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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.

Background The association between smoking and several health outcomes among those from the most deprived communities in the UK has not previously been detailed. The aim of this study is to examine the impact of smoking on health outcomes specifically among a particularly deprived population in a developed country (Liverpool; one of the most deprived local authorities in England). Methods The Liverpool Lung Project recruited a prospective cohort of 8753 participants from across Liverpool, aged 45–79 years between 1998 and 2008. Participants were followed annually through the Hospital Episode Statistics until 31 January 2013. Logistic regression models were used to identify health outcomes of smoking. Results From our study population, 5195 were smokers and 3558 were non-smokers. Smoking was associated with male gender (OR 1.62, 95% CI 1.48 to 1.77), pneumonia (1.28, 95% CI 1.10 to 1.49), chronic obstructive pulmonary disease (1.30, 95% CI 1.14 to 1.48), emphysema (5.46, 95% CI 3.48 to 8.55), bronchitis (1.85, 95% CI 1.65 to 2.07), other cancers (1.69, 95% CI 1.44 to 1.99), lung cancer (6.0, 95% CI 3.72 to 9.69), diabetes (1.21, 95% CI 1.02 to 1.43) and cardiovascular disease (1.45, 95% CI 1.25 to 1.67). Conclusions Smokers from deprived backgrounds in Liverpool showed increased risk of developing pneumonia, emphysema, chronic obstructive pulmonary disease, bronchitis, lung cancer, other types of cancer, cardiovascular disease and diabetes. These findings are in line with the literature and may help to inform public health policies and ultimately work towards addressing smoking-related health inequalities.

Objectives This Liverpool Healthy Lung Programme is a response to high rates of lung cancer and respiratory diseases locally and aims to diagnose lung cancer at an earlier stage by proactive approach to those at high risk of lung cancer. The objective of this study is to evaluate the programme in terms of its likely effect on mortality from lung cancer and its delivery to deprived populations. Methods Persons aged 58–75 years, with a history of smoking or a diagnosis of chronic obstructive pulmonary disease (COPD) 2 according to general practice records were invited for lung health check in a community health hub setting. A detailed risk assessment and spirometry were performed in eligible patients. Those with a 5% or greater five-year risk of lung cancer were referred for a low dose CT 3 scan. Results A total of 4 566 subjects attended the appointment for risk assessment and 3 591 (79%) consented to data sharing. More than 80% of the patients were in the most deprived quintile of the index of multiple deprivation. Of those attending, 63% underwent spirometry and 43% were recommended for a CT scan. A total of 25 cancers were diagnosed, of which 16 (64%) were stage I. Comparison with the national stage distribution implied that the programme was reducing lung cancer mortality by 22%. Conclusions Community based proactive approaches to early diagnosis of lung cancer in health deprived regions are likely to be effective in early detection of lung cancer.

BackgroundEstimation of the clinical probability of malignancy in patients with pulmonary nodules will facilitate early diagnosis, determine optimum patient management strategies and reduce overall costs.MethodsData from the UK Lung Cancer Screening trial were analysed. Multivariable logistic regression models were used to identify independent predictors and to develop a parsimonious model to estimate the probability of lung cancer in lung nodules detected at baseline and at 3-month and 12-month repeat screening.ResultsOf 1994 participants who underwent CT scan, 1013 participants had a total of 5063 lung nodules and 52 (2.6%) of the participants developed lung cancer during a median follow-up of 4 years. Covariates that predict lung cancer in our model included female gender, asthma, bronchitis, asbestos exposure, history of cancer, early and late onset of family history of lung cancer, smoking duration, FVC, nodule type (pure ground-glass and part-solid) and volume as measured by semiautomated volumetry. The final model incorporating all predictors had excellent discrimination: area under the receiver operating characteristic curve (AUC 0.885, 95% CI 0.880 to 0.889). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected AUC 0.882, 95% CI 0.848 to 0.907). The risk model had a good calibration (goodness-of-fit χ[8] 8.13, p=0.42).ConclusionsOur model may be used in estimating the probability of lung cancer in nodules detected at baseline and at 3 months and 12 months from baseline, allowing more efficient stratification of follow-up in population-based lung cancer screening programmes.Trial registration number78513845.

