Search

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

Recent genome-wide association studies (GWASs) have identified common genetic variants at 5p15.33, 6p21-6p22 and 15q25.1 associated with lung cancer risk. Several other genetic regions including variants of CHEK2 (22q12), TP53BP1 (15q15) and RAD52 (12p13) have been demonstrated to influence lung cancer risk in candidate- or pathway-based analyses. To identify novel risk variants for lung cancer, we performed a meta-analysis of 16 GWASs, totaling 14 900 cases and 29 485 controls of European descent. Our data provided increased support for previously identified risk loci at 5p15 (P = 7.2 × 10(-16)), 6p21 (P = 2.3 × 10(-14)) and 15q25 (P = 2.2 × 10(-63)). Furthermore, we demonstrated histology-specific effects for 5p15, 6p21 and 12p13 loci but not for the 15q25 region. Subgroup analysis also identified a novel disease locus for squamous cell carcinoma at 9p21 (CDKN2A/p16(INK4A)/p14(ARF)/CDKN2B/p15(INK4B)/ANRIL; rs1333040, P = 3.0 × 10(-7)) which was replicated in a series of 5415 Han Chinese (P = 0.03; combined analysis, P = 2.3 × 10(-8)). This large analysis provides additional evidence for the role of inherited genetic susceptibility to lung cancer and insight into biological differences in the development of the different histological types of lung cancer. peerReviewed

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.

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.

The exceptional high mortality of lung cancer can be instigated to a high degree by late diagnosis. Despite the plethora of studies on potential molecular biomarkers for lung cancer diagnosis, very few have reached clinical implementation. In this study, we developed a panel of DNA methylation biomarkers and validated their diagnostic efficiency in bronchial washings from a large retrospective cohort. Candidate targets from previous high-throughput approaches were examined by pyrosequencing in an independent set of 48 lung tumor/normal paired. Ten promoters were selected and quantitative methylation-specific PCR (qMSP) assays were developed and used to screen 655 bronchial washings from the Liverpool Lung Project (LLP) subjects divided into training (194 cases and 214 controls) and validation (139 cases and 109 controls) sets. Three statistical models were used to select the optimal panel of markers and to evaluate the performance of the discriminatory algorithms. The final logit regression model incorporated hypermethylation at p16, TERT, WT1, and RASSF1. The performance of this 4-gene methylation signature in the validation set showed 82% sensitivity and 91% specificity. In comparison, cytology alone in this set provided 43% sensitivity at 100% specificity. The diagnostic efficiency of the panel did not show any biases with age, gender, smoking, and the presence of a nonlung neoplasm. However, sensitivity was predictably higher in central (squamous and small cell) than peripheral (adenocarcinomas) tumors, as well as in stage 2 or greater tumors. These findings clearly show the impact of DNA methylation-based assays in the diagnosis of cytologically occult lung neoplasms. A prospective trial is currently imminent in the LLP study to provide data on the enhancement of diagnostic accuracy in a clinical setting, including by additional markers. Cancer Res; 72(22); 5692–701. ©2012 AACR.

The Liverpool Lung Project (LLP) has previously developed a risk model for prediction of 5-year absolute risk of lung cancer based on five epidemiologic risk factors. SEZ6L, a Met430IIe polymorphic variant found on 22q12.2 region, has been previously linked with an increased risk of lung cancer in a case-control population. In this article, we quantify the improvement in risk prediction with addition of SEZ6L to the LLP risk model. Data from 388 LLP subjects genotyped for SEZ6L single-nucleotide polymorphism (SNP) were combined with epidemiologic risk factors. Multivariable conditional logistic regression was used to predict 5-year absolute risk of lung cancer with and without this SNP. The improvement in the model associated with the SEZ6L SNP was assessed through pairwise comparison of the area under the receiver operating characteristic curve and the net reclassification improvements (NRI). The extended model showed better calibration compared with the baseline model. There was a statistically significant modest increase in the area under the receiver operating characteristic curve when SEZ6L was added into the baseline model. The NRI also revealed a statistically significant improvement of around 12% for the extended model; this improvement was better for subjects classified into the two intermediate-risk categories by the baseline model (NRI, 27%). Our results suggest that the addition of SEZ6L improved the performance of the LLP risk model, particularly for subjects whose initial absolute risks were unable to discriminate into “low-risk” or “high-risk” group. This work shows an approach to incorporate genetic biomarkers in risk models for predicting an individual's lung cancer risk. Cancer Prev Res; 3(5); 664–9. ©2010 AACR.

