PNPLA3, obesity and heavy alcohol use in cirrhosis patients may exert a synergistic increase hepatocellular carcinoma risk.

BACKGROUND & AIMS: In patients with cirrhosis, continued heavy alcohol consumption and obesity may increase risk of hepatocellular carcinoma (HCC). We examined whether germline susceptibility to hepatic steatosis not only independently predisposes to HCC but may also act synergistically with other risk factors. METHODS: We analyzed data from 1911 patients in two multicenter prospective cohort studies in the U.S. We classified patients according to alcohol consumption (current heavy vs. not current heavy), obesity (body mass index [BMI] ≥30 vs. <30), and PNPLA3 I148M variant status (carrier of at least one G risk allele vs. noncarrier). We examined the independent and joint effects of these risk factors on risk of developing HCC using Cox regression with competing risks. RESULTS: Mean age was 59.6y, 64.3% male, 28.7% Hispanic, 18.3% non-Hispanic Black, 50.9% were obese, 6.2% had current heavy alcohol consumption, and 58.4% harbored at least one PNPLA3 G-allele. 116 patients developed HCC. Compared to PNPLA3 noncarriers without heavy alcohol consumption, HCC risk was 2.65-fold higher (hazard ratio [HR], 2.65; 95% confidence interval [CI], 1.20-5.86) for carriers who had current heavy alcohol consumption. Compared to noncarrier patients without obesity, HCC risk was higher (HR, 2.40; 95%CI, 1.33-4.31) for carrier patients who were obese. PNPLA3 and alcohol consumption effect was stronger among patients with viral etiology of cirrhosis (HR, 3.42; 95% CI, 1.31-8.90). PNPLA3 improved 1-year risk prediction for HCC when added to a clinical risk model. CONCLUSIONS: The PNPLA3 variant may help refine risk stratification for HCC in patients with cirrhosis with heavy alcohol consumption or obesity who may need specific preventive measures.

What is the additive value of nutritional deficiency to VA-FI in the risk assessment for heart failure patients?

OBJECTIVES: To assess the impact of adding the Prognostic Nutritional Index (PNI) to the U.S. Veterans Health Administration frailty index (VA-FI) for the prediction of time-to-death and other clinical outcomes in Veterans hospitalized with Heart Failure. METHODS: A retrospective cohort study of veterans hospitalized for heart failure (HF) from October 2015 to October 2018. Veterans ≥50 years with albumin and lymphocyte counts, needed to calculate the PNI, in the year prior to hospitalization were included. We defined malnutrition as PNI ≤43.6, based on the Youden index. VA-FI was calculated from the year prior to the hospitalization and identified three groups: robust (≤0.1), prefrail (0.1-0.2), and frail (>0.2). Malnutrition was added to the VA-FI (VA-FI-Nutrition) as a 32nd deficit with the total number of deficits divided by 32. Frailty levels used the same cut-offs as the VA-FI. We compared categories based on VA-FI to those based on VA-FI-Nutrition and estimated the hazard ratio (HR) for post-discharge all-cause mortality over the study period as the primary outcome and other adverse events as secondary outcomes among patients with reduced or preserved ejection fraction in each VA-FI and VA-FI-Nutrition frailty groups. RESULTS: We identified 37,601 Veterans hospitalized for HF (mean age: 73.4 ± 10.3 years, BMI: 31.3 ± 7.4 kg/m2). In general, VA-FI-Nutrition reclassified 1959 (18.6%) Veterans to a higher frailty level. The VA-FI identified 1,880 (5%) as robust, 8,644 (23%) as prefrail, and 27,077 (72%) as frail. The VA-FI-Nutrition reclassified 382 (20.3%) from robust to prefrail and 1577 (18.2%) from prefrail to frail creating the modified-prefrail and modified-frail categories based on the VA-FI-Nutrition. We observed shorter time-to-death among Veterans reclassified to a higher frailty status vs. those who remained in their original group (Median of 2.8 years (IQR:0.5,6.8) in modified-prefrail vs. 6.3 (IQR:1.8,6.8) years in robust, and 2.2 (IQR:0.7,5.7) years in modified-frail vs. 3.9 (IQR:1.4,6.8) years in prefrail). The adjusted HR in the reclassified groups was also significantly higher in the VA-FI-Nutrition frailty categories with a 38% increase in overall all-cause mortality among modified-prefrail and a 50% increase among modified-frails. Similar trends of increasing adverse events were also observed among reclassified groups for other clinical outcomes. CONCLUSION: Adding PNI to VA-FI provides a more accurate and comprehensive assessment among Veterans hospitalized for HF. Clinicians should consider adding a specific nutrition algorithm to automated frailty tools to improve the validity of risk prediction in patients hospitalized with HF.

