Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.

Despite promising results in using deep learning to infer genetic features from histological whole-slide images (WSIs), no prior studies have specifically applied these methods to lung adenocarcinomas from subjects who have never smoked tobacco (NS-LUAD) - a molecularly and histologically distinct subset of lung cancer. Existing models have focused on LUAD from predominantly smoker populations, with limited molecular scope and variable performance. Here, we propose a customized deep convolutional neural network based on ResNet50 architecture, optimized for multilabel classification for NS-LUAD, enabling simultaneous prediction of 16 molecular alterations from a single H&E-stained WSI.

Past exposure to PM10 and lung cancer risk in the EAGLE case-control study.

Within the EAGLE population-based case-control study, the present study aims to integrate previous analyses which suggested an increased lung cancer risk associated with particulate matter ≤ 10 μm (PM) exposure estimated 2-5 years before diagnosis (year 2000), by considering pollutant levels estimated 12-15 years before diagnosis (year 1990), i.e., in a potentially more relevant time window.

Genes associated with genetic and rare lung diseases and the risk of lung cancer.

Background We investigated whether markers, genes or terms of the associated with genetic or rare diseases (GARDs) that affect airway or lung function are associated with lung cancer. Methods Genes of interest were extracted from , , and Monarch Initiative. Individual SNP, gene level and gene-set analyses were performed for 52,207 SNPs, 1,677 genes or for 620 terms of the .

Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types.

Cutaneous squamous cell carcinoma (cSCC), basal cell carcinoma (BCC), and melanoma, the three major types of skin cancer, account for over 70% of all cancer cases. Despite their prevalence, the skin cancer microenvironment remains poorly characterized, both in the outer skin layer where the cancer originates and at the deeper junctional and dermal layers into which it progresses. To address this, we integrated 12 complementary spatial single-cell technologies to construct orthogonally-validated cell signatures, spatial maps, and interactomes for cSCC, BCC, and melanoma.

Genome-wide association study of early-stage non-small cell lung cancer prognosis: a pooled analysis in the International Lung Cancer Consortium.

Lung cancer is the leading cause of cancer mortality. To investigate genetic determinants for prognosis among patients diagnosed with early-stage non-small cell lung cancer (NSCLC), we conducted the first large-scale genome-wide association prognostic study using data from the International Lung Cancer Consortium (ILCCO) through a two-phase analysis. Phase 1 includes the discovery of genome-wide association studies analysis using a multivariable Cox PH model on 3428 NSCLC patients of European ancestry from 10 ILCCO participating studies to identify genetic variants associated with overall survival and validation analysis for genome-wide significant variants (P-value ≤5 × 10-8) using the Cancer Genome Atlas (TCGA).

The mutagenic forces shaping the genomes of lung cancer in never smokers.

Lung cancer in never smokers (LCINS) accounts for around 25% of all lung cancers and has been associated with exposure to second-hand tobacco smoke and air pollution in observational studies. Here we use data from the Sherlock-Lung study to evaluate mutagenic exposures in LCINS by examining the cancer genomes of 871 treatment-naive individuals with lung cancer who had never smoked, from 28 geographical locations. KRAS mutations were 3.

Examining the Role of Extrachromosomal DNA in 1,216 Lung Cancers.

The role of extrachromosomal DNA (ecDNA) in lung cancer, particularly in subjects who never smoked (LCINS), remains unclear. Examination of 1,216 whole-genome-sequenced lung cancers identified ecDNA in 18.9% of patients.

Inflammatory diseases and risk of lung cancer among individuals who have never smoked.

Lung cancer in never-smokers (LCINS) is a leading cause of cancer death globally, but no screening programs for LCINS exist. To identify medical conditions that could serve as markers of LCINS risk, we conducted a nested case-control study within the United Kingdom's Clinical Practice Research Datalink (CPRD-GOLD), consisting of 1581 LCINS cases and 14,318 never-smoking controls. Conditions significantly associated with LCINS 1-10 years before the index date were validated in an independent dataset, CPRD-Aurum (2188 LCINS cases, 19,597 never-smoking controls).

Mapping chromatin interactions at melanoma susceptibility loci uncovers distant cis-regulatory gene targets.

Genome-wide association studies (GWASs) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping chromatin interactions.

APOBEC affects tumor evolution and age at onset of lung cancer in smokers.