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.

The development of cancer is driven by the accumulation of many oncogenesis-related genetic alterations and tumorigenesis is triggered by complex networks of involved genes rather than independent actions. To explore the epistasis existing among oncogenesis-related genes in lung cancer development, we conducted pairwise genetic interaction analyses among 35,031 SNPs from 2027 oncogenesis-related genes. The genotypes from three independent genome-wide association studies including a total of 24,037 lung cancer patients and 20,401 healthy controls with Caucasian ancestry were analyzed in the study. Using a two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis, we identified significant genetic interactions between SNPs in RGL1:RAD51B (OR=0.44, p value=3.27x10-11 in overall lung cancer and OR=0.41, p value=9.71x10-11 in non-small cell lung cancer), SYNE1:RNF43 (OR=0.73, p value=1.01x10-12 in adenocarcinoma) and FHIT:TSPAN8 (OR=1.82, p value=7.62x10-11 in squamous cell carcinoma) in our analysis. None of these genes have been identified from previous main effect association studies in lung cancer. Further eQTL gene expression analysis in lung tissues provided information supporting the functional role of the identified epistasis in lung tumorigenesis. Gene set enrichment analysis revealed potential pathways and gene networks underlying molecular mechanisms in overall lung cancer as well as histology subtypes development. Our results provide evidence that genetic interactions between oncogenesis-related genes play an important role in lung tumorigenesis and epistasis analysis, combined with functional annotation, provides a valuable tool for uncovering functional novel susceptibility genes that contribute to lung cancer development by interacting with other modifier genes.

Incorporation of genetic variants such as single nucleotide polymorphisms (SNPs) into risk prediction models may account for a substantial fraction of attributable disease risk. Genetic data, from 2385 subjects recruited into the Liverpool Lung Project (LLP) between 2000 and 2008, consisting of 20 SNPs independently validated in a candidate-gene discovery study was used. Multifactor dimensionality reduction (MDR) and random forest (RF) were used to explore evidence of epistasis among 20 replicated SNPs. Multivariable logistic regression was used to identify similar risk predictors for lung cancer in the LLP risk model for the epidemiological model and extended model with SNPs. Both models were internally validated using the bootstrap method and model performance was assessed using area under the curve (AUC) and net reclassification improvement (NRI). Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 (DRD2), rs5744256 (IL-18), rs2306022 (ITGA11) with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing P

The publisher regrets the author Darcy Fidoe was incorrectly spelt as Darcy Fideo. The publisher would like to apologise for any inconvenience caused.

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.

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.

The aim of the present study was to apply the Charlson comorbidity index (CCI) to evaluate the impact of comorbidity on lung cancer mortality in individuals not exhibiting lung cancer at the commencement of follow-up. Data from 9,579 participants without lung cancer were extracted from the Liverpool Lung Project between 1999 and 2010 and linked to the Hospital Episode Statistics database. The occurrence of comorbidities was assessed one year prior to the individual inclusion date. CCI scores were computed and Cox regression analysis was used to evaluate the association between comorbidity and lung cancer mortality using a competitive risk approach. During a median follow-up of 11 years, 1,320/9,579 (13.7%) individuals developed lung cancer, of which 886 (67.1%) succumbed to lung cancer and 875 of the 9,579 individuals (9.1%) succumbed due to other causes. The severity of comorbidity was associated with higher lung cancer-specific mortality; low to moderate comorbidity exhibited a hazard ratio (HR) of 2.86 [95% confidence interval (CI), 1.17-7.02] and severe comorbidity exhibited an HR of 5.16 (95% CI, 2.07-12.89). Furthermore, the CCI score determined that the severity of comorbidity increased the risk of lung cancer-specific mortality. Thus, CCI score is a good predictor of lung cancer-specific mortality and the use of comorbidity burdens in the clinical management of lung cancer is recommended.

