Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls.

BACKGROUND: Although the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated. METHODS: The genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways. RESULTS: Of the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p ≤ 1.67 × 10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p ≤ 1.28 × 10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified. CONCLUSIONS: Sixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby. PLAIN LANGUAGE SUMMARY: The epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer.

Occupational exposure to nickel and hexavalent chromium and the risk of lung cancer in a pooled analysis of case-control studies (SYNERGY).

There is limited evidence regarding the exposure-effect relationship between lung-cancer risk and hexavalent chromium (Cr(VI)) or nickel. We estimated lung-cancer risks in relation to quantitative indices of occupational exposure to Cr(VI) and nickel and their interaction with smoking habits. We pooled 14 case-control studies from Europe and Canada, including 16 901 lung-cancer cases and 20 965 control subjects. A measurement-based job-exposure-matrix estimated job-year-region specific exposure levels to Cr(VI) and nickel, which were linked to the subjects' occupational histories. Odds ratios (OR) and associated 95% confidence intervals (CI) were calculated by unconditional logistic regression, adjusting for study, age group, smoking habits and exposure to other occupational lung carcinogens. Due to their high correlation, we refrained from mutually adjusting for Cr(VI) and nickel independently. In men, ORs for the highest quartile of cumulative exposure to CR(VI) were 1.32 (95% CI 1.19-1.47) and 1.29 (95% CI 1.15-1.45) in relation to nickel. Analogous results among women were: 1.04 (95% CI 0.48-2.24) and 1.29 (95% CI 0.60-2.86), respectively. In men, excess lung-cancer risks due to occupational Cr(VI) and nickel exposure were also observed in each stratum of never, former and current smokers. Joint effects of Cr(VI) and nickel with smoking were in general greater than additive, but not different from multiplicative. In summary, relatively low cumulative levels of occupational exposure to Cr(VI) and nickel were associated with increased ORs for lung cancer, particularly in men. However, we cannot rule out a combined classical measurement and Berkson-type of error structure, which may cause differential bias of risk estimates.

Natural history of monoclonal B-cell lymphocytosis among relatives in CLL families.

Chronic lymphocytic lymphoma (CLL) has one of the highest familial risks among cancers. Monoclonal B-cell lymphocytosis (MBL), the precursor to CLL, has a higher prevalence (13%-18%) in families with 2 or more members with CLL compared with the general population (5%-12%). Although, the rate of progression to CLL for high-count MBLs (clonal B-cell count ≥500/µL) is ∼1% to 5%/y, no low-count MBLs have been reported to progress to date. We report the incidence and natural history of MBL in relatives from CLL families. In 310 CLL families, we screened 1045 relatives for MBL using highly sensitive flow cytometry and prospectively followed 449 of them. MBL incidence was directly age- and sex-adjusted to the 2010 US population. CLL cumulative incidence was estimated using Kaplan-Meier survival curves. At baseline, the prevalence of MBL was 22% (235/1045 relatives). After a median follow-up of 8.1 years among 449 relatives, 12 individuals progressed to CLL with a 5-year cumulative incidence of 1.8%. When considering just the 139 relatives with low-count MBL, the 5-year cumulative incidence increased to 5.7%. Finally, 264 had no MBL at baseline, of whom 60 individuals subsequently developed MBL (2 high-count and 58 low-count MBLs) with an age- and sex-adjusted incidence of 3.5% after a median of 6 years of follow-up. In a screening cohort of relatives from CLL families, we reported progression from normal-count to low-count MBL to high-count MBL to CLL, demonstrating that low-count MBL precedes progression to CLL. We estimated a 1.1% annual rate of progression from low-count MBL, which is in excess of that in the general population.

Uncovering circadian rhythms in metabolic longitudinal data: A Bayesian latent class modeling approach.

