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• Rapid Acceleration of Diagnostics - Underserved Populations (RADx-UP) Collection of General Research Use Datasets

  This individual-level collection of datasets contains all the RADx Underserved Populations (RADx-UP) studies in dbGaP for General Research Use (GRU) and have no further limitations beyond those outlined in the model RADx Data Use Certification Agreement. Access to this study collection will include additional authorized individual-level GRU datasets that become available. Renewal of this study collection is required annually. Access to studies in this collection is on the RADx® Data Hub. To request access to this study collection, select phs003832 in the dbGaP when submitting a data access request.

  Study phs003832

• Rapid Acceleration of Diagnostics - Tech Program (RADx-Tech) Collection of General Research Use Datasets

  This individual-level collection of datasets contains all the RADx® Tech Programs (RADx-Tech) studies in dbGaP for General Research Use (GRU) and have no further limitations beyond those outlined in the model RADx Data Use Certification Agreement. Access to this study collection will include additional authorized individual-level GRU datasets that become available. Renewal of this study collection is required annually. Access to studies in this collection is on the RADx® Data Hub. To request access to this study collection, select phs003831 in the dbGaP when submitting a data access request.

  Study phs003831

• Rapid Acceleration of Diagnostics - Radical (RADx-rad) Collection of General Research Use Datasets

  This individual-level collection of datasets contains all the RADx® Radical (RADx-rad) studies in dbGaP for General Research Use (GRU) and have no further limitations beyond those outlined in the model RADx Data Use Certification Agreement. Access to this study collection will include additional authorized individual-level GRU datasets that become available. Renewal of this study collection is required annually. Access to studies in this collection is on the RADx® Data Hub. To request access to this study collection, select phs003834 in the dbGaP when submitting a data access request.

  Study phs003834

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Breast cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Dataset EGAD00001006293

• Chromosomal copy number heterogeneity predicts survival rates across cancers

  Survival rates of cancer patients vary widely within and between malignancies. While genetic aberrations are at the root of all cancers, individual genomic features cannot explain these distinct disease outcomes. In contrast, intra-tumour heterogeneity (ITH) has the potential to elucidate pan-cancer survival rates and the biology that drives cancer prognosis1,2. Unfortunately, a comprehensive and effective framework to measure ITH across cancers is missing3. Here, we introduce a scalable measure of chromosomal copy number heterogeneity that predicts patient survival across cancers. We show that the level of ITH can be derived from a single-sample copy number profile. Using gene-expression data we demonstrate that ongoing chromosomal instability underlies the observed heterogeneity. Analysing 11,534 primary cancer samples from 35 different malignancies, we find that copy number heterogeneity can be accurately deduced and predicts cancer survival across tissues of origin and stages of disease. Our results provide a unifying molecular explanation for the different survival rates observed between cancer types.

  Study EGAS00001004702

• Transcriptomics of Liver and PBMCs in Alcohol-Associated Liver Disease

  This study was designed to characterize the transcriptome of alcohol-associated hepatitis using RNA sequencing (RNA-seq) technology for both liver tissue biopsies and peripheral blood mononuclear cells (PBMCs) extracted from blood samples, in order to identify expressed genes that are markers of the disease. To distinguish genes that differentiate alcohol-associated hepatitis from other inflammatory liver diseases, we compared the RNA-seq data generated from participants with alcohol-associated hepatitis, alcohol-associated cirrhosis, chronic hepatitis C infection, non-alcohol-associated fatty liver disease, and healthy controls. We utilized computational approaches to demonstrate that gene expression data generated from either liver tissue or blood samples can be used to identify a small set of gene biomarkers to distinguish between these liver diseases. The raw RNA-seq data will be available through dbGaP.

  Study phs003112

• Evolutionary trajectories of IDH-mutant astrocytoma identify molecular grading markers related to cell cycling

  To study the evolutionary processes that drive malignant transformation in IDH-mutant astrocytomas, we performed multi-omics on a large cohort of matched initial and recurrent tumor samples. We find that genome-wide DNA-methylation levels decreased over time, mainly in patients with high-grade recurrences. DNA-demethylation was lifted from specific loci associated with DNA replication, and was associated with upregulation of cell cycling associated genes. Upregulation of cell cycling genes occurred predominantly in proliferating tumor cells, and was associated with tumor grade. Temporal DNA-demethylation and upregulation of cell cycling were hardly affected by chemo- and/or radiotherapy, indicating treatment does not affect the course of disease. Further, we defined a DNA-methylation based signature associated with tumor grade and poor prognostic genetic events, with a prognostic power stronger than CDKN2A/B deletion. We validated its prognostic power on primary tumor samples of patients included in the CATNON randomized phase 3 clinical trial. Therefore this model can serve as a molecular grading marker for IDH-mutant astrocytomas.

  Study EGAS00001007527

• CNV detection in targeted NGS panel data

  The aim of this study was to compare copy-number-variation (CNV) detection methods for targeted NGS panel data in a clinical diagnostic setting. We present targeted NGS panel data from 170 samples that were processed using the TruSight(TM) Cancer (TSC) panel (Illumina, San Diego, CA, USA), which targets 94 genes and 284 SNPs associated with a predisposition towards cancer. The samples are enriched for CNVs in the genes of interest. All CNVs have previously been assessed with MLPA and can therefore be considered as confirmed.

  Study EGAS00001002481

• Transcriptome analysis of samples derived from GBM tumors, and relevant cell lines established and maintained at 5% or 20% oxygen.

  The goal of the study was to compare the transcriptome of patient-derived serum-free GBM cell lines to that of parental GBM tumors. For each patient, three samples were analyzed: GBM tumor, cell line growing at 5% oxygen, and cell line growing at 20% oxygen. Analysis demonstrated the enrichement in cell cycle and DNA repair gene expression in cell lines when compared to parental tumor sample. Moreover, cells cultured at 5% oxygen maintained hypoxia, carbon metabolism and tumor progression transcriptional pattern characteristic for GBM.

  Study EGAS00001006267

• Human NXPE1 mediates variation in sialic acid O-acetylation in Colon Tissue

  Sialylation, and its modifications, have important consequences for development, differentiation and immune system regulation in nearly all human tissues. An accounting of all the genes responsible for modifying sialic acids is incomplete, with only one pathway for O-acetylation being well-defined in humans. Using whole genome sequencing of normal human colorectal tissue we identify a haplotype on chromosome 11 that is associated with differences in mild periodic acid Schiff staining (mPAS), a marker of acetylation of sialic acids. Of the genes in this region, only SNPs from NXPE1 correlate perfectly in a validation data set. NXPE1 protein expression by IHC also correlates with mPAS staining patterns in homozygous, as well as heterozygous colon crypts that have undergone spontaneous gain of the mPAS phenotype. Manipulation of NXPE1 expression with CRISPR-Cas9 causes changes to modified sialic acid levels in both 2D cell line and 3D normal colon organoid models. These findings suggest NXPE1 may cause O-acetylation of sialic acids in colorectal tissue, potentially defining NXPE as a new family of sialic acid-modifying genes.

  Study EGAS00001007704