A gene-based risk score for lung cancer susceptibility in smokers and ex-smokers

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.

Lung cancer screening: who pays? Who receives? The European perspectives

Lung cancer is the leading cause of cancer-related death worldwide, and its early detection is critical to achieving a curative treatment and to reducing mortality. Low-dose computed tomography (LDCT) is a highly sensitive technique for detecting noninvasive small lung tumors in high-risk populations. We here analyze the current status of lung cancer screening (LCS) from a European point of view. With economic burden of health care in most European countries resting on the state, it is important to reduce costs of screening and improve its effectiveness. Current cost-effectiveness analyses on LCS have indicated a favorable economic profile. The most recently published analysis reported an incremental cost-effectiveness ratio (ICER) of €3,297 per 1 life-year gained adjusted for the quality of life (QALY) and €2,944 per life-year gained, demonstrating a 90% probability of ICER being below €15,000 and a 98.1% probability of being below €25,000. Different risk models have been used to identify the target population; among these, the PLCOM2012 in particular allows for the selection of the population to be screened with high sensitivity. Risk models should also be employed to define screening intervals, which can reduce the general number of LDCT scans after the baseline round. Future perspectives of screening in a European scenario are related to the will of the policy makers to implement policy on a large scale and to improve the effectiveness of a broad screening of smoking-related disease, including cardiovascular prevention, by measuring coronary calcium score on LDCT. The employment of artificial intelligence (AI) in imaging interpretation, the use of liquid biopsies for the characterization of CT-detected undetermined nodules, and less invasive, personalized surgical treatments, will improve the effectiveness of LCS.

Targeting lung cancer screening to individuals at greatest risk: the role of genetic factors

Lung cancer (LC) is the most common global cancer. An individual’s risk of developing LC is mediated by an array of factors, including family history of the disease. Considerable research into genetic risk factors for LC has taken place in recent years, with both low-penetrance and high-penetrance variants implicated in increasing or decreasing a person’s risk of the disease. LC is the leading cause of cancer death worldwide; poor survival is driven by late onset of non-specific symptoms, resulting in late-stage diagnoses. Evidence for the efficacy of screening in detecting cancer earlier, thereby reducing lung-cancer specific mortality, is now well established. To ensure the cost-effectiveness of a screening programme and to limit the potential harms to participants, a risk threshold for screening eligibility is required. Risk prediction models (RPMs), which provide an individual’s personal risk of LC over a particular period based on a large number of risk factors, may improve the selection of high-risk individuals for LC screening when compared with generalised eligibility criteria that only consider smoking history and age. No currently used RPM integrates genetic risk factors into its calculation of risk. This review provides an overview of the evidence for LC screening, screening related harms and the use of RPMs in screening cohort selection. It gives a synopsis of the known genetic risk factors for lung cancer and discusses the evidence for including them in RPMs, focusing in particular on the use of polygenic risk scores to increase the accuracy of targeted lung cancer screening.

Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.

Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.

Association between chronic obstructive pulmonary disease and interleukins gene variants: A systematic review and meta-analysis

Interleukins are cytokines involved in systemic inflammation and immune system regulation. Many studies have investigated the association between common genetic variations in interleukin-coding genes and COPD susceptibility. In this study, a systematic review and meta-analysis was performed to evaluate the association between interleukin gene variations and COPD pathogenesis. Association studies were retrieved from PubMed and Google Scholar databases using the standard systematic search strategy. A total of 26 different studies evaluating eight polymorphisms in four interleukin genes were included in this study. In overall comparisons, IL1β-rs16944, -rs1143627, -rs1143634, IL13-rs20541 polymorphisms were found not to be associated with the increased risk for developing COPD. However, IL1RN-rs2234663 and IL13-rs1800925 showed a strong association with COPD. We showed that the CC genotype carriers of the IL6-rs1800795 are at significantly higher risk of developing COPD (OR = 1.31, 95% CI: 1.04-1.64, P = 0.01) compared to GG carriers. In case of IL6-rs1800796, individuals with CC and CG genotypes showed a lower risk to develop COPD (OR = 0.46, 95%CI: 0.32-0.66, P > 0.00). This updated meta-analysis strongly supports the association of IL1RN-rs2234663, IL6-rs1800795, -rs1800795 and IL13-rs1800925 variants with COPD.

Lung cancer risk to personalise annual and biennial follow-up computed tomography screening

BACKGROUND

All lung cancer CT screening trials used fixed follow-up intervals, which may not be optimal. We developed new lung cancer risk models for personalising screening intervals to 1 year or 2 years, and compared these with existing models.

METHODS

We included participants in the CT arm of the National Lung Screening Trial (2002-2010) who underwent a baseline scan and a first annual follow-up scan and were not diagnosed with lung cancer in the first year. True and false positives and the area under the curve of each model were calculated. Internal validation was performed using bootstrapping.

RESULTS

Data from 24 542 participants were included in the analysis. The accuracy was 0.785, 0.693, 0.697, 0.666 and 0.727 for the polynomial, patient characteristics, diameter, Patz and PanCan models, respectively. Of the 24 542 participants included, 174 (0.71%) were diagnosed with lung cancer between the first and the second annual follow-ups. Using the polynomial model, 2558 (10.4%, 95% CI 10.0% to 10.8%), 7544 (30.7%, 30.2% to 31.3%), 10 947 (44.6%, 44.0% to 45.2%), 16 710 (68.1%, 67.5% to 68.7%) and 20 023 (81.6%, 81.1% to 92.1%) of the 24 368 participants who did not develop lung cancer in the year following the first follow-up screening round could have safely skipped it, at the expense of delayed diagnosis of 0 (0.0%, 0.0% to 2.7%), 8 (4.6%, 2.2% to 9.2%), 17 (9.8%, 6.0% to 15.4%), 44 (25.3%, 19.2% to 32.5%) and 70 (40.2%, 33.0% to 47.9%) of the 174 lung cancers, respectively.