Non-small cell lung cancer is the most common type of lung cancer. Both environmental and genetic risk factors contribute to lung carcinogenesis. We conducted a genome-wide interaction analysis between single nucleotide polymorphisms (SNPs) and smoking status (never-versus ever-smokers) in a European-descent population. We adopted a two-step analysis strategy in the discovery stage: we first conducted a case-only interaction analysis to assess the relationship between SNPs and smoking behavior using 13 336 non-small cell lung cancer cases. Candidate SNPs with P-value

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.

The National Lung Cancer Screening trial (NLST) demonstrated that individuals assigned to the LDCT screening arm had a 20% lower mortality than those who were assigned to the conventional chest radiography. The NLST was thoroughly analyzed by the US Preventive Task Force on CT Screening and they recommended that lung cancer screening should be implemented. A number of other countries have also recommended implementation, whilst others are awaiting the outcome of the NELSON Trial. However, recommendations for the management of CT screen detected nodules have only recently had any clarity. The management of CT detected nodules in the NLST was based on the identification and reporting of 4 mm diameter nodules found on the CT screens but there was no NLST radiology protocol in place for the management of nodules. The use of volumetric analysis is not routinely used in the USA and there is still a reliance on utilising the CT nodule diameter as the management parameter. The first pulmonary risk model was developed by the Canadians, utilising data sets from the Pan-Canadian Early detection of Lung cancer (PanCan) and validated in the chemoprevention trial dataset at the British Columbian Agency. This Canadian model, known as the Brock Model, is currently available and has been integrated into the British Thoracic Society guidelines on the management of pulmonary nodules. The American College of Radiology setup a Lung Cancer Screening Committee subgroup on Lung-RADS, to standardize lung cancer screening CT reporting and provide management recommendations. However, it has been recommended that the Lung-RADS system should be revised as the system as it has never been studied in a prospective fashion. The NELSON trial introduced a third screening test, the "indeterminate" screening test result, this was done with the aim to reduce the false-positives CT screening results and also utilized by the UKLS trial successfully. On comparing the radiological CT screen volumetric and diameter based protocols in the NELSON trial, the sensitivity and negative predictive value appeared to be comparable, however a higher specificity and positive predictive value was found for the volume-based protocols, thus confirming the advantage of utilising the volumetric approach over diameter The British Thoracic Society (BTS) has undertaken an in-depth piece of work developing guidelines on the management of pulmonary nodules, utilising the wealth of data published by the NELSON team and support the use of volumetric analysis for the management of pulmonary nodules.

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 is among the most common and fatal malignancies worldwide, and is often diagnosed at an advanced stage. Self-assessment tools for individualised lung cancer risk could potentially refine our understanding of risk factors, and contribute to disease prevention and earlier detection, thus reducing the disease burden. The originally published algorithm of the validated Liverpool Lung Project risk prediction model for lung cancer was transformed into a web-based questionnaire called MyLungRisk (MLR, www.mylungrisk.org). This provides a user-friendly interface for individuals aged 50–79 years to estimate their risk of developing lung cancer in the next five years. It incorporates epidemiological risk factors such as age, gender, smoking duration, family history of lung cancer, previous history of pneumonia, previous diagnosis of cancer (other than lung cancer) and exposure to asbestos. Upon the completion of the web-based questionnaire, the individual is given an estimated lung cancer risk score for...

