Prostate Cancer (PC) has become a major health problem in industrialized world during the last decades of the 20th century. It is now the most commonly diagnosed solid tumor among men in developed countries [1,2]. This cancer has a complex, multi-factorial etiology with an estimated 45% of disease variation being attributed to genetic factors and 50% to environmental/lifestyle factors [3]. One of the strongest risk factors for this disease is family history of PC; having a first-degree relative diagnosed with PC is associated with a two- to three-fold elevation in the relative risk, and both early age at diagnosis and multiple affected family members are important predictors of risk in relatives [4,5]. Taken together, these results suggest an important inherited component to disease risk. Nevertheless, interpreting the genetic basis for PC has been challenging, particularly since unique high-risk genetic mutations have not been identified. Single nucleotide polymorphism (SNP) is the simplest form of DNA variation among individuals. These simple changes can be of transition or transversion type and they occur throughout the genome at a frequency of about one in 1,000 bp. They may be responsible for the diversity among individuals, genome evolution, the most common familial traits such as curly hair, inter-individual differences in drug response, and complex and common diseases such as cancer, cardiovascular, Parkinson’s , diabetes, obesity, hypertension, and psychiatric disorders. SNPs may change the encoded amino acids (non-synonymous) or can be silent (synonymous) or simply occur in the noncoding regions. They may influence promoter activity, messenger RNA (mRNA) conformation, and subcellular localization of mRNAs and proteins and hence may produce disease. Therefore, identification of numerous variations in genes and analysis of their effects may lead to a better understanding of their impact on gene function and health of an individual. The most promising results have emerged from genome-wide association studies (GWAS) of PC, which have identified numerous highly replicated and independent SNPs distributed throughout the human genome [6-14]. These SNPs individually confer modest risks of PC with Odds ratio (ORs of 1.05-1.30) and only a subset has been associated with aggressive metastatic PC [15]. In addition, some risk alleles affect serum prostate specific antigen (PSA) levels which may impact PC screening.

The set of currently characterized SNPs identified through large GWAS, however, do not explain the majority of the familial/hereditary risk for PC [16-19].

There has been an increasing focus on the role of single nucleotide polymorphisms in the development and progression of PC but also on their role in diagnostics and risk prediction. A SNP is a DNA sequence variation occurring when a single nucleotide (A, T, C, or G) in the genome differs from the normally expected nucleotide. These SNPs are known to underlie differences in our susceptibility to diseases. SNPs need to be determined only once and are easy to determine, making them interesting biomarkers. The rising interest in the role of SNPs in PC development and progression is illustrated by the number of studies being published on SNPs in the PC field. Further, research has focused on identifying the genetic foundations of prostate cancer. It has been recognized that a number of forms of genetic changes coupled with epigenetic and gene expression changes can increase the prediction to develop prostate cancer. Identifying relevant genetic changes offers the ability to develop novel biomarkers to allow early and accurate detection of prostate cancer as well as provide risk stratification of patients following their diagnosis. In connection to the diagnosis, the concept of personalized or individualized medicine has gained significant attention. Over the past twenty years, the scientific community has come to believe that carcinogenesis is the result of genetic and/or epigenetic changes to protein-coding oncogenes and tumor suppressor genes. In the case of solid-organ malignancies such as prostate cancer, these result typically from somatic genetic events. However, in addition to these somatic genetic changes, it has also become clear that many cancers, including prostate, exhibit loss of function of tumor suppressor genes due to epigenetic changes in expression. Epigenetic mechanisms include biochemical modification of histones supporting DNA, modification of the DNA itself and expression of non-coding RNAs, including miRNAs. Despite the high prevalence of prostate cancer, little is known about its cause. Many genes have been implicated in the development of both sporadic and particularly hereditary prostate. Unfortunately, attempts at identifying a reliable biomarker have thus far proved unsuccessful due in large part to the highly variable disease, multiple implicated epidemiological factors and advanced patient age at diagnosis [20,21]. Therefore, a better understanding of the genetic and molecular characteristics distinguishing indolent from lethal prostate cancers is necessary to understand the genetic polymorphisms and metabolic pathways underlying prostate cancer development offers the opportunity to explore new therapeutic interventions with the possibility of offering patient-specific targeted therapy. Therefore, it is interesting to determine the role of these SNPs in the clinical field.

