15041 results for "cancer studies using rna-seq"

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• Comprehensive Genomic Data Deposition for Pancreatic Cancer Precision Medicine Studies: Clinical Trials NCT02451982 and NCT02648282

  This submission includes specimens from 89 pancreatic cancer patients at Johns Hopkins Hospital, comprising 41 surgically resectable and 48 locally advanced pancreatic cancer (LAPC) cases. These samples originate from two clinical studies, NCT02451982 and NCT02648282, following the Johns Hopkins Medical Institution Institutional Review Board-approved registry protocol at the Pancreatic Cancer Precision Medicine Center of Excellence Program. Written consent for specimen use, obtained from participants in the original studies, covers additional research, including this one. Biopsy specimens collected through endoscopic ultrasound-guided fine needle core biopsies or surgically resected tumors are included. NCT02451982 is a platform trial evaluating immunotherapy combinations for surgically resectable pancreatic ductal adenocarcinoma in the neo-adjuvant and adjuvant settings. It investigates the GVAX pancreatic cancer vaccine with immune-boosting cyclophosphamide, exploring immune cell trafficking and the impact of PD-1 blocking. NCT02648282 focuses on the efficacy and safety of combining cyclophosphamide, pembrolizumab, GVAX, and stereotactic body radiotherapy (SBRT) in locally advanced pancreatic cancer, with an emphasis on anti-tumor activity. Both studies provide valuable insights into innovative pancreatic cancer treatment approaches.

  Study phs003600

• RNA Sequencing Analysis of Patient-Derived Xenograft Tissue PIM-084 Treated with L-NMMA+Alpelisib vs Vehicle Control

  This dataset contains RNA-seq analysis of vehicle control compared to L-NMMA+alpelisib treated metaplastic breast cancer PDX PIM084 tumors (RNA sequencing and analysis performed by Novogene). The submitted data contains the gene ID, gene name, normalized read counts from all samples from control group and combination treatment group, log2-fold change derived from DESeq2 R/EdgeR package, p-value, adjusted p-value, read counts from each group, and gene FPKM from the each sample in the group. Due to patient privacy, we are submitting only the processed and desensitized RNA sequencing analysis to this repository.

  Study phs003814

• Transdisciplinary Research Into Cancer of the Lung (TRICL) - Exome Plus Targeted Sequencing

