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List of tag SNPs for the TDP1 gene region

Description of the participant studies

Purpose: DNA topoisomerase inhibitors are commonly used for treating small-cell lung cancer (SCLC). Tyrosyl-DNA phosphodiesterase (TDP1) repairs DNA damage caused by this class of drugs and may therefore influence treatment outcome. In this study, we investigated whether common TDP1 single-nucleotide polymorphisms (SNP) are associated with overall survival among SCLC patients.Experimental Design: Two TDP1 SNPs (rs942190 and rs2401863) were analyzed in 890 patients from 10 studies in the International Lung Cancer Consortium (ILCCO). The Kaplan–Meier method and Cox regression analyses were used to evaluate genotype associations with overall mortality at 36 months postdiagnosis, adjusting for age, sex, race, and tumor stage.Results: Patients homozygous for the minor allele (GG) of rs942190 had poorer survival compared with those carrying AA alleles, with a HR of 1.36 [95% confidence interval (CI): 1.08–1.72, P = 0.01), but no association with survival was observed for patients carrying the AG genotype (HR = 1.04, 95% CI, 0.84–1.29, P = 0.72). For rs2401863, patients homozygous for the minor allele (CC) tended to have better survival than patients carrying AA alleles (HR = 0.79; 95% CI, 0.61–1.02, P = 0.07). Results from the Genotype Tissue Expression (GTEx) Project, the Encyclopedia of DNA Elements (ENCODE), and the ePOSSUM web application support the potential function of rs942190.Conclusions: We found the rs942190 GG genotype to be associated with relatively poor survival among SCLC patients. Further investigation is needed to confirm the result and to determine whether this genotype may be a predictive marker for treatment efficacy of DNA topoisomerase inhibitors. Clin Cancer Res; 23(24); 7550–7. ©2017 AACR.

Background: Exposure to low doses of the radioactive gas radon, as found indoors in dwellings, has been consistently shown to be a risk factor for lung cancer (LC). The linear-no-threshold hypothesis (LNT) is often applied to estimate excess odds ratios or population attributable risks, albeit this LNT assumption remains debated. Aim/Method: We investigate the profile of radon-induced LC-risk in a sample of 8,927 cases and 5,562 controls of the International Lung Cancer Consortium (ILCCO), contributed by studies with sufficient exposure heterogeneity. Spatial indoor-radon exposure in the residential area (sIR) obtained from the national surveys were linked to the participants’ residential geo-location. Parametric linear- and spline-functions were fitted within framework of logistic regression. Results: We observed a U-shaped dose-risk relation, with the lowest risk exposure level (LRE) being 57.6 Bq/m³ (95%.CI: 56.1-59.2 Bq/m³). The risk of overall-LC at 25 Bq/m³ (OR=1.31, 95%-CI: 1.01-1.59) was comparable to that at 100 Bq/m³ (OR=1.34, 95%-CI: 1.20-1.45). Regarding histological subtypes, we observed the strongest risk for small-cell LC, and weak association for squamous-cell LC with no association below 58 Bq/m³. Conclusion: Our results showed a U-shaped risk-profile for radon-induced LC risk at very low exposure levels (sIR

BackgroundEarly detection of lung cancer saves lives, as demonstrated by the two largest published low-dose CT screening trials. Optimal implementation depends on our ability to identify those most at risk.MethodsVersion 2 of the Liverpool Lung Project risk score (LLPv2) was developed from case-control data in Liverpool and further adapted when applied for selection of subjects for the UK Lung Screening Trial. The objective was to produce version 3 (LLPv3) of the model, by calibration to national figures for 2017. We validated both LLPv2 and LLPv3 using questionnaire data from 75 958 individuals, followed up for lung cancer over 5 years. We validated both discrimination, using receiver operating characteristic (ROC) analysis, and absolute incidence, by comparing deciles of predicted incidence with observed incidence. We calculated proportionate difference as the percentage excess or deficit of observed cancers compared with those predicted. We also carried out Hosmer-Lemeshow tests.ResultsThere were 599 lung cancers diagnosed over 5 years. The discrimination of both LLPv2 and LLPv3 was significant with an area under the ROC curve of 0.81 (95% CI 0.79 to 0.82). However, LLPv2 overestimated absolute risk in the population. The proportionate difference was −58.3% (95% CI −61.6% to −54.8%), that is, the actual number of cancers was only 42% of the number predicted.In LLPv3, calibrated to national 2017 figures, the proportionate difference was −22.0% (95% CI −28.1% to −15.5%).ConclusionsWhile LLPv2 and LLPv3 have the same discriminatory power, LLPv3 improves the absolute lung cancer risk prediction and should be considered for use in further UK implementation studies.

Differences by sex in lung cancer incidence and mortality have been reported which cannot be fully explained by sex differences in smoking behavior, implying existence of genetic and molecular basis for sex disparity in lung cancer development. However, the information about sex dimorphism in lung cancer risk is quite limited despite the great success in lung cancer association studies. By adopting a stringent two-stage analysis strategy, we performed a genome-wide gene–sex interaction analysis using genotypes from a lung cancer cohort including ~ 47 000 individuals with European ancestry. Three low-frequency variants (minor allele frequency

Background Aberrant Wnt signalling, regulating cell development and stemness, influences the development of many cancer types. The Aryl hydrocarbon receptor (AhR) mediates tumorigenesis of environmental pollutants. Complex interaction patterns of genes assigned to AhR/Wnt-signalling were recently associated with lung cancer susceptibility. Aim To assess the association and predictive ability of AhR/Wnt-genes with lung cancer in cases and controls of European descent. Methods Odds ratios (OR) were estimated for genomic variants assigned to the Wnt agonist and the antagonistic genes DKK2, DKK3, DKK4, FRZB, SFRP4 and Axin2. Logistic regression models with variable selection were trained, validated and tested to predict lung cancer, at which other previously identified SNPs that have been robustly associated with lung cancer risk could also enter the model. Further, decision trees were created to investigate variant x variant interaction. All analyses were performed for overall lung cancer and for subgroups. Results No genome-wide significant association of AhR/Wnt-genes with overall lung cancer was observed, but within the subgroups of ever smokers (e.g. maker rs2722278 SFRP4; OR = 1.20; 95%-CI: 1.13–1.27; p = 5.6 10− 10) and never smokers (e.g. maker rs1133683 Axin2; OR = 1.27; 95%-CI: 1.19–1.35; p = 1.0 10− 12). Although predictability is poor, AhR/Wnt-variants are unexpectedly overrepresented in optimized prediction scores for overall lung cancer and for small cell lung cancer. Remarkably, the score for never-smokers contained solely two AhR/Wnt-variants. The optimal decision tree for never smokers consists of 7 AhR/Wnt-variants and only two lung cancer variants Conclusions The role of variants belonging to Wnt/AhR-pathways in lung cancer susceptibility may be underrated in main-effects association analysis. Complex interaction patterns in individuals of European descent have moderate predictive capacity for lung cancer or subgroups thereof, especially in never smokers.

