The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis

Membranes with Proton-Conducting Polymer Components 2793 4. Nonfluorinated Acid Ionomer Membranes 2793 4.1. Poly(arylene ether)-Based Membranes 2793 4.1.1. Modification of SPEEK Membrane 2793 4.1.2. Poly(arylene ether)s with Cross-Linkable Groups 2794 4.1.3. Poly(arylene ether)s with Pendant Sulfonated Groups or Side-Chains 2799 4.1.4. Poly(arylene ether)s with Backbones Containing Heteroatoms Such as F, N, S, and P 2800 4.1.5. Multiblock Copoly(arylene ether)s Synthesized by the Coupling Reaction of Hydrophilic and Hydrophobic Macromonomers 2803 4.2. Polyimide-Based Membranes 2804 4.2.1. SPIs Based on NTDA 2804 4.2.2. SPIs based on BNTDA 2806 4.2.3. SPIs Based on Other Dianhydrides 2807 4.3. Hydrocarbon Polymer with Aliphatic Main-Chain-Based Membranes 2807 4.4. Glass Membranes 2809 5. Polybenzimidazole/H 3 PO 4 Membranes 2809 5.1. Modifications to mPBI 2810 5.1.1. Optimizations of Preparation and Operation of Acid-Doped mPBI System 2810 5.1.2. Modified mPBI/H 3

Recurrence was frequent and associated with the presence of residual mood symptoms at initial recovery. Targeting residual symptoms in maintenance treatment may represent an opportunity to reduce risk of recurrence.

BackgroundThe association between hepatitis B virus (HBV) mutations and hepatocarcinogenesis remains controversial because of conflicting data in the literature. We conducted a meta-analysis of case–control and cohort studies to examine HBV PreS, enhancer II (EnhII), basal core promoter (BCP), and precore mutations in relation to the risk of hepatocellular carcinoma (HCC).MethodsWe searched databases for studies of these associations that were published in English or Chinese up to August 31, 2008. HBV mutation–specific odds ratios and relative risks were pooled by use of a random-effects model and stratified by potential confounders. All statistical tests were two-sided.ResultsOf the 43 studies included in this meta-analysis, 40 used a case–control design. The 43 studies evaluated a total of 11 582 HBV-infected participants, of whom 2801 had HCC. Statistically significant summary odds ratios of HCC were obtained for any PreS mutation (3.77, 95% confidence interval [CI] = 2.57 to 5.52), C1653T in EnhII (2.76, 95% CI = 2.09 to 3.64), T1753V (2.35, 95% CI = 1.63 to 3.40), and A1762T/G1764A in BCP (3.79, 95% CI = 2.71 to 5.29). PreS mutations were more strongly associated with an increased risk of HCC in subjects who were infected with HBV genotype C than in those who were infected with HBV genotype B, whereas the opposite was true for A1762T/G1764A. C1653T, T1753V, and A1762T/G1764A were more strongly associated with an increased risk of HCC in hepatitis B e antigen (HBeAg)–positive subjects than in HBeAg-negative subjects. PreS mutations, C1653T, T1753V, and A1762T/G1764A accumulated during the progression of chronic HBV infection from the asymptomatic carrier state to HCC (Ptrend < .001 for each mutation). PreS mutations, C1653T, C1653T + T1753V, and A1762T/G1764A-based combinations of mutations had specificities greater than 80% for the prediction of HCC. The precore mutations G1896A and C1858T were not associated with the risk of HCC, regardless of HBeAg status and HBV genotype.ConclusionsHBV PreS mutations, C1653T, T1753V, and A1762T/G1764A are associated with an increased risk of HCC. These mutations alone and in combination may be predictive for hepatocarcinogenesis.

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 ...