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Abstract Lung Cancer remains the leading cause of cancer deaths in the USA and worldwide. Non-small cell lung cancer (NSCLC) harbors high transcriptomic intratumor heterogeneity (RNA-ITH) that limits the reproducibility of expression-based prognostic models. In this study, we used multiregional RNA-seq data (880 tumor samples from 350 individuals) from both public (TRACERx) and internal (MDAMPLC) cohorts to investigate the effect of RNA-ITH on prognosis in localized NSCLC at the gene, signature, and tumor microenvironment levels. At the gene level, the maximal expression of hazardous genes (expression negatively associated with survival) but the minimal expression of protective genes (expression positively associated with survival) across different regions within a tumor were more prognostic than the average expression. Following that, we examined whether multiregional expression profiling can improve the performance of prognostic signatures. We investigated 11 gene signatures collected from previous publications and one signature developed in this study. For all of them, the prognostic prediction accuracy can be significantly improved by converting the regional expression of signature genes into sample-specific expression with a simple function—taking the maximal expression of hazardous genes and the minimal expression of protective genes. In the tumor microenvironment, we found a similar rule also seems applicable to immune ITH. We calculated the infiltration levels of major immune cell types in each region of a sample based on expression deconvolution. Prognostic analysis indicated that the region with the lowest infiltration level of protective or highest infiltration level of hazardous immune cells determined the prognosis of NSCLC patients. Our study highlighted the impact of RNA-ITH on the prognostication of NSCLC, which should be taken into consideration to optimize the design and application of expression-based prognostic biomarkers and models. Multiregional assays have the great potential to significantly improve their applications to prognostic stratification.

Abstract Lung cancer remains the leading cause of cancer mortality, despite declining smoking rates. Previous lung cancer GWAS have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging. Here, we perform multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program cohort (approximately 95% male cases) and a previous study of European-ancestry individuals, jointly comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We then carry out conditional meta-analyses on cigarettes per day and identify two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in squamous cell lung carcinoma. Overall, we report twelve novel risk loci for overall lung cancer, lung adenocarcinoma, and squamous cell lung carcinoma, nine of which are externally replicated. Finally, we perform PheWAS on polygenic risk scores for lung cancer, with and without conditioning on smoking. The unconditioned lung cancer polygenic risk score is associated with smoking status in controls, illustrating a reduced predictive utility in non-smokers. Additionally, our polygenic risk score demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.

Abstract Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, underlying mechanisms and target genes are largely unknown, as most risk-associated variants might regulate gene expression in a context-specific manner. Here, we generate a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Identified candidate cis-regulatory elements (cCREs) are largely cell type-specific, with 37% detected in one cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs combined with transcription factor footprinting prioritize the variants for 68% of the GWAS loci. CCV-colocalization and trait relevance score indicate that epithelial and immune cell categories, including rare cell types, contribute to lung cancer susceptibility the most. A multi-level cCRE-gene linking system identifies candidate susceptibility genes from 57% of the loci, where most loci display cell-category-specific target genes, suggesting context-specific susceptibility gene function.

Abstract The spatial arrangement of immune cells within the tumor microenvironment (TME) and their interactions play critical roles in the initiation and development of cancer. Several advanced technologies such as imaging mass cytometry (IMC) providing the immunological landscape of the TME with single-cell resolution. In this study, we develop a new method to quantify the spatial proximity between different cell types based on single-cell spatial data. Using this method on IMC data from 416 lung adenocarcinoma patients, we show that the proximity between different cell types is more correlated with patient prognosis compared to the traditional features such immune cell density and fractions. Consistent with previous reports, our results validate that proximity of T helper (Th) and B cells to cancer cells is associated with survival benefits. More importantly, we discover that the proximity of M2 macrophages to multiple immune cells is associated with poor prognosis. When Th/B cells are stratified into M2-distal and M2-proximal, the abundance of the former but not the latter category of Th/B cells is correlated with enhanced patient survival. Additionally, the abundance of M2-distal and M2-proximal cytotoxic T cells (Tc) is respectively associated with good and poor prognosis. Our results indicate that the prognostic effect of Th, Tc, and B cells in the tumor microenvironment is modulated by the nearby M2 macrophages. The proposed new method proposed can be readily applied to all single-cell spatial data for revealing functional impact of immune cell interactions.

Abstract 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.

Abstract 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

Abstract 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

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.

Abstract 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.

Abstract 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.

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.

Abstract Lung cancer is the leading cause of cancer-related mortality in the United States. Investigating epidemiological and clinical parameters can contribute to an improved understanding of disease development and management. In this cross-sectional, case–control study, we used the All of Us database to compare healthcare access, family history, smoking-related behaviors, and psychiatric comorbidities in light smoking controls, matched smoking controls, and primary and secondary lung cancer patients. We found a decreased odds of primary lung cancer patients versus matched smoking controls reporting inability to afford follow-up or specialist care. Additionally, we found a significantly increased odds of secondary lung cancer patients having comorbid anxiety and insomnia when compared to matched smoking controls. Our study provides a profile of the psychiatric disease burden in lung cancer patients and reports key epidemiological factors in patients with primary and secondary lung cancer. By using two controls, we were able to separate smoking behavior from lung cancer and identify factors that were mediated by heavy smoking alone or by both smoking and lung cancer.

