An expanded risk prediction model for lung cancer

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data

BACKGROUND

Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data.

METHODS

Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features.

RESULTS

For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer.

CONCLUSION

This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Development of lung cancer risk prediction models based on F-18 FDG PET images

OBJECTIVE

We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET).

METHODS

We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classified into the lung cancer group (n = 136); those who did not were classified into the control group (n = 659). The healthy individuals were then randomly assigned to either the training (n = 585) or validation sets (n = 210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set.

RESULTS

The lung SUVR and lung MH index in the lung cancer group were significantly higher than in the control group (p < 0.001 and p < 0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07, p < 0.001; male: OR 2.08, p = 0.015; BMI: OR 0.93, p = 0.057; current or past smoker: OR 5.60, p < 0.001; lung SUVR: OR 1.13, p < 0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06, p < 0.001; male: OR 1.87, p = 0.045; BMI: OR 0.93, p = 0.010; current or past smoker: OR 4.78, p < 0.001; lung MH index: OR 1.33, p < 0.001). In the validation data, combined prediction models 1 and 2 exhibited very good discrimination [area under the receiver operator curve (AUC): 0.867 and 0.901, respectively].

CONCLUSIONS

The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers to provide information independent of the clinical parameters, related to lung cancer risk.

[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.

Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study

Background

Patients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to develop and compare machine learning models to identify future diagnoses of COPD combined with NSCLC patients based on the patient's disease and routine clinical data.

Methods

Data were obtained from 237 patients with COPD combined with NSCLC as well as NSCLC admitted to Ningxia Hui Autonomous Region People's Hospital from October 2013 to July 2022. Six machine learning algorithms (K-nearest neighbor, logistic regression, eXtreme gradient boosting, support vector machine, naïve Bayes, and artificial neural network) were used to develop prediction models for NSCLC combined with COPD. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, Mathews correlation coefficient (MCC), Kappa, area under the receiver operating characteristic curve (AUROC)and area under the precision-recall curve (AUPRC) were used as performance indicators to evaluate the performance of the models.

Results

135 patients with NSCLC combined with COPD, 102 patients with NSCLC were included in the study. The results showed that pulmonary function and emphysema were important risk factors and that the support vector machine-based identification model showed optimal performance with accuracy:0.946, recall:0.940, specificity:0.955, precision:0.972, npv:0.920, F1 score:0.954, MCC:0.893, Kappa:0.888, AUROC:0.975, AUPRC:0.987.

Conclusion

The use of machine learning tools combining clinical symptoms and routine examination data features is suitable for identifying the risk of concurrent NSCLC in COPD patients.

Lung cancer screening an asbestos exposed population: Existing lung cancer risk criteria are not sufficient

BACKGROUND AND OBJECTIVE

Asbestos is a major risk factor for lung cancer, with or without tobacco smoke exposure. Low dose computed tomography (LDCT) screening for early lung cancer is effective but only when targeting high risk populations. This study aimed to analyse the effectiveness of LDCT screening in an asbestos exposed population and to compare lung cancer screening program (LCSP) eligibility criteria.

METHODS

Participants in an asbestos health surveillance program, the Western Australia Asbestos Review Program, underwent at least one LDCT scan and lung function assessment as part of annual review between 2012 and 2017. Lung cancer cases were confirmed through linkage to the WA cancer registry. Theoretical eligibility for different screening programs was calculated.

RESULTS

Five thousand seven hundred and two LDCT scans were performed on 1743 individuals. The median age was 69.8 years, 1481 (85.0%) were male and 1147 (65.8%) were ever-smokers (median pack-year exposure of 20.0). Overall, 26 lung cancers were detected (1.5% of the population; 3.5 cases per 1000 person-years of observation). Lung cancer was early stage in 86.4% and four (15.4%) cases were never smokers. Based on current lung screening program criteria, 1299 (74.5%) of this population, including the majority (17, 65.4%) of lung cancer cases, would not have been eligible for any LCSP.

CONCLUSION

This population is at raised risk despite modest tobacco exposure. LDCT screening is effective at identifying early-stage lung cancer in this population and existing lung cancer risk criteria do not capture this population adequately.

