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1.  Evaluation of the Lung Cancer Risks at Which to Screen Ever- and Never-Smokers: Screening Rules Applied to the PLCO and NLST Cohorts 
PLoS Medicine  2014;11(12):e1001764.
Martin Tammemägi and colleagues evaluate which risk groups of individuals, including nonsmokers and high-risk individuals from 65 to 80 years of age, should be screened for lung cancer using computed tomography.
Please see later in the article for the Editors' Summary
Lung cancer risks at which individuals should be screened with computed tomography (CT) for lung cancer are undecided. This study's objectives are to identify a risk threshold for selecting individuals for screening, to compare its efficiency with the U.S. Preventive Services Task Force (USPSTF) criteria for identifying screenees, and to determine whether never-smokers should be screened. Lung cancer risks are compared between smokers aged 55–64 and ≥65–80 y.
Methods and Findings
Applying the PLCOm2012 model, a model based on 6-y lung cancer incidence, we identified the risk threshold above which National Lung Screening Trial (NLST, n = 53,452) CT arm lung cancer mortality rates were consistently lower than rates in the chest X-ray (CXR) arm. We evaluated the USPSTF and PLCOm2012 risk criteria in intervention arm (CXR) smokers (n = 37,327) of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). The numbers of smokers selected for screening, and the sensitivities, specificities, and positive predictive values (PPVs) for identifying lung cancers were assessed. A modified model (PLCOall2014) evaluated risks in never-smokers. At PLCOm2012 risk ≥0.0151, the 65th percentile of risk, the NLST CT arm mortality rates are consistently below the CXR arm's rates. The number needed to screen to prevent one lung cancer death in the 65th to 100th percentile risk group is 255 (95% CI 143 to 1,184), and in the 30th to <65th percentile risk group is 963 (95% CI 291 to −754); the number needed to screen could not be estimated in the <30th percentile risk group because of absence of lung cancer deaths. When applied to PLCO intervention arm smokers, compared to the USPSTF criteria, the PLCOm2012 risk ≥0.0151 threshold selected 8.8% fewer individuals for screening (p<0.001) but identified 12.4% more lung cancers (sensitivity 80.1% [95% CI 76.8%–83.0%] versus 71.2% [95% CI 67.6%–74.6%], p<0.001), had fewer false-positives (specificity 66.2% [95% CI 65.7%–66.7%] versus 62.7% [95% CI 62.2%–63.1%], p<0.001), and had higher PPV (4.2% [95% CI 3.9%–4.6%] versus 3.4% [95% CI 3.1%–3.7%], p<0.001). In total, 26% of individuals selected for screening based on USPSTF criteria had risks below the threshold PLCOm2012 risk ≥0.0151. Of PLCO former smokers with quit time >15 y, 8.5% had PLCOm2012 risk ≥0.0151. None of 65,711 PLCO never-smokers had PLCOm2012 risk ≥0.0151. Risks and lung cancers were significantly greater in PLCO smokers aged ≥65–80 y than in those aged 55–64 y. This study omitted cost-effectiveness analysis.
The USPSTF criteria for CT screening include some low-risk individuals and exclude some high-risk individuals. Use of the PLCOm2012 risk ≥0.0151 criterion can improve screening efficiency. Currently, never-smokers should not be screened. Smokers aged ≥65–80 y are a high-risk group who may benefit from screening.
Please see later in the article for the Editors' Summary
Editors' Summary
Lung cancer is the most commonly occurring cancer in the world and the most common cause of cancer-related deaths. Like all cancers, lung cancer occurs when cells acquire genetic changes that allow them to grow uncontrollably and to move around the body (metastasize). The most common trigger for these genetic changes in lung cancer is exposure to cigarette smoke. Symptoms of lung cancer include a persistent cough and breathlessness. If lung cancer is diagnosed when it is confined to the lung (stage I), the tumor can often be removed surgically. Stage II tumors, which have spread into nearby lymph nodes, are usually treated with surgery plus chemotherapy or radiotherapy. For more advanced lung cancers that have spread throughout the chest (stage III) or the body (stage IV), surgery is rarely helpful and these tumors are treated with chemotherapy and radiotherapy alone. Overall, because most lung cancers are not detected until they are advanced, less than 17% of people diagnosed with lung cancer survive for five years.
Why Was This Study Done?
Screening for lung cancer—looking for early disease in healthy people—could save lives. In the US National Lung Screening Trial (NLST), annual screening with computed tomography (CT) reduced lung cancer mortality by 20% among smokers at high risk of developing cancer compared with screening with a chest X-ray. But what criteria should be used to decide who is screened for lung cancer? The US Preventive Services Task Force (USPSTF), for example, recommends annual CT screening of people who are 55–80 years old, have smoked 30 or more pack-years (one pack-year is defined as a pack of cigarettes per day for one year), and—if they are former smokers—quit smoking less than 15 years ago. However, some experts think lung cancer risk prediction models—statistical models that estimate risk based on numerous personal characteristics—should be used to select people for screening. Here, the researchers evaluate PLCOm2012, a lung cancer risk prediction model based on the incidence of lung cancer among smokers enrolled in the US Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). Specifically, the researchers use NLST and PLCO screening trial data to identify a PLCOm2012 risk threshold for selecting people for screening and to compare the efficiency of the PLCOm2012 model and the USPSTF criteria for identifying “screenees.”
What Did the Researchers Do and Find?
By analyzing NLST data, the researchers calculated that at PLCOm2012 risk ≥0.0151, mortality (death) rates among NLST participants screened with CT were consistently below mortality rates among NLST participants screened with chest X-ray and that 255 people with a PLCOm2012 risk ≥0.0151 would need to be screened to prevent one lung cancer death. Next, they used data collected from smokers in the screened arm of the PLCO trial to compare the efficiency of the PLCOm2012 and USPSTF criteria for identifying screenees. They found that 8.8% fewer people had a PLCOm2012 risk ≥0.0151 than met USPSTF criteria for screening, but 12.4% more lung cancers were identified. Thus, using PLCOm2012 improved the sensitivity and specificity of the selection of individuals for lung cancer screening over using UPSTF criteria. Notably, 8.5% of PLCO former smokers with quit times of more than 15 years had PLCOm2012 risk ≥0.0151, none of the PLCO never-smokers had PLCOm2012 risk ≥0.0151, and the calculated risks and incidence of lung cancer were greater among PLCO smokers aged ≥65–80 years than among those aged 55–64 years.
What Do These Findings Mean?
Despite the absence of a cost-effectiveness analysis in this study, these findings suggest that the use of the PLCOm2012 risk ≥0.0151 threshold rather than USPSTF criteria for selecting individuals for lung cancer screening could improve screening efficiency. The findings have several other important implications. First, these findings suggest that screening may be justified in people who stopped smoking more than 15 years ago; USPSTF currently recommends that screening stop once an individual's quit time exceeds 15 years. Second, these findings do not support lung cancer screening among never-smokers. Finally, these findings suggest that smokers aged ≥65–80 years might benefit from screening, although the presence of additional illnesses and reduced life expectancy need to be considered before recommending the provision of routine lung cancer screening to this section of the population.
Additional Information
Please access these websites via the online version of this summary at
The US National Cancer Institute provides information about all aspects of lung cancer for patients and health-care professionals, including information on lung cancer screening (in English and Spanish)
Cancer Research UK also provides detailed information about lung cancer and about lung cancer screening
The UK National Health Service Choices website has a page on lung cancer that includes personal stories
MedlinePlus provides links to other sources of information about lung cancer (in English and Spanish)
Information about the USPSTF recommendations for lung cancer screening is available
PMCID: PMC4251899  PMID: 25460915
2.  Risk Prediction for Breast, Endometrial, and Ovarian Cancer in White Women Aged 50 y or Older: Derivation and Validation from Population-Based Cohort Studies 
PLoS Medicine  2013;10(7):e1001492.
