11925 results for "cancer rna-seq"

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• ABHD11 Autoimmunity Study DAC

  This DAC oversees controlled access to human RNA-seq data generated as part of the project “ABHD11 inhibition and T cell function in autoimmunity” at Swansea University. The DAC will evaluate and approve access requests based on scientific merit and alignment with participant consent. Data requests will be considered from academic researchers and healthcare professionals working on immunology or autoimmunity.

  Dac EGAC50000000750

• Single cell mRNA sequencing of primary GBM - SF 10345

  We collected fresh tissue from an untreated GBM (SF10345) directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine, resulting in sequencing libraries from 96 individual cells.

  Dataset EGAD00001002271

• Single cell mRNA sequencing of primary GBM - SF 10282

  We collected fresh tissue from an untreated GBM (SF10282) directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine, resulting in sequencing libraries from 96 individual cells.

  Dataset EGAD00001002270

• Single cell mRNA sequencing of primary GBM - SF 10360

  We collected fresh tissue from an untreated GBM (SF10360) directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine, resulting in sequencing libraries from 96 individual cells.

  Dataset EGAD00001002272

• Single cell mRNA sequencing of primary GBM - SF 10679

  We collected fresh tissue from an untreated GBM (SF10679) directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine, resulting in sequencing libraries from 96 individual cells.

  Dataset EGAD00001003113

• Single cell mRNA sequencing of primary GBM - SF 10281

  We collected fresh tissue from an untreated GBM (SF10281) directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine, resulting in sequencing libraries from 96 individual cells.

  Dataset EGAD00001003114

• Single cell mRNA sequencing of primary GBM - SF 10592

  We collected fresh tissue from an untreated GBM (SF10592) directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine, resulting in sequencing libraries from 96 individual cells.

  Dataset EGAD00001003112

• Islet RFX6 Study Manuscript Data

  This submission includes raw FASTQ files (for bulk RNA-seq and 10X joint snATAC+snRNA multiome profiling experiments), sample phenotype files, and genotypes for the data included in the manuscript.

  Dataset EGAD00001008777

• Gene expression in human monocyte differentiation

  This dataset consists of RNA-seq data from human monocytes, monocyte-derived dendritic cells or monocyte-derived macrophages as well as monocyte-derived cells that were subjected to siRNA treatment targeting CTCF or RAD21. In total, it includes 63 samples.

  Dataset EGAD00001007956

• Microinjection of hIPSC-derived intestinal organoids with Salmonella Typhimurium

  To generate an RNA-Seq dataset for organoids apically stimulated with Salmonella Typhimurium.These data are part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001001363

• Single-Nuclei RNA-seq of healthy (post-mortem) and temporal lobe epilepsy (biopsy) subjects.

  To identify dysfunctional neuronal subtypes underlying seizure activity in the human brain, we have performed single-nucleus transcriptomics analysis of >110,000 neuronal transcriptomes derived from temporal cortex samples of multiple temporal lobe epilepsy and non-epileptic subjects.

  Dataset EGAD00001006575

• RnaSeq data from 414 tumor samples with non muscle invasive bladder cancer

  A better understanding of the molecular landscape of non-muscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and identify potential therapeutic targets. Here, we perform a comprehensive genomic analysis of patients diagnosed with NMIBC based on whole-exome- (n=438), shallow whole-genome- (n=362), and total RNA-sequencing (n=414). This dataset contains 414 BAM files corresponding to the full total RNA-sequencing dataset.

  Dataset EGAD50000000733

• Multi-omic dataset utilized in the analysis of a phase II study of epigenetic priming followed by nivolumab in lung cancer

  This dataset includes sequence files from whole exome sequencing and bulk RNA sequencing of tissue and blood biospecimens from a phase II clinical trial investigating epigenetic priming followed by immune checkpoint blockade in non-small cell lung cancer (NSCLC; NCT01928576). There are 78 whole exome sequencing files and 36 bulk RNA sequencing files included in this dataset.

  Dataset EGAD50000001336

• Targeted gene expression analysis on FFPE specimen from NSCLC patients

  This study measured the expression of a panel of over 300 cancer immunotherapy-relavant genes in tumors after neoadjuvant chemoimmunotherapy. It was performed on RNA from FFPE tumor tissue. Differential gene expression analysis was performed between patients achieving or not achieving an event-free survival of 12 months.

  Study EGAS50000001140

• Concatenated long-read single-cell RNA sequencing of momentum biopsies of ovarian cancer patients

  Concatenated long-read single-cell RNA sequencing samples prepared using 10X and the HIT-scIsoSeq protocol. The sequencing was performed on Sequel II Pacbio machine. 3 ovarian cancer patients, 5 omentum biopsies samples: 3 metastasis samples (one per patient), 2 healthy samples (one per patient except Patient2). 4 bam files per metastasis sample, 2 bam files per healthy sample.

  Dataset EGAD00001009814

• Low T cell diversity is associated with poor outcome in bladder cancer - Single cell RNAseq and TCRseq data

  T cells are crucial effector cells in the endogenous defense against cancer, yet the clinical impact of their quantity, diversity, and dynamics remains underexplored. In this study we performed single cell RNAseq paired with single cell TCR-seq on tumor and blood samples from 4 patients (n samples = 8). The goal was to investigate T cell subtypes of expanded and non-expanded T cell clones in blood and in tumors.

  Study EGAS50000000938

• Chordoma_Sequencing_Project_RNAseq

  This is a continuation of the Chordoma Sequencing Project. All cancers arise due to somatically acquired abnormalities in DNA sequence. Systematic sequencing of cancer genomes allows acquisition of complete catalogues of all classes of somatic mutation present in cancer. These mutation catalogues will allow identification of the somatically mutated cancer genes that are operative and characterise patterns of somatic mutation that may reflect previous exogenous and endogenous mutagenic exposures. In this application, we aim to perform whole genome sequencing on 10 chordoma matched genome pairs. RNA Sequencing/Methylation and SNP6 and an additional sequencing of three cancer cell lines will be added to this work.

  Study EGAS00001000410

• Plasma cell‐Free transcriptome profiling in chronic liver disease

  Chronic liver disease (CLD) encompasses a wide range of hepatic disorders that progressively compromise liver function. In order to support the development of non-invasive diagnostic tools and improve patient stratification, we generated a high-throughput extracellular RNA sequencing dataset from blood plasma samples collected from patients at various stages of liver disease. Samples were obtained from two independent cohorts of 112 (cohort 1) and 489 (cohort 2) patients using standardized collection and processing protocols, and profiled using total RNA sequencing (SMARTer Stranded Total RNA-Seq Kit v3 - Pico Input Mammalian), resulting in 112 and 820 extracellular transcriptomic profiles for cohort 1 and 2, respectively. This dataset is provided with full documentation of the preprocessing analysis pipeline and accompanying metadata, enabling its use as an independent validation cohort for biomarker discovery and as a complementary resource for integrative multi-omics studies, even in cases with incomplete data modalities. The publicly available resource is intended to facilitate reproducibility and foster further research in the field of liver disease diagnostics

  Study EGAS50000000545

• Whole genome sequencing data of pediatric B-other subtype acute lymphoblastic leukemia

  For a long time, approximately 70% of childhood B-ALL cases could be classified routinely, and the remaining 30% were classified into the “B-other” group lacking the known genetic features. However, more recently, new genomic tools have uncovered new subtypes among the B-other groups. Approximately 10% of B-ALL cases lack the recurrent genetic abnormalities, and are referred as "B-other". They are typically more heterogeneous than other subtypes of B-ALL, and their prognoses are more variable. In a more recent update of the B-ALL classification, 27 subtypes were identified (Arber et al., 2022). The identification of some of these subtypes is dependent on NGS methods, such as RNA sequencing, which are not routinely used in all clinical laboratories. For example, ETV6::RUNX1-like and DUX4-rearranged subtypes have been identified using RNA sequencing. For this study, subtype predictions for some of the cases is acquired from ALLIUM (Krali et al., NPJ Precis Oncol., 2023) and ALLCatchR (Beder et al., Blood, 2022) based on the RNA-seq profiles.

