Addressing the contribution of previously described genetic and epidemiological risk factors associated with increased prostate cancer risk and aggressive disease within men from South Africa

Abstract

Background: Although African ancestry represents a significant risk factor for prostate cancer, few studies have investigated the significance of prostate cancer and relevance of previously defined genetic and epidemiological prostate cancer risk factors within Africa. We recently established the Southern African Prostate Cancer Study (SAPCS), a resource for epidemiological and genetic analysis of prostate cancer risk and outcomes in Black men from South Africa. Biased towards highly aggressive prostate cancer disease, this is the first reported data analysis.

Methods: The SAPCS is an ongoing population-based study of Black men with or without prostate cancer. Pilot analysis was performed for the first 837 participants, 522 cases and 315 controls. We investigate 46 pre-defined prostate cancer risk alleles and up to 24 epidemiological measures including demographic, lifestyle and environmental factors, for power to predict disease status and to drive on-going SAPCS recruitment, sampling procedures and research direction.

Results: Preliminary results suggest that no previously defined risk alleles significantly predict prostate cancer occurrence within the SAPCS. Furthermore, genetic risk profiles did not enhance the predictive power of prostate specific antigen (PSA) testing. Our study supports several lifestyle/environmental factors contributing to prostate cancer risk including a family history of cancer, diabetes, current sexual activity and erectile dysfunction, balding pattern, frequent aspirin usage and high PSA levels.

Conclusions: Despite a clear increased prostate cancer risk associated with an African ancestry, experimental data is lacking within Africa. This pilot study is therefore a significant contribution to the field. While genetic risk factors (largely European-defined) show no evidence for disease prediction in the SAPCS, several epidemiological factors were associated with prostate cancer status. We call for improved study power by building on the SAPCS resource, further validation of associated factors in independent African-based resources, and genome-wide approaches to define African-specific risk alleles.

Figure 1

Current content of prostate cancer risk alleles. (A) Chromosomal distribution and discovery population of published prostate cancer risk alleles achieving genome-wide significance (P-value < 10^-6). Each dot represents one single nucleotide polymorphism (SNP), except where a numerical value indicates the number of SNPs that dot represents (applicable to regions 8q24 and 17q12). Dots are color coded to represent the discovery population of each SNP. (B) Classification of each SNP represented in Figure (A), relative to known, characterized genes.

Figure 2

Stages 1 to 3 of SAPCS data analysis. Depicting study size, study inclusion (Bantu population groups and participating urological clinics), data analysis and study power at first three stages of SAPCS collection. *Study Power represents power to detect an OR = 1.4, given a probability of exposure in controls = 0.1 in single hypothesis testing.

Figure 3

Receiver operating characteristic (ROC) curves for genetic risk model 2 compared to PSA levels alone and combined with genetic risk model. Genetic risk model 2 for 38 SNPs results in an AUC of 0.671. PSA levels more accurately predict prostate cancer occurrence in the SAPCS study population with an AUC of 0.919 (difference between areas = 0.248, 95% CI 0.176-0.319, P < 0.0001). The predictive power of PSA was not improved when combined with genetic risk model 2 for 38 SNPs (AUC = 0.890, difference between areas = 0.0292, 95% CI -0.003-0.062, P = 0.0784).

Figure 4

Multiple logistic regression analysis for association between multiple variables and prostate cancer risk. For each variable, odds ratios (ORs) are represented by dots and Confidence intervals (CI) are represented by horizontal lines extending from dots. Dots are color coded according to variable groupings indicated on left hand side of Y-axis. Reference groups for variables with multiple outcomes are represented by a single triangle (no CI) positioned along the Y-axis at X = 1. The X-axis limit was set at a value of 6.0. CI exceeding this limit are indicated by arrows. PSA levels are not represented on this plot due to values exceeding this limit. *indicates variable is associated with over-all prostate cancer risk at a significance level <0.05.