Incremental value of pulmonary function and sputum DNA image cytometry in lung cancer risk prediction

Hypercapnia and lung function parameters in chronic obstructive pulmonary disease

BACKGROUND

In advanced chronic obstructive pulmonary disease (COPD), hypercapnia may occur due to severe bronchial obstruction with lung hyperinflation. Non-invasive ventilation (NIV) provides the standard of care intended to achieve physiological PCO2 levels, thereby reducing overall mortality. The present study aimed to evaluate pulmonary function parameters derived from spirometry (forced vital capacity [FVC], forced expiratory volume in 1 s [FEV1]), body plethysmography (residual volume [RV], total lung capacity [TLC]), and lung diffusion capacity for carbon monoxide (single-breath method [DCO-SB], alveolar-volume corrected values [DCO-VA]) as predictors of chronic hypercapnia in patients with advanced COPD.

METHODS

This monocentric, retrospective observational study included 423 COPD patients. Receiver operating characteristic (ROC) curve analysis and cross-validation were used to assess lung function parameters' diagnostic accuracy for predicting chronic hypercapnia, with the resulting performance expressed as area under the ROC curve (AUROC). We performed univariable and multivariable binary logistic regression analysis to determine if these parameters were independently associated with chronic hypercapnia, with probabilities reported as odds ratios [OR] with 95% confidence intervals [95%CI].

RESULTS

FVC% (AUROC 0.77 [95%CI 0.72-0.81], P < 0.01) and FEV1% (AURIC 0.75 [95%CI 0.70-0.79], P < 0.01) exhibited reasonable accuracy in the prediction of chronic hypercapnia, whereas lung diffusion capacity performed poorly (AUROC 0.64 [95%CI 0.58-0.71] for DCO-SB%, P < 0.01). FVC% (OR 0.95 [95%CI 0.93-0.97], P < 0.01) and FEV1% (OR 0.97 [95%CI 0.94-0.99], P = 0.029) were the only parameters associated independently with chronic hypercapnia in logistic regression analysis. FVC and FEV1 thresholds that best separated hypercapnic from normocapnic subjects reached 56% and 33% of predicted values.

CONCLUSIONS

Routinely collected pulmonary function parameters, particularly FVC% and FEV1%, may predict chronic hypercapnia during COPD progression.

Facing an un-met need in lung cancer screening: The never smokers

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide and the second most common cancer in both men and women. In addition to smoking, other risk factors, such as environmental tobacco smoke, air pollution, biomass combustion, radon gas, occupational exposure, lung disease, family history of cancer, geographic variability, and genetic factors, play an essential role in developing LC. Current screening guidelines and eligibility criteria have limited efficacy in identifying LC cases (50 %), as most screening programs primarily target subjects with a smoking history as the leading risk factor. Implementing LC screening programs in people who have never smoked (PNS) can significantly impact cancer-specific survival and early disease detection. However, the available evidence regarding the feasibility and effectiveness of such programs is limited. Therefore, further research on LC screening in PNS is warranted to determine the necessary techniques for accurately identifying individuals who should be included in screening programs.

[Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort]

Objective

To identify the risk factors related to lifestyle behaviors that affect the incidence of lung cancer, to build a lung cancer risk prediction model to identify, in the population, individuals who are at high risk, and to facilitate the early detection of lung cancer.

Methods

The data used in the study were obtained from the UK Biobank, a database that contains information collected from 502 389 participants between March 2006 and October 2010. Based on domestic and international guidelines for lung cancer screening and high-quality research literature on lung cancer risk factors, high-risk population identification criteria were determined. Univariate Cox regression was performed to screen for risk factors of lung cancer and a multifactor lung cancer risk prediction model was constructed using Cox proportional hazards regression. Based on the comparison of Akaike information criterion and Schoenfeld residual test results, the optimal fitted model assuming proportional hazards was selected. The multiple factor Cox proportional hazards regression was performed to consider the survival time and the population was randomly divided into a training set and a validation set by a ratio of 7:3. The model was built using the training set and the performance of the model was internally validated using the validation set. The area under the receiver operating characteristic (ROC) curve ( AUC) was used to evaluate the efficacy of the model. The population was categorized into low-risk, moderate-risk, and high-risk groups based on the probability of occurrence of 0% to <25%, 25% to <75%, and 75% to 100%. The respective proportions of affected individuals in each risk group were calculated.

Results

The study eventually covered 453 558 individuals, and out of the cumulative follow-up of 5 505 402 person-years, a total of 2 330 cases of lung cancer were diagnosed. Cox proportional hazards regression was performed to identify 10 independent variables as predictors of lung cancer, including age, body mass index (BMI), education, income, physical activity, smoking status, alcohol consumption frequency, fresh fruit intake, family history of cancer, and tobacco exposure, and a model was established accordingly. Internal validation results showed that 8 independent variables (all the 10 independent variables screened out except for BMI and fresh fruit intake) were significant influencing factors of lung cancer ( P<0.05). The AUC of the training set for predicting lung cancer occurrence at one year, five years, and ten years were 0.825, 0.785, and 0.777, respectively. The AUC of the validation set for predicting lung cancer occurrence at one year, five years, and ten years were 0.857, 0.782, and 0.765, respectively. 68.38% of the individuals who might develop lung cancer in the future could be identified by screening the high-risk population.

Conclusion

We established, in this study, a model for predicting lung cancer risks associated with lifestyle behaviors of a large population. Showing good performance in discriminatory ability, the model can be used as a tool for developing standardized screening strategies for lung cancer.

Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study

BACKGROUND

It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally.

OBJECTIVE

The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women.

METHODS

Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set.

RESULTS

In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk-predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination.

CONCLUSIONS

We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women.

Detection of COPD in the SUMMIT Study lung cancer screening cohort using symptoms and spirometry

BACKGROUND

COPD is a major comorbidity in lung cancer screening (LCS) cohorts, with a high prevalence of undiagnosed COPD. Combining symptom assessment with spirometry in this setting may enable earlier diagnosis of clinically significant COPD and facilitate increased understanding of lung cancer risk in COPD. In this study, we wished to understand the prevalence, severity, clinical phenotype and lung cancer risk of individuals with symptomatic undiagnosed COPD in a LCS cohort.

