11762 results for "cancer rna-seq"

in 33.03 milliseconds.

• Sequencing data for oesophageal and related samples - Ogden et al (WGS, RNA)

  Data supporting: "Widespread reorganisation of the regulatory chromatin landscape facilitates resistance to inhibition of oncogenic ERBB2 signalling" Ogden et al. WGS for tumour and normal samples. RNAseq for tumour samples.

  Dataset EGAD00001007496

• Single-cell ATAC-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell ATAC sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015715

• Single-cell ATAC-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell ATAC sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015719

• UNITO_Molpheno_Closed

  This dataset contains Whole Genome Sequencing, RNA-sequencing and ATAC-sequencing data obtained from PBMCs derived from blood samples of one patient with complex genomic rearrangements and the biological parents. The patient has multiple congenital anomalies and delayed development. Data access is closed.

  Dataset EGAD00001004863

• UMCU Molpheno Closed

  This dataset contains Whole Genome Sequencing and, if available, RNA-sequencing and/or ATAC-sequencing data obtained from PBMCs derived from blood samples of two patients with intellectual disability and/or multiple congenital anomalies and eight parents included in the University Medical Center Utrecht (The Netherlands). Data access is closed.

  Dataset EGAD00001004864

• Raw microarray gene expression data from NPC samples_Cohort 1

  We retrospectively collected 150 non-metastatic, pretreatment, formalin-fixed, paraffin-embedded (FFPE) nasopharyngeal carcinoma (NPC) samples as validation cohort 1. Also, we prospectively collected 32 FFPE samples from NPC patients enrolled in a trial evaluating anti-PD-1 antibody as validation cohort 2. Total RNA was extracted and hybridised to an Affymetrix HTA 2.0 microarray. In this study, we investigated the immune status of the tumour microenvironment (TME) based on gene expression profiles to classify NPC into biologically distinct immune subtypes, and clarify their associations with prognosis and immunotherapy response.

  Dataset EGAD00001006273

• The data access committee for Single molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer

  Dac EGAC00001003253

• Expression-Based Subtypes Define Pathologic Response to Neoadjuvant Immune-Checkpoint Inhibitors in Muscle-Invasive Bladder Cancer

  Dac EGAC00001002276

• EGAD00010000474

  blood-based gene expression from breast cancer cases and age-matched controls in case-control serie 2 (CC2)

  Dataset EGAD00010000474

• EGAD00010000476

  blood-based gene expression from breast cancer cases and age-matched controls in case-control serie 1 (CC1)

  Dataset EGAD00010000476

• EGAD00010000478

  blood-based gene expression from breast cancer cases and age-matched controls in case-control serie 3 (CC3)

  Dataset EGAD00010000478

• EGAD00010000498

  Affymetrix SNP6.0 genotype data for prostate cancer patients

  Dataset EGAD00010000498

• OCAC OncoArray data

  Germline genotype data on 56,479 ovarian cancer cases and controls

  Dataset EGAD00010001192

• Transcriptome sequencing of Gingivo-buccal Cancer : ICGC-India Project_Batch05

  Dataset EGAD00001004430

• Blood dataset - Case

  blood-based gene expression from breast cancer cases

  Dataset EGAD00010001063

• EPIC

  EPIC array data from 72 tumor samples with muscle invasive bladder cancer.

  Dataset EGAD00010001921

• SAFIR02_Agilent

  Genomics to select patients with metastatic breast cancer for targeted therapy (microarray_agilent)

  Dataset EGAD00010002243

• Somatic mutations in healthy and leukemic blood progenitors reveal evolutionary mechanisms underlying childhood leukemia and differential patient outcome

  Study EGAS00001004593

• Gene expression profiles of disseminated breast cancer cells

  Study EGAS00001004597

• Novel de novo pathogenic variant in the GNAI1 as a cause of severe disorders of intellectual development with autistic features

  Study EGAS00001005355

• Single-cell multi-omics of human clonal hematopoiesis reveals that DNMT3A R882 mutations perturb early progenitor states through selective hypomethylation.

  Study EGAS00001006364

• Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens

  Study EGAS00001007404

• Targeted gene panel sequencing of leiomyosarcoma

  We conducted MSK-IMPACT sequencing of leiomyosarcoma.

