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

Background: The magnitude of risk conferred by the interaction between tobacco and alcohol use on the risk of head and neck cancers is not clear because studies have used various methods to quantify the excess head and neck cancer burden. Methods: We analyzed individual-level pooled data from 17 European and American case-control studies (11,221 cases and 16,168 controls) participating in the International Head and Neck Cancer Epidemiology consortium. We estimated the multiplicative interaction parameter (y) and population attributable risks (PAR). Results: A greater than multiplicative joint effect between ever tobacco and alcohol use was observed for head and neck cancer risk (y = 2.15; 95% confidence interval, 1.53-3.04). The PAR for tobacco or alcohol was 72% (95% confidence interval, 61-79%) for head and

Our results represent the most precise estimates available of the independent association of each of the two main risk factors of head and neck cancer, and they exemplify the strengths of large-scale consortia in cancer epidemiology.

The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH). Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention.

Genome wide association studies (GWAS) and large scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ~14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS comprising of 15,748 breast cancer cases and 18,084 controls, and 46,785 cases and 42,892 controls from 41 studies genotyped on a 200K custom array (iCOGS). Analyses were restricted to women of European ancestry. Genotypes for more than 11M SNPs were generated by imputation using the 1000 Genomes Project reference panel. We identified 15 novel loci associated with breast cancer at P<5×10−8. Combining association analysis with ChIP-Seq data in mammary cell lines and ChIA-PET chromatin interaction data in ENCODE, we identified likely target genes in two regions: SETBP1 on 18q12.3 and RNF115 and PDZK1 on 1q21.1. One association appears to be driven by an amino-acid substitution in EXO1.

SummaryWhile several lung cancer susceptibility loci have been identified, much of lung cancer heritability remains unexplained. Here, 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated GWAS analysis of lung cancer on 29,266 patients and 56,450 controls. We identified 18 susceptibility loci achieving genome wide significance, including 10 novel loci. The novel loci highlighted the striking heterogeneity in genetic susceptibility across lung cancer histological subtypes, with four loci associated with lung cancer overall and six with lung adenocarcinoma. Gene expression quantitative trait analysis (eQTL) in 1,425 normal lung tissues highlighted RNASET2, SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2, and the telomere-related genes, OFBC1 and RTEL1. Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer.

The Breast Cancer Association Consortium (BCAC) has been established to conduct combined case-control analyses with augmented statistical power to try to confirm putative genetic associations with breast cancer. We genotyped nine SNPs for which there was some prior evidence of an association with breast cancer: CASP8 D302H (rs1045485), IGFBP3 -202 C --> A (rs2854744), SOD2 V16A (rs1799725), TGFB1 L10P (rs1982073), ATM S49C (rs1800054), ADH1B 3' UTR A --> G (rs1042026), CDKN1A S31R (rs1801270), ICAM5 V301I (rs1056538) and NUMA1 A794G (rs3750913). We included data from 9-15 studies, comprising 11,391-18,290 cases and 14,753-22,670 controls. We found evidence of an association with breast cancer for CASP8 D302H (with odds ratios (OR) of 0.89 (95% confidence interval (c.i.): 0.85-0.94) and 0.74 (95% c.i.: 0.62-0.87) for heterozygotes and rare homozygotes, respectively, compared with common homozygotes; P(trend) = 1.1 x 10(-7)) and weaker evidence for TGFB1 L10P (OR = 1.07 (95% c.i.: 1.02-1.13) and 1.16 (95% c.i.: 1.08-1.25), respectively; P(trend) = 2.8 x 10(-5)). These results demonstrate that common breast cancer susceptibility alleles with small effects on risk can be identified, given sufficiently powerful studies.

Genetic variations in DNA repair genes are thought to modulate DNA repair capacity and are suggested to be related to cancer risk. However, epidemiologic findings have been inconsistent. The authors conducted meta-analyses of associations between genes in the base excision repair pathway and cancer risk, focusing on three key genes: 8-oxoguanine DNA glycosylase (OGG1), apurinic/apyrimidinic endonuclease (APE1/APEX1), and x-ray repair cross-complementing group 1 (XRCC1). They found increased lung cancer risk among subjects carrying the OGG1 Cys/Cys genotype (odds ratio (OR) = 1.24, 95% confidence interval (CI): 1.01, 1.53), using 3,253 cases and 3,371 controls from seven studies; this is consistent with experimental evidence that this isoform exhibits decreased activity. They found a protective effect of the XRCC1 194Trp allele for tobacco-related cancers (OR = 0.86, 95% CI: 0.77, 0.95), using 4,895 cases and 5,977 controls from 16 studies; this is compatible with evidence of lower mutagen sensitivity for this allele. The XRCC1 399Gln/399Gln genotype was associated with increased risk of tobacco-related cancers among light smokers (OR = 1.38, 95% CI: 0.99, 1.94) but decreased risk among heavy smokers (OR = 0.71, 95% CI: 0.51, 0.99), suggesting effect modification by tobacco smoking. There was no association between cancer risk and the APE1/APEX1 Asp148Glu and XRCC1 Arg280His polymorphisms. Recommendations for future studies include pooling of individual data to facilitate evaluation of multigenic effects and detailed analysis of effect modification by environmental exposure.