Researchers in biology and medicine have increasingly focused on characterizing circadian rhythms and their potential impact on disease. Understanding circadian variation in metabolomics, the study of chemical processes involving metabolites may provide insight into important aspects of biological mechanism. Of scientific importance is developing a statistical rigorous approach for characterizing different types of 24-hour patterns among high dimensional longitudinal metabolites. We develop a latent class approach to incorporate variation in 24-hour patterns across metabolites where profiles are modeled with finite mixtures of distinct shape-invariant circadian curves that themselves incorporate variation in amplitude and phase across metabolites. An efficient Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. When the model was fit separately by individual to the data from a small group of participants, two distinct 24-hour rhythms were identified, with one being sinusoidal and the other being more complex with multiple peaks. Interestingly, the latent pattern associated with circadian variation (simple sinusoidal curve) had a similar phase across the three participants, while the more complex latent pattern reflecting diurnal variation differed across individual. The results suggested that this modeling framework can be used to separate 24-hour rhythms into an endogenous circadian and one or more exogenous diurnal patterns in describing human metabolism.

Causal relationships between body mass index, smoking and lung cancer: Univariable and multivariable Mendelian randomization.

At the time of cancer diagnosis, body mass index (BMI) is inversely correlated with lung cancer risk, which may reflect reverse causality and confounding due to smoking behavior. We used two-sample univariable and multivariable Mendelian randomization (MR) to estimate causal relationships of BMI and smoking behaviors on lung cancer and histological subtypes based on an aggregated genome-wide association studies (GWASs) analysis of lung cancer in 29 266 cases and 56 450 controls. We observed a positive causal effect for high BMI on occurrence of small-cell lung cancer (odds ratio (OR) = 1.60, 95% confidence interval (CI) = 1.24-2.06, P = 2.70 × 10-4 ). After adjustment of smoking behaviors using multivariable Mendelian randomization (MVMR), a direct causal effect on small cell lung cancer (ORMVMR = 1.28, 95% CI = 1.06-1.55, PMVMR = .011), and an inverse effect on lung adenocarcinoma (ORMVMR = 0.86, 95% CI = 0.77-0.96, PMVMR = .008) were observed. A weak increased risk of lung squamous cell carcinoma was observed for higher BMI in univariable Mendelian randomization (UVMR) analysis (ORUVMR = 1.19, 95% CI = 1.01-1.40, PUVMR = .036), but this effect disappeared after adjustment of smoking (ORMVMR = 1.02, 95% CI = 0.90-1.16, PMVMR = .746). These results highlight the histology-specific impact of BMI on lung carcinogenesis and imply mediator role of smoking behaviors in the association between BMI and lung cancer.

A Likelihood Ratio Test for Gene-Environment Interaction Based on the Trend Effect of Genotype Under an Additive Risk Model Using the Gene-Environment Independence Assumption.

Several statistical methods have been proposed for testing gene-environment (G-E) interactions under additive risk models using data from genome-wide association studies. However, these approaches have strong assumptions from underlying genetic models, such as dominant or recessive effects that are known to be less robust when the true genetic model is unknown. We aimed to develop a robust trend test employing a likelihood ratio test for detecting G-E interaction under an additive risk model, while incorporating the G-E independence assumption to increase power. We used a constrained likelihood to impose 2 sets of constraints for: 1) the linear trend effect of genotype and 2) the additive joint effects of gene and environment. To incorporate the G-E independence assumption, a retrospective likelihood was used versus a standard prospective likelihood. Numerical investigation suggests that the proposed tests are more powerful than tests assuming dominant, recessive, or general models under various parameter settings and under both likelihoods. Incorporation of the independence assumption enhances efficiency by 2.5-fold. We applied the proposed methods to examine the gene-smoking interaction for lung cancer and gene-apolipoprotein E $\varepsilon$4 interaction for Alzheimer disease, which identified 2 interactions between apolipoprotein E $\varepsilon$4 and loci membrane-spanning 4-domains subfamily A (MS4A) and bridging integrator 1 (BIN1) genes at genome-wide significance that were replicated using independent data.

A Robust Test for Additive Gene-Environment Interaction Under the Trend Effect of Genotype Using an Empirical Bayes-Type Shrinkage Estimator.

Evaluating gene by environment (G × E) interaction under an additive risk model (i.e., additive interaction) has gained wider attention. Recently, statistical tests have been proposed for detecting additive interaction, utilizing an assumption on gene-environment (G-E) independence to boost power, that do not rely on restrictive genetic models such as dominant or recessive models. However, a major limitation of these methods is a sharp increase in type I error when this assumption is violated. Our goal was to develop a robust test for additive G × E interaction under the trend effect of genotype, applying an empirical Bayes-type shrinkage estimator of the relative excess risk due to interaction. The proposed method uses a set of constraints to impose the trend effect of genotype and builds an estimator that data-adaptively shrinks an estimator of relative excess risk due to interaction obtained under a general model for G-E dependence using a retrospective likelihood framework. Numerical study under varying levels of departures from G-E independence shows that the proposed method is robust against the violation of the independence assumption while providing an adequate balance between bias and efficiency compared with existing methods. We applied the proposed method to the genetic data of Alzheimer disease and lung cancer.

