Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides.

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann-Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Models trained through this workflow achieved an error (mean absolute error of 0.43 J mol-1 K-1) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures.

Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification.

BACKGROUND: Although polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored. METHODS: Lung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold. RESULTS: Considerable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12-3.50, P-value = 4.13 × 10-15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99-2.49, P-value = 5.70 × 10-46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72-0.74). CONCLUSIONS: Lung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS.

A Prospective Observational Study of Physical Activity Levels and Physical Fitness of People at High Risk for Lung Cancer.

Introduction: Physical activity (PA) is a potentially modifiable risk factor for lung cancer, with previous research revealing that people who engage in more PA have lower risk of developing lung cancer. PA levels of lung cancer screening participants have not previously been explored. Methods: Participants at a single Australian International Lung Screen Trial site were eligible for assessment of self-reported PA levels (International Physical Activity Questionnaire and Physical Activity Scale for the Elderly) and physical assessments (6-min walk distance, hand grip muscle strength, daily step count, and body composition) at a single time point during lung cancer screening. Statistics were predominantly descriptive, with parametric data presented as mean and SD and nonparametric data presented as median and interquartile range (IQR). Results: A total of 178 participants were enrolled in this study, with a median age of 61 years. Of the participants, 61% were men and 51% were people who currently smoke. The median total International Physical Activity Questionnaire score was 1756 MET/min/wk (IQR 689, 4049). Mean total Physical Activity Scale for the Elderly score was 160 (SD 72), higher than described in healthy sedentary adults. The median daily step count was 7237 steps (IQR 5353, 10,038) and mean 6-minute walk distance was 545 m (SD 92). Median grip strengths were within predicted normal range, with an elevated median percentage body fat and low skeletal muscle mass found on body composition. Conclusion: Almost a quarter of International Lung Screen Trial participants assessed reported low levels of PA and have a potentially modifiable risk factor to improve health outcomes. Larger studies are needed to characterize the burden of inactivity among high-risk lung cancer screening populations.

Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review.

BACKGROUND: Lung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related death worldwide. Although smoking is the primary cause of the cancer, lung cancer is also commonly diagnosed in people who have never smoked. Currently, the proportion of people who have never smoked diagnosed with lung cancer is increasing. Despite this alarming trend, this population is ineligible for lung screening. With the increasing proportion of people who have never smoked among lung cancer cases, there is a pressing need to develop prediction models to identify high-risk people who have never smoked and include them in lung cancer screening programs. Thus, our systematic review is intended to provide a comprehensive summary of the evidence on existing risk prediction models for lung cancer in people who have never smoked. METHODS: Electronic searches will be conducted in MEDLINE (Ovid), Embase (Ovid), Web of Science Core Collection (Clarivate Analytics), Scopus, and Europe PMC and Open-Access Theses and Dissertations databases. Two reviewers will independently perform title and abstract screening, full-text review, and data extraction using the Covidence review platform. Data extraction will be performed based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). The risk of bias will be evaluated independently by two reviewers using the Prediction model Risk-of-Bias Assessment Tool (PROBAST) tool. If a sufficient number of studies are identified to have externally validated the same prediction model, we will combine model performance measures to evaluate the model's average predictive accuracy (e.g., calibration, discrimination) across diverse settings and populations and explore sources of heterogeneity. DISCUSSION: The results of the review will identify risk prediction models for lung cancer in people who have never smoked. These will be useful for researchers planning to develop novel prediction models, and for clinical practitioners and policy makers seeking guidance for clinical decision-making and the formulation of future lung cancer screening strategies for people who have never smoked. SYSTEMATIC REVIEW REGISTRATION: This protocol has been registered in PROSPERO under the registration number CRD42023483824.

Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.

BACKGROUND: Low-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen. METHODS: Using four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess three machine learning (ML) models (eXtreme Gradient Boosted Trees, random forest, least absolute shrinkage and selection operator (LASSO)) for their ability to accurately predict risk of malignancy for pulmonary nodules. We report model performance based on the area under the curve (AUC) and calibration metrics in the held-out test set. RESULTS: The LASSO model yielded the best predictive performance in cross-validation and was fit in the full training set based on optimised hyperparameters. Our radiomics model had a test-set AUC of 0.93 (95% CI 0.90 to 0.96) and outperformed the established Pan-Canadian Early Detection of Lung Cancer model (AUC 0.87, 95% CI 0.85 to 0.89) for nodule assessment. Our model performed well among both solid (AUC 0.93, 95% CI 0.89 to 0.97) and subsolid nodules (AUC 0.91, 95% CI 0.85 to 0.95). CONCLUSIONS: We developed highly accurate ML models based on radiomic and epidemiological features from four international lung cancer screening studies that may be suitable for assessing indeterminate screen-detected pulmonary nodules for risk of malignancy.

Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies.

BACKGROUND: Clinical, molecular, and genetic epidemiology studies displayed remarkable differences between ever- and never-smoking lung cancer. METHODS: We conducted a stratified multi-population (European, East Asian, and African descent) association study on 44,823 ever-smokers and 20,074 never-smokers to identify novel variants that were missed in the non-stratified analysis. Functional analysis including expression quantitative trait loci (eQTL) colocalization and DNA damage assays, and annotation studies were conducted to evaluate the functional roles of the variants. We further evaluated the impact of smoking quantity on lung cancer risk for the variants associated with ever-smoking lung cancer. RESULTS: Five novel independent loci, GABRA4, intergenic region 12q24.33, LRRC4C, LINC01088, and LCNL1 were identified with the association at two or three populations (P < 5 × 10-8). Further functional analysis provided multiple lines of evidence suggesting the variants affect lung cancer risk through excessive DNA damage (GABRA4) or cis-regulation of gene expression (LCNL1). The risk of variants from 12 independent regions, including the well-known CHRNA5, associated with ever-smoking lung cancer was evaluated for never-smokers, light-smokers (packyear ≤ 20), and moderate-to-heavy-smokers (packyear > 20). Different risk patterns were observed for the variants among the different groups by smoking behavior. CONCLUSIONS: We identified novel variants associated with lung cancer in only ever- or never-smoking groups that were missed by prior main-effect association studies. IMPACT: Our study highlights the genetic heterogeneity between ever- and never-smoking lung cancer and provides etiologic insights into the complicated genetic architecture of this deadly cancer.

Validation of the psychosocial consequences of screening in lung cancer questionnaire in the international lung screen trial Australian cohort.

BACKGROUND: Evaluation of psychosocial consequences of lung cancer screening with LDCT in high-risk populations has generally been performed using generic psychometric instruments. Such generic instruments have low coverage and low power to detect screening impacts. This study aims to validate an established lung cancer screening-specific questionnaire, Consequences Of Screening Lung Cancer (COS-LC), in Australian-English and describe early results from the baseline LDCT round of the International Lung Screen Trial (ILST). METHODS: The Danish-version COS-LC was translated to Australian-English using the double panel method and field tested in Australian-ILST participants to examine content validity. A random sample of 200 participants were used to assess construct validity using Rasch item response theory models. Reliability was assessed using classical test theory. The COS-LC was administered to ILST participants at prespecified timepoints including at enrolment, dependent of screening results. RESULTS: Minor linguistic alterations were made after initial translation of COS-LC to English. The COS-LC demonstrated good content validity and adequate construct validity using psychometric analysis. The four core scales fit the Rasch model, with only minor issues in five non-core scales which resolved with modification. 1129 Australian-ILST participants were included in the analysis, with minimal psychosocial impact observed shortly after baseline LDCT results. CONCLUSION: COS-LC is the first lung cancer screening-specific questionnaire to be validated in Australia and has demonstrated excellent psychometric properties. Early results did not demonstrate significant psychosocial impacts of screening. Longer-term follow-up is awaited and will be particularly pertinent given the announcement of an Australian National Lung Cancer Screening Program. TRIAL REGISTRATION: NCT02871856.

Distinct karyotypic and mutational landscape in trisomy AML.

Trisomy karyotype occurs in 5%-10% of AML. Its mutational landscape and prognostic significance are not well defined. A cohort of 156 trisomy AML patients was analysed, with reference to 615 cytogenetically normal (CN) AML patients. Trisomy AML showed distinct mutational landscape with more prevalent SMC1A, N/KRAS, ASXL1 and BCOR but fewer CEBPAbZIP and NPM1 mutations in patients ≤60, and fewer NPM1 mutations in those >60. NRAS mutations were associated with poor outcome in trisomy AML, whereas DNMT3A and FLT3-ITD mutations had neutral effect. Trisomy AML appeared biologically distinct from CN-AML.

Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls.

BACKGROUND: Although the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated. METHODS: The genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways. RESULTS: Of the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p ≤ 1.67 × 10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p ≤ 1.28 × 10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified. CONCLUSIONS: Sixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby. PLAIN LANGUAGE SUMMARY: The epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer.

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Open-Source Deep Learning and Quantitative Measurement Initiative.

INTRODUCTION: With global adoption of computed tomography (CT) lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open-source, cloud-based, globally distributed, screening CT imaging data set and computational environment that are compliant with the most stringent international privacy regulations that also protect the intellectual properties of researchers, the International Association for the Study of Lung Cancer sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be used for clinically relevant AI research. METHODS: In this second phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans. RESULTS: A total of 1394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness more than or equal to 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high-quality CT scans. CONCLUSIONS: These initial experiments revealed that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based data sets.

Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer.

Low-dose computed tomography (LDCT) screening for lung cancer substantially reduces mortality from lung cancer, as revealed in randomized controlled trials and meta-analyses. This review is based on the ninth CT screening symposium of the International Association for the Study of Lung Cancer, which focuses on the major themes pertinent to the successful global implementation of LDCT screening and develops a strategy to further the implementation of lung cancer screening globally. These recommendations provide a 5-year roadmap to advance the implementation of LDCT screening globally, including the following: (1) establish universal screening program quality indicators; (2) establish evidence-based criteria to identify individuals who have never smoked but are at high-risk of developing lung cancer; (3) develop recommendations for incidentally detected lung nodule tracking and management protocols to complement programmatic lung cancer screening; (4) Integrate artificial intelligence and biomarkers to increase the prediction of malignancy in suspicious CT screen-detected lesions; and (5) standardize high-quality performance artificial intelligence protocols that lead to substantial reductions in costs, resource utilization and radiologist reporting time; (6) personalize CT screening intervals on the basis of an individual's lung cancer risk; (7) develop evidence to support clinical management and cost-effectiveness of other identified abnormalities on a lung cancer screening CT; (8) develop publicly accessible, easy-to-use geospatial tools to plan and monitor equitable access to screening services; and (9) establish a global shared education resource for lung cancer screening CT to ensure high-quality reading and reporting.

CYP2A6 Activity and Cigarette Consumption Interact in Smoking-Related Lung Cancer Susceptibility.

Cigarette smoke, containing both nicotine and carcinogens, causes lung cancer. However, not all smokers develop lung cancer, highlighting the importance of the interaction between host susceptibility and environmental exposure in tumorigenesis. Here, we aimed to delineate the interaction between metabolizing ability of tobacco carcinogens and smoking intensity in mediating genetic susceptibility to smoking-related lung tumorigenesis. Single-variant and gene-based associations of 43 tobacco carcinogen-metabolizing genes with lung cancer were analyzed using summary statistics and individual-level genetic data, followed by causal inference of Mendelian randomization, mediation analysis, and structural equation modeling. Cigarette smoke-exposed cell models were used to detect gene expression patterns in relation to specific alleles. Data from the International Lung Cancer Consortium (29,266 cases and 56,450 controls) and UK Biobank (2,155 cases and 376,329 controls) indicated that the genetic variant rs56113850 C>T located in intron 4 of CYP2A6 was significantly associated with decreased lung cancer risk among smokers (OR = 0.88, 95% confidence interval = 0.85-0.91, P = 2.18 × 10-16), which might interact (Pinteraction = 0.028) with and partially be mediated (ORindirect = 0.987) by smoking status. Smoking intensity accounted for 82.3% of the effect of CYP2A6 activity on lung cancer risk but entirely mediated the genetic effect of rs56113850. Mechanistically, the rs56113850 T allele rescued the downregulation of CYP2A6 caused by cigarette smoke exposure, potentially through preferential recruitment of transcription factor helicase-like transcription factor. Together, this study provides additional insights into the interplay between host susceptibility and carcinogen exposure in smoking-related lung tumorigenesis. SIGNIFICANCE: The causal pathway connecting CYP2A6 genetic variability and activity, cigarette consumption, and lung cancer susceptibility in smokers highlights the need for behavior modification interventions based on host susceptibility for cancer prevention.

Canadian Association of Radiologists Thoracic Imaging Referral Guideline.

