14976 results for "cancer studies using rna-seq"

in 17.84 milliseconds.

• MAITS in HCC

  Hepatocellular Carcinoma (HCC) is a leading cause of cancer-related death and can be considered a prototype of inflammation-derived cancer arising from chronic liver injury. The cell composition of the HCC tumor immune microenvironment (TiME) has a major impact on cancer biology as the TiME can have divergent capacities on tumor initiation, progress, and response to therapy. Recent development of multi-omics and single-cell technologies help us to comprehensively quantify the cellular heterogeneity and spatial organization of the TiME and to further our understanding of antitumor immunity. We investigated the cellular composition of liver cancer patient samples (n=8) using single-cell RNA-seq. For this purpose, mononuclear cells were isolated from human liver cancer samples. Three locations within the tumor-bearing liver were used: adjacent liver, rim and tumor core. Then, cells were FACS sorted (either CD45+ cells or MAIT cells) and subjected to 10X Chromium-based scRNA-seq. We investigated immune cell changes and the cellular heterogeneity of the HCC TiME with a focus on MAIT cells. Annotated H5 files are provided. Clinical metadata including TMN stage, sex, gender, ethnicity, pretreatment, and histopathological reports are available for all patient samples. Further details on the study can be obtained in our paper once it’s published.A complementary dataset derived from ultrahighplex CO-Detection by indEXing (CODEX) from paired patient samples can be accessed through The Cancer Imaging Archives (TCIA) under DOI: https://doi.org/10.7937/bh0r-y074.

  Study phs003279

• mUM patient biopsies pre and post treatment with tebentafusp

  The dataset includes 141 RNA-seq samples form mUM patietns. Tumour biopsies were obtained from uveal melanoma patients pre and post treatment with tebentafusp. Biopsies, which were either snap frozen or put in RNA later, were analysed by bulk RNA sequencing (50 million reads per sample) using the Illumina NovaSeq system.

  Dataset EGAD50000001258

• Bulk RNA sequencing of Singapore colorectal cancer patients (SG-BULK)

  RNA-seq libraries were prepared using the KAPA Stranded RNA-Seq Kit with RiboErase (Kapa Biosystems, Wilmington, MA) and sequenced to a target depth of 200-M reads on the Illumina HiSeq platform (Illumina, San Diego, CA).

  Dataset EGAD00001008512

• The evolutionary dynamics of human colorectal cancer using single-gland spatial multi-omic profiling of DNA, RNA and chromatin.

  Study EGAS00001006088

• The evolutionary dynamics of human colorectal cancer using single-gland spatial multi-omic profiling of DNA, RNA and chromatin.

  Study EGAS00001006122

• The evolutionary dynamics of human colorectal cancer using single-gland spatial multi-omic profiling of DNA, RNA and chromatin.

  Study EGAS00001005827

• Single Cell and Spatial Transcriptomic Profiling of Haemophilus ducreyi Infection

  We investigated the immune response in end-point pustules formed by Haemophilus ducreyi in volunteers using an experimental infection model. Six volunteers were infected in the skin of the upper arm at three sites with 41-85 CFU of H. ducreyi loaded on an allergy testing device; a sham-inoculated (e.g., wound) site was also performed. Five volunteers (4 men and 1 woman; 2 Asians and 3 Whites; aged 27-46 years) formed pustules 6-8 days post infection; 1 volunteer spontaneously resolved all sites. If a volunteer formed pustules, biopsies were performed to collect their pustule and wound sites. Biopsy pairs for all 5 pustule formers were subjected to single cell (sc) RNA-seq. Sequencing libraries were prepared using the 10X Genomics Single-cell 3' RNA-seq kit. Spatial transcriptomics was performed on biopsy pairs from 4 of the 5 pustule formers. Ten micron sections were placed on 10X Genomics Visium slides for fresh-frozen tissue, and libraries were prepared with the 10X Genomics Visium spatial 3' gene expression kit. All samples were sequenced on an Illumina NovaSeq 6000. Count tables were produced with CellRanger and SpaceRanger, respectively, and Seurat was used to analyze the data. We identified 13 major cell types by scRNA-seq; the proportion of some immune cell subsets was increased in pustules compared to wound sites. We also localized the major cell types within the tissue by using the scRNA-seq data to deconvolve the spatial transcriptomic data. The FASTQ files and count tables from CellRanger and SpaceRanger are provided.