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.

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.

Clinically, our ability to predict disease outcome for patients with early stage lung cancer is currently poor. To address this issue, tumour specimens were collected at surgery from non-small cell lung cancer (NSCLC) patients as part of the European Early Lung Cancer (EUELC) consortium. The patients were followed-up for three years post-surgery and patients who suffered progressive disease (PD, tumour recurrence, metastasis or a second primary) or remained disease-free (DF) during follow-up were identified. RNA from both tumour and adjacent-normal lung tissue was extracted from patients and subjected to microarray expression profiling. These samples included 36 adenocarcinomas and 23 squamous cell carcinomas from both PD and DF patients. The microarray data was subject to a series of systematic bioinformatics analyses at gene, network and transcription factor levels. The focus of these analyses was 2-fold: firstly to determine whether there were specific biomarkers capable of differentiating between PD and DF patients, and secondly, to identify molecular networks which may contribute to the progressive tumour phenotype. The experimental design and analyses performed permitted the clear differentiation between PD and DF patients using a set of biomarkers implicated in neuroendocrine signalling and allowed the inference of a set of transcription factors whose activity may differ according to disease outcome. Potential links between the biomarkers, the transcription factors and the genes p21/CDKN1A and Myc, which have previously been implicated in NSCLC development, were revealed by a combination of pathway analysis and microarray meta-analysis. These findings suggest that neuroendocrine-related genes, potentially driven through p21/CDKN1A and Myc, are closely linked to whether or not a NSCLC patient will have poor clinical outcome.

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci., Author Summary This work describes a new methodology for analyzing genome-wide case-control association studies of diseases with strong correlations to clinical covariates, such as age in prostate cancer and body mass index in type 2 diabetes. Currently, researchers either ignore these clinical covariates or apply approaches that ignore the disease's prevalence and the study's ascertainment strategy. We take an alternative approach, leveraging external prevalence information from the epidemiological literature and constructing a statistic based on the classic liability threshold model of disease. Our approach not only improves the power of studies that ascertain individuals randomly or based on the disease phenotype, but also improves the power of studies that ascertain individuals based on both the disease phenotype and clinical covariates. We apply our statistic to seven datasets over six different diseases and a variety of clinical covariates. We found that there was a substantial improvement in test statistics relative to current approaches at known associated variants. This suggests that novel loci may be identified by applying our method to existing and future association studies of these diseases.

In the identification of subjects with lung cancer, increased DNA methylation of the SHOX2 gene locus in bronchial aspirates has previously been proven to be a clinically valuable biomarker. This is particularly true in cases where the cytological and histological results following bronchoscopy are undetermined. This previous case control study was conducted using research assay components and a complex work flow. To facilitate the use in a diagnostic setting, a CE marked in vitro diagnostic test kit to quantify SHOX2 DNA methylation in bronchial aspirates was developed and characterized. The presented assay for measuring SHOX2 DNA methylation in bronchial aspirates is based on two major steps: generation of bisulfite converted template DNA from patient samples followed by subsequent determination of SHOX2 biomarker methylation by real-time PCR. Individual kits for DNA preparation, real-time PCR analysis and work flow control were developed. This study describes the analytical performance (reproducibility, accuracy, interfering substances, cross-reactivity) of the in vitro diagnostic (IVD) test kit 'Epi proLung BL Reflex Assay'. In addition, the intended use of the test was validated in a clinical performance evaluation (case control) study comprised of 250 patients (125 cases, 125 controls). The results describe the test as a robust and reliable diagnostic tool for identifying patients with lung cancer using Saccomanno-fixed bronchial lavage specimens (AUC [95% confidence intervals] = 0.94 [0.91-0.98], sensitivity 78% [69-86]/specificity 96% [90-99]). This test may be used as a diagnostic adjunct to existing clinical and pathological investigations in lung cancer.