Background: Somatic EGFR mutations define a subset of non–small cell lung cancers (NSCLC) that have clinical impact on NSCLC risk and outcome. However, EGFR-mutation-status is often missing in epidemiologic datasets. We developed and tested pragmatic approaches to account for EGFR-mutation-status based on variables commonly included in epidemiologic datasets and evaluated the clinical utility of these approaches. Methods: Through analysis of the International Lung Cancer Consortium (ILCCO) epidemiologic datasets, we developed a regression model for EGFR-status; we then applied a clinical-restriction approach using the optimal cut-point, and a second epidemiologic, multiple imputation approach to ILCCO survival analyses that did and did not account for EGFR-status. Results: Of 35,356 ILCCO patients with NSCLC, EGFR-mutation-status was available in 4,231 patients. A model regressing known EGFR-mutation-status on clinical and demographic variables achieved a concordance index of 0.75 (95% CI, 0.74–0.77) in the training and 0.77 (95% CI, 0.74–0.79) in the testing dataset. At an optimal cut-point of probability-score = 0.335, sensitivity = 69% and specificity = 72.5% for determining EGFR-wildtype status. In both restriction-based and imputation-based regression analyses of the individual roles of BMI on overall survival of patients with NSCLC, similar results were observed between overall and EGFR-mutation-negative cohort analyses of patients of all ancestries. However, our approach identified some differences: EGFR-mutated Asian patients did not incur a survival benefit from being obese, as observed in EGFR-wildtype Asian patients. Conclusions: We introduce a pragmatic method to evaluate the potential impact of EGFR-status on epidemiological analyses of NSCLC. Impact: The proposed method is generalizable in the common occurrence in which EGFR-status data are missing.

Introduction: Aberrant Wnt signalling, regulating cell development and stemness, is observed in many cancer entities. Aryl hydrocarbon receptor (AhR) mediates tumorigenesis of environmental pollutants. Complex interaction patterns of genes assigned to AhR/Wnt-signalling were recently associated to lung cancer susceptibility. Aim: To assess the association and predictive ability of AhR/Wnt-genes with lung cancer in cases and controls of European descent. Methods: Odds ratios (OR) were estimated for genomic variants assigned to the genes DKK2, DKK3, DKK4, FRZB, SFRP4 and Axin2 and other lung cancer-related genes. Logistic regression models with variable selection were trained, validated and tested to predict lung cancer. Further, decision trees were created to investigate variant x variant interaction. All analyses were performed for overall lung cancer and for subgroups. Results: No association with overall lung cancer was observed, but within the subgroups of ever smokers (e.g. maker rs2722278 SFRP4; OR=1.20; 95%-CI: 1.13-1.27; p=5.6 10-10) and never smokers. Although predictability is poor, AhR/Wnt-variants are unexpected overrepresented in optimized prediction scores for overall lung cancer and for small cell lung cancer. Remarkable, the score for never-smokers contained solely two AhR/Wnt-variants. The optimal decision tree for never smokers consists of 7 AhR/Wnt-variants and only two lung cancer variants, no assigned to any CHRN gene. Conclusions: The role of variants belonging to Wnt/AhR-pathways in lung cancer susceptibility may be underrated in main-effects association analysis. Complex interaction patterns in individuals of European descent have moderate predictive capacity for lung cancer or subgroups thereof, especially in never smokers.

Highlights • PD-L1 expression was assessed for heterogeneity in 107 NSCLC patients. • Intra-tumoural heterogeneity was observed in 78% of cases. • Inter-tumoural heterogeneity was observed in 53% of cases. • 23% of cases had clinically relevant changes between primary and secondary tumours. • Sample site selection is an important consideration for testing PD-L1., Objectives PD-L1 expression on tumour cells can guide the use of anti-PD-1/PD-L1 immune modulators to treat patients with non-small cell lung cancer (NSCLC). Heterogeneity of PD-L1 expression both within and between tumour sites is a well-documented phenomenon that compromises its predictive power. Our aim was to better characterise the pattern and extent of PD-L1 heterogeneity with a view to optimising tumour sampling and improve its accuracy as a biomarker. Materials and methods Expression of PD-L1 was assessed by immunochemistry using the SP263 clone in 107 resected primary NSCLCs and their nodal metastases. Intra-tumoural heterogeneity, defined as ‘small-scale’ (mm²), ‘medium-scale’ (cm²) and ‘large-scale’ (between tumour blocks), was assessed by digital imaging using a novel ‘squares method’. Inter-tumoural heterogeneity between the primary tumours and their nodal metastases and between N1 and N2 nodal stages was also assessed. Results The majority of tumours demonstrated intra-tumoural heterogeneity (small-scale 78%, medium-scale 50%, large-scale 46%). Inter-tumoural heterogeneity between the primary and nodal metastases was present in 53% of cases and, in 17%, between N1 and N2 disease. These differences were occasionally sufficient to lead to discrepancy across the ≥1%, ≥25% and ≥50% cut-offs used to guide therapy. Conclusion Heterogeneity of PD-L1 expression is common, variable in scale and extent, and carries significant implications for its accuracy as a predictive biomarker. Extensive sampling reduces, but cannot eliminate, this inaccuracy.

Background: Imputation of untyped markers is a standard tool in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy with which genotypes are imputed is fundamental. Several accuracy measures have been proposed and some are implemented in imputation software, unfortunately diversely across platforms. In the present paper we introduce I’am hiQ, an independent pair of accuracy measures that can be applied to dosage files, the output of all imputation software. I’am (imputation accuracy measure) quantifies the average amount of individual-specific versus population-specific genotype information in a linear manner. hiQ (heterogeneity in quantities of dosages) addresses the inter-individual heterogeneity between dosages of a marker across the sample at hand. Results: Applying both measures to a large case-control sample of the International Lung Cancer Consortium (ILCCO), comprising 27,065 individuals, we found meaningful thresholds for I’am and hiQ suitable to classify markers of poor accuracy. We demonstrate how Manhattan-like plots and moving averages of I’am and hiQ can be useful to identify regions enriched with less accurate imputed markers, whereas these regions would by missed when applying the accuracy measure info (implemented in IMPUTE2). Conclusion: We recommend using I’am hiQ additional to other accuracy scores for variant filtering before stepping into the analysis of imputed GWAS data.

Recent studies suggest that rare variants exhibit stronger effect sizes and might play a crucial role in the etiology of lung cancers (LC). Whole exome plus targeted sequencing of germline DNA was performed on 1045 LC cases and 885 controls in the discovery set. To unveil the inherited causal variants, we focused on rare and predicted deleterious variants and small indels enriched in cases or controls. Promising candidates were further validated in a series of 26,803 LCs and 555,107 controls. During discovery, we identified 25 rare deleterious variants associated with LC susceptibility, including 13 reported in ClinVar. Of the five validated candidates, we discovered two pathogenic variants in known LC susceptibility loci, ATM p.V2716A (Odds Ratio [OR] 19.55, 95%CI 5.04–75.6) and MPZL2 p.I24M frameshift deletion (OR 3.88, 95%CI 1.71–8.8); and three in novel LC susceptibility genes, POMC c.*28delT at 3′ UTR (OR 4.33, 95%CI 2.03–9.24), STAU2 p.N364M frameshift deletion (OR 4.48, 95%CI 1.73–11.55), and MLNR p.Q334V frameshift deletion (OR 2.69, 95%CI 1.33–5.43). The potential cancer-promoting role of selected candidate genes and variants was further supported by endogenous DNA damage assays. Our analyses led to the identification of new rare deleterious variants with LC susceptibility. However, in-depth mechanistic studies are still needed to evaluate the pathogenic effects of these specific alleles.

