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• Integrative Single-Cell Analysis of Transcriptome, Epigenome, and Lineage in HIV Latency and Activation

  With the development of triple combination antiretroviral therapy, routine HIV treatment eliminates nearly all actively infected cells. Nevertheless, the small reservoir of latently infected cells, which can remain dormant for long periods of time before becoming active and producing new virus particles, represents a crucial barrier to completely curing the disease. Identifying markers that identify latently infected cells or the biochemical factors that control latency activation could enable the effective use of a “shock and kill” strategy, where specific targeting or activation of latently infected cells eliminates the viral reservoir. Our recent work suggests that the global transcriptomic and epigenomic changes during hematopoietic differentiation affect viral latency and activation. Additionally, we recently found that global inhibition of histone deacetylase activity increases viral activation in these cells, further implicating epigenomic changes during activation. These results raise fundamental questions: What are the markers of latently infected cells? How do the transcriptomic and epigenomic states of a cell affect latency and activation? How does the differentiation state relate to viral latency? Here, we leverage our experimental platform for identifying latently and actively infected cells, single-cell transcriptome and epigenome sequencing, and our recently developed computational integration methods to investigate these questions. Our interdisciplinary team combines expertise in HIV basic science, HIV clinical treatment, and bioinformatics to develop an experimental and computational framework for integrated gene expression, chromatin accessibility, and lineage into a single picture of viral latency and activation. Specifically, this project will (1) use single-cell RNA-seq and single-cell ATAC-seq to map the diversity of infected cells, (2) investigate the relationship between hematopoietic differentiation state and viral activation, (3) determine viral integration sites through single-cell RNA-seq, (4) computationally integrate single-cell transcriptome and epigenome profiles, and (5) computationally infer cell lineage relationships among viral genomes and infected cells. To accomplish these goals, we will carry out the following aims: (1) Characterize lineage, transcriptomic and epigenomic diversity of single latently and actively infected primary cells. (2) Investigate latency and activation during in vitro differentiation. (3) Survey single cell diversity of re-activated and in vitro infected cells from cART-suppressed patients. Together, these aims will produce a comprehensive, integrated transcriptomic and epigenomic atlas of the HIV reservoir, identify DNA and RNA biomarkers of latency, and characterize clonal expansion patterns. Our work also develops a broadly applicable experimental and computational framework, laying a foundation for the discovery of novel insights into HIV latency and activation.

  Study phs002915

• Single-Cell Mitochondrial Mutation Lineage Tracing of Non-Dysplastic and Dysplastic Barrett's Esophagus

  This study addresses a critical knowledge gap in understanding Barrett's esophagus (BE), a common metaplastic condition and precursor to esophageal adenocarcinoma, by identifying lineage connections and cell states underlying its development and malignant transformation. We collected biopsies from 13 Barrett's esophagus patients undergoing routine surveillance endoscopy, sampling metaplastic tissue alongside normal squamous esophagus and gastric cardia tissues when clinically appropriate. Our novel approach integrated single-cell RNA sequencing of nearly 180,000 cells with mitochondrial DNA mutation analysis for lineage tracing, coupled with spatial transcriptomics and comprehensive whole-exome sequencing. For one patient with high-grade dysplasia, we performed detailed whole-exome sequencing analysis at 100x coverage on dissociated Barrett's esophagus cells alongside matched normal esophagus and gastric cardia tissues. This genomic profiling revealed critical genetic drivers of malignant transformation, including a truncating mutation in the mutation cluster region of APC (a negative WNT regulator), mutations in the tumor suppressor genes CDKN2A and TP53, and significant copy number alterations including loss of CDKN2A on chromosome 9. These genetic alterations corresponded with the transcriptional changes observed in our single-cell data, particularly WNT pathway dysregulation and the expansion of LGR5-expressing stem cells, providing molecular evidence for how single clones can initiate the dysplastic transformation. Data deposited in dbGaP consist specifically of the whole-exome sequencing raw files, processed variant calls, and copy number analyses from the single patient with high-grade dysplasia and matched normal esophagus and stomach control tissue. Whole exome sequencing was performed on an Illumina NovaSeq 6000. Sequencing data was preprocessed following the Genome Analysis Toolkit's (GATK) Best Practices. Sequencing data was analyzed with Mutect2 (GATK, v4.2.5.0) to identify short somatic mutations including single-nucleotide polymorphisms and insertions and deletions. The Barrett's esophagus sample was run with the two matched normals, as well as the publicly available germline resource somatic-hg38_af-only-gnomad.hg38.vcf.gz (GATK). Somatic variant calls were filtered using FilterMutectCalls (GATK) and loaded into the Integrative Genomics Viewer (v2.12.3) for identification of high-quality variants in known esophageal adenocarcinoma regulators. Copy number alterations in the Barrett's esophagus sample were estimated using CNVkit (v0.9.9). CNV, Mutect2, and SVC analyses will be made available through dbGAP. Copy number alterations in the Barrett's esophagus sample were estimated using CNVkit (v0.9.9) with the default run settings; a pooled reference was generated from the normal esophagus and stomach samples. Copy number results were plotted directly within CNVkit using the function scatter.