Background This study aimed to show that SHOX2 DNA methylation is a tumor marker in patients with suspected lung cancer by using bronchial fluid aspirated during bronchoscopy. Such a biomarker would be clinically valuable, especially when, following the first bronchoscopy, a final diagnosis cannot be established by histology or cytology. A test with a low false positive rate can reduce the need for further invasive and costly procedures and ensure early treatment. Methods Marker discovery was carried out by differential methylation hybridization (DMH) and real-time PCR. The real-time PCR based HeavyMethyl technology was used for quantitative analysis of DNA methylation of SHOX2 using bronchial aspirates from two clinical centres in a case-control study. Fresh-frozen and Saccomanno-fixed samples were used to show the tumor marker performance in different sample types of clinical relevance. Results Valid measurements were obtained from a total of 523 patient samples (242 controls, 281 cases). DNA methylation of SHOX2 allowed to distinguish between malignant and benign lung disease, i.e. abscesses, infections, obstructive lung diseases, sarcoidosis, scleroderma, stenoses, at high specificity (68% sensitivity [95% CI 62-73%], 95% specificity [95% CI 91-97%]). Conclusions Hypermethylation of SHOX2 in bronchial aspirates appears to be a clinically useful tumor marker for identifying subjects with lung carcinoma, especially if histological and cytological findings after bronchoscopy are ambiguous.

Killer immunoglobulin-like receptor (KIR) and human leukocyte antigen (HLA) genotypes were analyzed from panels of lung (non–small-cell lung cancer [NSCLC] and small-cell lung cancer [SCLC]), colon, and kidney cancer patients and compared with normal control subjects. No significant differences were noted between KIR gene frequencies in patients compared with normal subjects. When combinations of KIR genes and their HLA ligands were considered, there were significant decreases in frequencies of both KIR2DL2 and KIR2DL3 in homozygotes for their ligand HLA-C1, and an increase in the frequency of KIR3DL1 and its ligand HLA-Bw4 in kidney cancer patients compared with controls. Both associations were partly attributable to changes in ligand frequencies alone. NSCLC patients showed a significant increase in the frequency of KIR2DL1 and its ligand HLA-C2 and a corresponding decrease in frequency of KIR2DL3 and its ligand HLA-C1 in homozygotes. In NSCLC, the Ile80 form of HLA-Bw4 was decreased in KIR3DL1+ HLA-Bw4+ patients, whereas in SCLC the Ile80 form was increased and the Thr80 form decreased in KIR3DS1+ HLA-Bw4+ patients. These findings are consistent with increased co-expression of high-affinity inhibitory KIRs and their ligands, potentially resulting in decreased natural killer cell function, and hence with natural killer cells having a protective role in lung and kidney cancer but not colon cancer.

Computed tomography screening for early diagnosis of lung cancer is one of the more potentially useful strategies, aside from smoking cessation programmes, for reducing mortality and improving the current poor survival from this disease. The long-term 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 could potentially play a major role in the selection of high-risk individuals who would benefit most from screening intervention programmes for the early detection of lung cancer. Improvements of developed lung cancer risk prediction models (through incorporation of objective clinical factors and genetic and molecular biomarkers for precise and accurate estimation of risks), demonstration of their clinical usefulness in decision making, and their use in future screening programmes are the focus of current research.

