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• National Cancer Institute (NCI) Genome Wide Association Study (GWAS) of Lung Cancer in Never Smokers

  A genomewide study of lung cancer in never smokers Abstract and specific aims In the United States, lung cancer incidence and mortality rates have been steadily declining over the past decade, following decline in the prevalence of tobacco smoking. However, lung cancer remains the leading cause of cancer death, killing more patients than breast, colon, and prostate cancers combined. Although tobacco smoke is the predominant risk factor for development of lung cancer, some patients develop the disease without a history of tobacco smoking. About 10 - 15% of all lung cancers occur in lilfetime never smokers. This figure will increase as the proportion of never smokers increases in the population. Even at present rates, lung cancer in never smokers, if considered a separate disease, is 6th to 8th top cause of cancer death. The growing number of never smokers in the USA and other countries emphasizes the importance of understanding the epidemiology and biology underlying lung cancer in this group. Genetic polymorphisms associated with the risk of lung cancer in never smokers are expected to overlap with those associated with the risk of lung cancer in ever smokers only partially. Epidemiological, molecular and clinical data suggest that molecular mechanisms of LC may differ in smokers and non-smokers, implying that lung cancer in never smokers is a different disease compared to the lung cancer in smokers. One can expect that there should be stronger genetic component in the control lung cancer in never smokers because effects of the genetic factors in never smokers are unmasked by the lack of tobacco smoke exposure. The genetic epidemiology of lung cancer in never smokers has not been well explored, largely because of difficulties in accruing the needed sample size for association studies. We propose a multicenter (total 14 sites from the US and Europe) genomewide association study of lung cancer in never smokers with the following specific aims: Aim 1: To identify candidate SNPs influencing risk for lung cancer in never smokers using Discovery sample. In the Discovery phase we will genotype 1256 Caucasian cases and 1365 age- and gender-matched never smoker controls using the Illumina Human660W-Quad platform. In addition, we will include in the analysis 284 cases and 175 matched controls already genotyped on the 610Quad platform. In this phase we will only include the study sites that have collected blood specimens (MDACC, Mayo Clinic, Karmanos Cancer Institute, The University of Liverpool Cancer research Centre, Institute of Cancer Research in Sutton, and Lunenfeld Research Institute in Toronto, Canada). All the samples will be sent to the independent lab for genotyping, to reduce site-specific technical artifacts. The final sample will consist of 1540 cases and 1540 controls matched by study site. Aim 2: To perform the second phase (validation) analysis of significant SNPs identified in aim 1 using an independent set of cases and controls. SNPs associated with risk at the significance level of 0.01 or below in the discovery set will be included in the replication phase. The proposed threshold guarantees an adequate power to retain SNPs with the typical effect size of 1.3. We plan to carry 6000-7000 SNPs for validation. The independent replication set will include 800 cases and 800 controls, mostly from sites that collected tissue (Mayo Clinic, Karmanos Cancer Institute, UT Southwestern) or buccal specimens (UCLA), but also blood samples (Imperial College London, University of Pennsylvana, German Cancer Research Center, Heidelberg, National Research Center for Environment and Health, Neuherberg, Carmel Medical Center, Haifa). We will then perform a joint analysis to test the significance of the SNPs identified in the first stage using a stringent critical p-value of 10-7. There will be 2340 cases and 2340 controls in the joint set. Based on our experience with GWAS in smokers and assuming that genetic component in lung cancer risk in never smokers can be higher than genetic component in smokers, we expect to identify about 5-10 candidate regions associated with lung cancer risk in never smokers. Aim 3: To identify and explore pathways associated with the risk of lung cancer in never smokers. Results of the number of studies on the molecular mechanisms and drug response suggest that lung cancer in never smokers is a different disease and different pathways will be associated with lung cancer risk in non-smokers and smokers. To identify pathways and molecular functions associated with lung cancer risk in never smokers we will apply Ingenuity and DAVID bioinformatics tools. We will use at least 300 top candidate genes identified in joint and discovery analysis. The reason why we select rather large number of candidate genes for functional annotation is two-fold: 1. Both algorithms are looking for enrichment of pathways and function by most significant genes and they produce statistically robust results only when number of genes is relatively high. 2. Despite the fact that this study will be largest possible for never smokers we still are underpowered to detect SNPs with relatively small effect size. But though those SNPs will not reach genome wide level of significance they will tend to be on the top of the list. In other words genes from the gray zone (significant on individual level and non-significant for genome wide level) are expected to be enriched by true discoveries. True discoveries are likely to be associated with limited number of pathways / functions while false positives are expected to be uniformly distributed across functions and pathways. Therefore significant clustering of the gene to a given function will suggest that that those genes are true discoveries. This is the first GWAS aiming at identifying the genetic control of susceptibility to lung cancer in Caucasian never smokers. We will combine the available resources from the multiple sites to achieve the sample size sufficient for this study. The study will identify genetic architecture of the predisposition to the lung cancer in never smokers.

  Study phs000634

• Na?ve Treg-specific genomic DNA hypomethylation for autoimmune disease susceptibility

  Naturally occurring regulatory T (Treg) cells, which are specifically expressing the transcription factor FOXP3, CD25 and CTLA-4, are indispensable for the maintenance of immunological self-tolerance and homeostasis. Mutations of the FOXP3, CD25 and CTLA4 genes compromise the function of natural FOXP3+CD25+CD4+ Treg cells and cause severe autoimmune diseases showing Mendelian inheritance. Also, altered Treg-specific epigenetic changes, such as DNA hypomethylation and enhancer activation, impair the expression of these molecules in developing Treg cells, causing autoimmune diseases. It is obscure, however, whether Treg cells significantly contribute to the development of polygenic common autoimmune diseases (such as type 1 diabetes and rheumatoid arthritis), which afflict ~10% of the population. Recent genome-wide association study (GWAS) of common autoimmune diseases has revealed many autoimmune disease-associated single nucleotide polymorphisms (SNPs) in enhancer regions of immune cells, for example, in non-coding regions of T cell activation-associated genes such as CD25 and CTLA4. These findings, especially the involvement of common T-cell genes in the functions of both Treg cells and effector T cells, prompt one to ask whether autoimmune disease-associated causative SNPs impact on the functions of either disease-causing effector T cells or disease-suppressing Treg cells. Here, we have explored epigenetic profiles of human Treg and conventional T (Tconv) cells in na?ve or activated states, and found that autoimmune disease-associated SNPs are predominantly enriched in Treg-specific CpG demethylated regions (Treg-DRs), which were mostly included in Treg-specific super-enhancers. Treg-DRs are developmentally established in na?ve Treg cells before antigenic stimulation, distinct from activation-induced CpG hypomethylation in Treg or Tconv cells, and highly associated with Treg cell-specific transcriptional and epigenetic changes. Taken together, genetic variants affecting endogenous development of natural Treg cells and their Treg-intrinsic functions are causative of common autoimmune diseases, indicating natural Treg cells as a key target for their treatment and prevention.

  Study JGAS000145

• NSIGHT North Carolina Newborn Exome Sequencing for Universal Screening (NC NEXUS)

  Since newborn screening (NBS) began in the 1960s, technological advances have resulted in its use in an increasing number of disorders. Recent developments in whole-genome sequencing and its simpler corollary, whole-exome sequencing (WES), now afford the opportunity to comprehensively define the variation within an individual's genome in a rapid and affordable manner. Many challenges arise with the clinical application of genome-scale sequencing and in deriving practical benefits to infants and children. Its utility in NBS has yet to be demonstrated and its application in the pediatric population requires special examination, not only for potential clinical benefits but also for the unique ethical challenges it presents. In this proposal, we outline a highly interdisciplinary approach to identify, confront, and overcome the major challenges that must be met in order to implement deep sequencing technology to enhance current newborn screening in a diverse pediatric population. Overarching Aim 1 will evaluate the utility of WES as a diagnostic tool to extend the utility of current NBS. Using diverse cohorts of infants and young children with known conditions identified through NBS, we will examine the sensitivity and specificity of WES. We will also utilize WES in cohorts of children with known conditions not currently screened as potential candidates for NBS in the future. Overarching Aim 2 will develop and assess a framework for analyzing WES in a clinically oriented framework based on principles of ethics and evidence-based medicine. We will develop strategies to guide clinicians, clinical laboratories, and patients/families in their decisions regarding the inevitable incidental findings that will be detected, in ways that respect the child and protect his/her future autonomy, while also respecting parental interests and rights. Overarching Aim 3 will explore ethical, legal, and social issues (ELSI) involved in informed decision making and develop best practices regarding the return of results after testing. We will develop novel decision support tools and evaluate their usefulness in parental decision making, and examine the burdens placed on clinicians as this new technology is deployed in the vulnerable and special population that are newborns and their families.

  Study phs002095

• EGA file encryption types

  EGA file encryption The European Genome-Phenome Archive (EGA) stands as a significant initiative in the field of genomics, facilitating the secure storage and sharing of vast amounts of genomic data. Jointly hosted by the Centre for Genomic Regulation (CRG) and the European Bioinformatics Institute (EMBL-EBI), the EGA offers a robust infrastructure for managing encrypted data, ensuring the privacy and integrity of sensitive genomic information. Both CRG and EMBL-EBI play pivotal roles in the EGA, each institution bringing unique expertise in managing different types of encryptions. EMBL-EBI EGACryptor encryption system, while CRG utilises Crypt4GH, resulting in two distinct encryption file types: cip and c4gh, respectively. How can I check the file encryption? To access your dataset of interest on the European Genome-Phenome Archive (EGA) and determine the corresponding encryption file extension, please follow these step-by-step instructions: Utilise the search or navigation features provided on the EGA website to locate the dataset you are interested in. Once you find the dataset webpage, click on the "Files" tab, typically located on the dataset's webpage. Upon reaching the "Files" tab, you will be presented with a table containing comprehensive information about the files associated with the dataset. This table will include various columns, such as "File Name," "Size," and "Location." The "Location" column is particularly relevant for determining the encryption file extension. Figure 1: File table showing all the information available for the files in a dataset. This table is specific for each dataset. To identify the encryption file extension corresponding to a specific file, locate the "Location" column in the table. If the file is archived in Spain, the file will have the .c4gh extension, indicating that it is encrypted using Crypt4gh. Conversely, if the file is archived in the UK, the encrypted file extension will be .cip, indicating the usage of EGACryptor for encryption. It is worth noting that a file can be available in both locations, meaning it will be accessible with both file extensions (.c4gh and .cip). This allows users to choose the encryption method that aligns with their preferred platform or analysis tools.

  Documentation check-encryption-type

• NEOPREDICT-Lung: Neoadjuvant Immunotherapy for Resectable NSCLC

  Links via: DOI: https://doi.org/10.1038/s41591-024-02965-0 PubMed: https://pubmed.ncbi.nlm.nih.gov/38689060/ Journal: https://www.nature.com/articles/s41591-024-02965-0 Abstract Antibodies targeting the immune checkpoint molecules PD-1, PD-L1 and CTLA-4 administered alone or in combination with chemotherapy are standard of care in most patients with metastatic non-small cell lung cancers. When given prior to curative surgery, tumor responses and improved event-free survival are achieved. New antibody combinations may be more efficacious and tolerable. In an ongoing, open-label phase II study, 60 biomarker-unselected, treatment-naïve patients with resectable non-small cell lung cancer were randomized to receive two preoperative doses of nivolumab (anti-PD-1) with or without relatlimab (anti-LAG-3) antibody therapy. The primary study endpoint was feasibility of surgery within 43 days, which was met by all patients. Curative resection was achieved in 95% of patients. Secondary endpoints included pathological and radiographic response rates, pathologically complete resection rates, disease-free and overall survival rates, and safety. Major pathological (≤10% viable tumor cells) and objective radiographic responses were achieved in 27% and 10% (nivolumab), and 30% and 27% (nivolumab and relatlimab) of patients, respectively. In 100% (nivolumab) and 90% (nivolumab and relatlimab) of patients tumors and lymph nodes were pathologically completely resected. With 12 months median duration of follow-up, disease-free survival and overall survival rates at 12 months were 89% and 93% (nivolumab), and 93% and 100% (nivolumab and relatlimab). Both treatments were safe with grade ≥ 3 treatment-emergent adverse events reported in 10% and 13% of patients per study arm. Exploratory analyses provided insights into biological processes triggered by preoperative immunotherapy. This study establishes the feasibility and safety of dual targeting of PD-1 and LAG-3 prior to lung cancer surgery.

  Study EGAS00001007753

• 16S rRNA Rectal Mucus

  16S rRNA sequencing of rectal mucus samples from patients suspected to have colorectal cancer. This is 1 of 4 sequencing experiments on the same sample type.

  Study EGAS50000001262

• Enzymatic Methyl-Seq Rectal Mucus

  Enzymatic methyl sequencing of rectal mucus samples from patients suspected to have colorectal cancer. This is 1 of 4 sequencing experiments on the same sample type.

  Study EGAS50000001314

• BBMRI - BIOS project - Freeze 1 - Fastq files

  BBMRI - BIOS project - Freeze 1 - Fastq files

  Dataset EGAD00001001622

• BBMRI - BIOS project - Freeze 1 - Bam files

  BBMRI - BIOS project - Freeze 1 - Bam files

  Dataset EGAD00001001623

• Single-cell profiling of co-cultures of GSCCs and macrophages

  This dataset contains scRNA-seq data from 8 co-cultures of GSCCs and macrophages, and 1 monoculture of macrophages. Samples were individually labeled and pooled using MULTI-seq technology and processed with 10x Genomics Technology.

  Dataset EGAD00001011275