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Abstract Background The purpose of this study is to validate a new five-tiered prognostic classification system to better discriminate cancer-specific mortality in men diagnosed with primary non-metastatic prostate cancer. Methods We applied a recently described five-strata model, the Cambridge Prognostic Groups (CPGs 1-5), in two international cohorts and tested prognostic performance against the current standard three-strata classification of low-, intermediate- or high-risk disease. Diagnostic clinico-pathological data for men obtained from the Prostate Cancer data Base Sweden (PCBaSe) and the Singapore Health Study were used. The main outcome measure was prostate cancer mortality (PCM) stratified by age group and treatment modality. Results The PCBaSe cohort included 72,337 men, of whom 7162 died of prostate cancer. The CPG model successfully classified men with different risks of PCM with competing risk regression confirming significant intergroup distinction (p < 0.0001). The CPGs were significantly better at stratified prediction of PCM compared to the current three-tiered system (concordance index (C-index) 0.81 vs. 0.77, p < 0.0001). This superiority was maintained for every age group division (p < 0.0001). Also in the ethnically different Singapore cohort of 2550 men with 142 prostate cancer deaths, the CPG model outperformed the three strata categories (C-index 0.79 vs. 0.76, p < 0.0001). The model also retained superior prognostic discrimination in the treatment sub-groups: radical prostatectomy (n = 20,586), C-index 0.77 vs. 074; radiotherapy (n = 11,872), C-index 0.73 vs. 0.69; and conservative management (n = 14,950), C-index 0.74 vs. 0.73. The CPG groups that sub-divided the old intermediate-risk (CPG2 vs. CPG3) and high-risk categories (CPG4 vs. CPG5) significantly discriminated PCM outcomes after radical therapy or conservative management (p < 0.0001). Conclusions This validation study of nearly 75,000 men confirms that the CPG five-tiered prognostic model has superior discrimination compared to the three-tiered model in predicting prostate cancer death across different age and treatment groups. Crucially, it identifies distinct sub-groups of men within the old intermediate-risk and high-risk criteria who have very different prognostic outcomes. We therefore propose adoption of the CPG model as a simple-to-use but more accurate prognostic stratification tool to help guide management for men with newly diagnosed prostate cancer.

Insulin-like growth factors (IGFs) and insulin-like growth factor binding proteins (IGFBPs) have been implicated in the aetiology of several cancers. To better understand whether anthropometric, behavioural and sociodemographic factors may play a role in cancer risk via IGF signalling, we examined the cross-sectional associations of these exposures with circulating concentrations of IGFs (IGF-I and IGF-II) and IGFBPs (IGFBP-1, IGFBP-2 and IGFBP-3). The Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group dataset includes individual participant data from 16,024 male controls (i.e. without prostate cancer) aged 22-89 years from 22 prospective studies. Geometric means of protein concentrations were estimated using analysis of variance, adjusted for relevant covariates. Older age was associated with higher concentrations of IGFBP-1 and IGFBP-2 and lower concentrations of IGF-I, IGF-II and IGFBP-3. Higher body mass index was associated with lower concentrations of IGFBP-1 and IGFBP-2. Taller height was associated with higher concentrations of IGF-I and IGFBP-3 and lower concentrations of IGFBP-1. Smokers had higher concentrations of IGFBP-1 and IGFBP-2 and lower concentrations of IGFBP-3 than nonsmokers. Higher alcohol consumption was associated with higher concentrations of IGF-II and lower concentrations of IGF-I and IGFBP-2. African Americans had lower concentrations of IGF-II, IGFBP-1, IGFBP-2 and IGFBP-3 and Hispanics had lower IGF-I, IGF-II and IGFBP-3 than non-Hispanic whites. These findings indicate that a range of anthropometric, behavioural and sociodemographic factors are associated with circulating concentrations of IGFs and IGFBPs in men, which will lead to a greater understanding of the mechanisms through which these factors influence cancer risk.

Background: Little is known about disease trajectories for men with castration-resistant prostate cancer (CRPC). Objective: To create a state transition model that estimates time spent in the CRPC state and its outcomes. Design, setting, and participants: The model was generated using population-based prostate-specific antigen data from 40% of the Swedish male population, which were linked to nationwide population-based databases. We compared the observed and predicted cumulative incidence of transitions to and from the CRPC state. Outcome measurements and statistical analysis: We measured time spent in the CRPC state and the proportion of men who died of prostate cancer during follow-up by CRPC risk category. Results and limitations: Time spent in the CRPC state varied from 1.1 yr for the highest risk category to 3.9 yr for the lowest risk category. The proportion of men who died from prostate cancer within 10 yr ranged from 93% for the highest risk category to 54% for the lowest. There was good agreement between the model estimates and observed data. Conclusions: There is large variation in the time spent in the CRPC state, varying from 1 yr to 4 yr according to risk category. Patient summary: It is possible to accurately estimate the disease trajectory and duration for men with castration-resistant prostate cancer.