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.

The Journey for Lung Cancer Screening where we Stand Today

Background

Lung cancer remains a leading cause of cancer mortality worldwide with many patients presenting with advanced disease.

Objective

We reviewed the available literature for lung cancer screening using low dose computed tomography (LDCT). We reviewed the National Lung Screening Trial (NLST), Early Lung Cancer Action Program (ELCAP) and the (Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trials. We also look at different lung cancer risk prediction models that may aid in identifying target populations and also discuss the cost-effectiveness of LDCT screening in different groups of smokers and ex-smokers. Lastly, we discuss recent guideline changes that have occurred in line with new and emerging evidence on lung cancer screening.

Conclusion

LDCT has been shown reduce lung cancer mortality in certain groups of current and former smokers and should be considered to help in the early diagnosis of lung cancer.

Lung cancer: Premalignant biology and medical prevention

Lung cancer (both adenocarcinoma and squamous cell) progress through a series of pre-malignant histologic changes before the development of invasive disease. Each of these carcinogenic cascades is defined by genetic and epigenetic alterations in pulmonary epithelial cells. Additionally, alterations in the immune response, progenitor cell function, mutational burden, and microenvironmental mediated survival of mutated clones contribute to the risk of pre-malignant lesions progressing to cancer. Medical preventions studies have been completed and current and future trials are informed by the improved understanding of pre-malignancy. This will lead to precision chemoprevention trials based on lesional biology and histologic characteristics.

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.

Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data

Rationale: Most lung cancers are diagnosed at an advanced stage. Presymptomatic identification of high-risk individuals can prompt earlier intervention and improve long-term outcomes. Objectives: To develop a model to predict a future diagnosis of lung cancer on the basis of routine clinical and laboratory data by using machine learning. Methods: We assembled data from 6,505 case patients with non-small cell lung cancer (NSCLC) and 189,597 contemporaneous control subjects and compared the accuracy of a novel machine learning model with a modified version of the well-validated 2012 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial risk model (mPLCOm2012), by using the area under the receiver operating characteristic curve (AUC), sensitivity, and diagnostic odds ratio (OR) as measures of model performance. Measurements and Main Results: Among ever-smokers in the test set, a machine learning model was more accurate than the mPLCOm2012 for identifying NSCLC 9-12 months before clinical diagnosis (P < 0.00001) and demonstrated an AUC of 0.86, a diagnostic OR of 12.3, and a sensitivity of 40.1% at a predefined specificity of 95%. In comparison, the mPLCOm2012 demonstrated an AUC of 0.79, an OR of 7.4, and a sensitivity of 27.9% at the same specificity. The machine learning model was more accurate than standard eligibility criteria for lung cancer screening and more accurate than the mPLCOm2012 when applied to a screening-eligible population. Influential model variables included known risk factors and novel predictors such as white blood cell and platelet counts. Conclusions: A machine learning model was more accurate for early diagnosis of NSCLC than either standard eligibility criteria for screening or the mPLCOm2012, demonstrating the potential to help prevent lung cancer deaths through early detection.

Molecular approaches to lung cancer prevention

Lung cancer is generally diagnosed at advanced stages when surgical resection is not possible. Late diagnosis, along with development of chemoresistance, results in high mortality. Preventive approaches, including smoking cessation, chemoprevention and early detection are needed to improve survival. Smoking cessation combined with low-dose computed tomography screening has modestly improved survival. Chemoprevention has also shown some promise. Despite these successes, most lung cancer cases remain undetected until advanced stages. Additional early detection strategies may further improve survival and treatment outcome. Molecular alterations taking place during lung carcinogenesis have the potential to be used in early detection via noninvasive methods and may also serve as biomarkers for success of chemopreventive approaches. This review focuses on the utilization of molecular biomarkers to increase the efficacy of various preventive approaches.

