Comprehensive Genomic Characterization of 102 Cervical Adenocarcinoma Tumors.

Background and Objectives: Cervical adenocarcinoma (CAC) is a histologically distinct subtype of cervical cancer with a rising incidence in many regions. While the roles of key driver mutations are known, a comprehensive understanding of its genomic landscape, particularly variations across different populations and tumor stages, remains incomplete. This study aims to characterize the somatic genomic landscape of CAC by identifying recurrent mutations, copy number alterations (CNAs), and patterns of co-occurrence, with a focus on variations across racial groups and between primary and metastatic tumors. Materials and Methods: We conducted a comprehensive genomic analysis of 102 tumor samples from 99 patients diagnosed with cervical adenocarcinoma using data from the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) database. Results: The most frequently mutated genes were PIK3CA (25.5%), TP53 (21.6%), ARID1A (20.6%), and KRAS (16.7%). Significant amplification of ERBB2 was also observed (n = 3; 4.83%). Our analysis revealed notable genomic disparities across racial groups, with TP53 mutations being significantly more frequent in White patients compared to Asian and Black patients (p = 0.0236). Furthermore, we identified significant co-occurrence between mutations in KRAS and MSH2 (p = 0.011) as well as ATM and STK11 (p = 0.037). In comparing tumor types, mutations in BCL6 were found to be significantly enriched in metastatic samples. Conclusions: This study validates the primary drivers of cervical adenocarcinoma and reveals novel findings, including notable racial disparities in TP53 mutation frequency and unique patterns of co-occurring mutations. These findings highlight the genomic heterogeneity of the disease and suggest that ancestry and tumor evolution may influence its molecular pathogenesis, offering potential avenues for the development of targeted therapies and personalized biomarkers.