15038 results for "cancer studies using rna-seq"

in 23.46 milliseconds.

• Small cell carcinoma of the ovary, hypercalcemic type, displays frequent inactivating germline and somatic mutations in SMARCA4

  Small cell carcinoma of the ovary of hypercalcemic type (SCCOHT) is an extremely rare, aggressive cancer affecting children and young women. We identified germline and somatic inactivating mutations in the SWI/SNF chromatin-remodeling gene SMARCA4 in 75% (9/12) of SCCOHT patients in addition to SMARCA4 protein loss in 82% (14/17) of SCCOHT tumors, but in only 0.4% (2/485) of other primary ovarian tumors. These data implicate SMARCA4 in SCCOHT oncogenesis.

  Study EGAS00001000714

• Barcelona_kids_with_melanoma

  This set of samples is composed of eight young people (7-16 years old) that have developed melanoma with first-degree relatives that have also developed cancer, which suggests a genetic component to their disease. Here we want to sequence these samples in order to find the causative mutations. As these samples do not carry any of the high-penetrance mutations known to date, finding the genes(s) responsible will offer new insights into the genetic mechanisms underlying predisposition to melanoma.

  Study EGAS00001000733

• Comprehensive investigation of genome architecture of gastric adenocarcinoma with whole-genome sequencing in the Chinese population.

  In this study, we performed whole-genome sequencing (WGS) of 214 paired GC tumors and adjacent normal tissues to identify novel potential biomarkers. We extracted mutational signatures which induce somatic mutations and classified GC tumors into four groups with significantly different prognosis. Moreover, we observed copy number changes and structural variations frequently occurring in some cancer related genes. Our study covers a comprehensive genetic framework that can be used in clinical trials and treatment modalities in GC.

  Study EGAS00001002404

• MYD88/TLR mutations in CLL

  The Chronic Lymphocytic Leukemia (CLL) Genome Project aims to identify genetic alterations involved in the development and progression of the CLL, which are still unknown, with the objective of generating a comprehensive catalogue of genetic alterations in 500 independent tumours. The CLL Genome Project, as a contributing member of the International Cancer Genome Consortium (ICGC), has the purposes of creating diagnostic tools, discovering therapeutic targets and developing new strategies that will allow a customized therapy for CLL in order to make it more precise and effective.

  Study EGAS00001000772

• Genetic ancestry contributes to somatic mutations in lung cancers from admixed Latin American populations

  Inherited lung cancer risk, particularly in non-smokers, is poorly understood. Genomic and ancestry analysis of 1,153 lung cancers from Latin America revealed striking associations between Native American ancestry and their somatic landscape, including tumor mutational burden (TMB), and specific driver mutations in EGFR, KRAS, and STK11. A local Native American ancestry risk score predicted EGFR and KRAS mutation frequency more strongly than global ancestry, suggesting that germline genetics (rather than environmental exposure) underlie these disparities.

  Study EGAS00001004752

• Whole Exome Sequencing of 15 Tumor/Normal pairs of inflammatory hepatocellular adenomas

  The French ICGC project on liver tumors is coordinated by Pr Jessica Zucman-Rossi and funded by Inca (French Institute for Cancer). The aim of the present project is to identify the catalog of somatic and germline mutations in liver tumors. The present series corresponds to 15 Tumor/Normal pairs of Whole Exome Sequencing (WES) of IHCA samples. Aligned bam files can be both in hg19 (CHC750T/CHC750N; CHC2189T/CHC2189N; CHC2615T/CHC2614N) or hg38 (other samples).

  Study EGAS00001003686

• Lung cancer organoids addition

  Organoids are self-organizing 3D structures grown from stem cells that recapitulate essential aspects of organ structure and function. Here we describe a method to establish long-term culture conditions of human airway epithelial organoids that contain all major cell populations and allow personalized human disease modelling. We collected macroscopically inconspicuous lung tissue from non-small-cell lung cancer (NSCLC) patients undergoing medically indicated surgery and isolated epithelial cells to engineer 3D organoids. We exploit the potential to derive sub-clones from AOs to demonstrate the feasibility of CRISPR gene editing. Finally, we show that AOs readily allow modelling of viral infections such as RSV and for the first time demonstrate the possibility to study neutrophil-epithelium interaction in an organoid model. Taken together, we anticipate that human AOs will find broad applications in the study of adult human airway epithelium in health and disease.

