Bevan, Graham H.

5082 Background: We evaluated the association of germ-line variation in 10 candidate genes important in prostate cancer biology (NKX3-1, JAK2, SLCO2B1, STAT3, CYP19A1, HSD17B4, TRMT11, PRMT3, HSD17B12, NCOA4) with failure of AA in advanced prostate cancer patients. Methods: Patients (N=619) were enrolled for genotyping at the time of AA failure. Candidate genes were selected from the literature and previous pilot studies in smaller cohorts which had identified variation in TRMT11 and HSD17B12 to be associated with AA failure. Genes were tagged using single nucleotide polymorphisms (SNPs) from HapMap with minor allele frequency of >5% and r2≥0.8. DNA was extracted from peripheral blood and genotyped using Illumina Veracode platform. The primary endpoint was time to failure on AA, defined as time from initiating continuous AA to two serial PSA increases over the nadir or clinical or imaging based progression, whichever came first. Principal component analysis was used for gene-levels tests. Association with the primary endpoint was assessed using proportional hazards regression models at the gene-level and at the SNP level. For SNP level results we estimated per allele (ordinal model) hazard ratios (HR) and 95% confidence intervals (CI) using Cox regression, adjusted for Gleason score (GS). Results: We successfully genotyped 60 SNPs (from 10 genes) in 519 subjects. The median age of the cohort was 71 years (range 43, 92). The GS distribution was 51% subjects with GS≥8; 33% with GS=7 and 17% with GS<7. Median time to AA failure for the cohort was 2.3 years (IQ range: 1.0-4.5). In gene level analyses adjusting for GS, TRMT11 (p=0.004), HSD17B12 (p=0.016), and JAK2 (p=0.044) genes were associated with time to AA failure. Four of the 18 genotyped SNPs in JAK2 were associated with AA failure after adjustment for GS (Table). Conclusions: Variation in the JAK2 gene is significantly associated with time to AA failure. Validation is needed to develop these as predictive biomarkers for AA in advanced prostate cancer. [Table: see text]

75 Background: We investigated the association of germline variation in ten candidate genes important in prostate cancer biology (NKX3-1, JAK2, SLCO2B1, STAT3, CYP19A1, HSD17B4, TRMT11, PRMT3, HSD17B12, NCOA4) with overall survival (OS) in a cohort of metastatic CRPC stage patients. Methods: Patients (N=497) were enrolled at the time of androgen deprivation therapy failure for D2 stage prostate cancer. Candidate genes were selected from the literature and pilot studies. Genes were tagged using SNPs from HapMap with minor allele frequency (MAF) of >5% and r2≥0.8. DNA was extracted from peripheral blood and genotyped using Illumina Veracode platform. The primary endpoint was OS, defined as time from development of CRPC to death. Principle components analysis was used for gene-levels tests. To account for multiple testing we calculated the false discovery rate (FDR). For SNP level results we estimated per allele (ordinal model) hazard ratios (HR) and 95% confidence intervals (CI) using Cox regression and adjusted for age and Gleason score (GS). Results: We successfully genotyped 60 SNPs (from 10 genes) in 497 Caucasian subjects. The median age of the CRPC cohort was 71 years (range 43,93). The GS distribution was 51% subjects with GS≥8; 32% with GS7 and 17% with GS<7. After a median follow up of 2.1 years (IQ range: 1.2-3.6), 297 subjects had died. In unadjusted gene level analyses, NKX3-1 (p=0.04; FDR=0.03) was associated with OS. After adjusting for age and GS the result remained significant (p=0.03). Two of the 5 genotyped SNPs in this gene were associated with OS after adjustment for age and GS: rs10503733 (dominant model p=0.03, HR=0.77; 95 CI=0.60-0.98; A>C; MAF = 0.22) and rs7005368 (dominant model p=0.05, HR=1.38, 95CI=1.0-1.92; G>A; MAF=0.07). Both SNPs are in Hardy Weinberg Equilibrium and in complete LD (D'=1), although r2 is low (r2=0.025) due to different allele frequencies. Conclusions: Variation in the homeobox NKX-1 gene, a prostate specific tumor suppressor gene involved in prostate development and carcinogenesis is significantly associated with OS in CRPC after accounting for age and GS. Further validation is needed to develop these as prognostic biomarkers.

BACKGROUNDProstate cancer (PCa) is a leading cause of cancer death of men worldwide. In hormone‐sensitive prostate cancer (HSPC), androgen deprivation therapy (ADT) is widely used, but an eventual failure on ADT heralds the passage to the castration‐resistant prostate cancer (CRPC) stage. Because predicting time to failure on ADT would allow improved planning of personal treatment strategy, we aimed to develop a predictive personalization algorithm for ADT efficacy in HSPC patients.METHODSA mathematical mechanistic model for HSPC progression and treatment was developed based on the underlying disease dynamics (represented by prostate‐specific antigen; PSA) as affected by ADT. Following fine‐tuning by a dataset of ADT‐treated HSPC patients, the model was embedded in an algorithm, which predicts the patient's time to biochemical failure (BF) based on clinical metrics obtained before or early in‐treatment.RESULTSThe mechanistic model, including a tumor growth law with a dynamic power and an elaborate ADT‐resistance mechanism, successfully retrieved individual time‐courses of PSA (R2 = 0.783). Using the personal Gleason score (GS) and PSA at diagnosis, as well as PSA dynamics from 6 months after ADT onset, and given the full ADT regimen, the personalization algorithm accurately predicted the individual time to BF of ADT in 90% of patients in the retrospective cohort (R2 = 0.98).CONCLUSIONSThe algorithm we have developed, predicting biochemical failure based on routine clinical tests, could be especially useful for patients destined for short‐lived ADT responses and quick progression to CRPC. Prospective studies must validate the utility of the algorithm for clinical decision‐making. Prostate 76:48–57, 2016. © 2015 Wiley Periodicals, Inc.