Risks of childhood leukaemia and lymphoma were investigated for specific work-related exposures of mothers in the UK Childhood Cancer Study. Interviews with parents of 1881 leukaemia and lymphoma cases (0-14 years) and 3742 controls collected job histories recording exposure to eight specific agents. Exposure was (1) self-reported and (2) reviewed, based mainly on exposure probability and exposure level. Completeness, consistency and sufficiency evaluated data quality. Of all job exposures which were self-reported as exposed, 33% cases and 34% controls remained classified as exposed after review, with the remainder designated as partially exposed or unexposed. No review of underreporting of exposure was made. Data quality was ‘good’ for 26% of cases and 24% of controls. For self-reported exposure, significant risks of acute lymphoblastic leukaemia (ALL) were observed for solvents and petrol in all time windows. For reviewed exposure, solvents remained significant for ALL during pregnancy and postnatally. Restricting analyses to good-quality information removed all significant results. Refinement of exposure assessment revealed misclassification of self-reported exposures and data quality influenced risk assessment. Maternal exposure to solvents should further be investigated. These findings must invoke caution in the interpretation of risks reliant on self-reported occupational data.

Lung cancer is the leading cause of cancer mortality worldwide, with more than 1 million deaths each year (1). The disease is usually diagnosed at an advanced stage, when surgical resection or other treatment options are less effective, thus leading to poor survival (2). The results from the NLST (National Lung Screening Trial), sponsored by the National Cancer Institute, showed a 20% decrease in lung cancer deaths and a 6% decrease in all-cause mortality when smokers were screened annually for 3 years with low-dose spiral computed tomography (CT) compared with standard chest radiography (3). The ongoing evaluations of CT screening have indicated the need to identify persons at high risk for the disease to maximize the benefit–harm ratio (4–6). Although this approach has been endorsed by the International Association for the Study of Lung Cancer in its recent report (7), no tools adequately identify which patients should be targeted to maximize the screening benefit. Many risk models have been developed to predict individual risk for lung cancer within a specified period by using patient characteristics and epidemiologic, social, and clinical risk factors (8). These include risk models by Bach and colleagues (9), Spitz and colleagues (10), Tammemagi and colleagues (11), and the Liverpool Lung Project (LLP) (12). The LLP risk model, developed from the LLP case– control (LLCC) study, provides a single unified model for smokers (current and former) and nonsmokers, whereas the Bach model was developed for predicting risk only in smokers and the Spitz model requires 3 separate models for predicting risk in current, former, or nonsmokers. In addition, the LLP model accounts for important lung cancer risk factors besides age, sex, and smoking duration, including history of pneumonia, history of nonlung cancer, asbestos exposure, and family history. This makes it simpler than Tammemagi and colleagues’ model, which includes many smoking-related variables that may be difficult to obtain in a clinical setting. A clinically useful risk model needs to perform well in independent data sets and be generalizable to external populations (13–16). Traditional statistical measures for evaluating risk models are the area under the receiver-operating characteristic curve (AUC), which quantifies the model’s ability to discriminate between patients with and those without disease, and the calibration accuracy, which compares the predicted and observed disease probabilities (13, 16, 17). A key limitation of these predictive measures is that they focus purely on the mathematical accuracy of the model and do not assess predicted net benefit, which is based on both accuracy and tradeoffs between the harms and benefits of using the model for making clinical decisions (such as who should receive lung cancer CT screening). Two recently proposed decision-theoretical methods that put risk model assessment into clinical perspective are decision curve analysis and the relative utility curve method (18–21). These methods explicitly incorporate the predicted harms and benefits of clinical decisions based on the model predictions at a given risk threshold. A risk threshold is the absolute risk for lung cancer at which one might choose to initiate screening. A person whose risk, as predicted by the model, is above the threshold would be stratified as high-risk and selected for screening. The decision curve and relative utility curve estimate the predicted net benefit of the model across all possible risk thresholds, making it easier to evaluate the effect of various risk thresholds. Our study presents the validation of the LLP risk model in 2 independent case–control studies and a population-based prospective cohort study by assessing its discriminative performance. In addition, we evaluate the potential clinical effect of using the model for making decisions about lung cancer CT screening.

