SummaryWhile several lung cancer susceptibility loci have been identified, much of lung cancer heritability remains unexplained. Here, 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated GWAS analysis of lung cancer on 29,266 patients and 56,450 controls. We identified 18 susceptibility loci achieving genome wide significance, including 10 novel loci. The novel loci highlighted the striking heterogeneity in genetic susceptibility across lung cancer histological subtypes, with four loci associated with lung cancer overall and six with lung adenocarcinoma. Gene expression quantitative trait analysis (eQTL) in 1,425 normal lung tissues highlighted RNASET2, SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2, and the telomere-related genes, OFBC1 and RTEL1. Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer.

Introduction: The relationships between morbid obesity, changes in body mass index (BMI) before cancer diagnosis, and lung cancer outcomes by histology (SCLC and NSCLC) have not been well studied. Methods: Individual level data analysis was performed on 25,430 patients with NSCLC and 2787 patients with SCLC from 16 studies of the International Lung Cancer Consortium evaluating the association between various BMI variables and lung cancer overall survival, reported as adjusted hazard ratios (aHRs) from Cox proportional hazards models and adjusted penalized smoothing spline plots. Results: Overall survival of NSCLC had putative U-shaped hazard ratio relationships with BMI based on spline plots: being underweight (BMI < 18.5 kg/m 2 ; aHR ¼ 1.56; 95% confidence interval [CI]:1.43-1.70) or morbidly overweight (BMI > 40 kg/m 2 ; aHR ¼ 1.09; 95% CI: 0.95-1.26) at the time of diagnosis was associated with worse stage-specific prognosis, whereas being overweight (25 kg/m 2 BMI < 30 kg/ m 2 ; aHR ¼ 0.89; 95% CI: 0.85-0.95) or obese (30 kg/m 2 BMI 40 kg/m 2 ; aHR ¼ 0.86; 95% CI: 0.82-0.91) was associated with improved survival. Although not significant, a similar pattern was seen with SCLC. Compared with an increased or stable BMI from the period between young adulthood until date of diagnosis, a decreased BMI was associated with worse outcomes in NSCLC (aHR ¼ 1.24; 95% CI: 1.2-1.3) and SCLC patients (aHR¼1.26 (95% CI: 1.0-1.6). Decreased BMI was consistently associated with worse outcome, across clinicodemographic subsets. Conclusions: Both being underweight or morbidly obese at time of diagnosis is associated with lower stage-specific survival in independent assessments of NSCLC and SCLC patients. In addition, a decrease in BMI at lung cancer diagnosis relative to early adulthood is a consistent marker of poor survival.

BackgroundArsenic, a common groundwater pollutant, is associated with adverse reproductive health but few studies have examined its effect on maternal health.MethodsA prospective cohort was recruited in Bangladesh from 2008–2011 (N = 1,458). At enrollment (<16 weeks gestational age [WGA]), arsenic was measured in personal drinking water using inductively-coupled plasma mass spectrometry. Questionnaires collected health data at enrollment, at 28 WGA, and within one month of delivery. Adjusted odds ratios (aORs) and 95% confidence intervals (95% CI) for self-reported health symptoms were estimated for each arsenic quartile using logistic regression.ResultsOverall, the mean concentration of arsenic was 38 μg/L (Standard deviation, 92.7 μg/L). A total of 795 women reported one or more of the following symptoms during pregnancy (cold/flu/infection, nausea/vomiting, abdominal cramping, headache, vaginal bleeding, or swollen ankles). Compared to participants exposed to the lowest quartile of arsenic (≤0.9 μg/L), the aOR for reporting any symptom during pregnancy was 0.62 (95% CI = 0.44-0.88) in the second quartile, 1.83 (95% CI = 1.25-2.69) in the third quartile, and 2.11 (95% CI = 1.42-3.13) in the fourth quartile where the mean arsenic concentration in each quartile was 1.5 μg/L, 12.0 μg/L and 144.7 μg/L, respectively. Upon examining individual symptoms, only nausea/vomiting and abdominal cramping showed consistent associations with arsenic exposure. The odds of self-reported nausea/vomiting was 0.98 (95% CI: 0.68, 1.41), 1.52 (95% CI: 1.05, 2.18), and 1.81 (95% CI: 1.26, 2.60) in the second, third and fourth quartile of arsenic relative to the lowest quartile after adjusting for age, body mass index, second-hand tobacco smoke exposure, educational status, parity, anemia, ferritin, medication usage, type of sanitation at home, and household income. A positive trend was also observed for abdominal cramping (P for trend <0.0001). A marginal negative association was observed between arsenic quartiles and odds of self-reported cold/flu/infection (P for trend = 0.08). No association was observed between arsenic and self-reported headache (P for trend = 0.19).ConclusionModerate exposure to arsenic contaminated drinking water early in pregnancy was associated with increased odds of experiencing nausea/vomiting and abdominal cramping. Preventing exposure to arsenic contaminated drinking water during pregnancy could improve maternal health.

Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.