11745 results for "cancer rna-seq"

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• Pheno-seq profiles of single clonal tumor spheroids derived from a patient with colorectal cancer

  Pheno-seq is a new approach that integrates high-throughput imaging and transcriptomic profiling of clonal spheroids/organoids to dissect functional tumor cell heterogeneity in 3D cell culture systems. The method is based on the iCELL8 technology (TakaraBio) that uses barcoded nanowells and a micro-solenoid valve dispenser. The CRC_spheroid dataset contains demultiplexed RNA-sequencing profiles (FASTQ file format, NextSeq 500) of 95 clonal tumor spheroids derived from a patient with colorectal cancer.

  Dataset EGAD00001004131

• Genomic Profiling of Melanoma

  This study reports on genomic profiling [whole-exome sequencing (WXS), bulk RNA sequencing (RNA-seq), and/or single-cell RNA sequencing (scRNA-seq)] of patient-derived melanoma samples and matched normal samples. The goal is to elucidate the genetic and cellular basis of sun-exposed and sun-shielded melanomas and to relate these findings to survival and regional metastasis. Specimens were collected by the Tissue Resource Core of the Yale SPORE in Skin Cancer with participants' informed signed consent according to Health Insurance Portability and Accountability Act (HIPAA) regulations with a Human Investigative Committee protocol. The melanomas used for sequencing were from fresh or snap-frozen tumors, or from short-term cell cultures. The cell cultures were routinely checked for mycoplasma contamination and were discarded when found positive. Matching normal DNA was from circulating lymphocytes or normal skin. We also include 77 WES-sequenced samples from the Yale Spitzoid neoplasm repository, which collects specimen and data according to a protocol approved by the Yale Human Investigative Committee.

  Study phs000933

• ProstOmics - Bulk Transcriptomics

  Bulk transcriptomics data from 176 prostate tissue samples (37 patients, 27 with post-surgery relapse) that were acquired using the SENSE mRNA-Seq Library Prep Kit V2 and Illumina NextSeq 500 instrument. The raw transcriptomics data is single-read RNA-seq. The individual samples have information on patient origin and sample type (cancer, cancer-adjacent field-effect normal or normal sample far from cancer). Patient metadata include information of age at surgery, time (months) until reported relapse, pre-surgery PSA and post-surgery T-stage.

  Dataset EGAD50000000604

• SAPCS Blood RNA-seq of prostate cancer patients

  The study aims to quantify the gene expression in blood samples in 20 prostate cancer patients under the Southern African Prostate Cancer Study (SAPCS). The objective is to identify a list of differentially expressed genes.

  Study EGAS50000000702

• Rio Dataset

  Homologous recombination DNA repair deficiency and PARP inhibition activity in primary triple negative breast cancer. RNA-Seq data for paired baseline and end of treatment samples

  Dataset EGAD00001006120

• RNA-seq study of a Princess Margaret Cancer Centre human acute myeloid leukemia patient cohort

  The PMCC AML RNAseq dataset consists of 81 AML patient samples (clinical data in Supplemental Table 11 of manuscript), processed in two batches. These patient samples are able to engraft in the NSG (NOD.Cg PrkdcscidIl2rgtm1Wjl /SzJ) mouse model. Five patients (90543, 598, 90240, 110484, 100500) were included in both batches. Viaably frozen material from the Leukemia Tissue Bank at Princess Margaret Cancer Centre/ University Health Network were thawed by dropwise addition of X-VIVO + 50% fetal calf serum supplemented with DNase (100μg/mL final concentration, Roche). RNA was extracted from bulk peripheral blood mononuclear cells (PBMC) using the RNeasy Micro Kit (Qiagen Inc.). A paired-end 76 base-pair flow-cell lane Illumina High seq 2000 yielded an average of 240 million sequence reads aligning to genome per sample at the Genome Sciences Centre, BC Cancer Agency for cohort 1. Cohort 2 was subjected to 125 bp, paired-end RNA-sequencing on the Illumina HiSeq 2500 with an average of 50 million reads/sample at the Centre for Applied Genomics, Sick Kids Hospital.

  Dataset EGAD00001006576

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

  In this project, single cells extracted from two high-grade serous ovarian cancer cell-lines, one primary (OV2295) and one metastatic (OV2295R2), where split into two 384 well plates and libraries were prepared using Smart-seq3 single cell RNA sequencing (Hagemann-Jensen et al, 2020).

  Study EGAS00001006868

• Transcriptome profiling for Korean Early Onset Gastric Cancer

  To identify transcriptome profile in this unique subset of gastric cancers, RNA-seq analyses were performed using frozen cancer tissue. Adjacent normal tissue of the same patients were used in differently expressed gene selection and fusion gene prediction.

  Dataset EGAD00001002187

• Japanese liver cancer RNAseq

  RIKEN collection of RNA-seq reads for 458 liver cancer samples and matched normal liver from 247 donors.

