Lung cancer susceptibility model based on age, family history and genetic variants

Polygenic Risk Score Is Associated with Developing and Dying from Lung Cancer in the National Lung Screening Trial

Background: Epidemiological studies suggest lung cancer results from the combined effects of smoking and genetic susceptibility. The clinical application of polygenic risk scores (PRSs), derived from combining the results from multiple germline genetic variants, have not yet been explored in a lung cancer screening cohort. Methods: This was a post hoc analysis of 9191 non-Hispanic white subjects from the National Lung Screening Trial (NLST), a sub-study of high-risk smokers randomised to annual computed tomography (CT) or chest X-ray (CXR) and followed for 6.4 years (mean). This study's primary aim was to examine the relationship between a composite polygenic risk score (PRS) calculated from 12 validated risk genotypes and developing or dying from lung cancer during screening. Validation was undertaken in the UK Biobank of unscreened ever-smokers (N = 167,796) followed for 10 years (median). Results: In this prospective study, we found our PRS correlated with lung cancer incidence (p < 0.0001) and mortality (p = 0.004). In an adjusted multivariable logistic regression analysis, PRS was independently associated with lung cancer death (p = 0.0027). Screening participants with intermediate and high PRS scores had a higher lung cancer mortality, relative to those with a low PRS score (rate ratios = 1.73 (95%CI 1.14-2.64, p = 0.010) and 1.89 (95%CI 1.28-2.78, p = 0.009), respectively). This was despite comparable baseline demographics (including lung function) and comparable lung cancer characteristics. The PRS's association with lung cancer mortality was validated in an unscreened cohort from the UK Biobank (p = 0.002). Conclusions: In this biomarker-based cohort study, an elevated PRS was independently associated with dying from lung cancer in both screening and non-screening cohorts.

[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.

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.

Chr15q25 genetic variant (rs16969968) independently confers risk of lung cancer, COPD and smoking intensity in a prospective study of high-risk smokers

IMPORTANCE

While cholinergic receptor nicotinic alpha 5 (CHRNA5) variants have been linked to lung cancer, chronic obstructive pulmonary disease (COPD) and smoking addiction in case-controls studies, their corelationship is not well understood and requires retesting in a cohort study.

OBJECTIVE

To re-examine the association between the CHRNA5 variant (rs16969968 AA genotype) and the development of lung cancer, relative to its association with COPD and smoking.

METHODS

In 9270 Non-Hispanic white subjects from the National Lung Screening Trial, a substudy of high-risk smokers were followed for an average of 6.4 years. We compared CHRNA5 genotype according to baseline smoking exposure, lung function and COPD status. We also compared the lung cancer incidence rate, and used multiple logistic regression and mediation analysis to examine the role of the AA genotype of the CHRNA5 variant in smoking exposure, COPD and lung cancer.

RESULTS

As previously reported, we found the AA high-risk genotype was associated with lower lung function (p=0.005), greater smoking intensity (p<0.001), the presence of COPD (OR 1.28 (95% CI 1.10 to 1.49) p=0.0015) and the development of lung cancer (HR 1.41, (95% CI 1.03 to 1.93) p=0.03). In a mediation analyses, the AA genotype was independently associated with smoking intensity (OR 1.42 (95% CI 1.25 to 1.60, p<0.0001), COPD (OR 1.25, (95% CI 1.66 to 2.53), p=0.0015) and developing lung cancer (OR 1.37, (95% CI 1.03 to 1.82) p=0.03).

CONCLUSION

In this large-prospective study, we found the CHRNA5 rs 16 969 968 AA genotype to be independently associated with smoking exposure, COPD and lung cancer (triple whammy effect).

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.

The potential impact of chronic obstructive pulmonary disease in lung cancer screening: implications for the screening clinic

Introduction: Following the findings of the National Lung Screening Trial (NLST), lung cancer screening is now recommended in the United States. However, post-hoc analyses of the NLST suggest that reducing lung cancer mortality through screening is highly dependent on the underlying characteristics of the screening participants, in particular, the presence of chronic obstructive pulmonary disease (COPD). Areas covered: In this review, we outline how outcomes in lung cancer screening are significantly affected by the presence of airflow limitation, as caused by COPD, and how this might impact the assessment of eligible smokers in a lung cancer screening clinic. Expert opinion: There is growing evidence showing that CT-based screening for lung cancer reduces lung cancer mortality. The benefits of screening exceed those seen in the NLST when screening is carried out in lower risk populations, for a longer duration, and when outcomes are compared with usual care control cohorts. In this article, we review data from a post-hoc analysis of the NLST. We suggest that whilst worsened airflow limitation is associated with greater lung cancer risk, there is also more aggressive lung cancer, reduced lung cancer operability, and for advanced COPD, reduced benefits from screening. We advocate an 'outcomes-based' approach to screening over a 'risk-based' approach.