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

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

Progress on pivotal role and application of exosome in lung cancer carcinogenesis, diagnosis, therapy and prognosis

Lung cancer is often diagnosed at an advanced stage and has a poor prognosis. Conventional treatments are not effective for metastatic lung cancer therapy. Although some of molecular targets have been identified with favorable response, those targets cannot be exploited due to the lack of suitable drug carriers. Lung cancer cell-derived exosomes (LCCDEs) receive recent interest in its role in carcinogenesis, diagnosis, therapy, and prognosis of lung cancer due to its biological functions and natural ability to carry donor cell biomolecules. LCCDEs can promote cell proliferation and metastasis, affect angiogenesis, modulate antitumor immune responses during lung cancer carcinogenesis, regulate drug resistance in lung cancer therapy, and be now considered an important component in liquid biopsy assessments for detecting lung cancer. Therapeutic deliverable exosomes are emerging as promising drug delivery agents specifically to tumor high precision medicine because of their natural intercellular communication role, excellent biocompatibility, low immunogenicity, low toxicity, long blood circulation ability, biodegradable characteristics, and their ability to cross various biological barriers. Several studies are currently underway to develop novel diagnostic and prognostic modalities using LCCDEs, and to develop methods of exploiting exosomes for use as efficient drug delivery vehicles. Current status of lung cancer and extensive applicability of LCCDEs are illustrated in this review. The promising data and technologies indicate that the approach on LCCDEs implies the potential application of LCCDEs to clinical management of lung cancer patients.

Extracellular vesicles: novel communicators in lung diseases

The lung is the organ with the highest vascular density in the human body. It is therefore perceivable that the endothelium of the lung contributes significantly to the circulation of extracellular vesicles (EVs), which include exosomes, microvesicles, and apoptotic bodies. In addition to the endothelium, EVs may arise from alveolar macrophages, fibroblasts and epithelial cells. Because EVs harbor cargo molecules, such as miRNA, mRNA, and proteins, these intercellular communicators provide important insight into the health and disease condition of donor cells and may serve as useful biomarkers of lung disease processes. This comprehensive review focuses on what is currently known about the role of EVs as markers and mediators of lung pathologies including COPD, pulmonary hypertension, asthma, lung cancer and ALI/ARDS. We also explore the role EVs can potentially serve as therapeutics for these lung diseases when released from healthy progenitor cells, such as mesenchymal stem cells.

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.

Technical advance in targeted NGS analysis enables identification of lung cancer risk-associated low frequency TP53, PIK3CA, and BRAF mutations in airway epithelial cells

BACKGROUND

Standardized Nucleic Acid Quantification for SEQuencing (SNAQ-SEQ) is a novel method that utilizes synthetic DNA internal standards spiked into each sample prior to next generation sequencing (NGS) library preparation. This method was applied to analysis of normal appearing airway epithelial cells (AEC) obtained by bronchoscopy in an effort to define a somatic mutation field effect associated with lung cancer risk. There is a need for biomarkers that reliably detect those at highest lung cancer risk, thereby enabling more effective screening by annual low dose CT. The purpose of this study was to test the hypothesis that lung cancer risk is characterized by increased prevalence of low variant allele frequency (VAF) somatic mutations in lung cancer driver genes in AEC.

METHODS

Synthetic DNA internal standards (IS) were prepared for 11 lung cancer driver genes and mixed with each AEC genomic (g) DNA specimen prior to competitive multiplex PCR amplicon NGS library preparation. A custom Perl script was developed to separate IS reads and respective specimen gDNA reads from each target into separate files for parallel variant frequency analysis. This approach identified nucleotide-specific sequencing error and enabled reliable detection of specimen mutations with VAF as low as 5 × 10- 4 (0.05%). This method was applied in a retrospective case-control study of AEC specimens collected by bronchoscopic brush biopsy from the normal airways of 19 subjects, including eleven lung cancer cases and eight non-cancer controls, and the association of lung cancer risk with AEC driver gene mutations was tested.

RESULTS

TP53 mutations with 0.05-1.0% VAF were more prevalent (p < 0.05) and also enriched for tobacco smoke and age-associated mutation signatures in normal AEC from lung cancer cases compared to non-cancer controls matched for smoking and age. Further, PIK3CA and BRAF mutations in this VAF range were identified in AEC from cases but not controls.

