A cross-trait study of lung cancer and its related respiratory diseases based on large-scale exome sequencing population.

Background: Genome-wide association studies (GWASs) explain the genetic susceptibility between diseases and common variants. Nevertheless, with the appearance of large-scale sequencing profiles, we could explore the rare coding variants in disease pathogenesis. Methods: We estimated the genetic correlation of nine respiratory diseases and lung cancer in the UK Biobank (UKB) by linkage disequilibrium score regression (LDSC). Then, we performed exome-wide association studies at single-variant level and gene-level for lung cancer and lung cancer-related respiratory diseases using the whole-exome sequencing (WES) data of 427,934 European participants. Cross-trait meta-analysis was conducted by association analysis based on subsets (ASSET) to identify the pleiotropic variants, while in-silico functional analysis was performed to explore their function. Causal mediation analysis was used to explore whether these pleiotropic variants lead to lung cancer is mediated by affecting the chronic respiratory diseases. Results: Five respiratory diseases [emphysema, pneumonia, asthma, chronic obstructive pulmonary disease (COPD), and fibrosis] were genetically correlated with lung cancer. We identified 102 significant independent variants at single-variant levels for lung cancer and five lung cancer-related diseases. 15:78590583:G>A (missense variant in CHRNA5) was shared in lung cancer, emphysema, and COPD. Meanwhile, 14 significant genes and 87 suggestive genes were identified in gene-based association tests, including HSD3B7 (lung cancer), SRSF2 (pneumonia), TNXB (asthma), TERT (fibrosis), MOSPD3 (emphysema). Based on the cross-trait meta-analysis, we detected 145 independent pleiotropic variants. We further identified abundant pathways with significant enrichment effects, demonstrating that these pleiotropic genes were functional. Meanwhile, the proportion of mediation effects of these variants ranged from 6 to 23 (emphysema: 23%; COPD: 20%; pneumonia: 20%; fibrosis: 7%; asthma: 6%) through these five respiratory diseases to the incidence of lung cancer. Conclusions: The identified shared genetic variants, genes, biological pathways, and potential intermediate causal pathways provide a basis for further exploration of the relationship between lung cancer and respiratory diseases.

Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer.

Plasma proteins are promising biomarkers and potential drug targets in lung cancer. To evaluate the causal association between plasma proteins and lung cancer, we performed proteome-wide Mendelian randomization meta-analysis (PW-MR-meta) based on lung cancer genome-wide association studies (GWASs), protein quantitative trait loci (pQTLs) of 4,719 plasma proteins in deCODE and 4,775 in Fenland. Further, causal-protein risk score (CPRS) was developed based on causal proteins and validated in the UK Biobank. 270 plasma proteins were identified using PW-MR meta-analysis, including 39 robust causal proteins (both FDR-q < 0.05) and 78 moderate causal proteins (FDR-q < 0.05 in one and p < 0.05 in another). The CPRS had satisfactory performance in risk stratification for lung cancer (top 10% CPRS:Hazard ratio (HR) (95%CI):4.33(2.65-7.06)). The CPRS [AUC (95%CI): 65.93 (62.91-68.78)] outperformed the traditional polygenic risk score (PRS) [AUC (95%CI): 55.71(52.67-58.59)]. Our findings offer further insight into the genetic architecture of plasma proteins for lung cancer susceptibility.

Systematic characterization of m6A proteomics across 12 cancer types: a multi-omics integration study.