Introduction: Early detection of lung cancer by screening of high risk populations (identified by epidemiological and life-style factors) has the potential to save many lives. However, effective screening is reliant on minimally invasive techniques, such as CT screening, bronchioalveolar lavage (BAL) and blood tests, and the identification of suitable biomarkers. CT screening is effective in reducing mortality, but generates a large proportion of indeterminate nodules that must be further characterised. MicroRNAs (miRNA) have great potential as biomarkers due to their tissue-specific and cancer-specific expression patterns. We have identified tumour-specific miRNAs for non-small cell lung cancer (NSCLC), using a combination of screening on TaqMan microRNA TLDA cards and validation with qRTPCR assays, with the aim of utilising these as biomarkers in the early detection setting. Methods: Our sample group consisted of 31 frozen samples from 20 Liverpool Lung Project (LLP) NSCLC patients, including 10 adenocarcinomas (Ad), 10 squamous cell carcinomas (SCC) & matched normal tissue. Two further validation sets consisted of equal numbers of Ad and SCC tumour/normal pairs (124 in total). MiRNA was prepared from tumour and normal specimens using Qiagen MicroRNeasy kits. Reverse transcription and pre-amplification was performed using Applied Biosystems MegaPlex Pools and miRNAs were quantified on a 7900HT Real-Time PCR System with TaqMan Array Human MiRNA Card Set v3.0 (covering 754 human miRNAs). Ct values were exported using SDS v2.3 data and RQ Manager software and further analysed in Bioconductor. Validation qRTPCR was performed with individual miRNA assays, following reverse transcription with MegaPlex pools. Results: When Benjamin-Hoechst-adjusted-p value 4.0 fold-change in the cancer group. A subset of 22 miRNAs including miR-34a, miR-96, let-7g and miR-183 was identified with the greatest expression in tumours. Differential expression of all 22 miRNAs was confirmed in an independent set of 24 tumour/normal pairs. Using these 22 validated miRNAs we performed discriminative modelling and identified a model based on just 8 markers that gave a specificity of 100% and a sensitivity of 98%. This panel was validated, with 97% specificity and 91% sensitivity, in a 2nd independent sample set containing 48 tumours and paired normal samples. Conclusion: A number of miRNAs was identified that showed good discriminatory power individually, but greatest sensitivity and specificity when combined as an 8 member panel. The lung cancer specific miRNAs we have identified provide a potential source of early detection biomarkers. Their applicability to minimally-invasive samples is being evaluated in a range of samples including plasma and bronchial lavage. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4133. doi:1538-7445.AM2012-4133