Ruth Pfeiffer and colleagues describe models to calculate absolute risks for breast, endometrial, and ovarian cancers for white, non-Hispanic women over 50 years old using easily obtainable risk factors.
Please see later in the article for the Editors' Summary
Breast, endometrial, and ovarian cancers share some hormonal and epidemiologic risk factors. While several models predict absolute risk of breast cancer, there are few models for ovarian cancer in the general population, and none for endometrial cancer.
Methods and Findings
Using data on white, non-Hispanic women aged 50+ y from two large population-based cohorts (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [PLCO] and the National Institutes of Health–AARP Diet and Health Study [NIH-AARP]), we estimated relative and attributable risks and combined them with age-specific US-population incidence and competing mortality rates. All models included parity. The breast cancer model additionally included estrogen and progestin menopausal hormone therapy (MHT) use, other MHT use, age at first live birth, menopausal status, age at menopause, family history of breast or ovarian cancer, benign breast disease/biopsies, alcohol consumption, and body mass index (BMI); the endometrial model included menopausal status, age at menopause, BMI, smoking, oral contraceptive use, MHT use, and an interaction term between BMI and MHT use; the ovarian model included oral contraceptive use, MHT use, and family history or breast or ovarian cancer. In independent validation data (Nurses' Health Study cohort) the breast and ovarian cancer models were well calibrated; expected to observed cancer ratios were 1.00 (95% confidence interval [CI]: 0.96–1.04) for breast cancer and 1.08 (95% CI: 0.97–1.19) for ovarian cancer. The number of endometrial cancers was significantly overestimated, expected/observed = 1.20 (95% CI: 1.11–1.29). The areas under the receiver operating characteristic curves (AUCs; discriminatory power) were 0.58 (95% CI: 0.57–0.59), 0.59 (95% CI: 0.56–0.63), and 0.68 (95% CI: 0.66–0.70) for the breast, ovarian, and endometrial models, respectively.
These models predict absolute risks for breast, endometrial, and ovarian cancers from easily obtainable risk factors and may assist in clinical decision-making. Limitations are the modest discriminatory ability of the breast and ovarian models and that these models may not generalize to women of other races.
Please see later in the article for the Editors' Summary
Editors' Summary
In 2008, just three types of cancer accounted for 10% of global cancer-related deaths. That year, about 460,000 women died from breast cancer (the most frequently diagnosed cancer among women and the fifth most common cause of cancer-related death). Another 140,000 women died from ovarian cancer, and 74,000 died from endometrial (womb) cancer (the 14th and 20th most common causes of cancer-related death, respectively). Although these three cancers originate in different tissues, they nevertheless share many risk factors. For example, current age, age at menarche (first period), and parity (the number of children a woman has had) are all strongly associated with breast, ovarian, and endometrial cancer risk. Because these cancers share many hormonal and epidemiological risk factors, a woman with a high breast cancer risk is also likely to have an above-average risk of developing ovarian or endometrial cancer.
Why Was This Study Done?
Several statistical models (for example, the Breast Cancer Risk Assessment Tool) have been developed that estimate a woman's absolute risk (probability) of developing breast cancer over the next few years or over her lifetime. Absolute risk prediction models are useful in the design of cancer prevention trials and can also help women make informed decisions about cancer prevention and treatment options. For example, a woman at high risk of breast cancer might decide to take tamoxifen for breast cancer prevention, but ideally she needs to know her absolute endometrial cancer risk before doing so because tamoxifen increases the risk of this cancer. Similarly, knowledge of her ovarian cancer risk might influence a woman's decision regarding prophylactic removal of her ovaries to reduce her breast cancer risk. There are few absolute risk prediction models for ovarian cancer, and none for endometrial cancer, so here the researchers develop models to predict the risk of these cancers and of breast cancer.
What Did the Researchers Do and Find?
Absolute risk prediction models are constructed by combining estimates for risk factors from cohorts with population-based incidence rates from cancer registries. Models are validated in an independent cohort by testing their ability to identify people with the disease in an independent cohort and their ability to predict the observed numbers of incident cases. The researchers used data on white, non-Hispanic women aged 50 years or older that were collected during two large prospective US cohort studies of cancer screening and of diet and health, and US cancer incidence and mortality rates provided by the Surveillance, Epidemiology, and End Results Program to build their models. The models all included parity as a risk factor, as well as other factors. The model for endometrial cancer, for example, also included menopausal status, age at menopause, body mass index (an indicator of the amount of body fat), oral contraceptive use, menopausal hormone therapy use, and an interaction term between menopausal hormone therapy use and body mass index. Individual women's risk for endometrial cancer calculated using this model ranged from 1.22% to 17.8% over the next 20 years depending on their exposure to various risk factors. Validation of the models using data from the US Nurses' Health Study indicated that the endometrial cancer model overestimated the risk of endometrial cancer but that the breast and ovarian cancer models were well calibrated—the predicted and observed risks for these cancers in the validation cohort agreed closely. Finally, the discriminatory power of the models (a measure of how well a model separates people who have a disease from people who do not have the disease) was modest for the breast and ovarian cancer models but somewhat better for the endometrial cancer model.
What Do These Findings Mean?
These findings show that breast, ovarian, and endometrial cancer can all be predicted using information on known risk factors for these cancers that is easily obtainable. Because these models were constructed and validated using data from white, non-Hispanic women aged 50 years or older, they may not accurately predict absolute risk for these cancers for women of other races or ethnicities. Moreover, the modest discriminatory power of the breast and ovarian cancer models means they cannot be used to decide which women should be routinely screened for these cancers. Importantly, however, these well-calibrated models should provide realistic information about an individual's risk of developing breast, ovarian, or endometrial cancer that can be used in clinical decision-making and that may assist in the identification of potential participants for research studies.
Additional Information
Please access these websites via the online version of this summary at
This study is further discussed in a PLOS Medicine Perspective by Lars Holmberg and Andrew Vickers
The US National Cancer Institute provides comprehensive information about cancer (in English and Spanish), including detailed information about breast cancer, ovarian cancer, and endometrial cancer;
Information on the Breast Cancer Risk Assessment Tool, the Surveillance, Epidemiology, and End Results Program, and on the prospective cohort study of screening and the diet and health study that provided the data used to build the models is also available on the NCI site
Cancer Research UK, a not-for-profit organization, provides information about cancer, including detailed information on breast cancer, ovarian cancer, and endometrial cancer
The UK National Health Service Choices website has information and personal stories about breast cancer, ovarian cancer, and endometrial cancer; the not-for-profit organization Healthtalkonline also provides personal stories about dealing with breast cancer and ovarian cancer
PMCID: PMC3728034  PMID: 23935463
3.  Predictors of Adverse Smoking Outcomes in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial 
The impact of lung cancer screening on smoking behavior is unclear. The aims of this ancillary study of the Prostate Lung Colorectal and Ovarian Cancer Screening Trial were to produce risk prediction models to identify individuals at risk of relapse or continued smoking and to evaluate whether cancer-screening variables affect long-term smoking outcomes.
Participants completed a baseline questionnaire at trial enrollment and a supplemental questionnaire 4–14 years after enrollment, which assessed several cancer-related variables, including family history of cancer, comorbidities, and tobacco use. Multivariable logistic regression models were used to predict smoking status at completion of the supplemental questionnaire. The models’ predictive performances were evaluated by assessing discrimination via the receiver operator characteristic area under the curve (ROC AUC) and calibration. Models were internally validated using bootstrap methods.