  Study EGAS50000001497

• Triple Negative BC RNA Sequencing

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Dataset EGAD00001001339

• The transition from quiescent to activated states in human hematopoietic stem cells is governed by dynamic 3D genome reorganization

  To elucidate the epigenetic changes which occur when human long-term hematopoietic stem cells (LT-HSC) become activated we performed Bulk ATAC-Seq on 13 sorted bulk hematopoietic populations from cord bloodas well as single-cell ATAC-Seq upon CD34+CD38-CD45RA- cells enriched for HSC as well as CD34+/CD38+ progenitor cells both from cord blood. These studies revealed gains of chromatin accessibility around CTCF binding sites during HSPC activation, as such we additionally performed Low-C to directly profile the 3D conformation of human cord-blood derived LT-HSC and Short-term hematopoietic stem cells (ST-HSC), as well as Hi-C , ATAC-Seq and CTCF ChIP-Seq upon the OCIAML-2 cell line in which CTCF sites gained during LT-HSC activation are enriched. Finally we transduced human cord-blood LT-HSC with an shCTCF vector; in-vitro cultured LT-HSC cells harbouring shCTCF were used to perform RNA-Seq, and scATAC-Seq was performed on CD34+/CD38- human CB cells transduced with shCTCF, four weeks post xeno-transplantation into mice. Collectively these studies have helped us demonstrate the role of 3D chromatin conformation changes during human LT-HSC activation.

  Dataset EGAD00001006447

• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

  Blog upcycling-and-merging-data-to-challenge-glioblastoma

• Clonal Evolution and Heterogeneity of Osimertinib Acquired Resistance Mechanisms in EGFR Mutant Lung Cancer

  Current understanding of acquired resistance to osimertinib in EGFR-mutant lung cancer is largely based on the use of targeted sequencing platforms and cell-free DNA. In this study we examined the clonal evolution and heterogeneity of acquired resistance mechanisms to osimertinib through whole exome and RNA-sequencing of prospectively collected pre-and post-osimertinib resistant tumors and metastases at rapid autopsy. We enrolled 34 patients with histologically confirmed EGFR mutant advanced lung adenocarcinoma in a prospective Phase II clinical trial to evaluate osimertinib treatment and the use of local ablative therapy (LAT) for oligoprogressive disease (NCT02759835) in EGFR mutant NSCLC from April 2016 until data cutoff in September 2019. Patients were not selected based on oligometastatic status, rather 32/34 patients had multiple organ involvement, including lung, liver, adrenal, brain and bone. Biopsies or surgeries were performed pre-osimertinib treatment and at first progression on osimertinib. A subset of patients also underwent biopsies at second progression on osimertinib, and following end of life in-patient hospice and rapid autopsy upon death. 63% (n=15/24) of first-line osimertinib and 50% (n=5/10) of second-line osimertinib treated patients had RECIST defined partial response. Overall, 21 patients had RECIST defined first progression on osimertinib. Two patients on this trial discontinued treatment and were lost to follow up. Of the 21 patients who had RECIST defined progression, 14 patients underwent LAT (8 first-line and 6 second-line). Twelve patients had paired pre- and post-osimertinib resistant tumors and three had post-osimertinib resistant tumors available for analysis. Four patients (LAT002, LAT006, LAT014 and LAT021) consented for a rapid autopsy protocol and underwent rapid autopsy after deatH. To elucidate acquired osimertinib resistance mechanisms, we performed whole-exome (WES), targeted sequencing and RNA-seq on 66 tumors and matched germline DNA from tumor tissue obtained from a total of 15 patients.

  Study phs002001

• To profile the landscape of sebaceous tumours_RNAseq

  Sebaceous tumours are a rare cutaneous cancer with potential for aggressive behaviour. However, limited information is available on these cancers with few published cases. Here we wish to exome sequence these cancers to define the first genomic landscape for this malignancy. We will extract RNA from formalin-fixed, paraffin-embedded (FFPE) cores. Cores may be obtained from lesional and non-lesional tissues of primaries as well as matching metastases. The extracted RNA will be used for RNA sequencing.

  Dataset EGAD00001015368

• Characterization of High-Grade Serous Ovarian Cancer Subtypes via Single-Cell and Spatial-Transcriptomics Profiling

  We measured gene expression in single-cell RNA sequencing samples from patients with high-grade serous ovarian cancer (HGSOC), for a study on improving HGSOC subtype definition by taking into account varying cell type proportions within tumors. The initial dataset (added October, 2020) contains 3 samples (3 individuals). The next dataset (added November, 2022) contains 34 samples from 8 individuals. The samples in the second dataset were sequenced in five different ways: 1) rRNA depletion and bulk RNA sequencing of tumor chunks, 2) rRNA depletion and bulk RNA sequencing of dissociated cells, 3) poly-A capture and bulk RNA sequencing of dissociated cells, 4) poly-A capture and scRNA sequencing of dissociated cells, 5) barcoding and pooling cells and scRNA sequencing (2 pools of 4 samples each). The third dataset (added June, 2024) contains data from one spatially resolved transcriptomics experiment (4 samples, one per Visium capture area, describing one tumor from one individual) and scRNA sequencing on 5 samples (5 individuals).

  Study phs002262

• Synergy study: "Tissue resident CD8+ T cell clonal expansion in advanced triple negative breast cancer is associated with response to chemoimmunotherapy"

  Dataset of 10x Genomics 5'prime single-cell RNA sequencing of 40 tumor biopsy samples or triple negative breast cancer. Samples are taken before and after immuno checkpoint blockade treatment. Processed according the 10X genomics protocol, sequenced on Immumina NovaSeq6000. Raw sequencing reads are further processed with Cellranger 3.0 to get gene-expression matrices.

  Study EGAS50000000527

• scRNA dataset for 15 samples

  This dataset includes single cell (sc)RNA-seq of N=3 patient-derived xenograft (PDX) samples profiled at baseline (no drug treatment) and N=12 samples from one PDX profiled during the course of treatment (N=1 sample each for 4 treatment arms x 3 timepoints). The dataset includes bam files that have been purged of reads mapping to cells where a majority of the reads from that cell mapped to the mouse genome. All scRNA-seq data were generated using the Chromium Next GEM Single Cell 5' Reagent Kits v2 (Dual Index) from 10X Genomics.

  Dataset EGAD50000001424

• Single-nuclei multiomic analysis of KOLF2.1J human induced pluripotent stem cells-derived differentiated dopaminergic neurons

  Datasets consists of 12 samples of nuclei from hIPSCs differentiated optimized protocols for generating dopaminergic neurons. 6 single nuclei (RNA-ATAC) samples of XMAS protocol with cells collected at days 0,11,16,28,42 and 56 after differentiation. 3 snRNA-seq samples from AMS protocol of cells collected at days 14,16 and 28 after differentiation and 3 snRNA-seq samples with AMS protocol until day 14 grafted and grafts processed after 5 months grafting.

  Dataset EGAD50000002054

• Single-cell RNA sequencing dissects gene-environment interactions on gene expression and regulation in immune cells.

  This study aims to dissect gene-environment interactions on gene expression and regulation in immune cells using scRNA-seq data from 120 donors being stimulated with 3 pathogens, for 2 different timepoints. More specifically, we identified context- and cell-type-specific eQTLs and co-expression QTLs in PBMCs.

  Study EGAS00001005376

• T cell transcriptional gradient

  There are two datasets: 1. scRNA-seq of human cutaneous immune cells from psoriasis patients. These include pre- and post-Tildrakizumab treated patients and come in a BAM file format. 19006FL-25-01 19006FL-38-01 19006FL-32-01-03 19006FL-33-01 19006FL-28-01-05 19006FL-35-01-01 2. RNA-seq of ZFP36L2 CRISPIR deleted Human T cells are FASTQ files. 19006XR-30-05 19006XR-30-04 19006XR-30-02 19006XR-30-01 19006XR-26-05 19006XR-26-04 19006XR-26-02 19006XR-26-01 19006R-22-04 19006R-22-08 19006R-22-05 19006R-22-01

  Dataset EGAD00001009677

• Single-cell RNA sequencing on 12346 single T cells from 14 non-small cell lung cancer (NSCLC) patients

  Deep single-cell RNA sequencing data for 12346 T cells from tumour, adjacent normal tissue and peripheral blood of treatment-naïve NSCLC patients

  Dataset EGAD00001003999

• CD8+ Tumor-Infiltrating Lymphocyte Abundance is a Positive Prognostic Indicator in Nasopharyngeal Cancer

  This retrospective tissue study explores the prognostic significance of tumour-infiltrating lymphocyte (TIL) populations in NPC, using a combination of RNA sequencing and immunohistochemistry.