Summary: Background: Tumour-promoting inflammation is a “hallmark” of cancer and conventional epidemiological studies have reported links between various inflammatory markers and cancer risk. The causal nature of these relationships and, thus, the suitability of these markers as intervention targets for cancer prevention is unclear. Methods: We meta-analysed 6 genome-wide association studies of circulating inflammatory markers comprising 59,969 participants of European ancestry. We then used combined cis-Mendelian randomization and colocalisation analysis to evaluate the causal role of 66 circulating inflammatory markers in risk of 30 adult cancers in 338,294 cancer cases and up to 1,238,345 controls. Genetic instruments for inflammatory markers were constructed using genome-wide significant (P 70% was employed to indicate support for shared causal variants across inflammatory markers and cancer outcomes. Findings were replicated in the FinnGen study and then pooled using meta-analysis. Findings: We found strong evidence to support an association of genetically-proxied circulating pro-adrenomedullin concentrations with increased breast cancer risk (OR: 1.19, 95% CI: 1.10–1.29, q-value = 0.033, PPH4 = 84.3%) and suggestive evidence to support associations of interleukin-23 receptor concentrations with increased pancreatic cancer risk (OR: 1.42, 95% CI: 1.20–1.69, q-value = 0.055, PPH4 = 73.9%), prothrombin concentrations with decreased basal cell carcinoma risk (OR: 0.66, 95% CI: 0.53–0.81, q-value = 0.067, PPH4 = 81.8%), and interleukin-1 receptor-like 1 concentrations with decreased triple-negative breast cancer risk (OR: 0.92, 95% CI: 0.88–0.97, q-value = 0.15, PPH4 = 85.6%). These findings were replicated in pooled analyses with the FinnGen study. Though suggestive evidence was found to support an association of macrophage migration inhibitory factor concentrations with increased bladder cancer risk (OR: 2.46, 95% CI: 1.48–4.10, q-value = 0.072, PPH4 = 76.1%), this finding was not replicated when pooled with the FinnGen study. For 22 of 30 cancer outcomes examined, there was little evidence (q-value ≥0.20) that any of the 66 circulating inflammatory markers examined were associated with cancer risk. Interpretation: Our comprehensive joint Mendelian randomization and colocalisation analysis of the role of circulating inflammatory markers in cancer risk identified potential roles for 4 circulating inflammatory markers in risk of 4 site-specific cancers. Contrary to reports from some prior conventional epidemiological studies, we found little evidence of association of circulating inflammatory markers with the majority of site-specific cancers evaluated. Funding: Cancer Research UK (C68933/A28534, C18281/A29019, PPRCPJT∖100005), World Cancer Research Fund (IIG_FULL_2020_022), National Institute for Health Research (NIHR202411, BRC-1215-20011), Medical Research Council (MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/6, and MC_UU_00011/4), Academy of Finland Project 326291, European Union's Horizon 2020 grant agreement no. 848158 (EarlyCause), French National Cancer Institute (INCa SHSESP20, 2020-076), Versus Arthritis (21173, 21754, 21755), National Institutes of Health (U19 CA203654), National Cancer Institute (U19CA203654).

Abstract Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

OBJECTIVES/GOALS: Clinical and translational science needs to address roadblocks to translational processes. We conducted a survey at two institutions, a private medical school and a large public university, to understand the frequency and distribution of barriers and roadblocks to research. METHODS/STUDY POPULATION: We reviewed the literature to compile a pool of barriers and roadblocks and convened a panel of relevant stakeholders to develop a 20-item questionnaire. Survey respondents were asked to select and prioritize the five leading clinical and translational roadblocks, provide information regarding their academic degrees and rank/position, complete open-ended items regarding their areas of research, and optionally add additional remarks in a comment box. The survey was disseminated in August 2022 via REDCap to faculty and staff with active research protocols at Baylor College of Medicine and the University of Houston. RESULTS/ANTICIPATED RESULTS: In total, 227 respondents completed the survey. Their disciplines were basic science (29.5%), translational research (52.9%), clinical research (55.5%), community-engaged research (9.7%), and educational research (9.7%). Respondents identified 1) lack of access to trained research coordinators, 2) lack of understanding about different resources that facilitate research, 3) complex regulatory environment and delays, 4) fragmented infrastructure for administrative and fiscal processes, and 5) inadequate funding for pilot projects to foster new research. Other roadblocks included lack of established community stakeholder partnerships, inadequate access to medical record data, and limited biostatistical support. In the comments, several respondents noted that all items included were important. DISCUSSION/SIGNIFICANCE: Research workforce recruitment/training was the highest priority followed by lack of access to information and administrative bottlenecks. We are building an online portal to increase awareness and simplify access to competency-based training and research services. Initiatives are underway to address other roadblocks.

Genomic medicine can enhance prevention and treatment. First, we propose that advances in genomics have the potential to enhance assessment of disease risk, improve prognostic predictions, and guide treatment development and application. Clinical implementation of polygenic risk scores (PRSs) has emerged as an area of active research. The pathway from genomic discovery to implementation is an iterative process. Second, we provide examples on how genomic medicine has the potential to solve problems in prevention and treatment using two examples: Lung cancer screening and evidence-based tobacco treatment are both under-utilized and great opportunities for genomic interventions. Third, we discuss the translational process for developing genomic interventions from evidence to implementation by presenting a model to evaluate genomic evidence for clinical implementation, mechanisms of genomic interventions, and patient desire for genomic interventions. Fourth, we present potential challenges in genomic interventions including a great need for evidence in all diverse populations, little evidence on treatment algorithms, challenges in accommodating a dynamic evidence base, and implementation challenges in real world clinical settings. Finally, we conclude that research to identify genomic markers that are associated with smoking cessation success and the efficacy of smoking cessation treatments is needed to empower people of all diverse ancestry. Importantly, genomic data can be used to help identify patients with elevated risk for nicotine addiction, difficulty quitting smoking, favorable response to specific pharmacotherapy, and tobacco-related health problems.

The genetic basis of primary sclerosing cholangitis has only been partially uncovered. Here, the authors perform a multitrait genome-wide association study to provide insight into the genetic etiology of primary sclerosing cholangitis risk and possible therapeutic drug targets.