Of the 31 694 former smokers on the baseline questionnaire, 1042 (3.3%) had relapsed (ie, reported being a current smoker on the supplemental questionnaire). Of the 6807 current smokers on the baseline questionnaire, 4439 (65.2%) reported continued smoking on the supplemental questionnaire. Relapse was associated with multiple demographic, medical, and tobacco-related characteristics. This model had a bootstrap median ROC AUC of 0.862 (95% confidence interval [CI] = 0.858 to 0.866) and a calibration slope of 1.004 (95% CI = 0.978 to 1.029), indicating excellent discrimination and calibration. Predictors of continued smoking also included multiple demographic, medical, and tobacco-related characteristics. This model had an ROC AUC of 0.611 (95% CI = 0.605 to 0.614) and a slope of 1.006 (95% CI = 0.962 to 1.041), indicating modest discrimination. Neither the trial arm nor the lung-screening result was statistically significantly associated with smoking outcomes.
These models, if validated externally, may have public health utility in identifying individuals at risk for adverse smoking outcomes, who may benefit from relapse prevention and smoking cessation interventions.
PMCID: PMC3490843  PMID: 23104210
A combination of biomarkers in a multivariate model may predict disease with greater accuracy than a single biomarker employed alone. We developed a non-linear method of multivariate analysis, weighted digital analysis (WDA), and evaluated its ability to predict lung cancer employing volatile biomarkers in the breath.
WDA generates a discriminant function to predict membership in disease vs no disease groups by determining weight, a cutoff value, and a sign for each predictor variable employed in the model. The weight of each predictor variable was the area under the curve (AUC) of the receiver operating characteristic (ROC) curve minus a fixed offset of 0.55, where the AUC was obtained by employing that predictor variable alone, as the sole marker of disease. The sign (±) was used to invert the predictor variable if a lower value indicated a higher probability of disease. When employed to predict the presence of a disease in a particular patient, the discriminant function was determined as the sum of the weights of all predictor variables that exceeded their cutoff values. The algorithm that generates the discriminant function is deterministic because parameters are calculated from each individual predictor variable without any optimization or adjustment. We employed WDA to re-evaluate data from a recent study of breath biomarkers of lung cancer, comprising the volatile organic compounds (VOCs) in the alveolar breath of 193 subjects with primary lung cancer and 211 controls with a negative chest CT.
The WDA discriminant function accurately identified patients with lung cancer in a model employing 30 breath VOCs (ROC curve AUC = 0.90; sensitivity = 84.5%, specificity = 81.0%). These results were superior to multi-linear regression analysis of the same data set (AUC= 0.74, sensitivity = 68.4, specificity = 73.5%). WDA test accuracy did not vary appreciably with TNM (tumor, node, metastasis) stage of disease, and results were not affected by tobacco smoking (ROC curve AUC =0.92 in current smokers, 0.90 in former smokers). WDA was a robust predictor of lung cancer: random removal of 1/3 of the VOCs did not reduce the AUC of the ROC curve by >10% (99.7% CI).
A test employing WDA of breath VOCs predicted lung cancer with accuracy similar to chest computed tomography. The algorithm identified dependencies that were not apparent with traditional linear methods. WDA appears to provide a useful new technique for non-linear multivariate analysis of data.
PMCID: PMC2497457  PMID: 18420034
5.  Development and Validation of a Lung Cancer Risk Prediction Model for African-Americans 
Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self- reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67−0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57−0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction model for lung cancer that is specific to African-Americans and thus more precise in predicting their risks. These findings highlight the importance of conducting further ethnic-specific analyses of disease risk.
PMCID: PMC2854402  PMID: 19138969
6.  Current and Former Smoking and Risk for Venous Thromboembolism: A Systematic Review and Meta-Analysis 
PLoS Medicine  2013;10(9):e1001515.
In a meta-analysis of 32 observational studies involving 3,966,184 participants and 35,151 events, Suhua Wu and colleagues found that current, ever, and former smoking was associated with risk of venous thromboembolism.
Please see later in the article for the Editors' Summary
Smoking is a well-established risk factor for atherosclerotic disease, but its role as an independent risk factor for venous thromboembolism (VTE) remains controversial. We conducted a meta-analysis to summarize all published prospective studies and case-control studies to update the risk for VTE in smokers and determine whether a dose–response relationship exists.
Methods and Findings
We performed a literature search using MEDLINE (source PubMed, January 1, 1966 to June 15, 2013) and EMBASE (January 1, 1980 to June 15, 2013) with no restrictions. Pooled effect estimates were obtained by using random-effects meta-analysis. Thirty-two observational studies involving 3,966,184 participants and 35,151 VTE events were identified. Compared with never smokers, the overall combined relative risks (RRs) for developing VTE were 1.17 (95% CI 1.09–1.25) for ever smokers, 1.23 (95% CI 1.14–1.33) for current smokers, and 1.10 (95% CI 1.03–1.17) for former smokers, respectively. The risk increased by 10.2% (95% CI 8.6%–11.8%) for every additional ten cigarettes per day smoked or by 6.1% (95% CI 3.8%–8.5%) for every additional ten pack-years. Analysis of 13 studies adjusted for body mass index (BMI) yielded a relatively higher RR (1.30; 95% CI 1.24–1.37) for current smokers. The population attributable fractions of VTE were 8.7% (95% CI 4.8%–12.3%) for ever smoking, 5.8% (95% CI 3.6%–8.2%) for current smoking, and 2.7% (95% CI 0.8%–4.5%) for former smoking. Smoking was associated with an absolute risk increase of 24.3 (95% CI 15.4–26.7) cases per 100,000 person-years.
Cigarette smoking is associated with a slightly increased risk for VTE. BMI appears to be a confounding factor in the risk estimates. The relationship between VTE and smoking has clinical relevance with respect to individual screening, risk factor modification, and the primary and secondary prevention of VTE.
Please see later in the article for the Editors' Summary
Editors' Summary
Blood normally flows throughout the human body, supplying its organs and tissues with oxygen and nutrients. But, when an injury occurs, proteins called clotting factors make the blood gel (coagulate) at the injury site. The resultant clot (thrombus) plugs the wound and prevents blood loss. Occasionally, a thrombus forms inside an uninjured blood vessel and partly or completely blocks the blood flow. Clot formation inside one of the veins deep within the body, usually in a leg, is called deep vein thrombosis (DVT) and can cause pain, swelling, and redness in the affected limb. DVT can be treated with drugs that stop the blood clot from getting larger (anticoagulants) but, if left untreated, part of the clot can break off and travel to the lungs, where it can cause a life-threatening pulmonary embolism. DVT and pulmonary embolism are collectively known as venous thromboembolism (VTE). Risk factors for VTE include having an inherited blood clotting disorder, oral contraceptive use, prolonged inactivity (for example, during a long-haul plane flight), and having surgery. VTEs are present in about a third of all people who die in hospital and, in non-bedridden populations, about 10% of people die within 28 days of a first VTE event.
Why Was This Study Done?
Some but not all studies have reported that smoking is also a risk factor for VTE. A clear demonstration of a significant association (a relationship unlikely to have occurred by chance) between smoking and VTE might help to reduce the burden of VTE because smoking can potentially be reduced by encouraging individuals to quit smoking and through taxation policies and other measures designed to reduce tobacco consumption. In this systematic review and meta-analysis, the researchers examine the link between smoking and the risk of VTE in the general population and investigate whether heavy smokers have a higher risk of VTE than light smokers. A systematic review uses predefined criteria to identify all the research on a given topic; meta-analysis is a statistical method for combining the results of several studies.
What Did the Researchers Do and Find?
The researchers identified 32 observational studies (investigations that record a population's baseline characteristics and subsequent disease development) that provided data on smoking and VTE. Together, the studies involved nearly 4 million participants and recorded 35,151 VTE events. Compared with never smokers, ever smokers (current and former smokers combined) had a relative risk (RR) of developing VTE of 1.17. That is, ever smokers were 17% more likely to develop VTE than never smokers. For current smokers and former smokers, RRs were 1.23 and 1.10, respectively. Analysis of only studies that adjusted for body mass index (a measure of body fat and a known risk factor for conditions that affect the heart and circulation) yielded a slightly higher RR (1.30) for current smokers compared with never smokers. For ever smokers, the population attributable fraction (the proportional reduction in VTE that would accrue in the population if no one smoked) was 8.7%. Notably, the risk of VTE increased by 10.2% for every additional ten cigarettes smoked per day and by 6.1% for every additional ten pack-years. Thus, an individual who smoked one pack of cigarettes per day for 40 years had a 26.7% higher risk of developing VTE than someone who had never smoked. Finally, smoking was associated with an absolute risk increase of 24.3 cases of VTE per 100,000 person-years.