CONCLUSIONS

Application of SNAQ-SEQ to measure mutations in the 0.05-1.0% VAF range enabled identification of an AEC somatic mutation field of injury associated with lung cancer risk. A biomarker comprising TP53, PIK3CA, and BRAF somatic mutations may better stratify individuals for optimal lung cancer screening and prevention outcomes.

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.

Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine

BACKGROUND

Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate.

OBJECTIVE

The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of new incident lung cancer within the next 1 year in the general population.

METHODS

Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients with at least one EHR between April 1, 2016, and March 31, 2018, who had no history of lung cancer. A retrospective cohort (N=873,598) and a prospective cohort (N=836,659) were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018.

RESULTS

The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer.

CONCLUSIONS

We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance.

Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models

Introduction

Low-dose CT (LDCT) is currently used in lung cancer screening of high-risk populations for early lung cancer diagnosis. However, 96% of individuals with detected nodules are false positives.

Methods

In order to develop an efficient early lung cancer predictor from clinical, demographic and LDCT features, we studied a total of 218 subjects with lung cancer or benign nodules. Probabilistic graphical models (PGMs) were used to integrate demographics, clinical data and LDCT features from 92 subjects (training cohort) from the Pittsburgh Lung Screening Study cohort.

Results

Learnt PGMs identified three variables directly (causally) linked to malignant nodules and the largest benign nodule and used them to build the Lung Cancer Causal Model (LCCM), which was validated in a separate cohort of 126 subjects. Nodule and vessel numbers and years since the subject quit smoking were sufficient to discriminate malignant from benign nodules. Comparison with existing predictors in the training and validation cohorts showed that (1) incorporating LDCT scan features greatly enhances predictive accuracy; and (2) LCCM improves cancer detection over existing methods, including the Brock parsimonious model (p<0.001). Notably, the number of surrounding vessels, a feature not previously used in predictive models, significantly improves predictive efficiency. Based on the validation cohort results, LCCM is able to identify 30% of the benign nodules without risk of misclassifying cancer nodules.

Discussion

LCCM shows promise as a lung cancer predictor as it is significantly improved over existing models. Validated in a larger, prospective study, it may help reduce unnecessary follow-up visits and procedures.

Risk prediction models for lung cancer: Perspectives and dissemination

Objective

The objective was to systematically assess lung cancer risk prediction models by critical evaluation of methodology, transparency and validation in order to provide a direction for future model development.

Methods

Electronic searches (including PubMed, EMbase, the Cochrane Library, Web of Science, the China National Knowledge Infrastructure, Wanfang, the Chinese BioMedical Literature Database, and other official cancer websites) were completed with English and Chinese databases until April 30th, 2018. Main reported sources were input data, assumptions and sensitivity analysis. Model validation was based on statements in the publications regarding internal validation, external validation and/or cross-validation.

Results

Twenty-two studies (containing 11 multiple-use and 11 single-use models) were included. Original models were developed between 2003 and 2016. Most of these were from the United States. Multivariate logistic regression was widely used to identify a model. The minimum area under the curve for each model was 0.57 and the largest was 0.87. The smallest C statistic was 0.59 and the largest 0.85. Six studies were validated by external validation and three were cross-validated. In total, 2 models had a high risk of bias, 6 models reported the most used variables were age and smoking duration, and 5 models included family history of lung cancer.

Conclusions

The prediction accuracy of the models was high overall, indicating that it is feasible to use models for high-risk population prediction. However, the process of model development and reporting is not optimal with a high risk of bias. This risk affects prediction accuracy, influencing the promotion and further development of the model. In view of this, model developers need to be more attentive to bias risk control and validity verification in the development of models.