Abstract Limited efforts have been made in assessing the effect of genome-wide profiling of RNA splicing-related variation on lung cancer risk. In the present study, we first identified RNA splicing-related genetic variants linked to lung cancer in a genome-wide profiling analysis and then conducted a two-stage (discovery and replication) association study in populations of European ancestry. Discovery and validation were conducted sequentially with a total of 29,266 cases and 56,450 controls from both the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium as well as the OncoArray database. For those variants identified as significant in the two datasets, we further performed stratified analyses by smoking status and histological type and investigated their effects on gene expression and potential regulatory mechanisms. We identified three genetic variants significantly associated with lung cancer risk: rs329118 in JADE2 (P = 8.80E−09), rs2285521 in GGA2 (P = 4.43E−08), and rs198459 in MYRF (P = 1.60E−06). The combined effects of all three SNPs were more evident in lung squamous cell carcinomas (P = 1.81E−08, P = 6.21E−08, and P = 7.93E−04, respectively) than in lung adenocarcinomas and in ever smokers (P = 9.80E−05, P = 2.70E−04, and P = 2.90E−05, respectively) than in never smokers. Gene expression quantitative trait analysis suggested a role for the SNPs in regulating transcriptional expression of the corresponding target genes. In conclusion, we report that three RNA splicing-related genetic variants contribute to lung cancer susceptibility in European populations. However, additional validation is needed, and specific splicing mechanisms of the target genes underlying the observed associations also warrants further exploration.

Summary: Background: The causal relevance of polyunsaturated fatty acids (PUFAs) for risk of site-specific cancers remains uncertain. Methods: Using a Mendelian randomization (MR) framework, we assessed the causal relevance of PUFAs for risk of cancer in European and East Asian ancestry individuals. We defined the primary exposure as PUFA desaturase activity, proxied by rs174546 at the FADS locus. Secondary exposures were defined as omega 3 and omega 6 PUFAs that could be proxied by genetic polymorphisms outside the FADS region. Our study used summary genetic data on 10 PUFAs and 67 cancers, corresponding to 562,871 cases and 1,619,465 controls, collected by the Fatty Acids in Cancer Mendelian Randomization Collaboration. We estimated odds ratios (ORs) for cancer per standard deviation increase in genetically proxied PUFA exposures. Findings: Genetically elevated PUFA desaturase activity was associated (P

Background: The COVID-19 pandemic during lockdown has highlighted the importance of identifying individuals most at risk of infection with SARS-CoV-2, underscoring the need to assess factors contributing to susceptibility to disease. With the rapidly evolving nature of the pandemic and its new variants, there is an inadequate understanding on whether there are certain factors such as a specific symptom or collection of symptoms that combined with life-style behaviors may be useful to predict susceptibility. The study aims to explore such factors from pre-vaccination data to guide public health response to potential new waves. Methods: An anonymous electronic survey was distributed through social media during the lockdown period in the United States from April to June 2020. Respondents were questioned regarding COVID testing, presenting symptoms, demographic information, comorbidities, and confirmation of COVID‐19 test results. Stepwise logistic regression was used to identify predictors for COVID‐19 perceived susceptibility. Selected classifiers were assessed for prediction performance using area under receiver operating characteristic (AUROC) curve analysis. Results: A total of 130 participants deemed as susceptible because they self-reported their perception of having COVID-19 (but without the evidence of positive test) were compared with 130 individuals with documented negative test results. Participants had a mean age of 45 years, and 165 (63%) were female. Final multivariable model showed significant associations with perceived susceptibility for the following variables: fever (OR:33.5; 95%CI: 3.9,85.9), body ache (OR:3.0; 95%CI:1.1,6.4), contact history (OR:2.7; 95%CI:1.1,6.4), age> 50 (OR:2.7; 95%CI:1.1, 6.6) and smoking (OR:3.3; 95%CI: 1.2,9.1) after adjusting for other symptoms and presence of comorbid conditions. The AUROC ranged from poor to fair (0.65–0.76) for cluster of symptoms but improved to a good model (AUROC = 0.803) after inclusion of sociodemographic and lifestyle behaviors e.g., age and smoking tobacco. Conclusions: Fever and body aches suggest association with perceived COVID‐19 susceptibility in the presence of demographic and lifestyle behaviors. Using other constitutional and respiratory symptoms with fever and body aches, the parsimonious classifier correctly predicts 80.3% of COVID‐19 perceived susceptibility. A larger cohort of respondents will be needed to study and refine classifier performance in future lockdowns and with expected surge of new variants of COVID-19 pandemic.

Using SNP-based microarray data from The Cancer Genome Atlas (TCGA), we investigated isochromosomes (deletion of one arm and duplication of the other arm) and related acquired uniparental disomy in 12 tumor types. We observed a high frequency of isochromosomes (25.98%) across all type of tumors except thyroid cancers. The highest frequency of isochromosomes was found in lung squamous cell carcinoma (54.18%). Moreover, whole-chromosome arm acquired uniparental disomy (aUPD) was common in the deleted arms of isochromosomes. These data are consistent with whole-chromosome arm aUPD likely being a consequence of isochromosomes formation. Our findings implicated aUPD as occurring through error-prone DNA repair of a deleted arm or segment of a chromosome that leads to homozygosity for existing alterations. Isochromosomes were significantly more frequent in TP53 mutated samples than wild types in 6 types of tumors with loss of TP53 function potentially contributing to development of isochromosomes. Isochromosomes are common alterations in cancer, and losing one arm of a chromosome could result in duplication of the lost arm. Duplication of the remaining arm leads promulgation of the effects on any defects in the remaining allele, due to subsequent homozygosity.

Abstract Background Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs). Results Here, we presented a computational framework that takes advantage of recently developed methods to infer the mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3942 genes and 186,132 eQTLs for 4751 genes from 15,122,700 genetic variants for 13,476 genes on the autosomes, respectively. Interestingly, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.