Several epidemiologic studies have indicated an increased risk of lung cancer among welders. We used the SYNERGY project database to assess welding as a risk factor for developing lung cancer. The database includes data on 15,483 male lung cancer cases and 18,388 male controls from 16 studies in Europe, Canada, China, and New Zealand conducted between 1985 and 2010. Odds ratios and 95% confidence intervals between regular or occasional welding and lung cancer were estimated, with adjustment for smoking, age, study center, and employment in other occupations associated with lung cancer risk. Overall, 568 cases and 427 controls had ever worked as welders and had an odds ratio of developing lung cancer of 1.44 (95% confidence interval: 1.25, 1.67) with the odds ratio increasing for longer duration of welding. In never and light smokers, the odds ratio was 1.96 (95% confidence interval: 1.37, 2.79). The odds ratios were somewhat higher for squamous and small cell lung cancers than for adenocarcinoma. Another 1,994 cases and 1,930 controls had ever worked in occupations with occasional welding. Work in any of these occupations was associated with some elevation of risk, though not as much as observed in regular welders. Our findings lend further support to the hypothesis that welding is associated with an increased risk of lung cancer. © The Author 2013.

Low-dose computed tomography screening is a strategy for early diagnosis of lung cancer. The success of such screening will be dependent upon identifying populations at sufficient risk in order to maximise the benefit-to-harm ratio of the intervention. To facilitate this, the lung cancer risk prediction community has established several risk models with good predictive performance. This review focuses on current progress in risk modelling for lung cancer prediction, with some views on future development. J. Surg. Oncol. 2013 108:304–311. © 2013 Wiley Periodicals, Inc.

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

Purpose: The current project sought to examine whether delivery of lung cancer risk projections (calculated using the Liverpool Lung Project [LLP] risk model) predicted follow-up smoking status. Design: Two single-blinded randomized controlled trials. Setting: Stop Smoking Services in Liverpool (United Kingdom). Participants: Baseline current smokers (N = 297) and baseline recent former smokers (N = 216) were recruited. Intervention: Participants allocated to intervention groups were provided with personalized lung cancer risk projections, calculated using the LLP risk model. Measures: Baseline and follow-up questionnaires explored sociodemographics, smoking behavior, and lung cancer risk perceptions. Analysis: Bivariate analyses identified significant differences between randomization groups, and logistic regression models were developed to investigate the intervention effect on the outcome variables. Results: Lung cancer risk projections were not found to predict follow-up smoking status in the trial of baseline current smokers; however, they did predict follow-up smoking status in the trial of baseline recent former smokers (odds ratio: 1.91; 95% confidence interval: 1.03-3.55). Conclusion: The current study suggests that lung cancer risk projections may help maintain abstinence among individuals who have quit smoking, but the results did not provide evidence to suggest that lung cancer risk projections motivate current smokers to quit.

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.

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.

Lung cancer is the most common cancer in the world, therefore creating a huge public health concern. The aim of this study is to determine the change in age-standardised incidence rate trend of lung cancer in England between 2002 and 2011 and use these findings to anticipate the potential burden of the disease by gender in the year 2020. Lung cancer incidence data (ICD-10 code C33-34) from 2002 and 2011 and mid-year population estimates for the same period were obtained from Office of National Statistics. Age-standardised incidence rates were calculated, by gender and region. Poisson regression analysis was used to describe the time incidence trend and projections were estimated up to year 2020. A total of 318, 417 lung cancer cases were identified. Incidence rates decreased in men by an average annual percentage change (AAPC) of -1.0% and increased in women by +1.9%. Projection analysis showed that by year 2020, provided the rates remain the same, English women will have the same lung cancer incidence rates as their male counterparts. This study demonstrated that there would be 5,848 excess lung cancer cases by 2020 with female population accounting for 85% (4,996) of the excess cases. Therefore, in addition to the development of high quality preventive intervention strategies, future public health also needs to prioritise targets at the implementation phase, in a manner that engage women living in regions that have demonstrated very high AAPC values.

Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P > 5×10(-8)) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P = 4.6×10(-7)) and MTMR2 at 11q21 (rs10501831, P = 3.1×10(-6)) with SCC, as well as GAREM at 18q12.1 (rs11662168, P = 3.4×10(-7)) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P = 1.05×10(-4) for KCNIP4, represented by rs9799795) and AC (P = 2.16×10(-4) for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.