  Dataset EGAD00001004943

• Enrichment of homologous recombination repair alteration in prostate cancer brain metastases

  Whole exome sequencing from 51 patients with brain metastases from prostate cancer

  Dataset EGAD00001008033

• Garvan/St Vincent’s Prostate Cancer Tissue and Data

  The cohort (n=53) consists of prostate cancer patients from Australia. For each patient, a pair of blood and tumour samples were collected. The sequencing data was mapped to hg38 reference. Blood BAMs are named with “-B” as suffix and tumour BAMs are named with “-T” as suffix.

  Dataset EGAD00001009066

• Undifferentiated sarcomas develop through distinct evolutionary pathways

  Undifferentiated sarcomas (USARC) of adults are diverse, rare and aggressive soft tissue cancers. Recent efforts have confirmed that USARC exhibit one of the highest burdens of structural aberrations across human cancer. Here, we sought to unravel the genomic basis of this structural complexity by integrating whole genome sequencing, ploidy analysis and methylation profiling of 53 USARC. We identified whole genome doubling as a prevalent and pernicious force in USARC tumourigenesis. Deconvolution of the complex copy number and rearrangement landscapes show distinct signatures associated with chromothripsis, early-haploidy, and successive whole-genome-doubling events, suggesting four divergent models of sarcoma development. We show similar distinct evolutionary tumourigenic pathways in different sarcoma subtypes from the Cancer Genome Atlas. Thirteen percent of tumours exhibited a hypermutator phenotype, opening new avenues for clinical management such as immunotherapy, whilst the period prior to and between genome doubling events may represent clinically relevant interventional points in USARC.

  Dataset EGAD00001004162

• Metabolic reprogramming towards OXPHOS identifies a novel therapeutic target for mantle cell lymphoma

  Metabolic reprogramming is linked to cancer cell growth and proliferation, metastasis, and therapeutic resistance in a multitude of cancers. Targeting dysregulated metabolic pathways to overcome resistance, an urgent clinical need in all relapsed/refractory cancers, remains difficult. Through genomics analysis of clinical specimens, we show that metabolic reprogramming towards oxidative phosphorylation (OXPHOS) and glutaminolysis is associated with therapeutic resistance to the Bruton’s tyrosine kinase inhibitor ibrutinib in mantle cell lymphoma (MCL), an incurable B-cell lymphoma with poor clinical outcomes. Inhibition of OXPHOS with a novel, clinically applicable small molecule, IACS-010759, which targets complex I of the mitochondrial electron transport chain, results in significant growth inhibition in vitro and in vivo in ibrutinib-resistant patient-derived cancer models. This work suggests that targeting metabolic pathways to subvert therapeutic resistance is a clinically viable approach to treat highly refractory malignancies.

  Dataset EGAD00001004577

• The landscape of somatic mutation in normal colorectal epithelial cells

  The colorectal adenoma-carcinoma sequence has provided a paradigmatic framework for understanding the successive somatic genetic events and consequent clonal expansions leading to cancer. As for most cancer types, however, understanding of the earliest phases of colorectal neoplastic change, which may occur in morphologically normal tissue, is comparatively limited because of the difficulty of detecting somatic mutations in normal cells. Each colorectal crypt is a small clone of cells derived from a single recently-existing stem cell. Here, we sequenced hundreds of normal crypts from 42 individuals. Signatures of multiple mutational processes were revealed, some ubiquitous and continuous, others only found in some individuals, in some crypts or during some phases of the cell lineage from zygote to adult cell. Likely driver mutations were present in ~1% of normal colorectal crypts in middle-aged individuals, indicating that adenomas and carcinomas are rare outcomes of a pervasive process of neoplastic change across morphologically normal colorectal epithelium.