Will That Pulmonary Nodule Become Cancerous? A Risk Prediction Model for Incident Lung Cancer

This prospective investigation derived a prediction model for identifying risk of incident lung cancer among patients with visible lung nodules identified on computed tomography (CT). Among 2,924 eligible patients referred for evaluation of a pulmonary nodule to the Stony Brook Lung Cancer Evaluation Center between January 1, 2002 and December 31, 2015, 171 developed incident lung cancer during the observation period. Cox proportional hazard models were used to model time until disease onset. The sample was randomly divided into discovery (n = 1,469) and replication (n = 1,455) samples. In the replication sample, concordance was computed to indicate predictive accuracy and risk scores were calculated using the linear predictions. Youden index was used to identify high-risk versus low-risk patients and cumulative lung cancer incidence was examined for high-risk and low-risk groups. Multivariable analyses identified a combination of clinical and radiologic predictors for incident lung cancer including ln-age, ln-pack-years smoking, a history of cancer, chronic obstructive pulmonary disease, and several radiologic markers including spiculation, ground glass opacity, and nodule size. The final model reliably detected patients who developed lung cancer in the replication sample (C = 0.86, sensitivity/specificity = 0.73/0.81). Cumulative incidence of lung cancer was elevated in high-risk versus low-risk groups [HR = 14.34; 95% confidence interval (CI), 8.17-25.18]. Quantification of reliable risk scores has high clinical utility, enabling physicians to better stratify treatment protocols to manage patient care. The final model is among the first tools developed to predict incident lung cancer in patients presenting with a concerning pulmonary nodule.

Combining ctnnb1 genetic variability with epidemiologic factors to predict lung cancer susceptibility

OBJECTIVE

Early detection and diagnosis of lung cancer remain challenging but would improve patient prognosis. The goal of this study is to develop a model to estimate the risk of lung cancer for a given individual.

METHODS

We conducted a case-control study to develop a predictive model to identify individuals at high risk for lung cancer. Clinical data from 500 lung cancer patients and 500 population-based age- and gender-matched controls were used to develop and evaluate the model. Associations between environmental variants together with single nucleotide polymorphisms (SNPs) of beta-catenin (ctnnb1) and lung cancer risk were analyzed using a logistic regression model. The predictive accuracy of the model was determined by calculating the area under the receiver operating characteristic (ROC) curve.

RESULTS

Prior diagnosis of chronic obstructive pulmonary disease (COPD), pulmonary tuberculosis, family history of cancer, and smoking are lung cancer risk factors. The area under the curve (AUC) was 0.740, and the sensitivity, specificity, and Youden index were 0.718, 0.660, and 0.378, respectively.

CONCLUSION

Our risk prediction model for lung cancer is useful for distinguishing high-risk individuals.

From systems biology to P4 medicine: applications in respiratory medicine

Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

Systems biology and network medicine have the potential to transform medical research and practicehttp://ow.ly/r3jR30hf35x

SNP rs16969968 as a Strong Predictor of Nicotine Dependence and Lung Cancer Risk in a North Indian Population

Background:

The 15q24-25 loci contain genes (CHRNA5 and CHRNA3) encoding nicotinic acetylcholine receptor subunits. We here determined for the first time the association of genetic variants rs16969968 and rs3743074 in CHRNA5 and CHRNA3, respectively, on nicotine dependence and lung cancer risk in a North Indian population by a case-control approach.

Methods:

Venous blood samples were obtained from 324 participants (108 lung cancer patients and 216 healthy individuals). DNA was extracted and PCR amplified with primers flanking the SNPs rs16969968 and rs3743074. Amplicons were subjected to sequencing and logistic regression was used to analyze association between variables.

Results:

The risk variant SNP rs16969968 in both heterozygous and homozygous forms appeared to exert a significant effect on nicotine dependence [GA (OR=2.77) and AA (OR=2.53)]. As expected, smoking was strongly associated with lung cancer (OR= 2.62). Risk allele rs16969968 in CHRNA5 also showed a significant association with increased lung cancer risk in our cohort, alone (OR= 4.99) and with smoking as a co-variable (OR= 4.28). Comparison of our analysis with other populations suggested that individuals with rs16969968 risk allele in the Indian population are more susceptible to lung cancer.

Conclusion:

Overall, the results strongly indicated that, in our cohort North Indian population, the genetic variant rs16969968, but not rs3743074, is significantly associated with both nicotine dependence and increased risk of lung cancer. While the results are significant, there is further need to increase the sample size and improve precision of our risk prediction.