The Canadian Association of Radiologists (CAR) Thoracic Expert Panel consists of radiologists, respirologists, emergency and family physicians, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 24 clinical/diagnostic scenarios, a rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 30 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop 48 recommendation statements across the 24 scenarios. This guideline presents the methods of development and the referral recommendations for screening/asymptomatic individuals, non-specific chest pain, hospital admission for non-thoracic conditions, long-term care admission, routine pre-operative imaging, post-interventional chest procedure, upper respiratory tract infection, acute exacerbation of asthma, acute exacerbation of chronic obstructive pulmonary disease, suspect pneumonia, pneumonia follow-up, immunosuppressed patient with respiratory symptoms/febrile neutropenia, chronic cough, suspected pneumothorax (non-traumatic), clinically suspected pleural effusion, hemoptysis, chronic dyspnea of non-cardiovascular origin, suspected interstitial lung disease, incidental lung nodule, suspected mediastinal lesion, suspected mediastinal lymphadenopathy, and elevated diaphragm on chest radiograph.

Addressing Inequity in Spatial Access to Lung Cancer Screening.

BACKGROUND: The successful implementation of an equitable lung cancer screening program requires consideration of factors that influence accessibility to screening services. METHODS: Using lung cancer cases in British Columbia (BC), Canada, as a proxy for a screen-eligible population, spatial access to 36 screening sites was examined using geospatial mapping and vehicle travel time from residential postal code at diagnosis to the nearest site. The impact of urbanization and Statistics Canada's Canadian Index of Multiple Deprivation were examined. RESULTS: Median travel time to the nearest screening site was 11.7 min (interquartile range 6.2-23.2 min). Urbanization was significantly associated with shorter drive time (p < 0.001). Ninety-nine percent of patients with ≥60 min drive times lived in rural areas. Drive times were associated with sex, ethnocultural composition, situational vulnerability, economic dependency, and residential instability. For example, the percentage of cases with drive times ≥60 min among the least deprived situational vulnerability group was 4.7% versus 44.4% in the most deprived group. CONCLUSIONS: Populations at risk in rural and remote regions may face more challenges accessing screening services due to increased travel times. Drive times increased with increasing sociodemographic and economic deprivations highlighting groups that may require support to ensure equitable access to lung cancer screening.

New insights into the biology and development of lung cancer in never smokers-implications for early detection and treatment.

Lung cancer is the leading cause of cancer deaths worldwide. Despite never smokers comprising between 10 and 25% of all cases, lung cancer in never smokers (LCNS) is relatively under characterized from an etiological and biological perspective. The application of multi-omics techniques on large patient cohorts has significantly advanced the current understanding of LCNS tumor biology. By synthesizing the findings of multi-omics studies on LCNS from a clinical perspective, we can directly translate knowledge regarding tumor biology into implications for patient care. Primarily focused on never smokers with lung adenocarcinoma, this review details the predominance of driver mutations, particularly in East Asian patients, as well as the frequency and importance of germline variants in LCNS. The mutational patterns present in LCNS tumors are thoroughly explored, highlighting the high abundance of the APOBEC signature. Moreover, this review recognizes the spectrum of immune profiles present in LCNS tumors and posits how it can be translated to treatment selection. The recurring and novel insights from multi-omics studies on LCNS tumor biology have a wide range of clinical implications. Risk factors such as exposure to outdoor air pollution, second hand smoke, and potentially diet have a genomic imprint in LCNS at varying degrees, and although they do not encompass all LCNS cases, they can be leveraged to stratify risk. Germline variants similarly contribute to a notable proportion of LCNS, which warrants detailed documentation of family history of lung cancer among never smokers and demonstrates value in developing testing for pathogenic variants in never smokers for early detection in the future. Molecular driver subtypes and specific co-mutations and mutational signatures have prognostic value in LCNS and can guide treatment selection. LCNS tumors with no known driver alterations tend to be stem-like and genes contributing to this state may serve as potential therapeutic targets. Overall, the comprehensive findings of multi-omics studies exert a wide influence on clinical management and future research directions in the realm of LCNS.

The impact of the multi-disciplinary molecular tumour board and integrative next generation sequencing on clinical outcomes in advanced solid tumours.