Genome-wide association studies (GWAS) have identified SNPs linked with lung cancer risk. Our aim was to discover the genes, non-coding RNAs, and regulatory elements within GWAS-identified risk regions that are deregulated in non-small cell lung carcinoma (NSCLC) to identify novel, clinically targetable genes and mechanisms in carcinogenesis. A targeted bisulphite-sequencing approach was used to comprehensively investigate DNA methylation changes occurring within lung cancer risk regions in 17 NSCLC and adjacent normal tissue pairs. We report differences in differentially methylated regions between adenocarcinoma and squamous cell carcinoma. Among the minimal regions found to be differentially methylated in at least 50% of the patients, 7 candidates were replicated in 2 independent cohorts (n = 27 and n = 87) and the potential of 6 as methylation-dependent regulatory elements was confirmed by functional assays. This study contributes to understanding the pathways implicated in lung cancer initiation and progression, and provides new potential targets for cancer treatment.

Abstract The COVID-19 pandemic has produced broad clinical manifestations, from asymptomatic infection to hospitalization and death. Despite progress from genomic and clinical epidemiology research, risk factors for developing severe COVID-19 are incompletely understood and identification of modifiable risk factors is desperately needed. We conducted linkage disequilibrium score regression (LDSR) analysis to estimate cross-trait genetic correlation between COVID-19 severity and various polygenic phenotypes. To attenuate the genetic contribution of smoking and BMI, we further conducted sensitivity analyses by pruning genomic regions associated with smoking/BMI and repeating LDSR analyses. We identified robust positive associations between the genetic architecture of severe COVID-19 and both BMI and smoking. We observed strong positive genetic correlation (rg) with diabetes (rg = 0.25) and shortness of breath walking on level ground (rg = 0.28) and novel protective associations with vitamin E (rg = − 0.53), calcium (rg = − 0.33), retinol (rg = − 0.59), Apolipoprotein A (rg = − 0.13), and HDL (rg = − 0.17), but no association with vitamin D (rg = − 0.02). Removing genomic regions associated with smoking and BMI generally attenuated the associations, but the associations with nutrient biomarkers persisted. This study provides a comprehensive assessment of the shared genetic architecture of COVID-19 severity and numerous clinical/physiologic parameters. Associations with blood and plasma-derived traits identified biomarkers for Mendelian randomization studies to explore causality and nominates therapeutic targets for clinical evaluation.

Abstract Background Epidemiological and experimental evidence has linked chronic inflammation to cancer aetiology. It is unclear whether associations for specific inflammatory biomarkers are causal or due to bias. In order to examine whether altered genetically predicted concentration of circulating cytokines are associated with cancer development, we performed a two-sample Mendelian randomisation (MR) analysis. Methods Up to 31,112 individuals of European descent were included in genome-wide association study (GWAS) meta-analyses of 47 circulating cytokines. Single nucleotide polymorphisms (SNPs) robustly associated with the cytokines, located in or close to their coding gene (cis), were used as instrumental variables. Inverse-variance weighted MR was used as the primary analysis, and the MR assumptions were evaluated in sensitivity and colocalization analyses and a false discovery rate (FDR) correction for multiple comparisons was applied. Corresponding germline GWAS summary data for five cancer outcomes (breast, endometrial, lung, ovarian, and prostate), and their subtypes were selected from the largest cancer-specific GWASs available (cases ranging from 12,906 for endometrial to 133,384 for breast cancer). Results There was evidence of inverse associations of macrophage migration inhibitory factor with breast cancer (OR per SD = 0.88, 95% CI 0.83 to 0.94), interleukin-1 receptor antagonist with endometrial cancer (0.86, 0.80 to 0.93), interleukin-18 with lung cancer (0.87, 0.81 to 0.93), and beta-chemokine-RANTES with ovarian cancer (0.70, 0.57 to 0.85) and positive associations of monokine induced by gamma interferon with endometrial cancer (3.73, 1.86 to 7.47) and cutaneous T-cell attracting chemokine with lung cancer (1.51, 1.22 to 1.87). These associations were similar in sensitivity analyses and supported in colocalization analyses. Conclusions Our study adds to current knowledge on the role of specific inflammatory biomarker pathways in cancer aetiology. Further validation is needed to assess the potential of these cytokines as pharmacological or lifestyle targets for cancer prevention.

Abstract Background Lung adenocarcinoma, the most common type of lung cancer, has a high level of morphologic heterogeneity and is composed of tumor cells of multiple histological subtypes. It has been reported that immune cell infiltration significantly impacts clinical outcomes of patients with lung adenocarcinoma. However, it is unclear whether histologic subtyping can reflect the tumor immune microenvironment, and whether histologic subtyping can be applied for therapeutic stratification of the current standard of care. Methods We inferred immune cell infiltration levels using a histological subtype-specific gene expression dataset. From differential gene expression analysis between different histological subtypes, we developed two gene signatures to computationally determine the relative abundance of lepidic and solid components (denoted as the L-score and S-score, respectively) in lung adenocarcinoma samples. These signatures enabled us to investigate the relationship between histological composition and clinical outcomes in lung adenocarcinoma using previously published datasets. Results We found dramatic immunological differences among histological subtypes. Differential gene expression analysis showed that the lepidic and solid subtypes could be differentiated based on their gene expression patterns while the other subtypes shared similar gene expression patterns. Our results indicated that higher L-scores were associated with prolonged survival, and higher S-scores were associated with shortened survival. L-scores and S-scores were also correlated with global genomic features such as tumor mutation burdens and driver genomic events. Interestingly, we observed significantly decreased L-scores and increased S-scores in lung adenocarcinoma samples with EGFR gene amplification but not in samples with EGFR gene mutations. In lung cancer cell lines, we observed significant correlations between L-scores and cell sensitivity to a number of targeted drugs including EGFR inhibitors. Moreover, lung cancer patients with higher L-scores were more likely to benefit from immune checkpoint blockade therapy. Conclusions Our findings provided further insights into evaluating histology composition in lung adenocarcinoma. The established signatures reflected that lepidic and solid subtypes in lung adenocarcinoma would be associated with prognosis, genomic features, and responses to targeted therapy and immunotherapy. The signatures therefore suggested potential clinical translation in predicting patient survival and treatment responses. In addition, our framework can be applied to other types of cancer with heterogeneous histological subtypes.