Introduction: DNA topoisomerase inhibitors (e.g. etoposide, irinotecan, teniposide, topotecan) are commonly used for treating small cell lung cancer (SCLC). Tyrosyl-DNA phosphodiesterase (TDP1) plays a role in repairing DNA damage caused by DNA topoisomerase inhibitors and it is believed to be responsible for resistance to this class of drugs. The purpose of this study was to determine whether common TDP1 polymorphisms are associated with overall survival among SCLC patients. Method: Two common TDP1 SNPs (rs942190 and rs2401863, r2(European)=0.63 and r2(East Asian)=0.26) were analyzed in 898 patients from 10 studies in the International Lung Cancer Consortium, conducted in the U.S., Canada, Japan, Italy, Spain and the United Kingdom. Genotype data were obtained from either the OncoArray Consortium or other existing genome-wide association studies. Cox proportional hazard regression models with robust standard error were used to evaluate genotype associations for overall survival at 36 months post-diagnosis, adjusting for age, sex, race and tumor stage. Results: Patients homozygous for the minor allele (GG) of TDP1 SNP rs942190 were associated with poorer survival compared to those carrying both major alleles (AA), with a hazard ratio (HR) of 1.36 (95% confidence interval (CI): 1.07-1.72, p-value=0.01), but there was no association with survival for patients with the heterozygous genotype (HR=1.02, 95% CI: 0.83-1.27, p-value=0.83). For rs2401863, patients carrying both minor alleles (CC) tended to have better survival than patients carrying AA alleles, but this was within the limits of chance given no significant association (HR=0.79, 95% CI: 0.61-1.02, p-value=0.07). In addition, multi-tissue expression quantitative trait loci (eQTL) analyses from the Genotype-Tissue Expression Project indicated a potential effect of rs942190 on lung tissue, with higher TDP1 gene expression for the GG than AG or AA genotypes. Conclusion: We found that among SCLC patients the TDP1 rs942190 GG genotype was associated with poor overall survival. Further study could determine if this genotype can serve as a predictive marker for the treatment efficacy of DNA topoisomerase inhibitors in SCLC. Citation Format: Pawadee Lohavanichbutr, Lori C. Sakoda, Christopher I. Amos, Susanne M. Arnold, David C. Christiani, Michael P. Davies, John K. Field, Eric B. Haura, Rayjean J. Hung, Takashi Kohno, Maria Teresa Landi, Geoffrey Liu, Yi Liu, Michael W. Marcus, Grainne M. O'Kane, Matthew B. Schabath, Kouya Shiraishi, Stacey A. Slone, Adonina Tardón, Ping Yang, Kazushi Yoshida, Ruyang Zhang, Xuchen Zong, Noel S. Weiss, Chu Chen. Common TDP1 polymorphisms in relation to survival among small cell lung cancer patients in a multicenter study from the International Lung Cancer Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2248. doi:10.1158/1538-7445.AM2017-2248