  Dataset EGAD00001004193

• A compendium of mutational signatures due to environmental exposures

  Whole-genome-sequencing (WGS) of human tumours has revealed distinct mutation patterns that hint at the causative origins of cancer. We examined mutational-signatures in 324 WGS of human induced pluripotent stem cells (iPSCs) following exposure to known or suspected environmental carcinogens. 79 agents were tested with or without metabolic activation at concentrations that produced measurable cytotoxicity; 41 yielded characteristic substitution mutational signatures. Some exhibit similarity with signatures found in human tumours. Additionally, 6 agents produced double-substitution signatures and 8 produced indel signatures. Investigating mutation asymmetries across genome topography reveals fully functional mismatch and transcription-coupled repair pathways in iPSCs. Primary adducts induced by environmental carcinogens can be resolved by disparate repair/replicative pathways, resulting in an assortment of signature outcomes even for a single mutagen. This compendium of experimentally-induced mutational-signatures permits further exploration of roles of environmental agents in cancer aetiology, and underscores how human stem cell DNA is directly vulnerable to environmental agents.

  Dataset EGAD00001004583

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? TThe most suitable archive for your data is dependent on the type of data you are wishing to submit and whether the data require public or controlled access. Public access is defined as complete and open access to all submitted data. On the contrary, controlled access, exerted by the EGA, requires formal applications to be made to access the submitted data files and metadata. EGA only accepts human-derived data subject to controlled access. If your submission contains other types of data, please choose the appropriate repository for it (see image below): ENA, EVA, ArrayExpress, BioSD and GWAS catalog.  Should your submission be subjected to controlled access? Data access conditions are normally defined in the original informed consent agreements signed by the participants involved in your study. All data submitted to the EGA is subject to controlled access. These consents prevent the derived data files, potentially identifiable, from being dispersed by open and public access. Controlled access data often refers to human data derived from medical research and consortium projects. If in doubt, consult the informed consent agreements that apply to your study The EGA enables you to hold a submission before publication. What data types can be submitted to the EGA? Data types accepted by the EGA can be split into three categories: Sequences: both in generic and platform-specific formats. Array-based: from raw signal files to processed matrices. Phenotypes: all possible phenotype formats are accepted. All manufacturer-specific raw data formats derived from major next generation sequencing platforms are accepted. Also generic sequence formats: flat reads in a FASTQ file, aligned sequences (BAM or CRAM files) as well as sequence variation files in VCF format. All array-based technologies are accepted, including raw data, intensity and analysis files, without any restriction on data formats accepted. We also accept and distribute phenotype data (associated to the samples) in almost any format: from an image to a README file. How long does a submission take? Submissions to EGA come in a variety of formats and sizes, thus it is difficult for us to exactly predict how long a submission will take. We, therefore, advise all of our submitters to allow as much time as possible to make a submission. Based on previous records, we anticipate that the submission process may take at least one month. The submitter’s familiarity with the procedures, possible technical issues that may arise during submission and the amount of data that is being submitted are the main factors that will affect the length of the submission process. However, each step of a regular submission should be considered when estimating the time it would take: Encryption of the files Upload of the files Metadata submission Archival of the files Release of the study and datasets to EGA webpage For example, the upload of the files depends on the submission size, while metadata submission mainly relies on each submitter’s expertise. Further, some steps (e.g. answering to inquiries) depend on the EGA Helpdesk team, which may take some days to be processed during busy times Is data deposited in the EGA secure? The EGA set-up consists of a secure computing facility for data processing, a shared EBI set-up for data submissions and distribution of data via data requests made through the EGA website. Data is also copied in the Barcelona Supercomputing Center (BSC) infrastructure, where all stored and distributed data is encrypted Data is encrypted along the submission process and stored securely, granting its access to authorised users exclusively. During the download process, through our Python Client or Aspera, all requested data is downloaded over secure https connections. All data at the EGA is encrypted, and only accessible (for log-in and download) through secure protocols. For further information please, visit our security overview. What documentation do I need to provide? All submissions require policy’s documentation: 'Data Access Agreement (DAA)', 'Data Processing Agreement (DPA)' and 'Authorized Submitters Formulary'. The data processors (EGA) and the data owners will also sign the DPA. Will all metadata be public? Among the submitted metadata we need to make the distinction between identifiable and unidentifiable metadata: (1) the former may allow the identification of the human the sample derived from (e.g. detailed geographical providence, personal name, family ancestry…); (2) while the latter can be used to interpret the data without compromising the anonymity of the patients. The majority of the metadata submitted to the EGA corresponds to the unidentifiable category (e.g. sequencer's model). This type of metadata is publicly available on the EGA website and other EBI resources/partners’ websites. On the other hand, some parts samples’ metadata are subject to being identifiable, and thus only accessible by authorized data requesters, with the exception of: 5 submitter-defined attributes of the sample: alias, title, subject_id, gender and phenotype. It is the submitter’s responsibility not to submit sensitive metadata in these public fields. 