INTRODUCTION: The relationship between Body-Mass-Index (BMI) and lung cancer prognosis is heterogeneous. We evaluated the impact of sex, smoking and race on the relationship between BMI and overall survival (OS) in non-small-cell-lung-cancer (NSCLC). METHODS: Data from 16 individual ILCCO studies were pooled to assess interactions between BMI and the following factors on OS: self-reported race, smoking status and sex, using Cox models (adjusted hazard ratios; aHR) with interaction terms and adjusted penalized smoothing spline plots in stratified analyses. RESULTS: Among 20,937 NSCLC patients with BMI values, females = 47 %; never-smokers = 14 %; White-patients = 76 %. BMI showed differential survival according to race whereby compared to normal-BMI patients, being underweight was associated with poor survival among white patients (OS, aHR = 1.66) but not among black patients (aHR = 1.06; p(interaction) = 0.02). Comparing overweight/obese to normal weight patients, Black NSCLC patients who were overweight/obese also had relatively better OS (p(interaction) = 0.06) when compared to White-patients. BMI was least associated with survival in Asian-patients and never-smokers. The outcomes of female ever-smokers at the extremes of BMI were associated with worse outcomes in both the underweight (p(interaction)

This research was supported by funding from the National Institutes of Health (US NCI R25T CA112355 and R01 CA201358; PI: Witte) and the Canadian Institute for Health Research (Foundation grant FDN 167273, PI: Hung; Canada Research Chair, PI: Hung). The OncoArray project was supported by NIH U19 CA203654 (MPI: Hung, Amos, Brennan, Lin). The Boston Lung Cancer Study was funded by NIH (NCI) U01CA209414 (PI: Christiani). The lung eQTL study at Laval University was supported by the Fondation de l’Institut universitaire de cardiologie et de pneumologie de Québec and the Canadian Institutes of Health Research (MOP − 123369). Y.B. holds a Canada Research Chair in Genomics of Heart and Lung Diseases. The EAGLE study was supported by the Intramural Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS. The Multiethnic Cohort Study is supported by National Institutes of Health (CA164973). The CARET study was supported by the National Institutes of Health/National Cancer Institute: UM1 CA167462 (PI: Goodman), U01 CA6367307 (PIs: Omen, Goodman); R01 CA111703 (PI: Chen), 5R01 CA151989-01A1 (PI: Doherty) and U01 CA167462 (PI: Chen). M.B.S. was supported in part by a Cancer Center Support Grant (P30 CA076292) and by NIH P50 CA119997. R.M.M. is supported by a CRUK programme grant, the Integrative Cancer Epidemiology Programme (C18281/A19169), and by the National Institute for Health Research (NIHR) Bristol Biomedical Research Centre based at University Hospitals Bristol NHS Foundation Trust and the University of Bristol., Kachuri, L., Johansson, M., Rashkin, S.R., Graff, R.E., Bossé, Y., Manem, V., Caporaso, N.E., Landi, M.T., Christiani, D.C., Vineis, P., Liu, G., Scelo, G., Zaridze, D., Shete, S.S., Albanes, D., Aldrich, M.C., Tardón, A., Rennert, G., Chen, C., Goodman, G.E., Doherty, J.A., Bickeböller, H., Field, J.K., Davies, M.P., Dawn Teare, M., Kiemeney, L.A., Bojesen, S.E., Haugen, A., Zienolddiny, S., Lam, S., Le Marchand, L., Cheng, I., Schabath, M.B., Duell, E.J., Andrew, A.S., Manjer, J., Lazarus, P., Arnold, S., McKay, J.D., Emami, N.C., Warkentin, M.T., Brhane, Y., Obeidat, M., Martin, R.M., Relton, C., Davey Smith, G., Haycock, P.C., Amos, C.I., Brennan, P., Witte, J.S., Hung, R.J.

The CAPUA study was supported by FIS-FEDER/Spain grant numbers FIS-01/310, FIS-PI03-0365, and FIS-07-BI060604, FICYT/Asturias grant numbers FICYT PB02-67 and FICYT IB09-133, and the University Institute of Oncology (IUOPA), of the University of Oviedo and the Ciber de Epidemiologia y Salud Pública. CIBERESP, SPAIN (...), Ji, X., Mukherjee, S., Landi, M.T., Bosse, Y., Joubert, P., Zhu, D., Gorlov, I., Xiao, X., Han, Y., Gorlova, O., Hung, R.J., Brhane, Y., Carreras-Torres, R., Christiani, D.C., Caporaso, N., Johansson, M., Liu, G., Bojesen, S.E., Le Marchand, L., Albanes, D., Bickeböller, H., Aldrich, M.C., Bush, W.S., Tardon, A., Rennert, G., Chen, C., Byun, J., Dragnev, K.H., Field, J.K., Kiemeney, L.F., Lazarus, P., Zienolddiny, S., Lam, S., Schabath, M.B., Andrew, A.S., Bertazzi, P.A., Pesatori, A.C., Diao, N., Su, L., Song, L., Zhang, R., Leighl, N., Johansen, J.S., Mellemgaard, A., Saliba, W., Haiman, C., Wilkens, L., Fernandez-Somoano, A., Fernandez-Tardon, G., Heijden, E.H.F.M., Kim, J.H., Davies, M.P.A., Marcus, M.W., Brunnström, H., Manjer, J., Melander, O., Muller, D.C., Overvad, K., Trichopoulou, A., Tumino, R., Goodman, G.E., Cox, A., Taylor, F., Woll, P., Wichmann, E., Muley, T., Risch, A., Rosenberger, A., Grankvist, K., Johansson, M., Shepherd, F., Tsao, M.-S., Arnold, S.M., Haura, E.B., Bolca, C., Holcatova, I., Janout, V., Kontic, M., Lissowska, J., Mukeria, A., Ognjanovic, S., Orlowski, T.M., Scelo, G., Swiatkowska, B., Zaridze, D., Bakke, P., Skaug, V., Butler, L.M., Offit, K., Srinivasan, P., Bandlamudi, C., Hellmann, M.D., Solit, D.B., Robson, M.E., Rudin, C.M., Stadler, Z.K., Taylor, B.S., Berger, M.F., Houlston, R., McLaughlin, J., Stevens, V., Nickle, D.C., Obeidat, M., Timens, W., Artigas, M.S., Shete, S., Brenner, H., Chanock, S., Brennan, P., McKay, J.D., Amos, C.I.

