Abstract
BACKGROUND: Prostate cancer is the most common cancer in urology. While the genomic profiles and prognostic biomarkers of metastatic castration-resistant prostate cancer (mCRPC) have been well studied, the genomic characteristics of Chinese metastatic hormone-sensitive prostate cancer (mHSPC) and their association with clinical features remain incompletely characterized. This study aimed to characterize the genomic landscape of mHSPC in a Chinese cohort and explore correlations with clinical characteristics, with comparisons to data from the Stand Up to Cancer-Prostate Cancer Foundation (SU2C-PCF) cohort. METHODS: In this retrospective study, 52 patients diagnosed with mHSPC were enrolled, and tumor samples were subjected to targeted sequencing. Clinical, demographic, and pathological data were collected. The association between genomic alterations and clinical features was analyzed using Pearson's chi-squared test tests. Comparative analysis was performed using the SU2C-PCF cohort. RESULTS: Compared to the SU2C-PCF cohort, Chinese patients with mHSPC had significantly higher mutation frequencies in FOXA1 (36.54% vs. 12.0%, P < 0.001), KDM6A (15.4% vs. 4.0%, P < 0.01), PIK3CA (15.4% vs. 4.7%, P = 0.01), and CTNNB1 (11.5% vs. 4.0%, P = 0.047), while AR mutations were significantly more frequent in the SU2C-PCF cohort (1.9% vs. 22.7%, P < 0.001). Homologous recombination repair gene mutations were detected in 36.54% of patients. PIK3CA mutations were associated with low PSA levels (50% vs. 11%, P = 0.044), TP53 mutations were significantly enriched in patients with high Gleason scores (43% vs. 0%, P < 0.001), and KDM6A (7% vs. 30%, P = 0.046) and SPOP (3% vs. 30%, P = 0.012) mutations were significantly enriched in patients with low Gleason scores. CONCLUSIONS: Chinese patients with mHSPC have a distinct genomic landscape and show high genomic heterogeneity, TP53 mutation was significantly enriched in patients with mHSPC with high Gleason scores. TRIAL REGISTRATION: Not applicable. This study is a retrospective observational analysis.