4755 results for "*oma"

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• DAC for Breast Invasive Lobular Carcinoma CDH1 study

  We developed an artificial intelligence (AI)-model applied to histological images trained using CDH1 biallelic mutations, pathognomonic for breast invasive lobular carcinoma (ILC), as ground truth. We evaluated the performance of the AI-model to predict the presence of CDH1 biallelic mutations and to diagnose ILC. Subsequently, we investigated the molecular underpinning of cases predicted by the model to harbor a CDH1 biallelic mutations but lacking these alterations according to targeted sequencing. We then subjected one ILC lacking CDH1 biallelic mutations and lacking CDH1 promoter methylation to whole genome sequencing (WGS) to determine the molecular basis of its lobular phenotype.

  Dac EGAC50000000333

• Disease recurrence after pathologic response

  In this study we aim to gain insight into the mechanisms of immune evasion after initial pathologic response to neoadjuvant immune checkpoint inhibition in macroscopic stage III melanoma by pooling data of the neoadjuvant OpACIN, OpACIN-neo and PRADO trials. Here we used RNAseq data from 21 patients(paired end)

  Dataset EGAD50000000698

• Disease recurrence after pathologic response

  In this study we aim to gain insight into the mechanisms of immune evasion after initial pathologic response to neoadjuvant immune checkpoint inhibition in macroscopic stage III melanoma by pooling data of the neoadjuvant OpACIN, OpACIN-neo and PRADO trials. Therefore we analyzed in-depth paired baseline and recurrent tumor samples through a.o. DNA sequencing analyses.

  Dataset EGAD50000000699

• Disease recurrence after pathologic response

  In this study we aim to gain insight into the mechanisms of immune evasion after initial pathologic response to neoadjuvant immune checkpoint inhibition in macroscopic stage III melanoma by pooling data of the neoadjuvant OpACIN, OpACIN-neo and PRADO trials. Therefore we analyzed in-depth paired baseline(n=10) and recurrent tumor samples of the lymph nodes(n=10) and brain metastasis (n=2) through Whole Exome Sequencing (WES) analyses.

  Dataset EGAD50000000700

• Combined single-cell transcriptomics and T-cell receptor sequencing reveal heterogeneity of mycosis fungoides between and within patients and identify a CD4+ cytotoxic subtype

  Comprehensive analysis of the malignant cells present in the skin lesions of Mycosis fungoides, the most common subtype of cutaneous T-cell lymphoma, is complicated by the fact that malignant from reactive T cells are challenging to distinguish with certainty. To this end, combined single-cell RNA and TCR sequencing allows unambiguous identification of MF cells, thereby revealing a marked heterogeneity between and within patients with unexpected functional phenotypes. It should be further noted, that mRNA expression of established CTCL protein markers in single-cell transcriptomics to identify MF cells should be used with caution; however, our study uncovered an exciting gene expression pattern that serves as a beacon separating malignant from reactive T cells.

  Study EGAS50000000226

• Single-cell transcriptomics of neuroblastoma tumors

  Single-cell RNA-sequencing (CITE-seq) and single-nucleus RNA-sequencing data were generated from neuroblastoma patient tumors to uncover transcriptional heterogeneity at a single-cell resolution. In addition, we used the data to validate our findings of the NBAtlas.

  Study EGAS50000000223

• Single-cell transcriptomics of neuroblastoma tumors

  Single-cell, single-nucleus and CITE-sequencing of neuroblastoma tumors with 10X Genomics

  Dataset EGAD50000000328

• Whole Genome - Uveal Melanoma

  Whole genome sequencing on 1 uveal melanoma and corresponding germline sample. Libraries were prepared using the Kapa HyperPrep kit (Roche, 07962363001). Paired-end libraries (2 x 150 bp) were sequenced on a NovaSeq 6000 instrument (Illumina).

  Dataset EGAD50000000764

• Exome - Uveal Melanomas

  Whole exome sequencing on 11 uveal melanoma samples and corresponding germline samples. Libraries were prepared using the SureSelectXT2 Clinical Research Exome V2 kit (Agilent, 5190-9500 and G9621B). Paired-end libraries (2 x 100 bp) were sequenced on HiSeq 2000/2500 or NovaSeq 6000 instruments (Illumina).

