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• single-cell RNA-Seq samples of CRC patients

  The dataset contains samples of 11 CRC patients (2 samples for each patient, tumor and normal adjacent tissue site, 22 samples in total). Dataset is composed by fastq file (paired end) type from 10x single-cell RNA-Seq.

  Dataset EGAD00001009634

• Investigation_of_mutational_signatures_associated_with_DNMT3A_deficiency_

  Mutational signatures have been shown to be attributable to specific genetic contexts, such as mutations in DNA repair genes. DNMT3A is a DNA methyltransferase that helps maintain the DNA methylation pattern in a site-specific manner and may participate in DNA repair or the stress response. We have identified an adult individual who is a germline mosaic for a DNMT3A mutation. We have obtained clonal lymphoblastoid cells (LCLs) from the subject representing both WT and mutant lines grown in the same individual for >50 years. These clones represent a unique opportunity to examine the mutational impact of the DNMT3A mutation in a well-controlled setting. Our goal is to perform WGS on whole blood, representing the pool, as well as several WT and several mutant clones, in order to investigate the contribution of DNMT3A to mutation rates and signatures.

  Study EGAS00001002329

• Dedifferentiated Melanoma (2021-02-02)

  De- and transdifferentiation of melanoma is a rare histopathological phenomenon that has not be characterised genetically. In this project we plan to sequence the genomes of de and transdifferentiated cases so as to define their genetic make-up. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006931

• Whole genome and transcriptome sequencing of lung cancer patients and cell lines at Genentech

  Version 1 Whole genome sequencing was applied to tumor and adjacent normal lung tissue in an individual non-small-cell lung cancer patient. We present an analysis of somatic changes identified throughout the tumor genome, including single-nucleotide variants, copy number variants, and large-scale chromosomal rearrangements. Over 50,000 high-confidence single-nucleotide variants were identified, revealing evidence of substantial smoking-related DNA damage as well as distinct mutational pressures within the tumor resulting in uneven distribution of somatic mutations across the genome. Version 2 Lung cancer is a highly heterogeneous disease in terms of both underlying genetic lesions and response to therapeutic treatments. We performed deep whole genome sequencing and transcriptome sequencing on 19 lung cancer cell lines and 3 lung tumor/normal pairs. Overall, our data show that cell line models exhibit similar mutation spectra to human tumor samples. Smoker and never-smoker cancer samples exhibit distinguishable patterns of mutations. A number of epigenetic regulators are frequently altered by mutations or copy number changes. A systematic survey of splice-site mutations identified over 100 splice site mutations associated with cancer specific aberrant splicing, including mutations in several known cancer-related genes. Differential usages of splice isoforms were also studied. Taken together, these data present a comprehensive genomic landscape of a large number of lung cancer samples and further demonstrate that cancer specific alternative splicing is a widespread phenomenon that has potential utility as therapeutic biomarkers.

  Study phs000299

• Mutational Signature and Transcriptomic Classification Analyses as the Decisive Diagnostic Tools for a Cancer of Unknown Primary with Neuroendocrine Differentiation

  PURPOSE: Cancer of unknown primary (CUP) is a group of metastatic tumors in which the standard diagnostic work-up fails to identify the site of origin of the tumor. The potential impact of precision oncology on this group of patients is large since their tumors might have actionable driver mutations that can provide treatment options otherwise not available for patients with these fatal cancers. This study investigated if comprehensive genomics analyses could inform on the origin of the tumor. PATIENT AND METHODS: Here we describe a patient whose tumor was misdiagnosed at least three times. Next-generation sequencing, a PDX mouse model and bioinformatics was used to identify an actionable mutation, predict resistance development to the targeted therapy, and to correctly diagnose the origin of the tumor. The Cancer Genome Atlas was used to benchmark the bioinformatics workflow. RESULTS: Despite the lack of a known primary tumor site and the absence of diagnostic immunohistochemical markers, the origin of the patient's tumor was established using the novel bioinformatics workflow. This included a mutational signature analysis of the sequenced metastases and comparison of their transcriptomic profiles to a pan-cancer panel of tumors from The Cancer Genome Atlas. We further discuss the strengths and limitations of the latter approaches in the context of three potentially incorrectly diagnosed TCGA lung tumors. CONCLUSION: Comprehensive genomics analyses could inform on the origin of tumors in patients suffering from CUP.

  Study EGAS00001003026

• DAC-2020-03-26-Lemola (DAC-039))

  DAC-2020-03-26-Lemola (DAC-039)), raw data in EGA, metadata in Harvard Dataverse

  Dataset EGAD50000000897

• T cell transcriptional gradient

  There are two datasets: 1. scRNA-seq of human cutaneous immune cells from psoriasis patients. These include pre- and post-Tildrakizumab treated patients and come in a BAM file format. 19006FL-25-01 19006FL-38-01 19006FL-32-01-03 19006FL-33-01 19006FL-28-01-05 19006FL-35-01-01 2. RNA-seq of ZFP36L2 CRISPIR deleted Human T cells are FASTQ files. 19006XR-30-05 19006XR-30-04 19006XR-30-02 19006XR-30-01 19006XR-26-05 19006XR-26-04 19006XR-26-02 19006XR-26-01 19006R-22-04 19006R-22-08 19006R-22-05 19006R-22-01

  Dataset EGAD00001009677

• Exome and transcriptome sequencing of Desmoplastic Small Round Cell Sarcoma

  To conduct exome and transcriptome profiling to identify secondary recurrent mutations, multi-site phylogenetic analyse and immune characterization of Desmoplastic Small Round Cell Sarcoma.

  Study EGAS00001004575

• Multi-modal spatial characterization of tumor-immune microenvironments in diffuse large B-cell lymphoma

  We have leveraged spatial transcriptomics together with proteomic and genomic analysis to investigate the spatial immunology and pathobiology of DLBCL. We define distinct cellular niches that differ in frequency across tumors, related to both Epstein-Barr virus (EBV) infection status and the anatomical site of the tumor, and show that residence within different cellular niches is associated with divergent functional states of T-cells and tumor B-cells due to different patterns of cell-cell communication. This provides a framework for understanding stereotyped patterns of spatial organization in DLBCL and immunological interactions associated with heterogeneity in immune cell infiltration and function across patients.

  Study EGAS50000001146

• Differential Transcription Start Site Usage in Brain-related Samples

  RNA-Seq is an effective method to study the transcriptome, but specialized methods are required to identify 5' ends of transcripts. Several published strategies exist for this specific purpose, but their relative merits have not been systematically analyzed. Here, we directly compare the performance of six such methods - testing five with cellular RNA as well as a novel spike-in RNA assay that helps address interpretation challenges that arise from uncertainties in annotation or RNA processing. Using a single human RNA sample, we constructed and sequenced 18 libraries with these methods and one standard, control RNA-Seq library. We find that the CAGE method performed best for mRNA and that most of its unannotated peaks are supported by evidence from other genomic methods. We then applied CAGE to eight brain-related samples and revealed sample-specific transcription start site (TSS) usage as well as a transcriptome-wide shift in TSS usage between fetal and adult brain.

  Study phs001463