Somatic mutations of esophageal adenocarcinoma: a comparison between Black and White patients.

Esophageal adenocarcinoma is the most common histological subtype of esophageal cancer in Western countries and shows poor prognosis with rapid growth. EAC is characterized by a strong male predominance and racial disparity. EAC is up to fivefold more common among Whites than Blacks, yet Black patients with EAC have poorer survival rates. The racial disparity remains largely unknown, and there is limited knowledge of mutations in EAC regarding racial disparities. We used whole-exome sequencing to show somatic mutation profiles derived from tumor samples from 18 EAC male patients. We identified three molecular subgroups based on the pre-defined esophageal cancer-specific mutational signatures. Group 1 is associated with age and NTHL1 deficiency-related signatures. Group 2 occurs primarily in Black patients and is associated with signatures related to DNA damage from oxidative stress and NTHL1 deficiency-related signatures. Group 3 is associated with defective homologous recombination-based DNA often caused by BRCA mutation in White patients. We observed significantly mutated race related genes (LCE2B in Black, SDR39U1 in White) were (q-value < 0.1). Our findings underscore the possibility of distinct molecular mutation patterns in EAC among different races. Further studies are needed to validate our findings, which could contribute to precision medicine in EAC.

Thrombosis risk prediction in lymphoma patients: A multi-institutional, retrospective model development and validation study.

Venous thromboembolism (VTE) poses a significant risk to cancer patients receiving systemic therapy. The generalizability of pan-cancer models to lymphomas is limited. Currently, there are no reliable risk prediction models for thrombosis in patients with lymphoma. Our objective was to create a risk assessment model (RAM) specifically for lymphomas. We performed a retrospective cohort study to develop Fine and Gray sub-distribution hazard model for VTE and pulmonary embolism (PE)/ lower extremity deep vein thrombosis (LE-DVT) respectively in adult lymphoma patients from the Veterans Affairs national healthcare system (VA). External validations were performed at the Harris Health System (HHS) and the MD Anderson Cancer Center (MDACC). Time-dependent c-statistic and calibration curves were used to assess discrimination and fit. There were 10,313 (VA), 854 (HHS), and 1858 (MDACC) patients in the derivation and validation cohorts with diverse baseline. At 6 months, the VTE incidence was 5.8% (VA), 8.2% (HHS), and 8.8% (MDACC), respectively. The corresponding estimates for PE/LE-DVT were 3.9% (VA), 4.5% (HHS), and 3.7% (MDACC), respectively. The variables in the final RAM included lymphoma histology, body mass index, therapy type, recent hospitalization, history of VTE, history of paralysis/immobilization, and time to treatment initiation. The RAM had c-statistics of 0.68 in the derivation and 0.69 and 0.72 in the two external validation cohorts. The two models achieved a clear differentiation in risk stratification in each cohort. Our findings suggest that easy-to-implement, clinical-based model could be used to predict personalized VTE risk for lymphoma patients.

Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices.