  Study phs003949

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Study EGAS50000000156

• California Pacific Medical Center Research Breast Health Cohort

  Description of Cohort: The California Pacific Medical Center (CPMC) Breast Health Cohort is a cohort study based at CPMC and is linked to the San Francisco Mammography Registry, one of the sites of the NCI-funded Breast Cancer Screening Consortium (U01CA063740). CPMC is a community hospital in San Francisco, which has one of the highest volumes for mammography in San Francisco. Between September 2004 and June 2007, >90,000 mammograms were performed at CPMC. The CPMC breast health cohort collects demographic and risk factor data on women receiving mammography through participation in the San Francisco Mammography Registry, as part of the Breast Cancer Screening Consortium (U01CA063740). The SFMR database collects information from all sources, including a questionnaire on demographic and risk factor information, the clinical results of the breast examination, the measures of breast density by Dr. John Shepherd and the women who agreed to donate a blood sample. By merging these various sources of information we have very efficiently developed a large sample of women who have donated blood and have had a measure of mamographic density. Blood Collection: Dr. Steve Cummings is leading an effort to collect and archive blood samples from women who are receiving mammography screening. All women who are sent for a screening mammogram at CPMC are considered eligible. Since the cohort began collecting blood samples in July 2004 until June 2007, samples have been collected from over 11,000 women. Measurement of Breast Density: Dr. John Shepherd is currently measuring breast density in a large fraction of the cohort using an automated approach with single X-ray absorptiometry. Dr. Shepherd has established a link with the CPMC mammography center that allows him to collect routine digital mammography information. Using the data from the mammogram, Dr. Shepherd and his group have developed the single X-ray absorptiometry (SXA) technique for measuring density which is described in more detail below. The table demonstrates the distribution of demographic variables and some breast cancer risk factors of women who donated blood and had a breast density measurement in the CPMC breast health cohort. Nearly 80% of the participants are Caucasian and most of the women are post-menopausal with a median age of ~52. Since it will be difficult to accrue a large enough sample from each ethnic group, our study will focus only on Caucasian women. Table: Demographic variables, reproductive history and family history of breast cancer among 2962 women participating in the CPMC cohort study who contributed blood samples between 1994-1997. Variable Median/Percentage Age (Median/IQR) 52 (46-59) Ethnicity Caucasian/White 0.76 Asian/Pacific Islander 0.141 Hispanic 0.029 Mixed Race/Ethnicity 0.039 African American/Black 0.022 American Indian 0.001 Other 0.009 First degree relative with breast cancer 0.17 Age at first birth Nulliparous 0.39 Age<20 0.043 Age>40 0.032 Age<30, ≥20 0.251 Age>30, ≤40 0.282 Measurement of Breast Density in Cohort: Measurement of breast density is accomplished using an automated technique for all mammograms obtained by Dr. Shepherd using Single X-Ray absorptiometry (SXA). SXA measurement of breast density is done on approximately 30% of all screening mammograms. Below we describe the method for measurement of breast density by SXA by Dr. Shepherd's group and its validation and association with breast cancer. As we demonstrate below, breast density, as measured by SXA, is an automated, highly reproducible measure of the density of breast tissue and is associated with breast cancer risk. SXA for Quantifying Breast Density: Single x-ray absorptiometry (SXA) was initially developed for measuring bone density. SXA can determine the fraction of each of two densities simultaneously using the fact the sample is a constant thickness, the thickness in known, and the total attenuation is known. In applying this technique to breast density, we assume a two compartment model: fat and non-fatty (fibroglandular tissue). We use a reference material composed of various concentrations of two materials: one which is the same density as fat and another which is the same density as fibroglandular tissue. The reference material (phantom) is placed in the X-ray field with each mammogram. We have been able to implement this in a way that is unintrusive to the patient and technologist at CPMC. Assuming this two-compartment model and a constant known breast thickness, we can then calculate the percent density at any region of the breast based on the assumption that % pixel grey-scale is proportion to the mass fractions of breast fat and lean tissue. If reference materials (a phantom) of fat and fibroglandular tissue are imaged with the patient's breast and the reference materials have the same thickness as the patient's breast, then the breast's grey-scale values can be converted to fat/fibroglandular mass fractions by interpolating between those two references. The total percent density is found by averaging the volume fraction over all breast pixels. The phantom being used for breast density assessment at CPMC began to be used in September 2004. The phantom does not have to be manipulated by the technologist and stays attached on the mammography device during standard craniocaudal (CC) views. Thus it creates minimal to no interference with the clinical mammogram. Reproducibility of breast density measures: Traditional measures of mammographic density require some human interpretation. A human reader outlines the area perceived to be dense and a computer then calculates the percent area outlined as a percent of the entire image. Thus, while traditional mammographic density is associated with breast cancer risk, it has some limitations. In a study by Drs. Shepherd, Kerlikowske, et al., the correlation coefficient (Pearson's R) between different readers was 0.8-0.9. In contrast to the traditional mammographic density measurement, the SXA measurement is fully automated and, therefore, the reproducibility of the measurement is higher. Dr. Shepherd and colleagues have performed a replication study of SXA as a measurement of breast density. They have estimated the correlation coefficient of the SXA measurement of breast density to be >0.98. Thus, as expected for an automated measure, SXA is a highly reproducible measure of mammographic breast density. Drs. Shepherd and Kerlikowske have recently analyzed the association between breast cancer risk and breast density as measured by SXA (Shepherd et al., Cancer Epi Biomarkers and Prev, 2011, PMID: 21610220). They found that women in the highest quintile of % volumetric density had an odds ratio of 4.1 (95% CI: 2.3 - 7.2) for breast cancer risk compared to women in the lowest quintile of volumetric density. Thus volumetric density appears to be a highly reproducible, automated measure of breast cancer.

