11769 results for "cancer rna-seq"

in 14.31 milliseconds.

• Whole genome sequencing data of pediatric B-other subtype acute lymphoblastic leukemia

  For a long time, approximately 70% of childhood B-ALL cases could be classified routinely, and the remaining 30% were classified into the “B-other” group lacking the known genetic features. However, more recently, new genomic tools have uncovered new subtypes among the B-other groups. Approximately 10% of B-ALL cases lack the recurrent genetic abnormalities, and are referred as "B-other". They are typically more heterogeneous than other subtypes of B-ALL, and their prognoses are more variable. In a more recent update of the B-ALL classification, 27 subtypes were identified (Arber et al., 2022). The identification of some of these subtypes is dependent on NGS methods, such as RNA sequencing, which are not routinely used in all clinical laboratories. For example, ETV6::RUNX1-like and DUX4-rearranged subtypes have been identified using RNA sequencing. For this study, subtype predictions for some of the cases is acquired from ALLIUM (Krali et al., NPJ Precis Oncol., 2023) and ALLCatchR (Beder et al., Blood, 2022) based on the RNA-seq profiles.

  Study EGAS50000001497

• Tumor Profiler Melanoma Study

  The Tumor Profiler Study is an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease. Technologies used are digital pathology, targeted NGS, single-cell RNA, single-cell DNA, bulk RNA, bulk proteotyping, single-cell CyTOF, imaging mass cytometry, pharmacoscopy, 4i Drug Response Profiling, and cell-free DNA. These technologies were applied to 240 tumor samples collected over 3 years across three cancer indications: metastatic melanoma, metastatic epithelial ovarian cancer, and acute myeloid leukemia (AML). Here, we publish the scRNA, scDNA, and bkRNA data of the melanoma cohort.

  Study EGAS50000000599

• Whole exome sequencing (WES), shallow whole genome sequencing (sWGS), ultra-deep targeted sequencing (TS), RNA whole transcriptome sequencing (RNAseq), targeted TCR sequencing in RNA (RNA-TCRseq) from breast cancer samples.

  Whole exome sequencing (WES), shallow whole genome sequencing (sWGS), ultra-deep targeted sequencing (TS), RNA whole transcriptome sequencing (RNAseq) bam files. Targeted TCR sequencing in RNA (RNA-TCRseq) cram files.

  Dataset EGAD00001003933

• Personalized Breast Cancer Vaccines Based on Genome Sequencing

  This study is from a phase I clinical trial of neoantigen DNA vaccines in triple negative breast cancer patients with persistent disease following neoadjuvant chemotherapy. The exome and RNA sequences used to identify somatic mutations, predict neoantigens, and design vaccines is within.

  Study phs002787

• Application of organoid culture system of human gastrointestinal and thoracic cancer in genome medicine

  In order to establish preclinical models, we have established a kind of lung cancer tumoroids derived from a mucus-producing solid pulmonary adenocarcinoma patient. We performed whole exome sequencing and RNA sequencing to analyze genetic background of the tumoroids.

  Study JGAS000664

• Application of organoid culture system of human gastrointestinal and thoracic cancer in genome medicine

  In order to establish preclinical models, we have established a kind of lung cancer tumoroids derived from a mucus-producing solid pulmonary adenocarcinoma patient. We performed whole exome sequencing and RNA sequencing to analyze genetic background of the tumoroids.

  Study JGAS000653

• Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification (H021)

  Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

  Study EGAS00001006706

• The evolutionary dynamics of human colorectal cancer using single-gland spatial multi-omic profiling of DNA, RNA and chromatin.

  Study EGAS00001006088

• The evolutionary dynamics of human colorectal cancer using single-gland spatial multi-omic profiling of DNA, RNA and chromatin.

  Study EGAS00001006122

• The evolutionary dynamics of human colorectal cancer using single-gland spatial multi-omic profiling of DNA, RNA and chromatin.

  Study EGAS00001005827