Background: The integration of next-generation sequencing (NGS) comprehensive gene profiling (CGP) into clinical practice is playing an increasingly important role in oncology. Therefore, the HKU-HKSH Multi-disciplinary Molecular Tumour Board (MTB) was established to advance precision oncology in Hong Kong. A multicenter retrospective study investigated the feasibility of the HKU-HKSH MTB in determining genome-guided therapy for treatment-refractory solid cancers in Hong Kong. Methods: Patients who were presented at the HKU-HKSH MTB between August 2018 and June 2022 were included in this study. The primary study endpoints were the proportion of patients who receive MTB-guided therapy based on genomic analysis and overall survival (OS). Secondary endpoints included the proportion of patients with actionable genomic alterations, objective response rate (ORR), and disease control rate (DCR). The Kaplan-Meier method was used in the survival analyses, and hazard ratios were calculated using univariate Cox regression. Findings: 122 patients were reviewed at the HKU-HKSH MTB, and 63% (n = 77) adopted treatment per the MTB recommendations. These patients achieved a significantly longer median OS than those who did not receive MTB-guided therapy (12.7 months vs. 5.2 months, P = 0.0073). Their ORR and DCR were 29% and 65%, respectively. Interpretation: Our study demonstrated that among patients with heavily pre-treated advanced solid cancers, MTB-guided treatment could positively impact survival outcomes, thus illustrating the applicability of NGS CGPs in real-world clinical practice. Funding: The study was supported by the Li Shu Pui Medical Foundation. Dr Aya El Helali was supported by the Li Shu Pui Medical Foundation Fellowship grant from the Li Shu Pui Medical Foundation. Funders had no role in study design, data collection, data analysis, interpretation, or writing of the report.

Candidate pathway analysis of surfactant proteins identifies CTSH and SFTA2 that influences lung cancer risk.

Pulmonary surfactant is a lipoprotein synthesized and secreted by alveolar type II cells in lung. We evaluated the associations between 200,139 single nucleotide polymorphisms (SNPs) of 40 surfactant-related genes and lung cancer risk using genotyped data from two independent lung cancer genome-wide association studies. Discovery data included 18,082 cases and 13,780 controls of European ancestry. Replication data included 1,914 cases and 3,065 controls of European descent. Using multivariate logistic regression, we found novel SNPs in surfactant-related genes CTSH [rs34577742 C > T, odds ratio (OR) = 0.90, 95% confidence interval (CI) = 0.89-0.93, P = 7.64 × 10-9] and SFTA2 (rs3095153 G > A, OR = 1.16, 95% CI = 1.10-1.21, P = 1.27 × 10-9) associated with overall lung cancer in the discovery data and validated in an independent replication data-CTSH (rs34577742 C > T, OR = 0.88, 95% CI = 0.80-0.96, P = 5.76 × 10-3) and SFTA2 (rs3095153 G > A, OR = 1.14, 95% CI = 1.01-1.28, P = 3.25 × 10-2). Among ever smokers, we found SNPs in CTSH (rs34577742 C > T, OR = 0.89, 95% CI = 0.85-0.92, P = 1.94 × 10-7) and SFTA2 (rs3095152 G > A, OR = 1.20, 95% CI = 1.14-1.27, P = 4.25 × 10-11) associated with overall lung cancer in the discovery data and validated in the replication data-CTSH (rs34577742 C > T, OR = 0.88, 95% CI = 0.79-0.97, P = 1.64 × 10-2) and SFTA2 (rs3095152 G > A, OR = 1.15, 95% CI = 1.01-1.30, P = 3.81 × 10-2). Subsequent transcriptome-wide association study using expression weights from a lung expression quantitative trait loci study revealed genes most strongly associated with lung cancer are CTSH (PTWAS = 2.44 × 10-4) and SFTA2 (PTWAS = 2.32 × 10-6).

Clinical and genetic determinants of severe acute pancreatitis: A genetic association study in the UK Biobank.