  Study phs000395

• NTNU Data Access Committee for “ProstOmics: spatial and bulk multi-omics of prostate cancer” datasets archived in Federated EGA Norway

  The ProstOmics project of NTNU contains spatial and bulk multi-omics datasets of prostate cancer, and access request to these datasets are handled by this Data Access Committee on behalf of NTNU.

  Dac EGAC50000000277

• CRC organoid RNA-seq data access committee

  This DAC is responsible for granting access to RNA-seq data (paired-end FASTQ files) from 10 patient-derived colorectal cancer organoid patients. The data was ad hoc generated as part of the development of NovumRNA, a non-canonical tumor-specific antigen prediction pipeline.

  Dac EGAC50000000406

• TRIGR collaboration between University of Helsinkin and Children's Mercy Research Institute

  DAC for collaborations between by the University of Helsinki and Children's Mercy Hospital, such as the Trial to Reduce IDDM in the Genetically at Risk (TRIGR), used to study the evolving epigenomics of immune cells in type 1 diabetes at single nuclei resolution

  Dac EGAC50000000543

• Boyes Lab - DAC policy

  Access to this data is controlled. Upon request from a third party for access to the data, a separate Data Sharing Agreement would be put in place between the University of Leeds and the third party prior to EGA granting access to the data to the third party.

  Dac EGAC50000000283

• DAC Study assessing the efficacy and safety of durvalumab plus olaparib plus fulvestrant in selected metastatic or locally advanced ER-positive, HER2-negative breast cancer patients.

  DAC for Study assessing the efficacy and safety of durvalumab plus olaparib plus fulvestrant in selected metastatic or locally advanced ER-positive, HER2-negative breast cancer patients.

  Dac EGAC50000000694

• Department of Human Genetics at Yokohama City University (YCU) — Data Access Committee

  The Data Access Committee at Yokohama City University (Department of Human Genetics) is overseeing controlled access to data for our research manuscripts. Access is granted to bona fide researchers in line with the Data Access Agreement and DUO restrictions.

  Dac EGAC50000000770

• Plasma cell‐free transcriptome profiling in chronic liver disease

  RNAseq data from plasma samples corresponding to a Grand Challenge Programme project to find biomarkers for liver disease, including NAFLD, NASH, Cirrhosis and Hepatocelullarcarcinoma. A template of the DTA and DAA files is available in the following zenodo repository https://doi.org/10.5281/zenodo.19047046.

