5246 results for "*oma"

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• Targeting vulnerabilities in IDH mutant tumours: The model matters RNAseq study

  Introduction: Synthetic lethal interactions with IDH1 and IDH2 (IDH) mutations were identified in non-endogenous IDH mutant (IDHmut) AML and glioma models, but are absent in endogenous IDHmut chondrosarcoma cell lines. The translation into successful clinical applications has remained challenging, implying artificially created models do not fully recapitulate endogenous IDHmut tumour biology. The aim of this study was to elucidate if the model system is indeed an important factor to consider when studying therapeutic vulnerabilities in IDHmut tumours. Methods: Vector-based and CRISPR-Cas9 approaches were used to introduce or revert the IDH1 mutation in chondrosarcoma cell lines. These isogenic cell line pairs were used to examine the presence of known therapeutic vulnerabilities and their underlying biological mechanisms. Results: Vector-based IDHmut chondrosarcoma models showed the previously reported synthetic lethal interactions, but these treatment sensitivities were absent in the CRISPR-edited models. Interestingly, not all vector-based IDHmut cell lines displayed the same therapeutic vulnerabilities. Differences in treatment response were associated with multiple factors, including IDHmut protein expression and D-2-HG levels, in line with the fact that therapeutic vulnerabilities could be induced in the CRISPR-edited models by enhancing D-2-HG levels. Conclusion: Our findings demonstrate that synthetic lethal interactions observed in vector-based models are often a consequence of IDHmut protein overexpression and supra-physiological D-2-HG levels. These results highlight that relying on artificially created IDHmut models may lead to the identification of therapeutic vulnerabilities that are not present in IDHmut tumours, potentially explaining the poor translation of preclinical findings to clinical trials.

  Study EGAS50000001871

• RNA sequencing data and whole exome sequencing data of genetically modified IDH1 wildtype and mutant chondrosarcoma cell lines

  This dataset contains total RNA sequencing (polyA enriched) data of early and late passages of n=5 vector-based CH2879 empty vector and IDH1mut cell lines. Total RNA sequencing (polyA enriched) using 150bp paired-end reads was performed in triplicate using the NEBNext Ultra II Directional RNA Library Prep Kit (New England Biolabs) on the NovaSeq6000 platform (Illumina). Additionally, this dataset contains whole exome sequencing data of n=15 parental, mock, and CRISPR-edited IDH1mut and IDH1wt CH2879 and JJ012 cell lines. Whole exome sequencing using 150bp paired-end reads with a minimum coverage of 100x was performed in single using the SureSelect Human All Exon V8 library (Agilent Technologies) on the NovaSeq X platform (Illumina).

  Dataset EGAD50000002703

• Tumor-resident T cells and dendritic cells form an in situ archetype during immunotherapy response in melanoma.

  Tumor-resident (TR) T cells, known as tissue-resident memory (TRM) T cells in mice, play a central role in melanoma immunosurveillance, yet their contribution to immune checkpoint inhibitors (ICI) therapy has not been comprehensively explored. We performed spatial and single-cell profiling on 32 metastatic melanoma lymph node samples, from treatment-naïve, ICI-resistant, or ICI-responsive patients. Here we show that tumor areas in ICI-responders were enriched in both CD8+ and CD4+ TR. CD8+ TR were hyperexpanded, and both CD8+ and CD4+ TR cells upregulated cytotoxicity-related gene expression, suggesting functional anti-tumor immunity. Conversely, ICI-resistant tumors displayed chronic IFN-γ response pathways, linked to T cell exhaustion. We further identified a spatially organized immune triad composed of CD8⁺ TR, CD4⁺ TR, and type-3 dendritic cells (DC3) that is exclusive to responding tumors. These findings define coordinated cellular interactions within the tumor microenvironment that underpin successful immunotherapy and provide a framework for spatial biomarkers of response.

  Study EGAS50000001722

• TR T Cell and Melanoma Study Data Access Committee

  This Data Access Committee (DAC) governs access to controlled human genomic and transcriptomic data generated from melanoma patients enrolled in studies investigating tumour-resident immune cells and mechanisms of resistance to immune checkpoint inhibitors. The dataset includes bulk and single-cell RNA sequencing, TCR sequencing, and associated clinical metadata derived from metastatic melanoma samples. Access to these data will be granted to qualified researchers for ethically approved studies, in accordance with patient consent, institutional guidelines, and applicable data protection regulations. Requests will be evaluated based on scientific merit, data security measures, and compliance with the intended use of the data. All approved users must agree to a Data Access Agreement outlining conditions for data use, storage, and publication.

