Clinical and Molecular Characterization of Clear Cell Adenocarcinoma of the Urinary Tract: A Multi-Institutional Study.
Summary
Clear cell adenocarcinoma of the urinary tract (CCA-UT) is a rare and aggressive tumor with limited understanding of its clinicopathologic and molecular features. This multi-institutional study characterized 35 cases, showing a female predominance and advanced stage at presentation. Genomic alterations were found in 91% of cases, frequently involving chromatin modifiers such as ATRX, KMT2C, ARID1A, and ARID1B. The heterogeneous molecular profile of these tumors highlights the critical role of molecular analysis in identifying potential therapeutic targets.
Analysis
Clinique : CCA-UT is a rare and aggressive tumor, frequently diagnosed at an advanced stage (all cases ≥pT2 in this cohort). The 29% mortality rate underscores its severity. This retrospective study, despite its modest cohort size (35 cases), highlights the critical need for molecular characterization to guide therapeutic options, suggesting a medium-term clinical impact (3-5 years) for integrating these analyses into patient management. Biomol : This study employed whole-exome sequencing (WES) and RNA sequencing (RNA-seq) to characterize the molecular landscape of CCA-UT. Pathogenic/oncogenic alterations were identified in 91% of cases, with a high prevalence of mutations in chromatin modifier genes (ATRX, KMT2C, ARID1A, ARID1B). Mutations in ATM, NF1, and ERBB2 were also noted. The detection of homologous recombination deficiency (HRD) and BRCA mutations in some cases, along with recurrent copy number losses on Chr 1(p36.33-p35.3), suggests potential predictive biomarkers. RNA-seq analysis identified an EMT signature, though without prognostic significance in this cohort. Bioinfo : Bioinformatic analysis of whole-exome sequencing data enabled the identification of genomic alterations and recurrent mutations. RNA-seq data analysis revealed numerous differentially expressed genes and the enrichment of an epithelial-to-mesenchymal transition (EMT) gene signature. While no novel algorithms were presented, the application of these standard bioinformatic methods to a rare CCA-UT cohort provided a detailed molecular profile, which could eventually support clinical workflows for prioritizing targeted therapies.