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

The HUNT lung-SNP model: genetic variants plus clinical variables improve lung cancer risk assessment over clinical models

PURPOSE

The HUNT Lung Cancer Model (HUNT LCM) predicts individualized 6-year lung cancer (LC) risk among individuals who ever smoked cigarettes with high precision based on eight clinical variables. Can the performance be improved by adding genetic information?

METHODS

A polygenic model was developed in the prospective Norwegian HUNT2 study with clinical and genotype data of individuals who ever smoked cigarettes (n = 30749, median follow up 15.26 years) where 160 LC were diagnosed within six years. It included the variables of the original HUNT LCM plus 22 single nucleotide polymorphisms (SNPs) highly associated with LC. External validation was performed in the prospective Norwegian Tromsø Study (n = 2663).

RESULTS

The novel HUNT Lung-SNP model significantly improved risk ranking of individuals over the HUNT LCM in both HUNT2 (p < 0.001) and Tromsø (p < 0.05) cohorts. Furthermore, detection rate (number of participants selected to detect one LC case) was significantly better for the HUNT Lung-SNP vs. HUNT LCM in both cohorts (42 vs. 48, p = 0.003 and 11 vs. 14, p = 0.025, respectively) as well as versus the NLST, NELSON and 2021 USPSTF criteria. The area under the receiver operating characteristic curve (AUC) was higher for the HUNT Lung-SNP in both cohorts, but significant only in HUNT2 (AUC 0.875 vs. 0.844, p < 0.001). However, the integrated discrimination improvement index (IDI) indicates a significant improvement of LC risk stratification by the HUNT Lung-SNP in both cohorts (IDI 0.019, p < 0.001 (HUNT2) and 0.013, p < 0.001 (Tromsø)).

CONCLUSION

The HUNT Lung-SNP model could have a clinical impact on LC screening and has the potential to replace the HUNT LCM as well as the NLST, NELSON and 2021 USPSTF criteria in a screening setting. However, the model should be further validated in other populations and evaluated in a prospective trial setting.

Polygenic inheritance and its interplay with smoking history in predicting lung cancer diagnosis: a French-Canadian case-control cohort

BACKGROUND

The most near-term clinical application of genome-wide association studies in lung cancer is a polygenic risk score (PRS).

METHODS

A case-control dataset was generated consisting of 4002 lung cancer cases from the LORD project and 20,010 ethnically matched controls from CARTaGENE. A genome-wide PRS including >1.1 million genetic variants was derived and validated in UK Biobank (n = 5419 lung cancer cases). The predictive ability and diagnostic discrimination performance of the PRS was tested in LORD/CARTaGENE and benchmarked against previous PRSs from the literature. Stratified analyses were performed by smoking status and genetic risk groups defined as low (<20th percentile), intermediate (20-80th percentile) and high (>80th percentile) PRS.

FINDINGS

The phenotypic variance explained and the effect size of the genome-wide PRS numerically outperformed previous PRSs. Individuals with high genetic risk had a 2-fold odds of lung cancer compared to low genetic risk. The PRS was an independent predictor of lung cancer beyond conventional clinical risk factors, but its diagnostic discrimination performance was incremental in an integrated risk model. Smoking increased the odds of lung cancer by 7.7-fold in low genetic risk and by 11.3-fold in high genetic risk. Smoking with high genetic risk was associated with a 17-fold increase in the odds of lung cancer compared to individuals who never smoked and with low genetic risk.

INTERPRETATION

Individuals at low genetic risk are not protected against the smoking-related risk of lung cancer. The joint multiplicative effect of PRS and smoking increases the odds of lung cancer by nearly 20-fold.

FUNDING

This work was supported by the CQDM and the IUCPQ Foundation owing to a generous donation from Mr. Normand Lord.

Exploring the Role of Vitamin D, Vitamin D-Dependent Proteins, and Vitamin D Receptor Gene Variation in Lung Cancer Risk

Lung cancer has an unfavorable prognosis with a rate of low overall survival, caused by the difficulty of diagnosis in the early stages and resistance to therapy. In recent years, there have been new therapies that use specific molecular targets and are effective in increasing the survival chances of advanced cancer. Therefore, it is necessary to find more specific biomarkers that can identify early changes in carcinogenesis and allow the earliest possible treatment. Vitamin D (VD) plays an important role in immunity and carcinogenesis. Furthermore, the vitamin D receptor (VDR) regulates the expression of various genes involved in the physiological functions of the human organism. The genes encoding the VDR are extremely polymorphic and vary greatly between human populations. To date, there are significant associations between VDR polymorphism and several types of cancer, but the data on the involvement of VDR polymorphism in lung cancer are still conflicting. Therefore, in this review, our aim was to investigate the relationship between VDR single-nucleotide polymorphisms in humans and the degree of risk for developing lung cancer. The studies showcased different gene polymorphisms to be associated with an increased risk of lung cancer: TaqI, ApaI, BsmI, FokI, and Cdx2. In addition, there is a strong positive correlation between VD deficiency and lung cancer development. Still, due to a lack of awareness, the assessment of VD status and VDR polymorphism is rarely considered for the prediction of lung cancer evolution and their clinical applicability, despite the fact that studies have shown the highest risk for lung cancer given by TaqI gene polymorphisms and that VDR polymorphisms are associated with more aggressive cancer evolution.