Molecular genetics of prostate cancer

Evidence of a genetic contribution to the susceptibility to PC has been established through familial aggression, segregation and twin studies. Besides age and race, family history is the only well-established risk factor for PC [22-24]. Over the years, genetic epidemiological research has accrued much evidence in favor of a significant hereditary element for PC susceptibility. Familial aggregation of prostate cancer i.e., the occurrence of more than one PC case among first-degree relatives, has been recognized as early as the 1950s. Since then, epidemiological studies have shown that first-degree relatives of PC patients have a 2- to 3-fold increased risk of developing PC [25]. The highest risks of PC have been observed in men having multiple affected relatives, or relatives diagnosed at an early age. This familial risk of prostate cancer has been observed in multiple ethnic populations (Americans, Australians, Europeans, Asian-Americans, Caucasians, Asian Pacific and African-Americans decedents) [26]. The increasing rate of PC has been compared between monozygotic and dizygotic twins, making it possible to assess genetic and environmental factors. Such studies from Nordic countries and the USA consistently show a high increasing rate in monozygotic twins. From a meta-analysis of three twin registries in Sweden, Denmark, and Finland, the estimated heritability for PC was found to be 42%, which was the highest of all studied cancers. Family studies and twin studies may strongly suggest a genetic component in PC, but cannot be used to conclude the specific genetic mode of transmission. Several segregation studies have been performed to reveal the mode of inheritance of PC within families. Three studies from the USA and one Swedish study have showed a Mendelian autosomal dominant inheritance for familial aggregation of PC. While segregation analyses of Australian PC families observed that a dominantly inherited risk contributed to early-onset PC specifically, late-onset PC was mainly due to a recessive inheritance pattern. A more recent study by Gong et al. found that a multi-factorial model explained the segregation of PC better than did the previous research regarding Mendelian models. Since 1996, genome wide scans have yielded several loci that were believed to harbor PC susceptibility genes [27]. Smith et al. were the first to report a PC susceptibility locus linked to 1q24-25, designated HPC1 (hereditary prostate cancer 1), in a population of 91 high-risk PC families from the US and Sweden [28].

The mapping of HPC loci at 1q42.2-43 (PCP), Xq27-28 (HPCX), 1p36 (CAPB), 20q13 (HPC20), 8p22-23 and 17p11 followed and three genes have been successfully cloned within the linked loci (RNASEL at 1q24-25, MSR1 at 8p22-23, and HPC2/ELAC2 at 17p11). However, these genes are thought to only explain a small proportion of the occurrence of HPC. The failure to identify highly-penetrant loci has led to the hypothesis that susceptibility to common cancers is polygenic, that is due to a large number of variants each conferring a small increase in risk. In an attempt to explain the hereditary basis of PC, focused candidate gene studies have varied in their success. Some have been successful in detecting multiple associations with PC, while most of have been poorly replicated [29]. Association studies, which compare frequencies of genetic polymorphisms between cases and controls, offer a powerful approach to identify low-penetrant variants. Early association studies were based on studying limited numbers of polymorphisms in candidate genes that were suspected to be important in carcinogenesis. Technological advancements have allowed researchers to move pass hunts for candidate genes and now allow hundreds of thousands of single nucleotide polymorphism to be genotyped simultaneously. Such genome-wide association studies (GWAS) have identified over 30 germ line loci for PC [30]. The most notable PC region identified is 8q24. The most striking finding is that at least five distinct loci within chromosome 8q24 harbor germ line variants associated with PC. However, GWAS have revealed an association between particular single nucleotide polymorphisms and prostate cancer risk (2p15, 3p12, 6q25, 7p15, 7p21, 8q24, 9q33, 10q11, 10q26, 11q13, 17q12, 17q24.3, 19q13, and Xp11). In the early stages of cellular transformation, gene expression changes in MYC (v-myc myelocytomatosis viral oncogenes), MET (met protooncogene), TERT (telomerase reverse transcriptase), AMACR (alphamethyl acyl-CoA racemase) and cell cycle regulators such as CDKN1B (cyclin-dependent kinase inhibitor 1B, p27Kip1) are among the most common. The most prominent epigenetic modification observed in disease initiation is hyper methylation of the GSTP1 promoter, rendering this gene silent. In patients with clinically diagnosed prostate cancer, TP53 (tumor protein p53) is the most commonly mutated gene, along with a host of other less prevalent genetic alterations including BCL2 (B-cell CLL/lymphoma 2), PTEN, RB1, CDKN2A (cyclindependent kinase inhibitor 2A, p16), and TGFB1. In stages of more advanced and hormone-refractory disease, mutated and amplified AR is the most consistent change observed both at the genetic as well as at the protein level. Fusion of the TMPRSS2 gene with members of the ETS family (ETS, ERG, ETV1, and ETV4) is also commonly seen in prostate cancer [31-33].