Most solid tumors harbor somatic mutations attributed to off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B). However, how APOBEC3A/B enzymes affect tumor evolution in the presence of exogenous mutagenic processes is largely unknown. Here, multi-omics profiling of 309 lung cancers from smokers identifies two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis.

Genome-wide association study for lung cancer in 6531 African Americans reveals new susceptibility loci.

Despite lung cancer affecting all races and ethnicities, disparities are observed in incidence and mortality rates among different ethnic groups in the United States. Non-Hispanic African Americans had a high incidence rate of lung cancer at 55.8 per 100 000 people, as well as the highest death rate at 37.

Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition.

Understanding lung cancer evolution can identify tools for intercepting its growth. In a landscape analysis of 1024 lung adenocarcinomas (LUAD) with deep whole-genome sequencing integrated with multiomic data, we identified 542 LUAD that displayed diverse clonal architecture. In this group, we observed an interplay between mobile elements, endogenous and exogenous mutational processes, distinct driver genes, and epidemiological features.

Associations of self-identified race and ethnicity and genetic ancestry with mortality among cancer survivors.

Self-identified race and ethnicity (SIRE) and genetic ancestry (GA) are potentially associated with disparities in health outcomes; however, independent effects of SIRE and GA on mortality in cancer survivors including when adjusting for multiple risk factors are understudied. Among 23,445 cancer survivors in the Prostate, Lung, Colorectal, and Ovarian Screening Trial, SIRE was associated with mortality among prostate, colorectal, lung, ovarian, and breast cancer survivors; GA was associated with mortality among prostate, colorectal, and breast cancer survivors. Associations were strong when adjusting for age at cancer diagnosis, sex, and tumor characteristics, but attenuated when adjusting for individual-level factors and population-level socioeconomic status.

The impact of imprecise case definitions in electronic health record research: a melanoma case-study from the Million Veteran Program.

Cases for a disease can be defined broadly using diagnostic codes, or narrowly using gold-standard confirmation that often is not available in large administrative datasets. These different definitions can have significant impacts on the results and conclusions of studies. We conducted this study to assess how using melanoma phecodes versus histologic confirmation for invasive or in situ melanoma impacts the results of a genome-wide association study (GWAS) using the Million Veteran Program.

Sherlock-Genome: an R Shiny application for genomic analysis and visualization.

Motivation: Next-generation sequencing technologies, such as whole genome sequencing (WGS), have become prominent in cancer genomics. However, managing, visualizing, and integratively analyzing WGS results across various bioinformatic pipelines remains challenging, particularly for non-bioinformaticians, hindering the usability of WGS data for biological discovery.

Results: We developed Sherlock-Genome, an R Shiny app for data harmonization, visualization, and integrative analysis of WGS-based cancer genomics studies.

Mapping chromatin interactions at melanoma susceptibility loci and cell-type specific dataset integration uncovers distant gene targets of -regulation.

Genome-wide association studies (GWAS) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping cell-type specific chromatin interactions.

Socioeconomic Status, Smoking, and Lung Cancer: Mediation and Bias Analysis in the SYNERGY Study.

Background: Increased lung cancer risks for low socioeconomic status (SES) groups are only partially attributable to smoking habits. Little effort has been made to investigate the persistent risks related to low SES by quantification of potential biases.

Methods: Based on 12 case-control studies, including 18 centers of the international SYNERGY project (16,550 cases, 20,147 controls), we estimated controlled direct effects (CDE) of SES on lung cancer via multiple logistic regression, adjusted for age, study center, and smoking habits and stratified by sex.

The mutagenic forces shaping the genomic landscape of lung cancer in never smokers.

Lung cancer in never smokers (LCINS) accounts for up to 25% of all lung cancers and has been associated with exposure to secondhand tobacco smoke and air pollution in observational studies. Here, we evaluate the mutagenic exposures in LCINS by examining deep whole-genome sequencing data from a large international cohort of 871 treatment-naïve LCINS recruited from 28 geographical locations within the Sherlock- study. mutations were 3.

APOBEC shapes tumor evolution and age at onset of lung cancer in smokers.

APOBEC enzymes are part of the innate immunity and are responsible for restricting viruses and retroelements by deaminating cytosine residues. Most solid tumors harbor different levels of somatic mutations attributed to the off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B). However, how APOBEC3A/B enzymes shape the tumor evolution in the presence of exogenous mutagenic processes is largely unknown.