Missense variants in CYP4B1 associated with increased risk of lung cancer among Chinese Han population

INTRODUCTION

Understanding the etiology and risk factors of lung cancer (LC) is the key to developing scientific and effective prevention and control strategies for LC. CYP4B1 genetic polymorphism has been reported to be associated with susceptibility to various diseases. We aimed to explore the association between CYP4B1 genetic variants and LC susceptibility.

METHODS

One thousand three hundred thirty-nine participants were recruited to perform an association analysis through SNPStats online software. Statistical analysis of this study was mainly completed by SPSS 22.0 software. False-positive report probability analysis (FPRP) to detect whether the positive findings were noteworthy. Finally, the interaction of SNP-SNP in LC risk was evaluated by multi-factor dimensionality reduction.

RESULTS

We found evidence that missense variants in CYP4B1 (rs2297810, rs4646491, and rs2297809) are associated with LC susceptibility. In particular, genotype GA of CYP4B1-rs2297810 was significantly associated with an increased risk of LC in both overall and stratified analyses (genotype GA: OR (95% CI) = 1.35 (1.08-1.69), p = 0.010). CYP4B1-rs4646491 (overdominant: OR (95% CI) = 1.30 (1.04-1.62), p = 0.023) and CYP4B1-rs2297809 (genotype CT: OR (95% CI) = 1.26 (1.01-1.59), p = 0.046) are also associated with an increased risk of LC. FPRP analysis showed that all positive results in this study are noteworthy findings CONCLUSION: Three missense variants in CYP4B1 (rs2297810, rs4646491, and rs2297809) are associated with increasing risk of LC.

IPRS: Leveraging Gene-Environment Interaction to Reconstruct Polygenic Risk Score

Background: Polygenic risk score (PRS) is widely regarded as a predictor of genetic susceptibility to disease, applied to individuals to predict the risk of disease occurrence. When the gene-environment (G×E) interaction is considered, the traditional PRS prediction model directly uses PRS to interact with the environment without considering the interactions between each variant and environment, which may lead to prediction performance and risk stratification of complex diseases are not promising.

Methods: We developed a method called interaction PRS (iPRS), reconstructing PRS by leveraging G×E interactions. Two extensive simulations evaluated prediction performance, risk stratification, and calibration performance of the iPRS prediction model, and compared it with the traditional PRS prediction model. Real data analysis was performed using existing data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial study to predict genetic susceptibility, pack-years of smoking history, and G×E interactions in patients with lung cancer.

Results: Two extensive simulations indicated iPRS prediction model could improve the prediction performance of disease risk, the accuracy of risk stratification, and clinical calibration performance compared with the traditional PRS prediction model, especially when antagonism accounted for the majority of the interaction. PLCO real data analysis also suggested that the iPRS prediction model was superior to the PRS prediction model in predictive effect (p = 0.0205).

Conclusion: IPRS prediction model could have a good application prospect in predicting disease risk, optimizing the screening of high-risk populations, and improving the clinical benefits of preventive interventions among populations.

Association of smoking and polygenic risk with the incidence of lung cancer: a prospective cohort study

Background

Genetic variation increases the risk of lung cancer, but the extent to which smoking amplifies this effect remains unknown. Therefore, we aimed to investigate the risk of lung cancer in people with different genetic risks and smoking habits.

Methods

This prospective cohort study included 345,794 European ancestry participants from the UK Biobank and followed up for 7.2 [6.5–7.8] years.

Results

Overall, 26.2% of the participants were former smokers, and 9.8% were current smokers. During follow-up, 1687 (0.49%) participants developed lung cancer. High genetic risk and smoking were independently associated with an increased risk of incident lung cancer. Compared with never-smokers, HR per standard deviation of the PRS increase was 1.16 (95% CI, 1.11–1.22), and HR of heavy smokers (≥40 pack-years) was 17.89 (95% CI, 15.31–20.91). There were no significant interactions between the PRS and the smoking status or pack-years. Population-attributable fraction analysis showed that smoking cessation might prevent 76.4% of new lung cancers.

Conclusions

Both high genetic risk and smoking were independently associated with higher lung cancer risk, but the increased risk of smoking was much more significant than heredity. The combination of traditional risk factors and additional PRS provides realistic application prospects for precise prevention.

Genetic analysis of colorectal carcinoma using high throughput single nucleotide polymorphism genotyping technique within the population of Jammu and Kashmir

BACKGROUND

SNP genotyping has become increasingly more common place to understand the genetic basis of complex diseases like cancer. SNP-genotyping through MassARRAY™ is a cost-effective method to quantitatively analyse the variation of gene expression in multiple samples, making it a potential tool to identify the underlying causes of colorectal carcinogenesis.