CONCLUSIONS

The polynomial model, using both patient characteristics and baseline scan morphology, was significantly superior in assigning participants to 1-year or 2-year screening intervals. Implementing personalised follow-up intervals would enable hundreds of participants to skip a screening round per lung cancer diagnosis delayed.

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.

Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study

BACKGROUND

Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years). Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure (smoking status, smoking duration, cigarettes per day, pack-years smoked, time since smoking cessation) as risk predictors. In addition, some models considered factors such as gender, race, ethnicity, education, body mass index, chronic obstructive pulmonary disease, emphysema, personal history of cancer, personal history of pneumonia, and family history of lung cancer.

METHODS AND FINDINGS

Retrospective analyses were performed on 53,452 National Lung Screening Trial (NLST) participants (1,925 lung cancer cases and 884 lung cancer deaths) and 80,672 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) ever-smoking participants (1,463 lung cancer cases and 915 lung cancer deaths). Six-year lung cancer incidence and mortality risk predictions were assessed for (1) calibration (graphically) by comparing the agreement between the predicted and the observed risks, (2) discrimination (area under the receiver operating characteristic curve [AUC]) between individuals with and without lung cancer (death), and (3) clinical usefulness (net benefit in decision curve analysis) by identifying risk thresholds at which applying risk-based eligibility would improve lung cancer screening efficacy. To further assess performance, risk model sensitivities and specificities in the PLCO were compared to those based on the NLST eligibility criteria. Calibration was satisfactory, but discrimination ranged widely (AUCs from 0.61 to 0.81). The models outperformed the NLST eligibility criteria over a substantial range of risk thresholds in decision curve analysis, with a higher sensitivity for all models and a slightly higher specificity for some models. The PLCOm2012, Bach, and Two-Stage Clonal Expansion incidence models had the best overall performance, with AUCs >0.68 in the NLST and >0.77 in the PLCO. These three models had the highest sensitivity and specificity for predicting 6-y lung cancer incidence in the PLCO chest radiography arm, with sensitivities >79.8% and specificities >62.3%. In contrast, the NLST eligibility criteria yielded a sensitivity of 71.4% and a specificity of 62.2%. Limitations of this study include the lack of identification of optimal risk thresholds, as this requires additional information on the long-term benefits (e.g., life-years gained and mortality reduction) and harms (e.g., overdiagnosis) of risk-based screening strategies using these models. In addition, information on some predictor variables included in the risk prediction models was not available.

CONCLUSIONS

Selection of individuals for lung cancer screening using individual risk is superior to selection criteria based on age and pack-years alone. The benefits, harms, and feasibility of implementing lung cancer screening policies based on risk prediction models should be assessed and compared with those of current recommendations.

Lung Cancer Risk Prediction Using Common SNPs Located in GWAS-Identified Susceptibility Regions

INTRODUCTION

Genome-wide association studies (GWAS) have consistently identified specific lung cancer susceptibility regions. We evaluated the lung cancer-predictive performance of single-nucleotide polymorphisms (SNPs) in these regions.

METHODS

Lung cancer cases (N = 778) and controls (N = 1166) were genotyped for 77 SNPs located in GWAS-identified lung cancer susceptibility regions. Variable selection and model development used stepwise logistic regression and decision-tree analyses. In a subset nested in the Pittsburgh Lung Screening Study, change in area under the receiver operator characteristic curve and net reclassification improvement were used to compare predictions made by risk factor models with and without genetic variables.

RESULTS

Variable selection and model development kept two SNPs in each of three GWAS regions, rs2736100 and rs7727912 in 5p15.33, rs805297 and rs1802127 in 6p21.33, and rs8034191 and rs12440014 in 15q25.1. The ratio of cases to controls was three times higher among subjects with a high-risk genotype in every one as opposed to none of the three GWAS regions (odds ratio, 3.14; 95% confidence interval, 2.02-4.88; adjusted for sex, age, and pack-years). Adding a three-level classified count of GWAS regions with high-risk genotypes to an age and smoking risk factor-only model improved lung cancer prediction by a small amount: area under the receiver operator characteristic curve, 0.725 versus 0.717 (p = 0.056); overall net reclassification improvement was 0.052 across low-, intermediate-, and high- 6-year lung cancer risk categories (<3.0%, 3.0%-4.9%, ≥ 5.0%).

CONCLUSION

Specifying genotypes for SNPs in three GWAS-identified susceptibility regions improved lung cancer prediction, but probably by an extent too small to affect disease control practice.

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.

Host genetic variation influences gene expression response to rhinovirus infection

Rhinovirus (RV) is the most prevalent human respiratory virus and is responsible for at least half of all common colds. RV infections may result in a broad spectrum of effects that range from asymptomatic infections to severe lower respiratory illnesses. The basis for inter-individual variation in the response to RV infection is not well understood. In this study, we explored whether host genetic variation is associated with variation in gene expression response to RV infections between individuals. To do so, we obtained genome-wide genotype and gene expression data in uninfected and RV-infected peripheral blood mononuclear cells (PBMCs) from 98 individuals. We mapped local and distant genetic variation that is associated with inter-individual differences in gene expression levels (eQTLs) in both uninfected and RV-infected cells. We focused specifically on response eQTLs (reQTLs), namely, genetic associations with inter-individual variation in gene expression response to RV infection. We identified local reQTLs for 38 genes, including genes with known functions in viral response (UBA7, OAS1, IRF5) and genes that have been associated with immune and RV-related diseases (e.g., ITGA2, MSR1, GSTM3). The putative regulatory regions of genes with reQTLs were enriched for binding sites of virus-activated STAT2, highlighting the role of condition-specific transcription factors in genotype-by-environment interactions. Overall, we suggest that the 38 loci associated with inter-individual variation in gene expression response to RV-infection represent promising candidates for affecting immune and RV-related respiratory diseases.

