Prostate cancer is the most frequently diagnosed cancer in males in developed countries. To identify common prostate cancer susceptibility alleles, we genotyped 211,155 SNPs on a custom Illumina array (iCOGS) in blood DNA from 25,074 prostate cancer cases and 24,272 controls from the international PRACTICAL Consortium. Twenty-three new prostate cancer susceptibility loci were identified at genome-wide significance (P < 5 × 10−8). More than 70 prostate cancer susceptibility loci, explaining ~30% of the familial risk for this disease, have now been identified. On the basis of combined risks conferred by the new and previously known risk loci, the top 1% of the risk distribution has a 4.7-fold higher risk than the average of the population being profiled. These results will facilitate population risk stratification for clinical studies.

Prostate cancer (PrCa) is the most frequently diagnosed male cancer in developed countries. To identify common PrCa susceptibility alleles, we have previously conducted a genome-wide association study in which 541, 129 SNPs were genotyped in 1,854 PrCa cases with clinically detected disease and 1,894 controls. We have now evaluated promising associations in a second stage, in which we genotyped 43,671 SNPs in 3,650 PrCa cases and 3,940 controls, and a third stage, involving an additional 16,229 cases and 14,821 controls from 21 studies. In addition to previously identified loci, we identified a further seven new prostate cancer susceptibility loci on chromosomes 2, 4, 8, 11, and 22 (P=1.6×10 −8 to P=2.7×10 −33 ).Genome-wide association studies (GWAS) provide a powerful approach to identify common disease alleles. We previously conducted a GWAS 1 , based on genotyping of 541, 129 SNPs in 1,854 clinically detected PrCa cases and 1,894 controls (see Figure 1, stage 1). Follow-up genotyping of SNPs exhibiting strong evidence of association (P<10 −6 ), in a further 3,268 cases and 3,366 controls, allowed us to identify SNPs at 7 susceptibility loci associated with the disease at genome-wide levels of significance 1 . Other studies have identified an additional 8 loci [2][3][4][5][6][7][8][9] . These loci, however, explain only a small fraction of the familial risk of PrCa. Moreover, the strength of the associations that have been detected are generally small (perallele odds ratios, OR, 1.1-1.2), and the power of the existing studies to detect many of the susceptibility alleles has been limited. It is highly likely, therefore, that other PrCa predisposition loci exist, and that such loci should be detectable by studies with larger sample sizes.In an attempt to identify further susceptibility loci, we conducted a more extensive follow-up of SNPs showing evidence of association in stage 1 of our GWAS. We designed a panel of 47,120 SNPs, aiming to include all SNPs with a significant association in stage 1 at P-trend (1df)<.05 or P(2df)<.01 (see Online Methods). These SNPs were genotyped using the Illumina iSELECT platform in 3,894 PrCa cases and 4,055 controls from the United Kingdom (UK) and Australia ( Figure 1, stage 2). After quality control (QC) exclusions (as described in Online Methods), we utilised data from 43,671 SNPs in 3,650 PrCa cases and 3,940 controls. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptGenotype frequencies in cases and controls were compared using a 1 degree of freedom (df) Cochran-Armitage trend test (for QQ plots see Supplementary Figure 1). There was little evidence of inflation in the test statistics in the UK samples (estimated inflation factor λ=1.08), but there was more marked inflation in those from Australia (λ=1.23; λ=1.19 for stage 2 overall), suggestive of some population substructure. The Australian samples were selected from three studies (MCCS, RFPCS and EOPCS; see Supplementary Note for cohort descriptions), and further analysis revealed that ...

Purpose Clinicopathologic data from a population-based endometrial cancer cohort, unselected for age or family history, were analyzed to determine the optimal scheme for identification of patients with germline mismatch repair (MMR) gene mutations. Patients and Methods Endometrial cancers from 702 patients recruited into the Australian National Endometrial Cancer Study (ANECS) were tested for MMR protein expression using immunohistochemistry (IHC) and for MLH1 gene promoter methylation in MLH1-deficient cases. MMR mutation testing was performed on germline DNA of patients with MMR-protein deficient tumors. Prediction of germline mutation status was compared for combinations of tumor characteristics, age at diagnosis, and various clinical criteria (Amsterdam, Bethesda, Society of Gynecologic Oncology, ANECS). Results Tumor MMR-protein deficiency was detected in 170 (24%) of 702 cases. Germline testing of 158 MMR-deficient cases identified 22 truncating mutations (3% of all cases) and four unclassified variants. Tumor MLH1 methylation was detected in 99 (89%) of 111 cases demonstrating MLH1/PMS2 IHC loss; all were germline MLH1 mutation negative. A combination of MMR IHC plus MLH1 methylation testing in women younger than 60 years of age at diagnosis provided the highest positive predictive value for the identification of mutation carriers at 46% versus ≤ 41% for any other criteria considered. Conclusion Population-level identification of patients with MMR mutation-positive endometrial cancer is optimized by stepwise testing for tumor MMR IHC loss in patients younger than 60 years, tumor MLH1 methylation in individuals with MLH1 IHC loss, and germline mutations in patients exhibiting loss of MSH6, MSH2, or PMS2 or loss of MLH1/PMS2 with absence of MLH1 methylation.