METHODS

In the present study, SNP genotyping was carried out using Agena MassARRAY™, which is a cost-effective, robust, and sensitive method to analyse multiple SNPs simultaneously. We analysed 7 genes in 492 samples (100 cases and 392 controls) associated with CRC within the population of Jammu and Kashmir. These SNPs were selected based on their association with multiple cancers in literature.

RESULTS

This is the first study to explore these SNPs with colorectal cancer within the J&K population.7 SNPs with a call rate of 90% were selected for the study. Out of these, five SNPs rs2234593, rs1799966, rs2229080, rs8034191, rs1042522 were found to be significantly associated with the current study under the allelic model with an Odds Ratio OR = 2.981(1.731-5.136 at 95% CI); p value = 4.81E-05 for rs2234593,OR = 1.685(1.073-2.647 at 95% CI);; p value = 0.02292 for rs1799966, OR = 1.5 (1.1-2.3 at 95% CI), p value = 0.02 for rs2229080, OR = 1.699(1.035-2.791 at 95% CI); p value = 0.03521 for rs8034191, OR = 20.07 (11.26-35.75); p value = 1.84E-34 for rs1042522 respectively.

CONCLUSION

This is the first study to find the relation of Genetic variants with the colorectal cancer within the studied population using high throughput MassARRAY™ technology. It is further anticipated that the variants should be evaluated in other population groups that may aid in understanding the genetic complexity and bridge the missing heritability.

A Bayesian linear mixed model for prediction of complex traits

MOTIVATION

Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance.

RESULTS

We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive.

AVAILABILITYAND IMPLEMENTATION

The R-package is available at https://github.com/yhai943/BLMM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assessing Lung Cancer Absolute Risk Trajectory Based on a Polygenic Risk Model

Lung cancer is the leading cause of cancer-related death globally. An improved risk stratification strategy can increase efficiency of low-dose CT (LDCT) screening. Here we assessed whether individual's genetic background has clinical utility for risk stratification in the context of LDCT screening. On the basis of 13,119 patients with lung cancer and 10,008 controls with European ancestry in the International Lung Cancer Consortium, we constructed a polygenic risk score (PRS) via 10-fold cross-validation with regularized penalized regression. The performance of risk model integrating PRS, including calibration and ability to discriminate, was assessed using UK Biobank data (N = 335,931). Absolute risk was estimated on the basis of age-specific lung cancer incidence and all-cause mortality as competing risk. To evaluate its potential clinical utility, the PRS distribution was simulated in the National Lung Screening Trial (N = 50,772 participants). The lung cancer ORs for individuals at the top decile of the PRS distribution versus those at bottom 10% was 2.39 [95% confidence interval (CI) = 1.92-3.00; P = 1.80 × 10-14] in the validation set (P trend = 5.26 × 10-20). The OR per SD of PRS increase was 1.26 (95% CI = 1.20-1.32; P = 9.69 × 10-23) for overall lung cancer risk in the validation set. When considering absolute risks, individuals at different PRS deciles showed differential trajectories of 5-year and cumulative absolute risk. The age reaching the LDCT screening recommendation threshold can vary by 4 to 8 years, depending on the individual's genetic background, smoking status, and family history. Collectively, these results suggest that individual's genetic background may inform the optimal lung cancer LDCT screening strategy. SIGNIFICANCE: Three large-scale datasets reveal that, after accounting for risk factors, an individual's genetics can affect their lung cancer risk trajectory, thus may inform the optimal timing for LDCT screening.

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.

Epigenetic Inactivation of the Tumor Suppressor IRX1 Occurs Frequently in Lung Adenocarcinoma and Its Silencing Is Associated with Impaired Prognosis

Iroquois homeobox (IRX) encodes members of homeodomain containing genes which are involved in development and differentiation. Since it has been reported that the IRX1 gene is localized in a lung cancer susceptibility locus, the epigenetic regulation and function of IRX1 was investigated in lung carcinogenesis. We observed frequent hypermethylation of the IRX1 promoter in non-small cell lung cancer (NSCLC) compared to small cell lung cancer (SCLC). Aberrant IRX1 methylation was significantly correlated with reduced IRX1 expression. In normal lung samples, the IRX1 promoter showed lower median DNA methylation levels (<10%) compared to primary adenocarcinoma (ADC, 22%) and squamous cell carcinoma (SQCC, 14%). A significant hypermethylation and downregulation of IRX1 was detected in ADC and SQCC compared to matching normal lung samples (p < 0.0001). Low IRX1 expression was significantly correlated with impaired prognosis of ADC patients (p = 0.001). Reduced survival probability was also associated with higher IRX1 promoter methylation (p = 0.02). Inhibition of DNA methyltransferase (DNMT) activity reactivated IRX1 expression in human lung cancer cell lines. Induced DNMT3A and EZH2 expression was correlated with downregulation of IRX1. On the cellular level, IRX1 exhibits nuclear localization and expression of IRX1 induced fragmented nuclei in cancer cells. Localization of IRX1 and induction of aberrant nuclei were dependent on the presence of the homeobox of IRX1. By data mining, we showed that IRX1 is negatively correlated with oncogenic pathways and IRX1 expression induces the proapoptotic regulator BAX. In conclusion, we report that IRX1 expression is significantly associated with improved survival probability of ADC patients. IRX1 hypermethylation may serve as molecular biomarker for ADC diagnosis and prognosis. Our data suggest that IRX1 acts as an epigenetically regulated tumor suppressor in the pathogenesis of lung cancer.