  Dac EGAC50000000974

• Bulk RNA-seq (FASTQ) – melanoma samples

  This dataset comprises raw FASTQ files from bulk RNA sequencing of metastatic melanoma lymph node samples collected from patients with differential responses to immune checkpoint inhibitor therapy. RNA was extracted from tumour tissue, enriched for polyadenylated transcripts, and libraries were prepared using standard Illumina protocols. Sequencing was performed on an Illumina platform to generate high-quality paired-end reads. The dataset includes multiple patient samples with associated clinical annotations, enabling analysis of tumour-intrinsic transcriptional programs linked to immunotherapy response and resistance.

  Dataset EGAD50000002475

• Single-cell RNA-seq (10x) – melanoma samples

  This dataset comprises raw FASTQ files generated from single-cell RNA sequencing of metastatic melanoma lymph node samples using the 10x Genomics Chromium platform. Single-cell suspensions were prepared from tumour tissues, and libraries were constructed using 5′ or 3′ gene expression kits following the manufacturer’s protocol. Sequencing was performed on an Illumina platform to obtain high-throughput single-cell transcriptomic profiles. The dataset includes samples from patients with differing responses to immune checkpoint inhibitor therapy, enabling characterisation of tumour-resident immune cell populations and tumour–immune interactions associated with treatment response and resistance.

  Dataset EGAD50000002476

• Nanostring – melanoma samples

  This dataset comprises gene expression data generated using the Nanostring nCounter platform from metastatic melanoma samples. RNA extracted from tumour tissues was analysed using targeted gene expression panels, including immune-related and tumour-resident T cell signatures. The dataset includes RCC files and associated normalised counts derived from patients with differing responses to immune checkpoint inhibitor therapy. These data enable targeted profiling of immune and tumour transcriptional programs associated with treatment response and resistance.

  Dataset EGAD50000002477

• TCR-seq (10x VDJ) – melanoma samples

  This dataset comprises raw FASTQ files generated from T cell receptor (TCR) sequencing of metastatic melanoma samples using the 10x Genomics V(D)J platform. Single-cell suspensions were processed to generate paired gene expression and V(D)J libraries, with this dataset containing the V(D)J-enriched libraries targeting TCR α and β chains. Sequencing was performed on an Illumina platform to enable reconstruction of TCR clonotypes and analysis of clonal expansion. The dataset includes samples from patients with varying responses to immune checkpoint inhibitor therapy, supporting investigation of T cell clonality, antigen specificity, and immune dynamics associated with treatment response and resistance.

  Dataset EGAD50000002478

• Follicular Lymphoma Whole Genome and Transcriptome Sequencing

  Study EGAS00001006646

• Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens

  Study EGAS00001007404

• LySeqST: A targeted sequencing assay for robust genomic classification of diffuse large B-cell lymphoma

  Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous disease. Two genomic classification systems, LymphGen and DLBclass, are capable of reproducibly classifying single tumors into subtypes with prognostic relevance in the context of standard-of-care R-CHOP chemoimmunotherapy. Preliminary data from subgroup analyses of clinical trials suggest that the distinct biological features are targetable with drugs that exploit subtype-specific vulnerabilities, and large clinical trials are being designed to test this hypothesis. Although whole exome sequencing (WES) remains the gold standard for each of these classification systems, many laboratories may opt for smaller targeted sequencing panels that reduce costs associated with sequencing, data storage, and processing. Here, we describe the design and validation of a targeted sequencing panel (LySeqST) that captures the genomic features required specifically for LymphGen classification. We perform in silico and real-world validation vs. WES data and demonstrate that LySeqST can be used to accurately classify DLBCL tumors. In an unselected population-based cohort, we determine the real-world proportions and validate the prognostic relevance of LymphGen subtypes, confirming that only tumors expressing the dark zone gene expression signature are consistently associated with inferior outcomes. Our results support the use of LySeqST for accurate genomic classification of DLBCL.