  Study phs003754

• Single cell RNA-sequencing of tissues from NSCLC patients treated with chemoimmunotherapy

  The goal of this study is to characterize immune cell populations by single cell RNA-sequencing (scRNA-seq) in tumor and uninvolved normal tissues from non-small cell lung cancer (NSCLC) patients with resectable non-small cell lung cancer and who received neoadjuvant chemoimmunotherapy. scRNA-seq was performed on seven pairs of tumor and normal tissues as well as one lymph node (LN) sample. Data set includes pair-end fastq files for single cell RNA sequencing of 7 neo-immuno patients. (Total 15 samples and 50 runs).

  Dataset EGAD00001009778

• Whole Transcriptome Sequencing

  RNA-seq data of 15 snap-frozen tissue of peritoneal mesothelioma. The strand specific RNA library prepared using TruSeq (Illumina) and pair-end sequencing performed in Illumina HiSeq 4000. The datasets contains paired fastq files for each of 15 tumor samples.

  Dataset EGAD00001004504

• Processing of tissue and cfDNA samples of CRC patients using Active-seq

  In this study, we have developed a novel technology called Active-Seq (Azide Click Tagging for In Vitro Epigenomic sequencing), a base conversion-free technology used to profile the DNA methylation status of a sample, based on the isolation of DNA containing unmodified CpG sites using a mutated bacterial methyltransferase enzyme and a synthetically prepared cofactor analogue. We procured samples from colorectal cancer (CRC) patients including tumour tissue as well as normal adjacent tissues. The data enabled us to identify CRC-specific markers that could then be used to track and monitor the presence of tumour DNA in cell-free DNA plasma (cfDNA) samples procured in the same patients. The FASTQ files of all the colorectal cancer tissues, normal adjacent tissues as wells as the cfDNA samples are available in this submission. The data made available in this study have been used to outline an approach for tumour-informed disease profiling in colorectal cancer cfDNA samples using Active-Seq.

  Study EGAS50000001226

• Amplicon sequencing of various tumors

  In our previous studies on advanced esophageal, gastric, and colorectal carcinomas, measurement of circulating tumor DNA (ctDNA) in terms of variant allele frequency (VAF) in blood suggested: (1) patients with high VAF have a high chance of relapse; (2) VAF changes in response to treatment; and (3) ctDNA VAF elevation precedes imaging diagnosis of relapse. It is thus necessary to investigate whether these findings can be generalized in a wide cancer spectrum, types of treatment, and relapse phenotypes across cancer types. In the present study, we conduct a non-interventional observational study in a wide cancer spectrum on the basis of technical establishment for ctDNA using our unique digital PCR probe library.

  Study JGAS000366

• Integrative Molecular Characterization of Breast Cancer

  Version 1: We have performed integrative analyses of breast cancer tumors, including whole exome sequencing (WES) of tumor and normal pairs, whole transcriptome sequencing, and single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq), to determine molecular correlates of response to novel and immune therapies. We have focused on dissecting the role of anticancer immunity in response to these therapies and understanding biological mechanisms underlying therapeutic resistance. The goal of this work is to advance a precision medicine approach to breast cancer treatment by identifying molecular biomarkers to improve therapy selection and novel therapeutic targets to overcome resistance. Version 2: We performed bulk whole exome sequencing (WES) of fresh frozen tissue from primary hormone receptor-positive breast cancer. Additionally, where available, we performed bulk whole transcriptome sequencing for the same individuals (tumor only). Additionally, we performed single cell sequencing on fresh frozen tissue from primary hormone receptor-positive breast cancer.