Extracellular superoxide dismutase and its role in cancer

Reactive oxygen species (ROS) are increasingly recognized as critical determinants of cellular signaling and a strict balance of ROS levels must be maintained to ensure proper cellular function and survival. Notably, ROS is increased in cancer cells. The superoxide dismutase family plays an essential physiological role in mitigating deleterious effects of ROS. Due to the compartmentalization of ROS signaling, EcSOD, the only superoxide dismutase in the extracellular space, has unique characteristics and functions in cellular signal transduction. In comparison to the other two intracellular SODs, EcSOD is a relatively new comer in terms of its tumor suppressive role in cancer and the mechanisms involved are less well understood. Nevertheless, the degree of differential expression of this extracellular antioxidant in cancer versus normal cells/tissues is more pronounced and prevalent than the other SODs. A significant association of low EcSOD expression with reduced cancer patient survival further suggests that loss of extracellular redox regulation promotes a conducive microenvironment that favors cancer progression. The vast array of mechanisms reported in mediating deregulation of EcSOD expression, function, and cellular distribution also supports that loss of this extracellular antioxidant provides a selective advantage to cancer cells. Moreover, overexpression of EcSOD inhibits tumor growth and metastasis, indicating a role as a tumor suppressor. This review focuses on the current understanding of the mechanisms of deregulation and tumor suppressive function of EcSOD in 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.

Using a genetic/clinical risk score to stop smoking (GeTSS): randomised controlled trial

Background

As genetic tests become cheaper, the possibility of their widespread availability must be considered. This study involves a risk score for lung cancer in smokers that is roughly 50% genetic (50% clinical criteria). The risk score has been shown to be effective as a smoking cessation motivator in hospital recruited subjects (not actively seeking cessation services).

Methods

This was an RCT set in a United Kingdom National Health Service (NHS) smoking cessation clinic. Smokers were identified from medical records. Subjects that wanted to participate were randomised to a test group that was administered a gene-based risk test and given a lung cancer risk score, or a control group where no risk score was performed. Each group had 8 weeks of weekly smoking cessation sessions involving group therapy and advice on smoking cessation pharmacotherapy and follow-up at 6 months. The primary endpoint was smoking cessation at 6 months. Secondary outcomes included ranking of the risk score and other motivators.

Results

67 subjects attended the smoking cessation clinic. The 6 months quit rates were 29.4%, (10/34; 95% CI 14.1–44.7%) for the test group and 42.9% (12/28; 95% CI 24.6–61.2%) for the controls. The difference is not significant. However, the quit rate for test group subjects with a “very high” risk score was 89% (8/9; 95% CI 68.4–100%) which was significant when compared with the control group (p = 0.023) and test group subjects with moderate risk scores had a 9.5% quit rate (2/21; 95% CI 2.7–28.9%) which was significantly lower than for above moderate risk score 61.5% (8/13; 95% CI 35.5–82.3; p = 0.03).

Conclusions

Only the sub-group with the highest risk score showed an increased quit rate. Controls and test group subjects with a moderate risk score were relatively unlikely to have achieved and maintained non-smoker status at 6 months.

ClinicalTrials.gov ID NCT01176383 (date of registration: 3 August 2010)

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-017-2831-2) contains supplementary material, which is available to authorized users.

Data Mining for Identification of Molecular Targets in Ovarian Cancer

Ovarian cancer is possibly the sixth most common malignancy worldwide, in Mexico representing the fourth leading cause of gynecological cancer death more than 70% being diagnosed at an advanced stage and the survival being very poor. Ovarian tumors are classified according to histological characteristics, epithelial ovarian cancer as the most common (~80%). We here used high-density microarrays and a systems biology approach to identify tissue-associated deregulated genes. Non-malignant ovarian tumors showed a gene expression profile associated with immune mediated inflammatory responses (28 genes), whereas malignant tumors had a gene expression profile related to cell cycle regulation (1,329 genes) and ovarian cell lines to cell cycling and metabolism (1,664 genes).

Biomarkers of risk to develop lung cancer in the new screening era

Low-dose computed tomography for high-risk individuals has for the first time demonstrated unequivocally that early detection save lives. The currently accepted screening strategy comes at the cost of a high rate of false positive findings while still missing a large percentage of the cases. Therefore, there is increasing interest in developing strategies to better estimate the risk of an individual to develop lung cancer, to increase the sensitivity of the screening process, to reduce screening costs and to reduce the numbers of individuals harmed by screening and follow-up interventions. New molecular biomarkers candidates show promise to improve lung cancer outcomes. This review discusses the current state of biomarker research in lung cancer screening with the primary focus on risk assessment.