  Study EGAS00001006396

• PAMELA - 142 patients with HER2+ breast cancer

  RNA sequencing data of 141 samples from 141 patients with HER2+ breast cancer treated withletrozole or tamoxifen (SOLTI-1114 PAMELA trial)

  Dataset EGAD00001009054

• Differential expression profile of gluten-specific T cells identified by single-cell RNA-seq

  Gluten-specific CD4+ T cells drive the pathogenesis of celiac disease and circulating gluten-specific T cells can be identified by staining with HLA-DQ:gluten tetramers. In this first single-cell RNA-seq study of tetramer-sorted T cells from untreated celiac disease patients blood, we found that gluten-specific T cells showed distinct transcriptomic profiles consistent with activated effector memory T cells that shared features with Th1 and follicular helper T cells. Compared to non-specific cells, gluten-specific T cells showed differential expression of several genes involved in T-cell receptor signaling, translational processes, apoptosis, fatty acid transport, and redox potentials. Many of the gluten-specific T cells studied shared T-cell receptor with each other, indicating that circulating gluten-specific T cells belong to a limited number of clones. Moreover, the transcriptional profiles of cells that shared the same clonal origin were transcriptionally more similar compared with between clonally unrelated gluten-specific cells.

  Study EGAS00001005517

• Characterizing Advanced Breast Cancer Heterogeneity and Treatment Resistance through Serial Biopsies and Comprehensive Analytics

  This dataset accompanies the manuscript "Characterizing Advanced Breast Cancer Heterogeneity and Treatment Resistance Through Serial Biopsies and Comprehensive Analytics". In this study we discuss the clinical implications and consequences of molecular heterogeneity in a metastatic breast cancer patient identified through serial biopsies, multi-omic analysis, and longitudinal patient monitoring, performed through the precision oncology program, Serial Measurements of Molecular and Architectural Responses to Therapy (SMMART). This case study describes a metastatic breast cancer patient with a highly heterogenous tumor that began as ER positive/HER2 negative on diagnosis, then was found to be ER negative/HER2 positive in a metastatic liver lesion (Study biopsy #1), and finally triple negative (TNBC) in another metastatic liver biopsy (Study biopsy #2). Multi-omic assays included clinical genomic panel sequencing, clinical immunohistochemistry, a clinical protein profiling assay (Intracellular Signaling Protein Profiling), RNA sequencing, and RPPA. Here we provide the expression data from RNAseq and RPPA data on Study biopsy #2 for the patient discussed in this case report. Note: The first liver biopsy did not yield sufficient material for RNAseq or RPPA.Raw RNA FASTQ files from Study biopsy #2 of the liver Gene expression (RNAseq) files from Study biopsy #2Protein expression (RPPA) files (level 2, 3, 4) for Study biopsy #2

  Study phs002321

• RNA-seq of longitudinal human blood and nose swab samples from a controlled human infection experiment with SARS-CoV-2 virus

  Controlled human infection experiments enable longitudinal profiling of immune responses to a pathogen. 36 healthy volunteers aged 18-29 years, with no evidence of previous infection or vaccination, were inoculated with SARS-CoV-2 virus and quarantined for 14 days. Blood samples for RNA sequencing were collected into PAXgene tubes before virus challenge, 6 hours after challenge, daily thereafter for 14 days and on day 28. Mid-turbinate nose swabs for RNA sequencing were collected before virus challenge, and on days 1, 3, 5, 7, 10 and 14 after challenge, preserved in RNAprotect.

  Study EGAS50000000667

• Glioblastoma stem cell lines RNA-seq (Guilhamon et al.)

  RNA was extracted from GSCs using the Qiagen RNeasy Plus kit. RNA sample quality was measured by Qubit (Life Technologies) for concentration and by Agilent Bioanalyzer for RNA integrity. All samples had RIN above 9. Libraries were prepared using the TruSeq Stranded mRNA kit (Illumina). Two hundred nanograms from each sample were purified for polyA tail containing mRNA molecules using poly-T oligo attached magnetic beads, then fragmented post-purification. The cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers. This is followed by second strand cDNA synthesis using RNase H and DNA Polymerase I. A single “A” base was added and adapter ligated followed by purification and enrichment with PCR to create cDNA libraries. Final cDNA libraries were verified by the Agilent Bioanalyzer for size and concentration quantified by qPCR. All libraries were pooled to a final concentration of 1.8nM, clustered and sequenced on the Illumina NextSeq500 as a pair-end 75 cycle sequencing run using v2 reagents to achieve a minimum of ~40 million reads per sample.

  Dataset EGAD00001006813

• Development of Computational Approaches for Cell Hashing in scRNA-Seq

  As a test of cell overloading after cell hashing, a sample of peripheral blood mononuclear cells (PBMC) from a single subject was separated into naive T cells, memory T cells, and non-T cell PBMC populations using fluorescence activated cell sorting (FACS). Each population was separated into two technical replicates, then stained with BioLegend TotalSeq-A antibodies. All 6 samples were pooled, then loaded into 6 wells of a 10X Genomics Chromium 3' RNA-seq v3 chip at increasing cell counts: 16,000 (k), 24k, 32k, 48k, 64, and 80k cells per well. We also included a single sample that was not hashed or pooled, as well as a single sample that was stained with hashing antibodies, but not pooled in two separate wells.

  Study phs002695

• BAM files ChIP-Seq

  Data from the paper Context-specific Effects of TGFβ/SMAD3 in Cancer Are Modulated by the Epigenome. Tufegdzic et al, Cell Reports 2015

  Dataset EGAD00001001669

• Prostate cancer datasets WGS

  Low pass WGS: 48 samples (5 blood samples from 6 patient data): 22 Tumour cores and 26 normal/benign cores (Next Seq )

  Dataset EGAD00001004466

• Adult-type Granulosa Cell Tumour of the Ovary

  A recurrent mutation in FOXL2 (c.402C>G; p.C134W) is present in over 95% of adult-type granulosa cell tumours (AGCTs). In contrast, various loss-of-function mutations in FOXL2 lead to the development of blepharophimosis, ptosis and epicanthus inversus syndrome (BPES). BPES is characterized by an eyelid malformation often accompanied with primary ovarian insufficiency but not granulosa cell tumours. Two recent studies suggest that FOXL2 C402G is a gain or change-of-function mutation with altered DNA-binding specificity. Another study proposes that FOXL2 C402G is selectively targeted for degradation inducing somatic haploinsufficiency suggesting its role as a tumour suppressor. The latter study relies on data indicative of an FOXL2 allelic imbalance in AGCTs. Here we present RNA-seq data as genetic evidence that no real allelic imbalance is observed at the transcriptomic level in AGCTs. Additionally, there is no loss of protein expression in tumours harboring the mutated allele. These data and other features of this mutation as compared to other oncogenes and tumour suppressor genes argue strongly against FOXL2 being a tumour suppressor in this context. Given the likelihood that FOXL2 C402G is oncogenic, targeting the variant protein or its downstream consequences is the most viable path forward to identifying an effective treatment for this cancer.

  Study EGAS00001005414

• Counts: Brain transcriptome of hereditary cerebral haemorrhage with amyloidosis–Dutch type (HCHWA-D)

  These are the log2CPM (log2 counts per million) fragments per gene counts associated with the BAM files in EGAD00001003806, in tab separated format. Counts for 36 postmortem brain samples from 9 non-demented control subjects and 9 Hereditary cerebral hemorrhage with amyloidosis-Dutch type subjects are included (1 Frontal cortex sample and 1 Occipital cortex sample per subject). RNA samples were depleted for ribosomal RNA with the Ribo Zero Gold Human kit (Illumina) and strand specific RNA-Seq libraries were generated. Paired-end sequencing was performed on a HiSeq2500 Illumina system (2x50bp reads). Alignments were performed using GSNAP v2014-12-23 with setting "--npaths 1" on GRCh38 reference genome without the alternative contigs. Fragment per gene counting was performed using HTSeq-count v0.6.1p1 with setting "--stranded reverse". The gene annotation used for quantification were UCSC RefSeq genes for GRCh38 downloaded on 2015-07-13.