  Study EGAS50000001601

• Vaccine-Expanded Plasmablast-like B Cells Are Associated with Response to Dendritic Cell Therapy in Metastatic Melanoma

  Dendritic cells (DCs), either monocyte-derived (moDCs) or naturally circulating, have been employed in cancer treatment across both metastatic and adjuvant settings. DC vaccines (DC Vax) are capable of inducing tumor-specific immune responses, yet clinical outcomes have thus far fallen short of expectations. Here, we performed a multi-omics analysis of the apheresis product, including CD14⁺ monocytes, and the final DCVax formulation. Among all the components analyzed, the most striking finding was the unexpected presence of CD19⁺ B cells within the DC Vax formulation, which distinguished Responders from Non-Responders to moDC vaccination in metastatic melanoma. The abundance of B cells in DC Vax correlated with their peripheral frequency; however, within the vaccine, these cells acquired a CD20lowCD27hiCD38hi plasmablast-like phenotype, distinct from their autologous circulating B-cell precursors. Notably, a parallel observation was made in Responders, whose pre-treatment lesions were enriched in B cells organized as Tertiary Lymphoid Structures. These results underscore the importance of B cells in both baseline immunity and DCVax formulation, hinting at unexplored mechanisms by which they may influence vaccine effectiveness.

  Study EGAS50000001177

• Differential Wnt/β-Catenin Signaling Activities Shape Distinct Tumor Cell Phenotypes

  By profiling patient-derived hepatoblastoma organoids via single-cell transcriptomics and chromatin dynamics, we identify subtype-specific transcriptional and epigenetic profiles. Using CUT&RUN, we show that β-catenin engages distinct genomic regions, which is dictated by differential association with TCF/LEF family transcription factors. TCF7L2 binds to a motif, distinct from the classical Wnt consensus sequence, which is found in the promoter of hepatic target genes.

  Study EGAS50000001229

• Spatial transcriptomics of human meningioma samples.

  We performed spatial transcriptomic profiling of human meningioma samples to map gene expression patterns within their native tissue architecture.

  Study EGAS50000001556

• A single-cell atlas of meningioma.

  In this study, we examined the breadth of cell types and states in meningioma using the largest single-cell cohort of meningioma by generating more than 7.5 million total cells from meningioma and surrounding dura, profiled using single-nuclear RNA sequencing and high-resolution spatial transcriptomics.

  Study EGAS50000001589

• cell-free Methylated DNA by Immunoprecipitation and Sequencing (cfMeDIP) of human meningioma samples.

  Raw data for cell-free Methylated DNA by Immunoprecipitation and Sequencing (cfMeDIP) of human meningioma samples.

  Study EGAS50000001539

• Epigenomic profiling of neuroendocrine lung cancer

  Heterogeneity of neuroendocrine (NE) differentiation in pulmonary high-grade NE carcinoma, mainly small-cell lung cancer (SCLC), has been recently explored based on the expression of lineage transcription factors such as ASCL1, NEUROD1 and POU2F3. However, molecular classification determined by these factors is not settled and accumulating evidence suggest that NE differentiation among lung cancers is more heterogeneous. Here, we investigated super-enhancer profiles of formalin-fixed paraffin-embedded tumor tissues from patients with a wide range of lung cancer with NE components, mainly large cell neuroendocrine carcinoma (LCNEC).

  Study JGAS000903

• Plasma cell-free small RNA sequencing comparison of Illumina and Ultima Genomics in pancreatic cancer

  This study evaluates plasma-derived cell-free small RNA sequencing in a case-control cohort of 39 pancreatic ductal adenocarcinoma patients and 39 control subjects. Cell-free nucleic acids were isolated from plasma, small RNA libraries were prepared using the SMARTer smRNA-Seq workflow, and the same libraries were sequenced on both Illumina NovaSeq 6000 and Ultima Genomics UG 100 platforms. The project aims to compare platform performance for circulating small RNA and mature microRNA profiling, including read yield, alignment, RNA biotype composition, mature miRNA detection, expression concordance, and pancreatic cancer-associated differential expression. The dataset supports benchmarking of cross-platform reproducibility and assessment of biologically relevant plasma microRNA signals in pancreatic cancer.

  Study EGAS50000001907

• Plasma cell-free small RNA sequencing of pancreatic cancer and control samples

  This dataset contains plasma-derived cell-free small RNA sequencing data from 78 human samples, comprising 39 pancreatic ductal adenocarcinoma patients and 39 control subjects. Each sample was sequenced as a single-end FASTQ library on both Illumina NovaSeq 6000 and Ultima Genomics UG 100 platforms, yielding 156 submitted sequencing runs. The data were generated from SMARTer smRNA-Seq libraries to support comparative analysis of sequencing performance, mature microRNA detection, and pancreatic cancer-associated cell-free small RNA expression across the two platforms.