High dimensional model representation of log likelihood ratio: binary classification with SNP data

BACKGROUND

Developing binary classification rules based on SNP observations has been a major challenge for many modern bioinformatics applications, e.g., predicting risk of future disease events in complex conditions such as cancer. Small-sample, high-dimensional nature of SNP data, weak effect of each SNP on the outcome, and highly non-linear SNP interactions are several key factors complicating the analysis. Additionally, SNPs take a finite number of values which may be best understood as ordinal or categorical variables, but are treated as continuous ones by many algorithms.

METHODS

We use the theory of high dimensional model representation (HDMR) to build appropriate low dimensional glass-box models, allowing us to account for the effects of feature interactions. We compute the second order HDMR expansion of the log-likelihood ratio to account for the effects of single SNPs and their pairwise interactions. We propose a regression based approach, called linear approximation for block second order HDMR expansion of categorical observations (LABS-HDMR-CO), to approximate the HDMR coefficients. We show how HDMR can be used to detect pairwise SNP interactions, and propose the fixed pattern test (FPT) to identify statistically significant pairwise interactions.

RESULTS

We apply LABS-HDMR-CO and FPT to synthetically generated HAPGEN2 data as well as to two GWAS cancer datasets. In these examples LABS-HDMR-CO enjoys superior accuracy compared with several algorithms used for SNP classification, while also taking pairwise interactions into account. FPT declares very few significant interactions in the small sample GWAS datasets when bounding false discovery rate (FDR) by 5%, due to the large number of tests performed. On the other hand, LABS-HDMR-CO utilizes a large number of SNP pairs to improve its prediction accuracy. In the larger HAPGEN2 dataset FTP declares a larger portion of SNP pairs used by LABS-HDMR-CO as significant.

CONCLUSION

LABS-HDMR-CO and FPT are interesting methods to design prediction rules and detect pairwise feature interactions for SNP data. Reliably detecting pairwise SNP interactions and taking advantage of potential interactions to improve prediction accuracy are two different objectives addressed by these methods. While the large number of potential SNP interactions may result in low power of detection, potentially interacting SNP pairs, of which many might be false alarms, can still be used to improve prediction accuracy.

Individual and joint contributions of genetic and methylation risk scores for enhancing lung cancer risk stratification: data from a population-based cohort in Germany

BACKGROUND

Risk stratification for lung cancer (LC) screening is so far mostly based on smoking history. This study aimed to assess if and to what extent such risk stratification could be enhanced by additional consideration of genetic risk scores (GRSs) and epigenetic risk scores defined by DNA methylation.

METHODS

We conducted a nested case-control study of 143 incident LC cases and 1460 LC-free controls within a prospective cohort of 9949 participants aged 50-75 years with 14-year follow-up. Lifetime smoking history was obtained in detail at recruitment. We built a GRS based on 31 previously identified LC-associated single-nucleotide polymorphisms (SNPs) and a DNA methylation score (MRS) based on methylation of 151 previously identified smoking-associated cytosine-phosphate-guanine (CpG) loci. We evaluated associations of GRS and MRS with LC incidence by logistic regression models, controlling for age, sex, smoking status, and pack-years. We compared the predictive performance of models based on pack-years alone with models additionally including GRS and/or MRS using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

GRS and MRS showed moderate and strong associations with LC risk even after comprehensive adjustment for smoking history (adjusted odds ratio [95% CI] comparing highest with lowest quartile 1.93 [1.05-3.71] and 5.64 [2.13-17.03], respectively). Similar associations were also observed within the risk groups of ever and heavy smokers. Addition of GRS and MRS furthermore strongly enhanced LC prediction beyond prediction by pack-years (increase of optimism-corrected AUC among heavy smokers from 0.605 to 0.654, NRI 26.7%, p = 0.0106, IDI 3.35%, p = 0.0036), the increase being mostly attributable to the inclusion of MRS.

CONCLUSIONS

Consideration of MRS, by itself or in combination with GRS, may strongly enhance LC risk stratification.

Collagen type VI α5 gene variations may predict the risk of lung cancer development in Chinese Han population

The abundant expression of collagen type VI α5 (COL6A5) exists in lung tissue, and its role in lung cancer is still unknown. We performed a genetic association study with an attempt to detect the relationships between single nucleotide polymorphisms (SNPs) in COL6A5 and lung cancer predisposition in Chinese Han population. We finally selected six tag-SNPs to determine their genotypes among 510 lung cancer patients and 495 healthy controls with the MassARRAY platform. The associations of SNPs and lung cancer risk were estimated by logistic regression method with adjustment for confounding factors. Two available databases were used for gene expression and prognosis analysis. COL6A5 rs13062453, rs1497305, and rs77123808 were significantly associated with the risk of lung cancer in the whole population or stratified subgroups (p < 0.05). Among them, COL6A5 rs13062453 and rs1497305 were also linked to the susceptibility of lung adenocarcinoma. Additionally, rs1497305 was found to be strongly related to the TNM staging under five genetic models (p < 0.05). Results from databases suggested the important role of COL6A5 in lung cancer development. COL6A5 polymorphisms rs13062453, rs1497305 and rs77123808 were associated with lung cancer risk in Chinese Han population. These findings first yield new insight of COL6A5 in lung cancer.