METHODS

16 010 current or former smokers aged 55-77 years attended a lung health check as part of the SUMMIT Study. A respiratory consultation and spirometry were performed alongside LCS eligibility assessment. Those with symptoms, no previous COPD diagnosis and airflow obstruction were labelled as undiagnosed COPD. Baseline low-dose computed tomography (LDCT) was performed in those at high risk of lung cancer (PLCOm2012 score ≥1.3% and/or meeting USPSTF 2013 criteria).

RESULTS

Nearly one in five (19.7%) met criteria for undiagnosed COPD. Compared with those previously diagnosed, those undiagnosed were more likely to be male (59.1% versus 53.2%; p<0.001), currently smoking (54.9% versus 47.6%; p<0.001) and from an ethnic minority group (p<0.001). Undiagnosed COPD was associated with less forced expiratory volume in 1 s impairment (Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1 and 2: 85.3% versus 68.4%; p<0.001) and lower symptom/exacerbation burden (GOLD A and B groups: 95.6% versus 77.9%; p<0.001) than those with known COPD. Multivariate analysis demonstrated that airflow obstruction was an independent risk factor for lung cancer risk on baseline LDCT (adjusted OR 2.74, 95% CI 1.73-4.34; p<0.001), with a high risk seen in those with undiagnosed COPD (adjusted OR 2.79, 95% CI 1.67-4.64; p<0.001).

CONCLUSIONS

Targeted case-finding within LCS detects high rates of undiagnosed symptomatic COPD in those most at risk. Individuals with undiagnosed COPD are at high risk for lung cancer.

Distinct bronchial microbiome precedes clinical diagnosis of lung cancer

Resident microbial populations have been detected across solid tumors of diverse origins. Sequencing of the airway microbiota represents an opportunity for establishing a novel omics approach to early detection of lung cancer, as well as risk prediction of cancer development. We hypothesize that bacterial shifts in the pre-malignant lung may be detected in non-cancerous airway liquid biopsies collected during bronchoscopy. We analyzed the airway microbiome profile of near 400 patients: epithelial brushing samples from those with lung cancer, those who developed an incident cancer, and those who do not develop cancer after 10-year follow-up. Using linear discriminate analysis, we define and validate a microbial-based classifier that is able to predict incident cancer in patients before diagnosis with no clinical signs of cancer. Our results demonstrate the potential of using lung microbiome profiling as a method for early detection of lung cancer.

Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China

Background

About 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China.

Methods

A large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set.

Results

A total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk.

Conclusions

We developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers.

A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in China

OBJECTIVE

Two large randomized controlled trials (RCTs) have demonstrated that low dose computed tomography (LDCT) screening reduces lung cancer mortality. Risk-prediction models have been proved to select individuals for lung cancer screening effectively. With the focus on established risk factors for lung cancer routinely available in general cancer screening settings, we aimed to develop and internally validated a risk prediction model for lung cancer.

MATERIALS AND METHODS

Using data from the Cancer Screening Program in Urban China (CanSPUC) in Henan province, China between 2013 and 2019, we conducted a prospective cohort study consisting of 282,254 participants including 126,445 males and 155,809 females. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Using Cox proportional risk regression analysis, we developed the Henan Lung Cancer Risk Models based on simplified questionnaire. Model discrimination was evaluated by concordance statistics (C-statistics), and model calibration was evaluated by the bootstrap sampling, respectively.

RESULTS

By 2020, a total of 589 lung cancer cases occurred in the follow-up yielding an incident density of 64.91/100,000 person-years (pyrs). Age, gender, smoking, history of tuberculosis and history of emphysema were included into the model. The C-index of the model for 1-year lung cancer risk was 0.766 and 0.741 in the training set and validation set, respectively. In stratified analysis, the model showed better predictive power in males, younger participants, and former or current smoking participants. The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups.

CONCLUSIONS

We developed and internally validated a simple risk prediction model for lung cancer, which may be useful to identify high-risk individuals for more intensive screening for cancer prevention.

Utility of incorporating a gene-based lung cancer risk test on uptake and adherence in a community-based lung cancer screening pilot study

Based on the results of randomized control trials, screening for lung cancer using computed tomography (CT) is now widely recommended. However, adherence to screening remains an issue outside the clinical trial setting. This study examines the utility of biomarker-based risk assessment on uptake and subsequent adherence in a community screening study.

In a single arm pilot study, current or former smokers > 50 years old with 20 + pack year history were recruited following local advertising. One hundred and fifty seven participants volunteered to participate in the study that offered an optional gene-based lung cancer risk assessment followed by low-dose CT according to a standardised screening protocol.

All 157 volunteers who attended visit 1 underwent the gene-based risk assessment comprising of a clinical questionnaire and buccal swab. Of this group, 154 subsequently attended for CT screening (98%) and were followed prospectively for a median of 2.7 years. A participant’s adherence to screening was influenced by their baseline lung cancer risk category, with overall adherence in those with a positive scan being significantly greater in the “very high” risk group compared to “moderate” and “high” risk categories (71% vs 52%, Odds ratio = 2.27, 95% confidence interval of 1.02–5.05, P = 0.047). Those in the “moderate” risk group were not different to those in the “high” risk group (52% and 52%, P > 0.05).

In this proof-of-concept study, personalised gene-based lung cancer risk assessment was well accepted, associated with a 98% uptake for screening and increased adherence for those in the highest risk group.

Bronchial gene expression signature associated with rate of subsequent FEV1 decline in individuals with and at risk of COPD

BACKGROUND

COPD is characterised by progressive lung function decline. Leveraging prior work demonstrating bronchial airway COPD-associated gene expression alterations, we sought to determine if there are alterations associated with differences in the rate of FEV₁ decline.

METHODS

We examined gene expression among ever smokers with and without COPD who at baseline had bronchial brushings profiled by Affymetrix microarrays and had longitudinal lung function measurements (n=134; mean follow-up=6.38±2.48 years). Gene expression profiles associated with the rate of FEV₁ decline were identified by linear modelling.

RESULTS

Expression differences in 171 genes were associated with rate of FEV₁ decline (false discovery rate <0.05). The FEV₁ decline signature was replicated in an independent dataset of bronchial biopsies from patients with COPD (n=46; p=0.018; mean follow-up=6.76±1.32 years). Genes elevated in individuals with more rapid FEV₁ decline are significantly enriched among the genes altered by modulation of XBP1 in two independent datasets (Gene Set Enrichment Analysis (GSEA) p<0.05) and are enriched in mucin-related genes (GSEA p<0.05).