  The study was conducted under the auspices of the Transdisciplinary Research In Cancer of the Lung (TRICL) Research Team, which is a part of the Genetic Associations and MEchanisms in ONcology (GAME-ON) consortium, and associated with the International Lung Cancer Consortium (ILCCO). Ethics All participants provided written informed consent. All studies were reviewed and approved by institutional ethics review committees at the involved institutions. Sequencing data are derived from four sub-studies. The sub-studies that contributed include Harvard, Liverpool, Toronto, and IARC. The IARC and Toronto studies are described above. A description of the Harvard and Liverpool studies is provided below. Liverpool Lung Project: The Liverpool Lung Project (LLP)1 is a case control and cohort study, which has over 11,500 individuals, with detailed epidemiological, clinical and outcome data with associated specimens (i.e. tumour tissue, blood, plasma, sputum, bronchial lavage, EBUS and oral brushings). The participants have completed a detailed lifestyle questionnaire and updated data on clinical outcome and hospital events are collected through the Office of National Statistics, Cancer Registry and from Health Episode Statistics. The project is registered on the UK National Institute for Health Research (NIHR) lung cancer portfolio and has all the required ethical approvals and sponsorship arrangements in place. The LLP has detailed standard operating procedures (SOP) for all aspects of the recruitment, data, specimen collection as well as the data storage. The LLP Cohort study has 8,224 participants with blood and 7,761 with plasma samples. The LLP case-control samples have been incorporated into in a large number of international GWAS and molecular studies 2,3, methylation 4-7, microRNA 8and next generation studies 9-11, resulting in high ranking publications, as well as forming the basis for the LLP risk prediction model 12-14 which has been utilised in the UK lung cancer screening trial (UKLS) 15-17 Patient and control DNAs were derived from EDTA-venous blood samples. Harvard Samples. David Christiani at the Harvard University School of Public Health has been directing research studies to investigate etiological factors influencing lung cancer development since 1983 and has amassed a collection of 2000 controls and 5055 lung cancer cases. He has been actively collecting and storing snap frozen tumor samples since 1992. Around 1500 tumor samples have been collected and the average wet tumor yield is about 30 grams of tumor, of which 631 cases have completely annotated clinical and survival information. Pathology confirmation is provided by two pathologists. At the time of surgery, a minimum of 30 grams of wet lung tumor tissue and 30 grams of non-involved tissue from the same lobe is sectioned, flash frozen and sent to Dr. Christiani's lab for logging and storage. A blood sample for DNA and serum is collected. A structured interview by trained research staff is conducted on each case, and clinical outcomes and treatments is extracted and entered into the molecular epidemiology data base at Harvard. Fresh frozen samples have been collected from 1451 lung cancer and are available for study. Samples from this collaborative study have played key roles in major studies, including the initial finding describing EGFR mutations in lung cancer 22. Participants in this study are patients, > 18 years of age, with newly diagnosed histologically confirmed lung cancer. Samples that are included in the analysis have the following histologies: Adenocarcinoma: 8140/3, 8250/3, 8260/3, 8310/3, 8480/3 8560/3; LCC: 8012/3, 8031/3; squamous carcinoma: 8070/3, 8071/3, 8072/3, 8074/3; and other NSCLC: 8010/3, 8020/3, 8021/3, 8032/3, 8230/3. The Toronto Study: The Toronto study was conducted in the Great Toronto Area between 1997 and 2014. Cases were recruited at the hospitals in the network of University of Toronto and Lunenfeld- Tanenbaum Research Institute. At the time of recruitment in the clinical setting, provisional diagnoses of lung carcinoma were first assigned based on clinical criteria. Diagnoses for all cases included were histologically confirmed by the reference pathologist who is a specialist in pulmonary pathology, based on review of pathology reports from surgery, biopsy or cytology samples in 100% of cases. Diagnostic classification was done initially according to ICD-9, ICD-10, and ICD for oncology-2, and subsequently converted to ICD-O-3. Tumors were grouped into the major categories included in this analysis according to primary cancer type based on the ICD-3 definitions. Controls were randomly selected from individual visiting family medicine clinics and Ministry of Finance Municipal Tax Tapes. All subjects were interviewed using a standard questionnaire and information on lifestyle risk factors, occupational history, medical and family history was collected. Blood samples were collected from more than 85% of the subjects. IARC: The IARC data are derived from case-control studies conducted in Russia and include samples that have available tissue samples. Patient and control DNAs were derived from EDTA-venous blood samples. The lung cancer patients were classified according to ICD-O-3; SQ: 8070/3, 8071/3, 8072/3, 8074/3; AD: 8140/3, 8250/3, 8260/3, 8310/3, 8480/3, 8560/3, 8251/3, 8490/3, 8570/3, 8574/3; with tumous with overlapping histologies classified as mixed. The Lung Cancer Transdisciplinary Research Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" section of this top-level study page phs000876 Lung Cancer Transdisciplinary Research Cohort. phs000877 Meta Analysis phs000878 CIDR Lung Cancer phs001681 Affy Axiom Array

  Study phs000876

• TenK10K Phase 1:  Single Cell

  Understanding the genetic basis of gene expression can shed light on the regulatory mechanisms underlying complex traits and diseases. Single-cell resolved measures of RNA levels and single-cell expression quantitative trait loci (sc-eQTLs) have revealed genetic regulation that drives sub-tissue cell states and types across diverse human tissues. Here, we describe the first phase of TenK10K, the largest- to-date dataset of matched whole-genome sequencing (WGS) and single-cell RNA-sequencing (scRNA-seq). We leverage scRNA-seq data from over 5 million cells across 28 immune cell types and matched WGS from 1,925 individuals. This provides power to detect associations between rare and low-frequency genetic variants that have largely been uncharacterised in their impact on cell-specific gene expression. We map the effects of both common and rare variants in a cell type specific manner using SAIGE-QTL.

  Study EGAS50000001653

• FOXM1 is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer that is detectable using metabolic imaging (RNA-seq data)

  PIK3CA, encoding the PI3Kα isoform, is the most frequently mutated oncogene in estrogen receptor-positive breast cancer. Isoform-selective PI3K inhibitors are used clinically but intrinsic and acquired resistance limits their utility. Improved selection of patients that will benefit from these drugs requires novel predictive biomarkers. We show here that persistent FOXM1 expression following drug treatment is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer. Since the transcription factor drives expression of lactate dehydrogenase (LDH) but not hexokinase 2 (HK-II), changes in FOXM1 expression can be detected using magnetic resonance imaging of LDH-catalyzed hyperpolarized 13C label exchange between pyruvate and lactate but not by PET measurements of HK-II-mediated trapping of the glucose analog 2-deoxy-2-[18F]fluorodeoxyglucose.

