Clinicogenomic Landscape of Histologic Subtypes in Ovarian Cancer: Real-World Evidence From a Japanese Nationwide Cohort

PURPOSE The frequency of histologic subtypes in epithelial ovarian cancer (EOC) varies by geographic region. In Western countries, high-grade serous ovarian cancer (HGSOC) is the most common and well characterized, whereas non-HGSOC subtypes, particularly clear cell ovarian cancer (CCOV), are more prevalent in Asia. However, their clinicogenomic features remain poorly defined. PATIENTS AND METHODS This retrospective study analyzed real-world data from Japan's Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database. The cohort included 5,395 patients diagnosed with ovarian cancer between January 2016 and June 2025. Clinical characteristics, treatment patterns, and real-world overall survival (rwOS) were evaluated in histologic subtypes. Comprehensive genomic profiling was used to characterize mutational landscapes and to identify genomic biomarkers associated with rwOS. RESULTS EOCs were classified into five histologic subtypes: HGSOC (51%), low-grade serous (LGSOC; 2.7%), CCOV (30%), endometrioid (EOV; 8.9%), and mucinous (MOV; 7.3%). Compared with Western cohorts, HGSOC was less frequent and CCOV more prevalent. Distinct mutational profiles were observed across subtypes: TP53 mutations in 94% of HGSOC, frequent ARID1A and PIK3CA mutations in CCOV and EOV, KRAS mutations in LGSOC and MOV, and enriched CDKN2A and CDKN2B deletions in MOV. KEAP1 and NFE2L2 mutations in HGSOC and CCOV, respectively, increased in post-treatment tumors, suggesting ferroptosis-mediated therapeutic resistance. Biomarker analyses identified subtype-specific associations with rwOS: PIK3CA mutations in HGSOC and CDKN2A / CDKN2B deletions in CCOV, EOV, and MOV with shorter rwOS, BRCA1 , and BRCA2 alterations in HGSOC with longer rwOS. A subclass of TP53 mutations that destabilize p53 protein structure was associated with shorter survival in HGSOC and EOV. CONCLUSION This large clinicogenomic study in an Asian population highlights unique mutational landscapes and survival associations, which may inform personalized treatment strategies.