The modification patterns of N6-methyladenosine (m6A) regulators and interacting genes are deeply involved in tumors. However, the effect of m6A modification patterns on human proteomics remains largely unknown. We evaluated the molecular characteristics and clinical relevance of m6A modification proteomics patterns among 1013 pan-cancer samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). More than half of the m6A proteins were expressed at higher levels in tumor tissues and presented oncogenic characteristics. Furthermore, we performed multi-omics analyses integrating with transcriptomics data of m6A regulators and interactive coding and non-coding RNAs and developed a m6A multi-omics signature to identify potential m6A modification target proteins across global proteomics. It was significantly associated with overall survival in nine cancer types, tumor mutation burden (P = 0.01), and immune checkpoints including PD-L1 (P = 4.9 × 10-8) and PD-1 (P < 0.01). We identified 51 novel proteins associated with the multi-omics signature (PFDR < 0.05). These proteins were functional through pathway enrichment analyses. The protein with the highest hit frequency was CHORDC1, which was significantly up-regulated in tumor tissues in nine cancer types. Its higher abundance was significantly associated with a poorer prognosis in seven cancer types. The identified m6A target proteins might provide infomation for the study of molecular mechanism of cancer.

A cross-disorder study to identify causal relationships, shared genetic variants, and genes across 21 digestive disorders.

Digestive disorders are a significant contributor to the global burden of disease and seriously affect human quality of life. Research has already confirmed the presence of pleiotropic genetic loci among digestive disorders, and studies have explored shared genetic factors among pan-cancers, including various malignant digestive disorders. However, most cross-phenotype studies within the digestive tract system have been limited to a few traits, with no systematic coverage of common benign and malignant digestive disorders. Here, we analyzed data from the UK Biobank to investigate 21 digestive disorders, exploring the genetic correlations and causal relationships between diseases, as well as the common genetic factors and potential biological pathways driving these relationships. Our findings confirmed the extensive genetic correlation and causal relationship between digestive disorders, providing important insights into the genetic etiology, causality, disease prevention, and clinical treatment of diseases.

A Large-Scale Exome-Wide Association Study Identifies Novel Germline Mutations in Lung Cancer.

Rationale: Genome-wide association studies have identified common variants of lung cancer. However, the contribution of rare exome-wide variants, especially protein-coding variants, to cancers remains largely unexplored. Objectives: To evaluate the role of human exomes in genetic predisposition to lung cancer. Methods: We performed exome-wide association studies to detect the association of exomes with lung cancer in 30,312 patients and 652,902 control subjects. A scalable and accurate implementation of a generalized mixed model was used to detect the association signals for loss-of-function, missense, and synonymous variants and gene-level sets. Furthermore, we performed association and Bayesian colocalization analyses to evaluate their relationships with intermediate exposures. Measurements and Main Results: We systematically analyzed 216,739 single-nucleotide variants in the human exome. The loss-of-function variants exhibited the most notable effects on lung cancer risk. We identified four novel variants, including two missense variants (rs202197044TET3 [Pmeta (P values of meta-analysis) = 3.60 × 10-8] and rs202187871POT1 [Pmeta = 2.21 × 10-8]) and two synonymous variants (rs7447927TMEM173 [Pmeta = 1.32 × 10-9] and rs140624366ATRN [Pmeta = 2.97 × 10-9]). rs202197044TET3 was significantly associated with emphysema (odds ratio, 3.55; Pfdr = 0.015), whereas rs7447927POT1 was strongly associated with telomere length (ß = 1.08; Pfdr (FDR corrected P value) = 3.76 × 10-53). Functional evidence of expression of quantitative trait loci, splicing quantitative trait loci, and isoform expression was found for the four novel genes. Gene-level association tests identified several novel genes, including POT1 (protection of telomeres 1), RTEL1, BSG, and ZNF232. Conclusions: Our findings provide insights into the genetic architecture of human exomes and their role in lung cancer predisposition.

A strategy to reduce the false-positive rate after low-dose computed tomography in lung cancer screening: A multicenter prospective cohort study.