A multi-parameterized artificial neural network for lung cancer risk prediction

The objective of this study is to train and validate a multi-parameterized artificial neural network (ANN) based on personal health information to predict lung cancer risk with high sensitivity and specificity. The 1997-2015 National Health Interview Survey adult data was used to train and validate our ANN, with inputs: gender, age, BMI, diabetes, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. We identified 648 cancer and 488,418 non-cancer cases. For the training set the sensitivity was 79.8% (95% CI, 75.9%-83.6%), specificity was 79.9% (79.8%-80.1%), and AUC was 0.86 (0.85-0.88). For the validation set sensitivity was 75.3% (68.9%-81.6%), specificity was 80.6% (80.3%-80.8%), and AUC was 0.86 (0.84-0.89). Our results indicate that the use of an ANN based on personal health information gives high specificity and modest sensitivity for lung cancer detection, offering a cost-effective and non-invasive clinical tool for risk stratification.

RNAi-based therapeutics for lung cancer: biomarkers, microRNAs, and nanocarriers

INTRODUCTION

Despite the current advances in the discovery of the lung cancer biomarkers and, consequently, in the diagnosis, this pathology continues to be the primary cause of cancer-related death worldwide. In most cases, the illness is diagnosed in an advanced stage, which limits the current treatment options available and reduces the survival rate. Therefore, RNAi-based therapy arises as a promising option to treat lung cancer.

AREAS COVERED

This review provides an overview on the exploitation of lung cancer biology to develop RNAi-based therapeutics to be applied in the treatment of lung cancer. Furthermore, the review analyzes the main nanocarriers designed to deliver RNAi molecules and induce antitumoral effects in lung cancer, and provides updated information about current RNAi-based therapeutics for lung cancer in clinical trials.

EXPERT OPINION

RNAi-based therapy uses nanocarriers to perform a targeted and efficient delivery of therapeutic genes into lung cancer cells, by taking advantage of the known biomarkers in lung cancer. These therapeutic genes are key regulatory molecules of crucial cellular pathways involved in cell proliferation, migration, and apoptosis. Thereby, the characteristics and functionalization of the nanocarrier and the knowledge of lung cancer biology have direct influence in improving the therapeutic effect of this therapy.

Drug response prediction model using a hierarchical structural component modeling method

BACKGROUND

Component-based structural equation modeling methods are now widely used in science, business, education, and other fields. This method uses unobservable variables, i.e., "latent" variables, and structural equation model relationships between observable variables. Here, we applied this structural equation modeling method to biologically structured data. To identify candidate drug-response biomarkers, we first used proteomic peptide-level data, as measured by multiple reaction monitoring mass spectrometry (MRM-MS), for liver cancer patients. MRM-MS is a highly sensitive and selective method for proteomic targeted quantitation of peptide abundances in complex biological samples.

RESULTS

We developed a component-based drug response prediction model, having the advantage that it first combines collapsed peptide-level data into protein-level information, facilitating subsequent biological interpretation. Our model also uses an alternating least squares algorithm, to efficiently estimate both coefficients of peptides and proteins. This approach also considers correlations between variables, without constraint, by a multiple testing problem. Using estimated peptide and protein coefficients, we selected significant protein biomarkers by permutation testing, resulting in our model for predicting liver cancer response to the tyrosine kinase inhibitor sorafenib.

CONCLUSIONS

Using data from a cohort of liver cancer patients, we then "fine-tuned" our model to successfully predict drug responses, as demonstrated by a high area under the curve (AUC) score. Such drug response prediction models may eventually find clinical translation in identifying individual patients likely to respond to specific therapies.

Blood based biomarkers beyond genomics for lung cancer screening

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Associations between expression levels of nucleotide excision repair proteins in lymphoblastoid cells and risk of squamous cell carcinoma of the head and neck

