15130 results for "cancer studies using rna-seq"

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• RNA-seq data

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

  Dataset EGAD00001008553

• Genomic Analysis of Nucleic Acid Sequences from Pancreatic Cancer

  Our study aims to perform multi-omics analysis of pancreatic ductal adenocarcinoma. We performed dual single-cell RNA sequencing and whole genome sequencing on fresh tumor specimens and dual bulk RNA-sequencing and 16S amplicon sequencing on frozen tumor specimens to identify human and non-human nucleic acid sequences. Additionally, we identified microbial sequences and found that bacterial taxonomic profiles from the same sample strongly correlated across technologies.

  Study phs003035

• Bulk RNASeq of metastatic colorectal cancer organoids with ATOH1 knockout

  Bulk RNA sequencing was performed on mCRC organoids subjected to cetuximab treatment, comparing ATOH1 knockout and control conditions. The dataset is intended to elucidate the contribution of ATOH1-regulated secretory cell population to cetuximab persistence mechanisms.

  Study EGAS50000001421

• Bulk RNA sequencing data of high-grade serous ovarian cancer samples (set 18-19)

  The dataset contains RNA sequencing data of 66 high-grade serous carcinoma (HGSC) patients sequenced with Novoseq 6000. The 155 samples are fresh frozen tissue samples that have been collected from different tissues and time points before and during treatment. The files provided are paired fastq files.

  Dataset EGAD50000002370

• Extrachromosomal DNA Amplification Contributes to Small Cell Lung Cancer Heterogeneity and is Associated with Worse Outcomes

  Small-cell lung cancer (SCLC) is a very aggressive neuroendocrine lung cancer often associated with oncogenic MYC amplifications, which are known to drive SCLC heterogeneity marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states. The genetic mechanisms of MYC amplification and phenotypic plasticity between cell states is not known. Using data from cell-lines and a few patient-derived samples along with integrated whole-genome sequencing, long-range optical mapping, single-cell DNA sequencing, and fluorescence in situ hybridization, we have attempted to successfully characterize extrachromosomal DNA (ecDNA) as the primary source of MYC amplifications and driver fusions in SCLC.

  Study phs003190

• Extracellular vesicles from TGF-β-activated cancer-associated fibroblasts remodel the tumor microenvironment through EV surface-associated proteins.

  In this study, we explored the function of extracellular vesicles (EVs) derived from non-activated cancer-associated fibroblasts (CAFs) and TGF-β-activated CAFs. We tested these two types of EVs on other cell types in the tumor environment, including cancer cells, CD8+ T cells, and CAFs. We further explored the protein cargo in EVs altered by TGF-β treatment using quantitative proteomics, and selected several highly upregulated proteins for further analysis. We found that the majority of these proteins were presented on the EV surface, and they regulated the target cells in an EV-cell contact dependent manner.

  Study EGAS50000000922

• Single cell atlas of large B-cell lymphoma

  The dataset comprises multi-omics profiles from 232 biopsies, including 217 from patients with large B-cell lymphoma (114 newly diagnosed and 103 relapsed/refractory) and 15 benign control samples. Specifically: single-nucleus RNA sequencing (snRNA-seq) was performed on all 232 biopsies; single-nucleus ATAC sequencing was conducted on 96 biopsies; bulk RNA sequencing was generated for 208 biopsies; whole-exome sequencing (WES) was performed on 174 tumor biopsies with 118 matched germline samples; Low-pass whole-genome sequencing (lpWGS) was performed on 174 tumor biopsies with 41 matched germline samples. All raw data files are provided in FASTQ format.

  Dataset EGAD50000001491

• RNA seq of intestinal biopsies and blood cells from patients with celiac disease and controls

  This study presents RNA sequenced from several intestinal phenotypes such as from patients with damaged intestinal mucosa as well as from those without a damaged intestine but still an autoantibody response to tissue transglutaminase (“potential celiac disease”) or those with treated celiac disease. By combining phenotypes in a much larger number of tissue samples from both peripheral blood and mucosal biopsies, we found highly differentially expressed genes, but also identify genes involved in controlling and triggering celiac disease associated changes. This study identifies molecular mechanisms involved in celiac disease and new possible targets for treatment and for identification of individuals at risk.