What Do These Findings Mean?
These findings indicate that cigarette smoking is associated with a statistically significant, slightly increased risk for VTE among the general population and reveal a dose-relationship between smoking and VTE risk. They cannot prove that smoking causes VTE—people who smoke may share other unknown characteristics (confounding factors) that are actually responsible for their increased risk of VTE. Indeed, these findings identify body mass index as a potential confounding factor that might affect the accuracy of estimates of the association between smoking and VTE risk. Although the risk of VTE associated with smoking is smaller than the risk associated with some well-established VTE risk factors, smoking is more common (globally, there are 1.1 billion smokers) and may act synergistically with some of these risk factors. Thus, smoking behavior should be considered when screening individuals for VTE and in the prevention of first and subsequent VTE events.
Additional Information
Please access these Web sites via the online version of this summary at
The US National Heart Lung and Blood Institute provides information on deep vein thrombosis (including an animation about how DVT causes pulmonary embolism), and information on pulmonary embolism
The UK National Health Service Choices website has information on deep vein thrombosis, including personal stories, and on pulmonary embolism; SmokeFree is a website provided by the UK National Health Service that offers advice on quitting smoking
The non-profit organization US National Blood Clot Alliance provides detailed information about deep vein thrombosis and pulmonary embolism for patients and professionals and includes a selection of personal stories about these conditions
The World Health Organization provides information about the dangers of tobacco (in several languages), from the US National Cancer Institute, offers online tools and resources to help people quit smoking
MedlinePlus has links to further information about deep vein thrombosis, pulmonary embolism, and the dangers of smoking (in English and Spanish)
PMCID: PMC3775725  PMID: 24068896
7.  MicroRNAs derived from circulating exosomes as non-invasive biomarkers for screening and diagnose lung cancer 
Lung cancer is formerly the highest cause of mortality among tumor pathologies worldwide. There are no validated techniques for an early detection of pulmonary cancer lesions other than low-dose helical CT-scan. Unfortunately, this method have some downside effects.
Recent studies have laid the basis for development of exosomes-based techniques to screen/diagnose lung cancers. As the isolation of circulating exosomes is a minimally invasive procedure, this technique opens new possibilities for diagnostic applications.
We used a first set of 30 plasma samples from as many patients, including 10 patients affected by Lung Adenocarcinomas, 10 with Lung Granulomas and 10 healthy smokers matched for age and sex as negative controls. Wide range microRNAs analysis (742 microRNAs) was performed by quantitative RT-PCR. Data were compared by lesion characteristics using WEKA software for statistics and modeling. Subsequently, selected microRNAs were evaluated on an independent larger group of samples (105 specimens: 50 Lung Adenocarcinomas, 30 Lung Granulomas and 25 healthy smokers).
This analysis led to the selection of 4 microRNAs to perform a screening test (miR-378a, miR-379, miR-139-5p and miR-200b-5p), useful to divide population into 2 groups: nodule (lung adenocarcinomas+carcinomas) and non-nodule (healthy former smokers). Six microRNAs (miR-151a-5p, miR-30a-3p, miR-200b-5p, miR-629, miR-100 and miR-154-3p) were selected for a second test on the “nodule” population to discriminate between lung adenocarcinoma and granuloma.
“Screening test” has shown 97.5% sensitivity, 72.0% specificity, AUC ROC of 90.8%. “Diagnostic test” had 96.0% sensitivity, 60.0% specificity, AUC ROC of 76.0%.
Further evaluation is needed to confirm the predictive power of those models on higher cohorts of samples.
PMCID: PMC4123222  PMID: 23945385
exosome; screening test; diagnostic test; lung Adenocarcinoma; microRNA
8.  Lung Cancer Occurrence in Never-Smokers: An Analysis of 13 Cohorts and 22 Cancer Registry Studies  
PLoS Medicine  2008;5(9):e185.
Better information on lung cancer occurrence in lifelong nonsmokers is needed to understand gender and racial disparities and to examine how factors other than active smoking influence risk in different time periods and geographic regions.
Methods and Findings
We pooled information on lung cancer incidence and/or death rates among self-reported never-smokers from 13 large cohort studies, representing over 630,000 and 1.8 million persons for incidence and mortality, respectively. We also abstracted population-based data for women from 22 cancer registries and ten countries in time periods and geographic regions where few women smoked. Our main findings were: (1) Men had higher death rates from lung cancer than women in all age and racial groups studied; (2) male and female incidence rates were similar when standardized across all ages 40+ y, albeit with some variation by age; (3) African Americans and Asians living in Korea and Japan (but not in the US) had higher death rates from lung cancer than individuals of European descent; (4) no temporal trends were seen when comparing incidence and death rates among US women age 40–69 y during the 1930s to contemporary populations where few women smoke, or in temporal comparisons of never-smokers in two large American Cancer Society cohorts from 1959 to 2004; and (5) lung cancer incidence rates were higher and more variable among women in East Asia than in other geographic areas with low female smoking.
These comprehensive analyses support claims that the death rate from lung cancer among never-smokers is higher in men than in women, and in African Americans and Asians residing in Asia than in individuals of European descent, but contradict assertions that risk is increasing or that women have a higher incidence rate than men. Further research is needed on the high and variable lung cancer rates among women in Pacific Rim countries.
Michael Thun and colleagues pooled and analyzed comprehensive data on lung cancer incidence and death rates among never-smokers to examine what factors other than active smoking affect lung cancer risk.
Editors' Summary
Every year, more than 1.4 million people die from lung cancer, a leading cause of cancer deaths worldwide. In the US alone, more than 161,000 people will die from lung cancer this year. Like all cancers, lung cancer occurs when cells begin to divide uncontrollably because of changes in their genes. The main trigger for these changes in lung cancer is exposure to the chemicals in cigarette smoke—either directly through smoking cigarettes or indirectly through exposure to secondhand smoke. Eighty-five to 90% of lung cancer deaths are caused by exposure to cigarette smoke and, on average, current smokers are 15 times more likely to die from lung cancer than lifelong nonsmokers (never smokers). Furthermore, a person's cumulative lifetime risk of developing lung cancer is related to how much they smoke, to how many years they are a smoker, and—if they give up smoking—to the age at which they stop smoking.
Why Was This Study Done?
Because lung cancer is so common, even the small fraction of lung cancer that occurs in lifelong nonsmokers represents a large number of people. For example, about 20,000 of this year's US lung cancer deaths will be in never-smokers. However, very little is known about how age, sex, or race affects the incidence (the annual number of new cases of diseases in a population) or death rates from lung cancer among never-smokers. A better understanding of the patterns of lung cancer incidence and death rates among never-smokers could provide useful information about the factors other than cigarette smoke that increase the likelihood of not only never-smokers, but also former smokers and current smokers developing lung cancer. In this study, therefore, the researchers pooled and analyzed a large amount of information about lung cancer incidence and death rates among never smokers to examine what factors other than active smoking affect lung cancer risk.
What Did the Researchers Do and Find?
The researchers analyzed information on lung cancer incidence and/or death rates among nearly 2.5 million self-reported never smokers (men and women) from 13 large studies investigating the health of people in North America, Europe, and Asia. They also analyzed similar information for women taken from cancer registries in ten countries at times when very few women were smokers (for example, the US in the late 1930s). The researchers' detailed statistical analyses reveal, for example, that lung cancer death rates in African Americans and in Asians living in Korea and Japan (but not among Asians living in the US) are higher than those in people of the European continental ancestry group. They also show that men have higher death rates from lung cancer than women irrespective of racial group, but that women aged 40–59 years have a slightly higher incidence of lung cancer than men of a similar age. This difference disappears at older ages. Finally, an analysis of lung cancer incidence and death rates at different times during the past 70 years shows no evidence of an increase in the lung cancer burden among never smokers over time.