3 anonymised fields that pinpoint the sample record in archivals: sample’s EGA stable ID (EGAN…), BioSample ID (SAMEA…) and submitter’s center name. During Submission Are there any sample specific requirements for EGA? All samples submitted to the EGA must include the attributes of biological sex, subject ID (anonymised individual identifier) and phenotype information. These are critical for data findability and its analysis, and we highly recommend using controlled ontology terms where applicable. For example: defining tumour and non-tumour samples and/or defining disease state. The EGA recommends using the Experimental Factor Ontology Database to find ontologized terms that describe your sample phenotypes. How do I get an accession number to use in my publication? You will receive your study accession number (EGAS…) upon complete your submission, either: Programmatically. As soon as the metadata is submitted and validated your study will be assigned an accession number that will be given in the submission’s response. Manually registering your study and relevant metadata using the online metadata submission tool: the EGA submitter Portal. How are files uploaded to the EGA? Data files are uploaded into private submission drop boxes (i.e. environments to which you are granted access and where you can transfer your files) using INBOX or FTP. These spaces are provided as part of the submission procedure. Before uploading any file, you must encrypt your files, . Only encrypted files shall be uploaded to the drop boxes. Why does data need to to encrypted for my submitted files? It is one of the security steps the EGA has implemented. In case of a security breach, people without the proper encryption key will not be able to read or use the information that could have been leaked. This measure is essential when working with sensitive data, such as controlled access human data. All submitters must use crypt4gh to create EGA compliant files prior to uploading them. This encryption is GPG-based, using EGA’s public key. Why are my files not available if I see them in the INBOX? There exists a time window between the data upload and the availability of such files via the Submitter Portal. For this reason, some metadata (run and analysis objects) cannot be registered until at least 24 hours after the files have been uploaded to your box. Why are MD5 sum values generated for my submitted files? We require pre- and post- encryption MD5 (message-digest) checksum values to be provided for all submitted files. These 128-bit values are computed using the content of each file, creating unique sequences that allow us to ensure that file integrity has been maintained during the transfer process. In other words, if the MD5 checksums we generate and those you generated match, we infer that the content of the transferred files is correct (i.e. files are not corrupted or truncated). MD5 checksums are computed automatically using the crypt4gh tool provided. Your submission will not be accepted and may be significantly delayed if you do not provide MD5 checksum values for all data files in the required format. How can I check if my files are correctly uploaded to the inbox? It is important to check the status pf your file so you know whether your files are in the inbox, being processed, or if there is any issue with one of them. In order to check this, you should: Look for the file locally. Drag and drop it to the file table. Then bars will appear on the table, which means that we are processing it. Green: The files checksum are correct and your file will move to “ingested files”. No further actions are needed from your end.. Red: The files checksum does not match and your file needs to be re-uploaded. Please re-upload relevant files to your inbox using the same path. After Submission How do I use my accession number in my publication? We suggest the use of the below template, using your study accession ID (EGAS…) : Data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found at https://ega-archive.org and "The European Genome-phenome Archive of human data consented for biomedical research" Your study ID will be the one that groups your whole submission, and thus its usage is recommended as such. Nevertheless, all metadata submitted to EGA hold a unique and persistent identifier (starting with EGA…) that can be used to identify specific records. For example, you could reference a specific dataset (EGAD…) or sample (EGAN…) in your publications (see full list of identifiers). How do I make my data searchable? Once you have finalized your submission, you can schedule the data release. Please take into account that the release process needs time for the files to be archived in our system, and for the Helpdesk team to validate your submission. Can I withdraw (meta)data from the EGA? We have methods in place for the secure removal of deposited (meta)data. Contact EGA-helpdesk for further details. EGA complies with FAIRness of (meta)data, and thus, even when the data is removed we keep an entry for their identifiers in our system. In other words, we execute a soft delete on canceled objects (e.g. a study): metadata is still stored in our systems, but it loses all links, cannot be queried and data files cannot be retrieved anymore. The reason behind this behaviour is so that queries using withdrawn data properly respond back (see example of a canceled study). What happens to the data once it has been submitted to the EGA? When the data is submitted, the submitter can choose either keep their data private or schedule the release of their data.

