4752 results for "*oma"

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• A single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

  Nodal B-cell non-Hodgkin lymphomas (B-NHL) are a clinically diverse set of cancers thought to originate from distinct stages of B-cell maturation. To characterize intratumor heterogeneity across B-NHL entities, we performed CITE-Seq (cellular indexing of transcriptomes and epitopes) on 51 lymph node samples including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL) and marginal zone lymphoma (MZL, n = 11) tumors, in addition to non-malignant reactive lymph nodes (rLN, n = 8). We also performed 5 prime scRNA-seq with immune receptor profiling for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL, and 2 MZL).

  Study EGAS50000000342

• A single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

  CITE-Seq (cellular indexing of transcriptomes and epitopes) of 51 lymph node (LN) samples from including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL), and marginal zone lymphomas (MZL, n = 11), in addition to non-malignant reactive lymph nodes (rLN, n = 8). Of the malignant LN samples, 20 were collected at the time of initial diagnosis and 23 were from patients who had previously undergone one or more lines of systemic treatment. Relapse samples were collected at least 3 months after cessation of systemic treatment. This dataset also includes 5 prime RNA sequencing and immune receptor sequencing (10X Genomics) for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL (GCB) and 2 MZL).

  Dataset EGAD50000000497

• ISA Nutrition 2015

  Comprehensive data on lifestyle-related modifiable factors, sociodemographic, anthropometric, economic, biochemical, and genetic markers related to the occurrence of cardiometabolic diseases as part of the observational cross-sectional survey (2015 Health Survey of Sao Paulo with Focus on Nutrition (2015 ISA-Nutrition), a population-based study. Data of 805218 SNPs for 841 individuals was genotyped using the Axioma 2.0 Precision Medicine Research Array in the Thermo Fisher Scientific laboratory (Affymetrix Inc, Santa Clara, CA).

  Dataset EGAD00010002678

• A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1 Inactivating Mechanisms

  We developed an artificial intelligence (AI)-model applied to histological images 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 cases of predicted by the model to harbor a CDH1 biallelic mutations but lacking these alterations according to targeted sequencing. Among this analyses, we subjected to whole genome sequencing (WGS) one ILC case lacking CDH1 biallelic mutations by targeted sequencing and lacking CDH1 promoter methylation to determine the molecular basis of its lobular phenotype.

  Study EGAS50000000485

• Breast Invasive Lobular Carcinoma CDH1 WGS dataset

  We subjected to whole genome sequencing (WGS) one ILC case lacking CDH1 biallelic mutations. Both tumor and normal samples were sequenced.

  Dataset EGAD50000000696

• 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