CONCLUSION

We have identified and replicated an airway gene expression signature associated with the rate of FEV₁ decline. Aspects of this signature are related to increased expression of XBP1-regulated genes, a transcription factor involved in the unfolded protein response, and genes related to mucin production. Collectively, these data suggest that molecular processes related to the rate of FEV₁ decline can be detected in airway epithelium, identify a possible indicator of FEV₁ decline and make it possible to detect, in an early phase, ever smokers with and without COPD most at risk of rapid FEV₁ decline.

Aryl hydrocarbon receptor deficiency causes the development of chronic obstructive pulmonary disease through the integration of multiple pathogenic mechanisms

Emphysema, a component of chronic obstructive pulmonary disease (COPD), is characterized by irreversible alveolar destruction that results in a progressive decline in lung function. This alveolar destruction is caused by cigarette smoke, the most important risk factor for COPD. Only 15%-20% of smokers develop COPD, suggesting that unknown factors contribute to disease pathogenesis. We postulate that the aryl hydrocarbon receptor (AHR), a receptor/transcription factor highly expressed in the lungs, may be a new susceptibility factor whose expression protects against COPD. Here, we report that Ahr-deficient mice chronically exposed to cigarette smoke develop airspace enlargement concomitant with a decline in lung function. Chronic cigarette smoke exposure also increased cleaved caspase-3, lowered SOD2 expression, and altered MMP9 and TIMP-1 levels in Ahr-deficient mice. We also show that people with COPD have reduced expression of pulmonary and systemic AHR, with systemic AHR mRNA levels positively correlating with lung function. Systemic AHR was also lower in never-smokers with COPD. Thus, AHR expression protects against the development of COPD by controlling interrelated mechanisms involved in the pathogenesis of this disease. This study identifies the AHR as a new, central player in the homeostatic maintenance of lung health, providing a foundation for the AHR as a novel therapeutic target and/or predictive biomarker in chronic lung disease.

Analysis of lung cancer risk model (PLCOM2012 and LLPv2) performance in a community-based lung cancer screening programme

INTRODUCTION

Low-dose CT (LDCT) screening of high-risk smokers reduces lung cancer (LC) specific mortality. Determining screening eligibility using individualised risk may improve screening effectiveness and reduce harm. Here, we compare the performance of two risk prediction models (PLCOM2012 and Liverpool Lung Project model (LLPv2)) and National Lung Screening Trial (NLST) eligibility criteria in a community-based screening programme.

METHODS

Ever-smokers aged 55-74, from deprived areas of Manchester, were invited to a Lung Health Check (LHC). Individuals at higher risk (PLCOM2012 score ≥1.51%) were offered annual LDCT screening over two rounds. LLPv2 score was calculated but not used for screening selection; ≥2.5% and ≥5% thresholds were used for analysis.

RESULTS

PLCOM2012 ≥1.51% selected 56% (n=1429) of LHC attendees for screening. LLPv2 ≥2.5% also selected 56% (n=1430) whereas NLST (47%, n=1188) and LLPv2 ≥5% (33%, n=826) selected fewer. Over two screening rounds 62 individuals were diagnosed with LC; representing 87% (n=62/71) of 6-year incidence predicted by mean PLCOM2012 score (5.0%). 26% (n=16/62) of individuals with LC were not eligible for screening using LLPv2 ≥5%, 18% (n=11/62) with NLST criteria and 7% (n=5/62) with LLPv2 ≥2.5%. NLST eligible Manchester attendees had 2.5 times the LC detection rate than NLST participants after two annual screens (≈4.3% (n=51/1188) vs 1.7% (n=438/26 309); p<0.0001). Adverse measures of health, including airflow obstruction, respiratory symptoms and cardiovascular disease, were positively correlated with LC risk. Coronary artery calcification was predictive of LC (adjOR 2.50, 95% CI 1.11 to 5.64; p=0.028).

CONCLUSION

Prospective comparisons of risk prediction tools are required to optimise screening selection in different settings. The PLCOM2012 model may underestimate risk in deprived UK populations; further research focused on model calibration is required.

Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population

BACKGROUND

Low-dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false-positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high-risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population.

METHODS

During 2006 and 2015, a total of 122 497 people were observed prospectively for lung cancer incidence with the total person-years of 976 663. Stepwise multivariable-adjusted logistic regressions with P_entry  = .15 and P_stay  = .20 were conducted to select the candidate variables including demographics and metabolic markers such as high-sensitivity C-reactive protein (hsCRP) and low-density lipoprotein cholesterol (LDL-C) into the prediction model. We used the C-statistic to evaluate discrimination, and Hosmer-Lemeshow tests for calibration. Tenfold cross-validation was conducted for internal validation to assess the model's stability.

RESULTS

A total of 984 lung cancer cases were identified during the follow-up. The epidemiological model including age, gender, smoking status, alcohol intake status, coal dust exposure status, and body mass index generated a C-statistic of 0.731. The full model additionally included hsCRP and LDL-C showed significantly better discrimination (C-statistic = 0.735, P = .033). In stratified analysis, the full model showed better predictive power in terms of C-statistic in younger participants (<50 years, 0.709), females (0.726), and former or current smokers (0.742). The model calibrated well across the deciles of predicted risk in both the overall population (P_HL  = .689) and all subgroups.

CONCLUSIONS

We developed and internally validated an easy-to-use risk prediction model for lung cancer among the Chinese population that could provide guidance for screening and surveillance.

Determinants of impaired lung function and lung cancer prediction among never-smokers in the UK Biobank cohort

BACKGROUND

Impaired lung health represents a significant burden on global health, including chronic obstructive pulmonary disease (COPD) and lung cancer. Given its global health impact, it is important to understand the determinants of impaired lung function and its relation to lung cancer risk independent of smoking. However, to date, no study has evaluated determinants of impaired lung function in a cohort exclusively of never-smokers, who also represent a growing proportion of all lung cancers.

METHODS

A total of 222,274 never-smokers with reproducible spirograms were identified in the UK Biobank population-based cohort and included in the analysis. Baseline volumetric measures of lung function, including forced expiratory volume in 1-s (FEV1) and forced vital capacity (FVC), were used to define lung function impairment. Determinants of impaired lung function were evaluated using Poisson regression with robust variance estimation. The added value of lung function in lung cancer prediction was evaluated using Fine and Gray regression accounting for the competing risk of all-cause mortality.

