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Purpose: Prostate cancer is the second most common cancer diagnosed worldwide and the third most common cancer among men in India. This study's objective was to characterise the mutational landscape of Indian prostate cancer using whole-exome sequencing to identify population-specific polymorphisms. Methods: Whole-exome sequencing was performed of 58 treatment-naive primary prostate tumors of Indian origin. Multiple computational and statistical analyses were used to profile the known common mutations, other deleterious mutations, driver genes, prognostic biomarkers, and gene signatures unique to each clinical parameter. Cox analysis was performed to validate survival-associated genes. McNemar test identified genes significant to recurrence and receiver-operating characteristic (ROC) analysis was conducted to determine its accuracy. OncodriveCLUSTL algorithm was used to deduce driver genes. The druggable target identified was modeled with its known inhibitor using Autodock. Results: TP53 was the most commonly mutated gene in our cohort. Three novel deleterious variants unique to the Indian prostate cancer subtype were identified: POLQ, FTHL17, and OR8G1. COX regression analysis identified ACSM5, a mitochondrial gene responsible for survival. CYLC1 gene, which encodes for sperm head cytoskeletal protein, was identified as an unfavorable prognostic biomarker indicative of recurrence. The novel POLQ mutant, also identified as a driver gene, was evaluated as the druggable target in this study. POLQ, a DNA repair enzyme implicated in various cancer types, is overexpressed and is associated with a poor prognosis. The mutant POLQ was subjected to structural analysis and modeled with its known inhibitor novobiocin resulting in decreased binding efficiency necessitating the development of a better drug. Conclusion: In this pilot study, the molecular profiling using multiple computational and statistical analyses revealed distinct polymorphisms in the Indian prostate cancer cohort. The mutational signatures identified provide a valuable resource for prognostic stratification and targeted treatment strategies for Indian prostate cancer patients. The DNA repair enzyme, POLQ, was identified as the druggable target in this study. [ABSTRACT FROM AUTHOR]

Introduction: We analyzed the biochemical recurrence-free survival (BRFS) of patients with high-risk prostate cancer (HRCaP) as per the D'Amico classification undergoing radical prostatectomy (RP) at our center. We aimed to determine whether the number and type of risk factors (cT2c-T3b, prostate-specific antigen >20 ng/ml, Gleason score >7) are associated with biochemical recurrence (BCR) in HRCaP patients undergoing RP in the Indian population. Methods: Between 2006 and 2017, 192 patients underwent RP (open RP [ORP], laparoscopic RP [LRP], and robotic RP [RRP]) at our center, of which 109 had D'Amico HR disease. Preoperative, postoperative, and pathological outcome data were analyzed for patients with HR disease as per the D'Amico classification. Subgroups were formed to determine whether an increasing number of risk factors (1, 2, or 3) were associated with poorer oncological results and early BCR. The Kaplan–Meier method with log-rank test was used to test the difference in BRFS between the groups. Univariate and multivariate analyses were done to find significant variable against BCR. Results: According to the D'Amico criteria, 109 patients had HR, 63 patients had intermediate-risk, and 19 patients had low-risk disease. These 109 patients with HR disease were analyzed in our study (50 RRP, 33 ORP, and 26 LRP). A total of 59 (54.1%) patients had one HR factor (1HR), 44 (40%) had two HR factors (2HR), and 6 (5.5%) had three HR factors (3HR). The mean follow-up for our patient population was 21.5 ± 19 months (median 18 months; range, 0–108). Overall, the 2-year and 5-year BRFS was 45% and 35%, respectively (mean BRFS 46 ± 6 months). Two-year BRFS was 63%, 23%, and 22%, respectively, for 1HR, 2HR, and 3HR (logrank, P < 0.0001). The prognostic substratification based on the three risk factors was significantly predictive for adverse pathologic features and oncologic outcomes. Conclusion: Substratification based on the three well-defined criteria leads to a better identification of the more aggressive cancers and prediction of need for additional treatment modalities. Localized HRCaP includes a heterogeneous population of patients with variable oncological outcomes. [ABSTRACT FROM AUTHOR]