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• Multicenter International Lymphangioleiomyomatosis Efficacy of Sirolimus Trial (The MILES Trial)

  Lymphangioleiomyomatosis (LAM) is an uncommon, progressive, cystic lung disease that predominantly affects young women. It is believed to be caused by defects within cellular pathways that regulate nutrient uptake, cell size, cell migration, and cell proliferation. The disease is caused by mutations in tuberous sclerosis complex (TSC) genes. Individuals with Lymphangioleiomyomatosis (LAM) often experience pneumothorax and chylothorax, as well progressive loss of lung function. Lymphangioleiomyomatosis (LAM) is frequently fatal and existing therapies for the disease have not proven effective. Lung transplantation can be considered as a last option, but alternative treatments are needed. Sirolimus is an immunosuppressive drug that is often used in people who have had kidney transplants. It directly affects the genetic pathway that causes Lymphangioleiomyomatosis (LAM). This study will evaluate the safety and effectiveness of sirolimus in stabilizing or improving lung function in people with Lymphangioleiomyomatosis (LAM). Individuals interested in participating in this 2-year, double-blind study will first report to the study sites for pulmonary function testing to determine their eligibility for participation. Participants deemed eligible will be randomly assigned to receive either sirolimus or placebo for 1 year. Sirolimus or placebo will be administered in 2 tablet doses (2 mg for sirolimus) for the duration of the study. Study visits will occur at baseline, Week 3, every 3 months for 12 months, and Months 18 and 24. Study visits will include a physical exam, questionnaires, a pregnancy test, blood and urine collection, and functional lung tests. A 6-minute walk test will occur at most study visits; a chest x-ray will be taken at baseline and Month 24; and a volumetric computed tomography scan will occur at baseline Month 12, and Month 24. Adverse events, medication side effects, and lung function will be assessed at each visit.

  Study phs000605

• DNA methylation database for gynecological cancer detection, classification and assay development

  DNA methylation changes are associated with cancer the best predictor of the presence of cancer. In order to detect and classify gynecological cancer, we performed MeD-seq to generate DNA methylation profiles of vulvar, cervical, endometrial, fallopian tube and ovarian cancers and their associated healthy tissues. DNA methylation profiles were used to find general cancer, regional cancer and specific cancer biomarkers that can be used for cancer detection, classification and treatment monitoring.

  Study EGAS50000000417

• Sequencing files for "Transcriptional Mechanisms of Resistance to Anti-PD-1 Therapy"

  Files from whole exome sequencing of 26 tumors and two matched normals from one melanoma patient. The 26 tumors include the untreated primary, cutaneous metastases and distant metastases to internal organs.

  Dataset EGAD00001003200

• Somatic copy number alteration and fragmentation analysis in circulating tumor DNA for cancer screening and treatment monitoring in colorectal cancer patients

  Background: Analysis of circulating free DNA (cfDNA) is a promising tool for personalized management of colorectal cancer (CRC) patients. Untargeted cfDNA analysis using whole-genome sequencing (WGS) does not need a priori knowledge of the patient´s mutation profile. Methods: Here we established LIquid biopsy Fragmentation, Epigenetic signature and Copy Number Alteration analysis (LIFE-CNA) using WGS with ~6x coverage for detection of circulating tumor DNA (ctDNA) in CRC patients as a marker for CRC detection and monitoring. Results: We describe the analytical validity and a clinical proof-of-concept of LIFE-CNA using a total of 259 plasma samples collected from 50 patients with stage I-IV CRC and 61 healthy controls. To reliably distinguish CRC patients from healthy controls, we determined cutoffs for the detection of ctDNA based on global and regional cfDNA fragmentation patterns, transcriptionally active chromatin sites, and SCNAs. We further combined global and regional fragmentation pattern into a machine learning classifier to accurately predict ctDNA for cancer detection. By following individual patients throughout their course of disease, we show that LIFE-CNA enables the reliable prediction of response or resistance to treatment up to 3.5 months before commonly used CEA. Conclusion: In summary, we developed and validated a sensitive and cost-effective method for untargeted ctDNA detection at diagnosis as well as for treatment monitoring of all CRC patients based on genetic as well as non-genetic tumor-specific cfDNA features. Thus, once sensitivity and specificity have been externally validated, LIFE-CNA has the potential to be implemented into clinical practice. To the best of our knowledge, this is the first study to consider multiple genetic and non-genetic cfDNA features in combination with ML classifiers and to evaluate their potential in both cancer detection and treatment monitoring.