Community low-dose CT lung cancer screening: a prospective cohort study

BACKGROUND

The National Lung Screening Trial (NLST) in 2011 showed that low-dose CT (LDCT) screening in high-risk groups reduces lung cancer deaths. Major professional organizations, as well as the U.S. Preventative Services Task Force, have endorsed LDCT screening in these select populations. However, major questions remain about whether widespread deployment of CT screening can achieve results similar to the NLST, especially in the community setting.

METHODS

A prospective cohort study was initiated in November 2010. Participants at least 50 years old and with at least 20 pack-years of smoking history underwent LDCT screening in a community setting.

RESULTS

One hundred and fifty four participants underwent LDCT screening with median follow-up of 2.7 years. Compared with the NLST, there was a higher rate of positive screening tests (35.7 vs. 27.3 %), higher false positive rate (100 vs. 96.4 %), and poor adherence (43 vs. 95 %). Invasive diagnostic follow-up was uncommon and uncomplicated. No interval lung cancer was detected. Late follow-up was mostly attributed to participant or primary care provider preference (67.5 %), participants lost to follow-up (17.5 %), and lack of insurance (10 %).

CONCLUSIONS

These findings highlight the potential challenges of generalizing the NLST mortality benefits in the broad deployment of CT screening. Our results support current recommendations that LDCT screening be performed in a highly structured and integrated setting.

Lung cancer screening

Lung cancer is the leading cause of cancer death. Although smoking prevention and cessation programs have decreased lung cancer mortality, there remains a large at-risk population. Dismal long-term survival rates persist despite improvements in diagnosis, staging, and treatment. Early efforts to identify an effective screening test have been unsuccessful. Recent advances in multidetector computed tomography have allowed screening studies using low-dose computed tomography (LDCT) to be performed. This set the stage for the National Lung Screening Trial that found that annual LDCT screening benefits individuals at high risk for lung cancer. An understanding of the harmful effects of lung cancer screening is required to help maximize the benefits and decrease the risks of a lung cancer screening program. Although many questions remain regarding LDCT screening, a comprehensive lung cancer screening program of high-risk individuals will increase detection of preclinical and potentially curable disease, creating a new model of lung cancer surveillance and management.

Genetic test to stop smoking (GeTSS) trial protocol: randomised controlled trial of a genetic test (Respiragene) and Auckland formula to assess lung cancer risk

BACKGROUND

A gene-based estimate of lung cancer risk in smokers has been shown to act as a smoking cessation motivator in hospital recruited subjects. The objective of this trial is to determine if this motivator is as effective in subjects recruited from an NHS primary care unit.

METHOD/DESIGN

Subjects will be recruited by mailings using smoking entries on the GP electronic data-base (total practice population = 32,048) to identify smokers who may want to quit. Smoking cessation clinics based on medical centre premises will run for eight weeks. Clinics will be randomised to have the gene-based test for estimation of lung cancer risk or to act as controls groups. The primary endpoint will be smoking cessation at eight weeks and six months. Secondary outcomes will include ranking of the gene-based test with other smoking cessation motivators.

DISCUSSION

The results will inform as to whether the gene-based test is both effective as motivator and acceptable to subjects recruited from primary care.

TRIAL REGISTRATION

Registered with Clinical Trials.gov,

REGISTRATION NUMBER

NCT01176383.

Role of selected genetic variants in lung cancer risk in African Americans

INTRODUCTION

Black/white disparities in lung cancer incidence and mortality mandate an evaluation of underlying biological differences. We have previously shown higher risks of lung cancer associated with prior emphysema in African American compared with white patients with lung cancer.

METHODS

We therefore evaluated a panel of 1440 inflammatory gene variants in a two-phase analysis (discovery and replication), added top genome-wide association studies (GWAS) lung cancer hits from white populations, and 28 single-nucleotide polymorphisms (SNPs) from a published gene panel. The discovery set (477 self-designated African Americans cases, 366 controls matched on age, ethnicity, and gender) were from Houston, Texas. The external replication set (330 cases and 342 controls) was from the EXHALE study at Wayne State University.

RESULTS

In discovery, 154 inflammation SNPs were significant (p < 0.05) on univariate analysis, as was one of the gene panel SNPs (rs308738 in REV1, p = 0.0013), and three GWAS hits, rs16969968 p = 0.0014 and rs10519203 p = 0.0003 in the 15q locus and rs2736100, in the HTERT locus, p = 0.0002. One inflammation SNP, rs950286, was successfully replicated with a concordant odds ratio of 1.46 (1.14-1.87) in discovery, 1.37 (1.05-1.77) in replication, and a combined odds ratio of 1.40 (1.17-1.68). This SNP is intergenic between IRF4 and EXOC2 genes. We also constructed and validated epidemiologic and extended risk prediction models. The area under the curve (AUC) for the epidemiologic discovery model was 0.77 and 0.80 for the extended model. For the combined datasets, the AUC values were 0.75 and 0.76, respectively.

CONCLUSIONS

As has been reported for other cancer sites and populations, incorporating top genetic hits into risk prediction models, provides little improvement in model performance and no clinical relevance.

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.