Introduction: The UKLS CT screening trial has demonstrated that by utilizing a population based approach, a risk prediction algorithm (LLPv2), volumetric based CT nodule detection and a specific management pathway, 80% of the lung cancers detected were Stage I or II, 85% of which were suitable for surgical resection. In the Liverpool region, 2.4% of the screened population were found to have lung cancer at baseline. Methodology: Liverpool has one of the highest lung cancer mortality rates in England, and double the national average of cases diagnosed. It was decided to tackle this health inequality and the NHS Liverpool Clinical Commissioning Group (CCG) introduced the Liverpool Healthy Lung Programme (LHLP). LHLP, began in February 2016 and aims to increase knowledge in communities, promoting positive messages around lung health, recognizing and tackling fear and fatalism surrounding lung cancer. The LHLP is undertaking coordinated, focused public engagement (Breathe Freely) events, throughout the Liverpool region, starting in neighborhoods with the highest incidence of lung cancer. These events are followed by patients between the ages of 58-70 who have COPD, or have smoked, being invited to a local face to face lung health check, conducted by a lung-health nurse working in local community clinics and in partnership with General Practice. Summary & Findings: This assessment of lung health involved calculation of their risk of developing lung cancer in the next 5 years (My Lung Risk). Those who triggered a 5% threshold were offered low dose thoracic CT. In the Liverpool CCG Region 34,000 patients will be eligible: in the initial 12 weeks in the first neighborhood, 1014 of 2471 (41%) eligible individuals booked to attend the lung health check (865 attended), 351 (41%) triggered the offer of a CT appointment. 306 scans have been reported; 31 (10%) had lung nodules. 59 (20%) had a noncancer significant finding managed in General Practice. To date, September, 4 have confirmed lung cancer with surgical resection; 2 on follow-up. 406 patients (45%) were not previously diagnosed with COPD, 225 (45%) had abnormal spirometry, and were referred. 238 (28%) accepted lifestyle referrals (e.g. smoking, exercise). Conclusion: The project is over 3 years. Projections suggest 34,000 participants will be eligible. We are aiming for an ambitious 70% uptake meaning 24,000 will have a face to face lung health check. We estimate that ~150 cases of lung cancer will be diagnosed Citation Format: John K. Field, Edward S. Gaynor, Stephen W. Duffy, Katy Gardner, Cathy Hubbert, Dimitris Tsintzos, Michael W. Marcus, Lisa Jones, Rachel Avanitis, Michelle Timoney, Martin Ledson. Liverpool healthy lung project: a primary care initiative to identify hard to reach individuals with a high risk of developing lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4220. doi:10.1158/1538-7445.AM2017-4220

Screening for lung cancer The results of the US National Lung Screening Trial (NLST) were published in 2011 and are considered a landmark event in lung cancer research. This randomised study of 53,454 individuals showed that computed tomography (CT) scans are able to reduce lung cancer mortality by 20% through early detection, although with important cost and morbidity due to overdiagnosis and treatment of benign nodule. A number of European pilot trials have reported, we await the NELSON, which is the only statistically powered screening trial in Europe. There are now discussions on how to implement lung cancer screening throughout the world, within differing health care systems. The success of lung cancer screening will be dependent upon identifying populations at sufficient risk in order to maximise the benefit-to-harm ratio of the intervention. Risk prediction models Thus accurate selection of high-risk individuals for lung cancer screening requires robust methods for risk prediction. The discriminative performance of a risk model depends not only on the identification of individual risk factors, but also on the influence of these risk variables in the presence/absence of other variables, how accurately these factors can be measured, and the appropriateness of the population and statistical techniques used for modeling. However, the main practical application of a risk prediction model is its use by non-specialists for selection of suitable high-risk people for lung cancer screening/intervention. In addition, to being technically detailed and accurate, a risk model needs to be sufficiently user-friendly to be applied in the general population and/or primary care setting. In practical terms, this means that the risk variables should be straightforward to elicit, and the algorithm should be simple to run. Current lung cancer prediction models The Lung cancer risk prediction models which have been developed include Bach, Spitz, LLP and more recently the PLCO [1] and EPIC model. The UK Lung cancer Screening trial (UKLS) [2] has been the only RCT trial to date, to select high risk individuals from a population based study for a screening trial, utilising a validated risk prediction model [3]. The data already analysed from the UKLS population based approach will provide valuable information as to how to we should implement lung cancer screening, if it becomes a national programme. Utilisation of LLPv2 risk model on UKLS screening trial The LLPv2 risk model has been used to select high-risk individual in the UKLS. UKLS is a randomised controlled trial of LDCT for lung cancer screening, following the Wald single-screen design. In short, the UKLS randomised subjects based on their ≥5% risk of developing lung cancer in the next five years. Using this selection criterion shows that screening programme can potentially be more cost-effective if it is limited to the high-risk segment of the population [2]. Risk models to evaluate indeterminate nodules [4, 5] The basis of lung cancer CT screening is to identify lung nodules, which are at a level of suspicion whereby they are referred to a specialist clinical team for work-up and potential surgical intervention. It has been demonstrated that such nodules detected within screening trials are often very early stage disease and thus these patients have a very good clinical outcome. However, nodules are common in the scans of many patients, and experienced radiologists using volumetric techniques can now measure these nodules and determine whether they are growing. The major clinical problem concerns nodules which are less than 10mm in diameter or The data from these trials has already started to change the management of indeterminate CT screen detected nodules, thus proving the power of risk prediction modeling in lung cancer, which will contribute to the methodology currently under discussion on how to implement lung cancer CT screening programmes [6]. 1. Tammemagi, M.C., et al., Selection criteria for lung-cancer screening. N Engl J Med, 2013. 368(8): p. 728-36. 2. Field, J.K., et al., The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer. Health Technol Assess, 2016. 20(40): p. 1-146. 3. Raji, O.Y., et al., Predictive accuracy of the Liverpool Lung Project risk model for stratifying patients for computed tomography screening for lung cancer: a case-control and cohort validation study. Ann Intern Med, 2012. 157(4): p. 242-50. 4. McWilliams, A., et al., Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med, 2013. 369(10): p. 910-9. 5. Horeweg, N., et al., Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol, 2014. 15(12): p. 1332-41. 6. Field, J.K., et al., CT screening for lung cancer: Is the evidence strong enough? Lung Cancer, 2016. 91: p. 29-35. Citation Format: John K. Field, Michael W. Marcus. Risk prediction modeling in lung cancer: How can we improve? [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA17.