Squamous cell carcinoma of head and neck (SCCHN) is one of the most common malignancies worldwide, and nucleotide excision repair (NER) is involved in SCCHN susceptibility. In this analysis of 349 newly diagnosed SCCHN patients and 295 cancer-free controls, we investigated whether expression levels of eight core NER proteins were associated with risk of SCCHN. We quantified NER protein expression levels in cultured peripheral lymphocytes using a reverse-phase protein microarray. Compared with the controls, SCCHN patients had statistically significantly lower expression levels of ERCC3 and XPA (P = 0.001 and 0.001, respectively). After dividing the subjects by controls' median values of expression levels, we found a dose-dependent association between an increased risk of SCCHN and low expression levels of ERCC3 (adjusted OR, 1.75, and 95% CI: 1.26-2.42; P_trend = 0.008) and XPA (adjusted OR, 1.88; 95% CI, 1.35-2.60; P_trend = 0.001). We also identified a significant multiplicative interaction between smoking status and ERCC3 expression levels (P = 0.014). Finally, after integrating demographic and clinical variables, we found that the addition of ERCC3 and XPA expression levels to the model significantly improved the sensitivity of the expanded model on SCCHN risk. In conclusion, reduced protein expression levels of ERCC3 and XPA were associated with an increased risk of SCCHN. However, these results need to be confirmed in additional large studies.

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

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.

Lung cancer screening-low dose CT for lung cancer screening: recent trial results and next steps

Screening for lung cancer using low-dose CT has already been implemented in North America following the results of the National Lung Screening Trial. Outside North America, clinicians and researchers are addressing issues that may have a major impact on the success of screening programmes by reviewing results of existing trials and by designing new research and pilot programmes. This review summarizes the work that has been done to try to answer the remaining questions and highlights potential barriers which may affect screening uptake and cost-effectiveness.

Reduced mRNA expression of nucleotide excision repair genes in lymphocytes and risk of squamous cell carcinoma of the head and neck

Nucleotide excision repair (NER) plays a critical role in the development of smoking-related cancers. We hypothesize that mRNA expression levels of NER genes are associated with risk of the squamous cell carcinoma of head and neck (SCCHN). To test this hypothesis, we conducted a case-control study of 260 SCCHN patients and 246 cancer-free controls by measuring the mRNA expression levels of eight core NER genes in cultured peripheral lymphocytes. Compared with the controls, cases had statistically significantly lower expression levels of DDB1 and ERCC3 (P = 0.015 and 0.041, respectively). Because DDB1 and ERCC3 expression levels were highly correlated, we used DDB1 for further multivariate analyses and modeling. After dividing the subjects by controls' quartiles of expression levels, we found an association between an increased risk of SCCHN and low DDB1 expression levels [adjusted ORs and 95% CIs: 1.92 and 1.11-3.32, 1.48 and 0.85-2.59, 2.00 and 1.15-3.45 for the 2nd-4th quartiles, respectively, compared with the 1st quartile; Ptrend = 0.026]. We also identified a multiplicative interaction between sex and DDB1 expression levels (P = 0.007). Finally, the expanded model with gene expression levels, in addition to demographic and clinical variables, on SCCHN risk was significantly improved, especially among men. In conclusion, reduced DDB1 expression levels were associated with an increased risk of SCCHN. However, these results need to be validated in larger studies.

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

BACKGROUND

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

METHODS AND FINDINGS

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

CONCLUSIONS

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

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

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

Oral Cell DNA Adducts as Potential Biomarkers for Lung Cancer Susceptibility in Cigarette Smokers

This perspective considers the use of oral cell DNA adducts, together with exposure and genetic information, to potentially identify those cigarette smokers at highest risk for lung cancer, so that appropriate preventive measures could be initiated at a relatively young age before too much damage has been done. There are now well established and validated analytical methods for the quantitation of urinary and serum metabolites of tobacco smoke toxicants and carcinogens. These metabolites provide a profile of exposure and in some cases lung cancer risk, but they do not yield information on the critical DNA damage parameter that leads to mutations in cancer growth control genes such as KRAS and TP53. Studies demonstrate a correlation between changes in the oral cavity and lung in cigarette smokers, due to the field effect of tobacco smoke. Oral cell DNA is readily obtained in contrast to DNA samples from the lung. Studies in which oral cell DNA and salivary DNA have been analyzed for specific DNA adducts are reviewed; some of the adducts identified have also been previously reported in lung DNA from smokers. The multiple challenges of developing a panel of oral cell DNA adducts that could be routinely quantified by mass spectrometry are discussed.