What Do These Findings Mean?
Although some of the findings described above have been hinted at in previous, smaller studies, these and other findings provide a much more accurate picture of lung cancer incidence and death rates among never smokers. Most importantly the underlying data used in these analyses are now freely available and should provide an excellent resource for future studies of lung cancer in never smokers.
Additional Information.
Please access these Web sites via the online version of this summary at
The US National Cancer Institute provides detailed information for patients and health professionals about all aspects of lung cancer and information on smoking and cancer (in English and Spanish)
Links to other US-based resources dealing with lung cancer are provided by MedlinePlus (in English and Spanish)
Cancer Research UK provides key facts about the link between lung cancer and smoking and information about all other aspects of lung cancer
PMCID: PMC2531137  PMID: 18788891
9.  Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung Cancer 
Annals of internal medicine  2012;157(4):242-250.
External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit.
To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America.
Case–control and prospective cohort study.
Europe and North America.
Participants in the European Early Lung Cancer (EUELC) and Harvard case–control studies and the LLP population-based prospective cohort (LLPC) study.
5-year absolute risks for lung cancer predicted by the LLP model.
The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening.
The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model.
Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population risk thresholds for initiating lung cancer screening.
Primary Funding Source
Roy Castle Lung Cancer Foundation.
PMCID: PMC3723683  PMID: 22910935
10.  C-Reactive Protein and Risk of Lung Cancer 
Journal of Clinical Oncology  2010;28(16):2719-2726.
Chronic inflammation could play a role in lung carcinogenesis, underscoring the potential for lung cancer prevention and screening. We investigated the association of circulating high-sensitivity C-reactive protein (CRP, an inflammation biomarker) and CRP single nucleotide polymorphisms (SNPs) with prospective lung cancer risk.
Patients and Methods
We conducted a nested case-control study of 592 lung cancer patients and 670 controls with available prediagnostic serum and 378 patients and 447 controls with DNA within the screening arm of the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (N = 77,464). Controls were matched to patients on age, sex, entry year, follow-up time, and smoking. We measured CRP levels in baseline serum samples and genotyped five common CRP SNPs.
Elevated CRP levels were associated with increased lung cancer risk (odds ratio [OR], 1.98; 95% CI, 1.35 to 2.89; P-trend < .001 for fourth quartile [Q4, ≥ 5.6 mg/L] v Q1 [< 1.0 mg/L]). The CRP association did not differ significantly by histology, follow-up time, or smoking status, but was most apparent for squamous cell carcinomas (OR, 2.92; 95% CI, 1.30 to 6.54), 2 to 5 years before lung cancer diagnosis (OR, 2.33; 95% CI, 1.24 to 4.39), and among former smokers (OR, 2.48; 95% CI, 1.53 to 4.03) and current smokers (OR, 1.90; 95% CI, 1.06 to 3.41). Although CRP SNPs and haplotypes were associated with CRP levels, they were not associated with lung cancer risk. Ten-year standardized absolute risks of lung cancer were higher with elevated CRP levels among former smokers (Q4: 2.55%; 95% CI, 1.98% to 3.27% v Q1: 1.39%; 95% CI, 1.07% to 1.81%) and current smokers (Q4: 7.37%; 95% CI, 5.81% to 9.33% v Q1: 4.03%; 95% CI, 3.01% to 5.40%).
Elevated CRP levels are associated with subsequently increased lung cancer risk, suggesting an etiologic role for chronic pulmonary inflammation in lung carcinogenesis.
PMCID: PMC2881850  PMID: 20421535
11.  Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy 
BMC Medical Genomics  2015;8:18.
The gene expression profile of cytologically-normal bronchial airway epithelial cells has previously been shown to be altered in patients with lung cancer. Although bronchoscopy is often used for the diagnosis of lung cancer, its sensitivity is imperfect, especially for small and peripheral suspicious lesions. In this study, we derived a gene expression classifier from airway epithelial cells that detects the presence of cancer in current and former smokers undergoing bronchoscopy for suspect lung cancer and evaluated its sensitivity to detect lung cancer among patients from an independent cohort.
We collected bronchial epithelial cells (BECs) from the mainstem bronchus of 299 current or former smokers (223 cancer-positive and 76 cancer-free subjects) undergoing bronchoscopy for suspected lung cancer in a prospective, multi-center study. RNA from these samples was run on gene expression microarrays for training a gene-expression classifier. A logistic regression model was built to predict cancer status, and the finalized classifier was validated in an independent cohort from a previous study.
We found 232 genes whose expression levels in the bronchial airway are associated with lung cancer. We then built a classifier based on the combination of 17 cancer genes, gene expression predictors of smoking status, smoking history, and gender, plus patient age. This classifier had a ROC curve AUC of 0.78 (95% CI, 0.70-0.86) in patients whose bronchoscopy did not lead to a diagnosis of lung cancer (n = 134). In the validation cohort, the classifier had a similar AUC of 0.81 (95% CI, 0.73-0.88) in this same subgroup (n = 118). The classifier performed similarly across a range of mass sizes, cancer histologies and stages. The negative predictive value was 94% (95% CI, 83-99%) in subjects with a non-diagnostic bronchoscopy.
We developed a gene expression classifier measured in bronchial airway epithelial cells that is able to detect lung cancer in current and former smokers who have undergone bronchoscopy for suspicion of lung cancer. Due to the high NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing in patients whose bronchoscopy is non diagnostic.
Electronic supplementary material
The online version of this article (doi:10.1186/s12920-015-0091-3) contains supplementary material, which is available to authorized users.
PMCID: PMC4434538  PMID: 25944280
12.  Individualized Risk Prediction Model for Lung Cancer in Korean Men 
PLoS ONE  2013;8(2):e54823.
Lung cancer is the leading cause of cancer deaths in Korea. The objective of the present study was to develop an individualized risk prediction model for lung cancer in Korean men using population-based cohort data.
From a population-based cohort study of 1,324,804 Korean men free of cancer at baseline, the individualized absolute risk of developing lung cancer was estimated using the Cox proportional hazards model. We checked the validity of the model using C statistics and the Hosmer–Lemeshow chi-square test on an external validation dataset.
The risk prediction model for lung cancer in Korean men included smoking exposure, age at smoking initiation, body mass index, physical activity, and fasting glucose levels. The model showed excellent performance (C statistic = 0.871, 95% CI = 0.867–0.876). Smoking was significantly associated with the risk of lung cancer in Korean men, with a four-fold increased risk in current smokers consuming more than one pack a day relative to non-smokers. Age at smoking initiation was also a significant predictor for developing lung cancer; a younger age at initiation was associated with a higher risk of developing lung cancer.
This is the first study to provide an individualized risk prediction model for lung cancer in an Asian population with very good model performance. In addition to current smoking status, earlier exposure to smoking was a very important factor for developing lung cancer. Since most of the risk factors are modifiable, this model can be used to identify those who are at a higher risk and who can subsequently modify their lifestyle choices to lower their risk of lung cancer.
PMCID: PMC3567090  PMID: 23408946
13.  The LLP risk model: an individual risk prediction model for lung cancer 
British Journal of Cancer  2007;98(2):270-276.
Using a model-based approach, we estimated the probability that an individual, with a specified combination of risk factors, would develop lung cancer within a 5-year period.
Data from 579 lung cancer cases and 1157 age- and sex-matched population-based controls were available for this analysis. Significant risk factors were fitted into multivariate conditional logistic regression models. The final multivariate model was combined with age-standardised lung cancer incidence data to calculate absolute risk estimates.