  Dataset EGAD00010001457

• RNA-Seq of human longitudinal whole blood samples from PCR-positive and PCR-negative recent household contacts of COVID-19 index cases.

  Little is known about the transcriptomic profile of individuals who are exposed to SARS-CoV-2 yet resist becoming PCR positive. To investigate this, longitudinal whole-blood samples were taken (0, 7, 14, and 28 days after enrolment) from PCR positive and PCR negative SARS-CoV-2-naïve household contacts who were recently exposed to a COVID-19 index. Samples were also taken from pre- and post-pandemic unexposed controls. Total RNA was extracted from PAXgene tubes before undergoing poly(A) selection followed by globin and rRNA depletion. DNA libraries were constructed using the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina. All samples were then sequenced across 2 flowcells of an Illumina HiSeq 4000.

  Dataset EGAD50000000684

• Single-Nuclei RNA Sequencing and Spatial Transcriptomics of Human Heart Right Atrial Appendage and Pericardial Fluid in Cardiovascular Disease

  This dataset includes single-nuclei RNA sequencing (snRNA-seq) and spatial transcriptomics data from biopsies of the right atrial appendage and pericardial fluid, collected during open-heart surgeries. The samples represent various clinical contexts, including control, ischemic heart disease, heart failure, and myocardial infarction. For each biopsy, nuclei were isolated, and RNA sequencing libraries were prepared using the 10x Genomics Chromium v3 platform. Additionally, spatial transcriptomics was performed on the heart samples. The dataset provides raw sequencing data, which includes fastq files generated from these libraries, offering valuable insights into the cellular composition of heart tissues and associated fluids across different disease states.

  Dataset EGAD50000000927

• bulk RNAseq analysis of tumor from 3 non-muscle-invasive bladder cancer patients

  We performed screenings of patient-specific neo-epitopes by tumor-infiltrating CD8+ T lymphocytes of 4 non-muscle invasive bladder cancer patients. After the tumor resection, these patients went on to receive BCG treatment as they had high-risk disease. In order to select the neo-epitopes, we performed bulk RNAseq of the bladder tumor to select the mutated genes that were expressed in the tumor (TPM >1). For patient UC2, 3 different tumors were resected at the same time and RNA from these 3 tumors were sent separately for RNAseq. For patient UC4, no RNA was available, so this patient is not included in this study.

  Study EGAS50000001383

• RNAseq data of metastatic breast cancer samples

  Paired-end RNA sequencing data from 28 metastatic breast cancer samples. Sequencing was performed on the Illumina NovSeq 6000 and HiSeq 4000 using TruSeq Stranded mRNA Kit.

  Dataset EGAD50000000818

• Single cell mRNA sequencing of CD11b cells from a primary GBM - SF 10592i

  We collected fresh tissue from an untreated GBM directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine after selection of CD11b+ cells using magnetic beads.

  Dataset EGAD00001003109

• Single cell mRNA sequencing of CD11b cells from a primary GBM - SF 10679iA

  We collected fresh tissue from an untreated GBM directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine after selection of CD11b+ cells using magnetic beads.

  Dataset EGAD00001003110

• Single cell mRNA sequencing of CD11b cells from a primary GBM - SF 10360i

  We collected fresh tissue from an untreated GBM directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine after selection of CD11b+ cells using magnetic beads.

  Dataset EGAD00001003108

• RNA-seq TPM matrices

  Matrices of TPM-normalized counts from RNAseq data for the three phase II clinical trials (IMvigor210, POPLAR, IMmotion150) and the phase I clinical trial PCD4989g.

  Dataset EGAD00001006741

• BLUEPRINT: RNA-seq of progenitor cells

  Data supporting the paper Transcriptional diversity during lineage commitment of human blood progenitors

  Dataset EGAD00001000745

• Single cell mRNA sequencing of CD11b cells from a primary GBM - SF 10360i

  We collected fresh tissue from an untreated GBM directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine after selection of CD11b+ cells using magnetic beads.

  Dataset EGAD00001003111

• Whole-exome and whole-genome sequencing data

  Multi-omic data for lung neuroendocrine neoplasms, including the first multi-omic sequencing data for the understudied lung atypical carcinoids. The data includes Whole-exomes, whole-genomes, RNA-seq, and EPIC 850K methylation array data.

  Dataset EGAD00001005087

• Single cell mRNA sequencing of CD11b cells from a primary GBM - SF 10345i

  We collected fresh tissue from an untreated GBM directly from the operating room and subjected the biopsy to single-cell RNA-seq with the fluidigm C1 machine after selection of CD11b+ cells using magnetic beads.

  Dataset EGAD00001003107

• Single cell characterization of arrested B-lymphoid differentiation and leukemic cell states in ETV6-RUNX1-positive pediatric leukemia

  Leukemic bone marrow in primary ETV6-RUNX1 positive acute lymphoblastic leukemia samples (Six diagnostic and two 15 days after treatment) using Chromium 3' single cell RNA-seq. Samples sequenced on 1-3 lanes and raw fastq files provided for each sample.

  Dataset EGAD00001006074

• Cambridge Neoadjuvant Cancer of the Prostate (CANCAP03): A Window Study into the Effects of Olaparib ± Degarelix in Primary Prostate Cancer.

  Study: Cambridge Neoadjuvant Cancer of the Prostate (CANCAP03): A Window Study into the Effects of Olaparib ± Degarelix in Primary Prostate Cancer. EudraCT Number: 2014-004417-86 Clinicaltrials.gov Number: NCT02324998 Purpose: Investigate combined PARP and androgen inhibition in primary prostate cancer. Aim: To understand the biological mechanism(s) underlying clinical efficacy, especially in the absence of mutations in homologous recombination (HR) repair pathways. Dataset: Single-nuclear RNA sequencing data from post-treatment radical prostatectomy tissue cores for six patients, 3 from each of olaparib and olaparib and degarelix cohorts. Samples were processed for nuclear isolation and extraction, prior to single-nuclear RNA sequencing. 10x libraries were constructed using the 10x supplied protocol, briefly: individual nuclei were trapped into droplets with a single bead, and poly A transcripts were barcoded during reverse transcription. Complementary DNA was amplified, and libraries were prepared for sequencing on NovaSeq 6000 using S4 flow cell (100k reads per cell). FASTQ files were processed using CellRanger pipeline (10X Genomics). Outputs from the CellRanger pipeline were analysed using the Seurat package (v4.9.9.9049) in R (v4.2.0). Processed data is provided as a Seurat object.

  Study EGAS50000000880

• Human CD4 Memory T Cell Activation Time Course

  We performed a high-resolution time course RNA-seq experiment on CD4+ memory T cells during activation with anti-CD3/CD28 beads. We performed this in 24 adult individuals of European ancestry with no autoimmune diseases. We used 1 million cells as starting material, per time point (up to 8 time points per individual from 0 to 72 hours). We identified widespread dynamic allele-specific expression (dynASE) across the genome, i.e. ASE that changes with time after activation, which reflects cell state-dependent genetic regulatory effects. DynASE events were enriched in risk loci for multiple autoimmune diseases. This highlights the importance of studying multiple cellular states in order to elucidate the mechanisms of autoimmune disease. It also provides a successful framework to do it in other cellular systems relevant for other traits. We also showed an unprecedented amount of regulatory variation for HLA genes. HLA-DQB1 has 3 distinct regulatory haplotypes with expression profiles that could come from 3 different genes. We performed ATAC-seq in replicate for CD4+ memory T cells stimulated for 72 hours from another healthy individual. These data, together with genetic fine-mapping, CRISPR editing, EMSA and luciferase assays, helped us find a SNP in the promoter of HLA-DQB1 that causes at least part of the large cell state-dependent change in expression, at the RNA and protein levels.

  Study phs002259

• Genome-Wide Characterization of Pancreatic Adenocarcinoma Patients Using Next Generation Sequencing

  In this study, we whole genome sequenced tumor/normal pairs from three pancreatic adenocarcinoma patients to separately characterize each patient with respect to somatic alterations. For 2 patients for whom tumor RNA was available, we also performed RNA sequencing to evaluate gene expression changes. While additional sequencing is needed to improve our understanding of the disease, the information acquired from this study contributes to our knowledge base on pancreatic cancer and helps to establish a foundation for identifying and developing more efficacious treatments for patients.