  Dataset EGAD50000000767

• FASTQ Data from In Situ Hi-C on Medulloblastoma Tissue Samples

  29 Hi-C datasets [8 G3 (MB3667, MB3687, MB3690, MB3692, MB3693, MB4010, MB4037, MB4141), 13 G4 (MB0558, MB3510, MB3670, MB3689, MB3716, MB3760, MB3761, MB3807, MB4079, MB4132, MB4174, MBSF7, SNC_2_5), 7 SHH (MB3612, MB3661, MB3662, MB3695, MB3697, MB3724, MB4143), and 1 WNT (MB4036)]. Fresh tissue samples were obtained from The Hospital for Sick Children (Toronto, ON). In situ Hi-C libraries were generated using approximately 2.5 million dissociated cells as input. All Hi-C libraries were sequenced at 150 bp PE with a Hi-Seq X instrument (Illumina) at McGill Genome Centre (Montreal, QC).

  Dataset EGAD50000000771

• Medulloblastoma Hi-C Data Access Committee

  Contact Information Marco Gallo marco.gallo@bcm.edu

  Dac EGAC50000000357

• Target sequencing of 53 synovial sarcoma patients

  This dataset contains 138 .bam files sequenced with Illumina NovaSeq 6000. The files with the variant calling performed to the sequencing data, as well as the clinical data (phenotype) extracted during the sample extraction.

  Dataset EGAD50000000743

• 10X single-nuclei RNA sequencing

  Single-nuclei sequencing data from four neuroblastoma patients. Each patient was run on two lanes, resulting in two runs per patient. Data is provided in paired-end fastq files.

  Dataset EGAD50000000727

• Genomic and Transcriptomic sequencing of neuroblastoma patient

  Genomic and Transcriptomic single-cell sequencing of neuroblastoma patient. Data represents one 96-well plate that was processed with G&T sequencing, resulting in genomic and transcriptomic data from the same single cells. Dataset contains 95 bam files containing the DNA sequencing data and 95 bam files containing the RNA sequencing data.

  Dataset EGAD50000000728

• Genomic and Transcriptomic sequencing of neuroblastoma cell lines

  Genomic and Transcriptomic sequencing of neuroblastoma HSR and ecDNA cell lines. Data represents five 96-well plates that were processed with G&T sequencing, resulting in genomic and transcriptomic data from the same single cells. Dataset contains 95 bam files containing the DNA sequencing data and 95 bam files containing the RNA sequencing data of CHP212 cells, 380 bam files (190 DNA and 190 RNA) for TR14 cells, 188 bam files (94 DNA and 94 RNA) for Kelly cells and 192 bam files (96 DNA and 96 RNA) for IMR5/75 cells.

  Dataset EGAD50000000729

• NanoString Cancer

  RCC files of 17 Cartridges from metastatic melanoma

  Dataset EGAD00010002432

• Single-cell transcriptomics of hepatoblastoma

  10X Genomics single-cell transcriptomics of hepatoblastoma tissues using Chromium Next GEM Single-Cell 3’ Reagent Kits v3.1. Single-cell transcriptomics data (bam files) of PT9, post-chemotherapy tumor sample, and PT13, treatment naive tumor sample. Single-cell solutions were obtained from viably frozen tissue samples using enzymatic digestion.

  Dataset EGAD50000000794

• Single-cell multiome ATAC and gene expression profiling of hepatoblastoma tumor organoids

  10X Genomics single-cell multiome profiling of hepatoblastoma tumor organoids using Chromium Next GEM Single-Cell Multiome ATAC + Gene Expression Kit. Single-cell transcriptomics data (bam file) of multiplexed sample of hepatoblastoma tumor organoids: 3E, 8F1, 10F2, 13F2, 13E, 17E and 96F1. Single-cell solutions were obtained from fresh organoid samples using enzymatic digestion.

  Dataset EGAD50000000795

• Single-cell transcriptomic profiling of hepatoblastoma tumor organoids

  10X Genomics single-cell transcriptomic profiling of hepatoblastoma tumor organoids using Chromium Next GEM Single-Cell 3’ Reagent Kits v3.1 or 3’ CellPlex Multiplexing Kit. Single-cell transcriptomics data (bam files) of hepatoblastoma tumor organoids: 3E, 8F1, 10F2, 13F2, 13F2 late, 13E, 17E, 17F1, 22E, 27F1, 28F1, 31E, 96F1, 121E and 135. Single-cell solutions were obtained from fresh organoid samples using enzymatic digestion.