BACKGROUND: The ability of lung cancer screening to manage pulmonary nodules was limited because of the high false-positive rate in the current mainstream screening method, low-dose computed tomography (LDCT). We aimed to reduce overdiagnosis in Chinese population. METHODS: Lung cancer risk prediction models were constructed using data from a population-based cohort in China. Independent clinical data from two programs performed in Beijing and Shandong, respectively, were used as the external validation set. Multivariable logistic regression models were used to estimate the probability of lung cancer incidence in the whole population and in smokers and nonsmokers. RESULTS: In our cohort, 1,016,740 participants were enrolled between 2013 and 2018. Of 79,581 who received LDCT screening, 5165 participants with suspected pulmonary nodules were allocated into the training set, of which, 149 lung cancer cases were diagnosed. In the validation set, 1815 patients were included, and 800 developed lung cancer. The ages of patients and radiologic factors of nodules (calcification, density, mean diameter, edge, and pleural involvement) were included in our model. The area under the curve (AUC) values of the model were 0.868 (95% CI: 0.839-0.894) in the training set and 0.751 (95% CI: 0.727-0.774) in the validation set. The sensitivity and specificity were 70.5% and 70.9%, respectively, which could reduce the 68.8% false-positive rate in simulated LDCT screening. There was no substantial difference between smokers' and nonsmokers' prediction models. CONCLUSION: Our models could facilitate the diagnosis of suspected pulmonary nodules, effectively reducing the false-positive rate of LDCT for lung cancer screening.

Incremental value of radiomics with machine learning to the existing prognostic models for predicting outcome in renal cell carcinoma.

Purpose: To systematically evaluate the potential of radiomics coupled with machine-learning algorithms to improve the predictive power for overall survival (OS) of renal cell carcinoma (RCC). Methods: A total of 689 RCC patients (281 in the training cohort, 225 in the validation cohort 1 and 183 in the validation cohort 2) who underwent preoperative contrast-enhanced CT and surgical treatment were recruited from three independent databases and one institution. 851 radiomics features were screened using machine-learning algorithm, including Random Forest and Lasso-COX Regression, to establish radiomics signature. The clinical and radiomics nomogram were built by multivariate COX regression. The models were further assessed by Time-dependent receiver operator characteristic, concordance index, calibration curve, clinical impact curve and decision curve analysis. Result: The radiomics signature comprised 11 prognosis-related features and was significantly correlated with OS in the training and two validation cohorts (Hazard Ratios: 2.718 (2.246,3.291)). Based on radiomics signature, WHOISUP, SSIGN, TNM Stage and clinical score, the radiomics nomogram has been developed. Compared with the existing prognostic models, the AUCs of 5 years OS prediction of the radiomics nomogram were superior to the TNM, WHOISUP and SSIGN model in the training cohort (0.841 vs 0.734, 0.707, 0.644) and validation cohort2 (0.917 vs 0.707, 0.773, 0.771). Stratification analysis suggested that the sensitivity of some drugs and pathways in cancer were observed different for RCC patients with high-and low-radiomics scores. Conclusion: This study showed the application of contrast-enhanced CT-based radiomics in RCC patients, creating novel radiomics nomogram that could be used to predict OS. Radiomics provided incremental prognostic value to the existing models and significantly improved the predictive power. The radiomics nomogram might be helpful for clinicians to evaluate the benefit of surgery or adjuvant therapy and make individualized therapeutic regimens for patients with renal cell carcinoma.

Large-scale integration of the non-coding RNAs with DNA methylation in human cancers.

Characterizing influences of DNA methylation (DNAm) on non-coding RNAs (ncRNAs) is important to understand the mechanisms of gene regulation and cancer outcome. In our study, we describe the results of ncRNA quantitative trait methylation sites (ncQTM) analyses on 8,545 samples from The Cancer Genome Atlas (TCGA), 763 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 516 samples from Genotype-Tissue Expression (GTEx) to identify the significant associations between DNAm sites and ncRNAs (miRNA, long non-coding RNA [lncRNA], small nuclear RNA [snRNA], small nucleolar RNA [snoRNA], and rRNA) across 32 cancer types. With more than 22 billion tests, we identify 302,764 cis-ncQTMs (6.28% of all tested) and 79,841,728 trans-ncQTMs (1.15% of all tested). Most DNAm sites (70.6% on average) are in trans association, while only 25.2% DNAm sites are in cis association. Further, we develop a subtype named ncmcluster based on cancer-specific ncRNAs thatis associated with tumor microenvironment, clinical outcome, and biological pathways. To comprehensively describe the ncQTM patterns, we developed a database named Pancan-ncQTM (http://bigdata.njmu.edu.cn/Pancan-ncQTM/).