  Study phs000550

• Single-cell RNA sequencing of 6 follicular lymphoma tumors

  Follicular lymphoma (FL) is an indolent cancer of mature B-cells but carries increased risk of transformation to a more aggressive histology over time. We present here comprehensive profiling both tumor and immune compartments in 6 diagnostic FL biopsies by single-cell RNA sequencing. This confirmed results from 155 FL tumors characterized by mass cytometry (CyTOF) which revealed two distinct evolutionary trajectories with disparate risk of transformation and alternate biologies.

  Study EGAS00001005257

• Bulk RNA-seq of monocytes and in vitro cultured monocyte-derived macrophages of ANCA-associated vasculitis patients with active and stable disease and healthy controls

  To study monocyte and macrophage activation in ANCA-associtated vasculitis (AAV), we performed bulk RNA sequencing of bead-selected monocytes and in vitro cultured monocyte-derived macrophages from AAV patients and healthy controls. Overview patients included for sequencing monocytes: - AAV active disease, n=4, MPO-AAV=4 - AAV remission, n=10, PR3-AAV=5, MPO-AAV=5 - Healthy controls, n=6 Overview patients included for sequencing monocyte-derived macrophages: - AAV active, n=1, PR3-AAV=1 - AAV remission, n=3, PR3-AAV=3 - Healthy controls, n=3

  Dataset EGAD50000000450

• RNA-seq from B-ALL patients treated on the ALLG ALL09 study

  Dataset contains RNA-sequencing data (fastq files) from 46 patients treated on the Australasian Leukaemia and Lymphoma Group (ALLG) ALL09 study (ACTRN12618001734257). All patients were diagnosed with CD19 positive B-cell acute lymphoblastic leukaemia and were negative for Philadelphia chromosome positive disease. Patient samples (bone marrow or peripheral blood) were taken at diagnosis. mRNA was extracted from lymphoblasts and underwent paired-end sequencing using either the illumina NextSeq (75 bp PE) or MGI DNABSEQ-G400 (100bp PE) platforms. Transcriptomic data was used for identification of genomic drivers of acute lymphoblastic leukaemia (ALL).

  Dataset EGAD50000001603

• Bulk RNA-sequencing datasets from Caco-2 cells under normal or inflamed conditions treated with 4HTBZ or vehicle.

  This dataset contains 4 bulk RNA sequencing files (.fastq.gz) and a metadata file (.csv) from Caco-2/TC7 intestinal epithelial cells under normal or inflamed conditions, followed by treatment with 4HTBZ or vehicle. The cDNA libraries were prepared with the QuantSeq 3′ mRNA-Seq Kit (Lexogen, Inc.) and sequenced on an Illumina HiSeq4000 system. The metadata file provides sample information (experimental condition and code identifier). Raw sequencing reads were quality-checked with FastQC, trimmed with Trimmomatic, and aligned to the human reference genome (GRCh38) using HISAT2.

  Dataset EGAD50000001759

• Transcriptomics for IMMU-SCCHN1 cohort

  The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients with confirmed locally recurrent and/or metastatic squamous cell carcinoma of the head and neck (SCCHN) who receive first-line treatment with immune checkpoint inhibitors (ICI) or ICI combined with chemotherapy. This dataset contains transcriptomics data processed with the STAR pipeline. Two sequencing kits were employed: Roche KAPA for high-quality RNA samples and Takara SMART-Seq Stranded for samples with limited RNA quantities.

  Dataset EGAD50000002206

• Multi-region RNA-Seq data of 10 neuroblastoma cases

  To investigate intratumour heterogeneity and to better understand tumour evolution in neuroblastoma, we have performed a multi-region RNA sequencing on a total of 51 spatially separated tumor samples from 9 primary neuroblastomas (2 low-risk, 1 intermediate-risk and 6 high-risk) and 1 relapsed neuroblastoma. We also assessed the impact of chemotherapy on the clonal expansion by sequencing tumour regions from one medium risk and one high-risk tumour for which we had matched samples obtained at diagnosis and after chemotherapy.

  Dataset EGAD00001008133

• Pseudogene RNAseq

  RNA sequencing to validate findings of somatic pseudogenes acquired during cancer development

  Dataset EGAD00001000732

• Non-coding mutations reveal cancer driver cistromes in luminal breast cancer

  A dataset of ER+PR+ breast tumor samples that were analyzed in order to identify mutation enrichment in cis-regulatory elements and cistrome. The repository includes sequencing data from 88 patients. 26/88 were sequenced using ATAC-seq

  Dataset EGAD00001007650

• Sequencing_of_patient_samples_who_received_immune_checkpoint_inhibition___WES___NKI

  DNA and RNA isolated from FFPE blocks of patients who received immune checkpoint inhibition (ICI) are sequenced with the aim to identify therapeutically tractable vulnerabilities in tumors to prevent ICI resistance.

  Study EGAS00001003154

• Total RNA sequencing data from the Triple Negative Trial (TNT)

  BAM files from total RNA sequencing of samples from breast cancer patients in the TNT trial. Data includes 186 primary tumour samples and 13 matched recurrence samples.

  Dataset EGAD00001011141

• Deep single-cell RNA sequencing data for 11138 T cells from tumour, adjacent normal tissue and peripheral blood of treatment-naive CRC patients. The DATA ACCESS AGREEMENT is provided at https://github.com/zhangyybio/single-T-cell-data-access. Applicants can request access to the data by directly downloading it or by sending an email to cancerpku@pku.edu.cn. The process that is used to approve an application includes verifying the institution, participants and research purposes of the application. In general this process will take about two weeks. In principal, any scientific research program complying with the laws and bioethic regulation policies of China will be approved.

  T cells are central players in cancer immunotherapy1, yet some of their fundamental properties such as development and migration within tumours remain elusive. The enormous T cell receptor (TCR) repertoire, required for recognising foreign and self-antigens2,3, could serve as lineage tags to track these T cells in tumours4. Here, we obtained transcriptomes of 11,138 single T cells from 12 colorectal cancer (CRC) patients and developed STARTRAC (Single T-cell Analysis by Rna-seq and Tcr TRACking) indices to quantitatively analyse dynamic relationships among 20 identified T cell subsets with distinct functions and clonalities. While both CD8+ effector and “exhausted” T cells exhibited high clonal expansion, they were independently connected with tumour-resident CD8+ effector memory cells, implicating a TCR-based fate decision. Of the CD4+ T cells, the majority of tumour-infiltrating Tregs showed clonal exclusivity, whereas certain Treg clones were developmentally linked to multiple TH clones. Notably, we identified two IFNG+ TH1-like clusters in tumours, the GZMK+ TEM and CXCL13+ TH1-like clusters, which were associated with distinct IFN-γ-regulating transcription factors, EOMES/RUNX3 and BHLHE40, respectively. Only CXCL13+BHLHE40+ TH1-like cells were preferentially enriched in tumours of microsatellite-instable (MSI) patients, which might explain their favourable response rates to immune-checkpoint blockade. Furthermore, we found IGFLR1 to be highly expressed in both CXCL13+BHLHE40+ TH1-like and CD8+ exhausted T cells and possessed co-stimulatory functions. Our integrated STARTRAC analyses provides a powerful avenue to comprehensively dissect the T cell properties in CRC, which could shed new insights into the dynamic relationships of T cells in other cancers

  Study EGAS00001002791

• Characterization of Human Transcriptome by Computational and HTS Approaches

  We have deeply sequenced total RNA and whole exome of six different post-mortem human snap-frozen tissues (brain, liver, lung, striated muscle, kidney, heart) from three unrelated healthy Caucasian individuals (males, aged 47-54) with the aim to characterize post-transcriptional events due to alternative splicing and RNA editing. In addition, to facilitate the detection of RNA editing sites we have also resequenced the entire genome of each individual. Data are also used to investigate tissue-specific abundance of mitochondrial DNA from exomes and its correlation with mitochondrial transcription, mass and respiratory activity. Tissues were obtained from Cureline (South San Francisco, CA, USA). DNA and RNA were purified using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) and the RNeasy Plus Mini Kit (Qiagen, Hilden, Germany), respectively. Exome capture was performed using the TruSeq Exome Enrichment Kit (Illumina, San Diego, CA) and the sequencing was carried out at IGA Technology Services in Udine (Italy) on Illumina HiSeq 2000 (generating for each tissue approximately 40 millions of 100bp paired-end reads). Strand-oriented RNA reads (2x100bp) were generated using the TruSeq Stranded Total RNA Sample Prep Kit (Illumina, San Diego, CA, USA) and sequenced at IGA Technology Services in Udine (Italy) on Illumina HiSeq 2000 platform. Whole genome resequencing was performed at Personal Genomics in Italy on an Illumina HiSeq 2000 platform (2x100bp) with a mean coverage of 30X. MiRNA-Seq was instead performed at IBBE Institute (Italy) using the TruSeq Small RNA Sample Prep Kit on Illumina MiSeq platform. Preliminary bioinformatics analyses have been performed to check quality using FASTQC software and inconsistent read regions have been trimmed using trim_galore. All reads were mapped onto the human reference genome (version hg19) using GSNAP.