Prostate cancer is currently considered to be a complex multifactorial disease with the vast majority of familial clustering attributed to the interaction of multiple shared moderate to low penetrance susceptibility genes as well as shared environmental factors. However, even with these findings, there is no clear evidence that variants associated with PC risk are also associated with cancer aggressiveness or mortality.

Role of single nucleotide polymorphism

Single nucleotide Polymorphisms are minute variations in the DNA sequence that are passed on from parents to children. They are the most common type of genetic variation in humans. Formally, an allele, that is, a variation in DNA sequence, is defined to be polymorphic if it occurs in at least one percent of a population [34]. Therefore, although overall humans are very similar at the DNA sequence level, because the genome is large there is substantial latitude for individual genetic variation. SNPs occur about once in every 800 base pairs [35]. The Human Genome Project and advances in related technologies have supported the investigation of the relationship between genetic variation and many health outcomes, including prostate cancer.

Since 2001, about 1,000 publications have reported associations between prostate cancer and SNPs and other genetic variants. The vast majority of the studies have related to candidate genes, in which the genes and variants, usually SNPs, have been specifically selected for investigation based on biological and physiological information regarding the involvement of gene products in early developmental pathways, biochemical and cellular process of progression, and/ or clinical manifestations. For prostate cancer, the most intensively investigated associations have related to genes in the following pathways: adhesion molecules (CDH1); androgen metabolism (AR, ESR2, SRDA2,); angiogenesis (VEGF) angiotensin conversion (ACE); baseexcision repair (XRCC1); inflammation and immune response (IL8, IL10, MSR1, PTGS2, TNF); inhibition of cell growth (FGFR4, TGFB1, TGFBR1); insulin-like growth factor metabolism (IGF1, IGFBP3); one carbon metabolism (MTHFR, diverse genes139); oxidative response (MnSOD, hOGG1); substrate metabolism (CYP1A1, CYP3A4, CYP17, GSTM1, GSTT1, GSTP1, NAT1 and NAT2, UGT2B17); vitamin D metabolism (VDR); and, common variants of genes for which rare mutations are associated with increased cancer risk (ELAC/HPC2, RNASEL, TP53, MDM2,) [36-50]. In general, the results of candidate gene studies have been inconclusive, for reasons discussed in many interpretations [51,52].

There has been a cumulative focus on the role of single nucleotide polymorphisms in the development and progression of PC but also on their role in diagnostics and risk prediction. A SNP is a DNA sequence variation occurring when a single nucleotide (A, T, C, or G) in the genome differs from the normally expected nucleotide. These SNPs are known to underlie differences in our susceptibility to diseases. SNPs need to be determined only once and are easy to determine, making them interesting biomarkers. The rising interest in the role of SNPs in PC development and progression is exemplified by the number of studies being reported on SNPs in the PC field. In 2008, an extensive genome-wide association study compared SNPs between PC cases and controls. Since then, numerous GWAS studies have been conducted which elucidates approximately 40% of the familial risk. With ongoing GWAS, we could anticipate that more genetic variants will be found, explaining more of the PC familial risk. However, the question has been raised whether finding more PC risk associated SNPs will have added value over the currently known ones [53]. Advances in highthroughput genotyping, together with the completion of the HapMap and Human Genome Projects, have enabled the performance of GWAS and, even more recently, detailed whole-exome and whole genome sequencing studies. These genetic studies have generally been designed as case control studies involving relatively large cohorts of patients that evaluate associations between the frequencies of SNPs and disease status. The SNPs identified in GWAS are believed to be surrogates for the true causative locus within a linkage imbalance block that is biologically responsible for the association GWAS of PC have rapidly progressed after the identification of the 8q24 locus [54-56]. In fact, there are now more than 80 altered SNPs associated with increased PC risk identified by GWAS on 20 different chromosomes (Table 1). In general, the relative increased risk of developing PC based on any individual SNP is small, ranging from 1.02 to 1.5 fold [57-63]. However, this risk appears to be cumulative and increases with the number of risk alleles that an individual carries. Together with family history, it has been assessed that SNPs can explain approximately 30% of the familial risk of PC in men of various ethnic populations around the globe [58]. Several genome-wide association studies have identified close to 100 PC susceptibility variants, primarily in populations of European ancestry, to a lesser extent in African American men and recently, in African men. Even though many of these associations exceeded or were close to genome-wide significance levels, it remains necessary to confirm these findings in multiple independent populations to rule out false positive results, thereby improving the likelihood that they represent true associations. However, the SNPs detected through GWAS studies are mostly limited to “index SNPs,” excluding other SNPs which are in linkage disequilibrium. Clearly, these index SNPs are not necessarily the SNPs causative for its associated phenotype [62,63]. Consequently, molecular analyses will be essential to recognize the exact SNP within each linkage dominion which is the relevant SNP. SNPs that lie within an open reading frame can lead to changes in messenger RNA stability or translation efficiency, as well as changes in structure of the encoded proteins. However, most SNPs are located outside of the genes and are suspected to affect gene expression levels and genome organization. Therefore, with advances in genotyping and sequencing technologies at a fraction of the cost, continued discovery of novel SNPs associated with disease initiation, progression, it is interesting to determine the role of these SNPs in the clinical field and response to treatment is anticipated in the future. Translation of these discoveries into routine clinical diagnostic tests and screening tools that provide personalized medicine should be possible. Ultimately prospective, large clinical trials involving multi-institutional collaborations will be required to validate the usefulness of these SNPs and genetic markers in the management of PC. In the meantime, continued genetic studies that identify novel genetic variations in all forms of PC are critical to advancing our knowledge of the genetic basis of the disease.