Lung cancer and tobacco use pose significant global health challenges and require a comprehensive translational roadmap for improved prevention strategies. We propose the GREAT care paradigm ( G enomic Informed Care for Motivating High R isk Individuals E ligible for Evidence-b a sed Prevention), which employs polygenic risk scores (PRSs) to stratify disease risk and personalize interventions, such as lung cancer screening and tobacco treatment. We developed PRSs using large-scale multi-ancestry genome-wide association studies and adjusted for genetic ancestry for standardized risk stratification across diverse populations. We applied our PRSs to over 340,000 individuals of diverse ethnic background and found significant odds ratios for lung cancer and difficulty quitting smoking. These findings enable the evaluation of PRS-based interventions in ongoing trials aimed at motivating health behavior changes in high-risk patients. This pioneering approach enhances primary care with genomic insights, promising improved outcomes in cancer prevention and tobacco treatment, and is currently under assessment in clinical trials.

Serum biomarker signature is predictive of the risk of hepatocellular cancer in patients with cirrhosis.

BACKGROUND: Inflammatory and metabolic biomarkers have been associated with hepatocellular cancer (HCC) risk in phases I and II biomarker studies. We developed and internally validated a robust metabolic biomarker panel predictive of HCC in a longitudinal phase III study. METHODS: We used data and banked serum from a prospective cohort of 2266 adult patients with cirrhosis who were followed until the development of HCC (n=126). We custom designed a FirePlex immunoassay to measure baseline serum levels of 39 biomarkers and established a set of biomarkers with the highest discriminatory ability for HCC. We performed bootstrapping to evaluate the predictive performance using C-index and time-dependent area under the receiver operating characteristic curve (AUROC). We quantified the incremental predictive value of the biomarker panel when added to previously validated clinical models. RESULTS: We identified a nine-biomarker panel (P9) with a C-index of 0.67 (95% CI 0.66 to 0.67), including insulin growth factor-1, interleukin-10, transforming growth factor ß1, adipsin, fetuin-A, interleukin-1 ß, macrophage stimulating protein α chain, serum amyloid A and TNF-α. Adding P9 to our clinical model with 10 factors including AFP improved AUROC at 1 and 2 years by 4.8% and 2.7%, respectively. Adding P9 to aMAP score improved AUROC at 1 and 2 years by 14.2% and 7.6%, respectively. Adding AFP L-3 or DCP did not change the predictive ability of the P9 model. CONCLUSIONS: We identified a panel of nine serum biomarkers that is independently associated with developing HCC in cirrhosis and that improved the predictive ability of risk stratification models containing clinical factors.

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.

Clonal gene signatures predict prognosis in mesothelioma and lung adenocarcinoma.

Malignant pleural mesothelioma (MPM) is a rare but lethal pleural cancer with high intratumor heterogeneity (ITH). A recent study in lung adenocarcinoma has developed a clonal gene signature (ORACLE) from multiregional transcriptomic data and demonstrated high prognostic values and reproducibility. However, such a strategy has not been tested in other types of cancer with high ITH. We aimed to identify biomarkers from multi-regional data to prognostically stratify MPM patients. We generated a multiregional RNA-seq dataset for 78 tumor samples obtained from 26 MPM patients, each with one sample collected from a superior, lateral, and inferior region of the tumor. By integrating this dataset with the Cancer Genome Atlas MPM RNA-seq data, we selected 29 prognostic genes displaying high variability across different tumors but low ITH, which named PRACME (Prognostic Risk Associated Clonal Mesothelioma Expression). We evaluated PRACME in two independent MPM datasets and demonstrated its prognostic values. Patients with high signature scores are associated with poor prognosis after adjusting established clinical factors. Interestingly, the PRACME and the ORACLE signatures defined respectively from MPM and lung adenocarcinoma cross-predict prognosis between the two cancer types. Further investigation indicated that the cross-prediction ability might be explained by the high similarity between the two cancer types in their genomic regions with copy number variation, which host many clonal genes. Overall, our clonal signature PRACME provided prognostic stratification in MPM and this study emphasized the importance of multi-regional transcriptomic data for prognostic stratification based on clonal genes.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data.

The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

N-acetylaspartate from fat cells regulates postprandial body temperature.