  Study phs000870

• Unraveling the transcriptomic profile of utero-tubal lavage fluid of ovarian cancer patients

  mRNA capture seq of uterotubal lavage samples

  Dataset EGAD00001007933

• HGSOC-celllines-SmartSeq3

  Smart-seq3 scRNA-seq of cells from primary (OV2295) and metastatic (OV2295R2) high-grade serous ovarian cancer cell-line

  Dataset EGAD00001009798

• Comprehensive Epigenetic Landscape of Rheumatoid Arthritis Fibroblast-Like Synoviocytes

  We characterized the epigenetic landscape of rheumatoid arthritis fibroblast-like synoviocytes (FLS) compared with osteoarthritis FLS. Multiple technologies were used, including ChIP-seq to assay H3K27ac, H3K4me1, H3K4me3, H3K36me3, H3K27me3, and H3K9me3, ATAC-seq for chromatin accessibility, the transcriptome by RNA-seq and whole genomic bisulfite sequencing for DNA methylation. Integrative analysis was performed using a novel unbiased method to identify regions of the genome that have similar epigenetic marks. The regions that distinguished RA and osteoarthritis cells were primarily located in active enhancers and promoters. The regions and genes identified included immunological pathways. In addition, some unexpected pathways, most notably "Huntington's Disease Signaling", were discovered. The Huntington's Disease pathway was biologically validated for Huntingtin-interacting protein-1, which regulated invasive behavior of FLS. For a complete description, see R. Ai et al., Comprehensive epigenetic landscape of rheumatoid arthritis fibroblast-like synoviocytes. Nat Commun 9, 1921 (2018). Sequencing data of study participants are available through dbGaP's Authorized Access portal, while analyses of the sequencing data may be obtained through NCBI's GEO portal under GSE112658.

  Study phs001615

• Integrated Analysis of Multimodal Single-Cell Data

  The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce 'weighted nearest neighbor' analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. All CITE-seq and ECCITE-seq raw matrices are available in GEO database under the accession number GEO: GSE164378.

  Study phs002315

• Nivolumab and Ipilimumab and Radiation Therapy in Microsatellite Stable (MSS) and Microsatellite Instable (MSI) High Colorectal and Pancreatic Cancer

  This is a single-arm, non-randomized, phase 2 trial (NCT03104439) combining radiation, ipilimumab and nivolumab in patients with metastatic MSS Colorectal Cancer (CRC; n=40) and Pancreatic Ductal Adenocarcinoma Cancer (PDAC; n=25). Patients were previously treated population, and the primary endpoint was disease control rate. Pre- and post-treatment samples were subjected to RNA sequencing and whole exome sequencing. The primary data is shared via GEO and dbGaP at National Center for Biotechnology Information (NCBI), National Institutes of Health (NIH).

  Study phs002545

• RNAseq_of_ribosomal_footprints

  Cancer genes can affect ribosomal RNA processing and this can underlie their essentiality to cells, making them cell-essential in the same way as ribosomal genes themselves. We want to confirm this, in order to understand the results of our CRISPR drop-out screens.NOTE FROM BESPOKE TEAM: Run a single read 1 (forward read) of 30 bases, then an index 1 read as normal. This would fit a 50cycle kit

  Study EGAS00001001591

• Immunological hallmarks for clinical response to BCG in bladder cancer

  Intravesical Bacillus Calmette-Guerin (BCG) is an effective immunotherapy for non-muscle invasive bladder cancer (NMIBC). However, recurrence and progression remain frequent warranting deeper insights into its mechanism. We herein comprehensively profiled blood and tissues obtained from NMIBC patients before, during and after BCG treatment using cytometry by time-of-flight (CyTOF) and RNA sequencing to identify the key immune subsets crucial for anti-tumour activity.

  Study EGAS00001004764

• Wilm's tumor sequencing data

  No. of samples: 80 (28 ULP-WGS, 26 WES, 26 RNA-SEQ) File types: FASTQ (28 ULP-WGS, 19 WES, 18 RNA) and BAM (7 WES, 8 RNA) Technology used: Sequencing - Illumina Novoseq 6000; Map/Align - Illumina DRAGEN v3.7.5; Genome assembly - GrCh38p13 Filename nomenclature: - SampleName_Passage_SampleType_TissueType_SequencingType - Passage of: PX = unknown; PZ = from patient; P0 = first passage from patient on plastic; P1 = first passage from plastic/PDX/organoid - SampleType: STN = normal; STT = tumor - SampleType STN: 00 = tissue unknown; 01 = adjacent normal; 02 = fibroblast; 03 = germline blood; 21 = cell line from patient tissue; 22 = cell line from PDX; 23 = cell line from patient fibroblast - SampleType STT: 00 = tissue unknown; 01 = primary tumor; 21 = cell line from patient; 22 = cell line from PDX - TissueType: WT = Wilm's tumor; 00 = kidney unknown; 01 = kidney left; 02 = kidney right - SequencingType: 00 = unknown; 02 = ultra-low pass whole-genome sequencing; 20 = whole-exome; 61 = bulk RNA-sequencing

  Dataset EGAD00001011111

• Methylome sequencing of cell-free DNA and RRBS of solid tissue

  Early cancer detection by cell-free DNA (cfDNA) faces multiple challenges: low fraction of tumor cfDNA, molecular heterogeneity of cancer, and sample sizes not sufficient to reflect diverse patient population. We develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of cfDNA methylome (with >12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our system to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, thereby permitting the classification models to learn new features as training cohorts grow, and expanding their scope to other cancer types.

  Study EGAS00001006020

• Genomic Analysis of Metastatic Brain Cancer

  This study contains targeted exome and transcriptome sequencing of metastatic brain tumors from 68 patients. Sequencing data includes i) targeted Tempus xT exome sequencing from matched tumor/normal from 68 patients, and ii) whole-transcriptome RNA sequencing from a subset of 65 patients.

  Study phs002639

• Cold Ischemia Study

  This study aimed to evaluate the impact of cold ischemia time on the expression of cancer-related genes, particularly those involved in hypoxia, apoptosis, and epithelial-to-mesenchymal transition (EMT). We performed RNA sequencing on 54 normal colon mucosa samples from nine patients undergoing colorectal cancer surgeries, freezing samples at predefined intervals ranging from 0 to 60 minutes. A total of 44 differentially expressed genes (DEGs) (p < 0.05) were identified when comparing samples frozen immediately (T0) with those frozen after 60 minutes (T5).

  Study EGAS00001008233

• Mutation of FOXL2 in granulosa cell tumors of the ovary

  We generated paired-end RNA-Seq data using the Illumina Genome Analyzer II platform for four adult granulosa cell tumours (GCTs) of the ovary (1 primary, 3 recurrent). We screened this data for the presence of GCT specific sequence variants and detected a non-synonymous mutation in the FOXL2 gene that was found to be recurrent in 97% of 89 additional GCTs. To measure genomic stability of the four index cases, we generated Affmetrix SNP 6.0 data and analysed these data for copy number changes. Raw sequence reads as well as Affymetrix CEL files are available to applicants.