Abstract Background Aberrant Wnt signalling, regulating cell development and stemness, influences the development of many cancer types. The Aryl hydrocarbon receptor (AhR) mediates tumorigenesis of environmental pollutants. Complex interaction patterns of genes assigned to AhR/Wnt-signalling were recently associated with lung cancer susceptibility. Aim To assess the association and predictive ability of AhR/Wnt-genes with lung cancer in cases and controls of European descent. Methods Odds ratios (OR) were estimated for genomic variants assigned to the Wnt agonist and the antagonistic genes DKK2, DKK3, DKK4, FRZB, SFRP4 and Axin2. Logistic regression models with variable selection were trained, validated and tested to predict lung cancer, at which other previously identified SNPs that have been robustly associated with lung cancer risk could also enter the model. Furthermore, decision trees were created to investigate variant × variant interaction. All analyses were performed for overall lung cancer and for subgroups. Results No genome-wide significant association of AhR/Wnt-genes with overall lung cancer was observed, but within the subgroups of ever smokers (e.g., maker rs2722278 SFRP4; OR = 1.20; 95% CI 1.13–1.27; p = 5.6 × 10–10) and never smokers (e.g., maker rs1133683 Axin2; OR = 1.27; 95% CI 1.19–1.35; p = 1.0 × 10–12). Although predictability is poor, AhR/Wnt-variants are unexpectedly overrepresented in optimized prediction scores for overall lung cancer and for small cell lung cancer. Remarkably, the score for never-smokers contained solely two AhR/Wnt-variants. The optimal decision tree for never smokers consists of 7 AhR/Wnt-variants and only two lung cancer variants. Conclusions The role of variants belonging to Wnt/AhR-pathways in lung cancer susceptibility may be underrated in main-effects association analysis. Complex interaction patterns in individuals of European descent have moderate predictive capacity for lung cancer or subgroups thereof, especially in never smokers.

Background Polygenic risk scores (PRS) hold the promise to refine prognostication in hepatocellular cancer (HCC). The few available HCC PRS include germline risk variants identified among individuals of mostly European ancestry, but data are lacking on the transportability of these PRS in multiethnic U.S patients with cirrhosis from multiple etiologies. Methods We used data from 1644 patients with cirrhosis enrolled in two prospective cohort studies in the U.S. Patients were followed until HCC diagnosis, death, liver transplantation, or last study visit through June 30, 2021. The high-risk variants in PNPLA3-MBOAT7-TM6SF2-GCKR were combined in a PRS and we evaluated its association with HCC. Discriminatory accuracy was assessed using the C-statistic. Results During 4,759 person-years of follow-up, 93 patients developed HCC. Mean age was 59.8 years, 68.6% were male, 27.2% Hispanic, 25.1% non-Hispanic Black, 25.7% had NAFLD, 42.1% had heavy alcohol use, and 19.5% had active HCV. HCC risk increased by 134% per unit increase in PRS (HR = 2.30; 95% CI, 1.35–3.92). Compared to cirrhosis patients in the lowest tertile of the PRS, those in the highest tertile had 2-fold higher risk of HCC (HR = 2.05; 95% CI, 1.22–3.44). The PRS alone had modest discriminatory ability (C-statistic = 0.58; 95% CI, 0.52–0.63); however, adding PRS to a predictive model with traditional HCC risk factors had a C-statistic of 0.70 (95% CI, 0.64–0.76), increasing from 0.68 without the PRS (p = 0.0012). Conclusions Our findings suggest that PRS may enhance risk prediction for HCC in contemporary U.S. cirrhosis patients.

OBJECTIVES/GOALS: In 2020, Baylor College of Medicine held a datathon to introduce a data warehouse, identify its capabilities/limitations, foster collaborations, and engage trainees. The event was held again in 2022, and lessons learned (e.g., tools for data self-service or team communication) were applied. METHODS/STUDY POPULATION: Senior faculty reviewed proposals with an emphasis on feasibility, impact, and relevance to quality improvement or population health. Selected teams worked with Information Technology (IT) for 2 months and presented findings at a 1-day event. Surveys were administered to participants before and after the event to evaluate their background, team characteristics, collaborations, knowledge before and after the datathon, perceived value of the datathon, and plans for future work. Descriptive statistics of respondents’ self-reports were tabulated. RESULTS/ANTICIPATED RESULTS: In 2022, 19 of 36 projects were accepted (13/33 in 2020). At both events, most projects studied quality improvement or clinical outcomes. Of 82 participants in 2022, 54 completed surveys. In 2022, 72% had no datathon experience (48% in 2020). Median effort was 10 person-hours; median IT time was 20% (20 and 10%, in 2020). Seven respondents finished and 21 partially finished their projects (1 and 11, in 2020); 92% made new collaborations (91% in 2020). Respondents strongly agreed that: the experience was valuable (n=28), they would participate in future datathons (n=30), and they would use the warehouse for future work (n=25). Twenty-seven have planned abstracts; 25 have planned manuscripts. DISCUSSION/SIGNIFICANCE: The 2022 datathon had more participants with less experience, potentially due to improved promotion and training opportunities. Fewer person-hours and a higher percentage of IT time were required as compared to 2020, and more projects were completed, possibly due to increased IT efficiency.

Genetic susceptibility loci for oropharyngeal cancer have been reported but these studies have not always examined human papillomavirus (HPV) status. Here, the authors perform genome-wide analysis taking into account HPV16 serology status and report two independent loci in the HLA region, suggesting the protective role of HLA variants against HPV infection.