  Study EGAS50000000595

• Pancreatic Cancer OICR

  ICGC DCC Release 24, PACA-CA Exome sequence

  Dataset EGAD00001003264

• Capture Hi-C

  Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci.

  Dataset EGAD00001001243

• Exome reads

  Cancer exomes consisting of FASTQ paired-end reads from ovary samples

  Dataset EGAD00001003916

• Cell Line Dataset

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001349

• Genomic Analyses in Neoadjuvant Immunotherapy-Treated Head and Neck Cancers

  This is a phase 2 multicenter interventional trial involving the preoperative (neoadjuvant) administration of pembrolizumab, an anti-programmed cell death protein-1 (anti-PD-1) agent, to patients with pathologically confirmed, surgically resectable advanced head and neck squamous cell carcinoma (HNSCC). Participating centers were Dana-Farber/Harvard Cancer Center, Alvin J. Siteman Cancer Center, Washington University and Memorial Sloan Kettering Cancer Center. The trial (NCT02296684) consisted of two cohorts: Cohort 1 enrolled and treated patients received one dose of pembrolizumab prior to surgical resection with curative intent, and adjuvant pembrolizumab was planned for patients with high risk pathological features (positive margins and/or extranodal extension) in the surgical specimen. Cohort 2 enrolled and treated patients received 2 doses of pembrolizumab prior to surgical resection with curative intent. Adjuvant treatment was not dictated by the trial protocol for Cohort 2. The primary endpoint was rate of at least 50% pathological tumor response (pTR), defined as the presence of tumor necrosis/keratinous debris in the tumor bed following neoadjuvant pembrolizumab. pTR was graded as follows: pTR0 (<10%), pTR1 (10-49%) and pTR2 (≥50%). Baseline, pre-treatment tumor biopsy and post-neoadjuvant pembrolizumab surgically resected tumor specimens were dissociated, and CD3+CD45+ cells were analyzed for T cell receptor (TCR) expression and 5' gene expression analysis using single cell sequencing technologies for a subset of patients.

  Study phs002864

• Genomic Landscape of Human Skin at a Single-Cell Resolution

  Every cell in the human body has a unique set of somatic mutations, yet it remains difficult to comprehensively genotype an individual cell. Here, we developed solutions to overcome this obstacle in the context of normal human skin, thus offering the first glimpse into the genomic landscapes of melanocytes at single-cell resolution. We comprehensively genotyped 133 melanocytes from 19 sites across 6 donors. As expected, sun-shielded melanocytes had fewer mutations than sun-exposed melanocytes. However, within sun-exposed sites, melanocytes on chronically sun-exposed skin (e.g. the face) displayed a lower mutation burden than melanocytes on intermittently sun-exposed skin (e.g. the back). Melanocytes from donors with a history of skin cancer had higher mutation burdens than melanocytes from donors without skin cancer, implying that the mutation burden of normal skin can be harnessed to measure cumulative sun damage and skin cancer risk. Moreover, melanocytes from healthy skin commonly harbor pathogenic mutations, likely explaining the origins of the melanomas that arise in the absence of a pre-existing nevus. Phylogenetic analyses identified groups of related melanocytes, suggesting that melanocytes spread throughout skin as fields of clonally related cells, invisible to the naked eye. Overall, our study offers an unprecedented view into the genomic landscapes of individual melanocytes, revealing key insights into the causes and origins of melanoma.

  Study phs001979

• A comprehensive DNA methylation landscape of human and mouse cell lines derived from hematological malignancies

  Human cancer cell lines constitute useful models to study the primary disease and many relevant findings in tumor biology have been originated from them since the first immortalized cell line (HeLa) was obtained. The easy experimental intervention, the purity of the transformed cells and their versatility to undergo high-throughput screenings represent advantageous features of the established cancer cell lines. In recent years, major efforts have characterized in detail the multiomics make-up of hundreds of cancer cell lines and studied their association with sensitivity to anticancer drugs. Most of these studies have been genetic-centric and have not characterized in detail the epigenetic profiles underlying the characteristics of these cells or their impact on the efficacy of antitumoral compounds. In this regard, a past version of a DNA methylation microarray or a less accessible and more time-consuming such as reduced representation bisulfite sequencing have been used in those attempts to interrogate the epigenetic setting. Herein, we have obtained the DNA methylation profiles of 210 cell lines derived from hematological malignancies utilizing comprehensive DNA methylation microarrays that interrogates more than 850,000 and 285,000 CpG sites from human and mouse genomes, respectively. Importantly, we also provide a pharmacoepigenetic example of the potential use of this resource by showing how DNA methylation can predict response to nucleoside analogues.