Combining Telomerase Reverse Transcriptase Genetic Variant rs2736100 with Epidemiologic Factors in the Prediction of Lung Cancer Susceptibility

Genetic variants from a considerable number of susceptibility loci have been identified in association with cancer risk, but their interaction with epidemiologic factors in lung cancer remains to be defined. We sought to establish a forecasting model for identifying individuals with high-risk of lung cancer by combing gene single-nucleotide polymorphisms with epidemiologic factors. Genotyping and clinical data from 500 lung cancer cases and 500 controls were used for developing the logistic regression model. We found that lung cancer was associated with telomerase reverse transcriptase (TERT) rs2736100 single-nucleotide polymorphism. The TERT rs2736100 model was still significantly associated with lung cancer risk when combined with environmental and lifestyle factors, including lower education, lower BMI, COPD history, heavy cigarettes smoking, heavy cooking emission, and dietary factors (over-consumption of meat and deficiency in fish/shrimp, vegetables, dairy products, and soybean products). These data suggest that combining TERT SNP and epidemiologic factors may be a useful approach to discriminate high and low-risk individuals for lung cancer.

Incorporating epistasis interaction of genetic susceptibility single nucleotide polymorphisms in a lung cancer risk prediction model

Incorporation of genetic variants such as single nucleotide polymorphisms (SNPs) into risk prediction models may account for a substantial fraction of attributable disease risk. Genetic data, from 2385 subjects recruited into the Liverpool Lung Project (LLP) between 2000 and 2008, consisting of 20 SNPs independently validated in a candidate-gene discovery study was used. Multifactor dimensionality reduction (MDR) and random forest (RF) were used to explore evidence of epistasis among 20 replicated SNPs. Multivariable logistic regression was used to identify similar risk predictors for lung cancer in the LLP risk model for the epidemiological model and extended model with SNPs. Both models were internally validated using the bootstrap method and model performance was assessed using area under the curve (AUC) and net reclassification improvement (NRI). Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 (DRD2), rs5744256 (IL-18), rs2306022 (ITGA11) with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing P<0.0001. The apparent AUC of the epidemiological model was 0.75 (95% CI 0.73–0.77). When epistatic data were incorporated in the extended model, the AUC increased to 0.81 (95% CI 0.79–0.83) which corresponds to 8% increase in AUC (DeLong's test P=2.2e-¹⁶); 17.5% by NRI. After correction for optimism, the AUC was 0.73 for the epidemiological model and 0.79 for the extended model. Our results showed modest improvement in lung cancer risk prediction when the SNP epistasis factor was added.

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.

Mouse double minute-2 homolog (MDM2)-rs2279744 polymorphism associated with lung cancer risk in a Northeastern Chinese population

Background

Altered expression or function of mouse double minute-2 (MDM2) protein could contribute to lung carcinogenesis; thus, this study investigated MDM2-rs2279744 polymorphism together with other epidemiologic factors for their association with lung cancer risk.

Methods

A total of 500 lung cancer patients and 500 age and gender-matched healthy controls living in Northeastern China were recruited for genotyping of MDM2-rs2279744. Clinicopathological data was collected and subjected to univariate and multivariate analyses.

Results

In univariate analysis, the MDM2-rs2279744 G/G genotype versus T/T + T/G genotypes showed a tendency toward a higher incidence of lung cancer in the recessive model (P = 0.043). However, there were no significant differences when it was analyzed by the dominant, additive, or multiplicative models. A significantly increased lung cancer risk was observed associated with lower education level, lower body mass index, cancer family history, prior diagnosis of chronic obstructive pulmonary disease and pneumonia, exposure to pesticide or gasoline/diesel, tobacco smoking, and heavy cooking emissions when assessed by multivariate analyses. Moreover, MDM2-rs2279744 was still a significant risk factor even after incorporating environmental and lifestyle factors. However, there was no association between MDM2-rs2279744 and other factors.

Conclusions

The MDM2-rs2279744 G/G genotype was associated with a higher lung cancer risk, even after incorporating other epidemiologic factors.

Evidence for a genetical contribution to non-smoking-related lung cancer

BACKGROUND

The majority of lung cancers are smoking-related, with environmental and genetical factors contributing. The interplay between environmental and genetical contributions in non-smoking-related lung cancers is less clear.

METHODS

We analysed a population-based computerised genealogy resource linked to a state-wide cancer registry of lung cancer cases (n=5544) for evidence of a genetical contribution to lung cancer predisposition in smoking (n=1747) and non-smoking cases (n=784). Statistical methods were used to test for significant excess relatedness of cases and estimate relative risk (RR) in close and distant relatives of lung cancer cases.

RESULTS

Significant excess relatedness was observed for all lung cancer cases (p<0.001) and for the subsets of smoking-related (p<0.001) and non-smoking-related (p<0.001) cases when all pairwise relationships were considered. Only the non-smoking-related subset of cases showed significant excess relatedness when close relationships were ignored (p=0.020). First-degree, second-degree, and fourth-degree relatives of non-smoking-related lung cancer cases had significantly elevated RR. An even higher elevated RR was observed for first-degree, second-degree, third-degree and fourth-degree relatives of smoking-related lung cancer cases.

