Journal of the National Cancer Institute Advance Access originally published online on October 9, 2007
JNCI Journal of the National Cancer Institute 2007 99(20):1499-1501; doi:10.1093/jnci/djm186
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Published by Oxford University Press 2007.
EDITORIALS |
The Evidence for Prostate Cancer Risk Loci at 8q24 Grows Stronger
Affiliation of authors: Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
Correspondence to: Sharon A. Savage, MD, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd, EPS-7018, Rockville, MD 20892 (e-mail: savagesh{at}mail.nih.gov).
Genetic association studies that implicate chromosome 8q24 polymorphisms as risk factors for cancer are appearing at an astounding rate. Chromosomal band 8q24 became a genomic region of interest when two separate studies (1,2) reported that markers within a 1-Mb region (127.9–128.0 Mb) at this locus are statistically significantly associated with an increased risk of prostate cancer. With astonishing speed, multiple studies replicated and/or found similar results in the same region (3–9) (Table 1). The designs of these studies included genotyping of single-nucleotide polymorphisms (SNPs) in the predefined 8q24 region (5–9) and genome-wide association studies (3,4). Some of the studies replicated published findings for the same specific SNPs, while others found associations in different linkage disequilibrium blocks, all within the same 8q24 region.
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In this issue of the Journal, Zheng et al. (10) show that SNPs at two unlinked 8q24 loci are associated with an increased risk of prostate cancer among 1545 prostate cancer case patients and 576 control subjects recruited from The Johns Hopkins Hospital and University. The authors selected 18 SNPs at 8q24 for analysis based on 1) previously reported associations between the SNPs and prostate cancer risk (1); 2) their proximity in location to the nearby MYC gene region; and/or 3) the strength of their associations with prostate cancer in the National Cancer Institute (NCI)–supported public database of the Cancer Genetic Markers of Susceptibility (CGEMS) study (http://cgems.cancer.gov), which had not yet published its results (4).
Zheng et al. (10) reported that rs1447295 and three other SNPs that are in linkage disequilibrium with rs1447295 were associated with an increased risk of prostate cancer (P values for the associations ranged from 1.12 x 10–6 to 2.8 x 10–4), confirming earlier reports (1,2). They also found that a second SNP, rs6983267, located 71.7 kb from rs1447295 and not in linkage disequilibrium with rs1447295, was also associated with an increased risk of prostate cancer (P = 6.12 x 10–4). The CGEMS prostate cancer analysis (4), which was published in May 2007, also documented that rs1447295 and rs6983267 are associated with an increased risk of prostate cancer.
The mechanism by which the main risk SNPs, rs1447295 and rs6983267, contribute to an increased risk of prostate cancer remains undefined. Linkage scans and genome-wide association studies, which are not based on candidate genes but broadly interrogate the genome in an impartial, "agnostic" manner, have identified a very unusual genomic region associated with risks of prostate and other cancers (5,11–14). This gene-poor region would be difficult to find with the candidate gene approach, although the "retrospectoscope" reveals that 8q24 is a common location for somatic gains in prostate cancer (15). These findings also serve to remind us that large regions of the genome that were previously referred to as "junk DNA" may contain novel regulatory elements or even genes that we have yet to identify and understand.
As demonstrated by Zheng et al. (10), data sharing in genetic association studies has become a major contributor to the speed at which genomics is progressing. The rapid posting of Human Genome Project data in the public domain established a new research and intellectual property paradigm. This strategy ensured timely public access to vast quantities of genetic data for analyses by independent investigators eager to test new hypotheses or confirm previous hypotheses. Numerous SNP databases have since been generated through sequencing (e.g., SNP500Cancer and Seattle SNPs) and genotyping (i.e., HapMap) projects, making ethnicity-specific data readily available for allele frequency and haplotype analyses. Readily accessible genotype datasets that are annotated with key clinical features from numerous studies are now critical for the continued advance of genetic association research. The Genetic Association Database (http://geneticassociationdb.nih.gov), which compiles data from published association studies, is increasingly useful in guiding study design, SNP selection, and genetic association study replication.
The precedent set by the Human Genome Project and the advent of genome-wide association studies bring the need for innovative data-sharing policies to the forefront of the genetic association field. The National Institutes of Health (NIH) recently implemented a genome-wide association study data-sharing policy (http://grants.nih.gov/grants/guide/notice-files/NOT-OD-07-088.html) for its grantees and intramural investigators that articulates the expectation that investigators will make such datasets publicly available in the central NIH repository of genome-wide association study data, the Database of Genotypes and Phenotypes (dbGaP; http://www.ncbi.nlm.nih.gov/entrez/query/Gap/gap_tmpl/about.html). Posting should occur as soon as the appropriate quality-control assessments are complete. The primary investigators will have a 12-month period of exclusive data access to permit them to submit genome-wide association study dataset analyses for publication.
The NCI has already implemented this strategy without the exclusivity period. The net effect has been to dramatically accelerate the pace of genomic research and discovery, as exemplified by Zheng et al. (10), who increased the strength of their findings by taking advantage of prepublication, readily available genotype data from the CGEMS prostate cancer case–control study. They have provided the first independent confirmation of rs6983267 as a modifier of prostate cancer risk. The use of a prepublished dataset allowed the replication of an important new genetic association with unprecedented speed. The Genetic Association Information Network, a public–private partnership that conducts genome-wide association studies of common diseases, has recently initiated novel approaches to data sharing and collaborative analyses (16). We hope that a policy of more liberal early access to datasets of this kind will soon become the accepted standard worldwide.
Studies of the genetic contribution to disease risk have entered the genome-wide scan era, with SNP panels now exceeding 1000000 markers. Similar technologies for copy number polymorphisms and whole-genome sequencing will soon follow. Analytic strategies that require large, collaborative, multicenter, and/or international biospecimen-based studies have become the new standard for obtaining sufficient samples for adequately powered initial studies, for replication studies, and, potentially, for clinical intervention. Replication of genome-wide association study findings—an essential component of current analytic strategies aimed at mitigating the false discovery problem inherent in large-scale SNP association studies—would be impossible without the last decade's long-term investment in these large, expensive case–control and cohort studies and their related consortia (17). The prostate cancer risk genetic association studies described herein exemplify the value and power of such consortia.
As the evidence supporting associations between 8q24 genetic variants and an increased risk prostate cancer accumulates, we will have a better understanding of the functional role of these SNPs and the population attributable risk that they confer. Current population attributable risk estimates related to rs1147295 and rs6982367 vary widely (8%–68%), depending on the study design and target population, making generalizations about the applicability of these estimates to public health difficult (3,4,6). The associations reported thus far between polymorphisms at 8q24 and an increased risk of prostate cancer are statistically strong but, with one exception (9), had small effect sizes (i.e., odds ratios were generally <1.5). Wang et al. (9) reported an odds ratio of prostate cancer of 1.93 for SNP rs1447295 for familial prostate cancer case patients compared with control subjects. Zheng et al. (10) reported that men who had both risk alleles were 2.7 times more likely to develop prostate cancer than men with neither risk allele. This suggests that, in the future, studies based upon a panel of SNPs, each associated with modest increases in prostate cancer risk, might identify a subset of men who are at very high risk. In such a scenario, the goal of patient-specific screening and/or intervention, informed by individual genotype data, may be achievable.
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