  Study EGAS50000000627

• Somatic rearrangements causing oncogenic ectodomain deletions of FGFR1 in squamous cell lung cancer

  The discovery of frequent 8p11-p12 amplifications in squamous cell lung cancer (SQLC) has fueled hopes that FGFR1, located inside this amplicon, might be a therapeutic target. In a clinical trial, only 11% of patients with 8p11 amplification (detected by FISH) responded to FGFR kinase inhibitor treatment. To understand the mechanism of FGFR1 dependency, we performed deep genomic characterization of 52 SQLCs with 8p11-p12 amplification, including 10 tumors obtained from patients who had been treated with FGFR inhibitors. We discovered somatically altered variants of FGFR1 with deletion of exons 1-8 that resulted from intragenic tail-to-tail rearrangements. These ectodomain-deficient FGFR1 variants (ΔEC-FGFR1) were expressed in the affected tumors and were tumorigenic in both in vitro and in vivo models of lung cancer. Mechanistically, breakage-fusion-bridges were the source of 8p11-p12 amplification, resulting from frequent head-to-head and tail-to-tail rearrangements. Generally, tail-to-tail rearrangements within or in close proximity upstream of FGFR1 were associated with FGFR1 dependency. Thus, the genomic events shaping the architecture of the 8p11-p12 amplicon provide a mechanistic explanation for the emergence of FGFR1-driven SQLC. Specifically, we believe that FGFR1 ectodomain-deficient and FGFR1-centered amplifications caused by tail-to-tail rearrangements are a novel somatic genomic event that might be predictive of therapeutically relevant FGFR1 dependency.

  Study EGAS50000001368

• Neoadjuvant immune checkpoint blockade in mismatch-repair proficient colon cancers

  Immune checkpoint blockade has led to paradigm shifts in the treatment of various tumour types, yet limited efficacy has been observed in patients with metastatic mismatch-repair proficient (pMMR) colorectal cancer. Here we report an in-depth analysis of patients with early-stage pMMR colon cancer from the phase II NICHE study (ClinicalTrials.gov: NCT03026140). A total of 31 patients received neoadjuvant treatment of nivolumab plus ipilimumab followed by surgery. The response rate was 26% and included six patients with a major pathological response (≤10% residual viable tumour). One patient with an ongoing clinical complete response did not undergo surgery. Responses were observed despite a low tumour mutational burden in all tumours, while chromosomal genomic instability scores were significantly higher in responders compared to non-responders. Responding tumours were more likely to be TP53 mutant, KRASG12 wildtype and had significantly higher baseline expression of proliferation signatures and TCF1. Imaging mass cytometry also revealed a higher percentage of Ki-67+ cancer and Ki-67+ CD8+ T cells in responders compared to non-responders. Meanwhile, in non-responders, we observed an enrichment for an inflamed-fibrotic microenvironment associated with TGF-β expression, hinting towards possible resistance mechanisms. These results provide a comprehensive analysis of response to neoadjuvant ICB in early-stage pMMR colon cancers and identify potential biomarkers for patient selection.

  Study EGAS50000000856

• Machine Learning Signal Enrichment for Ultrasensitive Plasma Tumor Burden Monitoring

  In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by 300X compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables de novo mutation calling in melanoma and small cell lung cancer (SCLC) without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition (ICI).

  Study EGAS00001007451

• Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

  In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by 300X compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables de novo mutation calling in melanoma and small cell lung cancer (SCLC) without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition (ICI).