CHRNA5 rs16969968 polymorphism is associated with lung cancer risk: A meta-analysis

OBJECTIVE

To evaluate the genetic association between rs16969968 and lung cancer risk by meta-analysis.

DATA SOURCE

We searched eligible studies from MEDLINE, Web of Science and EMBASE up to Dec, 2017.

STUDY SELECTION

Association studies concerning rs16969968 and lung cancer risk were included. We assessed the association strength between this polymorphism and risk of lung cancer by calculating odds ratios (OR) and 95% confidence interval (95%CI).

RESULTS

A total of 26 data sets comprising 30 772 lung cancers and 90 954 controls were included. rs16969968 was found to be associated with lung cancer risk in population of European ancestry in all models (A vs. G: OR = 1.30, 95%CI 1.27-1.33, P < 0.001; AA + GA vs. GG: OR = 1.38, 95%CI 1.33-1.43, P < 0.001; AA vs. GG + GA: OR = 1.45, 95%CI 1.38-1.53, P < 0.001), consistent with previous genome-wide association study (GWAS). However, no association was observed in Asians (A vs. G: OR = 1.19. 95%CI 0.95-1.49, P = 0.131). The minor allele A may increase the risk of lung cancer in both smokers (OR = 1.33, 95%CI 1.29-1.39, P < 0.001) and nonsmokers (OR = 1.25, 95%CI 1.12-1.39, P < 0.001). There was no obvious publication bias in all analyses.

CONCLUSIONS

Our analysis provided more evidence that rs16969968 is a susceptibility locus of lung cancer in the Caucasians and that it may be not associated with the risk in the Asians.

Effects of ALOX5, IL6R and SFTPD gene polymorphisms on the risk of lung cancer: A case-control study in China

BACKGROUND

ALOX5, IL6R and SFTPD are all immune related genes that may be involved in the development of lung cancer. We sought to explore the effect of polymorphisms of these genes on the risk of lung cancer.

METHODS

Six single nucleotide polymorphisms (SNPs) were genotyped using a MassARRAY platform in a case-control cohort including 550 patients with lung cancer and 550 healthy controls.

RESULTS

The rs4845626-T and rs4329505-C alleles were associated with a decreased risk of lung cancer (p < 0.001), while the rs745986-G and rs2245121-A alleles were correlated with an increased risk of lung cancer (p < 0.01). The rs4845626-GT/GG and rs4329505-TC genotypes were protective against lung cancer (p < 0.001). However, the rs745986-AG and rs2245121-AG/AA genotypes were associated with an increased risk of lung cancer (p < 0.01). Stratification analysis showed that the rs4845626 and rs4329505 polymorphisms of IL6R were associated with a reduced risk of lung cancer in both smokers and nonsmokers (p < 0.05). However, rs892690, rs745986 and rs2115819 of ALOX5 were associated with an increased risk of disease in nonsmokers, while rs2245121 of SFTPD was correlated with a higher risk of disease in smokers (p < 0.05).

CONCLUSION

Our results provide candidate SNPs for early screening for lung cancer and new clues for further study of the pathogenesis of the disease.

Identification of risk loci and a polygenic risk score for lung cancer: a large-scale prospective cohort study in Chinese populations

BACKGROUND

Genetic variation has an important role in the development of non-small-cell lung cancer (NSCLC). However, genetic factors for lung cancer have not been fully identified, especially in Chinese populations, which limits the use of existing polygenic risk scores (PRS) to identify subpopulations at high risk of lung cancer for prevention. We therefore aimed to identify novel loci associated with NSCLC risk, and generate a PRS and evaluate its utility and effectiveness in the prediction of lung cancer risk in Chinese populations.

METHODS

To systematically identify genetic variants for NSCLC risk, we newly genotyped 19 546 samples from Chinese NSCLC cases and controls from the Nanjing Medical University Global Screening Array Project and did a meta-analysis of genome-wide association studies (GWASs) of 27 120 individuals with NSCLC and 27 355 without NSCLC (13 327 cases and 13 328 controls of Chinese descent as well as 13 793 cases and 14 027 controls of European descent). We then built a PRS for Chinese populations from all reported single-nucleotide polymorphisms that have been reported to be associated with lung cancer risk at genome-wide significance level. We evaluated the utility and effectiveness of the generated PRS in predicting subpopulations at high-risk of lung cancer in an independent prospective cohort of 95 408 individuals from the China Kadoorie Biobank (CKB) with more than 10 years' follow-up.