Computed tomography screening for lung cancer: has it finally arrived? Implications of the national lung screening trial

The National Lung Screening Trial (NLST) has provided compelling evidence of the efficacy of lung cancer screening using low-dose helical computed tomography (LDCT) to reduce lung cancer mortality. The NLST randomized 53,454 older current or former heavy smokers to receive LDCT or chest radiography (CXR) for three annual screens. Participants were observed for a median of 6.5 years for outcomes. Vital status was available in more than 95% of participants. LDCT was positive in 24.2% of screens, compared with 6.9% of CXRs; more than 95% of all positive LDCT screens were not associated with lung cancer. LDCT detected more than twice the number of early-stage lung cancers and resulted in a stage shift from advanced to early-stage disease. Complications of LDCT screening were minimal. Lung cancer-specific mortality was reduced by 20% relative to CXR; all-cause mortality was reduced by 6.7%. The major harms of LDCT are radiation exposure, high false-positive rates, and the potential for overdiagnosis. This review discusses the risks and benefits of LDCT screening as well as an approach to LDCT implementation that incorporates systematic screening practice with smoking cessation programs and offers opportunities for better determination of appropriate risk cohorts for screening and for better diagnostic prediction of lung cancer in the setting of screen-detected nodules. The challenges of implementation are considered for screening programs, for primary care clinicians, and across socioeconomic strata. Considerations for future research to complement imaging-based screening to reduce the burden of lung cancer are discussed.

Clinical applications of gene-based risk prediction for lung cancer and the central role of chronic obstructive pulmonary disease

Lung cancer is the leading cause of cancer death worldwide and nearly 90% of cases are attributable to smoking. Quitting smoking and early diagnosis of lung cancer, through computed tomographic screening, are the only ways to reduce mortality from lung cancer. Recent epidemiological studies show that risk prediction for lung cancer is optimized by using multivariate risk models that include age, smoking exposure, history of chronic obstructive pulmonary disease (COPD), family history of lung cancer, and body mass index. It has also been shown that COPD predates lung cancer in 65–70% of cases, conferring a four- to sixfold greater risk of lung cancer compared to smokers with normal lung function. Genome-wide association studies of smokers have identified a number of genetic variants associated with COPD or lung cancer. In a case–control study, where smokers with normal lungs were compared to smokers who had spirometry-defined COPD or histology confirmed lung cancer, several of these variants were shown to overlap, conferring the same susceptibility or protective effects on both COPD and lung cancer (independent of COPD status). In this perspective article, we show how combining clinical data with genetic variants can help identify heavy smokers at the greatest risk of lung cancer. Using this approach, we found that gene-based risk testing helped engage smokers in risk mitigating activities like quitting smoking and undertaking lung cancer screening. We suggest that such an approach could facilitate the targeted selection of smokers for cost-effective life-saving interventions.

Association between chronic obstructive pulmonary disease and lung cancer: a case-control study in Southern Chinese and a meta-analysis

Background

Lung cancer and chronic obstructive pulmonary disease (COPD) share a common risk factor in cigarette smoking and a large portion of patients with lung cancer suffer from COPD synchronously. We therefore hypothesized that COPD is an independent risk factor for lung cancer. Our aim was to investigate the intrinsic linkage of COPD (or emphysema, chronic bronchitis and asthma) and lung cancer.

Methods

The present hospital-based case-control study included 1,069 patients with newly diagnosed lung cancer and 1,132 age frequency matched cancer-free controls. The odds ratios (ORs) for the associations between each previous pulmonary disease and lung cancer were estimated with logistic regression models, adjusting for age, sex, family history of cancer, BMI and pack year smoking. In meta-analysis, the pooled effects of previous pulmonary diseases were analyzed with random effects models; and stratification analyses were conducted on smoking status and ethnicity.

Results

In the case-control study, previous COPD was associated with the odds for increased risk of lung cancer (OR = 1.29, 95% confidence interval [CI] = 1.00∼1.68); so were emphysema (OR = 1.55, 95%CI = 1.03∼2.32) and chronic bronchitis (OR = 1.22, 95%CI = 0.99∼1.67); while asthma was associated with odds for decreased risk of lung cancer (OR = 0.29, 95%CI = 0.16∼0.53). These associations were more pronounced in smokers (P<.05 for all strata), but not in non-smokers. In meta-analysis, 35 studies (22,010 cases and 44,438 controls) were identified. COPD was significantly associated with the odds for increased risk of lung cancer (pooled OR = 2.76; 95% CI = 1.85–4.11), so were emphysema (OR = 3.02; 95% CI = 2.41–3.79) and chronic bronchitis (OR = 1.88; 95% CI = 1.49–2.36); and these associations were more pronounced in smokers than in non-smokers (P<.001 respectively). No significant association was observed for asthma.

Conclusion

Previous COPD could increase the risk of lung cancer, especially in smokers.

A new explained-variance based genetic risk score for predictive modeling of disease risk

The goal of association mapping is to identify genetic variants that predict disease, and as the field of human genetics matures, the number of successful association studies is increasing. Many such studies have shown that for many diseases, risk is explained by a reasonably large number of variants that each explains a very small amount of disease risk. This is prompting the use of genetic risk scores in building predictive models, where information across several variants is combined for predictive modeling. In the current study, we compare the performance of four previously proposed genetic risk score methods and present a new method for constructing genetic risk score that incorporates explained variance information. The methods compared include: a simple count Genetic Risk Score, an odds ratio weighted Genetic Risk Score, a direct logistic regression Genetic Risk Score, a polygenic Genetic Risk Score, and the new explained variance weighted Genetic Risk Score. We compare the methods using a wide range of simulations in two steps, with a range of the number of deleterious single nucleotide polymorphisms (SNPs) explaining disease risk, genetic modes, baseline penetrances, sample sizes, relative risks (RR) and minor allele frequencies (MAF). Several measures of model performance were compared including overall power, C-statistic and Akaike's Information Criterion. Our results show the relative performance of methods differs significantly, with the new explained variance weighted GRS (EV-GRS) generally performing favorably to the other methods.