Non-invasive lung cancer diagnosis by detection of GATA6 and NKX2-1 isoforms in exhaled breath condensate

Lung cancer (LC) is the leading cause of cancer‐related deaths worldwide. Early LC diagnosis is crucial to reduce the high case fatality rate of this disease. In this case–control study, we developed an accurate LC diagnosis test using retrospectively collected formalin‐fixed paraffin‐embedded (FFPE) human lung tissues and prospectively collected exhaled breath condensates (EBCs). Following international guidelines for diagnostic methods with clinical application, reproducible standard operating procedures (SOP) were established for every step comprising our LC diagnosis method. We analyzed the expression of distinct mRNAs expressed from GATA6 and NKX2‐1, key regulators of lung development. The Em/Ad expression ratios of GATA6 and NKX2‐1 detected in EBCs were combined using linear kernel support vector machines (SVM) into the LC score, which can be used for LC detection. LC score‐based diagnosis achieved a high performance in an independent validation cohort. We propose our method as a non‐invasive, accurate, and low‐price option to complement the success of computed tomography imaging (CT) and chest X‐ray (CXR) for LC diagnosis.

Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network

INTRODUCTION

Identifying high-risk lung cancer individuals at an early disease stage is the most effective way of improving survival. The landmark National Lung Screening Trial (NLST) demonstrated the utility of low-dose computed tomography (LDCT) imaging to reduce mortality (relative to X-ray screening). As a result of the NLST and other studies, imaging-based lung cancer screening programs are now being implemented. However, LDCT interpretation results in a high number of false positives. A set of dynamic Bayesian networks (DBN) were designed and evaluated to provide insight into how longitudinal data can be used to help inform lung cancer screening decisions.

METHODS

The LDCT arm of the NLST dataset was used to build and explore five DBNs for high-risk individuals. Three of these DBNs were built using a backward construction process, and two using structure learning methods. All models employ demographics, smoking status, cancer history, family lung cancer history, exposure risk factors, comorbidities related to lung cancer, and LDCT screening outcome information. Given the uncertainty arising from lung cancer screening, a cancer state-space model based on lung cancer staging was utilized to characterize the cancer status of an individual over time. The models were evaluated on balanced training and test sets of cancer and non-cancer cases to deal with data imbalance and overfitting.

RESULTS

Results were comparable to expert decisions. The average area under the curve (AUC) of the receiver operating characteristic (ROC) for the three intervention points of the NLST trial was higher than 0.75 for all models. Evaluation of the models on the complete LDCT arm of the NLST dataset (N=25,486) demonstrated satisfactory generalization. Consensus of predictions over similar cases is reported in concordance statistics between the models' and the physicians' predictions. The models' predictive ability with respect to missing data was also evaluated with the sample of cases that missed the second screening exam of the trial (N=417). The DBNs outperformed comparison models such as logistic regression and naïve Bayes.

CONCLUSION

The lung cancer screening DBNs demonstrated high discrimination and predictive power with the majority of cancer and non-cancer cases.

Biomarker development in the precision medicine era: lung cancer as a case study

Precision medicine relies on validated biomarkers with which to better classify patients by their probable disease risk, prognosis and/or response to treatment. Although affordable 'omics'-based technology has enabled faster identification of putative biomarkers, the validation of biomarkers is still stymied by low statistical power and poor reproducibility of results. This Review summarizes the successes and challenges of using different types of molecule as biomarkers, using lung cancer as a key illustrative example. Efforts at the national level of several countries to tie molecular measurement of samples to patient data via electronic medical records are the future of precision medicine research.

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.

The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer

BACKGROUND

Lung cancer kills more people than any other cancer in the UK (5-year survival < 13%). Early diagnosis can save lives. The USA-based National Lung Cancer Screening Trial reported a 20% relative reduction in lung cancer mortality and 6.7% all-cause mortality in low-dose computed tomography (LDCT)-screened subjects.

OBJECTIVES

To (1) analyse LDCT lung cancer screening in a high-risk UK population, determine optimum recruitment, screening, reading and care pathway strategies; and (2) assess the psychological consequences and the health-economic implications of screening.

