patients into 6 distinct groups by consensus clustering analysis. By integrating morphological, IHC, special staining and mutational features, we found a combination of characters of FGFR1 p.D44del / microscopic tumour borders / solid nest flakes / nucleomorphs / CD117 might represent the strongest power of subtyping the 16 patients by a L1-regularized supportive virtual machine algorithm. These findings indicated that genetic mutations were diverse among eosinophilic renal cell tumours and particular mutations might facilitate the differential diagnosis. The integration of morphological, IHC, special staining and mutational features could enhance subtyping diagnostically challenging eosinophilic ATERCT.