FINDINGS

Lung function impairment was associated with low birthweight, ambient air pollution (PM2·5 μg/mm3), and overweight, after adjustment for other important risk factors. We observed modest improvement in discrimination by adding lung function to our lung cancer prediction model for never-smokers. The highest optimism-corrected AUC at 3 (0·700, 95% CI: 0·654-0·734) and 5 years (0·694, 95% CI: 0·658-0·736) included FEV1 (% of GLI predicted FEV1), while the highest AUC at 7 years was based on the inclusion of FEV1/FVC (0·722, 95% CI: 0·687-0·762).

INTERPRETATION

We identified several modifiable risk factors associated with increased risk of lung function impairment among lifetime never-smokers in UKB. We achieved moderate discrimination for lung cancer risk-prediction for never-smokers, and found modest improvement with the inclusion of lung function. FUND: This study was supported by a Canada Research Chair to RJH.

Using Sequential Decision Making to Improve Lung Cancer Screening Performance

Globally, lung cancer is responsible for nearly one in five cancer deaths. The National Lung Screening Trial (NLST) demonstrated the efficacy of low-dose computed tomography (LDCT) to identify early-stage disease, setting the basis for widespread implementation of lung cancer screening programs. However, the specificity of LDCT lung cancer screening is suboptimal, with a significant false positive rate. Representing this imaging-based screening process as a sequential decision making problem, we combined multiple machine learning-based methods to learn a partially-observable Markov decision process that simultaneously optimizes lung cancer detection while enhancing test specificity. Using NLST data, we trained a dynamic Bayesian network as an observational model and used inverse reinforcement learning to discover a rewards function based on experts' decisions. Our resultant predictive model decreased the false positive rate while maintaining a high true positive rate at a level comparable to human experts. Our model also detected a number of lung cancers earlier.

Inhaled corticosteroids and the risk of lung cancer in COPD: a population-based cohort study

Inhaled corticosteroids (ICSs) are often prescribed in patients with chronic obstructive pulmonary disease (COPD). Their impact on the risk of lung cancer, a leading cause of mortality in COPD patients, remains uncertain.

Population-based linked administrative data between the years 1997 and 2007 from the province of British Columbia, Canada, were used to evaluate the association between lung cancer risk and ICS use in COPD patients. COPD was defined on the basis of receipt of three COPD-related prescriptions in subjects ≥50 years of age. Exposure to ICS was incorporated into multivariable Cox regression models using several time-dependent methods (“ever” exposure, cumulative duration of use, cumulative dose, weighted cumulative duration of use and weighted cumulative dose).

There were 39 676 patients who met the inclusion criteria. The mean±sd age of the cohort was 70.7±11.1 years and 53% were female. There were 994 (2.5%) cases of lung cancer during follow-up. In the reference case analysis (time-dependent “ever” exposure), ICS exposure was associated with a 30% reduced risk of lung cancer (HR 0.70 (95% CI 0.61–0.80)). ICS exposure was associated with a decrease in the risk of lung cancer diagnosis over all five methods of quantifying exposure.

This population-based study suggests that ICS use reduces the risk of lung cancer in COPD patients.

Static lung hyperinflation is an independent risk factor for lung cancer in patients with chronic obstructive pulmonary disease

INTRODUCTION

Static hyperinflation, a hallmark characteristic of some patients with chronic obstructive pulmonary disease, is related to higher mortality and cardiovascular morbidity. However, information about its association with lung cancer is scarce. Our aim was to evaluate whether static hyperinflation is associated with future risk of lung cancer in COPD patients.

METHODS

A cohort of 848 COPD patients recruited outside the hospital setting was monitored for an average period of 4.3 years, totaling 2858 person-years, regarding diagnosis of cancer of any origin or lung cancer. Static hyperinflation was defined by functional residual capacity measured by plethysmography greater than 120% of the predicted value.

RESULTS

The incidence rates for cancer of any origin and lung cancer were 16.0 (95%CI, 15.1-17.8) and 8.7 (95%CI, 7.7-9.8) per 1000 patient-years, respectively. Among the patients with lung cancer, non-small cell lung cancer predominated (88%). In a stepwise multivariate Cox regression model, body mass index (BMI), pack-years, Charlson index, and postbronchodilator FEV₁/FVC ratio were retained as independent predictors of cancer of any origin. In contrast, features associated with a future risk of lung cancer included older age, low BMI, increased pack-years and presence of static hyperinflation (adjusted hazard ratio: 4.617, 95%CI: 1.007-21.172, p = 0.049).

CONCLUSION

In a general COPD outpatient population, static hyperinflation is an independent risk factor for the development of lung cancer, which might contribute towards justifying the excess mortality identified in COPD patients with hyperinflation.

Blood based biomarkers beyond genomics for lung cancer screening

While there is considerable interest at the present time in the development of so-called liquid biopsy approaches for cancer detection based notably on circulating tumor DNA, there are other types of potential biomarkers that show promise for lung cancer screening and early detection. Here we review approaches and some of the promising markers based on proteomics, metabolomics and the immune response to tumor antigens in the form of autoantibodies.

Chronic obstructive pulmonary disease (COPD) and lung cancer screening

The primary aim of lung cancer screening is to improve survival from lung cancer by identifying early stage non-small cell lung cancers and prolong survival through their surgical removal. In a post-hoc analysis of 10,054 screening participants from the National Lung Screening Trial (NLST) we show that the risk of lung cancer, according to the PLCOm2012 model, is closely related to the likelihood of having chronic obstructive pulmonary disease (COPD). Those at greatest risk for lung cancer have the highest prevalence of COPD and greater likelihood of dying of a non-lung cancer cause. This "competing cause of death" effect occurs because smokers eligible for lung cancer screening have a high prevalence of comorbid disease and greater likelihood of dying from cardiovascular disease, respiratory disease or other cancers. This means high risk smokers at greatest risk of lung cancer may not necessarily benefit from screening due to greater inoperability and premature death. In this analysis we show that the benefit of annual computed tomography (CT) screening is greatest in those with normal lung function or only mild-to-moderate COPD. We found no mortality benefit in those with severe or very severe COPD (GOLD 3-4). We also show that the efficiency of screening, based on optimizing the number of lung cancer deaths averted per 1,000 persons screened, is best achieved by screening those at intermediate risk. By combining clinical risk variables with a gene-based risk score, even greater reductions in lung cancer mortality can be achieved with CT. We suggest a biomarker-led outcomes-based approach may help to better define which eligible smokers might defer screening (low risk of lung cancer), discontinue screening (high risk of overtreatment with little benefit) or continue screening to achieve the greatest reduction in lung cancer mortality.