DESIGN

A pilot randomised controlled trial comparing intervention with usual care. A population-based risk questionnaire identified individuals who were at high risk of developing lung cancer (≥ 5% over 5 years).

SETTING

Thoracic centres with expertise in lung cancer imaging, respiratory medicine, pathology and surgery: Liverpool Heart & Chest Hospital, Merseyside, and Papworth Hospital, Cambridgeshire.

PARTICIPANTS

Individuals aged 50-75 years, at high risk of lung cancer, in the primary care trusts adjacent to the centres.

INTERVENTIONS

A thoracic LDCT scan. Follow-up computed tomography (CT) scans as per protocol. Referral to multidisciplinary team clinics was determined by nodule size criteria.

MAIN OUTCOME MEASURES

Population-based recruitment based on risk stratification; management of the trial through web-based database; optimal characteristics of CT scan readers (radiologists vs. radiographers); characterisation of CT-detected nodules utilising volumetric analysis; prevalence of lung cancer at baseline; sociodemographic factors affecting participation; psychosocial measures (cancer distress, anxiety, depression, decision satisfaction); and cost-effectiveness modelling.

RESULTS

A total of 247,354 individuals were approached to take part in the trial; 30.7% responded positively to the screening invitation. Recruitment of participants resulted in 2028 in the CT arm and 2027 in the control arm. A total of 1994 participants underwent CT scanning: 42 participants (2.1%) were diagnosed with lung cancer; 36 out of 42 (85.7%) of the screen-detected cancers were identified as stage 1 or 2, and 35 (83.3%) underwent surgical resection as their primary treatment. Lung cancer was more common in the lowest socioeconomic group. Short-term adverse psychosocial consequences were observed in participants who were randomised to the intervention arm and in those who had a major lung abnormality detected, but these differences were modest and temporary. Rollout of screening as a service or design of a full trial would need to address issues of outreach. The health-economic analysis suggests that the intervention could be cost-effective but this needs to be confirmed using data on actual lung cancer mortality.

CONCLUSIONS

The UK Lung Cancer Screening (UKLS) pilot was successfully undertaken with 4055 randomised individuals. The data from the UKLS provide evidence that adds to existing data to suggest that lung cancer screening in the UK could potentially be implemented in the 60-75 years age group, selected via the Liverpool Lung Project risk model version 2 and using CT volumetry-based management protocols.

FUTURE WORK

The UKLS data will be pooled with the NELSON (Nederlands Leuvens Longkanker Screenings Onderzoek: Dutch-Belgian Randomised Lung Cancer Screening Trial) and other European Union trials in 2017 which will provide European mortality and cost-effectiveness data. For now, there is a clear need for mortality results from other trials and further research to identify optimal methods of implementation and delivery. Strategies for increasing uptake and providing support for underserved groups will be key to implementation.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN78513845.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 20, No. 40. See the NIHR Journals Library website for further project information.

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.

Epidemiology of Lung Cancer

Lung cancer continues to be one of the most common causes of cancer death despite understanding the major cause of the disease: cigarette smoking. Smoking increases lung cancer risk 5- to 10-fold with a clear dose-response relationship. Exposure to environmental tobacco smoke among nonsmokers increases lung cancer risk about 20%. Risks for marijuana and hookah use, and the new e-cigarettes, are yet to be consistently defined and will be important areas for continued research as use of these products increases. Other known environmental risk factors include exposures to radon, asbestos, diesel, and ionizing radiation. Host factors have also been associated with lung cancer risk, including family history of lung cancer, history of chronic obstructive pulmonary disease and infections. Studies to identify genes associated with lung cancer susceptibility have consistently identified chromosomal regions on 15q25, 6p21 and 5p15 associated with lung cancer risk. Risk prediction models for lung cancer typically include age, sex, cigarette smoking intensity and/or duration, medical history, and occupational exposures, however there is not yet a risk prediction model currently recommended for general use. As lung cancer screening becomes more widespread, a validated model will be needed to better define risk groups to inform screening guidelines.