Combinations of lifestyle risk factors were modelled to create risk profiles. For example, a 77-year-old male non-smoker, with a family history of lung cancer (early onset) and occupational exposure to asbestos has an absolute risk of 3.17% (95% CI, 1.67–5.95). Choosing a 2.5% cutoff to trigger increased surveillance, gave a sensitivity of 0.62 and specificity of 0.70, while a 6.0% cutoff gave a sensitivity of 0.34 and specificity of 0.90. A 10-fold cross validation produced an AUC statistic of 0.70, indicating good discrimination.
If independent validation studies confirm these results, the LLP risk models' application as the first stage in an early detection strategy is a logical evolution in patient care.
PMCID: PMC2361453  PMID: 18087271
lung carcinoma; risk prediction; model
14.  Increased Levels of Circulating Interleukin 6, Interleukin 8, C-Reactive Protein, and Risk of Lung Cancer 
Previous studies that were based primarily on small numbers of patients suggested that certain circulating proinflammatory cytokines may be associated with lung cancer; however, large independent studies are lacking.
Associations between serum interleukin 6 (IL-6) and interleukin 8 (IL-8) levels and lung cancer were analyzed among 270 case patients and 296 control subjects participating in the National Cancer Institute-Maryland (NCI-MD) case–control study. Results were validated in 532 case patients and 595 control subjects in a nested case–control study within the prospective Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Association with C-reactive protein (CRP), a systemic inflammation biomarker, was also analyzed. Associations between biomarkers and lung cancer were estimated using logistic regression models adjusted for smoking, stage, histology, age, and sex. The 10-year standardized absolute risks of lung cancer were estimated using a weighted Cox regression model.
Serum IL-6 and IL-8 levels in the highest quartile were associated with lung cancer in the NCI-MD study (IL-6, odds ratio [OR] = 3.29, 95% confidence interval [CI] = 1.88 to 5.77; IL-8, OR = 2.06, 95% CI = 1.19 to 3.57) and with lung cancer risk in the PLCO study (IL-6, OR = 1.48, 95% CI = 1.04 to 2.10; IL-8, OR = 1.57, 95% CI = 1.10 to 2.24), compared with the lowest quartile. In the PLCO study, increased IL-6 levels were only associated with lung cancer diagnosed within 2 years of blood collection, whereas increased IL-8 levels were associated with lung cancer diagnosed more than 2 years after blood collection (OR = 1.57, 95% CI = 1.15 to 2.13). The 10-year standardized absolute risks of lung cancer in the PLCO study were highest among current smokers with high IL-8 and CRP levels (absolute risk = 8.01%, 95% CI = 5.77% to 11.05%).
Although increased levels of both serum IL-6 and IL-8 are associated with lung cancer, only IL-8 levels are associated with lung cancer risk several years before diagnosis. Combination of IL-8 and CRP are more robust biomarkers than either marker alone in predicting subsequent lung cancer.
PMCID: PMC3139587  PMID: 21685357
15.  Lung Cancer Risk Prediction: Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial Models and Validation 
Identification of individuals at high risk for lung cancer should be of value to individuals, patients, clinicians, and researchers. Existing prediction models have only modest capabilities to classify persons at risk accurately.
Prospective data from 70 962 control subjects in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) were used in models for the general population (model 1) and for a subcohort of ever-smokers (N = 38 254) (model 2). Both models included age, socioeconomic status (education), body mass index, family history of lung cancer, chronic obstructive pulmonary disease, recent chest x-ray, smoking status (never, former, or current), pack-years smoked, and smoking duration. Model 2 also included smoking quit-time (time in years since ever-smokers permanently quit smoking). External validation was performed with 44 223 PLCO intervention arm participants who completed a supplemental questionnaire and were subsequently followed. Known available risk factors were included in logistic regression models. Bootstrap optimism-corrected estimates of predictive performance were calculated (internal validation). Nonlinear relationships for age, pack-years smoked, smoking duration, and quit-time were modeled using restricted cubic splines. All reported P values are two-sided.
During follow-up (median 9.2 years) of the control arm subjects, 1040 lung cancers occurred. During follow-up of the external validation sample (median 3.0 years), 213 lung cancers occurred. For models 1 and 2, bootstrap optimism-corrected receiver operator characteristic area under the curves were 0.857 and 0.805, and calibration slopes (model-predicted probabilities vs observed probabilities) were 0.987 and 0.979, respectively. In the external validation sample, models 1 and 2 had area under the curves of 0.841 and 0.784, respectively. These models had high discrimination in women, men, whites, and nonwhites.
The PLCO lung cancer risk models demonstrate high discrimination and calibration.
PMCID: PMC3131220  PMID: 21606442
16.  Body Mass Index and Risk of Lung Cancer Among Never, Former, and Current Smokers 
Although obesity has been directly linked to the development of many cancers, many epidemiological studies have found that body mass index (BMI)—a surrogate marker of obesity—is inversely associated with the risk of lung cancer. These studies are difficult to interpret because of potential confounding by cigarette smoking, a major risk factor for lung cancer that is associated with lower BMI.
We prospectively examined the association between BMI and the risk of lung cancer among 448 732 men and women aged 50–71 years who were recruited during 1995–1996 for the National Institutes of Health–AARP Diet and Health Study. BMI was calculated based on the participant’s self-reported height and weight on the baseline questionnaire. We identified 9437 incident lung carcinomas (including 415 in never smokers) during a mean follow-up of 9.7 years through 2006. Multivariable Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) with adjustment for lung cancer risk factors, including smoking status. To address potential bias due to preexisting undiagnosed disease, we excluded potentially unhealthy participants in sensitivity analyses. All statistical tests were two-sided.
The crude incidence rate of lung cancer over the study follow-up period was 233 per 100 000 person-years among men and 192 per 100 000 person-years among women. BMI was inversely associated with the risk of lung cancer among both men and women (BMI ≥35 vs 22.5–24.99 kg/m2: HR = 0.81, 95% CI = 0.70 to 0.94 and HR = 0.73, 95% CI = 0.61 to 0.87, respectively). The inverse association was restricted to current and former smokers and was stronger after adjustment for smoking. Among smokers, the inverse association persisted even after finely stratifying on smoking status, time since quitting smoking, and number of cigarettes smoked per day. Sensitivity analyses did not support the possibility that the inverse association was due to prevalent undiagnosed disease.
Our results suggest that a higher BMI is associated with a reduced risk of lung cancer in current and former smokers. Our inability to attribute the inverse association between BMI and the risk of lung cancer to residual confounding by smoking or to bias suggests the need for considering other explanations.
PMCID: PMC3352831  PMID: 22457475
17.  Validation of a blood protein signature for non-small cell lung cancer 
Clinical Proteomics  2014;11(1):32.
CT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations.
Blood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n = 363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n = 138 in the first validation and n = 135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation.
Selecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.
PMCID: PMC4123246  PMID: 25114662
Lung cancer; Biomarker; SOMAmer; Proteomic; Squamous cell carcinoma; Diagnosis; Preanalytic variability; Sample bias
18.  Endobronchial miRNAs as biomarkers in lung cancer chemoprevention 
Lung cancers express lower levels of prostacyclin than normal lung tissues. Prostacyclin prevents lung cancer in a variety of mouse models. A randomized phase II trial comparing oral iloprost (a prostacyclin analogue) to placebo in high-risk subjects demonstrated improvement in bronchial histology in former, but not current, smokers. This placebo-controlled study offered the opportunity for investigation of other potential intermediate endpoint and predictive biomarkers to incorporate into chemoprevention trials.
Matched bronchial biopsies were obtained at baseline (BL) and at 6 months follow-up (FU) from 125 high-risk individuals who completed the trial: 31/29 and 37/28 current/former smokers in the iloprost and placebo arm, respectively. We analyzed the expression of 14 selected miRNAs by qRT-PCR in 496 biopsies.