Tracing Lung Cancer Risk Factors Through Mutational Signatures in Never-Smokers.

Epidemiologic studies often rely on questionnaire data, exposure measurement tools, and/or biomarkers to identify risk factors and the underlying carcinogenic processes. An emerging and promising complementary approach to investigate cancer etiology is the study of somatic "mutational signatures" that endogenous and exogenous processes imprint on the cellular genome. These signatures can be identified from a complex web of somatic mutations thanks to advances in DNA sequencing technology and analytical algorithms. This approach is at the core of the Sherlock-Lung study (2018-ongoing), a retrospective case-only study of over 2,000 lung cancers in never-smokers (LCINS), using different patterns of mutations observed within LCINS tumors to trace back possible exposures or endogenous processes. Whole genome and transcriptome sequencing, genome-wide methylation, microbiome, and other analyses are integrated with data from histological and radiological imaging, lifestyle, demographic characteristics, environmental and occupational exposures, and medical records to classify LCINS into subtypes that could reveal distinct risk factors. To date, we have received samples and data from 1,370 LCINS cases from 17 study sites worldwide and whole-genome sequencing has been completed on 1,257 samples. Here, we present the Sherlock-Lung study design and analytical strategy, also illustrating some empirical challenges and the potential for this approach in future epidemiologic studies.

Lung cancer risk in painters: results from the SYNERGY pooled case-control study consortium.

OBJECTIVES: We evaluated the risk of lung cancer associated with ever working as a painter, duration of employment and type of painter by histological subtype as well as joint effects with smoking, within the SYNERGY project. METHODS: Data were pooled from 16 participating case-control studies conducted internationally. Detailed individual occupational and smoking histories were available for 19 369 lung cancer cases (684 ever employed as painters) and 23 674 age-matched and sex-matched controls (532 painters). Multivariable unconditional logistic regression models were adjusted for age, sex, centre, cigarette pack-years, time-since-smoking cessation and lifetime work in other jobs that entailed exposure to lung carcinogens. RESULTS: Ever having worked as a painter was associated with an increased risk of lung cancer in men (OR 1.30; 95% CI 1.13 to 1.50). The association was strongest for construction and repair painters and the risk was elevated for all histological subtypes, although more evident for small cell and squamous cell lung cancer than for adenocarcinoma and large cell carcinoma. There was evidence of interaction on the additive scale between smoking and employment as a painter (relative excess risk due to interaction >0). CONCLUSIONS: Our results by type/industry of painter may aid future identification of causative agents or exposure scenarios to develop evidence-based practices for reducing harmful exposures in painters.

Tuberculosis infection and lung adenocarcinoma: Mendelian randomization and pathway analysis of genome-wide association study data from never-smoking Asian women.

We investigated whether genetic susceptibility to tuberculosis (TB) influences lung adenocarcinoma development among never-smokers using TB genome-wide association study (GWAS) results within the Female Lung Cancer Consortium in Asia. Pathway analysis with the adaptive rank truncated product method was used to assess the association between a TB-related gene-set and lung adenocarcinoma using GWAS data from 5512 lung adenocarcinoma cases and 6277 controls. The gene-set consisted of 31 genes containing known/suggestive associations with genetic variants from previous TB-GWAS. Subsequently, we followed-up with Mendelian Randomization to evaluate the association between TB and lung adenocarcinoma using three genome-wide significant variants from previous TB-GWAS in East Asians. The TB-related gene-set was associated with lung adenocarcinoma (p = 0.016). Additionally, the Mendelian Randomization showed an association between TB and lung adenocarcinoma (OR = 1.31, 95% CI: 1.03, 1.66, p = 0.027). Our findings support TB as a causal risk factor for lung cancer development among never-smoking Asian women.