  Study phs002419

• Transcriptional data of human isogenic iPSCs

  This dataset contains bulk RNA-seq data from 12 autosomally isogenic human iPSCs. Three biological replicas from XXY (sample ID 419295-7), XX (sample ID 419298-419300), XY (sample ID 419302, 419305-6), and XO (sample ID 419308, 419310, 419312) hiPSC. RNA for this dataset was collected using Trizol method, and polyA library type was implemented. The libraries were sequenced using Illumina device. The sample reads are deposited as fastq files. Analysis have been done using reference alignment to GRCh38 reference genome.

  Dataset EGAD50000001361

• Targeted RNA-Seq

  This dataset contains targeted RNA-Seq generated with the Illumina RNA Pan-Cancer panel. It comprises 2200 cancers from children and adults. The cancers represent a wide range of CNS, solid and haematopoietic tumours. Samples are from pathology specimens including FFPE, fresh frozen, bone marrow aspirate and peripheral blood. Samples were tested in a clinical diagnostic lab to identify relevant SNVs, indels and gene fusions.

  Dataset EGAD50000000980

• RNA sequencing of in vitro generated suppressive myeloid cells using parental and Sialidase expressing A549 cancer cell lines

  Study EGAS00001007618

• Shanghai Breast Cancer Genetics Study (SBCGS)

  The Shanghai Breast Cancer Genetics Study (SBCGS) includes data from four population-based studies conducted among Chinese women in urban Shanghai: the Shanghai Breast Cancer Study (SBCS), the Shanghai Breast Cancer Survival Study (SBCSS), the Shanghai Women's Health Study (SWHS), and the Shanghai Endometrial Cancer Study (SECS, contributing controls only). Shanghai Breast Cancer Study (SBCS): The SBCS is a population-based, case-control study conducted in urban Shanghai. Subject recruitment in the initial phase of the SBCS (SBCS-I) was conducted between August 1996 and March 1998. The second phase (SBCS-II) of recruitment occurred between April 2002 and February 2005. Breast cancer cases were identified through the population-based Shanghai Cancer Registry and supplemented by a rapid case-ascertainment system. Controls were randomly selected using the Shanghai Resident Registry. Approximately 3500 cases and 3500 controls were recruited in the study. Shanghai Breast Cancer Survival Study (SBCSS) and Shanghai Endometrial Cancer Study (SECS): The SBCSS also used the population-based Shanghai Cancer Registry to identify newly diagnosed breast cancer cases. A total of 5,046 breast cancer patients were recruited. In-person interviews were conducted to collect information on known breast cancer risk factors and anthropometrics by using protocols and questionnaires similar to those used in the SBCS. For genetic studies, controls for the SBCSS cases came from the SECS, which recruited healthy women between January 1997 and December 2003. With the exception of a few questions related specifically to breast or endometrial cancer risk, the questionnaires used in the SECS and the SBCS were very similar. Shanghai Women's Health Study (SWHS): The SWHS is a population-based prospective cohort study of approximately 75,000 adult women who were recruited between 1997 and 2000. The cohort has been followed by a combination of record linkage and active follow-ups. Breast cancer patients identified in the SWHS and non-cases were included in the current study.

  Study phs001088

• Shanghai Breast Cancer Genetics Study (SBCGS)

  The Shanghai Breast Cancer Genetics Study (SBCGS) includes data from four population-based studies conducted among Chinese women in urban Shanghai: the Shanghai Breast Cancer Study (SBCS), the Shanghai Breast Cancer Survival Study (SBCSS), the Shanghai Women's Health Study (SWHS), and the Shanghai Endometrial Cancer Study (SECS, contributing controls only). Shanghai Breast Cancer Study (SBCS): The SBCS is a population-based, case-control study conducted in urban Shanghai. Subject recruitment in the initial phase of the SBCS (SBCS-I) was conducted between August 1996 and March 1998. The second phase (SBCS-II) of recruitment occurred between April 2002 and February 2005. Breast cancer cases were identified through the population-based Shanghai Cancer Registry and supplemented by a rapid case-ascertainment system. Controls were randomly selected using the Shanghai Resident Registry. Approximately 3500 cases and 3500 controls were recruited in the study. Shanghai Breast Cancer Survival Study (SBCSS) and Shanghai Endometrial Cancer Study (SECS): The SBCSS also used the population-based Shanghai Cancer Registry to identify newly diagnosed breast cancer cases. A total of 5,046 breast cancer patients were recruited. In-person interviews were conducted to collect information on known breast cancer risk factors and anthropometrics by using a protocol and questionnaire similar to those used in the SBCS. For genetic studies, controls for the SBCSS cases came from the SECS, which recruited healthy women between January 1997 and December 2003. With the exception of a few questions related specifically to breast or endometrial cancer risk, the questionnaires used in the SECS and the SBCS were very similar. Shanghai Women's Health Study (SWHS): The SWHS is a population-based prospective cohort study of approximately 75,000 adult women who were recruited between 1997 and 2000. The cohort has been followed by a combination of record linkage and active follow-ups. Breast cancer patients identified in the SWHS and non-cases were included in the current study.