  Dataset EGAD50000001597

• High-depth whole genome sequencing of 26 premalignant breast lesions

  Ductal carcinoma in situ (DCIS) is a non-obligate precursor lesion of breast cancer, representing 20-25% of newly diagnosed breast cancer lesions. It is currently not possible to predict which DCIS patients will progress to invasive breast cancer. The samples deposited here are part of a larger study using high-depth whole-genome sequencing (WGS) to investigate the somatic mutation genomic landscape of DCIS. Comprehensive evaluation of DCIS genomes aims to uncover biological insights into breast cancer progression and potential opportunities to stratify DCIS patients for personalised treatment options or active surveillance. Due to specific restrictions imposed by the ethical approval at sample collection, the use of the germline data is restricted to filtering of somatic mutation calls only and cannot be used outside this purpose.

  Study EGAS50000001559

• METABRIC

  Solid tumors are complex tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Normal cell contamination can dilute cancer cell information and tissue architecture is generally not reflected in molecular assays. To address these challenges, we developed a computational approach based on standard Haematoxylin and Eosin-stained sections and demonstrated its power in a discovery cohort of 323 breast tumors and an independent validation cohort of 241 tumors. First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.

  Study EGAS00000000098

• Integrative analysis of whole genome sequencing, RNA sequencing and methylome array of 20 carcinosarcomas.

  Carcinosarcomas of the female genital tract, or GCS, are uncommon but clinically highly aggressive neoplasms with biphasic histology of carcinomatous (C) and sarcomatous (S) elements. These tumors represent a unique model to study the genetic and phenotypic diversity of cancer cells and the link between cellular plasticity and resistance to therapy. The aim of ICGC GCS program is to genomically characterize C and S components of 20 tumors, from endometrium or ovary, through an integrative analysis of whole genome sequencing, RNA sequencing and methylome array.

  Study EGAS00001002271

• Spatial transcriptome analysis for elucidating progression of early lung adenocarcinoma

  In this study, we performed spatial transcriptome analysis of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) to identify molecular events which occur during progression of ealry lung adenocarcinoma. For the analysis, we used cancer tissues which were precisely classified by Noguchi classification. Using the obtained data, we elucidated key molecular changes during tumor cell progression and their associated microenvironment statuses.

  Study JGAS000677

• Plasma whole genome sequencing from patients with stage IV colorectal cancer and microsatellite instability

  This dataset contains plasma WGS data from patients with stage IV colorectal cancer (CRC, n = 16) and healthy individuals (n = 21) used in the Pointy manuscript. Patients with CRC provided written consent and samples were collected as performed as described previously (Clinical-Trials.gov number NCT01876511; Georgiadis et al., 2019, Le et al., 2017). Plasma samples from 21 healthy control individuals were procured through BioIVT. Cell-free DNA (cfDNA) was extracted from plasma using the QIAamp Circulating Nucleic Acid Kit. Libraries were prepared with 5 to 250 ng of cfDNA using the NEBNext DNA Library Prep Kit. Libraries were sequenced on HiSeq2000/2500.

  Dataset EGAD00001008999

• Detection of cancer cell transcriptomes

  Single cell transcriptomes, generated using chromium 10X 3' sequencing, for two tumour types (AT/RT, and Ewing's sarcoma). For each individual, tumour and normal whole genome sequencing was also obtained using Illumina short read sequencing to an average depth of 30X. These data were used to validate the accuracy of a method for identifying cancer cell transcriptomes based on the allelic shift produced by copy number changes.

  Dataset EGAD00001009005

• Multi-region RNA sequencing of tumour regions and tumour-adjacent normal lung tissue

  TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC).

  Study EGAS00001006517

• 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.

  Dataset EGAD00001000653

• 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

• MP2PRT-MNG: Identifying Novel Molecular Markers of Response to Radiotherapy in Meningiomas Using Samples from the RTOG-0539 (NCT00895622)

  The Molecular Profiling to Predict Response to Treatment (MP2PRT) program is part of the NCI's Cancer Moonshot Initiative. The aim of this program is the retrospective characterization and analysis of biospecimens collected from completed NCI-sponsored trials of the National Clinical Trials Network and the NCI Community Oncology Research Program. NRG RTOG (Radiation Therapy Oncology Group)-0539, which was a nonrandomized phase II cooperative group trial in 2009, was selected for inclusion in this program. NRG RTOG-0539 assigned patients with histologically confirmed diagnosis of meningioma to 1 of 3 risk groups based on 2007 WHO grade, resection extent, and recurrence status, and radiotherapy was given per protocol to the intermediate- and high-risk cases. Under the MP2PRT program, DNA methylation profiling, RNA sequencing, and whole exome sequencing were performed on clinical trial cases with available tissue for molecular analyte extraction and subsequent sequencing. Raw molecular data with metadata are available for this study.