N-acetylaspartate (NAA), the brain's second most abundant metabolite, provides essential substrates for myelination through its hydrolysis. However, activities and physiological roles of NAA in other tissues remain unknown. Here, we show aspartoacylase (ASPA) expression in white adipose tissue (WAT) governs systemic NAA levels for postprandial body temperature regulation. Proteomics and mass spectrometry revealed NAA accumulation in WAT of Aspa knockout mice stimulated the pentose phosphate pathway and pyrimidine production. Stable isotope tracing confirmed higher incorporation of glucose-derived carbon into pyrimidine metabolites in Aspa knockout cells. Additionally, serum NAA positively correlates with the pyrimidine intermediate orotidine and this relationship predicted lower body mass index in humans. Using whole-body and tissue-specific knockout mouse models, we demonstrate that fat cells provided plasma NAA and suppressed postprandial body temperature elevation. Furthermore, exogenous NAA supplementation reduced body temperature. Our study unveils WAT-derived NAA as an endocrine regulator of postprandial body temperature and physiological homeostasis.

Prevalence of SARS-CoV-2 Infection among Children and Adults in 15 US Communities, 2021

During January-August 2021, the Community Prevalence of SARS-CoV-2 Study used time/location sampling to recruit a cross-sectional, population-based cohort to estimate SARS-CoV-2 seroprevalence and nasal swab sample PCR positivity across 15 US communities. Survey-weighted estimates of SARS-CoV-2 infection and vaccine willingness among participants at each site were compared within demographic groups by using linear regression models with inverse variance weighting. Among 22,284 persons >2 months of age and older, median prevalence of infection (prior, active, or both) was 12.9% across sites and similar across age groups. Within each site, average prevalence of infection was 3 percentage points higher for Black than White persons and average vaccine willingness was 10 percentage points lower for Black than White persons and 7 percentage points lower for Black persons than for persons in other racial groups. The higher prevalence of SARS-CoV-2 infection among groups with lower vaccine willingness highlights the disparate effect of COVID-19 and its complications.

Adaptive Time-Location Sampling for COMPASS: A SARS-CoV-2 Prevalence Study in Fifteen Diverse Communities in the United States.

BACKGROUND: COVID-19 has placed a disproportionate burden on underserved racial and ethnic groups, community members working in essential industries, those living in areas of high population density, and those reliant on in-person services such as transportation. The goal of this study was to estimate the cross-sectional prevalence of SARS-CoV-2 (active SARS-CoV-2 or prior SARS-CoV-2 infection) in children and adults attending public venues in 15 sociodemographically diverse communities in the United States and to develop a statistical design that could be rigorously implemented amidst unpredictable stay-at-home COVID-19 guidelines. METHODS: We used time-location sampling with complex sampling involving stratification, clustering of units, and unequal probabilities of selection to recruit individuals from selected communities. We safely conducted informed consent, specimen collection, and face-to-face interviews outside of public venues immediately following recruitment. RESULTS: We developed an innovative sampling design that adapted to constraints such as closure of venues, changing infection hotspots, and uncertain policies. We updated both the sampling frame and the selection probabilities over time using information acquired from prior weeks. We created site-specific survey weights that adjusted sampling probabilities for nonresponse and calibrated to county-level margins on age and sex at birth. CONCLUSIONS: Although the study itself was specific to COVID-19, the strategies presented in this article could serve as a case study that can be adapted for performing population-level inferences in similar settings and could help inform rapid and effective responses to future global public health challenges.

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.

Risk model-based management for second primary lung cancer among lung cancer survivors through a validated risk prediction model.