Reduced Expiratory Flow Rate among Heavy Smokers Increases Lung Cancer Risk. Results from the National Lung Screening Trial-American College of Radiology Imaging Network Cohort

RATIONALE

Although epidemiological studies consistently show that chronic obstructive pulmonary disease is associated with an increased risk of lung cancer, debate exists as to whether there is a linear relationship between the severity of airflow limitation and lung cancer risk.

OBJECTIVES

We examined this in a large, prospective study of older heavy smokers from the American College of Radiology Imaging Network subcohort of the National Lung Screening Trial (ACRIN). Airflow limitation was defined by prebronchodilator spirometry subgrouped according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1-4.

METHODS

In the National Lung Screening Trial-ACRIN cohort of 18,473 screening participants, 6,436 had airflow limitation (35%) and 12,037 (65%) had no airflow limitation. From these groups, 758 lung cancer cases were prospectively identified. Participants with airflow limitation were stratified according to GOLD groups 1 (n = 1,607), 2 (n = 3,528), 3 (n = 1,083), and 4 (n = 211). Lung cancer incidence at study end (mean follow-up, 6.4 yr) was compared between the GOLD groups and those with no airflow limitation (referent group).

MEASUREMENTS AND MAIN RESULTS

Compared with those with no airflow limitation, where lung cancer incidence was 3.78/1,000 person years, incidence rates increased in a simple linear relationship: GOLD 1 (6.27/1,000 person yr); GOLD 2 (7.86/1,000 person yr); GOLD 3 (10.71/1,000 person yr); and GOLD 4 (13.25/1,000 person yr). All relationships were significant versus the reference group at a P value of 0.0001 or less.

CONCLUSIONS

In a large prospective study of high-risk cigarette smokers, we report a strong linear relationship between increasing severity of airflow limitation and risk of lung cancer.

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

Purpose of review

Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice.

Recent findings

Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening.

Summary

Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

Detecting the Presence and Progression of Premalignant Lung Lesions via Airway Gene Expression

Purpose: Lung cancer is the leading cause of cancer-related death in the United States. The molecular events preceding the onset of disease are poorly understood, and no effective tools exist to identify smokers with premalignant lesions (PMLs) that will progress to invasive cancer. Prior work identified molecular alterations in the smoke-exposed airway field of injury associated with lung cancer. Here, we focus on an earlier stage in the disease process leveraging the airway field of injury to study PMLs and its utility in lung cancer chemoprevention.Experimental Design: Bronchial epithelial cells from normal appearing bronchial mucosa were profiled by mRNA-Seq from subjects with (n = 50) and without (n = 25) PMLs. Using surrogate variable and gene set enrichment analysis, we identified genes, pathways, and lung cancer-related gene sets differentially expressed between subjects with and without PMLs. A computational pipeline was developed to build and test a chemoprevention-relevant biomarker.Results: We identified 280 genes in the airway field associated with the presence of PMLs. Among the upregulated genes, oxidative phosphorylation was strongly enriched, and IHC and bioenergetics studies confirmed pathway findings in PMLs. The relationship between PMLs and squamous cell carcinomas (SCC) was also confirmed using published lung cancer datasets. The biomarker performed well predicting the presence of PMLs (AUC = 0.92, n = 17), and changes in the biomarker score associated with progression/stability versus regression of PMLs (AUC = 0.75, n = 51).Conclusions: Transcriptomic alterations in the airway field of smokers with PMLs reflect metabolic and early lung SCC alterations and may be leveraged to stratify smokers at high risk for PML progression and monitor outcome in chemoprevention trials. Clin Cancer Res; 23(17); 5091-100. ©2017 AACR.

Lung Cancer Risk Prediction Model Incorporating Lung Function: Development and Validation in the UK Biobank Prospective Cohort Study

Purpose Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally validate a model incorporating lung function using data from the UK Biobank prospective cohort study. Methods This analysis included 502,321 participants without a previous diagnosis of lung cancer, predominantly between 40 and 70 years of age. We used flexible parametric survival models to estimate the 2-year probability of lung cancer, accounting for the competing risk of death. Models included predictors previously shown to be associated with lung cancer risk, including sex, variables related to smoking history and nicotine addiction, medical history, family history of lung cancer, and lung function (forced expiratory volume in 1 second [FEV1]). Results During accumulated follow-up of 1,469,518 person-years, there were 738 lung cancer diagnoses. A model incorporating all predictors had excellent discrimination (concordance (c)-statistic [95% CI] = 0.85 [0.82 to 0.87]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected c-statistic = 0.84). The full model, including FEV1, also had modestly superior discriminatory power than one that was designed solely on the basis of questionnaire variables (c-statistic = 0.84 [0.82 to 0.86]; optimism-corrected c-statistic = 0.83; pFEV1 = 3.4 × 10-13). The full model had better discrimination than standard lung cancer screening eligibility criteria (c-statistic = 0.66 [0.64 to 0.69]). Conclusion A risk prediction model that includes lung function has strong predictive ability, which could improve eligibility criteria for lung cancer screening programs.

Airflow Limitation and Histology Shift in the National Lung Screening Trial. The NLST-ACRIN Cohort Substudy

RATIONALE

Annual computed tomography (CT) is now widely recommended for lung cancer screening in the United States, although concerns remain regarding the potential harms, including those from overdiagnosis.

OBJECTIVES

To examine the effect of airflow limitation on overdiagnosis by comparing lung cancer incidence, histology, and stage shift in a subgroup of the National Lung Screening Trial (NLST).

METHODS

In an NLST subgroup (n = 18,714), screening participants were randomized to annual computed tomography (CT, n = 9,357) or chest radiograph (n = 9,357) screening and monitored for a mean of 6.1 years. After baseline prebronchodilator spirometry, to identify the presence of airflow limitation, 18,475 subjects (99%) were assigned as having chronic obstructive pulmonary disease (COPD) or no COPD. Lung cancer prevalence, incidence, histology, and stage shift were compared after stratification by COPD.