Table 1: List of Single nucleotide polymorphisms associated with significantly increased risk of prostate cancer.

The purpose of this review was to establish the evidence base behind using single nucleotide polymorphism in prostate cancer risk assessment, which includes risk stratification, screening for undiagnosed disease, and assessing prognosis. The high incidence of prostate cancer, the problems associated with current test methods particularly prostate-specific antigen screening in asymptomatic men, the difficulty of determining prognosis in many affected men, and the lack of clarity on the utility of different therapeutic approaches, mean that other avenues need to be explored with some energy. Even fairly modest improvements in risk classification could translate into large health gains in absolute terms. It is of crucial conceptual importance to recognize that this review is based on a framework of risk prediction, as distinct from pivotal inference. In the situation of risk prediction, it is relevant to compare models that include standard risk factors with models that include the same risk factors together with single nucleotide polymorphisms. This contrasts with the situation of pivotal inference in which the SNP status of an individual is assigned at birth. In a clinically-oriented, test evaluation approach such concerns are secondary to assessing performance as a predictor of a particular outcome.

Furthermore, ever since the definition of the human genome, the basis for genetic variations that can lead to individual risks for diseases has become more and more clear. Genome-wide association studies have defined groups of SNPs which partially predict increased risk for PC. As suggested in this review, SNPs have a great potential in predicting patients’ risk for PC and therapy response, which could have an important impact in every day clinic. Although many authors have suggested that genetic information can improve risk prediction and therefore be useful in clinical practice, there are several studies showing contradicting results, limiting their current clinical use. These contradicting results could be explained by multiple reasons. First, studies performed on small, heterogeneous populations might result in high rates of false positive and negative data. Secondly, most conducted studies are based on SNPs which have been correlated with PC in GWAS studies. Since PC phenotypes are probably determined by a spectrum of genetic variation, with possible interdependencies of SNPs, GWAS studies are probably not sufficient to develop a full understanding of these variations in PC. Throughout this review, it has become clear that some challenges still remain for translational research on the role of SNPs in PC. Firstly, clinical studies on SNPs should be performed in well-powered studies, which could give more conclusive results. Secondly, the important challenge for further basic research is to identify the causative SNPs within each linkage equilibrium. Hopefully, these SNPs will not only function as predictors but also give evidences to important pathways in PC development, which could be therapeutic targets. It will only be after the enrichment of GWAS data by detailed SNP mapping and functional SNP testing that the most relevant SNPs can be analyzed in clinical research. Taken together, therefore, benefits from improvements in prostate cancer risk prediction, screening, and prognostic stratification will depend to a large extent on clearer evidence that surveillance, diagnostic, and treatment strategies in themselves lead to reductions in morbidity and mortality. In the future, we expect them to become critical to interpret individualized PC risk, inter-individual biomarker variation, and therapeutic response.