Identification of high-risk individuals will facilitate early diagnosis, reduce overall costs, and also improve the current poor survival from lung cancer. The Liverpool Lung Project prospective cohort of 8,760 participants ages 45 to 79 years, recruited between 1998 and 2008, was followed annually through the hospital episode statistics until January 31, 2013. Cox proportional hazards models were used to identify risk predictors of lung cancer incidence. C-statistic was used to assess the discriminatory accuracy of the models. Models were internally validated using the bootstrap method. During mean follow-up of 8.7 years, 237 participants developed lung cancer. Age [hazard ratio (HR), 1.04; 95% confidence interval (CI), 1.02–1.06], male gender (HR, 1.48; 95% CI, 1.10–1.98), smoking duration (HR, 1.04; 95% CI, 1.03–1.05), chronic obstructive pulmonary disease (HR, 2.43; 95% CI, 1.79–3.30), prior diagnosis of malignant tumor (HR, 2.84; 95% CI, 2.08–3.89), and early onset of family history of lung cancer (HR, 1.68; 95% CI, 1.04–2.72) were associated with the incidence of lung cancer. The LLPi risk model had a good calibration (goodness-of-fit χ2 7.58, P = 0.371). The apparent C-statistic was 0.852 (95% CI, 0.831–0.873) and the optimism-corrected bootstrap resampling C-statistic was 0.849 (95% CI, 0.829–0.873). The LLPi risk model may assist in identifying individuals at high risk of developing lung cancer in population-based screening programs. Cancer Prev Res; 8(6); 570–5. ©2015 AACR.