ILCCO Data Repository was supported by Cancer Care Ontario Research Chair of Population Health, National Institute of Health (U19 CA148127), and Lunenfeld-Tanenbuaum Research Institute, Sinai Health System. The Cancer de Pulmon en Asturias (CAPUA) study (PI: Adonina Tardón) was supported by Fondo de investigación sanitaria. Instituto de Salud Carlos III. Consorcio de Investigación Biomédica en Red (CIBER) del Área de Epidemiología y Salud Pública and University of Oviedo. The Environment And Genetics in Lung cancer Etiology (EAGLE) study (PI: Maria Teresa Landi) was supported by the Intramural Research Program of NIH, NCI, Division of Cancer Epidemiology and Genetics. The Epidemiology & Genetics of Lung cancer (EGLC) study (PI: Ping Yang) was supported by the National Cancer Institute and National Institute of Health (R03-CA77118, R01s-CA80127, CA84354, and HL107612). The Carotene and Retinol Efficacy Trial (CARET) (PIs: Gary E. Goodman, Chu Chen) was supported by the National Cancer Institute and National Institute of Health (5-UM1-CA-167462, U01-CA63673, and R01-CA111703) The Harvard Lung Cancer Study (LCS) (PI: David C. Christiani) was supported by National Institute of Health grants (R01CA092824, R01CA074386, and P30 ES000002) The Japan lung cancer study (PI: Kouya Shiraishi) was supported by the National Cancer Center Research and Development Fund (NCC Biobank). Kentucky Lung Cancer Research Initiative (LCRI) (PI: Susanne M. Arnold) was supported by the Department of Defense [Congressionally Directed Medical Research Program, U.S. Army Medical Research and Materiel Command Program] under award number: 10153006 (W81XWH-11-1-0781). This research was also supported by unrestricted infrastructure funds from the UK Center for Clinical and Translational Science, NIH grant UL1TR000117 and Markey Cancer Center NCI Cancer Center Support Grant (P30 CA177558) Shared Resource Facilities: Cancer Research Informatics, Biospecimen and Tissue Procurement, and Biostatistics and Bioinformatics. The Liverpool Lung Project (LLP) (PI: John K. Field) is supported by the Roy Castle Lung Cancer Foundation, UK. The work performed for the Toronto lung cancer study (PIs: Rayjean J. Hung, Geoffrey Liu) was supported by Ontario Institute for Cancer Research, the Canadian Cancer Society Research Institute (020214), Ontario Institute of Cancer and Cancer Care Ontario Chair Award to R.J.H. and G.L. and the Alan Brown Chair and Lusi Wong Programs at the Princess Margaret Hospital Foundation. The Total Lung Cancer (TLC) study (PI: Matthew B. Schabath) was supported by the following funding sources: James & Esther King Biomedical Research Program Grant (09KN-15), National Institutes of Health Specialized Programs of Research Excellence (SPORE) Grant (P50 CA119997), and an American Cancer Society Institutional Research Grant (93-032-13)., Lohavanichbutr, P., Sakoda, L.C., Amos, C.I., Arnold, S.M., Christiani, D.C., Davies, M.P.A., Field, J.K., Haura, E.B., Hung, R.J., Kohno, T., Landi, M.T., Liu, G., Liu, Y., Marcus, M.W., O'Kane, G.M., Schabath, M.B., Shiraishi, K., Slone, S.A., Tardón, A., Yang, P., Yoshida, K., Zhang, R., Zong, X., Goodman, G.E., Weiss, N.S., Chen, C.

BackgroundThe NLST reported a significant 20% reduction in lung cancer mortality with three annual low-dose CT (LDCT) screens and the Dutch-Belgian NELSON trial indicates a similar reduction. We present the results of the UKLS trial.MethodsFrom October 2011 to February 2013, we randomly allocated 4 055 participants to either a single invitation to screening with LDCT or to no screening (usual care). Eligible participants (aged 50-75) had a risk score (LLPv2) ≥ 4.5% of developing lung cancer over five years. Data were collected on lung cancer cases to 31 December 2019 and deaths to 29 February 2020 through linkage to national registries. The primary outcome was mortality due to lung cancer. We included our results in a random-effects meta-analysis to provide a synthesis of the latest randomised trial evidence.Findings1 987 participants in the intervention and 1 981 in the usual care arms were followed for a median of 7.3 years (IQR 7.1-7.6), 86 cancers were diagnosed in the LDCT arm and 75 in the control arm. 30 lung cancer deaths were reported in the screening arm, 46 in the control arm, (relative rate 0.65 [95% CI 0.41-1.02]; p=0.062). The meta-analysis indicated a significant reduction in lung cancer mortality with a pooled overall relative rate of 0.84 (95% CI 0.76-0.92) from nine eligible trials.InterpretationThe UKLS trial of single LDCT indicates a reduction of lung cancer death of similar magnitude to the NELSON and NLST trials and was included in a meta-analysis of nine randomised trials which provides unequivocal support for lung cancer screening in identified risk groups.FundingNIHR Health Technology Assessment programme; NIHR Policy Research programme; Roy Castle Lung Cancer Foundation.

Background Accumulating evidence indicates inherited risk in the aetiology of lung cancer, although smoking exposure is the major attributing factor. Family history is a simple substitute for inherited susceptibility. Previous studies have shown some possible yet conflicting links between family history of cancer and EGFR mutation in lung cancer. As EGFR-mutated lung cancer favours female, never-smoker, adenocarcinoma and Asians, it may be argued that there may be some underlying genetic modifiers responsible for the pathogenesis of EGFR mutation. Methods We searched four databases for all original articles on family history of malignancy and EGFR mutation status in lung cancer published up to July 2018. We performed a meta-analysis by using a random-effects model and odds ratio estimates. Heterogeneity and sensitivity were also investigated. Then we conducted a second literature research to curate case reports of familial lung cancers who studied both germline cancer predisposing genes and their somatic EGFR mutation status; and explored the possible links between cancer predisposing genes and EGFR mutation. Results Eleven studies have been included in the meta-analysis. There is a significantly higher likelihood of EGFR mutation in lung cancer patients with family history of cancer than their counterparts without family history, preferentially in Asians (OR = 1.35[1.06–1.71], P = 0.01), those diagnosed with adenocarcinomas ((OR = 1.47[1.14–1.89], P = 0.003) and those with lung cancer-affected relatives (first and second-degree: OR = 1.53[1.18–1.99], P = 0.001; first-degree: OR = 1.76[1.36–2.28, P Conclusions Potential genetic modifiers may contribute to somatic EGFR mutation in lung cancer, although current data is limited. Further studies on this topic are needed, which may help to unveil lung carcinogenesis pathways. However, caution is warranted in data interpretation due to limited cases available for the current study.