  Dataset EGAD00001001880

• Divergent WNT Signaling and Drug Sensitivity Profiles within Hepatoblastoma Tumors and Organoids

  Hepatoblastoma, the most prevalent pediatric liver cancer, almost always carries a WNT-activating CTNNB1 mutation, yet exhibits notable molecular heterogeneity. To characterize this heterogeneity and identify novel targeted therapies, we perform comprehensive analysis of hepatoblastomas and tumor-derived organoids using single-cell RNA-seq/ATAC-seq, spatial transcriptomics, and high-throughput drug profiling. We identify two distinct tumor epithelial signatures: hepatic ‘fetal’ and WNT-high ‘embryonal’, displaying divergent WNT signaling patterns. The fetal group is enriched for liver-specific WNT targets, while the embryonal group is enriched in canonical WNT target genes.

  Study EGAS50000000561

• RNA sequencing data from 10 patient derived colorectal cancer organoids

  This dataset comprises RNA-sequencing data from ten colorectal cancer (CRC) organoids, derived from histologically verified primary tumor tissues of patients undergoing surgical resection. Paired-end sequencing produced 20 FASTQ.gz files. The organoids were cultured in Geltrex-based droplets using PDO culture medium and harvested for RNA extraction. Total RNA was isolated and subjected to full-length mRNA sequencing, producing high-quality RNA-seq data. The dataset includes transcriptomic profiles of seven microsatellite stable (MSS) and three microsatellite instable (MSI) tumors, providing a valuable resource for the analysis of non-canonical tumor-specific antigens (ncTSAs) using the NovumRNA pipeline. This dataset aids in uncovering potential immunotherapy targets in CRC, particularly in hard-to-treat low-TMB MSS cases.

  Dataset EGAD50000000962

• RNA-seq of high-grade serous ovarian cancer in long-term survivors (MOCOG study)

  RNA-seq dataset of high-grade serous ovarian cancer (HGSC) tumours from long-term survivors performed as part of the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study. The dataset includes fastq files from 56 HGSC tumours (53 primary tumours and 3 recurrent tumours) from 53 long-term survivor patients. Libraries were generated using the Illumina Stranded mRNA Prep and 150 bp paired-end sequencing was performed to a minimum of 100 million reads on Illumina NovaSeq 6000 instruments.

  Dataset EGAD00001008537

• Chordoma_Sequencing_Project_Whole_Genome

  This is a continuation of the Chordoma Sequencing Project, prelim #G0175-122-CF. 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. The Chordoma Foundation will fund the RNA Sequencing/Methylation and SNP6 costs for this project while the whole genome and library sequencing will be covered under the CGP, 2012-2013 core WTSI internal funding. An additional sequencing of three cancer cell lines will be added to this work, financed by the Chordoma Foundation. In this project, the Chordoma foundation will pay for the additional costs of methylation, RNA Seq,

  Study EGAS00001000409

• RNA-seq of oropharynx cancer cases from University of Michigan

  This dataset consists of 67 bulk RNA-seq data (sorted aligned BAM files) from formalin-fixed paraffin-embedded (FFPE) oropharynx tumor samples from the University of Michigan (UM). Samples were collected as part of the UM Head and Neck SPORE between 2008-2014. Library preparation was performed using the ribo-depletion method with the FastSelect kit and sequenced on the NovaSeq 6000 at the UM Advanced Genomics Core, resulting in 150bp paired-end reads. Raw reads were trimmed using Cutadapt (v3.4) and aligned to hg38 and high-risk HPV genomes using STAR (v2.7.9a). 62 of 67 samples were determined to be HPV RNA-positive.

  Dataset EGAD50000001306

• RNA-seq SE

  Transcriptomics of single-end RNA-seq data

  Study EGAS00001006521

• RNA-seq PE

  Transcriptomics of paired-end RNA-seq data

  Study EGAS00001006522

• High-grade serous ovarian cancer refined with single-cell RNA-sequencing

  We performed single-cell RNA-sequencing of 18,403 cells unbiasedly collected from 7 treatment-naive HGSOC tumours. For each phenotypic cluster of tumour or stromal cells, we identified specific transcriptomic markers. Next, we explored which phenotypic clusters correlate with overall survival based on expression of these transcriptomic markers in 1467 bulk RNA-seq tumours. By evaluating molecular subtype signatures in single cells, we assessed to what extent a phenotypic cluster of tumour or stromal cells contributes to each molecular subtype.

  Study EGAS00001004987

• RNA-Seq on GeparSixto tissue samples

  RNA-Seq on GeparSixto tissue samples

  Study EGAS00001007124

• June 2016 data update for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  June 2016 data update (bam/fastq for CEMT0062, CEMT0068, CEMT0072, CEMT0086, CEMT0087 ChIP-Seq and RNA-Seq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001002239

• RNA-Seq and small RNA-Seq of tuberous sclerosis complex cortical tubers and age-matched controls.