OWL: an optimized and independently validated machine learning prediction model for lung cancer screening based on the UK Biobank, PLCO, and NLST populations.

BACKGROUND: A reliable risk prediction model is critically important for identifying individuals with high risk of developing lung cancer as candidates for low-dose chest computed tomography (LDCT) screening. Leveraging a cutting-edge machine learning technique that accommodates a wide list of questionnaire-based predictors, we sought to optimize and validate a lung cancer prediction model. METHODS: We developed an Optimized early Warning model for Lung cancer risk (OWL) using the XGBoost algorithm with 323,344 participants from the England area in UK Biobank (training set), and independently validated it with 93,227 participants from UKB Scotland and Wales area (validation set 1), as well as 70,605 and 66,231 participants in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial (PLCO) control and intervention subpopulations, respectively (validation sets 2 & 3) and 23,138 and 18,669 participants in the United States National Lung Screening Trial (NLST) control and intervention subpopulations, respectively (validation sets 4 & 5). By comparing with three competitive prediction models, i.e., PLCO modified 2012 (PLCOm2012), PLCO modified 2014 (PLCOall2014), and the Liverpool Lung cancer Project risk model version 3 (LLPv3), we assessed the discrimination of OWL by the area under receiver operating characteristic curve (AUC) at the designed time point. We further evaluated the calibration using relative improvement in the ratio of expected to observed lung cancer cases (RIEO), and illustrated the clinical utility by the decision curve analysis. FINDINGS: For general population, with validation set 1, OWL (AUC = 0.855, 95% CI: 0.829-0.880) presented a better discriminative capability than PLCOall2014 (AUC = 0.821, 95% CI: 0.794-0.848) (p < 0.001); with validation sets 2 & 3, AUC of OWL was comparable to PLCOall2014 (AUCPLCOall2014-AUCOWL < 1%). For ever-smokers, OWL outperformed PLCOm2012 and PLCOall2014 among ever-smokers in validation set 1 (AUCOWL = 0.842, 95% CI: 0.814-0.871; AUCPLCOm2012 = 0.792, 95% CI: 0.760-0.823; AUCPLCOall2014 = 0.791, 95% CI: 0.760-0.822, all p < 0.001). OWL remained comparable to PLCOm2012 and PLCOall2014 in discrimination (AUC difference from -0.014 to 0.008) among the ever-smokers in validation sets 2 to 5. In all the validation sets, OWL outperformed LLPv3 among the general population and the ever-smokers. Of note, OWL showed significantly better calibration than PLCOm2012, PLCOall2014 (RIEO from 43.1% to 92.3%, all p < 0.001), and LLPv3 (RIEO from 41.4% to 98.7%, all p < 0.001) in most cases. For clinical utility, OWL exhibited significant improvement in average net benefits (NB) over PLCOall2014 in validation set 1 (NB improvement: 32, p < 0.001); among ever smokers of validation set 1, OWL (average NB = 289) retained significant improvement over PLCOm2012 (average NB = 213) (p < 0.001). OWL had equivalent NBs with PLCOm2012 and PLCOall2014 in PLCO and NLST populations, while outperforming LLPv3 in the three populations. INTERPRETATION: OWL, with a high degree of predictive accuracy and robustness, is a general framework with scientific justifications and clinical utility that can aid in screening individuals with high risks of lung cancer. FUNDING: National Natural Science Foundation of China, the US NIH.

A trans-omics assessment of gene-gene interaction in early-stage NSCLC.