IntroductionThe relationships between morbid obesity, changes in body mass index (BMI) before cancer diagnosis, and lung cancer outcomes by histology (SCLC and NSCLC) have not been well studied.MethodsIndividual level data analysis was performed on 25,430 patients with NSCLC and 2787 patients with SCLC from 16 studies of the International Lung Cancer Consortium evaluating the association between various BMI variables and lung cancer overall survival, reported as adjusted hazard ratios (aHRs) from Cox proportional hazards models and adjusted penalized smoothing spline plots.ResultsOverall survival of NSCLC had putative U-shaped hazard ratio relationships with BMI based on spline plots: being underweight (BMI < 18.5 kg/m2; aHR= 1.56; 95% confidence interval [CI]:1.43-1.70) or morbidly overweight (BMI > 40 kg/m2; aHR= 1.09; 95% CI: 0.95-1.26) at the time of diagnosis was associated with worse stage-specific prognosis, whereas being overweight (25 kg/m2 ≤ BMI < 30 kg/m2; aHR= 0.89; 95% CI: 0.85-0.95) or obese (30 kg/m2 ≤ BMI ≤ 40 kg/m2; aHR= 0.86; 95% CI: 0.82-0.91) was associated with improved survival. Although not significant, a similar pattern was seen with SCLC. Compared with an increased or stable BMI from the period between young adulthood until date of diagnosis, a decreased BMI was associated with worse outcomes in NSCLC (aHR= 1.24; 95% CI: 1.2-1.3) and SCLC patients (aHR=1.26 (95% CI: 1.0-1.6). Decreased BMI was consistently associated with worse outcome, across clinicodemographic subsets.ConclusionsBoth being underweight or morbidly obese at time of diagnosis is associated with lower stage-specific survival in independent assessments of NSCLC and SCLC patients. In addition, a decrease in BMI at lung cancer diagnosis relative to early adulthood is a consistent marker of poor survival.

Background Long non-coding RNAs compose an important level of epigenetic regulation in normal physiology and disease. Despite the plethora of publications of lncRNAs in human cancer, the landscape is still unclear. Methods Microarray analysis in 44 NSCLC paired specimens was followed by qPCR-based validation in 29 (technical) and 38 (independent) tissue pairs. Cross-validation of the selected targets was achieved in 850 NSCLC tumours from TCGA datasets. Results Twelve targets were successfully validated by qPCR (upregulated: FEZF1-AS1, LINC01214, LINC00673, PCAT6, NUTM2A-AS1, LINC01929; downregulated: PCAT19, FENDRR, SVIL-AS1, LANCL1-AS1, ADAMTS9-AS2 and LINC00968). All of them were successfully cross validated in the TCGA datasets. Abnormal DNA methylation was observed in the promoters of FENDRR, FEZF1-AS1 and SVIL-AS1. FEZF1-AS1 and LINC01929 were associated with survival in the TCGA set. Conclusions Our study provides through multiple levels of internal and external validation, a comprehensive list of dysregulated lncRNAs in NSCLC. We therefore envisage this dataset to serve as an important source for the lung cancer research community assisting future investigations on the involvement of lncRNAs in the pathogenesis of the disease and providing novel biomarkers for diagnosis, prognosis and therapeutic stratification.

Lung cancer is the leading cause of cancer-related deaths in China, with over 690 000 lung cancer deaths estimated in 2018. The mortality has increased about five-fold from the mid-1970s to the 2000s. Lung cancer low-dose computerized tomography (LDCT) screening in smokers was shown to improve survival in the US National Lung Screening Trial, and more recently in the European NELSON trial. However, although the predominant risk factor, smoking contributes to a lower fraction of lung cancers in China than in the UK and USA. Therefore, it is necessary to establish Chinese-specific screening strategies. There have been 23 associated programmes completed or still ongoing in China since the 1980s, mainly after 2000; and one has recently been planned. Generally, their entry criteria are not smoking-stringent. Most of the Chinese programmes have reported preliminary results only, which demonstrated a different high-risk subpopulation of lung cancer in China. Evidence concerning LDCT screening implementation is based on results of randomized controlled trials outside China. LDCT screening programmes combining tobacco control would produce more benefits. Population recruitment (e.g. risk-based selection), screening protocol, nodule management and cost-effectiveness are discussed in detail. In China, the high-risk subpopulation eligible for lung cancer screening has not as yet been confirmed, as all the risk parameters have not as yet been determined. Although evidence on best practice for implementation of lung cancer screening has been accumulating in other countries, further research in China is urgently required, as China is now facing a lung cancer epidemic.

DNase I hypersensitive sites (DHS) are abundant in regulatory elements, such as promoter, enhancer and transcription factor binding sites. Many studies have revealed that disease-associated variants were concentrated in DHS-related regions. However, limited studies are available on the roles of DHS-related variants in lung cancer. In this study, we performed a large-scale case–control study with 20 871 lung cancer cases and 15 971 controls to evaluate the associations between regulatory genetic variants in DHS and lung cancer susceptibility. The expression quantitative trait loci (eQTL) analysis and pathway-enrichment analysis were performed to identify the possible target genes and pathways. In addition, we performed motif-based analysis to explore the lung-cancer-related motifs using sequence kernel association test. Two novel variants, rs186332 in 20q13.3 (C>T, odds ratio [OR] = 1.17, 95% confidence interval [95% CI]: 1.10–1.24, P = 8.45 × 10−7) and rs4839323 in 1p13.2 (T>C, OR = 0.92, 95% CI: 0.89–0.95, P = 1.02 × 10−6) showed significant association with lung cancer risk. The eQTL analysis suggested that these two SNPs might regulate the expression of MRGBP and SLC16A1, respectively. What’s more, the expression of both MRGBP and SLC16A1 was aberrantly elevated in lung tumor tissues. The motif-based analysis identified 10 motifs related to the risk of lung cancer (P < 1.71 × 10−4). Our findings suggested that variants in DHS might modify lung cancer susceptibility through regulating the expression of surrounding genes. This study provided us a deeper insight into the roles of DHS-related genetic variants for lung cancer.

