3773 results for "*oma"

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• Tracking the evolution of Therapy-Related Myeloid Neoplasms using chemotherapy signatures

  Patients treated with cytotoxic therapies, including autologous stem cell transplantation, are at risk for developing therapy-related myeloid neoplasms (tMN). Pre-leukemic clones (i.e., clonal hematopoiesis; CH) are detectable years before the development of these aggressive malignancies, though the genomic events leading to transformation and expansion are not well-defined. Here, leveraging distinctive chemotherapy-associated mutational signatures from whole-genome sequencing data and targeted sequencing of pre-chemotherapy samples, we reconstruct the evolutionary life-history of 39 therapy-related myeloid malignancies. A dichotomy is revealed, in which neoplasms with evidence of chemotherapy-induced mutagenesis from platinum and melphalan are hypermutated and enriched for complex structural variants (i.e., chromothripsis) while neoplasms with non-mutagenic chemotherapy exposures are genomically similar to de novo acute myeloid leukemia. Using chemotherapy-associated mutational signatures as temporal barcodes linked to a discrete clinical exposure in each patient’s life, we estimate that several complex events and genomic drivers are acquired after chemotherapy is administered. For patients with prior multiple myeloma who were treated with high-dose melphalan and autologous stem cell transplantation, we demonstrate that tMN can develop from either a reinfused CH clone that escapes chemotherapy exposure and is selected following reinfusion, or from TP53-mutant CH that survives direct myeloablative conditioning and acquires melphalan-induced DNA-damage. Overall, we reveal a novel mode of tMN progression that is not reliant on direct mutagenesis or even exposure to chemotherapy. Conversely, for tMN that evolve under the influence of chemotherapy-induced mutagenesis, distinct chemotherapies not only select pre-existing CH, but also promote the acquisition of recurrent genomic drivers.

  Study EGAS00001006903

• Single-cell multi-omics of relapse/refractory multiple myeloma patients (Hipo K08K/H067/K43R)

  Therapy resistance in cancers is ingrained in complex clonal dynamics. While the major clonal population in a tumour might respond well to the employed intervention, minor clones will eventually mutate, consequently acquiring a selective advantage and drug resistance. Hence, to improve cancer therapy, it is crucial to understand the mechanisms behind clonal evolution. Here, we applied single-cell transcriptomics and epigenomics, as well as whole-genome sequencing to 15 patients with relapsed/refractory multiple myeloma (RRMM). By profiling longitudinal samples throughout varying treatment regimens, we were able to interrogate patterns of evolution across patients. In particular, by defining clones based on copy number aberrations (CNAs), we found evidence for the majority of clones being diminished but not fully eradicated during treatment.

  Study EGAS00001006538

• Single cell RNA sequencing of relapsed/refractory multiple myeloma

  Study EGAS00001004805

• Integrated genomic analyses reveal molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma

  Atezolizumab (anti–programmed death-ligand 1 [anti-PD-L1]) and bevacizumab (anti-vascular endothelial growth factor [anti-VEGF]) combination therapy has become the new standard of care in patients with unresectable hepatocellular carcinoma. However, potential predictive biomarkers and mechanisms of response and resistance remain less understood. We report integrated molecular analyses of tumor samples from 358 patients enrolled in the GO30140 phase 1b or IMbrave150 phase 3 trials who were treated with atezolizumab combined with bevacizumab, atezolizumab alone, or sorafenib. Pre-existing immunity assessed by high expression of CD274 (PD-L1 mRNA), T-effector signature, and intratumoral CD8+ T cell density were associated with response and better clinical outcomes with the combination treatment. Less clinical benefit was associated with high regulatory T cell (Treg) to effector T-cell ratio and high expression of oncofetal genes such as GPC3 and AFP. Improved outcomes from the combination vs atezolizumab alone appeared to be associated with high expression of KDR (VEGFR2), Treg, and myeloid inflammation signatures, indicating anti-VEGF may contribute by targeting these axes. These findings were further validated by transcriptome analysis of paired pre- and post-treatment biopsies, in situ analysis by multiplex IHC and digital pathology, and in vivo study of an immunogenic hepatocellular carcinoma mouse model. Our findings highlight that anti-VEGF therapy might augment antitumor immunity and enhance anti–PD-L1 immunotherapy by targeting VEGF-mediated angiogenesis, Treg proliferation, and myeloid cell inflammation. Furthermore, our study identified candidate biomarkers for predicting response and resistance to anti–PD-L1 and anti-VEGF combination therapy.

  Study EGAS00001005503

• Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma

  Diffuse large B cell lymphoma (DLBCL) is the most common hematologic malignancy and is characterized by a striking degree of heterogeneity. We enrolled a total of 1001 DLBCL patients and comprehensively defined the landscape of genetic mutations, copy number alterations and expression through whole-exome and transcriptome sequencing. We identified 150 genetic drivers of DLBCL including many novel, clinically relevant genes (e.g. SPEN, SETD1B and KLHL14). Genetic drivers were highly enriched among essential genes identified by CRISPR screening in DLBCL cell lines including those of immediate potential therapeutic relevance such as MTOR, SYK, SF3B1 and XPO1. RHOA emerged as a critical driver gene from our genetic analysis and CRISPR screen; we further delineated its functional role in vitro and in vivo models to define its role as an essential oncogene in DLBCL. Our work thus identifies the functional landscape of genetic drivers and their clinical and therapeutic relevance in DLBCL patients.

  Study EGAS00001002606

• Methylome of medulloblastoma data (MB_COMICS cohort)

  Study EGAS00001007815

• How to use the EGA search box

  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an OR operator when a space is inserted between two words. Include AND (capitalised) between the keywords in your search in order to show only pages containing both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

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• Whole genome sequencing of primary and metastatic Melanoma cases in an Australian cohort.

  Melanoma is the fourth most common cancer in Australia and the leading cause of cancer death in young adults. The Australian Melanoma Genome Project (AMGP) is analysing whole genomes from melanomas. We include the results of whole genome sequencing (WGS) for a number of datasets that include cutaneous, acral and mucosal melanoma subtypes.

  Study EGAS00001001552

• Single cell RNA-sequencing of GSCs and GBM tumours

  Characterization of glioblastoma tumors and glioblastoma stem cell (GSCs) lines with single cell or single nuclei RNA-sequencing.

  Study EGAS00001004656

• BLUEPRINT DNA Methylation 450K data of mantle cell lymphoma

  We have applied an analytic strategy to decipher the DNA methylome of 86 mantle cell lymphomas (MCL) in light of the methylome of the entire B-cell lineage. In this way, we first identified two MCL subgroups that respectively carry epigenetic imprints of pre- and post-germinal center B cells. Secondly, we observed that pure tumor-specific changes are rare, as most (89-99%) DNA methylation alterations in MCL are within or in close proximity of those regions showing dynamic methylation in normal B cells. Several thousand of these differentially methylated regions in MCL show concurrent changes in enhancer-associated histone modifications, including a region located 650 Kb away from the MCL oncogene SOX11. At the clinical level, epigenetic and genetic changes co-evolve during MCL progression and the magnitude of epigenetic changes is associated with overall survival of the patients.

  Study EGAS00001001637