  Study phs000799

• Idiosyncratic and generic single nuclei and spatial transcriptional patterns in papillary and anaplastic thyroid cancers

  Thyroid cancer is the most common endocrine malignancy. This dataset encompasses two types of thyroid cancer : anaplastic which is the most de-differentiated and aggressive one, and papillary which is the most common one. We profiled 14 patients, including 10 papillary and 4 anaplastic thyroid carcinomas, using both single nuclei RNA sequencing and spatial transcriptomics to link single cell resolution RNA sequencing with tissue morphology and better understand inter and intratumoral thyroid cancer heterogeneity.

  Study EGAS00001007574

• RNA sequencing for gastric cancer ascites

  Whole transcriptome sequencing (RNA-seq) was performed on 39 PC specimens. Among them, 21 specimens also had WES data.

  Dataset EGAD00001004365

• Whole transcriptome and whole exome sequencing of BPDCN patients

  Here, seven patients with BPDCN were characterized using RNA-seq and WXS.

  Dataset EGAD00001010314

• Bulk RNA sequencing of metastatic colorectal cancer patient-derived xenografts with known response to irinotecan

  Standard polyA enriched RNA-seq profiles of patient-derived xenograft (PDX) models from NOD/SCID mice without treatment. These PDX are obtained from tumors from patients with metastatic colorectal cancer after serial passaging in mice.

  Dataset EGAD50000002138

• 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

• Whole-genome and single-cell sequencing of lethal metastatic castration resistant prostate cancer

  Contains a total of 9 patients with whole-genome sequencing of 30 tumours or germline samples, 10X single-cell multiome RNA-seq + ATAC-seq of 24 tumours, 10X 5' single-cell RNA-seq of 10 tumours, and snPATHO-seq of 2 primary tumours. All single-cell experiments have matched whole-genome sequencing. When matched tumour (and germline) whole-genome sequencing is missing, it can be found under EGAS00001006598.

  Dataset EGAD50000001869

• RNA sequencing of Notch inhibitor treated HCC PDX models across timepoints

  To study the effect of Notch inhibition on the transcriptional profiles our PDX models, we generated bulk RNA-seq data for two Notch inhibition sensitive and three Notch inhibition insensitive models across multiple timepoints (8h, 24h, 48h, 7d, 17d) using anti-JAG1, anti-Notch2, anti-Notch1 or anti-gD antibodies. 3-5 samples were collected for each treatment condition, resulting in a total of 96 samples. Isolated RNA was used as input for library preparation using TruSeq RNA Sample Preparation Kit v2 (Illumina). The libraries were multiplexed and sequenced on Illumina HiSeq 2500 (Illumina) to produce FASTQ files.

  Dataset EGAD50000000737

• Transcriptome sequencing of hepatocellular carcinoma biopsies

  RNA-sequencing of 122 hepatocellular carcinoma biopsies and 15 normal liver biopsies. RNA-seq library prep was performed with 200 ng total RNA using the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Gold (Illumina) according to manufacturer’s specifications. Single-end 126-bp sequencing was performed on an Illumina HiSeq 2500 using v4 SBS chemistry at the Genomics Facility Basel according to the manufacturer’s guidelines. Primary data analysis was performed with the Illumina RTA version 1.18.66.3.

  Dataset EGAD00001008205