  Documentation submission/metadata/submission/FAQ

• NIPT samples for systematic evaluation of BinDel, a software tool for detecting clinically significant microdeletions in low-coverage WGS-based NIPT samples

  This dataset contains three sets of samples. The first sample set contains euploid fetus pregnancies reported by NIPTIFY screening test and postnatal evaluation. Dataset was processed similarly to previously published guidelines from KU Leuven, with modifications [1]. Briefly, peripheral blood samples were collected in cell-free DNA BCT tubes (Streck, USA), and plasma was separated with standard dual centrifugation. Cell-free DNA was extracted from 3 ml plasma using MagMAX Cell-Free DNA Isolation Kit (ThermoFisher Scientific). Whole-genome libraries were prepared using the FOCUS (Fragmented DNA Compact Sequencing Assay, Competence Centre on Health Technologies, Estonia) NIPT method protocol with 12 cycles for the final PCR enrichment step. In the following quantification, equal amounts of 36 samples were pooled, and the quality and quantity of the pool were assessed on Agilent 2200 TapeStation (Agilent Technologies, USA). Whole genome sequencing was performed on the NextSeq 550 instrument (Illumina Inc.) with an average coverage of 0.32× (minimum 0.08 and maximum 0.42) and producing 85 bp single-end reads. The second sample set contains a single NIPT sample postnatally diagnosed with Prader-Willi syndrome. The sample was sequenced with Illumina NextSeq 500 platform, producing 85 bp single-end reads with an average per-sample coverage of 0.32× at the University of Tartu, Institute of Genomics Core Facility, according to the manufacturer’s standard protocols, as described previously [2]. The third sample set contains samples SC005 (SeraCare Life Sciences Inc lot #10446565), SC0042 (#10571706), and SC016 (#10560229). These are SeraCare Life Sciences Inc circulating cell-free DNA (ccfDNA) like mixture of human genomic DNA that consists of matched maternal and fetus. SC005 and SC0042 consist of matched DNA of maternal and fetus with DiGeorge Syndrome. SC016 is a custom-ordered DNA Mix with fetus DNA having a pathogenic loss of the terminal region of 20p13 and a pathogenic 3q29 duplication. SC016 was processed as the first sample set was processed, and SC0042 was processed as the second sample set was processed. Sample SC005 was processed once as was sample set 1 and once as was sample set 2 processed. This study was performed with the approval of the Research Ethics Committee of the University of Tartu (#352/M-12). 1. Bayindir B, Dehaspe L, Brison N, Brady P, Ardui S, Kammoun M, et al. Noninvasive prenatal testing using a novel analysis pipeline to screen for all autosomal fetal aneuploidies improves pregnancy management. Eur J Hum Genet. 2015;23: 1286– 1293. doi:10.1038/ejhg.2014.282 2. Žilina O, Rekker K, Kaplinski L, Sauk M, Paluoja P, Teder H, et al. Creating basis for introducing noninvasive prenatal testing in the Estonian public health setting. Prenat Diagn. 2019;39: 1262–1268. doi:10.1002/pd.5578

  Dataset EGAD00001009512

• Homopolymer switches WES dataset

  This project used NGS (next generation sequencing) in mismatch repair deficient colorectal cancer samples. The project investigated the role of secondary MMR (mismatch repair) gene mutations in tumor evolution. This dataset includes BAM files from multi-region Whole Exome Sequencing of 49 samples from 22 patient tumors.