MEASUREMENTS AND MAIN RESULTS

For screening participants with spirometric COPD (n = 6,436), there was a twofold increase in lung cancer incidence (incident rate ratio, 2.15; P < 0.001) and, when compared according to screening arm, no excess lung cancers and comparable histology. Compared with chest radiography, there was also a trend favoring reduced late-stage and increased early-stage cancers in the CT arm (P = 0.054). For those with normal baseline spirometry (n = 12,039), we found an excess of lung cancers during screening in the CT arm, almost exclusively early-stage adenocarcinoma-related cancers (histology shift and overdiagnosis). After correction for these excess cancers, stage shift was marginal (P = 0.077).

CONCLUSIONS

In the CT arm of the NLST-ACRIN (American College of Radiology Imaging Network) cohort, COPD status was associated with a doubling of lung cancer incidence, no apparent overdiagnosis, and a more favorable stage shift.

Risk Prediction Models for Lung Cancer: A Systematic Review

Many lung cancer risk prediction models have been published but there has been no systematic review or comprehensive assessment of these models to assess how they could be used in screening. We performed a systematic review of lung cancer prediction models and identified 31 articles that related to 25 distinct models, of which 11 considered epidemiological factors only and did not require a clinical input. Another 11 articles focused on models that required a clinical assessment such as a blood test or scan, and 8 articles considered the 2-stage clonal expansion model. More of the epidemiological models had been externally validated than the more recent clinical assessment models. There was varying discrimination, the ability of a model to distinguish between cases and controls, with an area under the curve between 0.57 and 0.879 and calibration, the model's ability to assign an accurate probability to an individual. In our review we found that further validation studies need to be considered; especially for the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial 2012 Model Version (PLCOM2012) and Hoggart models, which recorded the best overall performance. Future studies will need to focus on prediction rules, such as optimal risk thresholds, for models for selective screening trials. Only 3 validation studies considered prediction rules when validating the models and overall the models were validated using varied tests in distinct populations, which made direct comparisons difficult. To improve this, multiple models need to be tested on the same data set with considerations for sensitivity, specificity, model accuracy, and positive predictive values at the optimal risk thresholds.

Lung cancer in patients with chronic obstructive pulmonary disease. Development and validation of the COPD Lung Cancer Screening Score

RATIONALE

Patients with chronic obstructive pulmonary disease (COPD) are at high risk for lung cancer (LC) and represent a potential target to improve the diagnostic yield of screening programs.

OBJECTIVES

To develop a predictive score for LC risk for patients with COPD.

METHODS

The Pamplona International Early Lung Cancer Detection Program (P-IELCAP) and the Pittsburgh Lung Screening Study (PLuSS) databases were analyzed. Only patients with COPD on spirometry were included. By logistic regression we determined which factors were independently associated with LC in PLuSS and developed a COPD LC screening score (COPD-LUCSS) to be validated in P-IELCAP.

MEASUREMENTS AND MAIN RESULTS

By regression analysis, age greater than 60, body mass index less than 25 kg/m(2), pack-years history greater than 60, and emphysema presence were independently associated with LC diagnosis and integrated into the COPD-LUCSS, which ranges from 0 to 10 points. Two COPD-LUCSS risk categories were proposed: low risk (scores 0-6) and high risk (scores 7-10). In comparison with low-risk patients, in both cohorts LC risk increased 3.5-fold in the high-risk category.

CONCLUSIONS

The COPD-LUCSS is a good predictor of LC risk in patients with COPD participating in LC screening programs. Validation in two different populations adds strength to the findings.

Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials

BACKGROUND

The National Lung Screening Trial (NLST) demonstrated that low-dose computed tomography screening is an effective way of reducing lung cancer (LC) mortality. However, optimal screening strategies have not been determined to date and it is uncertain whether lighter smokers than those examined in the NLST may also benefit from screening. To address these questions, it is necessary to first develop LC natural history models that can reproduce NLST outcomes and simulate screening programs at the population level.

METHODS

Five independent LC screening models were developed using common inputs and calibration targets derived from the NLST and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). Imputation of missing information regarding smoking, histology, and stage of disease for a small percentage of individuals and diagnosed LCs in both trials was performed. Models were calibrated to LC incidence, mortality, or both outcomes simultaneously.

RESULTS

Initially, all models were calibrated to the NLST and validated against PLCO. Models were found to validate well against individuals in PLCO who would have been eligible for the NLST. However, all models required further calibration to PLCO to adequately capture LC outcomes in PLCO never-smokers and light smokers. Final versions of all models produced incidence and mortality outcomes in the presence and absence of screening that were consistent with both trials.

CONCLUSIONS

The authors developed 5 distinct LC screening simulation models based on the evidence in the NLST and PLCO. The results of their analyses demonstrated that the NLST and PLCO have produced consistent results. The resulting models can be important tools to generate additional evidence to determine the effectiveness of lung cancer screening strategies using low-dose computed tomography.

Personalizing and targeting therapy for COPD: the role of molecular and clinical biomarkers

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by persistent airflow limitation. It is the third leading cause of death worldwide, and there are currently no curative strategies for this disease. Many factors contribute to COPD susceptibility, progression and exacerbations. These include cigarette smoking, environmental and occupational pollutants, respiratory infections and comorbidities. As the clinical phenotypes of COPD are so variable, it has been difficult to devise an individualized treatment plan for patients with this complex chronic disease. This review will highlight how potential clinical, inflammatory, genomic and epigenomic biomarkers for COPD could be used to personalize treatment, leading to improved disease management and prevention for our patients.

A dynamic bronchial airway gene expression signature of chronic obstructive pulmonary disease and lung function impairment

RATIONALE

Molecular phenotyping of chronic obstructive pulmonary disease (COPD) has been impeded in part by the difficulty in obtaining lung tissue samples from individuals with impaired lung function.

OBJECTIVES

We sought to determine whether COPD-associated processes are reflected in gene expression profiles of bronchial airway epithelial cells obtained by bronchoscopy.

METHODS

Gene expression profiling of bronchial brushings obtained from 238 current and former smokers with and without COPD was performed using Affymetrix Human Gene 1.0 ST Arrays.