FINDINGS

We identified 19 susceptibility loci to be significantly associated with NSCLC risk at p≤5·0 × 10-8, including six novel loci. When applied to the CKB cohort, the PRS of the risk loci successfully predicted lung cancer incident cases in a dose-response manner in participants at a high genetic risk (top 10%) than those at a low genetic risk (bottom 10%; adjusted hazard ratio 1·96, 95% CI 1·53-2·51; ptrend=2·02 × 10-9). Specially, we observed consistently separated curves of lung cancer events in individuals at low, intermediate, and high genetic risk, respectively, and PRS was an independent effective risk stratification indicator beyond age and smoking pack-years.

INTERPRETATION

We have shown for the first time that GWAS-derived PRS can be effectively used in discriminating subpopulations at high risk of lung cancer, who might benefit from a practically feasible PRS-based lung cancer screening programme for precision prevention in Chinese populations.

FUNDING

National Natural Science Foundation of China, the Priority Academic Program for the Development of Jiangsu Higher Education Institutions, National Key R&D Program of China, Science Foundation for Distinguished Young Scholars of Jiangsu, and China's Thousand Talents Program.

Quantifying the Potential for Future Gene Therapy to Lower Lifetime Risk of Polygenic Late-Onset Diseases

Gene therapy techniques and genetic knowledge may sufficiently advance, within the next few decades, to support prophylactic gene therapy for the prevention of polygenic late-onset diseases. The risk of these diseases may, hypothetically, be lowered by correcting the effects of a subset of common low effect gene variants. In this paper, simulations show that if such gene therapy were to become technically possible; and if the incidences of the treated diseases follow the proportional hazards model with a multiplicative genetic architecture composed of a sufficient number of common small effect gene variants, then: (a) late-onset diseases with the highest familial heritability will have the largest number of variants available for editing; (b) diseases that currently have the highest lifetime risk, particularly those with the highest incidence rate continuing into older ages, will prove the most challenging cases in lowering lifetime risk and delaying the age of onset at a population-wide level; (c) diseases that are characterized by the lowest lifetime risk will show the strongest and longest-lasting response to such therapies; and (d) longer life expectancy is associated with a higher lifetime risk of these diseases, and this tendency, while delayed, will continue after therapy.

Quantifying the Potential for Future Gene Therapy to Lower Lifetime Risk of Polygenic Late-Onset Diseases

Gene therapy techniques and genetic knowledge may sufficiently advance, within the next few decades, to support prophylactic gene therapy for the prevention of polygenic late-onset diseases. The risk of these diseases may, hypothetically, be lowered by correcting the effects of a subset of common low effect gene variants. In this paper, simulations show that if such gene therapy were to become technically possible; and if the incidences of the treated diseases follow the proportional hazards model with a multiplicative genetic architecture composed of a sufficient number of common small effect gene variants, then: (a) late-onset diseases with the highest familial heritability will have the largest number of variants available for editing; (b) diseases that currently have the highest lifetime risk, particularly those with the highest incidence rate continuing into older ages, will prove the most challenging cases in lowering lifetime risk and delaying the age of onset at a population-wide level; (c) diseases that are characterized by the lowest lifetime risk will show the strongest and longest-lasting response to such therapies; and (d) longer life expectancy is associated with a higher lifetime risk of these diseases, and this tendency, while delayed, will continue after therapy.

Integrating of genomic and transcriptomic profiles for the prognostic assessment of breast cancer

PURPOSE

To evaluate the prognostic effect of the integration of genomic and transcriptomic profiles in breast cancer.

METHODS

Eight hundred and ten samples from the Cancer Genome Atlas (TCGA) data sets were randomly divided into the training set (540 subjects) and validation set (270 subjects). We first selected single-nucleotide polymorphism (SNPs) and genes associated with breast cancer prognosis in the training set to construct the prognostic prediction model, and then replicated the prediction efficiency in the validation set.

RESULTS

Four SNPs and three genes associated with the prognosis of breast cancer in the training set were included in the prognostic model. Patients were divided into the high-risk group and low-risk group based on the four SNPs and three genes signature-based genetic prognostic index. High-risk patients showed a significant worse overall survival [Hazard Ratio (HR) 9.43, 95% confidence interval (CI) 3.81-23.33, P < 0.001] than the low-risk group. Compared to the model constructed with only gene expression, the C statistics for the signature-based genetic prognostic index [area under curves (AUC) = 0.79, 95% CI 0.72-0.86] showed a significant increase (P < 0.001). Additionally, we further replicated the prognostic prediction model in the validation set as patients in the high-risk group also showed a significantly worse overall survival (HR 4.55, 95% CI 1.50-13.88, P < 0.001), and the C statistics for the signature-based genetic prognostic index was 0.76 (95% CI 0.65-0.86). The following time-dependent ROC revealed that the mean of AUCs were 0.839 and 0.748 in the training set and the validation set, respectively.

CONCLUSIONS

Our findings suggested that integrating genomic and transcriptomic profiles could greatly improve the predictive efficiency of the prognosis of breast cancer patients.