Abstract Background Over the past decades, approaches for diagnosing and treating cancer have seen significant improvement. However, the variability of patient and tumor characteristics has limited progress on methods for prognosis prediction. The development of high-throughput omics technologies now provides multiple approaches for characterizing tumors. Although a large number of published studies have focused on integration of multi-omics data and use of pathway-level models for cancer prognosis prediction, there still exists a gap of knowledge regarding the prognostic landscape across multi-omics data for multiple cancer types using both gene-level and pathway-level predictors. Methods In this study, we systematically evaluated three often available types of omics data (gene expression, copy number variation and somatic point mutation) covering both DNA-level and RNA-level features. We evaluated the landscape of predictive performance of these three omics modalities for 33 cancer types in the TCGA using a Lasso or Group Lasso-penalized Cox model and either gene or pathway level predictors. Results We constructed the prognostic landscape using three types of omics data for 33 cancer types on both the gene and pathway levels. Based on this landscape, we found that predictive performance is cancer type dependent and we also highlighted the cancer types and omics modalities that support the most accurate prognostic models. In general, models estimated on gene expression data provide the best predictive performance on either gene or pathway level and adding copy number variation or somatic point mutation data to gene expression data does not improve predictive performance, with some exceptional cohorts including low grade glioma and thyroid cancer. In general, pathway-level models have better interpretative performance, higher stability and smaller model size across multiple cancer types and omics data types relative to gene-level models. Conclusions Based on this landscape and comprehensively comparison, models estimated on gene expression data provide the best predictive performance on either gene or pathway level. Pathway-level models have better interpretative performance, higher stability and smaller model size relative to gene-level models.

Abstract The complex polygenic nature of lung cancer is not fully characterized. Our study seeks to identify novel phenotypes associated with lung cancer using cross-trait linkage disequilibrium score regression (LDSR). We measured pairwise genetic correlation (rg) and SNP heritability (h2) between 347 traits and lung cancer risk using genome-wide association study summary statistics from the UKBB and OncoArray consortium. Further, we conducted analysis after removing genomic regions previously associated with smoking behaviors to mitigate potential confounding effects. We found significant negative genetic correlations between lung cancer risk and dietary behaviors, fitness metrics, educational attainment, and other psychosocial traits. Alcohol taken with meals (rg = − 0.41, h2 = 0.10, p = 1.33 × 10–16), increased fluid intelligence scores (rg = − 0.25, h2 = 0.22, p = 4.54 × 10–8), and the age at which full time education was completed (rg = − 0.45, h2 = 0.11, p = 1.24 × 10–20) demonstrated negative genetic correlation with lung cancer susceptibility. The body mass index was positively correlated with lung cancer risk (rg = 0.20, h2 = 0.25, p = 2.61 × 10–9). This analysis reveals shared genetic architecture between several traits and lung cancer predisposition. Future work should test for causal relationships and investigate common underlying genetic mechanisms across these genetically correlated traits.

Patricia Mabry and coauthors discuss application of systems approaches in cancer research.

Abstract The genetic architecture of non-small cell lung cancer (NSCLC) is relevant to smoking status. However, the genetic contribution of long-term smoking cessation to the prognosis of NSCLC patients remains largely unknown. We conducted a genome-wide association study primarily on the prognosis of 1299 NSCLC patients of long-term former smokers from independent discovery (n = 566) and validation (n = 733) sets, and used in-silico function prediction and multi-omics analysis to identify single nucleotide polymorphisms (SNPs) on prognostics with NSCLC. We further detected SNPs with at least moderate association strength on survival within each group of never, short-term former, long-term former, and current smokers, and compared their genetic similarity at the SNP, gene, expression quantitative trait loci (eQTL), enhancer, and pathway levels. We identified two SNPs, rs34211819 TNS3 at 7p12.3 (P = 3.90 × 10−9) and rs1143149 SEPT7 at 7p14.2 (P = 9.75 × 10−9), were significantly associated with survival of NSCLC patients who were long-term former smokers. Both SNPs had significant interaction effects with years of smoking cessation (rs34211819 TNS3 : P interaction = 8.0 × 10−4; rs1143149 SEPT7 : P interaction = 0.003). In addition, in silico function prediction and multi-omics analysis provided evidence that these QTLs were associated with survival. Moreover, comparison analysis found higher genetic similarity between long-term former smokers and never-smokers, compared to short-term former smokers or current smokers. Pathway enrichment analysis indicated a unique pattern among long-term former smokers that was related to immune pathways. This study provides important insights into the genetic architecture associated with long-term former smoking NSCLC.

Abstract Recent studies suggest that rare variants exhibit stronger effect sizes and might play a crucial role in the etiology of lung cancers (LC). Whole exome plus targeted sequencing of germline DNA was performed on 1045 LC cases and 885 controls in the discovery set. To unveil the inherited causal variants, we focused on rare and predicted deleterious variants and small indels enriched in cases or controls. Promising candidates were further validated in a series of 26,803 LCs and 555,107 controls. During discovery, we identified 25 rare deleterious variants associated with LC susceptibility, including 13 reported in ClinVar. Of the five validated candidates, we discovered two pathogenic variants in known LC susceptibility loci, ATM p.V2716A (Odds Ratio [OR] 19.55, 95%CI 5.04–75.6) and MPZL2 p.I24M frameshift deletion (OR 3.88, 95%CI 1.71–8.8); and three in novel LC susceptibility genes, POMC c.*28delT at 3′ UTR (OR 4.33, 95%CI 2.03–9.24), STAU2 p.N364M frameshift deletion (OR 4.48, 95%CI 1.73–11.55), and MLNR p.Q334V frameshift deletion (OR 2.69, 95%CI 1.33–5.43). The potential cancer-promoting role of selected candidate genes and variants was further supported by endogenous DNA damage assays. Our analyses led to the identification of new rare deleterious variants with LC susceptibility. However, in-depth mechanistic studies are still needed to evaluate the pathogenic effects of these specific alleles.