Abstract Polygenic risk scores (PRS) for prostate cancer incidence have been proposed to optimize prostate cancer screening. Prediction of lethal prostate cancer is key to any stratified screening program to avoid excessive overdiagnosis. Herein, PRS for incident prostate cancer was evaluated in two population-based cohorts of unscreened middle-aged men linked to cancer and death registries: the Västerbotten Intervention Project (VIP) and the Malmö Diet and Cancer study (MDC). SNP genotypes were measured by genome-wide SNP genotyping by array followed by imputation or genotyping of selected SNPs using mass spectrometry. The ability of PRS to predict lethal prostate cancer was compared to PSA and a commercialized pre-specified model based on four kallikrein markers. The PRS was associated with incident prostate cancer, replicating previously reported relative risks, and was also associated with prostate cancer death. However, unlike PSA, the PRS did not show stronger association with lethal disease: the hazard ratio for prostate cancer incidence vs. prostate cancer metastasis and death was 1.69 vs. 1.65 in VIP and 1.25 vs. 1.25 in MDC. PSA was a much stronger predictor of prostate cancer metastasis or death with an area-under-the-curve of 0.78 versus 0.63 for the PRS. Importantly, addition of PRS to PSA did not contribute additional risk stratification for lethal prostate cancer. We have shown that a PRS that predicts prostate cancer incidence does not have utility above and beyond that of PSA measured at baseline when applied to the clinically relevant endpoint of prostate cancer death. These findings have implications for public health policies for delivery of prostate cancer screening. Focusing polygenic risk scores on clinically significant endpoints such as prostate cancer metastasis or death would likely improve clinical utility.

BackgroundExperimental and clinical evidence implicates testosterone in the aetiology of prostate cancer. Variation across the normal range of circulating free testosterone concentrations may not lead to changes in prostate biology, unless circulating concentrations are low. This may also apply to prostate cancer risk, but this has not been investigated in an epidemiological setting.ObjectiveTo examine whether men with low concentrations of circulating free testosterone have a reduced risk of prostate cancer.Design, setting, and participantsAnalysis of individual participant data from 20 prospective studies including 6933 prostate cancer cases, diagnosed on average 6.8 yr after blood collection, and 12 088 controls in the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group.Outcome measurements and statistical analysisOdds ratios (ORs) of incident overall prostate cancer and subtypes by stage and grade, using conditional logistic regression, based on study-specific tenths of calculated free testosterone concentration.Results and limitationsMen in the lowest tenth of free testosterone concentration had a lower risk of overall prostate cancer (OR=0.77, 95% confidence interval [CI] 0.69-0.86; p

IntroductionSex hormones have been implicated in the etiology of a number of diseases. To better understand disease etiology and the mechanisms of disease-risk factor associations, this analysis aimed to investigate the associations of anthropometric, sociodemographic and behavioural factors with a range of circulating sex hormones and sex hormone-binding globulin.MethodsStatistical analyses of individual participant data from 12,330 male controls aged 25-85 years from 25 studies involved in the Endogenous Hormones Nutritional Biomarkers and Prostate Cancer Collaborative Group. Analysis of variance was used to estimate geometric means adjusted for study and relevant covariates.ResultsOlder age was associated with higher concentrations of sex hormone-binding globulin and dihydrotestosterone and lower concentrations of dehydroepiandrosterone sulfate, free testosterone, androstenedione, androstanediol glucuronide and free estradiol. Higher body mass index was associated with higher concentrations of free estradiol, androstanediol glucuronide, estradiol and estrone and lower concentrations of dihydrotestosterone, testosterone, sex hormone-binding globulin, free testosterone, androstenedione and dehydroepiandrosterone sulfate. Taller height was associated with lower concentrations of androstenedione, testosterone, free testosterone and sex hormone-binding globulin and higher concentrations of androstanediol glucuronide. Current smoking was associated with higher concentrations of androstenedione, sex hormone-binding globulin and testosterone. Alcohol consumption was associated with higher concentrations of dehydroepiandrosterone sulfate, androstenedione and androstanediol glucuronide. East Asians had lower concentrations of androstanediol glucuronide and African Americans had higher concentrations of estrogens. Education and marital status were modestly associated with a small number of hormones.ConclusionCirculating sex hormones in men are strongly associated with age and body mass index, and to a lesser extent with smoking status and alcohol consumption.

