Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer.

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1ß pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility.

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: rg = 0.098, p = 2.3 × 10-8) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: rg = 0.137, p = 2.0 × 10-12). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis.

Increased risk of lung cancer in individuals with a family history of the disease: a pooled analysis from the International Lung Cancer Consortium.

BACKGROUND AND METHODS: Familial aggregation of lung cancer exists after accounting for cigarette smoking. However, the extent to which family history affects risk by smoking status, histology, relative type and ethnicity is not well described. This pooled analysis included 24 case-control studies in the International Lung Cancer Consortium. Each study collected age of onset/interview, gender, race/ethnicity, cigarette smoking, histology and first-degree family history of lung cancer. Data from 24,380 lung cancer cases and 23,305 healthy controls were analysed. Unconditional logistic regression models and generalised estimating equations were used to estimate odds ratios and 95% confidence intervals. RESULTS: Individuals with a first-degree relative with lung cancer had a 1.51-fold increase in the risk of lung cancer, after adjustment for smoking and other potential confounders (95% CI: 1.39, 1.63). The association was strongest for those with a family history in a sibling, after adjustment (odds ratios (OR) = 1.82, 95% CI: 1.62, 2.05). No modifying effect by histologic type was found. Never smokers showed a lower association with positive familial history of lung cancer (OR = 1.25, 95% CI: 1.03, 1.52), slightly stronger for those with an affected sibling (OR = 1.44, 95% CI: 1.07, 1.93), after adjustment. CONCLUSIONS: The occurrence of lung cancer among never smokers and similar magnitudes of the effect of family history on lung cancer risk across histological types suggests familial aggregation of lung cancer is independent of those risks associated with cigarette smoking. While the role of genetic variation in the aetiology of lung cancer remains to be fully characterised, family history assessment is immediately available and those with a positive history represent a higher risk group.

Genetic linkage studies.

Linkage analysis is used to map genetic loci by use of observations of related individuals. We provide an introduction to methods commonly used to map loci that predispose to disease. Linkage analysis methods can be applied to both major gene disorders (parametric linkage) and complex diseases (model-free or non-parametric linkage). Evidence for linkage is most commonly expressed as a logarithm of the odds score. We provide a framework for interpretation of these scores and discuss the role of simulation in assessment of statistical significance and estimation of power. Genetic and phenotypic heterogeneity can also affect the success of a study, and several methods exist to address such problems.