CONCLUSIONS

Non-smoking-related lung cancer cases show significant excess relatedness for close and distant relationships, providing strong evidence for a genetical contribution as well as an environmental contribution. Significant excess relatedness for only close family relationships in all lung cancer cases and in only smoking-related lung cancer cases implies environmental contribution. Additionally, the highest RR for lung cancer was observed in the relatives of smoking-related lung cancer, suggesting predisposition gene carriers who smoke are at highest risk for lung cancer. Screening and gene identification should focus on high-risk pedigrees.

An individual risk prediction model for lung cancer based on a study in a Chinese population

AIMS AND BACKGROUND

Early detection and diagnosis remains an effective yet challenging approach to improve the clinical outcome of patients with cancer. Low-dose computed tomography screening has been suggested to improve the diagnosis of lung cancer in high-risk individuals. To make screening more efficient, it is necessary to identify individuals who are at high risk.

METHODS AND STUDY DESIGN

We conducted a case-control study to develop a predictive model for identification of such high-risk individuals. Clinical data from 705 lung cancer patients and 988 population-based controls were used for the development and evaluation of the model. Associations between environmental variants and lung cancer risk were analyzed with a logistic regression model. The predictive accuracy of the model was determined by calculating the area under the receiver operating characteristic curve and the optimal operating point.

RESULTS

Our results indicate that lung cancer risk factors included older age, male gender, lower education level, family history of cancer, history of chronic obstructive pulmonary disease, lower body mass index, smoking cigarettes, a diet with less seafood, vegetables, fruits, dairy products, soybean products and nuts, a diet rich in meat, and exposure to pesticides and cooking emissions. The area under the curve was 0.8851 and the optimal operating point was obtained. With a cutoff of 0.35, the false positive rate, true positive rate, and Youden index were 0.21, 0.87, and 0.66, respectively.

CONCLUSIONS

The risk prediction model for lung cancer developed in this study could discriminate high-risk from low-risk individuals.

CRTC2 and PROM1 expression in non-small cell lung cancer: analysis by Western blot and immunohistochemistry

Accumulating evidence supports that genetic factors are another risk factors for lung cancer. Previously, we used whole exome sequencing with sanger sequencing to search for genetic-related mutations in one of four individuals from a pedigree with lung cancer history. Then, we used PCR-RFLP and direct-sequence in the sample size of 318 individuals with lung cancer (cases) and 272 controls. Recently, we detected two new genes including CRTC2 (CREB regulated transcription coactivator 2) and PROM1(human prominin-1,CD133). We investigated the CRTC2 mutation and PROM1 mutation of surgically resected NSCLC tissues (n=200). The presence or absence of CRTC2 and PROM1 mutation was analyzed by direct sequencing. The expression of CRTC2 and PROM1 was studied by western blot and immunohistochemical analysis of the lung cancer tissues which had the mutation of the two genes(cases), the samples without mutations(controls) and the normal lung tissue(controls). CRTC2 and PROM1 mutations in 5 NSCLC tissues and 3 NSCLC tissues out of the samples were identified. The positive results were closely correlated with clinicopathological features, such as male gender, adenocarcinoma, smoker status, and older age (≥55). We found that the CRTC2 and PROM1 expression were significantly higher in tissues of NSCLS with mutations than that without mutations and the normal lung tissue. The results imply that the high expression of CRTC2 and PROM1 may play an important role in the development and hereditary of NSCLC.

Metformin decreases lung cancer risk in diabetic patients in a dose-dependent manner

OBJECTIVES

Higher risk of lung cancer has been noted in patients with type 2 diabetes mellitus (DM). Some observational studies have shown a reduced risk of lung cancer in DM patients taking metformin, but a dose-response relationship has never been reported. The aim of this study is to exam the association between the dose of metformin and the incidence of lung cancer in a Chinese population.

MATERIALS AND METHODS

The dataset used for this nationwide population-based study is a cohort of 1 million subjects randomly sampled from individuals enrolled in the Taiwan National Health Insurance system. We enrolled all subjects with newly diagnosed type 2 DM between 1997 and 2007. Subjects with a diagnosis of neoplasm before DM diagnosis, those using metformin before DM diagnosis, those with polycystic ovary syndrome, and those with a DM diagnosis before their 15 years of age were excluded. The demographic data and duration, cumulative dose and intensity of metformin use were compared between patients developing lung cancer and those without lung cancer.

RESULTS

Totally, 47,356 subjects were identified. After adjusting for age, gender, and modified Charlson Comorbidity Index score, the utilization of metformin was an independent protecting factor, and the risk of developing lung cancer decreased progressively with either the higher cumulative dose or the higher intensity of metformin use.