Analysis of DNA methylation at the SHOX2 locus has been shown to identify lung cancer in bronchial aspirates of patients with disease, and can potentially provide more definitive information to clinicians when histologic and cytologic findings from bronchoscopy are ambiguous. In a previous training study on bronchial aspirate samples, valid measurements were obtained from a total of 523 patient samples (242 controls, 281 cases). DNA methylation of SHOX2 discriminated malignant from benign lung disease (e.g. abscesses, infections, obstructive lung diseases, sarcoidosis, scleroderma, stenoses) with 68% sensitivity and 95% specificity. A SHOX2 assay performance was then validated in a blinded and randomized case control study comprised of Saccomanno-fixed bronchial lavage samples from 250 patients (125 cases, 125 controls). Results showed the SHOX2 assay reliably identified patients with lung cancer (AUC = 0.95, sensitivity 78%, specificity 96%). Computed tomography (CT) is currently being evaluated as a screening tool for early detection of lung cancer in several large ongoing trials. The randomized National Lung Screening Trial (NLST) in the United States was halted early after 8-year results showed that screening heavy smokers with low-dose helical CT significantly reduced deaths from lung cancer when compared with chest x-ray screening. However, CT scans fail to detect pre-invasive lesions and early lung cancer in the central airways, specifically small cell lung cancer (SCLC) and early stages of squamous cell carcinoma, which comprise 17-29% of all lung cancers. The use of a biomarker assay in conjunction with a CT might help to identify these patients. This study aimed to develop a modified SHOX2 assay which detects circulating methylated SHOX2 in blood and to analyze the performance of this optimized SHOX2 assay in plasma. Quantitative real-time PCR was used to analyze DNA methylation of SHOX2 assay in plasma samples from a total of 371 individuals (lung cancer patients, healthy individuals, benign lung diseases and prostate cancer patients). Performance of the assay was calculated to determine sensitivity and specificity based on a clinical cut-off. Valid measurements were obtained from a total of 343 patient samples (155 controls, 188 cases). DNA methylation of SHOX2 discriminated malignant lung disease from controls at a sensitivity of 62% and a specificity of 90%. SCLC (80%) and squamous cell carcinoma (63%) were detected at higher sensitivity as compared to adenocarcinoma (42%). SHOX2 DNA methylation is a sensitive and specific biomarker for detecting the presence of malignant lung disease in blood plasma, with highest sensitivity for detection of small cell and squamous cell lung cancer. A blood-based test for detection of SHOX2 DNA methylation could be a useful tool to identify patients with lung cancer alone or in conjunction with CT. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4158. doi:10.1158/1538-7445.AM2011-4158

Background: The results from a recent CT screening trial for lung cancer showed evidence of mortality reduction in the screened arm of the study. The long term success of future National Screening Programme as an early diagnosis tool may be dependent upon identifying populations at sufficient risk of developing the disease such that the benefit:harm ratio of the intervention can be maximised. Risk prediction models can play an important role in risk stratification by providing an estimate of individual's risk of developing a disease at a future time, but require validation in independent populations before they can be successfully generalised. Within the Liverpool Lung Project (LLP), we have developed a risk prediction model for estimating individual's 5-year absolute risk of lung cancer. The model was based on five epidemiological risk factors namely smoking duration, prior diagnosis of pneumonia, family history of lung cancer, occupational exposure to asbestos and prior diagnosis of other cancer. We present here an independent validation and clinical utility of the model using data from two case-control studies and a cohort population from Europe and North America. Method: The 5-year absolute risk of lung cancer was estimated for subjects in the Harvard and European Early Lung Cancer (EUELC) case-control and LLP prospective cohort studies. The model's performance was assessed through its predictive accuracy (discrimination and calibration) and clinical utility. The area under the receiver-operator characteristic curve (AUC) measures the model's discriminatory power while calibration was assessed by comparing the observed and the expected lung cancer cases in the cohort population. The clinical relevance of the model was examined using the decision and relative utility curves. Results: There was an evidence of good discriminations in Harvard (AUC = 0.76) and LLP cohort (c-index = 0.77, 0.82) with no significant difference in discrimination by age, gender and smoking status. In general, the model calibration indicated an underestimation of absolute risk; this showed improvement with high absolute risks. The application of the model was associated with reasonably good clinical utility across the three datasets as demonstrated by its superior ‘net benefit’ against any other alternative strategy. Conclusion: The LLP risk model demonstrates good performance and evidence of clinical usefulness in three independent settings and can serve as an adjunct tool for clinicians or used in primary care for referring patients for early detection and prevention intervention. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1898. doi:10.1158/1538-7445.AM2011-1898