BACKGROUND: Recent therapeutic advances and screening technologies have improved survival among patients with lung cancer, who are now at high risk of developing second primary lung cancer (SPLC). Recently, an SPLC risk-prediction model (called SPLC-RAT) was developed and validated using data from population-based epidemiological cohorts and clinical trials, but real-world validation has been lacking. The predictive performance of SPLC-RAT was evaluated in a hospital-based cohort of lung cancer survivors. METHODS: The authors analyzed data from 8448 ever-smoking patients diagnosed with initial primary lung cancer (IPLC) in 1997-2006 at Mayo Clinic, with each patient followed for SPLC through 2018. The predictive performance of SPLC-RAT and further explored the potential of improving SPLC detection through risk model-based surveillance using SPLC-RAT versus existing clinical surveillance guidelines. RESULTS: Of 8448 IPLC patients, 483 (5.7%) developed SPLC over 26,470 person-years. The application of SPLC-RAT showed high discrimination area under the receiver operating characteristics curve: 0.81). When the cohort was stratified by a 10-year risk threshold of ≥5.6% (i.e., 80th percentile from the SPLC-RAT development cohort), the observed SPLC incidence was significantly elevated in the high-risk versus low-risk subgroup (13.1% vs. 1.1%, p < 1 × 10-6 ). The risk-based surveillance through SPLC-RAT (≥5.6% threshold) outperformed the National Comprehensive Cancer Network guidelines with higher sensitivity (86.4% vs. 79.4%) and specificity (38.9% vs. 30.4%) and required 20% fewer computed tomography follow-ups needed to detect one SPLC (162 vs. 202). CONCLUSION: In a large, hospital-based cohort, the authors validated the predictive performance of SPLC-RAT in identifying high-risk survivors of SPLC and showed its potential to improve SPLC detection through risk-based surveillance. PLAIN LANGUAGE SUMMARY: Lung cancer survivors have a high risk of developing second primary lung cancer (SPLC). However, no evidence-based guidelines for SPLC surveillance are available for lung cancer survivors. Recently, an SPLC risk-prediction model was developed and validated using data from population-based epidemiological cohorts and clinical trials, but real-world validation has been lacking. Using a large, real-world cohort of lung cancer survivors, we showed the high predictive accuracy and risk-stratification ability of the SPLC risk-prediction model. Furthermore, we demonstrated the potential to enhance efficiency in detecting SPLC using risk model-based surveillance strategies compared to the existing consensus-based clinical guidelines, including the National Comprehensive Cancer Network.

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.

An Atlas Characterizing the Shared Genetic Architecture of Inflammatory Bowel Disease with Clinical and Behavioral Traits.

BACKGROUND: Inflammatory bowel disease (IBD) development is a complex, multifactorial process that involves extrinsic and intrinsic factors such as host genetics, the immune system, the gut microbiome, and environmental risks. To help understand the genetic contribution of clinical, behavioral, psychiatric, and diet-related traits, we aim to provide a deep and comprehensive characterization of the shared genetic architecture between IBD and hundreds of potentially related traits. METHODS: Utilizing publicly available summary statistics from a previously published IBD genome-wide association study and hundreds of traits from the United Kingdom BioBank (UKBB), we performed linkage disequilibrium score regression (LDSR) analysis to estimate cross-trait genetic correlations between Crohn's disease (CD), ulcerative colitis (UC), and IBD summary statistics with the UKBB traits of interest. RESULTS: Nominally significant (P < .05) genetic correlations were observed for 181 traits in overall IBD, 239 traits in CD, and 94 traits in UC. We replicate the known association between smoking behavior and CD/UC, namely that current tobacco smoking has a positive genetic correlation with CD (rg = 0.12, P = 4.2 × 10-4), while "ever smoking" has a negative genetic correlation with UC (rg = -0.07, P = .042). Globally, all 3 strata (IBD, CD, and UC) demonstrated increased genetic correlations for psychiatric-related traits related to anxiety and depression. CONCLUSION: The present analysis reveals the shared genetic architecture between multiple traits and IBD, CD, and UC. Understanding the relevance of joint occurrences of IBD with psychiatric diseases may moderate management of these diseases for individuals jointly affected by them.

This study provides an atlas of the genetic correlation between hundreds of United Kingdom Biobank (UKBB) traits with inflammatory bowel disease (IBD), Crohn's disease (CD), and ulcerative colitis (UC). Notable strong correlations are seen between IBD and various psychiatric traits.

Common and distinct patterns of acquired uniparental disomy and homozygous deletions between lung squamous cell carcinomas and lung adenocarcinoma.