BACKGROUND AND AIM: The clinical severity of acute pancreatitis is unpredictable, ranging from self-limiting disease to life-threatening inflammation. The determinants of severe acute pancreatitis (SAP) are unclear. We aim to identify clinical variables and single nucleotide polymorphisms (SNP) associated with SAP. METHODS: We used UK Biobank data to conduct a case-control clinical and genetic association study. Pancreatitis patients were identified through national hospital and mortality records across the United Kingdom. Clinical covariates and SAP were analyzed for associations. Genotyped data that included 35 SNPs were assessed for independent associations with SAP and SNP to SNP interaction. RESULTS: A total of 665 patients with SAP and 3304 non-SAP patients were identified. Male sex and older age increased odds of developing SAP (odds ratio [OR] 1.48; 95% confiden interval [CI] 1.24-1.78, P < 0.0001) and (OR 1.23; 95% CI 1.17-1.29), P < 0.0001), respectively. SAP was associated with diabetes (OR 1.46; 95% CI 1.15-1.86, P = 0.002), chronic kidney disease (OR 1.74; 95% CI 1.26-2.42, P = 0.001), and cardiovascular disease (OR 2.00; 95% CI 1.54-2.61, P = 0.0001). A significant association was established between IL-10 rs3024498 and SAP (OR 1.24; 95% CI 1.09-1.41, P = 0.0014). Epistasis analysis revealed that the odds of SAP was greater by an interaction between TLR 5 rs5744174 and Factor V rs6025 (ORinteraction 7.53; P = 6.64 × 10-5 ). CONCLUSION: This study reports clinical risk factors for SAP. We also show evidence for an interaction between rs5744174 and rs6025 as determinants for SAP in addition to rs3024498 independently altering the severity of acute pancreatitis.

Air Pollution and Lung Cancer: A Review by International Association for the Study of Lung Cancer Early Detection and Screening Committee.

INTRODUCTION: The second leading cause of lung cancer is air pollution. Air pollution and smoking are synergistic. Air pollution can worsen lung cancer survival. METHODS: The Early Detection and Screening Committee of the International Association for the Study of Lung Cancer formed a working group to better understand issues in air pollution and lung cancer. These included identification of air pollutants, their measurement, and proposed mechanisms of carcinogenesis. The burden of disease and the underlying epidemiologic evidence linking air pollution to lung cancer in individuals who never and ever smoked were summarized to quantify the problem, assess risk prediction models, and develop recommended actions. RESULTS: The number of estimated attributable lung cancer deaths has increased by nearly 30% since 2007 as smoking has decreased and air pollution has increased. In 2013, the International Agency for Research on Cancer classified outdoor air pollution and particulate matter with aerodynamic diameter less than 2.5 microns in outdoor air pollution as carcinogenic to humans (International Agency for Research on Cancer group 1) and as a cause of lung cancer. Lung cancer risk models reviewed do not include air pollution. Estimation of cumulative exposure to air pollution exposure is complex which poses major challenges with accurately collecting long-term exposure to ambient air pollution for incorporation into risk prediction models in clinical practice. CONCLUSIONS: Worldwide air pollution levels vary widely, and the exposed populations also differ. Advocacy to lower sources of exposure is important. Health care can lower its environmental footprint, becoming more sustainable and resilient. The International Association for the Study of Lung Cancer community can engage broadly on this topic.

Consensus Statements on Precision Oncology in the China Greater Bay Area.

BACKGROUND: Next-generation sequencing comprehensive genomic panels (NGS CGPs) have enabled the delivery of tailor-made therapeutic approaches to improve survival outcomes in patients with cancer. Within the China Greater Bay Area (GBA), territorial differences in clinical practices and health care systems and strengthening collaboration warrant a regional consensus to consolidate the development and integration of precision oncology (PO). Therefore, the Precision Oncology Working Group (POWG) formulated standardized principles for the clinical application of molecular profiling, interpretation of genomic alterations, and alignment of actionable mutations with sequence-directed therapy to deliver clinical services of excellence and evidence-based care to patients with cancer in the China GBA. METHODS: Thirty experts used a modified Delphi method. The evidence extracted to support the statements was graded according to the GRADE system and reported according to the Revised Standards for Quality Improvement Reporting Excellence guidelines, version 2.0. RESULTS: The POWG reached consensus in six key statements: harmonization of reporting and quality assurance of NGS; molecular tumor board and clinical decision support systems for PO; education and training; research and real-world data collection, patient engagement, regulations, and financial reimbursement of PO treatment strategies; and clinical recommendations and implementation of PO in clinical practice. CONCLUSION: POWG consensus statements standardize the clinical application of NGS CGPs, streamline the interpretation of clinically significant genomic alterations, and align actionable mutations with sequence-directed therapies. The POWG consensus statements may harmonize the utility and delivery of PO in China's GBA.