  Study EGAS00000000040

• Genomic Features of Lung Adenocarcinoma from Individuals with <= 10 Pack-Year Smoking History

  Actionable oncogenic alterations are identified in a subset of lung adenocarcinomas. Many of these driver alterations are enriched in lung adenocarcinomas from individuals with minimal smoking history and are generally mutually exclusive of one another. We generated whole-exome and RNA-seq data from lung adenocarcinomas obtained from individuals with &#8804;10 pack-year smoking history. Most of these tumors harbored an established oncogenic alteration. We identified a novel OCLN-RASGRF1 fusion from one tumor that lacked a known oncogenic driver. Functional studies demonstrated this fusion activates RAS signaling and promotes tumorigenesis in cell models.

  Study phs002556

• Gene Expression and Epigenetic Analyses of Human Myocytes From Different Muscles

  The study contains expression and DNA methylation data of in vitro cultured myoblasts derived from biopsies of control, FSHD1 and FSHD2 patients from the tibialis anterior, quadricep, bicep and deltoid. Expression data is from bulk RNA-seq using the SmartSeq2 protocol for days 0 and 5 of in vitro differentiation of primary myocytes from the bicep and deltoid of FSHD1 patients. DNA methylation data was produced using the TruSeq Methyl Capture EPIC kit (Illumina) for days 0, 3 and 12 of in vitro differentiation of primary myocytes from the tibialis anterior and quadricep of healthy individuals and FSHD2 patients.

  Study phs002554

• Genetic and Epigenetic Determinants of Pediatric Obesity-Associated Asthma

  Fifty-nine obese and sixty-one normal-weight asthmatic children of African American or Hispanic background, age 7-11 years, with well controlled asthma, were recruited from clinics affiliated with Children&#39;s Hospital at Montefiore, Bronx, New York, USA. We isolated CD4+ T cells (Th cells) from a fasting sample of peripheral blood and conducted genome-wide assays of gene expression by Directional Transcriptome Sequencing (RNA-seq), DNA methylation by HELP-tagging, and examined genetic polymorphisms and single nucleotide polymorphisms (SNPs), informative about ancestry, by array-based genotyping using Multi-Ethnic Genotyping Array.

  Study phs001812

• Multi-Omic Investigation of Beckwith-Wiedemann Syndrome Wilms Tumor

  Wilms&#8217; Tumor (WT), also known as nephroblastoma, is one of the most common malignant kidney tumors diagnosed in children. One of the most common WT predisposition syndromes is Beckwith-Wiedemann Syndrome (BWS, OMIM: 130650), which affects approximately 1/10,000 live births. In this study, we perform Whole Exome Sequencing (WES), DNA methylation array, and messenger RNA-Sequencing (mRNA-Seq), to examine the genomic, methylomic, and transcriptomic signature of BWS WT. We analyzed six tumor-normal paired samples, three BWS tumor only samples, and four non-BWS control samples to identify BWS WT drivers.

  Study phs002769

• EWS-WT1 Fusion Isoforms Establish Oncogenic Programs and Therapeutic Vulnerabilities in Desmoplastic Small Round Cell Tumors

  Desmoplastic Small Round Cell Tumor (DSRCT) is an aggressive mesenchymal tumor driven by fusions between the disordered domain of the Ewing sarcoma RNA binding protein 1 (EWSR1) and the developmental transcription factor Wilms tumor 1 (WT1). We used genome-wide chromatin profiling to identify EWSR1-WT1-dependent gene regulatory networks and target genes. Our studies show that EWS-WT1 is a powerful activator of distal regulatory elements and controls an oncogenic gene expression program that characterizes primary DSRCTs. ChIP-seq profiles for histone marks in primary DSRCT samples are available through dbGaP.

  Study phs003682

• ABHD11 inhibition drives sterol metabolism to modulate T cell effector function and alleviate autoimmunity

  Chronic inflammation in autoimmunity is driven by hyperactive T cells with dysregulated metabolism. ABHD11 is a mitochondrial hydrolase that supports alpha-ketoglutarate dehydrogenase function and has been associated with remission in rheumatoid arthritis. To explore its role in T cell function, we used CRISPR/Cas9 to knock down ABHD11 in Jurkat T cells and performed RNA-Seq analysis. Differential gene expression revealed widespread transcriptional changes, with sterol-related pathways among the most significantly enriched. These findings highlight ABHD11 as a potential regulator of metabolic programming in T cells with implications for autoimmunity.

  Study EGAS50000001297

• Integrative understanding of human immune system by functional genomics and development of intervention strategies for the prevention of autoimmune diseases

  To elucidate the regulation of B cell recptor repertoire in B cell subsets and its contribution to autoimmune diseases, B cell subsets from 36 Systemic Lupus Erythematosus(SLE), 90 Systemic Sclerosis(SSc), 85 Myositis(IIM), 21 Mixed Connective Tissue Disease(MCTD), 18 Sjogren's syndrome(SjS), 27 Rheumatoid Arthritis(RA), 23 Behcet's disease(BD), 18 Adult Onset Still���s Disease(AOSD), 24 ANCA-associated Vasculitis(AAV), 16 Takayasu���s Arteritis(TAK) and 137 healthy controls(HC) were collected (Naive_B, USM_B, SM_B, DN_B, Plasmablast. RNA-seq and BCR-mapping was performed with each B cell subset samples.

  Study JGAS000485

• Transcriptomic profiling of skin biopsies from psoriasis patients following treatment with Zasocitinib

  The objective of this study is the exploratory analysis and characterization of psoriasis using RNA-seq-based profiling. Specifically, the cutaneous and serum biomarker profiles associated with the molecular, histological and clinical effects of Zasocitinib treatment in patients with moderate-to-severe plaque psoriasis were characterized. To evaluate differences between the transcriptomic profiles of Zasocitinib- and placebo-treated patients, exploratory biomarker endpoints included quantification of skin biomarkers, such as immune cell infiltration and mRNA expression levels in lesional and non-lesional skin, as well as quantification of circulating cytokines and other inflammatory biomarkers.

  Study EGAS50000001548

• An Integrated Approach to Patient Stratification and Therapy Selection in Acute Myeloid Leukemia

  As more clinically-relevant genomic features of myeloid malignancies are revealed, it has become clear that targeted clinical genetic testing is inadequate for risk stratification. Here, we developed and validated a clinical transcriptome-based assay for stratification of acute myeloid leukemia (AML). Comparison of RNA-Seq to whole genome and exome sequencing revealed that as a standalone assay, RNA-Seq offered the greatest diagnostic return, enabling identification of expressed gene fusions, single nucleotide and short insertion/deletion variants, and whole-transcriptome expression information. Expression data were used to develop a novel risk score which, when combined with molecular risk guidelines, allowed for the re-stratification of 22.1 to 25.3% of AML patients from three independent cohorts into correct risk groups. Within the adverse-risk subgroup, we identified a subset of patients characterized by dysregulated integrin signaling and RUNX1 or TP53 mutation. We show that these patients may benefit from therapy with inhibitors of focal adhesion kinase (PTK2), demonstrating additional utility of transcriptome-based testing for therapy selection in myeloid malignancy.

  Dataset EGAD00001006701

• RNA-seq data

  RNA-seq FASTQ files from 296 bulk pre-treatment tumors from IMblaze370

  Dataset EGAD00001008553

• Cell and Circuit-Specific Exploration of HIV Neurogenomics in Context of Opiate and Cocaine Misuse