Introduction: Inherited susceptibility to lung cancer risk in never-smokers is poorly understood. The major reason for this gap in knowledge is that this disease is relatively uncommon (except in Asians), making it difficult to assemble an adequate study sample. In this study we conducted a genome-wide association study on the largest, to date, set of European-descent never-smokers with lung cancer. Methods: We conducted a two-phase (discovery and replication) genome-wide association study in never-smokers of European descent. We further augmented the sample by performing a meta-analysis with never-smokers from the recent OncoArray study, which resulted in a total of 3636 cases and 6295 controls. We also compare our findings with those in smokers with lung cancer. Results: We detected three genome-wide statistically significant single nucleotide polymorphisms rs31490 (odds ratio [OR]: 0.769, 95% confidence interval [CI]: 0.722-0.820; p value 5.31 x 10(-16)), rs380286 (OR: 0.770, 95% CI: 0.723-0.820; p value 4.32 x 10(-16)), and rs4975616 OR: 0.778, 95% CI: 0.730-0.829; p value 1.04 x 10(-14)). All three mapped to Chromosome 5 CLPTM1L-TERT region, previously shown to be associated with lung cancer risk in smokers and in never-smoker Asian women, and risk of other cancers including breast, ovarian, colorectal, and prostate. Conclusions: We found that genetic susceptibility to lung cancer in never-smokers is associated to genetic variants with pan-cancer risk effects. The comparison with smokers shows that top variants previously shown to be associated with lung cancer risk only confer risk in the presence of tobacco exposure, underscoring the importance of gene-environment interactions in the etiology of this disease. (C) 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The CAPUA study was supported by FIS-FEDER/Spain grants FIS-01/310, FIS-PI03-0365, and FIS-07-BI060604; FICYT/Asturias grants FICYT PB02-67 and FICYT IB09-133; and the University Institute of Oncology (IUOPA) of the University of Oviedo and the Ciber de Epidemiologiay Salud Pública (CIBERESP), Spain. (...), Zhu, Y., Wei, Y., Zhang, R., Dong, X., Shen, S., Zhao, Y., Bai, J., Albanes, D., Caporaso, N.E., Landi, M.T., Zhu, B., Chanock, S.J., Gu, F., Lam, S., Tsao, M.-S., Shepherd, F.A., Tardon, A., Fernandez-Somoano, A., Fernandez-Tardon, G., Chen, C., Barnett, M.J., Doherty, J., Bojesen, S.E., Johansson, M., Brennan, P., McKay, J.D., Carreras-Torres, R., Muley, T., Risch, A., Wichmann, H.-E., Bickeboeller, H., Rosenberger, A., Rennert, G., Saliba, W., Arnold, S.M., Field, J.K., Davies, M.P.A., Marcus, M.W., Wu, X., Ye, Y., Le Marchand, L., Wilkens, L.R., Melander, O., Manjer, J., Brunnstrom, H., Hung, R.J., Liu, G., Brhane, Y., Kachuri, L., Andrew, A.S., Duell, E.J., Kiemeney, L.A., Van der Heijden, E.H.F.M., Haugen, A., Zienolddiny, S., Skaug, V., Grankvist, K., Johansson, M., Woll, P.J., Cox, A., Taylor, F., Teare, D.M., Lazarus, P., Schabath, M.B., Aldrich, M.C., Houlston, R.S., McLaughlin, J., Stevens, V.L., Shen, H., Hu, Z., Dai, J., Amos, C.I., Han, Y., Zhu, D., Goodman, G.E., Chen, F., Christiani, D.C.

CAPUA study was supported by FIS-FEDER/Spain grant numbers FIS-01/310, FIS-PI03-0365, and FIS-07-BI060604, FICYT/Asturias grant numbers FICYT PB02-67 and FICYT IB09-133, and the University Institute of Oncology (IUOPA), of the University of Oviedo and the Ciber de Epidemiología y Salud Pública. CIBERESP, SPAIN (...), Ferreiro-Iglesias, A., Lesseur, C., McKay, J., Hung, R.J., Han, Y., Zong, X., Christiani, D., Johansson, M., Xiao, X., Li, Y., Qian, D.C., Ji, X., Liu, G., Caporaso, N., Scelo, G., Zaridze, D., Mukeriya, A., Kontic, M., Ognjanovic, S., Lissowska, J., Szołkowska, M., Swiatkowska, B., Janout, V., Holcatova, I., Bolca, C., Savic, M., Ognjanovic, M., Bojesen, S.E., Wu, X., Albanes, D., Aldrich, M.C., Tardon, A., Fernandez-Somoano, A., Fernandez-Tardon, G., Le Marchand, L., Rennert, G., Chen, C., Doherty, J., Goodman, G., Bickeböller, H., Wichmann, H.-E., Risch, A., Rosenberger, A., Shen, H., Dai, J., Field, J.K., Davies, M., Woll, P., Teare, M.D., Kiemeney, L.A., van der Heijden, E.H.F.M., Yuan, J.-M., Hong, Y.-C., Haugen, A., Zienolddiny, S., Lam, S., Tsao, M.-S., Johansson, M., Grankvist, K., Schabath, M.B., Andrew, A., Duell, E., Melander, O., Brunnström, H., Lazarus, P., Arnold, S., Slone, S., Byun, J., Kamal, A., Zhu, D., Landi, M.T., Amos, C.I., Brennan, P.

Supported by FIS-FEDER/Spain grant numbers FIS-01/310, FIS-PI03-0365, and FIS-07-BI060604, FICYT/Asturias grant numbers FICYT PB02-67 and FICYT IB09-133, and the University Institute of Oncology (IUOPA) of the University of Oviedo and the Ciber de Epidemiologia y Salud Pública., Ji, X., Bossé, Y., Landi, M.T., Gui, J., Xiao, X., Qian, D., Joubert, P., Lamontagne, M., Li, Y., Gorlov, I., de Biasi, M., Han, Y., Gorlova, O., Hung, R.J., Wu, X., McKay, J., Zong, X., Carreras-Torres, R., Christiani, D.C., Caporaso, N., Johansson, M., Liu, G., Bojesen, S.E., Le Marchand, L., Albanes, D., Bickeböller, H., Aldrich, M.C., Bush, W.S., Tardon, A., Rennert, G., Chen, C., Teare, M.D., Field, J.K., Kiemeney, L.A., Lazarus, P., Haugen, A., Lam, S., Schabath, M.B.,,rew, A.S., Shen, H., Hong, Y.-C., Yuan, J.-M., Bertazzi, P.A., Pesatori, A.C., Ye, Y., Diao, N., Su, L., Zhang, R., Brhane, Y., Leighl, N., Johansen, J.S., Mellemgaard, A., Saliba, W., Haiman, C., Wilkens, L., Fernandez-Somoano, A., Fernandez-Tardon, G., van der Heijden, E.H.F.M., Kim, J.H., Dai, J., Hu, Z., Davies, M.P.A., Marcus, M.W., Brunnström, H., Manjer, J., Melander, O., Muller, D.C., Overvad, K., Trichopoulou, A., Tumino, R., Doherty, J., Goodman, G.E., Cox, A., Taylor, F., Woll, P., Brüske, I., Manz, J., Muley, T., Risch, A., Rosenberger, A., Grankvist, K., Johansson, M., Shepherd, F., Tsao, M.-S., Arnold, S.M., Haura, E.B., Bolca, C., Holcatova, I., Janout, V., Kontic, M., Lissowska, J., Mukeria, A., Ognjanovic, S., Orlowski, T.M., Scelo, G., Swiatkowska, B., Zaridze, D., Bakke, P., Skaug, V., Zienolddiny, S., Duell, E.J., Butler, L.M., Koh, W.-P., Gao, Y.-T., Houlston, R., McLaughlin, J., Stevens, V., Nickle, D.C., Obeidat, M., Timens, W., Zhu, B., Song, L., Artigas, M.S., Tobin, M.D., Wain, L.V., Gu, F., Byun, J., Kamal, A., Zhu, D., Tyndale, R.F., Wei, W.-Q., Chanock, S., Brennan, P., Amos, C.I.