[COPD and lung cancer: epidemiological and biological links]

Lung cancer and chronic obstructive lung disease (COPD) are two common fatal diseases. Apart from their common link to tobacco, these two diseases are usually considered to be the result of separate distinct mechanisms. In the past 15 years, numerous studies have produced arguments in favour of a relationship between these two pathologies that goes beyond a simple addition of risk factors. At the epidemiological level, there are data that demonstrate an increased incidence of bronchial carcinoma in patients with COPD. The links between these two pathologies are still unexplained but there are numerous arguments supporting a common physiopathology. Common genetic and epigenetic abnormalities, mechanical factors and signalisation pathways have been quoted. COPD and lung cancer appear to be two diseases possessing a genetic basis that creates a predisposition to environmental or toxic assaults, resulting in a different clinical manifestation in each disease. Consequently, improvements in the management of these two diseases will involve a more intensive investigation of their physiopathology, and require a closer collaboration between research centres and clinical units.

Abstract IA21: Risk prediction models for lung cancer

Background: There are an estimated 45 million current smokers in the US and 49 million former smokers. The challenge is to construct reliable risk prediction models to identify that fraction of smokers most likely to get lung cancer. High-risk individuals could undergo a program of screening surveillance that might not be appropriate for a lower risk population and receive the most intensive smoking cessation interventions. Models with improved discriminatory ability will also have clinical benefits. In the US, 150,000 patients are diagnosed with focal pulmonary lesions annually, with the prevalence of malignancy ranging from 10 to 70%. Current guidelines are to employ best clinical judgment and/or validated models. Finally, risk prediction tools could be incorporated into the design of smaller, more powerful, and “smarter” prevention trials. Cardiovascular and type 2 diabetes risk profiles using epidemiologic data have been effectively used for nearly two decades. In the cancer arena, risk prediction models for breast cancer have the longest history, although models have also been generated for prostate, lung, melanoma, ovary, colorectal, and bladder cancers. The advent of genome-wide association studies to identify low-penetrance common susceptibility alleles now heralds the possibility of incorporating panels of gene variants into existing models and to assess improvement in model performance. However, to date, the expanded models for many cancer sites have shown only modest incremental improvements in discrimination.

Lung Risk Models: We and others have explored various approaches for lung cancer prediction. Such tools hold promise, but their interpretation is complex.

Epidemiologic/clinical: Bach published the original risk prediction model based on data from the Carotene and Retinol Efficacy Trial (CARET) of 14,000 heavy smokers and &gt;4,000 asbestos-exposed men, mostly white (1). The variables included age, gender, asbestos exposure, smoking history, cigarettes per day, duration of smoking and of cessation. The Bach model has been validated in an independent data from the placebo arm of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention study. Our baseline model was based on data derived from an ongoing lung cancer case control study (2). Variables included were environmental tobacco smoke (for never and former smokers only), family cancer history, asbestos and dust exposures, prior respiratory disease, history of hay fever, and smoking history variables. All variables have strong biologically plausible etiologic roles, and are relatively easy to ascertain through patient interview. The validated area under the curve (AUC) statistics for the former and current smoker models were modest (0.63, and 0.58, respectively), although consistent with those from other risk prediction models. If age and smoking status (case-control matching variables) had been included in the models, the models would likely have performed far better.

The Liverpool Lung Project (LLP) variables (3) include smoking duration, prior diagnosis of pneumonia, occupational exposure to asbestos, prior cancer diagnosis and family history of lung cancer. Most recently a carefully constructed model based on data from 70,962 control subjects in the Prostate, Lung, Colorectal, Ovarian cancer screening trial (PLCO) was published (4). This model includes age, SES (education), BMI, family history of lung cancer, COPD, recent chest x-ray, smoking status (never, former, or current), pack-years smoked, and smoking duration. A second model also included smoking quit-time. External validation was performed with 44,223 PLCO intervention arm participants. 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.

Extended Models:

Functional Data: We have estimated the improvement in model performance by incorporating two measures of DNA repair capacity that have been shown in case-control analyses to be associated with increased lung cancer risk (5). Addition of the biomarker assays does improve the sensitivity of the models over epidemiologic and clinical data alone. These in vitro lymphocyte culture assays, however, are time-consuming and require some level of technical expertise. Therefore while feasible in a controlled academic setting, they are not applicable for widespread population-based implementation.

Genetic Data: Genetic data are stable, inexpensive to ascertain, accurate and amenable to high-throughput analysis. However, it remains uncertain whether enriched multimarker models give better discrimination. We have added three SNPS that were most significant in GWAS of lung cancer to the baseline lung epidemiologic model. These include a replicated SNP in the 15q25 chromosomal region that encompasses the nicotinic acetylcholine receptor subunit genes, CHRNA3 and CHRNA5, that have a defined role in nicotine dependence, and a hypothesized direct role in downstream signaling pathways that promote carcinogenesis. We also included two SNPs from the 5p15.33 locus (rs2736100 and rs401681) that contains two known genes: the TERT (human telomerase reverse transcriptase) gene and the CLPTM1L. The AUC showed only modest improvement. Such small increases in discriminative accuracy are unlikely to be of diagnostic or predictive utility.

Young (6) developed a risk model using a 20-SNP panel including metabolizing, inflammation, DNA repair, anti-oxidant, apoptosis and addiction genes. The final model included the 20 SNPs, age, history of COPD, family history of lung cancer and gender. When numeric scores were assigned to both the SNP and demographic data, and sequentially combined by a simple algorithm in a risk model, the composite score was found to be linearly related to lung cancer risk with a bimodal distribution.