Circulating markers of cellular immune activation in prediagnostic blood sample and lung cancer risk in the Lung Cancer Cohort Consortium (LC3).

Cell-mediated immune suppression may play an important role in lung carcinogenesis. We investigated the associations for circulating levels of tryptophan, kynurenine, kynurenine:tryptophan ratio (KTR), quinolinic acid (QA) and neopterin as markers of immune regulation and inflammation with lung cancer risk in 5,364 smoking-matched case-control pairs from 20 prospective cohorts included in the international Lung Cancer Cohort Consortium. All biomarkers were quantified by mass spectrometry-based methods in serum/plasma samples collected on average 6 years before lung cancer diagnosis. Odds ratios (ORs) and 95% confidence intervals (CIs) for lung cancer associated with individual biomarkers were calculated using conditional logistic regression with adjustment for circulating cotinine. Compared to the lowest quintile, the highest quintiles of kynurenine, KTR, QA and neopterin were associated with a 20-30% higher risk, and tryptophan with a 15% lower risk of lung cancer (all ptrend < 0.05). The strongest associations were seen for current smokers, where the adjusted ORs (95% CIs) of lung cancer for the highest quintile of KTR, QA and neopterin were 1.42 (1.15-1.75), 1.42 (1.14-1.76) and 1.45 (1.13-1.86), respectively. A stronger association was also seen for KTR and QA with risk of lung squamous cell carcinoma followed by adenocarcinoma, and for lung cancer diagnosed within the first 2 years after blood draw. This study demonstrated that components of the tryptophan-kynurenine pathway with immunomodulatory effects are associated with risk of lung cancer overall, especially for current smokers. Further research is needed to evaluate the role of these biomarkers in lung carcinogenesis and progression.

Obesity and related conditions and risk of inflammatory breast cancer: a nested case-control study.

PURPOSE: Inflammatory breast cancer (IBC) is a rare, poorly understood and aggressive tumor. We extended prior findings linking high body mass index (BMI) to substantial increased IBC risk by examining BMI associations before and after adjustment for well-characterized comorbidities using medical record data for diabetes, insulin resistance, and disturbances of cholesterol metabolism in a general community healthcare setting. METHODS: We identified 247 incident IBC cases diagnosed at Kaiser Permanente Northern California between 2005 and 2017 and 2470 controls matched 10:1 on birth year and geographic area and with ≥ 13 months of continuous enrollment prior to diagnosis/index date. We assessed exposures from 6 years up to one year prior to the diagnosis/index date, using logistic regression to calculate odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS: Before adjustment for comorbidities, ORs (95% CIs) for BMI of 25-< 30, 30-< 35, and ≥ 35 compared to < 25 kg/m2 were 1.5 (0.9-2.3), 2.0 (1.2-3.1), and 2.5 (1.4-4.4), respectively. After adjustment for pre-diabetes/diabetes, HDL-C and triglyceride levels, and dyslipidemia, corresponding ORs were 1.3 (0.8-2.1), 1.6 (0.9-2.9), and 1.9 (1.0-3.5). The OR for HDL-C levels < 50 mg/dL compared to ≥ 65 mg/dL was 2.0 (1.2-3.3) in the adjusted model. In a separate model the OR for a triglyceride/HDL-C ratio ≥ 2.50 compared to < 1.62 was 1.7 (1.1-2.8) after adjustment for BMI, pre-diabetes/diabetes, and dyslipidemia. Results did not differ significantly by estrogen receptor status. CONCLUSIONS: Obesity and measures of insulin resistance independently increased IBC risk as did obesity and low HDL-C levels. These findings, if confirmed, have implications for IBC prevention.

Genome-Wide Meta-Analyses of FTND and TTFC Phenotypes.