  Dataset EGAD50000000319

• Whole-genome sequencing of high-retrotransposition rate tumours

  We performed whole-genome sequencing of 137 tumours and their matched-normal (adjacent tissue) samples to identify tumours with high rates of somatic retrotransposition

  Study EGAS50000000414

• Dataset with WGBS data of CRC and colon tissue

  This dataset contains WGBS data from 20 samples, which are either from colorectal cancer or control colon tissue. Sequencing was performed on either Illumina HiSeq 2000 or HiSeq X and the sequences are paired.

  Dataset EGAD50000000772

• Dac for "ARHGAP11A Maintains Cortical Progenitor Identity Through RHOA–ROCK Signalling During Human Brain Development" with Julia.ladewig@zi-mannheim.de, Department of Translational Brain Research. Moritz Mall, m.mall@dkfz-heidelberg.de, German Cancer Research Center (DKFZ). Yannick Hass, yannick.hass@zi-mannheim.de, Department of Translational Brain Research. Anne Hoffrichter, anne.hoffrichter@zi-mannheim.de, Department of Translational Brain Research, Central Institute of Mental Health

  Dac EGAC00001003603

• cfDNA sWGS BAM — Metastatic colorectal cancer

  Plasma cirDNA sWGS for metastatic colorectal adenocarcinoma at diagnosis prior to systemic therapy. Libraries DSP/SSP (per sample). Sequencing by IntegraGen (2022–2024). Alignment: GRCh38 with BWA-MEM; BAMs sorted/indexed; delivered as primary mapped reads only (filters/MAPQ in analysis metadata). Controlled access

  Dataset EGAD50000001879

• WES dataset

  Associated with Molecular biomarkers in progression from refractory celiac disease to the lethal cancer variety enteropathy associated T cell lymphoma (EATL) Study. Whole Exome Sequencing (WES) of 63 samples. WES analysis done to detect mutations and copy number aberration predictive for progression from RCD to EATL.

  Dataset EGAD50000001164

• SCANDARE TNBC

  Prospective biobanking study in breast patients aiming at better understand the link between the molecular alterations of the tumor itself, its microenvironment and immune response.

  Study EGAS50000000970

• NGS_based_viability_screening_using_haploid_cell_line

  Identification of human deubiquitylating enzymes whose knock out result in hypersensitivity to DNA damaging agents, by comparing the sequence reads of ‘barcode region’ from mixed cell culture.

  Study EGAS00001001095

• Matched_Pair_Cancer_Cell_line_Whole_Genomes

  We propose to definitively characterise the somatic genetics of 29 matched pair cell lines through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing.

  Study EGAS00001000160

• MPN_TGS2_Follow_up___PT1_Vori_other

  Targetted capture and resequencing of 94 known myeloid genes across MPN trials (PT1 and Voriconazole study) as well as remaining MPN samples. New bait design Jan 2014.

  Study EGAS00001000765

• Exploring_the_landscape_of_somatic_mutations_in_normal_tissue___GI___MAP___WGS

  Somatic mutations (burdens and signatures) and clonal dynamics in normal human tissues from the gastrointestinal tract of individuals with tumour predisposition syndromes and DNA damage repair defects.

  Study EGAS00001004122

• To determine the genomic profile of Triple Negative Breast Cancer patient-derived xenografts (PDX cohort)

  Specific BRCA and immune configurations determine optimal response to platinum-based chemotherapy in triple negative breast and ovarian carcinomas (PDX_WGS)

  Study EGAS00001005995

• Res1_HT29_exp2_MC_03_03_22

  1 sample is pure plasmid DNA and 10 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Study EGAS00001006093

• Res1_H23_exp1_MC_04_03_22

  1 sample is pure plasmid DNA and 8 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Study EGAS00001006091

• Res1_HT29_exp1_MC_02_03_22

  1 sample is pure plasmid DNA and 10 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Study EGAS00001006092

• whole-genome sequencing of gastric cancer

  whole-genome sequencing of 168GCs identifies hot-spot tandem duplications

  Dataset EGAD00001004811

• Multi-region sequencing of colon tumour biopsies and matched normals

  Dataset of adenoma and colon cancer multi-region sequencing. Publication: Nat Ecol Evol. 2018 Oct;2(10):1661-1672. doi: 10.1038/s41559-018-0642-z. Epub 2018 Aug 31.

  Dataset EGAD00001004966