The role of insulin-like growth factors (IGF) in prostate cancer development is not fully understood. To investigate the association between circulating concentrations of IGFs (IGF-I, IGF-II, IGFBP-1, IGFBP-2, and IGFBP-3) and prostate cancer risk, we pooled individual participant data from 17 prospective and two cross-sectional studies, including up to 10,554 prostate cancer cases and 13,618 control participants. Conditional logistic regression was used to estimate the ORs for prostate cancer based on the study-specific fifth of each analyte. Overall, IGF-I, IGF-II, IGFBP-2, and IGFBP-3 concentrations were positively associated with prostate cancer risk (Ptrend all ≤ 0.005), and IGFBP-1 was inversely associated weakly with risk (Ptrend = 0.05). However, heterogeneity between the prospective and cross-sectional studies was evident (Pheterogeneity = 0.03), unless the analyses were restricted to prospective studies (with the exception of IGF-II, Pheterogeneity = 0.02). For prospective studies, the OR for men in the highest versus the lowest fifth of each analyte was 1.29 (95% confidence interval, 1.16-1.43) for IGF-I, 0.81 (0.68-0.96) for IGFBP-1, and 1.25 (1.12-1.40) for IGFBP-3. These associations did not differ significantly by time-to-diagnosis or tumor stage or grade. After mutual adjustment for each of the other analytes, only IGF-I remained associated with risk. Our collaborative study represents the largest pooled analysis of the relationship between prostate cancer risk and circulating concentrations of IGF-I, providing strong evidence that IGF-I is highly likely to be involved in prostate cancer development. Cancer Res; 76(8); 2288-300. ©2016 AACR.

Abstract Background PREDICT Prostate is an endorsed prognostic model that provides individualised long-term prostate cancer-specific and overall survival estimates. The model, derived from UK data, estimates potential treatment benefit on overall survival. In this study, we externally validated the model in a large independent dataset and compared performance to existing models and within treatment groups. Methods Men with non-metastatic prostate cancer and prostate-specific antigen (PSA)

Bone metastasis is the lethal end‐stage of prostate cancer (PC), but the biology of bone metastases is poorly understood. The overall aim of this study was therefore to explore molecular variability in PC bone metastases of potential importance for therapy. Specifically, genome‐wide expression profiles of bone metastases from untreated patients (n = 12) and patients treated with androgen‐deprivation therapy (ADT, n = 60) were analyzed in relation to patient outcome and to morphological characteristics in metastases and paired primary tumors. Principal component analysis and unsupervised classification were used to identify sample clusters based on mRNA profiles. Clusters were characterized by gene set enrichment analysis and related to histological and clinical parameters using univariate and multivariate statistics. Selected proteins were analyzed by immunohistochemistry in metastases and matched primary tumors (n = 52) and in transurethral resected prostate (TUR‐P) tissue of a separate cohort (n = 59). Three molecular subtypes of bone metastases (MetA‐C) characterized by differences in gene expression pattern, morphology, and clinical behavior were identified. MetA (71% of the cases) showed increased expression of androgen receptor‐regulated genes, including prostate‐specific antigen (PSA), and glandular structures indicating a luminal cell phenotype. MetB (17%) showed expression profiles related to cell cycle activity and DNA damage, and a pronounced cellular atypia. MetC (12%) exhibited enriched stroma–epithelial cell interactions. MetB patients had the lowest serum PSA levels and the poorest prognosis after ADT. Combined analysis of PSA and Ki67 immunoreactivity (proliferation) in bone metastases, paired primary tumors, and TUR‐P samples was able to differentiate MetA‐like (high PSA, low Ki67) from MetB‐like (low PSA, high Ki67) tumors and demonstrate their different prognosis. In conclusion, bone metastases from PC patients are separated based on gene expression profiles into molecular subtypes with different morphology, biology, and clinical outcome. These findings deserve further exploration with the purpose of improving treatment of metastatic PC.

Abstract Background Disease trajectories for chronic diseases can span over several decades, with several time-dependent factors affecting treatment decisions. Thus, there is a need for long-term predictions of disease trajectories to inform patients and healthcare professionals on the long-term outcomes and provide information on the need of future health care. Here, we propose a state transition model to describe and predict disease trajectories up to 25 years after diagnosis in men with prostate cancer (PCa), as a proof of principle. Methods States, state transitions, and transition probabilities were identified and estimated in Prostate Cancer data Base of Sweden (PCBaSeTraject), using nationwide population-based data from 118,743 men diagnosed with PCa. A state transition model in discrete time steps (i.e., 4 weeks) was developed and applied to capture all possible transitions (PCBaSeSim). Transition probabilities were estimated for changes in both treatment and comorbidity. These models combined yielded parameter estimates to run an individual-level simulation based on the state-transition model to obtain prediction estimates. Predicted estimates were then compared to real world data in PCBaSeTraject. Results PCBaSeSim estimates for the cumulative incidence of first and second transitions, death from PCa and death from other causes were compared to observed transitions in PCBaSeTraject. A good agreement was found between simulated and observed estimates. Conclusions We developed a reliable and accurate simulation tool, PCBaSeSim that provides information on disease trajectories for subjects with a chronic disease on an individual and population-based level.