CONCLUSIONS

This study revealed that the use of metformin decreased the risk of lung cancer in a dose-dependent manner in patients with type 2 DM. The chemo-preventive effect of metformin deserves further study.

Updates and controversies in the rapidly evolving field of lung cancer screening, early detection, and chemoprevention

Lung cancer remains the leading cause of cancer-related death in the United States. Cigarette smoking is a well-recognized risk factor for lung cancer, and a sustained elevation of lung cancer risk persists even after smoking cessation. Despite identifiable risk factors, there has been minimal improvement in mortality for patients with lung cancer primarily stemming from diagnosis at a late stage when there are few effective therapeutic options. Early detection of lung cancer and effective screening of high-risk individuals may help improve lung cancer mortality. While low dose computerized tomography (LDCT) screening of high risk smokers has been shown to reduce lung cancer mortality, the high rates of false positives and potential for over-diagnosis have raised questions on how to best implement lung cancer screening. The rapidly evolving field of lung cancer screening and early-detection biomarkers may ultimately improve the ability to diagnose lung cancer in its early stages, identify smokers at highest-risk for this disease, and target chemoprevention strategies. This review aims to provide an overview of the opportunities and challenges related to lung cancer screening, the field of biomarker development for early lung cancer detection, and the future of lung cancer chemoprevention.

Cancer stem cell marker Musashi-1 rs2522137 genotype is associated with an increased risk of lung cancer

Gene single nucleotide polymorphisms (SNPs) have been extensively studied in association with development and prognosis of various malignancies. However, the potential role of genetic polymorphisms of cancer stem cell (CSC) marker genes with respect to cancer risk has not been examined. We conducted a case-control study involving a total of 1000 subjects (500 lung cancer patients and 500 age-matched cancer-free controls) from northeastern China. Lung cancer risk was analyzed in a logistic regression model in association with genotypes of four lung CSC marker genes (CD133, ALDH1, Musashi-1, and EpCAM). Using univariate analysis, the Musashi-1 rs2522137 GG genotype was found to be associated with a higher incidence of lung cancer compared with the TT genotype. No significant associations were observed for gene variants of CD133, ALDH1, or EpCAM. In multivariate analysis, Musashi-1 rs2522137 was still significantly associated with lung cancer when environmental and lifestyle factors were incorporated in the model, including lower BMI; family history of cancer; prior diagnosis of chronic obstructive pulmonary disease, pneumonia, or pulmonary tuberculosis; occupational exposure to pesticide; occupational exposure to gasoline or diesel fuel; heavier smoking; and exposure to heavy cooking emissions. The value of the area under the receiver-operating characteristic (ROC) curve (AUC) was 0.7686. To our knowledge, this is the first report to show an association between a Musashi-1 genotype and lung cancer risk. Further, the prediction model in this study may be useful in determining individuals with high risk of lung cancer.

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.

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.

Previous lung diseases and lung cancer risk: a pooled analysis from the International Lung Cancer Consortium

To clarify the role of previous lung diseases (chronic bronchitis, emphysema, pneumonia, and tuberculosis) in the development of lung cancer, the authors conducted a pooled analysis of studies in the International Lung Cancer Consortium. Seventeen studies including 24,607 cases and 81,829 controls (noncases), mainly conducted in Europe and North America, were included (1984-2011). Using self-reported data on previous diagnoses of lung diseases, the authors derived study-specific effect estimates by means of logistic regression models or Cox proportional hazards models adjusted for age, sex, and cumulative tobacco smoking. Estimates were pooled using random-effects models. Analyses stratified by smoking status and histology were also conducted. A history of emphysema conferred a 2.44-fold increased risk of lung cancer (95% confidence interval (CI): 1.64, 3.62 (16 studies)). A history of chronic bronchitis conferred a relative risk of 1.47 (95% CI: 1.29, 1.68 (13 studies)). Tuberculosis (relative risk = 1.48, 95% CI: 1.17, 1.87 (16 studies)) and pneumonia (relative risk = 1.57, 95% CI: 1.22, 2.01 (12 studies)) were also associated with lung cancer risk. Among never smokers, elevated risks were observed for emphysema, pneumonia, and tuberculosis. These results suggest that previous lung diseases influence lung cancer risk independently of tobacco use and that these diseases are important for assessing individual risk.

The effect of metformin and thiazolidinedione use on lung cancer in diabetics

Background

Metformin and the thiazolidinediones (TZDs) may have a protective effect against the development of lung cancer.

Methods

Patients with diabetes mellitus (DM) were identified from the electronic medical records of the Cleveland Clinic. Diabetics with lung cancer were identified then verified by direct review of their records. Control subjects were matched with cancer subjects 1:1 by date of birth, sex, and smoking history. The frequency and duration of diabetic medication use was compared between the groups. The cancer characteristics were compared between those with lung cancer who had and had not been using metformin and/or a TZD.

