11925 results for "cancer rna-seq"

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• Immune Determinants of Resistance to PD-1 Blockade in Renal Cell Carcinoma

  We performed an integrated bulk and single-cell transcriptomic analysis of tumors from 102 advanced renal cell carcinoma patients enrolled in a phase II clinical trial of frontline nivolumab (NCT03117309) to investigate determinants of response to anti-PD1 monotherapy. The present study includes 112 archival or pre-treatment tumors of clear cell and non-clear cell histologies from 90 patients that underwent bulk RNA sequencing (RNA-seq). For 76 patients, 74 of whom had matched RNA-seq, whole exome sequencing (WES) was also performed on tumor samples as well as matched peripheral blood. Additionally, 19 fresh tumor samples from 17 patients, including pre and post-treatment samples and mixed histologies, were locally cryopreserved and centrally processed for single cell RNA (scRNA-seq) and TCR (scTCR-seq) sequencing. 7 patients had matched scRNA/scTCRseq, RNAseq, and WES.

  Study phs003618

• Multi-omics analysis of cocaine use disorder in postmortem brain tissue of the ventral striatum [Proteomics data to study EGAS50000000623]

  We performed a multi-omics analysis of cocaine use disorder in postmortem human brain tissue of the ventral striatum in N=41 individuals (N=20 CUD cases, N=21 non-affected individuals). From the postmortem brain tissue samples, we generated miRNA-seq, RNA-seq, proteomics, and snRNA-seq data. The miRNA-seq, RNA-seq and snRNA-seq datasets can be found in the EGA under study ID EGAS50000000623. Proteomics data is deposited under study ID EGAS00001007945 (this study). The proteomic dataset was obtained using TMT-labeling. Measurement was performed on an Orbitrap Fusion Lumos device.

  Study EGAS00001007945

• Ongoing Replication Stress Tolerance and Clonal T Cell Responses Distinguish Liver and Lung Recurrence and Patient Outcomes in Pancreatic Ductal Adenocarcinoma

  Patients with metastatic pancreatic ductal adenocarcinoma (PDAC) survive longer when disease spreads to the lung but not to the liver. We generated overlapping, multi-omic datasets to identify molecular and cellular features that distinguish patients whose disease develops liver metastasis (liver cohort) from those whose disease progression results in lung metastasis without liver metastases (lung cohort). Lung cohort patients generally survived longer than liver cohort patients, independent of tumor subtype. We developed a pORG gene signature that distinguishes primary tumors in the liver and lung cohorts. We identified ongoing replication stress (RS) response pathways in high pORG/liver cohort tumors, while low pORG/lung cohort tumors had greater densities of lymphocytes and shared T cell clonal responses. Our study demonstrates that liver-avid PDAC is associated with tolerance to ongoing RS, limited tumor immunity, and less favorable outcomes; whereas low RS, lung-avid/liver-averse tumors are associated with active tumor immunity that may account for favorable outcomes. As expected, we found high frequencies of KRAS, TP53, CDKN2A, and SMAD4 gene alterations in our tumor samples. High pORG activity (GSVA scores) in our primary tumors appear to be positively correlated with alteration frequencies in both TP53 and CDKN2A. We did not see a correlation with alterations in KRAS as almost all the tumor samples have KRAS alterations. From a de-identified dataset of 1,873 patients diagnosed with and/or treated for PDAC at our institution between 2004 and 2020, we identified 422 patients for which we had specimens with sequencing data (N=374) and/or specific evidence of disease metastasis site(s) from the OHSU cancer registry or disease-relevant computed tomography (CT) scans to allow cohort classification. Note that our study includes RNA-Seq, DNA-Seq panel, and TCR-Seq (T-cell Receptor Sequencing) data, but that this dbGaP submission just includes the patients/samples with RNA-Seq and/or DNA-Seq panel data. Therefore, this submission includes 290 samples from 278 patients. TCR sequence data is available on the Adaptive Biotechnologies platform. Clinical course timepoints, patient demographics, stage, grade, nodal involvement, resection margins, and angiolymphatic invasion were provided as deidentified data by the OHSU cancer registry with quality control data verification in a subset by pathologists (BB and TM). We reviewed all available computed tomography (CT) scans for all patients with primary tumor resection dates recorded by the cancer registrar, with tumor samples analyzed by RNA-seq, DNA-seq, or TCR-seq, and/or with additional information indicating metastatic spread (e.g., metastatic samples received for related studies). We abstracted the site of all lesions proven to be metastatic by biopsy and/or that clearly increased in size during progression or decreased in size during treatment as long as a radiologist described the lesion as “likely”, “suspicious for”, “concerning for”, or “favor” metastasis. Clinical imaging was reviewed by a radiologist (AG) to validate patient assignments to the liver, lung, and neither liver nor lung (other recurrence site) cohorts. Time to recurrence was calculated from the earliest of either the recurrence date provided by the OHSU cancer registry, or the date of earliest lesion abstracted from CT reports. The subject attributes reported in this submission were up to date at the time of our data freeze, which occurred July 2021.