Spira et al (7) have advanced the notion that upper airway gene expression in smokers may serve as a relatively noninvasive surrogate marker of the physiologic response of the lung to tobacco smoke and could be used in large-scale screening and chemoprevention studies for lung cancer. Gene expression profiles in cytologically normal large airway epithelium were obtained via bronchoscopic brushings and were predictive of cancer status in a combined clinicogenomic model (P &lt; 0.005). There was a significant improvement in performance of the clinicogenomic relative to the clinical model (P &lt; 0.05). Use of the clinicogenomic model may reduce invasive diagnostic procedures for individuals without lung cancer.

The ROC curve may not be sensitive to differences in probabilities between models, and therefore insufficient to assess the impact of adding a new predictor. A substantial gain in performance may not yield a substantial increase in AUC, and only a very large independent association of the new marker with risk will yield a meaningful larger AUC. As an example, the Framingham Risk Score that is widely applied has an AUC of about 0.80. New metrics need to be developed to compare nested models.

In summary, the ability to accurately predict risk of lung cancer among former and current smokers has public health, clinical and financial implications for primary prevention, surveillance programs, screening programs and chemoprevention trials.

How the genetics of lung cancer may overlap with COPD

Over the last 30years, epidemiological studies have shown that COPD is the single most important risk factor for lung cancer after smoking exposure. Recent genetic studies using genome-wide approaches suggest that the genetic risk factors predisposing smokers to COPD and lung cancer may overlap. The genes identified by these studies suggest that this overlapping genetic susceptibility may be mediated through receptors expressed on the bronchial epithelium that implicate molecular pathways underlying both COPD and lung cancer. Furthermore, it appears that aberrant inflammatory and/or immune-modulatory pathways leading to excess matrix metalloproteinases, growth factors and airway remodelling in COPD may also be promoting malignant transformation of the bronchial epithelium. The process linking inflammation, remodelling and cancer formation is called epithelial-mesenchymal transition. There are several clinical implications arising from the COPD-lung cancer overlap. First, if COPD is a precursor disease to lung cancer then efforts to prevent COPD, might be even more important. Second, if drugs targeting the overlapping molecular pathways can be identified, chemoprevention that reduce the propensity to COPD and lung cancer is an attractive option. Finally, if low-dose computerized tomography can identify treatable lung cancer, gene-based tests of susceptibility might help identify those smokers who should undergo radiological screening.

Association of the CHRNA5-A3-B4 gene cluster with heaviness of smoking: a meta-analysis

INTRODUCTION

Variation in the CHRNA5-A3-B4 gene cluster is a promising candidate region for smoking behavior and has been linked to multiple smoking-related phenotypes (e.g., nicotine dependence) and diseases (e.g., lung cancer). Two single nucleotide polymorphisms (SNPs), rs16969968 in CHRNA5 and rs1051730 in CHRNA3, have generated particular interest.

METHODS

We evaluated the published evidence for association between rs16969968 (k = 27 samples) and rs1051730 (k = 44 samples) SNPs with heaviness of smoking using meta-analytic techniques. We explored which SNP provided a stronger genetic signal and investigated study-level characteristics (i.e., ancestry, disease state) to establish whether the strength of association differed across populations. We additionally tested for small study bias and explored the impact of year of publication.

RESULTS AND CONCLUSIONS

Meta-analysis indicated compelling evidence of an association between the rs1051730/rs16966968 variants and daily cigarette consumption (fixed effects: B = 0.91, 95% CI = 0.77, 1.06, p < .001; random effects: B = 1.01, 95% CI = 0.81, 1.22, p < .001), equivalent to a per-allele effect of approximately 1 cigarette/day. SNP rs1051730 was found to provide a stronger signal than rs16966968 in stratified analyses (p(diff) = .028), although this difference was only qualitatively observed in the subset of samples that provided data on both SNPs. While the functional relevance of rs1051730 is unknown, it may be a strong tagging SNP for functional haplotypes in this region.

Genetic variants associated with the risk of chronic obstructive pulmonary disease with and without lung cancer

Chronic obstructive pulmonary disease (COPD) is a strong risk factor for lung cancer. Published studies about variations of genes encoding glutathione metabolism, DNA repair, and inflammatory response pathways in susceptibility to COPD were inconclusive. We evaluated 470 single-nucleotide polymorphisms (SNP) from 56 genes of these three pathways in 620 cases and 893 controls to identify susceptibility markers for COPD risk, using existing resources. We assessed SNP- and gene-level effects adjusting for sex, age, and smoking status. Differential genetic effects on disease risk with and without lung cancer were also assessed; cumulative risk models were established. Twenty-one SNPs were found to be significantly associated with risk of COPD (P < 0.01); gene-based analyses confirmed two genes (GCLC and GSS) and identified three additional genes (GSTO2, ERCC1, and RRM1). Carrying 12 high-risk alleles may increase risk by 2.7-fold; eight SNPs altered COPD risk without lung cancer by 3.1-fold and 4 SNPs altered the risk with lung cancer by 2.3-fold. Our findings indicate that multiple genetic variations in the three selected pathways contribute to COPD risk through GCLC, GSS, GSTO2, ERCC1, and RRM1 genes. Functional studies are needed to elucidate the mechanisms of these genes in the development of COPD, lung cancer, or both.