A prospective study of dietary polyunsaturated fatty acids intake and lung cancer risk

Animal studies have shown that polyunsaturated fatty acids (PUFAs) have antineoplastic and anti-inflammatory properties. Results from epidemiologic studies on specific types of PUFAs for lung cancer risk, however, are inconclusive. We prospectively evaluated the association of specific types of dietary PUFA intakes and lung cancer risk in two population-based cohort studies, the Shanghai Women's Health Study (SWHS) and Shanghai Men's Health Study (SMHS) with a total of 121,970 study participants (i.e., 65,076 women and 56,894 men). Dietary fatty acid intakes were derived from data collected at the baseline using validated food frequency questionnaires (FFQs). Cox proportional hazards model was performed to assess the association between PUFAs and lung cancer risk. Total, saturated and monounsaturated fatty acid intakes were not significantly associated with lung cancer risk. Total PUFAs intake was inversely associated with lung cancer risk [HRs and respective 95% CIs for quintiles 2-5 vs quintile 1: 0.84 (0.71-0.98), 0.97 (0.83-1.13), 0.86 (0.74-1.01) and 0.85 (0.73-1.00), ptrend  = 0.11]. However, DHA intake was positively associated with lung cancer risk [HRs and 95% CIs: 1.01 (0.86-1.19), 1.20 (1.03-1.41), 1.21 (1.03-1.42) and 1.24 (1.05-1.47), ptrend  = 0.001]. The ratio of n-6 PUFAs to n-3 PUFAs (i.e., 7:1) was inversely associated with lung cancer risk, particularly among never-smokers and adenocarcinoma patients. Total PUFAs and the ratio between n-6 PUFAs and n-3 PUFAs were inversely associated with lung cancer risk. This study highlights an important public health impact of PUFA intakes toward intervention/prevention programs of lung cancer.

EBT: a statistic test identifying moderate size of significant features with balanced power and precision for genome-wide rate comparisons

Motivation

In genome-wide rate comparison studies, there is a big challenge for effective identification of an appropriate number of significant features objectively, since traditional statistical comparisons without multi-testing correction can generate a large number of false positives while multi-testing correction tremendously decreases the statistic power.

Results

In this study, we proposed a new exact test based on the translation of rate comparison to two binomial distributions. With modeling and real datasets, the exact binomial test (EBT) showed an advantage in balancing the statistical precision and power, by providing an appropriate size of significant features for further studies. Both correlation analysis and bootstrapping tests demonstrated that EBT is as robust as the typical rate-comparison methods, e.g. χ 2 test, Fisher's exact test and Binomial test. Performance comparison among machine learning models with features identified by different statistical tests further demonstrated the advantage of EBT. The new test was also applied to analyze the genome-wide somatic gene mutation rate difference between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), two main lung cancer subtypes and a list of new markers were identified that could be lineage-specifically associated with carcinogenesis of LUAD and LUSC, respectively. Interestingly, three cilia genes were found selectively with high mutation rates in LUSC, possibly implying the importance of cilia dysfunction in the carcinogenesis.

Availability and implementation

An R package implementing EBT could be downloaded from the website freely: http://www.szu-bioinf.org/EBT .

Contact

wangyj@szu.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Decade of GWAS Results in Lung Cancer

Genome-wide association studies (GWAS) were successful to identify genetic factors robustly associated with lung cancer. This review aims to synthesize the literature in this field and accelerate the translation of GWAS discoveries into results that are closer to clinical applications. A chronologic presentation of published GWAS on lung cancer susceptibility, survival, and response to treatment is presented. The most important results are tabulated to provide a concise overview in one read. GWAS have reported 45 lung cancer susceptibility loci with varying strength of evidence and highlighted suspected causal genes at each locus. Some genetic risk loci have been refined to more homogeneous subgroups of lung cancer patients in terms of histologic subtypes, smoking status, gender, and ethnicity. Overall, these discoveries are an important step for future development of new therapeutic targets and biomarkers to personalize and improve the quality of care for patients. GWAS results are on the edge of offering new tools for targeted screening in high-risk individuals, but more research is needed if GWAS are to pay off the investment. Complementary genomic datasets and functional studies are needed to refine the underlying molecular mechanisms of lung cancer preliminarily revealed by GWAS and reach results that are medically actionable. Cancer Epidemiol Biomarkers Prev; 27(4); 363-79. ©2018 AACRSee all articles in this CEBP Focus section, "Genome-Wide Association Studies in Cancer."

Collective effects of common SNPs and risk prediction in lung cancer

Lung cancer is the leading cause of cancer deaths in both men and women in the US. While most sporadic lung cancer cases are related to environmental factors such as smoking, genetic susceptibility may also play an important role and a number of lung cancer associated single-nucleotide polymorphisms (SNPs) have been identified although many remain to be found. The collective effects of genome-wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in lung cancer using previously published SNPs data sets (US and Finland samples) and found higher MAC in cases relative to matched controls. A set of 5400 SNPs with MA (MAF < 0.5) more common in cases (P < 0.08) and linkage disequilibrium (LD) r² = 0.3 was found to have the best predictive accuracy. These results identify higher MAC in lung cancer susceptibility and provide a meaningful genetic method to identify those at risk of lung cancer.