Mapping molecular sub-types in breast cancer (BC) tumours is a rapidly evolving area due to growing interest in, for example, targeted therapy and screening high-risk populations for early diagnosis. We report a new concept for profiling BC molecular sub-types based on volatile organic compounds (VOCs). For this purpose, breath samples were collected from 276 female volunteers, including healthy, benign conditions, ductal carcinoma in situ (DCIS) and malignant lesions. Breath samples were analysed by gas chromatography mass spectrometry (GC-MS) and artificially intelligent nanoarray technology. Applying the non-parametric Wilcoxon/Kruskal-Wallis test, GC-MS analysis found 23 compounds that were significantly different (p < 0.05) in breath samples of BC patients with different molecular sub-types. Discriminant function analysis (DFA) of the nanoarray identified unique volatolomic signatures between cancer and non-cancer cases (83% accuracy in blind testing), and for the different molecular sub-types with accuracies ranging from 82 to 87%, sensitivities of 81 to 88% and specificities of 76 to 96% in leave-one-out cross-validation. These results demonstrate the presence of detectable breath VOC patterns for accurately profiling molecular sub-types in BC, either through specific compound identification by GC-MS or by volatolomic signatures obtained through statistical analysis of the artificially intelligent nanoarray responses.

Although several lung cancer susceptibility loci have been identified, much of the heritability for lung cancer 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 genome-wide association study (GWAS) analysis of lung cancer in 29,266 cases and 56,450 controls. We identified 18 susceptibility loci achieving genome-wide significance, including 10 new loci. The new loci highlight the striking heterogeneity in genetic susceptibility across the histological subtypes of lung cancer, with four loci associated with lung cancer overall and six loci associated with lung adenocarcinoma. Gene expression quantitative trait locus (eQTL) analysis in 1,425 normal lung tissue samples highlights 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.

NCI, National Cancer Institute; NIHR, National Institute for Health Research, Carreras-Torres, R., Johansson, M., Haycock, P.C., Wade, K.H., Relton, C.L., Martin, R.M., Smith, G.D., Albanes, D., Aldrich, M.C., Andrew, A., Arnold, S.M., Bickeböller, H., Bojesen, S.E., Brunnström, H., Manjer, J., Brüske, I., Caporaso, N.E., Chen, C., Christiani, D.C., Christian, W.J., Doherty, J.A., Duell, E.J., Field, J.K., Davies, M.P.A., Marcus, M.W., Goodman, G.E., Grankvist, K., Haugen, A., Hong, Y.-C., Kiemeney, L.A., Van Der Heijden, E.H.F.M., Kraft, P., Johansson, M.B., Lam, S., Landi, M.T., Lazarus, P., Le Marchand, L., Liu, G., Melander, O., Park, S.L., Rennert, G., Risch, A., Haura, E.B., Scelo, G., Zaridze, D., Mukeriya, A., Savić, M., Lissowska, J., Swiatkowska, B., Janout, V., Holcatova, I., Mates, D., Schabath, M.B., Shen, H., Tardon, A., Teare, M.D., Woll, P., Tsao, M.-S., Wu, X., Yuan, J.-M., Hung, R.J., Amos, C.I., McKay, J., Brennan, P.

Deregulation of mitotic spindle genes has been reported to contribute to the development and progression of malignant tumours. The aim of the present study was to explore the association between the expression profiles of Aurora kinases (AURKA, AURKB and AURKC), cytoskeleton-associated protein 5 (CKAP5), discs large-associated protein 5 (DLGAP5), kinesin-like protein 11 (KIF11), microtubule nucleation factor (TPX2), monopolar spindle 1 kinase (TTK), and β-tubulins (TUBB) and (TUBB3) genes and clinicopathological characteristics in human non-small cell lung carcinoma (NSCLC). Reverse transcription-quantitative polymerase chain reaction-based RNA gene expression profiles of 132 NSCLC and 44 adjacent wild-type tissues were generated, and Cox's proportional hazard regression was used to examine associations. With the exception of AURKC, all genes exhibited increased expression in NSCLC tissues. Of the 10 genes examined, only AURKA was significantly associated with prognosis in NSCLC. Multivariate Cox's regression analysis demonstrated that AURKA mRNA expression [hazard ratio (HR), 1.81; 95% confidence interval (CI), 1.16–2.84; P=0.009], age (HR, 1.03; 95% CI, 1.00–1.06; P=0.020), pathological tumour stage 2 (HR, 2.43; 95% CI, 1.16–5.10; P=0.019) and involvement of distal nodes (pathological node stage 2) (HR, 3.14; 95% CI, 1.24–7.99; P=0.016) were independent predictors of poor prognosis in patients with NSCLC. Poor prognosis of patients with increased AURKA expression suggests that those patients may benefit from surrogate therapy with AURKA inhibitors.

This study was funded by National Cancer Institution, USA (R21 CA202529, TWG YFH)., Wang, T; Moon, JY; Wu, YQ; Amos, CI; Hung, RJ; Tardon, A; Andrew, A; Chen, C; Christiani, DC; Albanes, D; van der Heijdendr, EHFM; Duell, E; Rennert, G; Goodman, G; Liu, G; Mckay, JD; Yuan, JM; Field, JK; Manjer, J; Grankvist, K; Kiemeney, LA; Le Marchand, L; Teare, MD; Schabath, MB; Johansson, M; Aldrich, MC; Davies, M; Johansson, M; Tsao, MS; Caporaso, N; Lazarus, P; Lam, S; Bojesen, SE; Arnold, S; Wu, XF; Zong, XC; Hong, YC; Ho, GYF

Background: Volatile organic compounds (VOCs) are potential biomarkers for cancer detection in breath, but it is unclear if they reflect specific mutations. To test this, we have compared human bronchial epithelial cell (HBEC) cell lines carrying the KRASV12 mutation, knockdown of TP53 or both with parental HBEC cells. Methods: VOC from headspace above cultured cells were collected by passive sampling and analysed by thermal desorption gas chromatography mass spectrometry (TD-GC–MS) or sensor array with discriminant factor analysis (DFA). Results: In TD-GC–MS analysis, individual compounds had limited ability to discriminate between cell lines, but by applying DFA analysis combinations of 20 VOCs successfully discriminated between all cell types (accuracies 80–100%, with leave-one-out cross validation). Sensor array detection DFA demonstrated the ability to discriminate samples based on their cell type for all comparisons with accuracies varying between 77% and 93%. Conclusions: Our results demonstrate that minimal genetic changes in bronchial airway cells lead to detectable differences in levels of specific VOCs identified by TD-GC–MS or of patterns of VOCs identified by sensor array output. From the clinical aspect, these results suggest the possibility of breath analysis for detection of minimal genetic changes for earlier diagnosis or for genetic typing of lung cancers.

Lung cancer mortality is strongly associated with the predominant diagnosis of late stage lesions that hampers effective therapy. Molecular biomarkers for early lung cancer detection is an unmet public health need and the lung cancer research community worldwide is putting a lot of effort to utilise major lung cancer population programmes in order to develop such molecular tools. The study of cancer epigenetics in the last decade has radically altered our views in cancer pathogenesis, providing new insights in biomarker development for risk assessment, early detection and therapeutic stratification. DNA methylation and miRNAs have rapidly emerged as potential biomarkers in body fluids showing promise to assist the clinical management of lung cancer. These new developments are exemplified in this review, demonstrating the huge potential of clinical cancer epigenetics, but also critically discussing the necessary validation steps to bring epigenetic biomarkers towards clinical implementation and the weaknesses of current biomarker studies.