  Study phs003707

• Targeted sequencing data on sequential liquid biopsy samples from CSPC patients

  Sequencing data from newly diagnosed high emtastatic burder prostate cancer patients receiving an AR pathway inhibitor (ARPI) or docetaxel. Targeted sequencing data of prostate cancer relevant genes using the PCF-SELECT capture panel. cfDNA from PreADT, baseline, on-treatment (Cycle1 to 6) taken on ARPI or Docetaxel were sequenced as well as patient-matched WBC DNA

  Dataset EGAD50000001960

• Whole exome sequencing of endometriod ovarian cancer tumours

  Whole Exome sequencing data of tumour samples for 112 patients with endometrioid ovarian carcinoma in FASTQ format. Data was derived as summarized below: Library Preparation: Libraries were prepared from each DNA sample using the Illumina TruSeq Exome Library Prep kit (#FC-150-1002) according to the provided protocol using modifications for working with FFPE sourced material. Libraries were quantified using the Qubit 2.0 Fluorometer and the Qubit DNA HS assay (#Q32854) and the size distribution of fragments was assessed using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Library QC: Exome-captured sequencing library pools were quantified using the Qubit 2.0 Fluorometer and the Qubit DNA HS assay (#Q32854) and the size distribution of fragments was assessed using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Fragment size and quantity measurements were used to calculate molarity for each library pool. Sequencing: Sequencing was performed using the NextSeq 500/550 High-Output v2 (150 cycle) Kit (# FC-404-2002) on the NextSeq 550 platform (Illumina Inc, #SY-415-1002).

  Dataset EGAD00001006389

• Brain Mapping by Integrated Neurotechnologies for Disease Studies: Human Brain Aging Imaging Study

  Compare the MRI data with amyloid PET images and cognitive data.

  Study JGAS000277

• Recalibrated whole-exome sequencing alignment data of papillary thyroid cancer of Saudi Arabia

  The dataset consists of samples from papillary thyroid cancer patients. A total of 11 DNA samples from blood/normal and cancer tissue are subjected to whole exome sequencing using Illumina. The fastq files generated were aligned with reference genome ‘hg19’, duplicates were marked, realignment around indels and quality recalibration were performed to produce good quality variants. The recalibrated “.bam” files are included with this dataset.

  Dataset EGAD00001004490

• Targeted sequencing of cell-free DNA and white blood cells from 24 men with metastatic prostate cancer

  This dataset consists of aligned DNA sequencing data in BAM file format from cell-free DNA and white blood cells from 24 men with metastatic prostate cancer. One cell-free DNA sample and one white blood cell sample is available for each patient, resulting in 48 total BAM files in this dataset. The sequencing was performed using a hybrid capture-based targeted panel of 73 prostate cancer driver genes.

  Dataset EGAD00001004486

• Recalibrated whole-exome sequencing alignment files of Saudi papillary thyroid cancer

  The dataset consists of samples from papillary thyroid cancer patients. A total of 181 DNA samples from blood/normal and cancer tissue are subjected to whole exome sequencing using Illumina. The fastq files generated were aligned with reference genome ‘hg19’, duplicates were marked, realignment around indels and quality recalibration were performed to produce good quality variants. The recalibrated “.bam” files are included with this dataset.

  Dataset EGAD00001003358

• scRNA seq and scTCR seq data from 5 melanoma patients

  The Chromium Controller and Chromium X platfor of 10X Genomics were used for single cell partitioning and barcoding. Each cell's transcriptome was barcoded during reverse transcription, pooled cDNA was amplified and Single Cell 5' Gene Expression (GEX), V(D)J and Feature Barcode (FB) Libraries were prepared according to the manufacturer's protocols (CG000330 and CG000331, 10X Genomics). All libraries were quantified and normalized on library QC data generated on the Bioanalyzer system according to manufacturer's protocols (G2938-90321 and G2938-90024, Agilent Technologies). Based on the expected target cell counts, a balanced library sub-pool of samples was compsed for SC5'GEX, V(D)J and FB libraries. Library sub-pools were quantified by qPCR, accodring to the KAPA Library Quantification Kit Illumina(R) Platforms protocol (KR0405, KAPA Biosystems). Based on qPCR results a final sequencing pool was composed. Paired end sequencing was performed on a NovaSeq 6000 Instrument (Illumina) using NovaSeq 6000 Reagent Kits v1.5 100 cycles (cat. no. 20028401, 20028319, 20028316 Illumina), using 28 cycles for Read 1, 10 cycles for Read i7, 10 cycles for Read i5 and 90 cycles for Read 2.

  Dataset EGAD50000001155