Results

93,939 patients were identified as having DM. 522 lung cancers in 507 patients were confirmed. The matched control group was more likely to have used metformin and/or a TZD (61.0% vs. 41.2%, p < 0.001 for any use; 55.5% vs. 24.6%, p < 0.001 for >24 months vs. 0–12 months). In the group with lung cancer, those who had used metformin alone had a different histology distribution than those who received neither metformin nor a TZD, were more likely to present with metastatic disease (40.8% vs. 28.2%, p = 0.013), and had a shorter survival from the time of diagnosis (HR 1.47, p < 0.005).

Conclusions

The use of metformin and/or the TZDs is associated with a lower likelihood of developing lung cancer in diabetic patients. Diabetics who develop lung cancer while receiving metformin may have a more aggressive cancer phenotype.

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.

Associations between season of birth and the risk of lung cancer: epidemiological findings from Hungary

Lung cancer is the leading cause of cancer deaths worldwide. Both incidence and mortality of lung cancer are especially high in Hungary. Several investigations suggested recently that month of birth (MOB) is associated with the risks of several nonmalignant disorders as well as some malignant disorders. Only a few studies investigated previously the association between MOB and risk of lung cancer, but they provided inconsistent results. We, therefore, decided to investigate this issue in a large sample of individuals who died from lung cancer. Accordingly, we determined the MOB-associated risk of death by lung cancer between the years 1970 and 2009 among all individuals born in Hungary between 1925 and 1934. The final sample included about two million people. A total of 61,904 deaths by lung cancer occurred in this sample during the period investigated. Using analysis of variance (ANOVA), we did not find significant association between MOB and risk of lung cancer death, either in the whole population investigated (F = 1.492; p = .145) or in the female subpopulation (F = 1.535; p = .129). However, those males born in late spring (May-June) had a lower risk of lung cancer development (F = 2.577; p = .006). Results of the Edwards test also did not suggest consistent association between MOB and risk of lung cancer death in the whole investigated period (1925-1934) in any populations (i.e., whole population or male and female subpopulations). In conclusion, we did not find significant association between MOB and risk of lung cancer in our total sample (although results alluded to a weak association between MOB and risk of lung cancer development among males). The possible associations between MOB and the risk of lung cancer development (or smoking) would require confirmation (or refutation) in large studies from other populations.

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.

Nontypeable Haemophilus influenzae in chronic obstructive pulmonary disease and lung cancer

Chronic obstructive pulmonary disease (COPD) is predicted to become the third leading cause of death in the world by 2020. It is characterized by airflow limitation that is not fully reversible. The airflow limitation is usually progressive and associated with an abnormal inflammatory response of the lungs to noxious particles and gases, most commonly cigarette smoke. Among smokers with COPD, even following withdrawal of cigarette smoke, inflammation persists and lung function continues to deteriorate. One possible explanation is that bacterial colonization of smoke-damaged airways, most commonly with nontypeable Haemophilus influenzae (NTHi), perpetuates airway injury and inflammation. Furthermore, COPD has also been identified as an independent risk factor for lung cancer irrespective of concomitant cigarette smoke exposure. In this article, we review the role of NTHi in airway inflammation that may lead to COPD progression and lung cancer promotion.

Incorporation of a genetic factor into an epidemiologic model for prediction of individual risk of lung cancer: the Liverpool Lung Project

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

Pathway genetic load allows simultaneous evaluation of multiple genetic associations

OBJECTIVE

Despite the general success of genome-wide association studies, much heritability remains unidentified in many disease states. Some of this 'missing' heritability may lie in epistatic interactions among multiple loci, which are typically ignored. We utilized a method for simultaneous evaluation of epistatic interactions between allelic variations within genes confined to a single pathway, which we have termed as pathway genetic load (PGL).

METHODS

In separate analyses, we evaluated the risk for sepsis and for death associated with alleles at six loci in the TLR4 signaling and response pathway previously known or suspected to be linked to the development of sepsis after traumatic injury. We evaluated 155 patients with > or =15% TBSA burns and without significant non-burn trauma [ISS < or =16], traumatic or anoxic brain injury or spinal cord injury, who survived > 48 h post-admission. Clinical data were collected prospectively and candidate genotypes were determined by TaqMan assay.

RESULTS

After adjustment for burn size, inhalation injury, age, gender and race, PGL was associated with increased probability for complicated sepsis (aOR=1.59; 95%CI=1.11-2.29; p=0.011) and death (aOR=1.75; 95%CI=1.11-2.76; p=0.017).

CONCLUSION

Relative size and variability of aORs indicate greater power to detect genetic associations with PGL compared to the analysis of loci individually by multivariate logistic regression.

