On the informative value of community-based indoor radon values in relation to lung cancer.

BACKGROUND: Radon is a radioactive gas and a major risk factor for lung cancer (LC). METHODS: We investigated the dose-response relationship between radon and LC risk in the International Lung Cancer Consortium with 8927 cases and 5562 controls from Europe, North America, and Israel, conducted between 1992 and 2016. Spatial indoor radon exposure in the residential area (sIR) obtained from national surveys was linked to the participants' residential geolocation. Parametric linear and spline functions were fitted within a logistic regression framework. RESULTS: We observed a non-linear spatial-dose response relationship for sIR < 200 Bq/m3. The lowest risk was observed for areas of mean exposure of 58 Bq/m3 (95% CI: 56.1-59.2 Bq/m3). The relative risk of lung cancer increased to the same degree in areas averaging 25 Bq/m3 (OR = 1.31, 95% CI: 1.01-1.59) as in areas with a mean of 100 Bq/m3 (OR = 1.34, 95% CI: 1.20-1.45). The strongest association was observed for small cell lung cancer and the weakest for squamous cell carcinoma. A stronger association was also observed in men, but only at higher exposure levels. The non-linear association is primarily observed among the younger population (age < 69 years), but not in the older population, which can potentially represent different biological radiation responses. CONCLUSIONS: The sIR is useful as proxy of individual radon exposure in epidemiological studies on lung cancer. The usual assumption of a linear, no-threshold dose-response relationship, as can be made for individual radon exposures, may not be optimal for sIR values of less than 200 Bq/m3.

Prognostic stratification in DLBCL patients with aberrant MYC gene.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease characterized by a subset of patients who exhibit treatment resistance and poor prognoses. Genomic assays have been widely employed to identify high-risk individuals characterized by rearrangements in the MYC, BCL2 and BCL6 genes. These patients typically undergo more aggressive therapeutic treatments; however, there remains a significant variation in their treatment outcomes. This study introduces an MYC signature score (MYCSS) derived from gene expression profiles, specifically designed to evaluate MYC overactivation in DLBCL patients. MYCSS was validated across several independent cohorts to assess its ability to stratify patients based on MYC-related genetic and molecular aberrations, enhancing the accuracy of prognostic evaluations compared to conventional MYC biomarkers. Our results indicate that MYCSS significantly refines prognostic accuracy beyond that of conventional MYC biomarkers focused on genetic aberrations. More importantly, we found that nearly 50% of patients identified as high risk by traditional MYC metrics actually share similar survival prospects with those having no MYC aberrations. These patients may benefit from standard GCB-based therapies rather than more aggressive treatments. MYCSS provides a robust signature that identifies high-risk patients, aiding in the precision treatment of DLBCL, and minimizing the potential for overtreatment.

Asbestos-Related lung Cancer: An underappreciated oncological issue.

Asbestos, a group of class I (WHO) carcinogenic fibers, is the main cause of mesothelioma. Asbestos inhalation also increases the risk to develop other solid tumours with lung cancer as the most prominent example [91]. The incidence of asbestos-related lung cancer (ARLC) is estimated to be to six times larger than the mesothelioma incidence thereby becoming an important health issue [86]. Although the pivotal role of asbestos in inducing lung cancer is well established, the precise causal relationships between exposures to asbestos, tobacco smoke, radon and 'particulate' (PM2.5) air pollution remain obscure and new knowledge is needed to establish appropriate preventive measures and to tailor existing screening practices[22,61,65]. We hypothesize that a part of the increasing numbers of lung cancer diagnoses in never-smokers can be explained by (historic and current) exposures to asbestos as well as combinations of different forms of air pollution (PM2.5, asbestos and silica).

Stratifying Lung Adenocarcinoma Risk with Multi-ancestry Polygenic Risk Scores in East Asian Never-Smokers.

Background: Lung adenocarcinoma (LUAD) among never-smokers is a public health burden especially prevalent in East Asian (EAS) women. Polygenic risk scores (PRSs), which quanefy geneec suscepebility, are promising for straefying risk, yet have mainly been developed in European (EUR) populaeons. We developed and validated single-and mule-ancestry PRSs for LUAD in EAS never-smokers, using the largest available genome-wide associaeon study (GWAS) dataset. Methods: We used GWAS summary staesecs from both EAS (8,002 cases; 20,782 controls) and EUR (2,058 cases; 5,575 controls) populaeons, as well as independent EAS individual level data. We evaluated several PRSs approaches: a single-ancestry PRS using 25 variants that reached genome-wide significance (PRS-25), a genome-wide Bayesian based approach (LDpred2), and a mule-ancestry approach that models geneec correlaeons across ancestries (CT-SLEB). PRS performance was evaluated based on the associaeon with LUAD and AUC values. We then esemated the lifeeme absolute risk of LUAD (age 30-80) and projected the AUC at different sample sizes using EAS-derived effect-size distribueon and heritability esemates. Findings: The CT-SLEB PRS showed a strong associaeon with LUAD risk (odds raeo=1.71, 95% confidence interval (CI): 1.61, 1.82) with an AUC of 0.640 (95% CI: 0.629, 0.653). Individuals in the 95 th percenele of the PRS had an esemated 6.69% lifeeme absolute risk of LUAD. Comparison of LUAD risk between individuals in the highest and lowest 20% PRS quaneles revealed a 3.92-fold increase. Projeceon analyses indicated that achieving an AUC of 0.70, which approaches the maximized prediceon poteneal of the PRS given the esemated geneec variance, would require a future study encompassing 55,000 EAS LUAD cases with a 1:10 case-control raeo. Interpretations: Our study underscores the poteneal of mule-ancestry PRS approaches to enhance LUAD risk straeficaeon in never-smokers, parecularly in EAS populaeons, and highlights the necessary scale of future research to uncover the geneec underpinnings of LUAD.