  Dataset EGAD50000000796

• Spatial transcriptomics of hepatoblastoma

  10X Genomics Visium Spatial transcriptomics analysis of hepatoblastoma and adjacent normal liver. Spatial data of normal liver and PT2 (fastq files), and and PT13, PT14 and PT16 (bam files) using the Visium Spatial Gene Expression Solution. All samples, except PT13, were collected post-chemotherapy.

  Dataset EGAD50000000797

• Spatially resolved transcriptomics reveals profound subclonal heterogeneity and T cell dysfunction in extramedullary myeloma

  Investigating the spatial architecture and microenvironment of EMD lesions in Multiple Myeloma patients using spatial whole transcriptome sequencing (10X Visium), tomography for spatially resolved transcriptomics (TOMO-seq), and single-cell RNA sequencing (scRNAseq)

  Study EGAS50000000227

• UniKilinikum Wuerzburg MSNZ AGRasche/AG Riedel EMD DAC

  This is a Data access committee for the Data presented in the paper tilted "Spatially resolved transcriptomics reveals profound subclonal heterogeneity and T cell dysfunction in extramedullary myeloma"

  Dac EGAC50000000173

• Contribution of specific cell types to the development of Barrett’s esophagus and carcinoma via germline genetic risk

  We sought to elucidate how individual cell types in Barrett’s esophagus (BE) and esophageal adenocarcinoma (EAC) contribute to disease development via germline genetic risk.

  Dac EGAC50000000235

• Tumor Evolution Analysis Uncovered Immune-Escape Related Mutations in Relapsed Diffuse Large B-Cell Lymphoma

  More than 60% of patients with primary diffuse large B-cell lymphoma (DLBCL) achieve durable remissions upon the chemoimmunotherapy with R-CHOP. However, the outcome of the relapse patients is extremely poor. To characterize the molecular features that underlie treatment resistance, we performed whole-exome sequencing (n=44) and deep targeted sequencing (n=27) of 44 diagnosis-relapse DLBCL pairs. We found that the overall landscape of genetic alterations was concordant between diagnosis and relapse DLBCL. By a deep comparison of the longitudinal sequencings, we identified relapse-enriched mutations that involved in evasion of immune surveillance (CD58, TNFRSF14, PKD1, FAS), resistance to DNA damaging agents (ATM, TP53), and critical tumor suppressor gene (ZFHX3). Knockdown of ATM and ZFHX3 resulted in increased drug resistance in both germinal center B cell like (GCB) and activated B cell like (ABC) DLBCL cell lines in vitro, while the knockdown of TNFRSF14 and FAS conferred resistance specifically in GCB cell lines. Furthermore, we summarized two evolutionary patterns of DLBCL relapse as the “durable retention” model with stable clone structure, and the “selective sweep” model which exhibited significant clonal switch. Of note, genetic alterations leading to attenuated immune responses usually presented as trunk mutations in the “durable retention” model (7/12) while were newly acquired or expanded upon relapse in the “selective sweep” model (9/16). Overall, our study demonstrated that relapse DLBCL derives from distinct clonal evolutionary patterns, and highlighted immune escape as an important contributor of tumor relapse.

  Study EGAS50000000032

• Cancer cell and tumor microenvironment biomarkers associated with disease-free survival with adjuvant nivolumab in the phase 3 CheckMate 274 trial

  In the CheckMate 274 trial, adjuvant nivolumab versus placebo demonstrated an improvement in the primary endpoint of disease-free survival (DFS) in patients with high-risk muscle-invasive urothelial carcinoma after radical resection. An extensive analysis of tumor biomarkers, including their associations with DFS, was performed. Analysis of genome-wide differential gene expression and known gene signatures found that immune-related genes and pathways, in particular an IFNγ signature, were predictive of DFS in nivolumab-treated patients. Predictive and prognostic associations, respectively, were found for tumor-infiltrating CD4 and CD8 T cells. Analysis of a composite predictive model suggested that CD4 gene expression and tumor cell PD-L1 expression had the greatest contributions to predicting outcomes in nivolumab-treated patients. These results reinforce recent studies establishing the importance of biomarkers of adaptive immunity, and in particular CD4 T cells, in influencing response to PD-1/PD-L1 blockade and establish the potential predictive rather than solely prognostic nature of such findings.

  Study EGAS50000000560