The detection of chromosomal aneusomy by fluorescence in situ hybridization in sputum predicts lung cancer incidence

Lung cancer usually is disseminated (advanced) and has a poor prognosis at diagnosis. Current and former smokers are at a high risk for lung cancer and are candidates for prevention and early detection strategies. Sputum is a potential source of biomarkers that might determine either lung cancer risk or the presence of early lung cancer, but no current sputum test is sufficiently sensitive and specific for effective screening. We used fluorescence in situ hybridization (FISH) to measure chromosomal aneusomy (CA) in sputum samples collected prospectively from 100 incident lung cancer cases and 96 controls (matched on age, gender, and date of collection) nested within an ongoing high-risk cohort. The CA-FISH assay was aimed at four DNA targets: epidermal growth factor receptor, MYC, 5p15, and CEP 6. The sensitivity of a positive CA-FISH assay (abnormal for two or more of the four markers) for lung cancer was substantially higher for samples collected within 18 months (76% sensitivity) than for samples collected more than 18 months (31%) before lung cancer diagnosis. Sensitivity was higher for squamous cell cancers (94%) than for other histologic types (69%). CA-FISH specificity based on samples collected within 18 months before diagnosis was 88%. The adjusted odds ratio (OR) of lung cancer for specimens collected within 18 months before a cancer diagnosis was higher for the CA-FISH assay [OR, 29.9; 95% confidence interval (95% CI), 9.5-94.1] than for previously studied ORs of cytologic atypia (OR, 1.8; 95% CI, 1.3-2.6) and gene promoter methylation (OR, 6.5; 95% CI, 1.2-35.5). Whether CA-FISH is an indicator of extreme risk for incident lung cancer or detects exfoliated cancer cells is unknown. The apparent promise of CA-FISH in sputum for assessing lung cancer risk and/or for lung cancer early detection now needs to be validated in a clinical screening trial.

Reliability of the Brazilian version of the Functional Assessment of Cancer Therapy-Lung (FACT-L) and the FACT-Lung Symptom Index (FLSI)

OBJECTIVES

The purpose of this study was to assess the reliability of the Brazilian version of the Functional Assessment of Cancer Therapy-Lung (FACT-L) with the FACT-Lung Symptom Index (FLSI) questionnaire.

INTRODUCTION

The assessment of quality of life in patients with lung cancer has become an important evaluative endpoint in current clinical trials. For lung cancer patients, one of the most common quality of life tools available is the FACT-L. Despite the amount of data available regarding this questionnaire, there are no data on its performance in Brazilian lung cancer patients.

METHODS

The FACT-L with the FLSI questionnaire was prospectively administered to 30 consecutive, stable, lung cancer outpatients at baseline and at 2 weeks.

RESULTS

The intraclass correlation coefficient between test and retest for the FACT-L ranged from 0.79 to 0.96 and for the FLSI was 0.87. There was no correlation between these questionnaire dimensions and clinical or functional parameters.

CONCLUSIONS

The Brazilian version of the FACT-L with FLSI questionnaire is reliable and is quick and simple to apply. This instrument can now be used to properly evaluate the quality of life of Brazilian lung cancer patients.

Influence of DNA repair RAD51 gene variants in overall survival of non-small cell lung cancer patients treated with first line chemotherapy

PURPOSE

Lung cancer continues to be the most frequent cancer with approximately one million people worldwide dying of this disease each year. Non-small-cell lung cancer (NSCLC) accounts for approximately 80% of all lung cancers. The RAD51 protein is the key protein for homologous recombination, an evolutionarily conserved mechanism for DNA damage repair and the generation of genetic diversity. We conducted this study in order to investigate the effect of the RAD51 G135C polymorphism in treatment response to combined platinum taxanes/gemcitabine first line chemotherapy in NSCLC patients.

METHODS

We analysed RAD51 G135C polymorphism in 243 NSCLC patients using PCR-RFLP methodology.

RESULTS

There were no statistically significant differences between the groups of NSCLC patients with the different genotypes regarding tumour stage (p = 0.232). Our results indicate that the mean survival rates were statistically different according to the patient's genotypes. The group of patients carrying the C allele presented a higher mean survival rate than the other patients (56.0 months vs. 41.7 months; p = 0.024). Moreover, regarding smoking history, our results demonstrate that overall survival time differed significantly according to the patient's genotypes in smoker and ex-smoker individuals (p = 0.034). No statistically significant differences were found in the genotype frequencies and overall survival rate among non-smoker NSCLC patients (p = 0.413).

CONCLUSIONS

This is the first study evaluating the effect of the RAD51 G135C polymorphism in NSCLC patient survival. Our results suggest that RAD51 genotypes could be useful molecular markers for predicting the clinical outcome of NSCLC patients.