  Utilizing frozen postmortem brain tissue from the Manhattan HIV Brain Bank, we performed cell-type-specific, genome-wide sequencing of three different types: 1) single-nucleus RNA-sequencing (snRNA-seq), 2) in situ Hi-C, and 3) HIV integration site sequencing (IS-seq). We assayed tissue from three different donor types: 1) HIV-uninfected control (HIV-), HIV-infected (HIV+), and HIV-infected with HIV encephalitis (HIVE), who served as a positive control for active CNS viral infection. The diagnosis of HIVE was made by a board-certified neuropathologist on routine histopathology, based on the presence of a microglial nodule encephalitis with characteristic multinucleated syncytial cells and staining for HIV p24 antigen. Other HIV-infected individuals (the HIV+ group) did not have this histologic evidence of active HIV brain replication, and were variable with regard to cART status and virologic control prior to death. Donor characteristics, including ante-mortem plasma HIV loads, CD4 counts, and cART status, were documented. Quality controls were used for each assay: For snRNA-seq we applied filters to remove low quality nuclei from analysis and performed experiments in which mouse tissue was mixed with human tissue to determine a reliable HIV read count threshold. For Hi-C, we used paired HIV- and HIVE samples and processed all samples in parallel, at the same time. Correlation analysis was used to ensure biological replicates were aligned. For IS-seq, each time the assay was run, a positive and negative control were included. This allowed us to ensure a lack of contamination in IS-seq libraries and that, when a particular sample failed to yield library, this was due to low levels of HIV rather than a failure of the assay. Altogether, we built an integrative dataset from 79 sequencing files, including snRNA-seq, and cell type specific Hi-C, IS-seq, and ChIP-seq datasets. Human autopsy brain samples were collected by the Manhattan HIV Brain Bank (MHBB, U24MH100931), using protocols under the supervision of the Icahn School of Medicine at Mount Sinai (ISMMS) Institutional Review Board. Written informed consent was obtained either from decedents or their primary next of kin. Major Findings: Reorganization of open/repressive (A/B) compartment structures in HIVE microglia encompassing 6.4% of the genome was linked to transcriptional activation of interferon (IFN) signaling and cell migratory pathways and was partially recapitulated by IFN-gamma stimulation of cultured microglia. In contrast, decreased expression and repressive compartmentalization of genes regulating neuronal health and signaling was seen in both HIVE and HIV+ microglia. IS-seq recovered 1,221 integration sites in the brain that displayed distinct genomic patterns as compared to peripheral lymphocyte integration and were enriched for chromosomal domains newly mobilized into a permissive chromatin environment in HIVE microglia. Viral transcription occurred in a subset of highly activated microglia comprising 0.003% of all nuclei in HIVE brain. Our findings point to a disruption of microglia-neuronal interactions in the HIV infected brain and an interrelation of retroviral integration and expression with interferon-associated remodeling of the microglial 3D genome during progression to HIVE. Search terms: HIV, postmortem brain, microglia, substance abuse, viral integration, chromosomal conformation capture, Hi-C, chromatin, single nuclei RNA-seq, Irf8, NeuN, HIV encephalitis, neuron-microglia interaction, cytokine

  Study phs003080

• Childhood Cancer Data Initiative (CCDI): Molecular Characterization across Pediatric Brain Tumors and Other Solid and Hematologic Malignancies for Research, Diagnostic, and Precision Medicine

  This study contains tumor and germline WGS, RNA-Seq, Clinical Panel Sequencing, and other omics and molecular data for patients with pediatric brain tumors and other solid and hematologic malignancies. This data has been collected from multiple organizations including the multi-institute Children's Brain Tumor Network (CBTN), the Pacific Pediatric Neuro-Oncology Consortium (PNOC), and the Children's Hospital of Philadelphia Division for Genomic Diagnostics (CHOP DGD). In addition to being available pre-publication and without embargo via dbGaP and the NCI Cancer Research Data Commons, this data is made available via Cavatica, PedcBioPortal and the Kids First Data Resource Portal. Collectively, this availability strives to accelerate the research process and enable members of the scientific and patient communities to partner and make discoveries faster than ever before.In 2019, the CBTN launched the Pediatric Brain Tumor Atlas, which now comprises the largest collection of childhood brain tumor data in the world. This resource is helping accelerate not only brain tumor research, but is also empowering discovery for other rare childhood conditions. Brain tumors are the most common form of cancer in children aged 0-19 in the United States, and are the largest cause of cancer-related deaths. The estimated number of new cases in 2019 is nearly 3,800, and thus brain tumors are a rare disease. Despite their relative rarity, the years of potential life lost due to brain tumors in 2009 was estimated at 47,631 years for children and adolescents aged 0-19 in the United States; this is a disproportionate amount of life lost compared to adult cancers and represents an unrecognized societal threat. There is an urgent need to improve therapies for these children. This sequencing cohort defines the largest, clinically annotated pediatric brain tumor cohort study to date and seeks to define the intersection of germline and somatic underpinnings of pediatric brain tumors across a shared developmental context of cancer and structural birth defects.

  Study phs002517

• TransNEO neoadjuvant breast cancer study

  Whole exome, shallow whole genome, and RNA-sequencing data from a cohort of 168 women with breast cancer receiving neoadjuvant chemotherapy.

  Dataset EGAD00001008269

• BAM Files MBD-SEQ

  Data from the paper Context-specific Effects of TGFβ/SMAD3 in Cancer Are Modulated by the Epigenome. Tufegdzic et al, Cell Reports 2015

  Dataset EGAD00001001668

• BS-seq in plasma of CRC patients

  We performed whole genome bisulfite sequencing of plasma DNA in 11 colorectal cancer patients to study the colonic DNA in plasma.

  Dataset EGAD00001004568

• Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation

  Droplet-based single-cell RNA-seq has emerged as a powerful technique for massively parallel cellular profiling. While these approaches offer the exciting promise to deconvolute cellular heterogeneity in diseased tissues, the lack of cost-effective, reliable, and user-friendly instrumentation has hindered widespread adoption of droplet microfluidic techniques. To address this, we have developed a microfluidic control instrument that can be easily assembled from 3D printed parts and commercially available components costing approximately $575. We adapted this instrument for massively parallel scRNA-seq and deployed it in a clinical environment to perform single-cell transcriptome profiling of disaggregated synovial tissue from 5 rheumatoid arthritis patients. We sequenced 20,387 single cells from synovectomies, revealing 13 transcriptomically distinct clusters. These encompass a comprehensive and unbiased characterization of the autoimmune infiltrate, including inflammatory T and NK subsets that contribute to disease biology. Additionally, we identified fibroblast subpopulations that are demarcated via THY1 (CD90) and CD55 expression. Further experiments confirm that these represent synovial fibroblasts residing within the synovial intimal lining and subintimal lining, respectively, each under the influence of differing microenvironments. We envision that this instrument will have broad utility in basic and clinical settings, enabling low-cost and routine application of microfluidic techniques, and in particular single-cell transcriptome profiling. Reprinted from [Stephenson et al., Nature Communications, 2018], with permission from the Nature Publishing Group.

  Study phs001529

• RNA-seq of longitudinal human blood and nose swab samples from a controlled human infection experiment with SARS-CoV-2 virus

  Controlled human infection experiments enable longitudinal profiling of immune responses to a pathogen. 36 healthy volunteers aged 18-29 years, with no evidence of previous infection or vaccination, were inoculated with SARS-CoV-2 virus and quarantined for 14 days. Blood samples (n=374) for RNA sequencing were collected into PAXgene tubes before virus challenge, 6 hours after challenge, daily thereafter for 14 days and on day 28. Mid-turbinate nose swabs (n=96) for RNA sequencing were collected before virus challenge, and on days 1, 3, 5, 7, 10 and 14 after challenge, preserved in RNAprotect. 18 of 36 participants developed a replicative SARS-CoV-2 infection as evidenced by consecutive PCR-positive swabs for the virus. For every participant, blood RNA from selected days were extracted and depleted for genomic DNA and globin mRNA, before cDNA libraries were constructed using KAPA RNA HyperPrep with RiboErase kits. Libraries were sequenced on the Illumina NovaSeq 6000 platform using NovaSeq 6000 S4 Reagent Kits (200 cycles). Nose swab RNA samples were extracted and depleted for genomic DNA before cDNA libraries were constructed using KAPA mRNA HyperPrep Kits. Libraries were sequenced on the Illumina NextSeq platform the using the NextSeq 500/550 High Output Kit (75 cycles).

  Dataset EGAD50000000942

• INSPIRE multi-timepoint cfMeDIP dataset

  This dataset contains raw sequencing data from multi-timepoint cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq) of plasma samples from the INSPIRE study (NCT02644369). Details about the study, including inclusion/exclusion criteria and interventions are available at https://clinicaltrials.gov/study/NCT02644369. Briefly, five cohorts were included, INS-A (head &amp; neck squamous cell carcinoma), INS-B (triple-negative breast cancer), INS-C (high-grade serous ovarian cancer), INS-D (melanoma), and INS-E (mixed solid tumors). All patients in the study were diagnosed with advanced cancer and received treatment with pembrolizumab. Plasma samples were collected at baseline and every 3 cycles until disease progression, death, loss of follow-up, or completion of the study. Plasma samples were first processed for cell-free DNA mutations and remaining samples were then processed by cfMeDIP-seq, prioritizing baseline and post-cycle 3 samples. In total, data from 204 timepoints from 87 distinct patients are deposited.

  Dataset EGAD00001011312