  Study EGAS00001004452

• MethylBERT enables read-level DNA methylation pattern identification and tumour deconvolution using a Transformer-based model

  DNA methylation (DNAm) is a key epigenetic mark that shows profound alterations in cancer. Read-level methylomes enable more in-depth DNAm analysis due to the broad coverage and preservation of rare cell-type signals, compared to array-based data such as 450K/EPIC array. Here, we propose MethylBERT, a novel Transformer-based model for read-level methylation pattern classification. MethylBERT identifies tumour-derived sequence reads based on their methylation patterns and genomic sequence and the method estimates tumour cell fractions within bulk samples. In our evaluation, MethylBERT outperforms existing deconvolution methods and demonstrates high accuracy regardless of methylation pattern complexity, read length and read coverage. Moreover, we show its applicability to cell-type deconvolution as well as non-invasive early cancer diagnostics using liquid biopsy samples. MethylBERT represents a significant advancement in read-level methylome analysis and enables accurate tumour purity estimation. The broad applicability of MethylBERT will enhance studies on both tumour and non-cancerous bulk methylomes.

  Study EGAS50000000806

• SYSCOL Human colorectal cancer project pilot RNA-seq and germline genotyping

  Genotyping by array and Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001000880

• RNA-Seq of kidney cancer organoid biobank

  Dataset EGAD00001005318

• RNA-seq data from the MESOMICS project (French project of molecular characterization of malignant pleural mesothelioma)

  The dataset includes fastq files from 109 tumor samples as well as RNA-seq gene expression R data, RNA-seq transcript expression R data, RNA-seq gene counts matrix, RNA-seq transcript counts matrix, RNA-seq gene FPKM matrix RNA-seq transcript FPKM matrix for the 109 samples. The sequencing was done with HiSeq 2000 instrument.

  Dataset EGAD00001007024

• RNA-Seq profiles from the CheckMate-649 Clinical Trial

  This dataset contains RNA sequencing (RNAseq) data of 814 patients from the CheckMate 649 clinical trial whose ICF allows data deposition into a public repository. Gene expression profiling was performed retrospectively using RNAseq on a subset of baseline tumor samples. Paired-end FASTQ files were processed on Seven Bridges platform (Seven Bridges Genomics).

  Dataset EGAD50000001105

• Characterizing Immune Profiles in Extrapulmonary Tuberculosis via RNA Sequencing

  This dataset provides insight into the immune response in extrapulmonary tuberculosis (EPTB), using bulk RNA-seq data to analyze different disease severities. Hierarchical clustering identified three distinct levels of severity, revealing disease progression driven by interferon and IL-1β-mediated signaling. Additionally, the dataset helped develop a diagnostic gene expression signature for both EPTB and pulmonary TB.

  Dataset EGAD50000000943

• SCLC study MGH - RNAseq dataset

  RNA-seq analyses were performed on cDNA libraries prepared from PolyA+ RNA using the Illumina TruSeq protocol for mRNA. The final libraries were sequenced with a paired-end 2×75 bp protocol aiming at 8.5 Gb per sample for a 30x mean coverage of the annotated transcriptome. All sequencing reactions were conducted on an Illumina HiSeq instrument (Illumina, San Diego, CA, USA).

  Dataset EGAD00001003969

• LongRNA_Monocytes

  This dataset consists of bulk RNA sequencing profiles generated from monocytes isolated from peripheral blood mononuclear cells (PBMCs) collected from 315 patients enrolled in the ImmunAID consortium. Each sample corresponds to a unique patient, providing a resource to investigate transcriptional variation in circulating monocytes across the cohort. PBMCs were isolated from whole blood, and monocytes were subsequently enriched using standard immunological protocols. Total RNA was extracted and libraries were prepared using the SMART-Seq mRNA Library Preparation Kit with Unique Molecular Identifiers (UMIs). Sequencing was performed on an Illumina NovaSeq 6000 platform following manufacturer-recommended protocols.