Epigenetic reprogramming is a characteristic of virtually of lung tumors. DNA methylation is unique for biomarker utilization in body fluids due to its chemical stability and the high frequency of abnormal hypermethylation in lung tumors. Despite the evidence from a wide range of studies in this field, very little is progressing to clinical exploitation, mainly due to the insufficient preclinical and clinical validation. In this study we have undertaken extensive preclinical validation of DNA methylation biomarkers in bronchial washings from 320 lung cancer cases and 320 age/sex matched controls organized in training (n=150 in each arm) and validation (n=170 in each arm) sets. Quantitative methylation specific PCR (qMSP) assays were developed and optimized through multiple sets of methylation standards. The hypermethylation detection threshold was set to 0.5% based on the reproducibility of signal detection. Among the promoters tested p16, RASSF1, WT1, RARbeta, TERT and CYGB proved to be highly discriminating between cases and controls while CDH13, TMEFF2, p73 and DAPK did not demonstrate a significant difference. Using a criterion of at least two positives from the discriminating markers in the validation set, this panel of six markers demonstrated an overall 70.5% sensitivity and 97.7% specificity. It is of note that when combining methylation biomarker with cytology, the positive detection rate was 81.5%. Regarding tumor histology, the detection sensitivity (combining training and validation sets) was higher in small cell carcinomas (95.1%) and squamous carcinomas (65.2%) than in adenocarcinomas (51.6%). Our results clearly demonstrate the clinical potential of DNA methylation in lung cancer diagnostics, although it is profound that this panel must be enriched by additional promoters. It also becomes apparent that combined multi-center studies of increased statistical power must be undertaken in order to promptly validate biomarker panels for clinical implementation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3015. doi:10.1158/1538-7445.AM2011-3015

Lung cancer risk prediction models will be essential tools for the identification of high-risk individuals if ongoing lung cancer CT screening trials report positive findings. The Liverpool Lung Project (LLP) risk model estimates an individual's 5-year absolute risk of lung cancer based on five epidemiological risk factors; smoking duration, prior diagnosis of pneumonia or another cancer; family history of lung cancer including age at onset; and occupational exposure to asbestos (Cassidy et al. The LLP Risk Model: an individual risk prediction model for lung cancer. British J Cancer; 98 (2):270-6, 2008). DNA genotyping indicated a role of the SEZ6L, a Met430IIe polymorphic variant found on 22q12.2 region to have increased risk of lung cancer (Gorlov et al. Cancer Research 7: 8406-11, 2007). Therefore, we quantified the improvement in risk prediction attributed to addition of SEZ6L in the LLP risk model. SEZ6L was genotyped in 200 lung cancer patients and 188 controls from the Liverpool Lung Project, and combined with previously identified epidemiological risk factors. Bivariate association between the SNP and the epidemiological risk factors was studied using the Pearson's chi-squared test; multivariable conditional logistic regression modelled individual risk profile, which was combined with local incidence data for prediction of absolute risk of lung cancer in the next 5-year. The improvements in the models associated with the SNP was assessed through a pairwise comparison of the area under the receiver operating characteristic curve (ROC-AUC) and the Net Reclassification Improvements (NRI). A higher proportion of homozygote mutant genotype was observed among patients (10%) compared to controls (3%). No interaction was observed between any of the epidemiological risk factors and the SEZ6L genotype, in combined or separate analysis of patients and controls. The extended model (model including the SNP genotype) showed better calibration compared to the baseline model (model with only epidemiological factors). There was a modest, but statistically significant increase when SEZ6L was incorporated into the risk model, the ROC-AUC increased from 0.72 (0.66-0.77) for the baseline model to 0.75 (0.71-0.80) for the extended model. The NRI also revealed a significant improvement of around 12% for the extended model; this improvement was even better for individuals classified into the two intermediate risk categories by the baseline model (NRI=27%). Our results suggest addition of SEZ6L improved the performance of the LLP risk model particularly for individuals whose initial absolute risks were unable to discriminate them into ‘low’ or ‘high’ risk group. However, given the small number of patients and controls included in this analysis, further validation of the observed improvement is essential. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2893.