Background Mechanisms related to the influence of diet on the development of cardiovascular disease are not entirely understood, and protein biomarkers may help to understand these pathways. Studies of biomarkers identified with multiplex proteomic methods and dietary patterns are largely lacking. Methods and Results Dietary patterns were generated through principal component analysis in 2 population‐based Swedish cohorts, the EpiHealth (EpiHealth study; n=20 817 men and women) and the SMCC (Swedish Mammography Cohort Clinical [n=4650 women]). A set of 184 protein cardiovascular disease biomarkers were measured with 2 high‐throughput, multiplex immunoassays. Discovery and replication multivariable linear regression analyses were used to investigate the associations between the principal component analysis–generated dietary patterns and the cardiovascular disease–associated protein biomarkers, first in the EpiHealth (n=2240) and then in the Swedish Mammography Cohort Clinical. Four main dietary patterns were identified in the EpiHealth, and 3 patterns were identified in the Swedish Mammography Cohort Clinical. The healthy and the Western/traditional patterns were found in both cohorts. In the EpiHealth, 57 protein biomarkers were associated with 3 of the dietary patterns, and 41 of these associations were replicated in the Swedish Mammography Cohort Clinical, with effect estimates ranging from 0.057 to 0.083 (P‐value range, 5.0×10−2–1.4×10−9) for each SD increase in the relative protein concentration. Independent associations were established between dietary patterns and the 21 protein biomarkers. Two proteins, myeloperoxidase and resistin, were associated with both the healthy and the light meal pattern but in opposite directions. Conclusions We have discovered and replicated independent associations between dietary patterns and 21 biomarkers linked to cardiovascular disease, which have a role in the pathways related to inflammation, endothelial and immune function, cell adhesion, and metabolism.

To investigate the association between lower urinary-tract infections, their associated antibiotics and the subsequent risk of developing PCa.Using data from the Swedish PCBaSe 3.0, we performed a matched case-control study (8762 cases and 43806 controls). Conditional logistic regression analysis was used to assess the association between lower urinary-tract infections, related antibiotics and PCa, whilst adjusting for civil status, education, Charlson Comorbidity Index and time between lower urinary-tract infection and PCa diagnosis.It was found that lower urinary-tract infections did not affect PCa risk, however, having a lower urinary-tract infection or a first antibiotic prescription 6-12 months before PCa were both associated with an increased risk of PCa (OR: 1.50, 95% CI: 1.23-1.82 and 1.96, 1.71-2.25, respectively), as compared to men without lower urinary-tract infections. Compared to men with no prescriptions for antibiotics, men who were prescribed ≥10 antibiotics, were 15% less likely to develop PCa (OR: 0.85, 95% CI: 0.78-0.91).PCa was not found to be associated with diagnosis of a urinary-tract infection or frequency, but was positively associated with short time since diagnoses of lower urinary-tract infection or receiving prescriptions for antibiotics. These observations can likely be explained by detection bias, which highlights the importance of data on the diagnostic work-up when studying potential risk factors for PCa.

IntroductionSex hormones have been implicated in the etiology of a number of diseases. To better understand disease etiology and the mechanisms of disease-risk factor associations, this analysis aimed to investigate the associations of anthropometric, sociodemographic and behavioural factors with a range of circulating sex hormones and sex hormone-binding globulin.MethodsStatistical analyses of individual participant data from 12,330 male controls aged 25-85 years from 25 studies involved in the Endogenous Hormones Nutritional Biomarkers and Prostate Cancer Collaborative Group. Analysis of variance was used to estimate geometric means adjusted for study and relevant covariates.ResultsOlder age was associated with higher concentrations of sex hormone-binding globulin and dihydrotestosterone and lower concentrations of dehydroepiandrosterone sulfate, free testosterone, androstenedione, androstanediol glucuronide and free estradiol. Higher body mass index was associated with higher concentrations of free estradiol, androstanediol glucuronide, estradiol and estrone and lower concentrations of dihydrotestosterone, testosterone, sex hormone-binding globulin, free testosterone, androstenedione and dehydroepiandrosterone sulfate. Taller height was associated with lower concentrations of androstenedione, testosterone, free testosterone and sex hormone-binding globulin and higher concentrations of androstanediol glucuronide. Current smoking was associated with higher concentrations of androstenedione, sex hormone-binding globulin and testosterone. Alcohol consumption was associated with higher concentrations of dehydroepiandrosterone sulfate, androstenedione and androstanediol glucuronide. East Asians had lower concentrations of androstanediol glucuronide and African Americans had higher concentrations of estrogens. Education and marital status were modestly associated with a small number of hormones.ConclusionCirculating sex hormones in men are strongly associated with age and body mass index, and to a lesser extent with smoking status and alcohol consumption.