In long-term longitudinal cohort studies the dropout of participants occurring as a result of withdrawal or lost to follow-up may have greater impact on the effect estimates, if characteristics of participants who drop out and those still active in the study differ significantly. The study aimed to investigate factors associated with dropout in a 5-year follow-up of individuals at 'high‑risk' of lung cancer. We studied 'high‑risk' group of 1,486 individuals aged 45-79 selected from the Liverpool Lung Prospective (LLP) cohort study using a strategy reflecting only age, smoking duration and history of pulmonary disease. Study subjects were recalled annually from 2005-2009 for follow-up collection of specimens and questionnaire data. The dropout rate over the follow-up time was investigated using the Kaplan‑Meier survival curve and the Cox proportional hazard model. Dropout rate was 31% after an average of 3 annual visits. Female gender hazard ratio (HR) 1.35 (95% CI 1.09-1.66), current smoking 1.26 (1.02-1.57), prior diagnosis of malignant disease 0.54 (0.36-0.79), home visits 0.67 (0.48-0.94) and systolic blood pressure 1.46 (1.10-1.94) were significantly associated with the dropout rate. Nearly 40% of individuals selected into the 'high‑risk' group by the old criteria were low risk with predicted 5-year absolute risk of less than 2.5%. In conclusion, follow-up of individuals is feasible within the LLP, but may be prone to selective withdrawal attributable to patient's state of health and mobility. We recommend future design of 'high‑risk' follow‑up studies to consider home visit as a useful strategy to encourage continued participation.

Increased lung cancer risks among hairdressers were observed in large registry-based cohort studies from Scandinavia, but these studies could not adjust for smoking. Our objective was to evaluate the lung cancer risk among hairdressers while adjusting for smoking and other confounders in a pooled database of 16 case-control studies conducted in Europe, Canada, China, and New Zealand between 1985 and 2010 (the Pooled Analysis of Case-Control Studies on the Joint Effects of Occupational Carcinogens in the Development of Lung Cancer). Lifetime occupational and smoking information was collected through interviews with 19,369 cases of lung cancer and 23,674 matched population or hospital controls. Overall, 170 cases and 167 controls had ever worked as hairdresser or barber. The odds ratios for lung cancer in women were 1.65 (95% confidence interval (CI): 1.16, 2.35) without adjustment for smoking and 1.12 (95% CI: 0.75, 1.68) with adjustment for smoking; however, women employed before 1954 also experienced an increased lung cancer risk after adjustment for smoking (odds ratio = 2.66, 95% CI: 1.09, 6.47). The odds ratios in male hairdressers/barbers were generally not elevated, except for an increased odds ratio for adenocarcinoma in long-term barbers (odds ratio = 2.20, 95% CI: 1.02, 4.77). Our results suggest that the increased lung cancer risks among hairdressers are due to their smoking behavior; single elevated risk estimates should be interpreted with caution and need replication in other studies. © The Author 2013.

Background\ud \ud The high relapse and mortality rate of small-cell lung cancer (SCLC) fuels the need for epidemiologic study to aid in its prevention.\ud \ud Methods\ud \ud We included 24 studies from the ILCCO collaboration. Random-effects panel logistic regression and cubic spline regression were used to estimate the effects of smoking behaviors on SCLC risk and explore their non-linearity. Further, we explored whether the risk of smoking on SCLC was mediated through COPD.\ud \ud Findings\ud \ud Significant dose–response relationships of SCLC risk were observed for all quantitative smoking variables. Smoking pack-years were associated with a sharper increase of SCLC risk for pack-years ranged 0 to approximately 50. The former smokers with longer cessation showed a 43%quit_for_5–9 years to 89%quit_for_≥ 20 years declined SCLC risk vs. subjects who had quit smoking < 5 years. Compared with non-COPD subjects, smoking behaviors showed a significantly higher effect on SCLC risk among COPD subjects, and further, COPD patients showed a 1.86-fold higher risk of SCLC. Furthermore, smoking behaviors on SCLC risk were significantly mediated through COPD which accounted for 0.70% to 7.55% of total effects.\ud \ud Interpretation\ud \ud This is the largest pooling study that provides improved understanding of smoking on SCLC, and further demonstrates a causal pathway through COPD that warrants further experimental study.\ud \ud Abbreviations\ud COPD, chronic obstructive pulmonary disease; CPG, cigarettes per day; ILCCO, International Lung Cancer Consortium; MeSH, medical subject headings; NSCLC, non-small cell lung cancer; OR, odds ratio; SCLC, small cell lung cancer.