Epigenome-wide gene-gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with PBonferroni ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (ßinteraction  = 0.018, P = 1.87 × 10-12 ), which mapped to RELA × HLA-G (ßinteraction  = 0.218, P = 8.82 × 10-11 ) and cg08872738 × cg27077312 (ßinteraction  = -0.010, P = 1.16 × 10-11 ), which mapped to TUBA1B × TOMM40 (ßinteraction =-0.250, P = 3.83 × 10-10 ). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.

Risk-stratified Approach for Never- and Ever-Smokers in Lung Cancer Screening: A Prospective Cohort Study in China.

Rationale: Over 40% of lung cancer cases occurred in never-smokers in China. However, high-risk never-smokers were precluded from benefiting from lung cancer screening as most screening guidelines did not consider them. Objectives: We sought to develop and validate prediction models for 3-year lung cancer risks for never- and ever-smokers, named the China National Cancer Center Lung Cancer models (China NCC-LCm2021 models). Methods: 425,626 never-smokers and 128,952 ever-smokers from the National Lung Cancer Screening program were used as the training cohort and analyzed using multivariable Cox models. Models were validated in two independent prospective cohorts: one included 369,650 never-smokers and 107,678 ever-smokers (841 and 421 lung cancers), and the other included 286,327 never-smokers and 78,469 ever-smokers (503 and 127 lung cancers). Measurements and Main Results: The areas under the receiver operating characteristic curves in the two validation cohorts were 0.698 and 0.673 for never-smokers and 0.728 and 0.752 for ever-smokers. Our models had higher areas under the receiver operating characteristic curves than other existing models and were well calibrated in the validation cohort. The China NCC-LCm2021 ⩾0.47% threshold was suggested for never-smokers and ⩾0.51% for ever-smokers. Moreover, we provided a range of threshold options with corresponding expected screening outcomes, screening targets, and screening efficiency. Conclusion: The construction of the China NCC-LCm2021 models can accurately reflect individual risk of lung cancer, regardless of smoking status. Our models can significantly increase the feasibility of conducting centralized lung cancer screening programs because we provide justified thresholds to define the high-risk population of lung cancer and threshold options to adapt different configurations of medical resources.

Novel trace elements detected in multiple stages of pregnancy impact neonatal birth weight by affecting gestational age.

Prenatal exposure to elements may be associated with birth weight via shortening of gestation. This study aimed to determine if prenatal exposure is associated with birth weight, and to explore the potential mediating role of gestational age in the association. Within an established Bangladesh prospective birth cohort (2008-2011), we analyzed the concentrations of 15 elements in maternal serum samples collected during the first (n = 780) and second (n = 610) trimesters using inductively coupled plasma mass spectrometry. Mediation analyses explored the relationships between these elements, gestational age, and birth weight. Serum concentrations of cobalt (Co) (first trimester: b = 56.5; 95% confidence interval [CI]: 13.5-99.0; false discovery rate [FDR]-q = 0.035; second trimester: b = 73.3; 95% CI: 20.4-130.2; FDR-q = 0.037) and antimony (Sb) in both trimesters (first trimester:b = 92.1; 95% CI: 66.0-118.9; FDR-q < 0.001; second trimester: b = 93.3; 95% CI: 67.3-118.4; FDR-q < 0.001), and strontium (Sr) in the first trimester (b = 142.4; 95% CI: 41.6-247.9; FDR-q = 0.035) were positively associated with birth weight, while negative associations were observed for barium (Ba) (first trimester: b = -154.8; 95% CI: -217.9 to 91.8; FDR-q <0.001; second trimester: b = -26.7; 95% CI: -44.9 to 10.2; FDR-q < 0.001). These elements act partially by affecting gestation age and appear to have heightened impact among smaller infants. Further research is needed to determine the biological underpinnings of these effects, which may inform strategies to avert low birth weight.

Association of longitudinal platelet count trajectory with ICU mortality: A multi-cohort study.