Predictive accuracy of combined genetic and environmental risk scores

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.

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.

Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations

In the past ten years, great successes have been accumulated by taking advantage of both candidate-gene studies and genome-wide association studies. However, limited studies were available to systematically evaluate the genetic effects for lung cancer risk with large-scale and different ethnic populations. We systematically reviewed relevant literatures and filtered out 241 important genetic variants identified in 124 articles. A two-stage case-control study within specific subgroups was performed to assess the effects [Training set: 2,331 cases vs. 3,077 controls (Chinese population); testing set: 1,937 cases vs. 1,984 controls (European population)]. Variable selection and model development were used LASSO penalized regression and genetic risk score (GRS) system. Further change in area under the receiver operator characteristic curves (AUC) made by the epidemiologic model with and without GRS was used to compare predictions. It kept 38 genetic variants in our study and the ratios of lung cancer risk for subjects in the upper quartile GRS was three times higher compared to that in the low quartile (odds ratio: 4.64, 95% CI: 3.87–5.56). In addition, we found that adding genetic predictors to smoking risk factor-only model improved lung cancer predictive value greatly: AUC, 0.610 versus 0.697 (P < 0.001). Similar performance was derived in European population and the combined two data sets. Our findings suggested that genetic predictors could improve the predictive ability of risk model for lung cancer and highlighted the application among different populations, indicating that the lung cancer risk assessment model will be a promising tool for high risk population screening and prediction.

Genome-wide association studies and epigenome-wide association studies go together in cancer control

Completion of the human genome a decade ago laid the foundation for: using genetic information in assessing risk to identify individuals and populations that are likely to develop cancer, and designing treatments based on a person's genetic profiling (precision medicine). Genome-wide association studies (GWAS) completed during the past few years have identified risk-associated single nucleotide polymorphisms that can be used as screening tools in epidemiologic studies of a variety of tumor types. This led to the conduct of epigenome-wide association studies (EWAS). This article discusses the current status, challenges and research opportunities in GWAS and EWAS. Information gained from GWAS and EWAS has potential applications in cancer control and treatment.

A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations

BACKGROUND

Although genome-wide association studies (GWAS) have identified many genetic variants that are strongly associated with lung cancer, these variants have low penetrance and serve as poor predictors of lung cancer in individuals. We sought to increase the predictive value of germline variants by considering their cumulative effects in the context of biologic pathways.

METHODS

For individuals in the Environment and Genetics in Lung Cancer Etiology study (1,815 cases/1,971 controls), we computed pathway-level susceptibility effects as the sum of relevant SNP variant alleles weighted by their log-additive effects from a separate lung cancer GWAS meta-analysis (7,766 cases/37,482 controls). Logistic regression models based on age, sex, smoking, genetic variants, and principal components of pathway effects and pathway-smoking interactions were trained and optimized in cross-validation and further tested on an independent dataset (556 cases/830 controls). We assessed prediction performance using area under the receiver operating characteristic curve (AUC).

RESULTS

Compared with typical binomial prediction models that have epidemiologic predictors (AUC = 0.607) in addition to top GWAS variants (AUC = 0.617), our pathway-based smoking-interactive multinomial model significantly improved prediction performance in external validation (AUC = 0.656, P < 0.0001).

CONCLUSIONS

Our biologically informed approach demonstrated a larger increase in AUC over nongenetic counterpart models relative to previous approaches that incorporate variants.

IMPACT

This model is the first of its kind to evaluate lung cancer prediction using subtype-stratified genetic effects organized into pathways and interacted with smoking. We propose pathway-exposure interactions as a potentially powerful new contributor to risk inference. Cancer Epidemiol Biomarkers Prev; 25(8); 1208-15. ©2016 AACR.

Risk factors assessment and risk prediction models in lung cancer screening candidates

From February 2015, low-dose computed tomography (LDCT) screening entered the armamentarium of diagnostic tools broadly available to individuals at high-risk of developing lung cancer. While a huge number of pulmonary nodules are identified, only a small fraction turns out to be early lung cancers. The majority of them constitute a variety of benign lesions. Although it entails a burden of the diagnostic work-up, the undisputable benefit emerges from: (I) lung cancer diagnosis at earlier stages (stage shift); (II) additional findings enabling the implementation of a preventive action beyond the realm of thoracic oncology. This review presents how to utilize the risk factors from distinct categories such as epidemiology, radiology and biomarkers to target the fraction of population, which may benefit most from the introduced screening modality.

Developing and evaluating polygenic risk prediction models for stratified disease prevention

Knowledge of genetics and its implications for human health is rapidly evolving in accordance with recent events, such as discoveries of large numbers of disease susceptibility loci from genome-wide association studies, the US Supreme Court ruling of the non-patentability of human genes, and the development of a regulatory framework for commercial genetic tests. In anticipation of the increasing relevance of genetic testing for the assessment of disease risks, this Review provides a summary of the methodologies used for building, evaluating and applying risk prediction models that include information from genetic testing and environmental risk factors. Potential applications of models for primary and secondary disease prevention are illustrated through several case studies, and future challenges and opportunities are discussed.