The expression of seven miRNAs was significantly correlated with histology at BL. The expression of miR-34c was inversely correlated with histology at BL (p<0.0001) and with change in histology at FU (p=0.0003), independent of treatment or smoking status. Several miRNAs were also found to be differentially expressed in current smokers as compared with former smokers. In current smokers, miR-375 was up-regulated at BL (p<0.0001) and down-regulated after treatment with iloprost (p=0.0023). No miRNA at baseline reliably predicted a response to iloprost.
No biomarker predictive of response to iloprost was found. MiR-34c was inversely correlated with BL histology and with histology changes. Mir-34c changes at FU could be used as a quantitative biomarker which parallels histologic response in formalin-fixed bronchial biopsies in future lung cancer chemoprevention studies.
PMCID: PMC4159305  PMID: 23268837
miRNAs; chemoprevention; lung cancer; iloprost
19.  Pro–Surfactant Protein B As a Biomarker for Lung Cancer Prediction 
Journal of Clinical Oncology  2013;31(36):4536-4543.
Preliminary studies have identified pro–surfactant protein B (pro-SFTPB) to be a promising blood biomarker for non–small-cell lung cancer. We conducted a study to determine the independent predictive potential of pro-SFTPB in identifying individuals who are subsequently diagnosed with lung cancer.
Patients and Methods
Pro-SFTPB levels were measured in 2,485 individuals, who enrolled onto the Pan-Canadian Early Detection of Lung Cancer Study by using plasma sample collected at the baseline visit. Multivariable logistic regression models were used to evaluate the predictive ability of pro-SFTPB in addition to known lung cancer risk factors. Calibration and discrimination were evaluated, the latter by an area under the receiver operating characteristic curve (AUC). External validation was performed with samples collected in the Carotene and Retinol Efficacy Trial (CARET) participants using a case-control study design.
Adjusted for age, sex, body mass index, personal history of cancer, family history of lung cancer, forced expiratory volume in one second percent predicted, average number of cigarettes smoked per day, and smoking duration, pro-SFTPB (log transformed) had an odds ratio of 2.220 (95% CI, 1.727 to 2.853; P < .001). The AUCs of the full model with and without pro-SFTPB were 0.741 (95% CI, 0.696 to 0.783) and 0.669 (95% CI, 0.620 to 0.717; difference in AUC P < .001). In the CARET Study, the use of pro-SFPTB yielded an AUC of 0.683 (95% CI, 0.604 to 0.761).
Pro-SFTPB in plasma is an independent predictor of lung cancer and may be a valuable addition to existing lung cancer risk prediction models.
PMCID: PMC3871515  PMID: 24248694
20.  Pathway-based identification of a smoking associated 6-gene signature predictive of lung cancer risk and survival 
Smoking is a prominent risk factor for lung cancer. However, it is not an established prognostic factor for lung cancer in clinics. To date, no gene test is available for diagnostic screening of lung cancer risk or prognostication of clinical outcome in smokers. This study sought to identify a smoking associated gene signature in order to provide a more precise diagnosis and prognosis of lung cancer in smokers.
Methods and materials
An implication network based methodology was used to identify biomarkers by modeling crosstalk with major lung cancer signaling pathways. Specifically, the methodology contains the following steps: 1) identifying genes significantly associated with lung cancer survival; 2) selecting candidate genes which are differentially expressed in smokers versus non-smokers from the survival genes identified in Step 1; 3) from these candidate genes, constructing gene coexpression networks based on prediction logic for the smoker group and the non-smoker group, respectively; 4) identifying smoking-mediated differential components, i.e., the unique gene coexpression patterns specific to each group; and 5) from the differential components, identifying genes directly co-expressed with major lung cancer signaling hallmarks.
A smoking-associated 6-gene signature was identified for prognosis of lung cancer from a training cohort (n=256). The 6-gene signature could separate lung cancer patients into two risk groups with distinct post-operative survival (log-rank P < 0.04, Kaplan-Meier analyses) in three independent cohorts (n=427). The expression-defined prognostic prediction is strongly related to smoking association and smoking cessation (P < 0.02; Pearson’s Chi-squared tests). The 6-gene signature is an accurate prognostic factor (hazard ratio = 1.89, 95% CI: [1.04, 3.43]) compared to common clinical covariates in multivariate Cox analysis. The 6-gene signature also provides an accurate diagnosis of lung cancer with an overall accuracy of 73% in a cohort of smokers (n=164). The coexpression patterns derived from the implication networks were validated with interactions reported in the literature retrieved with STRING8, Ingenuity Pathway Analysis, and Pathway Studio.
The pathway-based approach identified a smoking-associated 6-gene signature that predicts lung cancer risk and survival. This gene signature has potential clinical implications in the diagnosis and prognosis of lung cancer in smokers.
PMCID: PMC3351561  PMID: 22326768
implication networks based on prediction logic; gene coexpression networks based on formal logic; smoking; gene signature; lung cancer diagnosis and prognosis; signaling pathways
21.  Comparison of Traditional Cardiovascular Risk Models and Coronary Atherosclerotic Plaque as Detected by Computed Tomography for Prediction of Acute Coronary Syndrome in Patients With Acute Chest Pain 
The objective was to determine the association of four clinical risk scores and coronary plaque burden as detected by computed tomography (CT) with the outcome of acute coronary syndrome (ACS) in patients with acute chest pain. The hypothesis was that the combination of risk scores and plaque burden improved the discriminatory capacity for the diagnosis of ACS.
The study was a subanalysis of the Rule Out Myocardial Infarction Using Computer-Assisted Tomography (ROMICAT) trial—a prospective observational cohort study. The authors enrolled patients presenting to the emergency department (ED) with a chief complaint of acute chest pain, inconclusive initial evaluation (negative biomarkers, nondiagnostic electrocardiogram [ECG]), and no history of coronary artery disease (CAD). Patients underwent contrast-enhanced 64-multidetector-row cardiac CT and received standard clinical care (serial ECG, cardiac biomarkers, and subsequent diagnostic testing, such as exercise treadmill testing, nuclear stress perfusion imaging, and/or invasive coronary angiography), as deemed clinically appropriate. The clinical providers were blinded to CT results. The chest pain score was calculated and the results were dichotomized to ≥10 (high-risk) and <10 (low-risk). Three risk scores were calculated, Goldman, Sanchis, and Thrombolysis in Myocardial Infarction (TIMI), and each patient was assigned to a low-, intermediate-, or high-risk category. Because of the low number of subjects in the high-risk group, the intermediate- and high-risk groups were combined into one. CT images were evaluated for the presence of plaque in 17 coronary segments. Plaque burden was stratified into none, intermediate, and high (zero, one to four, and more than four segments with plaque). An outcome panel of two physicians (blinded to CT findings) established the primary outcome of ACS (defined as either an acute myocardial infarction or unstable angina) during the index hospitalization (from the presentation to the ED to the discharge from the hospital). Logistic regression modeling was performed to examine the association of risk scores and coronary plaque burden to the outcome of ACS. Unadjusted models were individually fitted for the coronary plaque burden and for Goldman, Sanchis, TIMI, and chest pain scores. In adjusted analyses, the authors tested whether the association between risk scores and ACS persisted after controlling for the coronary plaque burden. The prognostic discriminatory capacity of the risk scores and plaque burden for ACS was assessed using c-statistics. The differences in area under the receiver-operating characteristic curve (AUC) and c-statistics were tested by performing the −2 log likelihood ratio test of nested models. A p value <0.05 was considered statistically significant.
Among 368 subjects, 31 (8%) subjects were diagnosed with ACS. Goldman (AUC = 0.61), Sanchis (AUC = 0.71), and TIMI (AUC = 0.63) had modest discriminatory capacity for the diagnosis of ACS. Plaque burden was the strongest predictor of ACS (AUC = 0.86; p < 0.05 for all comparisons with individual risk scores). The combination of plaque burden and risk scores improved prediction of ACS (plaque + Goldman AUC = 0.88, plaque + Sanchis AUC = 0.90, plaque + TIMI AUC = 0.88; p < 0.01 for all comparisons with coronary plaque burden alone).