  Dataset EGAD50000002341

• snRNA-seq schizophrenia control Prefrontal cortex

  In this study, we performed RNA sequencing of single nuclei from the prefrontal cortex of donors with and without schizophrenia using the 10x Genomics Chromium Single Cell protocol. The project rationale is to determine cell type specific transcriptomic changes between the two groups and linking them to the risk for schizophrenia.

  Study EGAS50000001703

• Hyperdiploid Acute Lymphoblastic Leukemia RNA-Seq

  Few genetic drivers of children with hyperdiploid precursor B-cell acute lymphoblastic leukemia (ALL) have been identified to date. In an effort to detect novel genomic rearrangements that could be promoting leukemogenesis in this subset of patients, we sequenced ribosomal RNA-depleted transcriptomes isolated from 5 hyperdiploid acute lymphoblastic leukemia samples. In addition to looking for novel genomic rearrangements, we also sought to identify transcripts that could be created as a result of a novel posttranscriptional process. Surprisingly, we identified transcripts created as the result of exon circularization. The results of our studies are present in the paper "Circular RNAs Are the Predominant Transcript Isoform from Hundreds of Human Genes in Diverse Cell Types" published in PLoS ONE (PMID: 22319583).

  Study phs000522

• Genomic and Transcriptomic Data from Advanced Rare Cancer Patients Treated with Pembrolizumab

  This dataset comprises whole exome sequencing data and RNA-seq data of advanced rare cancer patients.

  Dataset EGAD50000002182

• Proteogenomic Landscape of Curable Prostate Cancer

  Aligned RNA-seq sequences in this dataset are from the Proteogenomic Landscape of Curable Prostate Cancer study

  Dataset EGAD00001004875

• Raw RNA-seq data for clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial

  ABACUS is a single arm phase 2 study that investigated 2 cycles of atezolizumab (1200mg Q3) prior to cystectomy in 95 patients with muscle invasive transitional cell cancer (T2-4N0M0). Pathological complete response (pCR) occurring in ≥20% of patients was the primary endpoint. Biomarker analysis on sequential tissue was a co-primary endpoint. This dataset includes the raw RNA-seq data.

  Dataset EGAD00001006205

• Dataset for synovial_sarcoma-RNA

  Rare cancer sequencing data of 23 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008844

• The Normal Human Tissue Sequencing Project: Non-Diseased, Non-Malignant Tissues

  The Normal Human Tissue Sequencing Project provides RNA expression and DNA methylation data from normal (non-diseased) human tissues. This data set from a variety of source tissue is a valuable resource for human disease studies.

  Study phs000819

• Comprehensive molecular and clinicopathological profiling of desmoid tumors

  Previous studies have not clearly identified a prognostic factor for desmoid tumors (DT). Whole-exome sequencing and/or RNA sequencing were performed in 64 cases of DT to investigate the molecular profiles in combination with the clinicopathological characteristics.

  Study JGAS000270

• BCAC TIIC Data

  Data on spatially-resolved tissue infiltrating immune cells (TIICs) from 17,265 breast cancer patients of European descent, from studies participating in the Breast Cancer Association Consortium, together with linked clinical data.

  Study EGAS50000001477

• Disease-Linked Regulatory DNA Variants and Homeostatic Transcription Factors in Epidermis

  The role of non-coding variants in polygenic skin disease and their connection to homeostatic transcription factors (TFs) in epidermis was investigated. Homeostatic TFs were identified by perturb-seq in differentiating keratinocytes, using either 10x or split-pool approaches to sequencing, with follow-up validation by siRNA and RNA-seq. TF binding events were profiled by population sampling Cleavage Under Targets & Release Using Nuclease (CUT&RUN), in which a random population of neonatal foreskin keratinocytes were assayed by CUT&RUN for homeostatic TFs under progenitor growth and differentiation conditions. Heterozygous alleles were identified from the TF binding data and examined for allele specific binding events (ASBs). ASBs were then compared with non-coding variants linked to skin disease. Variants with potentially pathogenic effects were identified. Promoters and high TF occupancy genomic regions were found to be "buffered" against variants causing ASBs.

  Study phs003977

• ICGC Breast Cancer Project, RNA seq

  A comprehensive characterisation and analysis of human breast cancers through genome-wide approaches through transcriptomics.

  Dataset EGAD00001001323

• Comparison of good PDX responders and bad PDX responders

  RNA-Seq raw data of untreated breast cancer xenografts known as good or bad acetalax responders.

  Dataset EGAD50000000876