INTRODUCTION: FTND (FagerstrÓ§m test for nicotine dependence) and TTFC (time to smoke first cigarette in the morning) are common measures of nicotine dependence (ND). However, genome-wide meta-analysis for these phenotypes has not been reported. METHODS: Genome-wide meta-analyses for FTND (N = 19,431) and TTFC (N = 18,567) phenotypes were conducted for adult smokers of European ancestry from 14 independent cohorts. RESULTS: We found that SORBS2 on 4q35 (p = 4.05 × 10-8), BG182718 on 11q22 (p = 1.02 × 10-8), and AA333164 on 14q21 (p = 4.11 × 10-9) were associated with TTFC phenotype. We attempted replication of leading candidates with independent samples (FTND, N = 7010 and TTFC, N = 10 061), however, due to limited power of the replication samples, the replication of these new loci did not reach significance. In gene-based analyses, COPB2 was found associated with FTND phenotype, and TFCP2L1, RELN, and INO80C were associated with TTFC phenotype. In pathway and network analyses, we found that the interconnected interactions among the endocytosis, regulation of actin cytoskeleton, axon guidance, MAPK signaling, and chemokine signaling pathways were involved in ND. CONCLUSIONS: Our analyses identified several promising candidates for both FTND and TTFC phenotypes, and further verification of these candidates was necessary. Candidates supported by both FTND and TTFC (CHRNA4, THSD7B, RBFOX1, and ZNF804A) were associated with addiction to alcohol, cocaine, and heroin, and were associated with autism and schizophrenia. We also identified novel pathways involved in cigarette smoking. The pathway interactions highlighted the importance of receptor recycling and internalization in ND. IMPLICATIONS: Understanding the genetic architecture of cigarette smoking and ND is critical to develop effective prevention and treatment. Our study identified novel candidates and biological pathways involved in FTND and TTFC phenotypes, and this will facilitate further investigation of these candidates and pathways.

Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.

Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population. As increasingly large-scale GWAS are being performed through consortia effort where it is preferable to share only summary-level information across studies, we also describe simple mechanisms for implementing score tests based on standard meta-analysis of "one-step" maximum-likelihood estimates across studies. Applications of the methods in simulation studies and a dataset from GWAS of lung cancer illustrate ability of the proposed methods to maintain type-I error rates for the underlying testing procedures. For analysis of imputed SNPs, similar to typed SNPs, the retrospective methods can lead to considerable efficiency gain for modeling of gene-environment interactions under the assumption of gene-environment independence. Methods are made available for public use through CGEN R software package.

Is high vitamin B12 status a cause of lung cancer?

Vitamin B supplementation can have side effects for human health, including cancer risk. We aimed to elucidate the role of vitamin B12 in lung cancer etiology via direct measurements of pre-diagnostic circulating vitamin B12 concentrations in a nested case-control study, complemented with a Mendelian randomization (MR) approach in an independent case-control sample. We used pre-diagnostic biomarker data from 5183 case-control pairs nested within 20 prospective cohorts, and genetic data from 29,266 cases and 56,450 controls. Exposures included directly measured circulating vitamin B12 in pre-diagnostic blood samples from the nested case-control study, and 8 single nucleotide polymorphisms associated with vitamin B12 concentrations in the MR study. Our main outcome of interest was increased risk for lung cancer, overall and by histological subtype, per increase in circulating vitamin B12 concentrations. We found circulating vitamin B12 to be positively associated with overall lung cancer risk in a dose response fashion (odds ratio for a doubling in B12 [ORlog2B12 ] = 1.15, 95% confidence interval (95%CI) = 1.06-1.25). The MR analysis based on 8 genetic variants also indicated that genetically determined higher vitamin B12 concentrations were positively associated with overall lung cancer risk (OR per 150 pmol/L standard deviation increase in B12 [ORSD ] = 1.08, 95%CI = 1.00-1.16). Considering the consistency of these two independent and complementary analyses, these findings support the hypothesis that high vitamin B12 status increases the risk of lung cancer.

Epigenome-wide analysis of DNA methylation in lung tissue shows concordance with blood studies and identifies tobacco smoke-inducible enhancers.

Smoking-associated DNA hypomethylation has been observed in blood cells and linked to lung cancer risk. However, its cause and mechanistic relationship to lung cancer remain unclear. We studied the association between tobacco smoking and epigenome-wide methylation in non-tumor lung (NTL) tissue from 237 lung cancer cases in the Environment And Genetics in Lung cancer Etiology study, using the Infinium HumanMethylation450 BeadChip. We identified seven smoking-associated hypomethylated CpGs (P < 1.0 × 10-7), which were replicated in NTL data from The Cancer Genome Atlas. Five of these loci were previously reported as hypomethylated in smokers' blood, suggesting that blood-based biomarkers can reflect changes in the target tissue for these loci. Four CpGs border sequences carrying aryl hydrocarbon receptor binding sites and enhancer-specific histone modifications in primary alveolar epithelium and A549 lung adenocarcinoma cells. A549 cell exposure to cigarette smoke condensate increased these enhancer marks significantly and stimulated expression of predicted target xenobiotic response-related genes AHRR (P = 1.13 × 10-62) and CYP1B1 (P < 2.49 × 10-61). Expression of both genes was linked to smoking-related transversion mutations in lung tumors. Thus, smoking-associated hypomethylation may be a consequence of enhancer activation, revealing environmentally-induced regulatory elements implicated in lung carcinogenesis.