Acquired uniparental disomy (aUPD) is a chromosomal alteration that can lead to homozygosity of existing aberrations. We used data from The Cancer Genome Atlas SNP-based arrays to identify distinct and common aUPD profiles in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Moreover, we tested relevance of aUPD for homozygous deletion (HMD), overall survival (OS), and recurrence-free survival (RFS). Overall, we found significantly higher aUPD (q = 5.34E-09) in LUSC than in LUAD. A significant portion of HMD was associated with aUPD in LUSC (24.9%) and LUAD (19.7%). We identified segmental, whole-chromosome arm and whole-chromosome aUPD, in which whole 7p arm aUPD was restricted to LUSC, while whole-chromosome 3 aUPD was observed only in LUAD, and whole-chromosome 21 aUPD was common to both LUSC and LUAD. The most frequent aUPD and HMD were observed at CDKN2A/B region in both LUAD and LUSC. In LUAD, aUPD and HMD at CDKN2A/B region were associated with shorter OS (q < 0.021 and q < 0.005), and RFS (q < 0.005 and q < 0.005), while heterozygous deletion was not associated with OS and RFS. In contrast, no association was found between aUPD at CDKN2A/B region and survival in LUSC. In LUAD, CTLA expression was significantly lower in samples with aUPD at CDKN2A/B regions than in samples without copy number and allele-based changes. Immune infiltration correlated with aUPD or HMD at CDKN2A/B, gain at HLA class I region, and aUPD at whole-chromosome q-arm or whole chromosome in LUAD, but not in LUSC. Both LUSC and LUAD have common and distinct patterns of aUPD regions with differing frequencies of occurrence and associations with outcome.

Large-scale whole exome sequencing studies identify two genes,CTSL and APOE, associated with lung cancer.

Common genetic variants associated with lung cancer have been well studied in the past decade. However, only 12.3% heritability has been explained by these variants. In this study, we investigate the contribution of rare variants (RVs) (minor allele frequency <0.01) to lung cancer through two large whole exome sequencing case-control studies. We first performed gene-based association tests using a novel Bayes Factor statistic in the International Lung Cancer Consortium, the discovery study (European, 1042 cases vs. 881 controls). The top genes identified are further assessed in the UK Biobank (European, 630 cases vs. 172 864 controls), the replication study. After controlling for the false discovery rate, we found two genes, CTSL and APOE, significantly associated with lung cancer in both studies. Single variant tests in UK Biobank identified 4 RVs (3 missense variants) in CTSL and 2 RVs (1 missense variant) in APOE stongly associated with lung cancer (OR between 2.0 and 139.0). The role of these genetic variants in the regulation of CTSL or APOE expression remains unclear. If such a role is established, this could have important therapeutic implications for lung cancer patients.

Ethnic differences of genetic risk and smoking in lung cancer: two prospective cohort studies.

BACKGROUND: The role of genetic background underlying the disparity of relative risk of smoking and lung cancer between European populations and East Asians remains unclear. METHODS: To assess the role of ethnic differences in genetic factors associated with smoking-related risk of lung cancer, we first constructed ethnic-specific polygenic risk scores (PRSs) to quantify individual genetic risk of lung cancer in Chinese and European populations. Then, we compared genetic risk and smoking as well as their interactions on lung cancer between two cohorts, including the China Kadoorie Biobank (CKB) and the UK Biobank (UKB). We also evaluated the absolute risk reduction over a 5-year period. RESULTS: Differences in compositions and association effects were observed between the Chinese-specific PRSs and European-specific PRSs, especially for smoking-related loci. The PRSs were consistently associated with lung cancer risk, but stronger associations were observed in smokers of the UKB [hazard ratio (HR) 1.26 vs 1.15, P = 0.028]. A significant interaction between genetic risk and smoking on lung cancer was observed in the UKB (RERI, 11.39 (95% CI, 7.01-17.94)], but not in the CKB. Obvious higher absolute risk was observed in nonsmokers of the CKB, and a greater absolute risk reduction was found in the UKB (10.95 vs 7.12 per 1000 person-years, P <0.001) by comparing heavy smokers with nonsmokers, especially for those at high genetic risk. CONCLUSIONS: Ethnic differences in genetic factors and the high incidence of lung cancer in nonsmokers of East Asian ethnicity were involved in the disparity of smoking-related risk of lung cancer.