Genetic variability in DNA repair and cell cycle control pathway genes and risk of smoking-related lung cancer

DNA repair and cell cycle control play an important role in the repair of DNA damage caused by cigarette smoking. Given this role, functionally relevant single nucleotide polymorphisms (SNPs) in genes in these pathways may well affect the risk of smoking-related lung cancer. We examined the relationship between 240 SNPs in DNA repair and cell cycle control pathway genes and lung cancer risk in a case-control study of white current and ex-cigarette smokers (722 cases and 929 controls). Additive, dominant, and recessive genetic models were evaluated for each SNP. A genetic risk summary score was also constructed. Odds ratios (OR) for lung cancer risk and 95% confidence intervals (95% CI) were estimated using logistic regression models. Thirty-eight SNPs were associated with lung cancer risk in our study population at P < 0.05. The strongest associations were observed for rs2074508 in GTF2H4 (P(additive)  = 0.003), rs10500298 in LIG1 (P(recessive)  = 2.7 × 10(-4)), rs747658 and rs3219073 in PARP1 (rs747658: P(additive)  = 5.8 × 10(-5); rs3219073: P(additive)  = 4.6 × 10(-5)), and rs1799782 and rs3213255 in XRCC1 (rs1799782: P(dominant)  = 0.006; rs3213255: P(recessive)  = 0.004). Compared to individuals with first quartile (lowest) risk summary scores, individuals with third and fourth quartile summary score results were at increased risk for lung cancer (OR: 2.21, 95% CI: 1.66-2.95 and OR: 3.44, 95% CI: 2.58-4.59, respectively; P(trend)  < 0.0001). Our data suggests that variation in DNA repair and cell cycle control pathway genes is associated with smoking-related lung cancer risk. Additionally, combining genotype information for SNPs in these pathways may assist in classifying current and ex-cigarette smokers according to lung cancer risk.

GSTM1 null genotype in COPD and lung cancer: evidence of a modifier or confounding effect?

Background

Studies over the past two decades have reported associations between GSTM1 (glutathione S-transferase mu 1) null genotype and chronic obstructive pulmonary disease (COPD) or lung cancer. However, a modifier or confounding effect from COPD mediating the GSTM1 association with lung cancer has not been previously explored.

Aim and methods

This variant was examined in a case-control study of current or former smokers with COPD (n = 669), lung cancer (n = 454), or normal lung function (n = 488). Sex, age, and smoking history were comparable between groups.

Results

The GSTM1 null genotype was found to be more frequent in smokers with COPD alone (odds ratio [OR] 1.30, 95% confidence interval [CI] 1.02–1.66, P = 0.031) and lung cancer (OR 1.26, 95% CI 0.96–1.65, P = 0.083) than in matched smokers with normal lung function (62%, 61%, and 56%, respectively). However, when smokers with lung cancer were subgrouped according to the presence of COPD, then the association with all COPD subjects (OR 1.34, 95% CI 1.07–1.70, P = 0.010) and with COPD and lung cancer (OR 1.50, 95% CI 1.06–2.12, P = 0.018) continued to be significant while that with lung cancer only was reduced (OR 1.11, 95% CI 0.78–1.56, P = 0.55). These associations were independent of age, sex, height, lung function, and smoking history.

Conclusion

Findings suggest that COPD is an important subphenotype of lung cancer and may underlie previously reported associations with the GSTM1 null genotype.

Genetic evidence linking lung cancer and COPD: a new perspective

Epidemiological studies indicate that tobacco smoke exposure accounts for nearly 90% of cases of chronic obstructive pulmonary disease (COPD) and lung cancer. However, genetic factors may explain why 10%–30% of smokers develop these complications. This perspective reviews the evidence suggesting that COPD is closely linked to susceptibility to lung cancer and outlines the potential relevance of this observation. Epidemiological studies show that COPD is the single most important risk factor for lung cancer among smokers and predates lung cancer in up to 80% of cases. Genome-wide association studies of lung cancer, lung function, and COPD have identified a number of overlapping “susceptibility” loci. With stringent phenotyping, it has recently been shown that several of these overlapping loci are independently associated with both COPD and lung cancer. These loci implicate genes underlying pulmonary inflammation and apoptotic processes mediated by the bronchial epithelium, and link COPD with lung cancer at a molecular genetic level. It is currently possible to derive risk models for lung cancer that incorporate lung cancer-specific genetic variants, recently identified “COPD-related” genetic variants, and clinical variables. Early studies suggest that single nucleotide polymorphism-based risk stratification of smokers might help better target novel prevention and early diagnostic strategies in lung cancer.

miR-1254 and miR-574-5p: serum-based microRNA biomarkers for early-stage non-small cell lung cancer

INTRODUCTION

The ability to diagnose non-small cell lung cancer (NSCLC) at an early stage may lead to improved survival. The aim of this study was to identify differentially expressed serum-based microRNAs (miRNAs) between patients with early-stage NSCLC and controls. These miRNAs may serve as biomarkers for NSCLC early detection.

METHODS

miRNA profiling was performed on total RNA extracted from serum obtained from 22 individuals (11 controls and 11 patients with early-stage NSCLC). Quantitative polymerase chain reaction (qPCR) was used to validate the profiling results in the discovery set and in a validation set of 31 controls and 22 patients with early-stage NSCLC. Additionally, six matched plasma samples (four NSCLC cases and two controls) and three serum mesothelioma samples were analyzed by qPCR. Receiver operating characteristic curves were generated for each possible combination of the miRNAs measured by qPCR.

RESULTS

The expression of hsa-miR-1254 and hsa-miR-574-5p was significantly increased in the early-stage NSCLC samples with respect to the controls. Receiver operating characteristic curves plotting these two miRNAs were able to discriminate early-stage NSCLC samples from controls with 82% and 77% of sensitivity and specificity, respectively, in the discovery cohort and with 73% and 71% of sensitivity and specificity, respectively, in the validation cohort. The mesothelioma and plasma samples did not seem to classify into either NSCLC or control groups.

CONCLUSIONS

Serum miRNAs are differentially expressed between patients with early-stage NSCLC and controls. The utility of miR-1254 and miR-574-5p serum-based biomarkers as minimally invasive screening and triage tools for subsequent diagnostic evaluation warrants additional validation.