Association between GWAS-identified lung adenocarcinoma susceptibility loci and EGFR mutations in never-smoking Asian women, and comparison with findings from Western populations.

To evaluate associations by EGFR mutation status for lung adenocarcinoma risk among never-smoking Asian women, we conducted a meta-analysis of 11 loci previously identified in genome-wide association studies (GWAS). Genotyping in an additional 10,780 never-smoking cases and 10,938 never-smoking controls from Asia confirmed associations with eight known single nucleotide polymorphisms (SNPs). Two new signals were observed at genome-wide significance (P < 5 × 10-8), namely, rs7216064 (17q24.3, BPTF), for overall lung adenocarcinoma risk, and rs3817963 (6p21.3, BTNL2) which is specific to cases with EGFR mutations. In further sub-analyses by EGFR status, rs9387478 (ROS1/DCBLD1) and rs2179920 (HLA-DPB1) showed stronger estimated associations in EGFR-positive compared to EGFR-negative cases. Comparison of the overall associations with published results in Western populations revealed that the majority of these findings were distinct, underscoring the importance of distinct contributing factors for smoking and non-smoking lung cancer. Our results extend the catalogue of regions associated with lung adenocarcinoma in non-smoking Asian women and highlight the importance of how the germline could inform risk for specific tumour mutation patterns, which could have important translational implications.

MEGSA: A Powerful and Flexible Framework for Analyzing Mutual Exclusivity of Tumor Mutations.

The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, for pathway-guided searches, or for expanding established small MEGS. We applied our method to the whole-exome sequencing data for 13 cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a MEGS with five genes (FLT3, IDH2, NRAS, KIT, and TP53) and a MEGS (NPM1, TP53, and RUNX1) whose mutation status was strongly associated with survival (p = 6.7 × 10(-4)). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23, and TBL1XR1), providing support for their role as cancer drivers.

Peritoneal mesothelioma and asbestos exposure: a population-based case-control study in Lombardy, Italy.

OBJECTIVES: Asbestos is the main risk factor for peritoneal mesothelioma (PeM). However, due to its rarity, PeM has rarely been investigated in community-based studies. We examined the association between asbestos exposure and PeM risk in a general population in Lombardy, Italy. METHODS: From the regional mesothelioma registry, we selected PeM cases diagnosed in 2000-2015. Population controls (matched by area, gender and age) came from two case-control studies in Lombardy on lung cancer (2002-2004) and pleural mesothelioma (2014). Assessment of exposure to asbestos was performed through a quantitative job-exposure matrix (SYN-JEM) and expert evaluation based on a standardised questionnaire. We calculated period-specific and gender-specific OR and 90% CI using conditional logistic regression adjusted for age, province of residence and education. RESULTS: We selected 68 cases and 2116 controls (2000-2007) and 159 cases and 205 controls (2008-2015). The ORs for ever asbestos exposure (expert-based, 2008-2015 only) were 5.78 (90% CI 3.03 to 11.0) in men and 8.00 (2.56 to 25.0) in women; the ORs for definite occupational exposure were 12.3 (5.62 to 26.7) in men and 14.3 (3.16 to 65.0) in women. The ORs for ever versus never occupational asbestos exposure based on SYN-JEM (both periods) were 2.05 (90% CI 1.39 to 3.01) in men and 1.62 (0.79 to 3.27) in women. In men, clear positive associations were found for duration, cumulative exposure (OR 1.33 (1.19 to 1.48) per fibres/mL-years) and latency. CONCLUSIONS: Using two different methods of exposure assessment we provided evidence of a clear association between asbestos exposure and PeM risk in the general population.

Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.