  Study EGAS00001007545

• New England-Based Case-Control Study of Ovarian Cancer

  The case-control data provided to dbGaP were collected under NCI grant number CA54419 which had 3 enrollment phases: I (1992-1997), II (1998-2002) and III (2003-2008). Altogether, CA54419 funded 15 years of data and specimen collection, resulting in one of the longest running population based case-control studies of ovarian cancer. The study goal was to examine reproductive factors, lifestyle exposures, and genetic background in relation to ovarian cancer risk. The project recruited cases from Eastern Massachusetts and New Hampshire, and became known as the New England-based Case-Control study (NECC) of ovarian cancer. 3957 ovarian cancer cases diagnosed at ages 18-80 and residing in Eastern Massachusetts and New Hampshire were identified through tumor boards and registries. Of these, 3083 (78%) were eligible and among those eligible, 2203 (71%) were enrolled. After excluding 127 non-epithelial and 35 mixed mesodermal tumors, 2041 cases with epithelial tumors of ovarian, primary peritoneal, and fallopian tube origin, including borderline malignancies were included. On average, cases were interviewed 10 months after their diagnosis. A pathologist reviewed pathology reports and recorded histologic subtype, grade, and stage. Mixed ovarian cancers described as predominantly one type were classified as that type. Undifferentiated, transitional cell tumors, or mixed serous/transitional cell tumors were counted as serous. Other mixed epithelial, malignant Brenner, and unspecified epithelial tumors were classified as other. 2100 controls were identified through random digit dialing (RDD), driver-license lists, and town-resident lists. During the first funding phase from 1992 to 1997, 420 (72%) controls identified through RDD and 102 (51%) through town-resident lists agreed to participate. From 1998 to 2008, only town-resident lists were used to identify potential controls. 4366 controls were identified, of whom 1426 (33%) were ineligible if they had died, moved, or were seriously ill or if they did not have a working telephone, speak English, or have at least one ovary. Of eligible controls, 1362 (46%) declined to participate by phone or via 'opt-out' postcard and 1578 (54%) were enrolled. Controls were frequency matched to cases by 5-year age groups and region of residence. In all phases, after written informed consent, demographic information, reproductive and medical history, and habits were assessed by in-person or telephone interview (in a few instances). All of the questions were framed with respect to a reference date defined as 1 year before the diagnosis date for women in the case group and the date of interview for those in the control group. This study was approved by institutional review boards at Dana Farber Harvard Cancer Center and Dartmouth Medical Center.

  Study phs001034

• Ludwig Center for Cancer Genetics and Therapeutics at Johns Hopkins

  Dac EGAC00001000507

• Data access committee for study - The subclonal architecture of metastatic breast cancer: Results from a prospective community-based rapid autopsy program "CASCADE".

  Dac EGAC00001000574

• EXOME_ARRAY_ANALYSIS

  EXOME ARRAY ANALYSIS OF ADVERSE REACTIONS TO FLUOROPYRIMIDINE-BASED THERAPY FOR GASTROINTESTINAL CANCER

  Dataset EGAD00010001499

• Whole Genome Sequencing of Gingivo-buccal Cancer: ICGC-India Project_YR03

  Dataset EGAD00001004478

• methylation_tnbc

  Raw methylation data from triple negative breast cancer.

  Dataset EGAD00010002077

• OICR-DAC, Ontario Institute for Cancer Research

  Dac EGAC00001000591

• OICR-DAC, Ontario Institute for Cancer Research

  Dac EGAC00001000710

• Targeted analysis of cell-free circulating tumor DNA is suitable for early relapse and actionable target detection in patients with neuroblastoma

  Study EGAS00001006027

• Neurodegenerative_TGS

  An investigation of clonal haematopoiesis in patients with neurodegenerative disease.

  Study EGAS00001002431

• Exome_sequencing_of_EBV_driven_lymphoma

  Exome sequencing of a case of lethal EBV-driven LPD

  Study EGAS00001001021

• BC WGS Dataset 3

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001351

• BC WGS Dataset 4

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001353

• Sharc-prostate

  Low coverage nanopore sequencing of prostate cancer tumors

  Dataset EGAD00001005473

• Liquid biopsy-based minimal residual disease monitoring for early risk stratification and decision-making in advanced non-small cell lung cancer

  Dac EGAC50000000832

• EPICC: Evolutionary Predictions in Colorectal Cancer

  ATAC-seq profiling bam files from colorectal carcinoma and adenoma.

  Dataset EGAD00001008140