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For many cancers there are only a few well-established risk factors. Here, we use summary data from genome-wide association studies (GWAS) in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to identify potentially causal relationships for over 3,000 traits. Our outcome datasets comprise 378,142 cases across breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers, as well as 485,715 controls. We complement this analysis by systematically mining the literature space for supporting evidence. In addition to providing supporting evidence for well-established risk factors (smoking, alcohol, obesity, lack of physical activity), we also find sex steroid hormones, plasma lipids, and telomere length as determinants of cancer risk. A number of the molecular factors we identify may prove to be potential biomarkers. Our analysis, which highlights aetiological similarities and differences in common cancers, should aid public health prevention strategies to reduce cancer burden. We provide a R/Shiny app to visualise findings. Mendelian randomisation can identify potential risk factors from large populations. Here, the authors analyse 3000 traits across multiple cancer types to search for potential risk factors and molecular biomarkers.

Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, but a comprehensive description of its genomic landscape is lacking. We report the whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project, providing for a detailed description of the somatic mutational landscape of ccRCC. We identify candidate driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for therapeutic interventions. Genomic characterisation identified patients with divergent clinical outcome; higher number of structural copy number alterations associated with poorer prognosis, whereas VHL mutations were independently associated with a better prognosis. The observations that higher T-cell infiltration is associated with better overall survival and that genetically predicted immune evasion is not common supports the rationale for immunotherapy. These findings should inform personalised surveillance and treatment strategies for ccRCC patients. The genomic landscape of clear cell renal cell carcinoma (ccRCC) remains to be comprehensively characterised. Here, whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project was used to identify potential drivers and clinical correlations to inform the development of therapies.

Tumor genomic profiling is increasingly seen as a prerequisite to guide the treatment of patients with cancer. To explore the value of whole-genome sequencing (WGS) in broadening the scope of cancers potentially amenable to a precision therapy, we analysed whole-genome sequencing data on 10,478 patients spanning 35 cancer types recruited to the UK 100,000 Genomes Project. We identified 330 candidate driver genes, including 74 that are new to any cancer. We estimate that approximately 55% of patients studied harbor at least one clinically relevant mutation, predicting either sensitivity or resistance to certain treatments or clinical trial eligibility. By performing computational chemogenomic analysis of cancer mutations we identify additional targets for compounds that represent attractive candidates for future clinical trials. This study represents one of the most comprehensive efforts thus far to identify cancer driver genes in the real world setting and assess their impact on informing precision oncology. Analysis of whole-genome sequencing data from over 10,000 tumor samples spanning 35 cancer types identifies putative driver genes and highlights new therapeutic opportunities.

Colorectal carcinoma (CRC) is a common cause of mortality 1 , but a comprehensive description of its genomic landscape is lacking 2 – 9 . Here we perform whole-genome sequencing of 2,023 CRC samples from participants in the UK 100,000 Genomes Project, thereby providing a highly detailed somatic mutational landscape of this cancer. Integrated analyses identify more than 250 putative CRC driver genes, many not previously implicated in CRC or other cancers, including several recurrent changes outside the coding genome. We extend the molecular pathways involved in CRC development, define four new common subgroups of microsatellite-stable CRC based on genomic features and show that these groups have independent prognostic associations. We also characterize several rare molecular CRC subgroups, some with potential clinical relevance, including cancers with both microsatellite and chromosomal instability. We demonstrate a spectrum of mutational profiles across the colorectum, which reflect aetiological differences. These include the role of Escherichia coli pks+ colibactin in rectal cancers 10 and the importance of the SBS93 signature 11 – 13 , which suggests that diet or smoking is a risk factor. Immune-escape driver mutations 14 are near-ubiquitous in hypermutant tumours and occur in about half of microsatellite-stable CRCs, often in the form of HLA copy number changes. Many driver mutations are actionable, including those associated with rare subgroups (for example, BRCA1 and IDH1 ), highlighting the role of whole-genome sequencing in optimizing patient care. Whole-genome sequencing of more than 2,000 colorectal carcinoma samples provides a highly detailed view of the genomic landscape of this cancer and identifies new driver mutations.

Background Epidemiological studies of the relationship between gallstone disease and circulating levels of bilirubin with risk of developing colorectal cancer (CRC) have been inconsistent. To address possible confounding and reverse causation, we examine the relationship between these potential risk factors and CRC using Mendelian randomisation (MR). Methods We used two-sample MR to examine the relationship between genetic liability to gallstone disease and circulating levels of bilirubin with CRC in 26,397 patients and 41,481 controls. We calculated the odds ratio per genetically predicted SD unit increase in log bilirubin levels (OR SD ) for CRC and tested for a non-zero causal effect of gallstones on CRC. Sensitivity analysis was applied to identify violations of estimator assumptions. Results No association between either gallstone disease ( P value = 0.60) or circulating levels of bilirubin (OR SD  = 1.00, 95% confidence interval (CI) = 0.96–1.03, P value = 0.90) with CRC was shown. Conclusions Despite the large scale of this study, we found no evidence for a causal relationship between either circulating levels of bilirubin or gallstone disease with risk of developing CRC. While the magnitude of effect suggested by some observational studies can confidently be excluded, we cannot exclude the possibility of smaller effect sizes and non-linear relationships.

