501 results for "cancer rna-seq"

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• NIBIT-EPI-MESO study samples

  Multi-omics analysis of pre-immuno checkpoint inhibitors (ICI) therapy lesions from 91 pleural mesothelioma (PM) patients enrolled in the multicenter NIBIT-EPI-MESO study. Neoplastic lesions were characterized using multiple molecular platforms, including RNA sequencing (n=82), Whole Exome Sequencing (WES) (n=87), Reduced Representation Bisulfite Sequencing (RRBS) (n=83) and cell-free methylated immunoprecipitation sequencing (cfMeDIP-seq) (n=18). Tumor samples were collected during the standard diagnostic pleuroscopy or thoracoscopy before ICI therapy. Matched Peripheral Blood Mononuclear Cells (PBMC) were collected from PM patients before ICI therapy.

  Study EGAS50000001478

• Molecular correlates for HPV-negative head and neck cancer engraftment prognosticate patient outcomes

  Head and neck squamous cell carcinoma (HNSCC) tumors were sampled from 88 patients. Samples were implanted subcutaenously into the flank of NOD/SCID/IL2R-/- mice. Mice were then monitored weekly for tumor growth and the time of initial palpation was recorded. Mice were euthanized when tumors reached 15 mm in diameter or after 6 months. If no tumor formed by 6 months, the patient tumor was defined as Non-engrafter (N). RNA-seq was performed on all primary tumor samples and successfully engrafted patient-derived xenograft (PDX) samples.

  Study EGAS50000000714

• Epigenetic profiling of colorectal cancer initiating cells (CC-ICs) to identify bivalently marked genes (H3K4me3 and H3K27me3 ChIP-seq), and investigation of changes in transcriptome following EZH2 inhibition using RNA-seq.

  Epigenetic regulation of transcription plays a crucial role in lineage commitment of embryonic stem cells. Promoters of key lineage-specific differentiation genes are found in a repressed bivalent state, having both activating H3K4me3 and repressive H3K27me3 histone marks, making them poised for transcription upon loss of H3K27me3 in response to environmental cues. Whether the tumour-initiating, self-renewing, cancer-initiating cells (C-ICs) have similar epigenetic regulatory mechanism that prevent lineage commitment is unknown. In order to investigate bivalently marked and repressed promoters, we used a patient-derived CC-IC enriched model to identify the changes in transcriptome following inhibition of EZH2, the H3K27 methyltransferase. We also performed ChIP-seq for H3K27me3 and H3K4me3 at baseline in order to identify repressed and bivalently marked promoters.

  Study EGAS00001003003

• A Phase 2 Study of Lamivudine in Patients with p53 Mutant Metastatic Colorectal Cancer

  This was an open-label, single-arm Phase II study of lamivudine in patients who had progressed on systemic therapy for advanced colorectal cancer with TP53 mutations. The phase II study had a two-stage design, with a target accrual of 20 evaluable patients for the first stage and a total of 32 patients for the whole study. The first 9 patients were treated with lamivudine at 150 mg PO bid continuously for 28-day cycles. Subsequent patients (10-32) received a higher dose of 600 mg PO bid continuously for 28-day cycles. Tumor assessments were performed every 8 weeks until documented disease progression by Response Evaluation Criteria in Solid Tumors (RECIST) or drug intolerance. Whole genome sequencing (WGS) and total RNA-seq were performed on pre- and post- treatment biopsies on this trial.

  Study phs002833

• Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

  Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via "mapping" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

  Study EGAS00001003452

• Inferring expressed genes by whole-genome sequencing of plasma DNA

  The analysis of cell-free DNA (cfDNA) in plasma represents a rapidly advancing field in medicine. cfDNA consists predominantly of nucleosome-protected DNA shed into the bloodstream by cells undergoing apoptosis. We performed whole-genome sequencing (WGS) of plasma DNA and identified two discrete regions at transcription start sites (TSS) where the nucleosome occupancy results in different read-depth coverage patterns in expressed and silent genes. By employing machine learning for gene classification we found that the plasma DNA read depth patterns from healthy donors reflected the expression signature of hematopoietic cells. In cancer patients with metastatic disease, we were able to classify expressed cancer driver genes in regions with somatic copy number gains with high accuracy. We could even determine the expressed isoform of genes with several TSSs as confirmed by RNA-Seq of the matching primary tumor. Our analyses provide functional information about the cells releasing their DNA into the circulation.

  Study EGAS00001001754

• Arriba: accurate and efficient detection of gene fusions from RNA-Seq (H021)

  The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (n=803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS. In addition, we confirmed the transforming potential of two novel fusions, RRBP1-RAF1 and RASGRP1-ATP1A1, in cellular assays. These results demonstrate Arriba’s utility in both basic cancer research and clinical translation.

  Study EGAS00001003554

• RNA-seq TPMs quantification for baseline tumor samples originating from IMpower150, and relevant clinical metadata

  This dataset includes full tumor transcriptomes from 558 advanced NSCLC tumors. These data originate from pre-treatment samples from a clinical trial for first-line non-small cell lung cancer (IMpower150). The patients in this trial were treated with the PD-L1 inhibitor atezolizumab +bevacizumab + chemotherapy or bevacizumab + chemotherapy or atezolizumab + chemotherapy.

  Dataset EGAD50000001814

• Processed bam files for the whole exome sequencing of primary lung adenocarcinoma samples

  We performed single cell RNA sequencing (scRNA-seq) for 208,506 cells derived from 58 lung adenocarcinomas from 44 patients, which covers primary tumour, lymph node and brain metastases, and pleural effusion in addition to normal lung tissues and lymph nodes. The extensive single cell profiles depicted a complex cellular atlas and dynamics during lung adenocarcinoma progression which includes cancer, stromal, and immune cells in the surrounding tumor microenvironments.

  Dataset EGAD00001010320

• ScRNA-seq of 6 human FL

  Follicular lymphoma (FL) is an indolent cancer of mature B-cells but carries increased risk of transformation to a more aggressive histology over time. We present here comprehensive profiling both tumor and immune compartments in 6 diagnostic FL biopsies by single-cell RNA sequencing. This confirmed results from 155 FL tumors characterized by mass cytometry (CyTOF) which revealed two distinct evolutionary trajectories with disparate risk of transformation and alternate biologies.

  Dataset EGAD00001007691