Background: Since lung cancer only occurs in a small fraction of long-term smokers, there is a need to develop risk prediction models to identify high-risk subgroups. Three lung cancer models, constructed using clinical and epidemiological variables, predicted absolute risk of lung cancer: one based on a cohort of patients recruited for the CARET study, and two constructed from case-control studies conducted in Houston, Texas and Liverpool, England. Given their potential application to primary chemo-prevention strategies and screening trials, it is important to compare the accuracy of these three models in an independent population. Methods: We used data for 3197 lung cancer patients and 1703 cancer-free controls recruited to an ongoing case-control study of lung cancer at Harvard School of Public Health and Massachusetts General Hospital (Boston, MA). We estimated 5-year lung cancer risk for each risk model and compared the discriminatory power, as measured by the area under the the receiver-operator characteristic curve, accuracy, as measured by the positive predictive value and negative predictive value, and clinical utility of these models, as measured with scaled rectangles. Results: Overall, the discriminatory power for the Liverpool Lung Project (LLP) (AUC = 0.69, 95% CI = 0.67–0.71) and Spitz models (AUC = 0.69, 95%CI = 0.66–0.71) were comparable, while the Bach model had significantly lower power (AUC =0.66, 95% CI = 0.64–0.69; P=0.02). Positive predictive values were highest with the Spitz model (0.882) compared to 0.809 for the Bach model and 0.759 for the LLP model. In contrast, the negative predictive values were highest for the LLP model (0.560) compared to 0.450 for the Spitz model and 0.447 for the Bach. The Spitz and Bach models had lower sensitivity but higher specificity compared to the LLP model. For instance, 26.6% of all lung cancer cases have a five-year absolute risk of lung cancer ≥ 2.5% for the Spitz model compared to 66.7% of all cases for the LLP model. However, only 5.6% of all healthy controls have a five-year absolute risk of lung cancer ≥ 2.5% with the Spitz model compared to 33.4% of all controls for the LLP model. Conclusion: We observed modest differences in discriminatory among the three lung cancer risk models. The level of the discriminatory powers of these three lung cancer risk models was moderate at best, which highlights the difficulty in developing effective risk models. There is considerable room for improvement in model performance by incorporating additional risk factors, such as genetic risk factors, to increase discriminatory power and accuracy, while still maintaining clinical utility. Citation Information: Cancer Prev Res 2010;3(1 Suppl):A127.

Item does not contain fulltext BACKGROUND AND METHODS: Familial aggregation of lung cancer exists after accounting for cigarette smoking. However, the extent to which family history affects risk by smoking status, histology, relative type and ethnicity is not well described. This pooled analysis included 24 case-control studies in the International Lung Cancer Consortium. Each study collected age of onset/interview, gender, race/ethnicity, cigarette smoking, histology and first-degree family history of lung cancer. Data from 24,380 lung cancer cases and 23,305 healthy controls were analysed. Unconditional logistic regression models and generalised estimating equations were used to estimate odds ratios and 95% confidence intervals. RESULTS: Individuals with a first-degree relative with lung cancer had a 1.51-fold increase in the risk of lung cancer, after adjustment for smoking and other potential confounders (95% CI: 1.39, 1.63). The association was strongest for those with a family history in a sibling, after adjustment (odds ratios (OR)=1.82, 95% CI: 1.62, 2.05). No modifying effect by histologic type was found. Never smokers showed a lower association with positive familial history of lung cancer (OR=1.25, 95% CI: 1.03, 1.52), slightly stronger for those with an affected sibling (OR=1.44, 95% CI: 1.07, 1.93), after adjustment. CONCLUSIONS: The occurrence of lung cancer among never smokers and similar magnitudes of the effect of family history on lung cancer risk across histological types suggests familial aggregation of lung cancer is independent of those risks associated with cigarette smoking. While the role of genetic variation in the aetiology of lung cancer remains to be fully characterised, family history assessment is immediately available and those with a positive history represent a higher risk group. 01 september 2012