Individual and cumulative effects of GWAS susceptibility loci in lung cancer: associations after sub-phenotyping for COPD

Epidemiological studies show that approximately 20–30% of chronic smokers develop chronic obstructive pulmonary disease (COPD) while 10–15% develop lung cancer. COPD pre-exists lung cancer in 50–90% of cases and has a heritability of 40–77%, much greater than for lung cancer with heritability of 15–25%. These data suggest that smokers susceptible to COPD may also be susceptible to lung cancer. This study examines the association of several overlapping chromosomal loci, recently implicated by GWA studies in COPD, lung function and lung cancer, in (n = 1400) subjects sub-phenotyped for the presence of COPD and matched for smoking exposure. Using this approach we show; the 15q25 locus confers susceptibility to lung cancer and COPD, the 4q31 and 4q22 loci both confer a reduced risk to both COPD and lung cancer, the 6p21 locus confers susceptibility to lung cancer in smokers with pre-existing COPD, the 5p15 and 1q23 loci both confer susceptibility to lung cancer in those with no pre-existing COPD. We also show the 5q33 locus, previously associated with reduced FEV₁, appears to confer susceptibility to both COPD and lung cancer. The 6p21 locus previously linked to reduced FEV₁ is associated with COPD only. Larger studies will be needed to distinguish whether these COPD-related effects may reflect, in part, associations specific to different lung cancer histology. We demonstrate that when the “risk genotypes” derived from the univariate analysis are incorporated into an algorithm with clinical variables, independently associated with lung cancer in multivariate analysis, modest discrimination is possible on receiver operator curve analysis (AUC = 0.70). We suggest that genetic susceptibility to lung cancer includes genes conferring susceptibility to COPD and that sub-phenotyping with spirometry is critical to identifying genes underlying the development of lung cancer.

Association of genetic polymorphisms of CXCL12/SDF1 gene and its receptor, CXCR4, to the susceptibility and prognosis of non-small cell lung cancer

BACKGROUND

The aim of this study was to evaluate the relations of chemokine CXCL12, previously known as stromal cell-derived factor-1 (SDF1), and its receptor, CXCR4, gene variants on non-small cell lung cancer (NSCLC) risk and disease severity.

METHODS

Through a case-control study design, genomic DNA samples of 247 NSCLC patients and 328 age and sex-matched controls were subjected to polymerase chain reaction-restriction fragment length polymorphism analysis. The validity of this technique was proven by direct sequencing of amplified products. Statistical analyses were conducted to explore the contribution of polymorphism of the CXCL12/SDF1 gene and CXCR4, in the susceptibility to and prognosis of NSCLC.

RESULTS

Overall, the genotype frequencies of CXCL12/SDF1 gene and CXCR4, were significantly different between lung cancer patients and controls (p<0.0001), and also different between patients with lung cancers of various stages (p<0.0001). Logistic regression analysis revealed that higher odds ratios (ORs) for lung cancer were seen for individuals with CXCL12/SDF1 AA (an OR of 1.95, 95% CI 1.08-3.50, p=0.018), or CXCR4 TT (an OR of 4.71, 95% CI 1.99-11.2, p<0.0001), and for individuals with both CXCL12/SDF1 AA and CXCR4 TT genotypes (an OR of 12.4, 95% CI 1.56-98.3, p=0.002). The patients carrying a homologous AA genotype at CXCL12/SDF1, or a homologous TT genotype at CXCR4, had a tendency to advanced disease and toward poorer prognoses compared with other patients.

CONCLUSION

A significant association between the polymorphisms of CXCL12/SDF1 and CXCR4, and the susceptibility to and prognosis of NSCLC was demonstrated.

FAM13A locus in COPD is independently associated with lung cancer - evidence of a molecular genetic link between COPD and lung cancer

Recent genome-wide association studies have reported a FAM13A variant on chromosome 4q22.1 is associated with lung function and COPD. We examined this variant in a case-control study of current or former smokers with chronic obstructive pulmonary disease (COPD, n = 458), lung cancer (n = 454), or normal lung function (n = 488). Sex, age, and smoking history were comparable between groups. We confirmed the FAM13A variant (rs7671167) confers a protective effect on smoking-related COPD alone (C allele odds ratio [OR] = 0.79, P = 0.013, and CC genotype OR = 0.71, P = 0.024) and those with COPD, both with and without lung cancer (C allele OR = 0.80, P = 0.008, and CC genotype OR = 0.70, P = 0.007). The FAM13A variant also confers a protective effect on lung cancer overall (C allele OR = 0.75, P = 0.002, and CC genotype OR = 0.64, P = 0.003) even after excluding those with co-existing COPD (C allele OR = 0.67, P = 0.0007, and CC genotype OR = 0.58, P = 0.006). This was independent of age, sex, height, lung function, and smoking history. This protective effect was confined to those with nonsmall cell lung cancer (C allele OR = 0.72, P = 0.0009, and CC genotype OR = 0.61, P = 0.003). This study suggests that genetic predisposition to COPD is shared with lung cancer through shared pathogenetic factors such as the 4q22.1 locus implicating the Rho-kinase pathway.

Translesion synthesis polymerases in the prevention and promotion of carcinogenesis

A critical step in the transformation of cells to the malignant state of cancer is the induction of mutations in the DNA of cells damaged by genotoxic agents. Translesion DNA synthesis (TLS) is the process by which cells copy DNA containing unrepaired damage that blocks progression of the replication fork. The DNA polymerases that catalyze TLS in mammals have been the topic of intense investigation over the last decade. DNA polymerase η (Pol η) is best understood and is active in error-free bypass of UV-induced DNA damage. The other TLS polymerases (Pol ι, Pol κ, REV1, and Pol ζ) have been studied extensively in vitro, but their in vivo role is only now being investigated using knockout mouse models of carcinogenesis. This paper will focus on the studies of mice and humans with altered expression of TLS polymerases and the effects on cancer induced by environmental agents.