MEASUREMENTS AND MAIN RESULTS

We identified 98 genes whose expression levels were associated with COPD status, FEV1% predicted, and FEV1/FVC. In silico analysis identified activating transcription factor 4 (ATF4) as a potential transcriptional regulator of genes with COPD-associated airway expression, and ATF4 overexpression in airway epithelial cells in vitro recapitulates COPD-associated gene expression changes. Genes with COPD-associated expression in the bronchial airway epithelium had similarly altered expression profiles in prior studies performed on small-airway epithelium and lung parenchyma, suggesting that transcriptomic alterations in the bronchial airway epithelium reflect molecular events found at more distal sites of disease activity. Many of the airway COPD-associated gene expression changes revert toward baseline after therapy with the inhaled corticosteroid fluticasone in independent cohorts.

CONCLUSIONS

Our findings demonstrate a molecular field of injury throughout the bronchial airway of active and former smokers with COPD that may be driven in part by ATF4 and is modifiable with therapy. Bronchial airway epithelium may ultimately serve as a relatively accessible tissue in which to measure biomarkers of disease activity for guiding clinical management of COPD.

Chemoprevention of lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines

BACKGROUND

Lung cancer is the most common cause of cancer death in men and women in the United States. Cigarette smoking is the main risk factor. Former smokers are at a substantially increased risk of developing lung cancer compared with lifetime never smokers. Chemoprevention refers to the use of specific agents to reverse, suppress, or prevent the process of carcinogenesis. This article reviews the major agents that have been studied for chemoprevention.

METHODS

Articles of primary, secondary, and tertiary prevention trials were reviewed and summarized to obtain recommendations.

RESULTS

None of the phase 3 trials with the agents β-carotene, retinol, 13-cis-retinoic acid, α-tocopherol, N-acetylcysteine, acetylsalicylic acid, or selenium has demonstrated beneficial and reproducible results. To facilitate the evaluation of promising agents and to lessen the need for a large sample size, extensive time commitment, and expense, surrogate end point biomarker trials are being conducted to assist in identifying the most promising agents for later-stage chemoprevention trials. With the understanding of important cellular signaling pathways and the expansion of potentially important targets, agents (many of which target inflammation and the arachidonic acid pathway) are being developed and tested which may prevent or reverse lung carcinogenesis.

CONCLUSIONS

By integrating biologic knowledge, additional early-phase trials can be performed in a reasonable time frame. The future of lung cancer chemoprevention should entail the evaluation of single agents or combinations that target various pathways while working toward identification and validation of intermediate end points.

Selection criteria for lung-cancer screening

BACKGROUND

The National Lung Screening Trial (NLST) used risk factors for lung cancer (e.g., ≥30 pack-years of smoking and <15 years since quitting) as selection criteria for lung-cancer screening. Use of an accurate model that incorporates additional risk factors to select persons for screening may identify more persons who have lung cancer or in whom lung cancer will develop.

METHODS

We modified the 2011 lung-cancer risk-prediction model from our Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to ensure applicability to NLST data; risk was the probability of a diagnosis of lung cancer during the 6-year study period. We developed and validated the model (PLCO(M2012)) with data from the 80,375 persons in the PLCO control and intervention groups who had ever smoked. Discrimination (area under the receiver-operating-characteristic curve [AUC]) and calibration were assessed. In the validation data set, 14,144 of 37,332 persons (37.9%) met NLST criteria. For comparison, 14,144 highest-risk persons were considered positive (eligible for screening) according to PLCO(M2012) criteria. We compared the accuracy of PLCO(M2012) criteria with NLST criteria to detect lung cancer. Cox models were used to evaluate whether the reduction in mortality among 53,202 persons undergoing low-dose computed tomographic screening in the NLST differed according to risk.

RESULTS

The AUC was 0.803 in the development data set and 0.797 in the validation data set. As compared with NLST criteria, PLCO(M2012) criteria had improved sensitivity (83.0% vs. 71.1%, P<0.001) and positive predictive value (4.0% vs. 3.4%, P=0.01), without loss of specificity (62.9% and. 62.7%, respectively; P=0.54); 41.3% fewer lung cancers were missed. The NLST screening effect did not vary according to PLCO(M2012) risk (P=0.61 for interaction).

CONCLUSIONS

The use of the PLCO(M2012) model was more sensitive than the NLST criteria for lung-cancer detection.

Systematic review with meta-analysis of the epidemiological evidence relating FEV1 decline to lung cancer risk

Background

Reduced FEV₁ is known to predict increased lung cancer risk, but previous reviews are limited. To quantify this relationship more precisely, and study heterogeneity, we derived estimates of β for the relationship RR(diff) = exp(βdiff), where diff is the reduction in FEV₁ expressed as a percentage of predicted (FEV₁%P) and RR(diff) the associated relative risk. We used results reported directly as β, and as grouped levels of RR in terms of FEV₁%P and of associated measures (e.g. FEV₁/FVC).

Methods

Papers describing cohort studies involving at least three years follow-up which recorded FEV₁ at baseline and presented results relating lung cancer to FEV₁ or associated measures were sought from Medline and other sources. Data were recorded on study design and quality and, for each data block identified, on details of the results, including population characteristics, adjustment factors, lung function measure, and analysis type. Regression estimates were converted to β estimates where appropriate. For results reported by grouped levels, we used the NHANES III dataset to estimate mean FEV₁%P values for each level, regardless of the measure used, then derived β using regression analysis which accounted for non-independence of the RR estimates. Goodness-of-fit was tested by comparing observed and predicted lung cancer cases for each level. Inverse-variance weighted meta-analysis allowed derivation of overall β estimates and testing for heterogeneity by factors including sex, age, location, timing, duration, study quality, smoking adjustment, measure of FEV₁ reported, and inverse-variance weight of β.

Results

Thirty-three publications satisfying the inclusion/exclusion criteria were identified, seven being rejected as not allowing estimation of β. The remaining 26 described 22 distinct studies, from which 32 independent β estimates were derived. Goodness-of-fit was satisfactory, and exp(β), the RR increase per one unit FEV₁%P decrease, was estimated as 1.019 (95%CI 1.016-1.021). The estimates were quite consistent (I² =29.6%). Mean age was the only independent source of heterogeneity, exp(β) being higher for age <50 years (1.024, 1.020-1.028).

Conclusions

Although the source papers present results in various ways, complicating meta-analysis, they are very consistent. A decrease in FEV₁%P of 10% is associated with a 20% (95%CI 17%-23%) increase in lung cancer risk.