Discovery of new myositis genetic associations through leveraging other immune-mediated diseases.

Genome-wide association studies (GWASs) have been successful at finding associations between genetic variants and human traits, including the immune-mediated diseases (IMDs). However, the requirement of large sample sizes for discovery poses a challenge for learning about less common diseases, where increasing volunteer numbers might not be feasible. An example of this is myositis (or idiopathic inflammatory myopathies [IIM]s), a group of rare, heterogeneous autoimmune diseases affecting skeletal muscle and other organs, severely impairing life quality. Here, we applied a feature engineering method to borrow information from larger IMD GWASs to find new genetic associations with IIM and its subgroups. Combining this approach with two clustering methods, we found 17 IMDs genetically close to IIM, including some common comorbid conditions, such as systemic sclerosis and Sjögren's syndrome, as well as hypo- and hyperthyroidism. All IIM subtypes were genetically similar within this framework. Next, we colocalized IIM signals that overlapped IMD signals, and found seven potentially novel myositis associations mapped to immune-related genes, including BLK, IRF5/TNPO3, and ITK/HAVCR2, implicating a role for both B and T cells in IIM. This work proposes a new paradigm of genetic discovery in rarer diseases by leveraging information from more common IMD, and can be expanded to other conditions and traits beyond IMD.

High-throughput characterization of functional variants highlights heterogeneity and polygenicity underlying lung cancer susceptibility.

Genome-wide association studies (GWASs) have identified numerous lung cancer risk-associated loci. However, decoding molecular mechanisms of these associations is challenging since most of these genetic variants are non-protein-coding with unknown function. Here, we implemented massively parallel reporter assays (MPRAs) to simultaneously measure the allelic transcriptional activity of risk-associated variants. We tested 2,245 variants at 42 loci from 3 recent GWASs in East Asian and European populations in the context of two major lung cancer histological types and exposure to benzo(a)pyrene. This MPRA approach identified one or more variants (median 11 variants) with significant effects on transcriptional activity at 88% of GWAS loci. Multimodal integration of lung-specific epigenomic data demonstrated that 63% of the loci harbored multiple potentially functional variants in linkage disequilibrium. While 22% of the significant variants showed allelic effects in both A549 (adenocarcinoma) and H520 (squamous cell carcinoma) cell lines, a subset of the functional variants displayed a significant cell-type interaction. Transcription factor analyses nominated potential regulators of the functional variants, including those with cell-type-specific expression and those predicted to bind multiple potentially functional variants across the GWAS loci. Linking functional variants to target genes based on four complementary approaches identified candidate susceptibility genes, including those affecting lung cancer cell growth. CRISPR interference of the top functional variant at 20q13.33 validated variant-to-gene connections, including RTEL1, SOX18, and ARFRP1. Our data provide a comprehensive functional analysis of lung cancer GWAS loci and help elucidate the molecular basis of heterogeneity and polygenicity underlying lung cancer susceptibility.

Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden.

Single nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the "all histology together" analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.

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.

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 2 multicenter prospective cohort studies in the United States. We classified patients according to alcohol consumption (current heavy vs not current heavy), obesity (body mass index ≥30 vs <30 kg/m2), 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 59.6 years, 64.3% were male, 28.7% were Hispanic, 18.3% were non-Hispanic Black, 50.9% were obese, 6.2% had current heavy alcohol consumption, and 58.4% harbored at least 1 PNPLA3 G-allele. One hundred sixteen patients developed HCC. Compared with 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 with 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.

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

Background: Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies. Polygenic risk scores (PRSs) are powerful tools for patient risk stratification but have not yet been widely used in primary care for lung cancer, particularly in diverse patient populations. Methods: We propose the GREAT care paradigm, which employs PRSs to stratify disease risk and personalize interventions. We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardized PRS distributions across all ancestries. We applied our PRSs to 796 individuals from the GISC Trial, 350,154 from UK Biobank (UKBB), and 210,826 from All of Us Research Program (AoU), totaling 561,776 individuals of diverse ancestry. Results: Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58 - 2.18) in UKBB and 2.39 (95% CI: 1.93 - 2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32 - 1.41) in UKBB and 1.32 (95% CI: 1.28 - 1.36) in AoU. Conclusion: Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations. This model will be evaluated in two cluster-randomized clinical trials aimed at motivating health behavior changes in high-risk patients of diverse ancestry. This pioneering approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.

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.

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.

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.

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.

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.

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.

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.