  Study EGAS00001006490

• Center for Common Disease Genomics [CCDG] - Cardiovascular: Cardiology Biobanking for Biomarker Discovery

  This study is a part of NHGRI's Center for Common Disease Genomics, which is a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Current estimates anticipate that the CCDG program will sequence approximately 140K whole genomes and 225K whole exomes during the life of the project. The Cardiovascular Disease working group of the CCDG considered five diseases: early-onset coronary artery disease (EOCAD), stroke, atrial fibrillation, congestive heart failure and type 2 diabetes. Atrial fibrillation will affect between 6-12 million individuals in the US by 2050. AF also is associated with increased risks of stroke, dementia, heart failure, death, and high health care costs. Many risk factors for AF have been identified, including advancing age, cardiovascular disease (CVD), and CVD risk factors. However, there is little knowledge how to prevent AF. Furthermore, therapies for AF are only partially effective, and are themselves associated with substantial morbidity. Previously, heritable forms of AF have been considered rare; yet in the last decade, it has been established that AF, and in particular early-onset forms of AF, are heritable. Genome-wide association studies (GWAS) provide a powerful tool to identify common variants underlying disease risk. The AFGen Consortium currently consists of investigators from more than 25 studies with >20,000 individuals with AF and >100,000 without AF. In the latest analyses, 14 loci have been identified for AF1. Broadly, the loci implicate genes related to cardiopulmonary development, cardiac-expressed ion channels, and cell signaling molecules.Source: https://ccdg.rutgers.edu/sites/default/files/CCDG_CVD_EOAF_FINAL_w_link.pdfThe BioHEART study is a multi-center, prospective, observational cohort study to identify new biomarkers in stable coronary artery disease (CAD) and acute myocardial infarction (MI) (ACTRN12618001322224). Participants are recruited at time of clinically indicated computed tomography (CT) coronary angiogram (CTCA). 1,950 of 5,000 planned participants have been recruited and serum/plasma/erythrocytes/monocytes stored, with follow-up ongoing. CTCA imaging studies provides volumetric quantification of total calcified and non-calcified plaque, which is assessed using established and novel scoring systems. Comprehensive molecular phenotyping is performed on blood samples using state-of-the-art genomic, transcriptomic, proteomics, metabolomic, lipidomic, and immunophenotyping. Outcome data will be used to build improved risk models for CAD.Reference: http://www.anzctr.org.au/ - ACTRN12618001322224

  Study phs002726

• Dataset of methylation data from whole blood DNA

  This dataset includes raw data (.idat) for the Illumina Human450k beadchip and methylation levels (.txt files). Methylation level were treated for normalization and background substraction. We removed probes with at least one of the following characteristics: (1) weak signal (p > 0.01) (2128 CpG sites), (2) SNP-enriched sites (4100 sites), (3) out of a CpG context (not on a CG) (3149 sites), or (4) located on sex chromosomes (11,129 sites). A total of 465,071 CpG sites were analyzed initially. Signal was then normalized, first by scaling to the internal controls using the methylumi R package, then by applying the method of subset-quantile within array normalization (SWAN) implemented in the minfi R package.

  Dataset EGAD00010001593

• Atezolizumab and Bevacizumab in Treating Patients With Rare Solid Tumors

  This phase Ib/II single-arm study evaluated atezolizumab + bevacizumab for patients with treatment-refractory metastatic HPV+ anal cancer. The primary endpoint was best overall response, and secondary endpoints included progression-free survival, overall survival, and safety/tolerability. Paired tissue biopsies were collected in patients before treatment and at the time of first restaging and analyzed for whole-exome sequencing, RNAseq (Bulk), and multiplex immunofluorescence. The overall response rate among 20 patients was 10%. WES and RNAseq data are provided here.

  Study phs003845

• Long-read data (PacBio)

  The goal of this project was to perform long-read RNA sequencing (LR-seq, PacBio) in combination with short-read RNA-seq for systematic characterization of the isoform diversity in primary breast tumor samples. We sequenced the full-length transcriptomes of 26 breast tumors and 4 normal breast samples.

  Dataset EGAD00001006597

• Relevance of TMPRSS2, CD163/CD206 and CD33 in clinical severity stratification of COVID-19

  Our main objective is to incorporate new markers to classify COVID-19 patients based on mild or severe disease progression. To achieve this, we characterize SNP genotypes in critical genes involved in SARS-CoV infection (ACE2 (rs2285666), MX1 (rs469390), and TMPRSS2 (rs2070788) variants) and study the monocyte population by mass cytometry (CyTOF). Finally, the results obtained will be integrated to search for the best set of predictors of disease aggressiveness.

  Study EGAS00001007003

• Comparison of 3 protocols for deriving pancreatic progenitors from hPSCs (RNA-seq)

  This dataset contains 26 mapped bam files. The samples were generated with 3 different protocols for deriving pancreatic progenitors from hPSC. Three parallel differentiations were performed, all done in a hPSC NKX6.1-GFP reporter line. For each protocol there are three cellular populations: total (presort), GFP+ and GFP- . In summary: 3 differentiations x 3 protocols x 3 cellular populations. We prepared Smart-Seq2 RNA-seq libraries for the 27 samples, 1 sample failed library preparation and is not therefore included in this dataset.

  Dataset EGAD00001004823