Clinico-Pathological Factors and AR-LBD Mutations in Early and Late Castration-Resistant Prostate Cancer.

Background: Prostate cancer (PCa) is not well understood because of its enormous biological heterogeneity and unreliable progression. We conducted this retrospective analysis to examine the variables predicting early and late progression to castration-resistant PCa (CRPC) for better management of this disease.

Methods: This single institutional retrospective study was conducted from January 2018 to January 2022. A total of 98 consecutive men meeting with the diagnosis of CRPC as per the inclusion criteria were included in the study and were stratified in four quartiles on the basis of time to CRPC (time to castration resistance [TTCR]) development. Early CRPC (1^st quartile, TTCR = 6-12 months) and late CRPC (4^th quartile, TTCR = 38-120 months) were then compared on the basis of different clinical, pathological and AR-LBD sequence to find the correlation with response duration.

Results: Median time to develop castration resistance was 25 ± 26.44 months. The mean age of the patients was 66.8 ± 9.20 years and median baseline PSA was calculated 100±685.06 ng/mL respectively. Higher Gleason score (≥7-10) was found to be significantly associated with early development of CRPC (p<0.001) and lower nadir PSA was significantly indicating late CRPC progression (p<0.005). No mutations were found in androgen receptor exon-5, 6, 7 except a homozygous mutation in the 7^th intronic region, which is involved in splice variants formation playing noteworthy role in CRPC development.

Conclusion: Time for metastatic PCa to CRPC ranges from 6-120 months revealing its heterogeneous nature. Early age presentation in the clinic and high initial PSA and high grade (GS>7) at diagnosis were positively associated with early CRPC while lower nadir PSA was correlated with late CRPC progression. No remarkable genomic mutations were discovered. Therefore, more data are needed and further research is required with large no. of patients to discover the predictive prognostic biomarkers for better patients' management.