Molecular approach of renal cell carcinoma using FISH method : a prospective study (23 cases) and state of the art

Abstract

Abstract INTRODUCTION: In Morocco, Kidney cancer is the 18th most common tumour and the 19th lethal cancer in 2020. The average age of diagnosis is 65 years and men are more affected. Molecular studies of renal cell carcinoma (RCC) allowed the detection of several genetic abnormalities in each histological subtype. These aberrations got different diagnostic value, prognostic implication and for some, therapeutic utility. The aim of our study is to evaluate the utility of fluorescence in-situ hybridization (FISH) in the diagnosis and the prognosis of renal cell carcinoma. MATERIAL AND METHODS: We included prospectively cases of RCC diagnosed after histological examination and immunohistochemistry analysis for some cases. The methodology consisted in highlighting by FISH method, molecular abnormalities for each histological subtypes using Zytolight® probes. Probes were chosen depending on the histological diagnosis. RESULTS: A total of 23 case of RCC were included. Clear cell carcinoma (ccRCC) represented 47,8% (11 cases) followed by papillary RCC (pRCC) with 26%, chromophobe RCC (chRCC) with 13%, a case with uncertain diagnosis, ccRCC or pRCC (4,3%), one case of renal oncocytoma (RO) (4,3%), and a case of tubulo-cystic RCC (4,3%). FISH method supported the morphological diagnosis in all cases except in one biopsy diagnosed histologically as a ccRCC and this method allowed the diagnosis correction to pRCC by the detection of chromosome 17 polysomy described in this histological subtype. FISH can also be used in the prognostic categorization of patients by detecting of some genetic aberrations with a prognostic implication like CDKN2a loss which predict a worse evolution. CONCLUSION: FISH method got an implication in the diagnostic approach of RCC, especially in cases with non-conclusive histology and immunohistochemistry. It can also be used in the prognosis in addition to other histo-prognostic factors. This method will lead to more precision in diagnosis and better care management personalization.