Abstract Background Research on venous thromboembolism (VTE) that relies only on the International Classification of Diseases (ICD) can misclassify outcomes. Our study aims to discover and validate an improved VTE computable phenotype for people with cancer. Methods We used a cancer registry electronic health record (EHR)–linked longitudinal database. We derived three algorithms that were ICD/medication based, natural language processing (NLP) based, or all combined. We then randomly sampled 400 patients from patients with VTE codes (n = 1111) and 400 from those without VTE codes (n = 7396). Weighted sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated on the entire sample using inverse probability weighting, followed by bootstrapped receiver operating curve analysis to calculate the concordance statistic (c statistic). Results Among 800 patients sampled, 280 had a confirmed acute VTE during the first year after cancer diagnosis. The ICD/medication algorithm had a weighted PPV of 95% and a weighted sensitivity of 81%, with a c statistic of 0.90 (95% confidence interval [CI], 0.89–0.91). Adding Current Procedural Terminology codes for inferior vena cava filter removal or early death did not improve the performance. The NLP algorithm had a weighted PPV of 80% and a weighted sensitivity of 90%, with a c statistic of 0.93 (95% CI, 0.92–0.94). The combined algorithm had a weighted PPV of 98% at the higher cutoff and a weighted sensitivity of 96% at the lower cutoff, with a c statistic of 0.98 (95% CI, 0.97–0.98). Conclusions Our ICD/medication‐based algorithm can accurately identify VTE phenotype among patients with cancer with a high PPV of 95%. The combined algorithm should be considered in EHR databases that have access to such capabilities.

Abstract Background Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of disease-onset. Methods To demonstrate efficacy, we evaluated this method on two simulation data sets to estimate the type I error rate and power. Simulations showed that ES-MDR identified interactions using less computational workload and allowed for adjustment of covariates. We applied ES-MDR on the OncoArray-TRICL Consortium data with 14,935 cases and 12,787 controls for lung cancer (SNPs = 108,254) to search over all two-way interactions to identify genetic interactions associated with lung cancer age-of-onset. We tested the best model in an independent data set from the OncoArray-TRICL data. Results Our experiment on the OncoArray-TRICL data identified many one-way and two-way models with a single-base deletion in the noncoding region of BRCA1 (HR 1.24, P = 3.15 × 10–15), as the top marker to predict age of lung cancer onset. Conclusions From the results of our extensive simulations and analysis of a large GWAS study, we demonstrated that our method is an efficient algorithm that identified genetic interactions to include in our models to predict survival outcomes.

Abstract Background Genomic profiling of solid human tumors by projects such as The Cancer Genome Atlas (TCGA) has provided important information regarding the somatic alterations that drive cancer progression and patient survival. Although researchers have successfully leveraged TCGA data to build prognostic models, most efforts have focused on specific cancer types and a targeted set of gene-level predictors. Less is known about the prognostic ability of pathway-level variables in a pan-cancer setting. To address these limitations, we systematically evaluated and compared the prognostic ability of somatic point mutation (SPM) and copy number variation (CNV) data, gene-level and pathway-level models for a diverse set of TCGA cancer types and predictive modeling approaches. Results We evaluated gene-level and pathway-level penalized Cox proportional hazards models using SPM and CNV data for 29 different TCGA cohorts. We measured predictive accuracy as the concordance index for predicting survival outcomes. Our comprehensive analysis suggests that the use of pathway-level predictors did not offer superior predictive power relative to gene-level models for all cancer types but had the advantages of robustness and parsimony. We identified a set of cohorts for which somatic alterations could not predict prognosis, and a unique cohort LGG, for which SPM data was more predictive than CNV data and the predictive accuracy is good for all model types. We found that the pathway-level predictors provide superior interpretative value and that there is often a serious collinearity issue for the gene-level models while pathway-level models avoided this issue. Conclusion Our comprehensive analysis suggests that when using somatic alterations data for cancer prognosis prediction, pathway-level models are more interpretable, stable and parsimonious compared to gene-level models. Pathway-level models also avoid the issue of collinearity, which can be serious for gene-level somatic alterations. The prognostic power of somatic alterations is highly variable across different cancer types and we have identified a set of cohorts for which somatic alterations could not predict prognosis. In general, CNV data predicts prognosis better than SPM data with the exception of the LGG cohort.

In cancer many gene variants may contribute to disease etiology, but the impact of a given gene variant may have varied effect size. Here, the authors analyse summary statistics of genome-wide association studies from fourteen cancers, and show the utility of polygenic risk scores may vary depending on cancer type.

In lung cancer, relatively few germline mutations are known to impact risk. Here the authors looked at rare variants in 39,146 individuals and find novel germline mutations associated with risk, as well as implicating ATM and a new candidate gene for lung cancer risk.

Abstract Background Cancer prognosis prediction is valuable for patients and clinicians because it allows them to appropriately manage care. A promising direction for improving the performance and interpretation of expression-based predictive models involves the aggregation of gene-level data into biological pathways. While many studies have used pathway-level predictors for cancer survival analysis, a comprehensive comparison of pathway-level and gene-level prognostic models has not been performed. To address this gap, we characterized the performance of penalized Cox proportional hazard models built using either pathway- or gene-level predictors for the cancers profiled in The Cancer Genome Atlas (TCGA) and pathways from the Molecular Signatures Database (MSigDB). Results When analyzing TCGA data, we found that pathway-level models are more parsimonious, more robust, more computationally efficient and easier to interpret than gene-level models with similar predictive performance. For example, both pathway-level and gene-level models have an average Cox concordance index of ~ 0.85 for the TCGA glioma cohort, however, the gene-level model has twice as many predictors on average, the predictor composition is less stable across cross-validation folds and estimation takes 40 times as long as compared to the pathway-level model. When the complex correlation structure of the data is broken by permutation, the pathway-level model has greater predictive performance while still retaining superior interpretative power, robustness, parsimony and computational efficiency relative to the gene-level models. For example, the average concordance index of the pathway-level model increases to 0.88 while the gene-level model falls to 0.56 for the TCGA glioma cohort using survival times simulated from uncorrelated gene expression data. Conclusion The results of this study show that when the correlations among gene expression values are low, pathway-level analyses can yield better predictive performance, greater interpretative power, more robust models and less computational cost relative to a gene-level model. When correlations among genes are high, a pathway-level analysis provides equivalent predictive power compared to a gene-level analysis while retaining the advantages of interpretability, robustness and computational efficiency.

The role of impaired lung function in lung cancer etiology is complex due to the relation of cigarette smoking to both conditions. Here, supported by Mendelian randomization analysis the authors find a link between pulmonary function impairment and lung cancer risk beyond smoking, implicating immune-related pathways

In 2020, Baylor College of Medicine held a datathon to inform potential users of a new data warehouse, allow users to address clinical questions, identify warehouse capabilities and limitations, foster collaborations, and engage trainees. Senior faculty selected proposals based on feasibility and impact. Selectees worked with Information Technology for 2 months and presented findings. A survey of participants showed diverse levels of experience, high perceived value of the datathon, high rates of collaboration, and significant increases in knowledge. A datathon can promote familiarity with a new data warehouse, guide data warehouse improvement, and promote collaboration.

Summary: Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.

Acquired uniparental disomy (aUPD) leads to homozygosity facilitating identification of monoallelically expressed genes. We analyzed single-nucleotide polymorphism array-based genotyping data of 448 head and neck squamous cell carcinoma (HNSCC) samples from The Cancer Genome Atlas to determine the frequency and distribution of aUPD regions and their association with survival, as well as to gain a better understanding of their influence on the tumor genome. We used expression data from the same dataset to identify differentially expressed genes between groups with and without aUPD. Univariate and multivariable Cox proportional hazards models were performed for survival analysis. We found that 82.14% of HNSCC samples carried aUPD; the most common regions were in chromosome 17p (31.25%), 9p (30.13%), and 9q (27.46%). In univariate analysis, five independent aUPD regions at chromosome 9p, two regions at chromosome 9q, and the CDKN2A region were associated with poor overall survival in all groups, including training and test sets and human papillomavirus (HPV)-negative samples. Forty-three genes in areas of aUPD including PD-L1 and CDKN2A were differentially expressed in samples with aUPD compared to samples without aUPD. In multivariable analysis, aUPD at the CDKN2A region was a significant predictor of overall survival in the whole cohort and in patients with HPV-negative HNSCC. aUPD at specific regions in the genome influences clinical outcomes of HNSCC and may be beneficial for selection of personalized therapy to prolong survival in patients with this disease.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Smoking and alcohol intake are major risk factors in head and neck squamous cell carcinomas (HNSCCs). Although the link between TP53 mutation and smoking has been well established, very little is known about the link between acquired uniparental disomy (aUPD) and smoking and/or alcohol consumption or other clinical characteristics. We used TCGA genomic data to investigate whether smoking, alcohol intake, clinical and demographic variables, HPV status and TP53 mutation are associated with aUPD at specific chromosomal regions. In multivariate analysis, we found association between aUPD regions and risk factors and clinical variables of disease. aUPD regions on chromosome 4q, 5q, 9p, 9q, 13q, 17p and CDKN2A occurred significantly more often in patients with TP53-mutated HNSCC than in those with wild-type HNSCC, while aUPD regions on chromosome 9p and at CDKN2A were significantly more frequent in females than in males. Besides, aUPD occurred more frequent in HPV-positive than in HPV-negative samples with all HNSCC and larynx cancers on chromosome 9q 15q and 17p. Moreover, aUPD on CDKN2A region occurred more often in alcohol drinkers than nondrinkers in patients with all HNSCC and oral cavity cancers, while aUPD region on chromosome 5q occurred less in alcohol drinkers than nondrinkers in patients with all HNSCC and oral cavity cancers. Similarly, aUPD region on chromosome 5q occurred less in smokers than nonsmokers in patients with all HNSCC and oral cavity cancers. In conclusion, aUPD regions are not random, and certain regions are associated with risk factors for disease, and with TP53 mutation status.

Similarities in cancers can be studied to interrogate their etiology. Here, the authors use genome-wide association study summary statistics from six cancer types based on 296,215 cases and 301,319 controls of European ancestry, showing that solid tumours arising from different tissues share a degree of common germline genetic basis.

This cross-sectional ecological study examined the relationship between neighborhood-level standard occupational groups in the USA and COVID-19 vaccine uptake using 774 census tract data, each consisting of approximately 1600 housing units. The neighborhood-level COVID-19 vaccination uptake data were retrieved from Harris County Public Health, Harris County, Texas. The standard occupational group data were from the US Census Bureau. We calculated the incidence rate ratios (IRRs) for vaccine uptake using bivariate and multivariable Poisson regression models. In the adjusted models, we found that the healthcare practitioner/technician (IRR: 1.008; 95% CI: 1.003–1.014; p = 0.001), business/management/legal (IRR: 1.011; 95% CI: 1.008–1.013; p < 0.001), computer/engineering/life/physical/social science (IRR: 1.018; 95% CI: 1.013–1.023; p < 0.001), and arts/design/entertainment/sports/media (IRR: 1.031; 95% CI: 1.018–1.044; p < 0.001) occupational groups were more likely to have received the full regimen of a COVID-19 vaccine. On the contrary, the building/installation/maintenance/repair (IRR: 0.991; 95% CI: 0.987–0.995; p < 0.001), construction/extraction/production (IRR: 0.991; 95% CI: 0.988–0.995; p < 0.001), transportation/material moving (IRR: 0.992; 95% CI: 0.987–0.997; p = 0.002), food preparation/serving related (IRR: 0.995; 95% CI: 0.990–0.999; p = 0.023), and personal care/services (IRR: 0.991; 95% CI: 0.985–0.998; p = 0.017) groups were less likely to have received the complete dose of a COVID-19 vaccine. White-collar workers were more likely to be vaccinated than blue-collar workers. We adjusted for age, sex, and race/ethnicity in the multivariable analysis. The low vaccine uptake among certain occupational groups remains a barrier to pandemic control. Engaging labor-centered stakeholders in the development of vaccination interventions may increase uptake.