  Dataset EGAD50000002771

• VIVO Biobank Neuroblastoma Project Data Accession Committee

  Access to neuroblastoma sample data for VIVO Biobank projects.

  Dac EGAC50000000977

• Frequency and prognostic significance of genetic abnormalities in a subgroup of intermediate risk neuroblastoma patients: A SIOPEN study

  Intermediate risk neuroblastoma includes a rare subgroup of patients over the age of 18 months at diagnosis with unresectable, localised tumours with unfavourable histology without MYCN amplification. This group is treated differently around the world with some patients receiving upfront myeloablative therapy (MAT) in addition to standard treatment with chemotherapy, surgery, radiotherapy and 13 cis retinoic acid, but the overall 5-year patient survival with or without MAT is similar (75%). This study aimed to understand the neuroblastoma biology of this rare patient subgroup.

  Study EGAS50000001908

• TNGS data for SIOPEN intermediate-risk neuroblastoma subgroup study

  Targeted next-generation sequencing data for a SIOPEN intermediate-risk neuroblastoma subgroup (age >18 months, localised, unresectable, non-MYCN-amplified neuroblastoma with unfavourable histology), with a total of 31 cases analysed. All but one sample have been analysed using the same workflow, with the exception being NB32. VCF files for the analyses are included in this dataset.

  Dataset EGAD50000002773

• WES data for SIOPEN intermediate-risk neuroblastoma subgroup study

  Whole exome sequencing (WES) data analysed for the SIOPEN intermediate-risk neuroblastoma subgroup (age >18 months, localised, unresectable, non-MYCN amplified neuroblastoma with unfavourable histology), data for a total of 30 cases available. Four samples were analysed as part of another relapse neuroblastoma study (NB02 to NB05), of which two have germline comparison available. The remaining 26 cases had a different analysis workflow, with all but one having germline comparison available. VCF files from WES analyses are included in this dataset.

  Dataset EGAD50000002774

• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

  Blog upcycling-and-merging-data-to-challenge-glioblastoma

• The last addition to the list of clonal evolution studies in the EGA

  Tumours of different types may arise from a single cell with at least one malignant mutation. Accumulation of subsequent hits generates lineages of cells with different cancer transformation power; thus, every clone follows an autonomous evolution, and several clones may co-exist into the same solid or liquid tumour. Clonal evolution studies are not new: the conceptual power of this approach has been recognized since the early ’80, especially in leukaemia patients. Nonetheless, these studies greatly benefit from recent technological improvements -machineries and protocols-, which enable ever-increasing sequencing depth, allowing the detection of low frequency variants and consequently more tumour clones. Note: the figure belongs to the original paper and is owned by the authors. Researchers at the University of Kyoto in Japan, have indeed used state of the art knowledge to investigate the clonal evolution of chronic myeloid leukemia (CML) in a large panel of patients. CML progresses from a majoritarian benign chronic phase into an often fatal blast crisis phase. It is nowadays known that blast crisis are triggered by genetic instability and disrupted DNA repair, which lead to accumulation of DNA mutations. The use of thyrosine kinase inhibitors targeting the Philadelphia chromosome have enormously improved the treatment of blast crisis, nonetheless resistant patients are still a medical challenge. Yotaro Ochi and collegues used Whole Exome Sequencing of paired chronic phase and blast crisis samples to depict the genetic landscape of progression from the first to the second stage of the disease (as illustrated in their figure). Their results were published in Nature Communication on this May 2021. They used a dataset previously deposited at the EGA (EGAS00001003071) and their new generated data have been made available under accession number EGAS00001005075. Their analysis of the occurring mutations contribute to understanding the mechanisms of CML evolution and therefore develop a protocol for early management of patients with a TKI resistance signature. Up to date, the EGA hosts 99 clonal evolution studies, developed in several cancer types, including myeloma, renal carcinomas and ovarian cancer, in addition to various leukaemia subtypes, where this approach has been more widely applied and produced important results and seminal papers. From a pan-cancer point of view, the definition of tumour architecture through clonal evolution studies is instrumental to unravel the mechanisms of disease progression and treatment-resistance insource. At the EGA, we are honoured to contribute to exciting and important science advances with our service!

  Blog the-last-addition-to-the-list-of-clonal-evolution-studies-in-the-ega