Risk scores (Goldman, Sanchis, TIMI) have modest discriminatory capacity and coronary plaque burden has good discriminatory capacity for the diagnosis of ACS in patients with acute chest pain. The combined information of risk scores and plaque burden significantly improves the discriminatory capacity for the diagnosis of ACS.
PMCID: PMC3424404  PMID: 22849339
22.  Double-strand break damage and associated DNA repair genes predispose smokers to gene methylation 
Cancer research  2008;68(8):3049-3056.
Gene promoter hypermethylation in sputum is a promising biomarker for predicting lung cancer. Identifying factors that predispose smokers to methylation of multiple gene promoters in the lung could impact strategies for early detection and chemoprevention. This study evaluated the hypothesis that double-strand break repair capacity and sequence variation in genes in this pathway are associated with a high methylation index in a cohort of current and former cancer-free smokers. A 50% reduction in the mean level of double-strand break repair capacity was seen in lymphocytes from smokers with a high methylation index, defined as ≥ 3 of 8 genes methylated in sputum, compared to smokers with no genes methylated. The classification accuracy for predicting risk for methylation was 88%. Single nucleotide polymorphisms within the MRE11A, CHEK2, XRCC3, DNA-Pkc, and NBN DNA repair genes were highly associated with the methylation index. A 14.5-fold increased odds for high methylation was seen for persons with ≥ 7 risk alleles of these genes. Promoter activity of the MRE11A gene that plays a critical role in recognition of DNA damage and activation of ATM was reduced in persons with the risk allele. Collectively, ours is the first population-based study to identify double-strand break DNA repair capacity and specific genes within this pathway as critical determinants for gene methylation in sputum, that is, in turn, associated with elevated risk for lung cancer.
PMCID: PMC2483467  PMID: 18413776
promoter methylation; DNA double strand break; single nucleotide polymorphism; DNA repair capacity; association study
23.  A Comprehensive Haplotype Analysis of the XPC Genomic Sequence Reveals a Cluster of Genetic Variants Associated with Sensitivity to Tobacco-Smoke Mutagens 
Toxicological Sciences  2010;115(1):41-50.
The impact of single-nucleotide polymorphisms (SNPs) of the DNA repair gene XPC on DNA repair capacity (DRC) and genotoxicity has not been comprehensively determined. We constructed a comprehensive haplotype map encompassing all common XPC SNPs and evaluated the effect of Bayesian-inferred haplotypes on DNA damage associated with tobacco smoking, using chromosome aberrations (CA) as a biomarker. We also used the mutagen-sensitivity assay, in which mutagen-induced CA in cultured lymphocytes are determined, to evaluate the haplotype effects on DRC. We hypothesized that if certain XPC haplotypes have functional effects, a correlation between these haplotypes and baseline and/or mutagen-induced CA would exist. Using HapMap and single nucleotide polymorphism (dbSNP) databases, we identified 92 SNPs, of which 35 had minor allele frequencies ≥ 0.05. Bayesian inference and subsequent phylogenetic analysis identified 21 unique haplotypes, which segregated into six distinct phylogenetically grouped haplotypes (PGHs A–F). A SNP tagging approach used identified 11 tagSNPs representing these 35 SNPs (r2 = 0.80). We utilized these tagSNPs to genotype a population of smokers matched to nonsmokers (n = 123). Haplotypes for each individual were reconstituted and PGH designations were assigned. Relationships between XPC haplotypes and baseline and/or mutagen-induced CA were then evaluated. We observed significant interaction among smoking and PGH-C (p = 0.046) for baseline CA where baseline CA was 3.5 times higher in smokers compared to nonsmokers. Significant interactions among smoking and PGH-D (p = 0.023) and PGH-F (p = 0.007) for mutagen-induced CA frequencies were also observed. These data indicate that certain XPC haplotypes significantly alter CA and DRC in smokers and, thus, can contribute to cancer risk.
PMCID: PMC2855352  PMID: 20106949
DNA nucleotide excision repair; XPC gene; polymorphism; haplotypes; biomarkers; chromosome; smoking; cancer
24.  Selection Criteria for Lung-Cancer Screening 
The New England journal of medicine  2013;368(8):728-736.
The National Lung Screening Trial (NLST) used risk factors for lung cancer (e.g., ≥30 pack-years of smoking and <15 years since quitting) as selection criteria for lung-cancer screening. Use of an accurate model that incorporates additional risk factors to select persons for screening may identify more persons who have lung cancer or in whom lung cancer will develop.
We modified the 2011 lung-cancer risk-prediction model from our Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to ensure applicability to NLST data; risk was the probability of a diagnosis of lung cancer during the 6-year study period. We developed and validated the model (PLCOM2012) with data from the 80,375 persons in the PLCO control and intervention groups who had ever smoked. Discrimination (area under the receiver-operating-characteristic curve [AUC]) and calibration were assessed. In the validation data set, 14,144 of 37,332 persons (37.9%) met NLST criteria. For comparison, 14,144 highest-risk persons were considered positive (eligible for screening) according to PLCOM2012 criteria. We compared the accuracy of PLCOM2012 criteria with NLST criteria to detect lung cancer. Cox models were used to evaluate whether the reduction in mortality among 53,202 persons undergoing low-dose computed tomographic screening in the NLST differed according to risk.
The AUC was 0.803 in the development data set and 0.797 in the validation data set. As compared with NLST criteria, PLCOM2012 criteria had improved sensitivity (83.0% vs. 71.1%, P<0.001) and positive predictive value (4.0% vs. 3.4%, P = 0.01), without loss of specificity (62.9% and. 62.7%, respectively; P = 0.54); 41.3% fewer lung cancers were missed. The NLST screening effect did not vary according to PLCOM2012 risk (P = 0.61 for interaction).
The use of the PLCOM2012 model was more sensitive than the NLST criteria for lung-cancer detection.
PMCID: PMC3929969  PMID: 23425165
25.  Risk Perceptions Among Participants Undergoing Lung Cancer Screening: Baseline Results from the National Lung Screening Trial 
Lung cancer screening could present a “teachable moment” for promoting smoking cessation and relapse prevention. Understanding the risk perceptions of older individuals who undergo screening will guide these efforts.
This paper examines National Lung Screening Trial (NLST) participants’ perceptions of risk for lung cancer and other smoking-related diseases. We investigated (1) whether risk perceptions of lung cancer screening participants differed between current and former smokers and (2) which factors (sociodemographic, smoking and medical history, cognitive, emotional, and knowledge) were associated with these risk perceptions.
We analyzed baseline data collected from 630 NLST participants prior to their initial screen. Participants were older (55–74 years), heavy (minimum 30 pack years) current or former smokers. A ten-item risk perception measure was developed to assess perceived lifetime risk of lung cancer and other smoking-related diseases.
The risk perception measure had excellent internal consistency (alpha=0.93). Former smokers had lower risk perceptions compared to current smokers. Factors independently associated with high risk perceptions among current smokers included having a personal history of a smoking-related disease, higher lifetime maximum number of cigarettes smoked daily, having lived with a smoker, high worry, high perceived severity of lung cancer and smoking-related diseases, and accurate knowledge of tenfold increased risk of lung cancer for a one pack per day smoker. Factors independently associated with high risk perceptions among former smokers included being White, past history of smoking within 30 min of waking, high worry, and accurate knowledge of tenfold increased risk of lung cancer for a one pack per day smoker.
Using a comprehensive risk perception measurement, we found that current and former smokers held different risk perceptions. Former and current smokers’ smoking and medical history, race, emotional concerns, behavior change cognitions, and knowledge should be considered during a prescreening risk communication session. We highlight the theoretical and risk communication implications for former and current smokers undergoing lung cancer screening.
PMCID: PMC2831282  PMID: 19711141
Risk perceptions; Lung cancer; Cigarette smoking

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