Background: Taxanes are mitotic poisons widely used in the treatment of non-small cell lung cancer (NSCLC), however, little is known about potential molecular modulators of response to these compounds. Aurora B (AURKB) is a critical regulator of the mitotic spindle assembly, previously shown overexpressed in NSCLC. Here we investigated the hypothesis that AURKB expression modulates the efficacy of taxanes in NSCLC cells. Methods: AURKB mRNA expression was determined by qPCR in 132 frozen NSCLC tissues and nine NSCLC cell lines. Aurora B expression was knocked down in cell lines using multiple shRNA constructs. Barasertib was used to specifically inhibit AURKB activity, determined by the level of H3S10 phosphorylation. Results: Frequent AURKB mRNA upregulation was observed in NSCLC tissues (P

Background: Diagnosis is jeopardised when limited biopsy material is available or histological quality compromised. Here we developed and validated a prediction algorithm based on microRNA (miRNA) expression that can assist clinical diagnosis of lung cancer in minimal biopsy material to improve clinical management. Methods: Discovery utilised Taqman Low Density Arrays (754 miRNAs) in 20 non-small cell lung cancer (NSCLC) tumour/normal pairs. In an independent set of 40 NSCLC patients, 28 miRNA targets were validated using qRT–PCR. A prediction algorithm based on eight miRNA targets was validated blindly in a third independent set of 47 NSCLC patients. The panel was also tested in formalin-fixed paraffin-embedded (FFPE) specimens from 20 NSCLC patients. The genomic methylation status of highly deregulated miRNAs was investigated by pyrosequencing. Results: In the final, frozen validation set the panel had very high sensitivity (97.5%), specificity (96.3%) and ROC-AUC (0.99, P=10−15). The panel provided 100% sensitivity and 95% specificity in FFPE tissue (ROC-AUC=0.97 (P=10−6)). DNA methylation abnormalities contribute little to the deregulation of the miRNAs tested. Conclusion: The developed prediction algorithm is a valuable potential biomarker for assisting lung cancer diagnosis in minimal biopsy material. A prospective validation is required to measure the enhancement of diagnostic accuracy of our current clinical practice.

The autophagy degradation pathway is a cellular pathway that sequesters cargo from the cytosol to autophagosomes that are transferred to lysosomes for degradation or recycled as precursor metabolites. The autophagy pathway allows the removal of damaged organelles/proteins and is emerging as an important aspect of multiple human pulmonary diseases. The autophagy process is important in both the function of the immune system and the control of inflammation. Xenophagy (autophagy of bacteria) is an example of selective autophagy which could play a role in host defense mechanisms in pulmomary diseases such as sepsis. Autophagy pathways involving the degradation of cytosolic cargo could play different roles in disease pathogenesis and progression. In the case of certain lung diseases, mitophagy is elevated and the cilia shorten (ciliophagy), which contribute to lung dysfunction in the pathogenesis of chronic obstructive pulmonary disease. In other types of lung diseases such as pulmonary vascular disease, autophagy may provide a protective role to allow cell proliferation, repair and control of cell death. Disruption of autophagy in cystic fibrosis and idiopathic pulmonary fibrosis could promote pathogenesis of the disease. In lung cancer, autophagy is a ‘double-edged sword’ it blocks progression, but at the same time promotes tumour growth. In this chapter, we will review the different types of autophagy, the role of autophagy and its significance to human lung diseases. In addition, we will discuss the potential of targeting autophagy with therapeutics for lung disease management.

Body mass index (BMI) is inversely associated with lung cancer risk in observational studies, even though it increases the risk of several other cancers, which could indicate confounding by tobacco smoking or reverse causality. We used the two-sample Mendelian randomization (MR) approach to circumvent these limitations of observational epidemiology by constructing a genetic instrument for BMI, based on results from the GIANT consortium, which was evaluated in relation to lung cancer risk using GWAS results on 16,572 lung cancer cases and 21,480 controls. Results were stratified by histological subtype, smoking status and sex. An increase of one standard deviation (SD) in BMI (4.65 Kg/m2) raised the risk for lung cancer overall (OR = 1.13; P = 0.10). This was driven by associations with squamous cell (SQ) carcinoma (OR = 1.45; P = 1.2 × 10−3) and small cell (SC) carcinoma (OR = 1.81; P = 0.01). An inverse trend was seen for adenocarcinoma (AD) (OR = 0.82; P = 0.06). In stratified analyses, a 1 SD increase in BMI was inversely associated with overall lung cancer in never smokers (OR = 0.50; P = 0.02). These results indicate that higher BMI may increase the risk of certain types of lung cancer, in particular SQ and SC carcinoma.

Born and raised in the West Midlands town of Dudley, at the heart of what is still known as the Black Country, Vince Newey attended Dudley Grammar School before going up to New College, Oxford, to study English in 1962. It was at Liverpool that he completed his BLitt, 'A Critical Examination of the Poetry of William Cowper', and later his PhD, 'Studies in the Literature of Selfhood and Subjective Experience', based on work he had by then published. An effective administrator as well as a highly respected scholar, in 1989 Vince was appointed Professor of English at the University of Leicester, leading the Department as Head from 1991 to 2000, before taking early retirement in 2006. Vince's style of criticism is more than instructive and intellectually expansive, though it is always both of these: it is, in the best sense, creative, enlivening and revivifying.

Chromosome 5p15.33 has been identified as a lung cancer susceptibility locus, however the underlying causal mechanisms were not fully elucidated. Previous fine-mapping studies of this locus have relied on imputation or investigated a small number of known, common variants. This study represents a significant advance over previous research by investigating a large number of novel, rare variants, as well as their underlying mechanisms through telomere length. Variants for this fine-mapping study were identified through a targeted deep sequencing (average depth of coverage greater than 4000×) of 576 individuals. Subsequently, 4652 SNPs, including 1108 novel SNPs, were genotyped in 5164 cases and 5716 controls of European ancestry. After adjusting for known risk loci, rs2736100 and rs401681, we identified a new, independent lung cancer susceptibility variant in LPCAT1: rs139852726 (OR = 0.46, P = 4.73×10(-9)), and three new adenocarcinoma risk variants in TERT: rs61748181 (OR = 0.53, P = 2.64×10(-6)), rs112290073 (OR = 1.85, P = 1.27×10(-5)), rs138895564 (OR = 2.16, P = 2.06×10(-5); among young cases, OR = 3.77, P = 8.41×10(-4)). In addition, we found that rs139852726 (P = 1.44×10(-3)) was associated with telomere length in a sample of 922 healthy individuals. The gene-based SKAT-O analysis implicated TERT as the most relevant gene in the 5p15.33 region for adenocarcinoma (P = 7.84×10(-7)) and lung cancer (P = 2.37×10(-5)) risk. In this largest fine-mapping study to investigate a large number of rare and novel variants within 5p15.33, we identified novel lung and adenocarcinoma susceptibility loci with large effects and provided support for the role of telomere length as the potential underlying mechanism.

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations.