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• UK10K COHORT ALSPAC

  The UK10K project proposes a series of complementary genetic approaches to find new low-frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome-wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein-coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will directly analyse quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The Avon Longitudinal Study of Parents and Children (ALSPAC) is a two-generation prospective study. Pregnant women living in one of three health districts in the former county of Avon with an expected delivery date between April 1991 and December 1992 were eligible to be enrolled in the study, and this formed the initial point of contact for the development of a large, family based resource. Information has been collected on the children and the mothers through retrieval of biological materials (e.g. antenatal blood samples, placentas), biological sampling (e.g. collection of cord blood, umbilical cord, milk teeth, hair, toenails, blood and urine), self-administered questionnaires, data extraction from medical notes, linkage to routine information systems and at repeat research clinics.

  Study EGAS00001000090

• Exploring_the_landscape_of_somatic_mutations_in_normal_tissue___POL___TGS_

  Somatic mutations (burdens and signatures) and clonal dynamics in individuals with germline DNA polymerase mutations.

  Study EGAS00001003754

• Diabetes Control and Complications Trial (DCCT) and Epidemiology of Diabetes Interventions and Complications Study (EDIC)

  The Diabetes Control and Complications Trial (DCCT, 1982-93) and the Epidemiology of Diabetes Interventions and Complications follow-up study (EDIC, 1994-2016) have been ongoing for more than twenty years. After a mean follow-up of approximately 16 years, the DCCT-EDIC cohort of 1,441 Type 1 diabetics had remained remarkably complete with more than 90% of the original cohort being actively followed. Taken together, the DCCT clinical trial and subsequent EDIC longitudinal follow-up provide a uniquely rich source of information on the impact of intensive therapy on glycemia and the its long-term complications for persons with Type 1 diabetes. The DCCT was a multicenter, randomized clinical trial (1, 2) designed to compare intensive with conventional diabetes therapy with regard to their effects on the development and progression of the early complications of Type 1 diabetes. The DCCT trial found that "intensive therapy effectively delays the onset and slows the progression of diabetic retinopathy, nephropathy, and neuropathy" in patients with Type 1 diabetes (1). The goal of the EDIC follow-up was to examine the longer term effects of the original DCCT interventions, especially as they apply to late-occurring complications, such as cardiovascular disease and more advanced stages of retinal and renal disease (3). The EDIC study has been remarkably fruitful in discovering the long term "imprinting" effects (metabolic memory) of the previous intensive therapy, and in delineating the interactions among risk factors with regard to microvascular complications (4-6). In addition, EDIC established, for the first time, the role of intensive therapy and chronic glycemia on atherosclerosis (7-9). Note: This study description has been prepared using materials authored by the DCCT-EDIC Data Coordinating Center. DETAILED DESCRIPTION OF STUDY

  Study phs000086

• Gene-Environment Interactions (GxE) and Complex Traits

  Functional variants associated with complex traits tend to fall in non-coding regions and affect regulatory mechanisms that are not yet well characterized. Furthermore, it is generally difficult to determine in which tissues and conditions they may have a functional impact. This is because the effect of a genetic variant on a molecular pathway, and ultimately on the individual's phenotype, may be modulated by "environmental" factors. We denominate such variants "gene-expression environment-specific quantitative trait nucleotides" GxE-QTNs. Achieving a better understanding of the mechanisms underlying GxE is a critical step in understanding the link between genotype and complex phenotype. It is also crucial to develop computationally efficient and statistically sound methods capable to integrate tissue/condition-specific functional genomics data to predict and validate when a sequence variant is functional. In this study we developed novel experimental and computational approaches to screen, analyze and functionally characterize genetic variants for complex traits modulated by environmental exposures. To identify and characterize genes with GxE, we analyzed allele specific gene expression in a panel of five relevant tissues (e.g. the vascular endothelium for cardiovascular diseases) under 50 controlled environmental conditions (e.g. glucocorticoids treatment, as a proxy for stress exposure). These data should be useful to develop computational tools that integrate different sources of evidence including data collected by ENCODE, RoadMap Epigenome and GTEx projects to functionally annotate GWAS variants. The experimental and computational tools developed by this project have widespread applicability, for example, can be used to tackle the functional basis of complex traits in other environmental contexts (e.g. other types of stress and hormonal levels) and genetic backgrounds. This resource represents the first comprehensive catalog of genetic variants that interact with environmental exposure in determining human complex traits.

  Study phs001176

• Effects of Metformin on Transcriptomic and Metabolomic Profiles in Breast Cancer Survivors Enrolled in the Randomized Placebo-Controlled MetBreCS Trial

  Metformin reduces the incidence of breast cancer in patients with obesity and type 2 diabetes. However, our knowledge of the effects of metformin on breast cancer recurrence is limited. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI >25 kg/m2 after treatment with metformin. To identify metformin-regulated signaling pathways, we integrated the transcriptomic, metabolomic and steroid hormone profiles using bivariate and functional analyses. We identified MS4A1, HBA2, MT-RNR1 and MT-RNR2 expression to be differentially expressed in breast tissues from metformin-treated women. We also found metformin-associated down-regulation of EGFL6 and FDCSP in postmenopausal women. The integration of transcriptomic and metabolomic profiles revealed down-regulation of immune response genes associated with reduced levels of arginine and citrulline in the metformin-treated group. The integration of transcriptomic and steroid hormone profiles showed an enrichment of steroid hormone biosynthesis and metabolism pathways with highly negatively correlated CYP11A1 and CYP1B1 expression in breast tissue from postmenopausal metformin-treated women. Our results indicate that breast cancer survivors treated with metformin have specific changes in breast tissue gene expression that may prevent the development of new tumors. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI >25 kg/m2 after treatment with metformin using bulk RNAseq (total mRNA sequencing), that is deposited in Federated EGA. The steroid hormones, and metabolomics and the insulin sensitivity serum biomarker data underlying this article may be shared upon reasonable request to the PI of the MetBreCS study (Dr. Bernardo Bonanni), following approval by the Data and Safety Monitoring Board at IEO, Milan.

  Study EGAS50000000874

• 1 Intratumoral genetic heterogeneity and clonal evolution following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal tumors.

  Purpose: Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment forpatients with locally advanced rectal cancer. However, little is known about the effect of nCRT in the mutational landscape and intratumoral genetic heterogeneity (ITGH) of rectal tumors. Moreover, response to nCRT vary substantially among patients and the genetic determinants of treatment response are not fully understood. Here, we sought to determine the impact of nCRT in the mutational landscape and ITGH of rectal tumors and to identify putative drivers of therapeutic resistance. Patients and Methods: We analyzed whole-exome sequencing (WES) and clinical data from 79 primary non-treated rectal cancers obtained from The Cancer Genome Atlas (TCGA). We also compared the mutational landscape of a matched set of peripheral blood cells, irradiated tumor-adjacent colonic mucosa and pre and post-treatment tumor samples to determine the iatrogenic effects of nCRT and its impact in tissue genetic heterogeneity. Finally, we performed WES of 7 matched pre and post-treatment tumor samples to examine how nCRT affects the clonal architecture of rectal tumors. Results: We show that rectal tumors exhibit remarkable ITGH, which increases with disease progression. We also show that nCRT, per se, does not introduce novel somatic mutations, nor alters the clonal architecture of the irradiated colonic mucosa. Instead, we show that it acts as a potent selective pressure, favoring the expansion of tumor cell subpopulations more prone to resist therapy and increasing ITGH in the residual tumor. Conclusion: Our results reveal the highly heterogeneous and dynamic clonal architecture of rectal tumors undergoing nCRT. Our results also uncover putative genetic determinants of response to nCRT and a possible role for ITGH in predicting treatment outcome.

  Study EGAS00001003250

• The Immune Microenvironment, Genome–Wide Copy Number Aberrations and Survival in Mesothelioma

  IntroductionResults of recent clinical studies of immune checkpoint inhibitors in malignant pleural mesothelioma (MPM) have dampened initial enthusiasm. However, the immune environment and targets of these treatments such as program cell death protein 1 (PD-1) and its ligand PD-L1 have not been well characterised in MPM. Using a large cohort of patients, we investigated PD-L1 expression, immune infiltrates and genome-wide copy number status and correlated them to clinicopathological features.Patients and methodsTissue microarrays were constructed and stained with PD-L1(clone E1L3N, CST), CD4, CD8 and FOXP3 antibodies. PD-L1 positivity was defined as ≥5% membranous staining regardless of intensity and PD-L1+hi as ≥50%. Genomic DNA from a representative subset of 113 patients was used for genome-wide copy number analysis. The percent genome alteration (PGA) was computed as a proxy for genomic instability, and statistical analyses were used to relate copy number aberrations (CNA) to other variables.ResultsAmongst 329 patients evaluated, PD-L1+ was detected in 130/311 (41.7%), but PD-L1+hi was only seen in 30/311 (9.6%). PD-L1+ correlated with non–epithelioid histology and increased infiltration with CD4+, CD8+ and FOXP3+ lymphocytes. PD-L1+hi expression correlated with worse prognosis (HR = 2.37; 95%CI= 1.57-3.56; P < 0.001) on univariate analysis, but not in multivariate analysis. Higher PGA was associated with epithelioid histology and poorer survival (HR = 1.59; 95%CI= 1.01-2.5; P = 0.04), but not PD-L1 expression.ConclusionPD-L1 expression was associated with non-epithelioid MPM, poor clinical outcome and increased immunological infiltrates. Increased genomic instability did not correlate with PD-L1 expression, but was associated with poorer survival.

  Study EGAS00001002323

• Comprehensive characterization of pre- and post-treatment samples of breast cancer reveal potential mechanisms of chemotherapy resistance

  When locally advanced breast cancer is treated with neoadjuvant chemotherapy, the recurrence risk is significantly higher if no complete pathologic response is achieved. Identification of the underlying resistance mechanisms is essential to select treatments with maximal efficacy and minimal toxicity. Here we employed gene expression profiles derived from 317 HER2-negative treatment-naïve breast cancer biopsies of patients who underwent neoadjuvant chemotherapy, deep whole exome and RNA-sequencing profiles of 22 matched pre- and post-treatment tumors, and treatment outcome data to identify biomarkers of response and resistance mechanisms. Molecular profiling of treatment-naïve breast cancer samples revealed that expression levels of proliferation, immune response and extracellular matrix (ECM) organization combined predict response to chemotherapy. Triple negative patients with high proliferation, high immune response and low ECM expression had a significantly better treatment response and survival benefit (HR 0.29, 95% CI 0.10-0.85; p=0.02), while in ER+ patients the opposite was seen (HR 4.73, 95% CI 1.51-14.8; 0=0.008). The characterization of paired pre-and post-treatment samples revealed that aberrations of known cancer genes were either only present in the pre-treatment sample (CDKN1B) or in the post-treatment sample(TP53, APC, CTNNB1). Proliferation-associated genes were frequently down-regulated in post-treatment ER+ tumors, but not in triple negative tumors. Genes involved in ECM were upregulated in the majority of post-chemotherapy samples. Genomic and transcriptomic differences between pre- and post-chemotherapy samples are common and may reveal potential mechanisms of therapy resistance. Our results show a wide range of distinct, but related mechanisms, with a prominent role for proliferation- and ECM-related genes.

  Study EGAS00001005876

• Immune microenvironment and lineage tracing help deciphering Rosette-forming GlioNeuronal Tumors: a multi-omic analysis of 9 cases

  Rosette-forming GlioNeuronal Tumor (RGNT) is a rare central nervous system neoplasm containing two components, glial and neuronal. The delineation of histological diagnosis of RGNT from similar low-grade tumors such as pilocytic astrocytoma (PA) and ganglioglioma (GG) may be challenging. We performed here a comprehensive molecular analysis of a cohort of tumors with same histology features to identify molecular characteristics of RGNT. A cohort of 9 rosette-forming tumors histologically-diagnosed were analyzed at molecular level using multimodal approaches as Whole Exome Sequencing (WES), RNAseq and methylome. In our cohort, 3 tumors were plotted within the Methylation Class-RGNT (MC-RGNT) characterized by FGFR1 mutation associated with PIK3CA or NF1 mutations. Transcriptome analysis was performed in 7 cases. RNAseq identified a “Hot” and a “Cold” transcriptomic group; the latter includes the 3 MC-RGNT and 1 MC-Pilocytic Astrocytoma and exhibited a “Cold” immune tumor microenvironment in comparison with the “Hot” group. The distinct immune cell content of both groups was confirmed by quantitative immunostainings. Get Set Enrichment Analysis showed that the “Cold” group had upregulated NOTCH pathway and mainly oligodendrocyte precursor cell and neuronal phenotypes while the “Hot” group exhibited predominantly astrocytic and neural stem cell phenotypes. In silico deconvolution identified the cerebellar granule cell lineage as a putative origin and as the cell context to understand the effects of genetic alterations and NOTCH signaling. Histological diagnosis of rosette-forming tumors encompasses heterogeneous tumor entities. Our study identified distinct tumor cell contexts and microenvironments as key features to better understand and manage these neoplasms.

  Study EGAS00001006502

• Genomic Psychiatry Cohort (GPC) Whole Genome Sequencing and Genotyping Study

  The Center for Genomic Psychiatry at the University of Southern California (USC), SUNY Downstate and an extensive network of academic medical centers have created the Genomic Psychiatry Cohort (GPC). The GPC consists of a large clinical cohort of patients with schizophrenia, bipolar disorder, and healthy controls. The data generation of genotyping and whole genome sequencing of the GPC has been done in collaboration with the USC and the Broad Institute of MIT and Harvard. Genome-wide association studies are being performed with the samples to identify genetic risks for psychiatric disease. Genotyping is also being used to determine ancestry informative markers and replication of candidate genes. Whole genome sequencing identifies functional variants associated with disease risk.

  Study phs001020