  Study phs003597

• Epigenomic dataset of Human Hepatocellular Carcinoma for EpiHK

  This dataset is generated for EpiHK. It contains Histone PTM ChIP-seq, WGBS, RNA-seq of Human Hepatocellular Carcinoma and tumor adjacent normal tissue sample.

  Study EGAS50000000039

• Transcriptome Sequencing PPGL (2)

  RNA-Seq data (n=81) library preparation was performed as previously reported by Calsina et al 2023 using QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen, 015). The prepared libraries were subsequently sequenced on NovaSeq™ system (Illumina). To combine the new CNIO samples (n=81) with the CNIO+TCGA dataset generated in Calsina et al., 2023, RNA data were processed using ComBat-seq as described in that same article.

  Study EGAS50000000013

• RNA seq reads

  RNA seq reads constituting of FASTQ paired end reads from 5 FHD/FHDL patients

  Dataset EGAD00001003790

• Paired-end RNA-seq analysis of GBM, additional patient.

  Dataset contains one paired-end RNA-seq sample.

  Dataset EGAD00001011990

• NSD2 E1099K Drives Relapse in Pediatric Acute Lymphoblastic Leukemia by Disrupting 3D Chromatin Organization

  NSD2 p.E1099K (EK) is a mutation shown to be enriched in patients that relapse with pediatric Acute Lymphoblastic Leukemia (ALL). This work aims to uncover 3D chromatin architecture-related mechanisms responsible for drug resistance or therapy failure in these patients. We investigated this by performing Hi-C, ATAC-Seq, and RNA-Seq on three B-ALL cell lines heterozygous for EK either expressing a NSD2 targeting small RNA (sRNA) or a non-targeting small hairpin RNAs (shRNA). We also performed RNA-Seq on three matched diagnosis/relapse B-ALL samples with the relapse enriched EK mutation.

  Study phs003195

• RNA-seq

  Bulk RNA-seq data of 8 RNA samples were generated. Libraries were prepared using the Stranded mRNA Library Prep, Ligation Kit (Illumina) following manufacturer’s recommendations and sequenced on a NextSeq 2000 (2x50 bp, Illumina).

  Dataset EGAD00001010308

• Pathogenesis and Immunity in Endemic Burkitt Lymphoma

  This genomic landscape of Burkitt lymphoma represents a multimodal sequencing of tumors and control tissues and individuals to better understand the etiology, and molecular pathogenesis of Burkitt lymphoma including the roles of the associated Plasmodium falciparum malaria and EBV infections. Comprehensive sequencing set includes genomic, transcriptomic, and epigenomic datasets in concert with variable clinical phenotypes and outcome information such as anatomical presentation site, in-hospital survival rates, and EBV genome type. This deposit includes additional small RNA-seq data from endemic BL (eBL) tumors and RNA-sequencing data from sorted NK cell subsets from the peripheral blood of eBL patients. The additional small RNA-seq data are from tumor specimens from 17 eBL cases, of the previously deposited polyA RNA-seq data from 28 eBL cases (phs1282.V1). The additional RNAseq data from Fluorescence-activated cell sorting (FACS) of NK cell subsets, isolated from the peripheral blood of 7 eBL cases. The additional RNAseq data are from eBL tumor samples of 5 eBL cases, BL cell lines established from these tumor samples and also NSG mouse tumors created by implanting the established BL cell lines into NSG mice. The dataset also includes RNAseq data of the NSG mouse tumors after treatment with Rituximab (RIT) and Phosphate buffered saline (PBS). The fifth batch of data includes mRNA-seq from different passages of a single eBL cell line that have variable EBV phenotypes (low passage with lytic EBV and high passage with latent EBV). This batch also includes lytic and latent lymphoblastoid cell line (LCL) clones from a single blood donor generated through infection with the strain of EBV isolated from the aforementioned eBL cell line. For initial transcriptome analysis see: Kaymaz et al. "Comprehensive Transcriptome and Mutational Profiling of Endemic Burkitt Lymphoma Reveals EBV Type-specific Difference." Molecular Cancer Research, 2017, PMID: 28465297. For initial microRNA analysis see: Oduor et al. "Human and Epstein-Barr Virus miRNA Profiling as Predictive Biomarkers for Endemic Burkitt Lymphoma." Frontiers in Microbiology 8, 2017, PMID: 28400759. For initial NK subsets RNA expression analysis see: Forconi et al. "A new hope for CD56negCD16pos NK cells as unconventional cytotoxic mediators: an adaptation to chronic diseases." Frontiers in Cellular and Infection Microbiology, 2020, PMID: 32373555.For initial eBL cell line and mouse avatar RNA expression analysis see: Saikumar et al. "Endemic Burkitt lymphoma avatar mouse models for exploring inter-patient tumor variation and testing targeted therapies." Life Science Alliance, 2023, PMID: 36878637. For general overview of cohort study see: Buckle et al. "Factors influencing survival among Kenyan children diagnosed with endemic Burkitt lymphoma between 2003 and 2011: A historical cohort study." Int J Cancer. 15:1231, 2016, PMID: 27136063.

  Study phs001282