  Dac EGAC50000000360

• Whole exome sequencing for primary lung adenocarcinoma samples

  We performed whole exome sequencing (WES) of 11 formalin-fixed paraffin-embedded lung tumour tissues and paired blood samples.

  Study EGAS00001003680

• PETAL trial Whole Exome Sequencing (WES) from Normal Samples

  Associated with Molecular profiling of DLBCL patients treated in the PETAL trial. Whole Exome Sequencing (WES) of 55 Normal Samples. WES was used for Molecular Profiling by simultaneous screening of mutations. Data is Complementary to sWGS.

  Dataset EGAD50000002507

• Clinical Utility of BRCA Research by Inocras, CMC and SMC (CUBRICS)

  This dataset consists of transcriptome data available for most cases of the whole-genome sequences from 1,364 Korean breast cancers

  Dataset EGAD50000001993

• single cell RNA seq

  A selection of samples was analyzed using single-cell RNA sequencing to explore cellular heterogeneity. 4 inflammatory diseases were chosen for this purpose: Still’s disease, recurrent pericarditis, familial Mediterranean fever (FMF), and chronic non-bacterial osteomyelitis (CNO).

  Dataset EGAD50000002022

• T cell receptor repertoire in cell-free DNA as a proxy for tumor infiltrates in patients treated with pembrolizumab

  Bulk TCR sequences from tumor, peripheral blood mononuclear cell (PBMC), and cell-free DNA (cfDNA) samples from 81 patients with solid tumors treated with pembrolizumab.

  Dataset EGAD50000001857

• Nanopore Telomere Sequencing of II.3, II.4, and III.4

  This datasets contains the putative telomeric sequences isolated by TARPON in ubam format after using a Duplex Capture Based Enrichment Protocol for patients II.2, II.4, and III.4.

  Dataset EGAD50000002363

• Multiomic Dissection of Movement and Neurocognitive Toxicity Following BCMA-Targeting CAR T Cell Therapy

  Raw sequencing data (GEX, TCR, adn CSP libraries) for longitudinal cerebrospinal fluid and PBMC samples from a multiple myeloma patient treated with cilta-cel who developed MNT.

  Dataset EGAD50000002339

• HOVON152 Trial Targetted Sequencing

  Associated with Molecular profiling of HGBCL-DH-BCL2 patients treated in the HOVON-152 triaL. Targeted sequencing of 30 samples from 30 High-grade B-cell lymphoma patients. Targeted sequencing was used for identify genomic alterations associated with patient outcome.

  Dataset EGAD50000002093

• Germline Pan-cancer Lynch syndrome sequencing dataset from India

  The dataset consists of germline whole exome sequencing data generated on the Illumina NovaSeq X Plus platform for 48 patients with MSI-high/ MMRd gastro-intestinal and genito-urinary cancers, in order to detect Lynch syndrome.

  Dataset EGAD50000002088

• Long-read whole-genome sequencing dataset of aplastic anemia

  This dataset contains long-read whole-genome sequencing data generated from patients with aplastic anemia and RCC. Genomic DNA was prepared for long-read sequencing and sequenced using single-molecule real-time technology on a PacBio platform.

  Dataset EGAD50000002257

• BCR repertoir sequences from human buffy coat purified B-Cells with DIS3 and control ASO

  This dataset contains BCR sequences of buffy coat purified B-cells from 5 healthy donors. For each sample, sequences were obtained in two conditions : with control Aso and Dis Aso treatment.

  Dataset EGAD50000001601

• Guardant ctDNA variant analysis

  This table describes each variant detected using Guardant 360 for each sample included in this analysis.

  Dataset EGAD50000001340

• WGS FASTQ files studied in Allogeneic induced pluripotent stem (iPS) cell-derived cartilage transplantation for articular cartilage damage in knee joints: a phase I, first-in-human study

  This data set contains 5 paired fastq files (WGS).

  Dataset EGAD50000001666

• SIA Subtyping Shallow Whole Genome Sequencing (sWGS)

  Associated with Molecular classification of small intestinal adenocarcinomas. Shallow Whole Genome Sequencing (sWGS) generated 214 .fastq files from 127 small intestine adenocarcinomas from 125 patients. DNA libraries for shallow whole genome sequencing were prepared using TruSeq Nano kits (Illumina).

  Dataset EGAD50000001760