Objective: Platelet (PLT) engages in immune and inflammatory responses, all of which are related to the prognosis of critically ill patients. Although thrombocytopenia at ICU admission contributes to in-hospital mortality, PLT is repeatedly measured during ICU hospitalization and the role of longitudinal PLT trajectory remains unclear. We aimed to identify dynamic PLT trajectory patterns and evaluate their relationships with mortality risk and thrombocytopenia. Methods: We adopted a three-phase, multi-cohort study strategy. Firstly, longitudinal PLT trajectory patterns within the first four ICU days and their associations with 28-day survival were tested in the eICU Collaborative Research Database (eICU-CRD) and independently validated in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Secondly, the relationships among PLT trajectory patterns, thrombocytopenia, and 28-day mortality were explored and validated. Finally, a Mortality GRade system for ICU dynamically monitoring patients (Mortality-GRID) was developed to quantify the mortality risk based on longitudinal PLT, which was further validated in the Molecular Epidemiology of Acute Respiratory Distress Syndrome (MEARDS) cohort. Results: A total of 35,332 ICU patients were included from three cohorts. Trajectory analysis clustered patients into ascending (AS), stable (ST), or descending (DS) PLT patterns. DS patients with high baseline PLT decline quickly, resulting in poor prognosis. AS patients have low baseline PLT but recover quickly, favoring a better prognosis. ST patients maintain low PLT, having a moderate prognosis in between (HR ST vs AS = 1.26, 95% CI: 1.14-1.38, P = 6.15 × 10-6; HR DS vs AS = 1.58, 95% CI: 1.40-1.79, P = 1.41 × 10-13). The associations remained significant in patients without thrombocytopenia during the entire ICU hospitalization and were robust in sensitivity analyses and stratification analyses. Further, the trajectory pattern was a warning sign of thrombocytopenia, which mediated 27.2% of the effects of the PLT trajectory on 28-day mortality (HR indirect = 1.11, 95% CI: 1.06-1.17, P = 9.80 × 10-6). Mortality-GRID well predicts mortality risk, which is in high consistency with that directly estimated in MEARDS (r = 0.98, P = 1.30 × 10-23). Conclusion: Longitudinal PLT trajectory is a complementary predictor to baseline PLT for patient survival, even in patients without risk of thrombocytopenia. Mortality-GRID could identify patients at high mortality risk.

Sex disparity of lung cancer risk in non-smokers: a multicenter population-based prospective study based on China National Lung Cancer Screening Program.

BACKGROUND: Non-smokers account for a large proportion of lung cancer patients, especially in Asia, but the attention paid to them is limited compared with smokers. In non-smokers, males display a risk for lung cancer incidence distinct from the females-even after excluding the influence of smoking; but the knowledge regarding the factors causing the difference is sparse. Based on a large multicenter prospective cancer screening cohort in China, we aimed to elucidate the interpretable sex differences caused by known factors and provide clues for primary and secondary prevention. METHODS: Risk factors including demographic characteristics, lifestyle factors, family history of cancer, and baseline comorbidity were obtained from 796,283 Chinese non-smoking participants by the baseline risk assessment completed in 2013 to 2018. Cox regression analysis was performed to assess the sex difference in the risk of lung cancer, and the hazard ratios (HRs) that were adjusted for different known factors were calculated and compared to determine the proportion of excess risk and to explain the existing risk factors. RESULTS: With a median follow-up of 4.80 years, 3351 subjects who were diagnosed with lung cancer were selected in the analysis. The lung cancer risk of males was significantly higher than that of females; the HRs in all male non-smokers were 1.29 (95% confidence interval [CI]: 1.20-1.38) after adjusting for the age and 1.38 (95% CI: 1.28-1.50) after adjusting for all factors, which suggested that known factors could not explain the sex difference in the risk of lung cancer in non-smokers. Known factors were 7% (|1.29-1.38|/1.29) more harmful in women than in men. For adenocarcinoma, women showed excess risk higher than men, contrary to squamous cell carcinoma; after adjusting for all factors, 47% ([1.30-1.16]/[1.30-1]) and 4% ([7.02-6.75]/[7.02-1])) of the excess risk was explainable in adenocarcinoma and squamous cell carcinoma. The main causes of gender differences in lung cancer risk were lifestyle factors, baseline comorbidity, and family history. CONCLUSIONS: Significant gender differences in the risk of lung cancer were discovered in China non-smokers. Existing risk factors did not explain the excess lung cancer risk of all non-smoking men, and the internal causes for the excess risk still need to be explored; most known risk factors were more harmful to non-smoking women; further exploring the causes of the sex difference would help to improve the prevention and screening programs and protect the non-smoking males from lung cancers.

Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges.

Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.

A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians.

INTRODUCTION: Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC). METHODS: Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers. RESULTS: With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10-13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10-13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10-13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10-13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10-4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification. CONCLUSIONS: Important G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.

APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance.

BACKGROUND: Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions. METHODS: Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review. FINDINGS: APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10-69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness. INTERPRETATION: APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO). FUNDING: National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).

One-off low-dose CT for lung cancer screening in China: a multicentre, population-based, prospective cohort study.

BACKGROUND: Lung cancer is the leading cause of cancer death worldwide. Data on the effectiveness of one-off low-dose CT (LDCT) in reducing lung cancer mortality and all-cause mortality are needed to inform screening programmes in countries with limited medical resources. We aimed to evaluate the effectiveness of one-off LDCT screening in the early detection of lung cancer in China. METHODS: A multicentre, population-based, prospective cohort study was done in 12 cities of eight provinces across China, recruiting individuals aged 40-74 years who were asymptomatic for lung cancer with no lung cancer history. Participants were classified as at high risk or low risk of lung cancer using a sex-specific risk score that incorporated cigarette smoking, level of physical activity, occupational exposures, history of chronic respiratory diseases, family history of lung cancer, diet, and passive smoking (women only). Participants at high risk were invited for a one-off LDCT scan and were classified into screened and non-screened groups on the basis of whether or not they had the scan. Lung cancer incidence density, lung cancer mortality, and all-cause mortality were calculated for the screened and non-screened groups. The effectiveness of a one-off LDCT scan was evaluated by a comparison of the screened and non-screened groups in terms of lung cancer mortality and all-cause mortality in the period from cohort entry until administrative censoring (June 20, 2020). Inverse probability weighting was adopted to account for potential imbalanced factors between the two groups and Cox proportional hazards model was used to estimate the weighted associations between mortality and one-off LDCT scans. FINDINGS: Between Feb 19, 2013, and Oct 31, 2018, 1 032 639 individuals were assessed for eligibility. 1 016 740 participants were enrolled in the study, of whom 3581 had a lung cancer diagnosis after a median follow-up of 3·6 years (IQR 2·8-5·1). Among the 223 302 participants at high risk, 79 581 (35·6%) had an LDCT scan (screened group) and 143 721 (64·4%) did not (non-screened group). After inverse probability weighting, lung cancer incidence density was 47·0% higher (hazard ratio 1·47 [95% CI 1·27-1·70]; p<0·0001), lung cancer mortality was 31·0% lower (0·69 [95% CI 0·53-0·92]; p=0·010) and all-cause mortality was 32·0% lower (0·68 [0·57-0·82]; p<0·0001) for participants in the screened group compared with those in the non-screened group. INTERPRETATION: One-off LDCT screening was associated with significantly lower lung cancer mortality and all-cause mortality in a large population in China. Our results point to the promise of one-off LDCT screening in countries with limited medical resources. Further studies are needed to explore interactions by subgroup-including sex, age, smoking status, and economic status-to develop population-specific screening strategies. FUNDING: Ministry of Finance and National Health Commission of the People's Republic of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Lung cancer risk prediction models based on pulmonary nodules: A systematic review.

BACKGROUND: Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models. METHODS: The keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed. RESULTS: A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets. CONCLUSION: The existing models showed good discrimination for identifying high-risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.