  This dataset contains both standard RNA-Seq and small RNA-Seq of TSC related cortical tubers and age matched cortical controls. For the standard RNA-Seq paired-end sequencing was carried out. Each sample was split across multiple lanes. For the files available here the multiple lanes have been merged together, resulting in one forward and one reverse .fastq file for each sample. Small RNA-Seq was carried out on the same samples that underwent standard RNA-Seq. Again paired-end sequencing was carried out. The files here are raw and will need to be undergo quality control and trimming.

  Dataset EGAD00001003444

• Ribodepletion RNA-Seq of 8 samples and polyA RNA-Seq of 22 samples

  Ribodepletion RNA-Seq of 8 samples and polyA RNA-Seq of 22 samples

  Dataset EGAD00001004993

• Response and Resistance to ER-Directed Therapy in Metastatic Breast Cancer

  Despite the reduction in cancer recurrence and mortality provided by hormonal therapy, estrogen receptor positive (ER+) metastatic breast cancer (MBC) remains the most common cause of breast cancer death, resulting in more than 20,000 deaths in the U.S each year. Although several therapeutic options exist to target the estrogen receptor, resistance to these therapies invariably occurs. Genetic mechanisms of acquired resistance to targeted therapies in ER+ MBC are not well understood. To address this, we developed a prospective biopsy protocol where multiple research biopsies are collected from metastatic samples and used for pathology, ER/PR/HER2, OncoPanel (a CLIA 300 gene sequencing panel), whole exome sequencing (WES), transcriptome sequencing (RNA-Seq), and single-cell RNA-seq (scRNA-seq). In some cases, tissue is used for cell line and xenograft generation. Archival primary biopsies are also obtained when possible for parallel genomic studies. Patients are followed and serial metastatic biopsies are obtained. This data will be used to determine the landscape of genomic alterations in treatment-resistant ER+ MBC, and compared to data generated by TCGA for treatment-naive ER+ primary breast cancer. Comparison of genomic studies from resistant, metastatic specimens with corresponding pre-treatment primary samples will allow us to identify genomic association with resistance to specific therapies. Analysis of the serial metastatic biopsies collected over the course of multiple treatments will also shed light on tumor evolution under selective therapeutic pressure.NoteA subset of the MBC blood biopsies that are included in our study can be accessed and downloaded from the phs001977 accession.Another subset of patients in this study participated in a clinical trial (NCT02871791) and have their data deposited in the current study as well.

  Study phs001285

• Comparison of capture-based method for transcriptome profiling of formalin-fixed paraffin embedded tumor samples

  Background: The need for fresh frozen (FF) tissue limits implementing RNA sequencing (RNA-seq) in the clinic. The majority of clinical samples are stored as formalin-fixed, paraffin-embedded (FFPE) tissues. Exome capture platforms have been developed for RNA-seq from FFPE samples. However, these methods have not been systematically compared. Methods: Transcriptomic analysis of 32 FFPE tumor samples from 11 patients was performed using three exome capture-based methods: Agilent SureSelect V6, TWIST NGS Exome, and IDT XGen Exome Research Panel. We compared these methods to TruSeq RNA-seq of fresh frozen (FF-TruSeq) tumor samples from the same patients. We assessed the recovery of clinically relevant biological features. Results: The Spearman's correlation coefficients between the global expression profiles of the three capture-based methods from FFPE and matched FF-TruSeq were high (rho = 0.72-0.9, p < 0.05). A significant correlation between the expression of key immune genes between individual capture-based methods and FF-TruSeq (rho = 0.76-0.88, p < 0.05) was observed. All exome capture-based methods reliably detected outlier expression of actionable genes, including ERBB2, MET, NTRK1, and PPARG. In urothelial cancer samples, the Agilent assay was associated with the highest molecular subtype concordance with FF-TruSeq (Cohen's k = 0.7, p < 0.01). Both Agilent and IDT detected all the clinically relevant fusions that were initially identified in FF-TruSeq. Conclusion: All exome capture-based methods had comparable performance and concordance with FF-TruSeq. By enabling the interrogation of FFPE tumor samples, our findings will enable the implementation of RNA-seq in the clinic to guide precision oncology approaches.

  Study EGAS00001005255

• RNA-seq of lung neuroendocrine tumors

  RNA-seq of lung neuroendocrine tumors

  Study EGAS50000000058

• Comprehensive RNA repository of tissue and plasma from patients with esophageal cancer or precursor lesions

  A comprehensive RNA repository (both coding and non-coding) from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett’s esophagus. Per patient, a blood plasma sample, and a healthy esophageal and disease tissue sample were collected. This dataset includes both mRNA and small RNA sequencing data (fastq.gz files) of all tissue and plasma samples. In total,102 RNA-seq libraries from 51 samples (17 plasma and 34 tissue samples) were sequenced (plasma mRNA libraries were sequenced twice).

  Dataset EGAD00001006857