4913 results for "*oma"

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• SDR-seq_06_BCL

  SDR-seq_BCL data access. Two follicular lymphoma (FL) primary patient samples and one germinal center subtype diffuse large B-cell lymphoma (GCB) primary patient sample. FL1 and GCB1 are sequenced in Run1. FL2 is sequenced in Run2.

  Dataset EGAD50000000551

• Clinical Outcomes of 344 Diffuse Large B-Cell Lymphoma patients

  This study examines clinical outcomes of patients with diffuse large B-cell lymphoma (DLBCL). We analyzed a cohort of 344 patients from the German High-Grade Non-Hodgkin Lymphoma Study Group (DSHNHL). To protect patient confidentiality, all samples were pseudonymized. The dataset includes clinical key characteristics, such as patient age at diagnosis, sex, ECOG performance status, disease stage, serum lactate dehydrogenase levels, bulky disease, bone marrow involvement, extranodal involvement, and B-symptoms. We used these data alongside proteomic and transcriptomic profiles to explore relationships between clinical features and molecular signatures, reconstructing regulatory networks. Insights from this integrative analysis aim to identify key pathological drivers and biomarkers of DLBCL progression, ultimately informing improved risk stratification and targeted therapeutic approaches.

  Study EGAS50000001054

• Clinical Outcomes for 344 Diffuse Large B-Cell Lymphoma Patients

  This dataset contains clinical outcomes for 344 Diffuse Large B-Cell Lymphoma patients. For each subject we have recorded AGE (binarized: > 60, <= 60), SEX (male or female), LDH (serum lactate dehydrogenase), ECOG (Performance status 3 or 4), STAGE (Disease stage III or IV according to the Ann Arbor staging system), EXBM (Two or more extranodal sites of disease (outside of a lymph node), BSYMP (Presence of B symptoms), and BM (Bone marrow involvement). All data are delivered as a single XLSX file with one row per patient.

  Dataset EGAD50000001536

• DAC-Clinical Outcomes of Diffuse Large B-Cell Lymphoma patients

  Dac EGAC50000000595

• Spatial Transcriptomics Uncovers Tumor Microenvironment-Based Subtypes in Invasive Lobular Carcinoma

  Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer (BC); however, little is known about its tumor microenvironment (TME). Here, we investigated the spatial architecture of 43 hormone-receptor-positive, HER2-negative (HR+/HER2-) primary ILC samples, using spatial transcriptomics and detailed morphological annotation. By integrating morphology, spatial, and single-cell data, we mapped TME-related cell types, exploring their proximity to tumor cells and their enrichment in specific compartments. Clustering of spatial transcriptomics spots revealed significant inter- and intra-tumor heterogeneity, identifying pathways and cell types linked with disease outcomes. Morphological and gene expression data were finally integrated with a multimodal approach, leading to the identification of 4 new ILC subtypes based on TME heterogeneity. By using derived, specific gene expression signatures, these subtypes were identified and validated in external bulk RNA-seq/microarray datasets (SCAN-B and METABRIC), where differences in prognosis emerged.

  Study EGAS50000001001

• Changes in AXL and/or MITF melanoma subpopulations in patients receiving immunotherapy

  Our data demonstrate large interpatient variability and variable therapy-induced changes independent of the type of therapy. We identify the presence of previously described AXL+ and MITF+ subpopulations in metastatic tissues both at the mRNA level and in situ at the protein level, and demonstrate that MITF+ melanoma cells are significantly decreased upon immunotherapy, while AXL+ melanoma cell numbers are stable. MITF+ tumor cells showed the most significant inverse correlation with CD8+ T cells. Our patient cohort also shows that immunotherapy-induced changes in the abundance of AXL+ or MITF+ tumor cells did not correlate with improved survival.

  Study EGAS50000001080

• Melanoma transcriptomic data from patients undergoing immunotherapy

  This dataset contains raw RNA sequencing data from melanoma patient tumor samples collected before and during immunotherapy. The data support investigation of treatment-related changes in tumor cell populations. Samples were analyzed as part of a study exploring cellular responses to immune-based therapies.

  Dataset EGAD50000001569

• Whole-genome sequencing data of poorly differentiated thyroid carcinomas and matched blood from six patients

  This dataset contains raw sequencing reads in FASTQ format from 12 samples, comprising paired tumour and matched blood DNA from six patients diagnosed with poorly differentiated thyroid carcinoma (PDTC). Genomic DNA was extracted from fresh-frozen tumour tissue and peripheral blood using standard protocols. Whole-genome sequencing was performed on the Illumina HiSeq X Ten platform, generating paired-end reads at an average depth of approximately 20X. These data support investigations into the somatic mutational landscape and genomic alterations associated with PDTC.

  Dataset EGAD50000001628

• DAC for "Cross-species Comparison Reveals Therapeutic Vulnerabilities Halting Glioblastoma Progression" with Prof. Dr. Ana Martin-Villalba(a.martin-villalba@dkfz-heidelberg.de), Santiago Cerrizuela(s.cerrizuela@dkfz-heidelberg.de)

  Dac EGAC00001003564

• The Oral Microbiome and Head and Neck Cancer

  Oral microbiota may influence head and neck squamous cell carcinoma (HNSCC) development, potentially related to carcinogen metabolism. The human oral cavity hosts a diverse microbiota, including bacteria and fungi. We performed shotgun sequencing and ITS1 sequencing on 236 HNSCC case participants who developed HNSCC during a mean follow-up of 5.1 years and 458 matched controls who remained HNSCC-free. Oral samples were obtained from a prospective nested case-control study within three epidemiological cohorts: the American Cancer Society Cancer Prevention Study II Nutrition Cohort (ACS CPS-II), the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO), and the Southern Community Cohort Study (SCCS). Control participants were selected using 2:1 frequency matching based on cohort, age, sex, race and ethnicity, and time since oral sample collection.

  Study phs004018