Given a statistical model that attempts to explain the data, calculating the Bayes' posterior distribution of the models parameters is desirable. The marginal likelihood of the model is also of interest, which is used for model comparison. However, for most applications, only estimates of these two measurements can be obtained with a class of methods that give consistent estimates being Monte Carlo algorithms. This thesis attempts to improve both the process in inferring a high-dimensional posterior distribution and the corresponding model marginal likelihood, on the condition that we can define an ordered set of statistical models in which deterministic transformations between each adjacent model can be applied. We propose an adaption of the sequential Monte Carlo algorithm, which we term the \"transformation Sequential Monte Carlo\" algorithm. The key feature of this algorithm is by defining a series of target distributions, that make use of said mentioned model transformations, we aim to infer high dimensional models by using easier to estimate posteriors from lower dimensional models with a model transformation applied. Our proposed algorithm has advantages over many established MC methods. One notable advantage is that we can tailor the algorithm if we wish to update a posterior distribution by including additional observations, but these observations also correspond to a new parameter set that needs to be inferred. Alternatively it is useful where the parameter space can become too large to explore using basic MC methods, for example if there exists an exponential or factorial relationship with observation size and the number of discrete values, but using a lower dimensional model and incorporating it into the model exploration assists with convergence. We test these strengths of tSMC under three applications, which include two population genetics applications being ancestral reconstruction under the coalescent and the other being the Structure algorithm.

This paper introduces methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. We show how this may be achieved through the use of sequential Monte Carlo (SMC) samplers (Del Moral et al., 2006, 2007), making use of the full flexibility of this framework in order that the method is computationally efficient. In particular, we introduce the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use in applications where the acceptance rate in reversible jump Markov chain Monte Carlo (RJMCMC) is low. We demonstrate this approach on the well-studied problem of model comparison for mixture models, and for the novel application of inferring coalescent trees sequentially, as data arrives.

To characterise the somatic alterations in colorectal cancer (CRC), we conducted whole-genome sequencing analysis of 2,023 tumours. We provide the most detailed high-resolution map to date of somatic mutations in CRC, and demonstrate associations with clinicopathological features, in particular location in the large bowel. We refined the mutational processes and signatures acting in colorectal tumorigenesis. In analyses across the sample set or restricted to molecular subtypes, we identified 185 CRC driver genes, of which 117 were previously unreported. New drivers acted in various molecular pathways, including Wnt (CTNND1, AXIN1, TCF3), TGF-beta/BMP (TGFBR1) and MAP kinase (RASGRF1, RASA1, RAF1, and several MAP2K and MAP3K loci). Non-coding drivers included intronic neo-splice site alterations in APC and SMAD4. Whilst there was evidence of an excess of mutations in functionally active regions of the non-coding genome, no specific drivers were called with high confidence. Novel recurrent copy number changes included deletions of PIK3R1 and PWRN1, as well as amplification of CCND3 and NEDD9. Putative driver structural variants included BRD4 and SOX9 regulatory elements, and ACVR2A and ANKRD11 hotspot deletions. The frequencies of many driver mutations, including somatic Wnt and Ras pathway variants, showed a gradient along the colorectum. The Pks-pathogenic E. coli signature and TP53 mutations were primarily associated with rectal cancer. A set of unreported immune escape driver genes was found, primarily in hypermutated CRCs, most of which showed evidence of genetic evasion of the anti-cancer immune response. About 25% of cancers had a potentially actionable mutation for a known therapy. Thirty-three of the new driver genes were predicted to be essential, 17 possessed a druggable structure, and nine had a bioactive compound available. Our findings provide further insight into the genetics and biology of CRC, especially tumour subtypes defined by genomic instability or clinicopathological features.Competing Interest StatementThe authors have declared no competing interest.

To determine the relationship between psychological stress with cognitive outcomes in a multi-centre longitudinal study of people with amnestic mild cognitive impairment (aMCI) we assessed three parameters of psychological stress (Recent Life Changes Questionnaire (RLCQ); the Perceived Stress Scale (PSS) and salivary cortisol) and their relationship with rates of cognitive decline over an 18 month follow up period and conversion to dementia over a 5.5 year period. In 133 aMCI and 68 cognitively intact participants the PSS score was higher in the aMCI compared with control group but neither the RLCQ scores nor salivary cortisol measures were different between groups. In the aMCI group the RLCQ and the PSS showed no significant association with cognitive function at baseline, cognitive decline or with conversion rates to dementia but high salivary cortisol levels were associated with RLCQ scores and poorer cognitive function at baseline and lower rates of cognitive decline. No relationship was found between salivary cortisol levels and conversion rate to dementia. We conclude that psychological stress as measured by the RLCQ or PSS was not associated with adverse cognitive outcomes in an aMCI population and hypothesise that this may reflect diminished cortisol production to psychological stress as the disease progresses.

The scientific literature contains evidence suggesting that women who have been treated for breast cancer may, as a result of their diagnosis, increase their phyto-oestrogen (PE) intake. In the present paper, we describe the creation of a dietary analysis database (based on Dietplan6) for the determination of dietary intakes of specific PE (daidzein, genistein, glycitein, formononetin, biochanin A, coumestrol, matairesinol and secoisolariciresinol), in a group of women previously diagnosed and treated for postmenopausal breast cancer. The design of the database, data evaluation criteria, literature data entry for 551 foods and primary analysis by LC–MS/MS of an additional thirty-four foods for which there were no published data are described. The dietary intake of 316 women previously treated for postmenopausal breast cancer informed the identification of potential food and beverage sources of PE and the bespoke dietary analysis database was created to, ultimately, quantify their PE intake. In order that PE exposure could be comprehensively described, fifty-four of the 316 subjects completed a 24 h urine collection, and their urinary excretion results allowed for the description of exposure to include those identified as ‘equol producers’.