Genetic predisposition to chronic obstructive pulmonary disease and/or lung cancer: important considerations when evaluating risk

Chronic obstructive pulmonary disease (COPD) is defined as a disease causing an airflow limitation that is not fully reversible. COPD is phenotypically complex and characterized by small-airway disease and/or emphysema that result from the interaction between host genetic susceptibility and environmental exposures. As in lung cancer, smoking exposure is the most important risk factor for the development of COPD, accounting for 80% to 90% of all cases. COPD affects an estimated 8% to 10% of the general adult population, 15% to 20% of the smoking population, and 50% to 80% of lung cancer patients (with substantial smoking histories). In prospective studies, COPD has been found to be an independent risk factor for lung cancer, conferring a three- to 10-fold increased risk of lung cancer when compared with smokers without COPD. These findings suggest that smokers have a host susceptibility to COPD alone, COPD and lung cancer (i.e., overlap), and lung cancer in the absence of COPD. This minireview focuses on important points that need to be addressed when studying genetic susceptibility factors for COPD and its complex relationship with susceptibility to lung cancer.

What to do when a smoker's CT scan is "normal"?: Implications for lung cancer screening

Lung cancer is the leading cause of cancer-related mortality in the United States and around the world. There are > 90 million current and ex-smokers in the United States who are at increased risk of lung cancer. The published data from the National Lung Screening Trial (NLST) suggest that yearly screening with low-dose thoracic CT scan in heavy smokers can reduce lung cancer mortality by 20% and all-cause mortality by 7%. However, to implement this program nationwide using the NLST inclusion and exclusion criteria would be extremely expensive, with CT scan costs alone > $2 billion per annum. In this article, we offer a possible low-cost strategy to risk-stratify smokers on the basis of spirometry measurements and emphysema scoring by radiologists on CT scans.

The state of molecular biomarkers for the early detection of lung cancer

Using biomarkers to select the most at-risk population, to detect the disease while measurable and yet not clinically apparent has been the goal of many investigations. Recent advances in molecular strategies and analytic platforms, including genomics, epigenomics, proteomics, and metabolomics, have identified increasing numbers of potential biomarkers in the blood, urine, exhaled breath condensate, bronchial specimens, saliva, and sputum, but none have yet moved to the clinical setting. Therefore, there is a recognized gap between the promise and the product delivery in the cancer biomarker field. In this review, we define clinical contexts where risk and diagnostic biomarkers may have use in the management of lung cancer, identify the most relevant candidate biomarkers of early detection, provide their state of development, and finally discuss critical aspects of study design in molecular biomarkers for early detection of lung cancer.

Technologies supporting analytical cytology: clinical, research and drug discovery applications

The tools and techniques developed for analytical cytology have become invaluable in expanding the development of cancer screening programs and biomarker discovery for personalized medicine. Detecting cellular, molecular, and functional changes of diseased tissue as defined by quantitative analytical methodologies has enhanced the field of medical diagnostics and prognostics. The focus of this review is to outline applications and recent technical advances in flow cytometry, laser scanning cytometry, image cytometry, and quantitative image analysis, as they pertain to clinical, research, and drug discovery applications.

Lung cancer risk prediction to select smokers for screening CT--a model based on the Italian COSMOS trial

Screening with low-dose helical computed tomography (CT) has been shown to significantly reduce lung cancer mortality but the optimal target population and time interval to subsequent screening are yet to be defined. We developed two models to stratify individual smokers according to risk of developing lung cancer. We first used the number of lung cancers detected at baseline screening CT in the 5,203 asymptomatic participants of the COSMOS trial to recalibrate the Bach model, which we propose using to select smokers for screening. Next, we incorporated lung nodule characteristics and presence of emphysema identified at baseline CT into the Bach model and proposed the resulting multivariable model to predict lung cancer risk in screened smokers after baseline CT. Age and smoking exposure were the main determinants of lung cancer risk. The recalibrated Bach model accurately predicted lung cancers detected during the first year of screening. Presence of nonsolid nodules (RR = 10.1, 95% CI = 5.57-18.5), nodule size more than 8 mm (RR = 9.89, 95% CI = 5.84-16.8), and emphysema (RR = 2.36, 95% CI = 1.59-3.49) at baseline CT were all significant predictors of subsequent lung cancers. Incorporation of these variables into the Bach model increased the predictive value of the multivariable model (c-index = 0.759, internal validation). The recalibrated Bach model seems suitable for selecting the higher risk population for recruitment for large-scale CT screening. The Bach model incorporating CT findings at baseline screening could help defining the time interval to subsequent screening in individual participants. Further studies are necessary to validate these models.

Lung cancer risk prediction: Prostate, Lung, Colorectal And Ovarian Cancer Screening Trial models and validation

INTRODUCTION

Identification of individuals at high risk for lung cancer should be of value to individuals, patients, clinicians, and researchers. Existing prediction models have only modest capabilities to classify persons at risk accurately.

METHODS

Prospective data from 70 962 control subjects in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) were used in models for the general population (model 1) and for a subcohort of ever-smokers (N = 38 254) (model 2). Both models included age, socioeconomic status (education), body mass index, family history of lung cancer, chronic obstructive pulmonary disease, recent chest x-ray, smoking status (never, former, or current), pack-years smoked, and smoking duration. Model 2 also included smoking quit-time (time in years since ever-smokers permanently quit smoking). External validation was performed with 44 223 PLCO intervention arm participants who completed a supplemental questionnaire and were subsequently followed. Known available risk factors were included in logistic regression models. Bootstrap optimism-corrected estimates of predictive performance were calculated (internal validation). Nonlinear relationships for age, pack-years smoked, smoking duration, and quit-time were modeled using restricted cubic splines. All reported P values are two-sided.

RESULTS

During follow-up (median 9.2 years) of the control arm subjects, 1040 lung cancers occurred. During follow-up of the external validation sample (median 3.0 years), 213 lung cancers occurred. For models 1 and 2, bootstrap optimism-corrected receiver operator characteristic area under the curves were 0.857 and 0.805, and calibration slopes (model-predicted probabilities vs observed probabilities) were 0.987 and 0.979, respectively. In the external validation sample, models 1 and 2 had area under the curves of 0.841 and 0.784, respectively. These models had high discrimination in women, men, whites, and nonwhites.

CONCLUSION

The PLCO lung cancer risk models demonstrate high discrimination and calibration.