5032 results for "*oma"

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• The Spatial Heterogeneity in Multiple Myeloma - from the Subclonal Architecture to the Immune Microenvironment (partly hipo_K08K)

  Multiple myeloma (MM) cells show pronounced heterogeneity not only within a given patient but also between spatially separated tumor loci in the bone marrow. Understanding this spatial heterogeneity is emerging as a critical challenge for the successful treatment of the disease. Yet, our understanding of spatial differences in the subclonal architecture, molecular signatures and interactions with the tumor microenvironment remains very limited. To address this shortcoming, we performed bulk and single-cell multi-region sequencing, including random bone marrow samples from the iliac crest and paired imaging-guided focal lesion specimens from 15 newly diagnosed MM patients. We found a median of 5 subclones per patient and unique subclones in focal lesions. Central features of spatial heterogeneity included a consistent down-regulation of the chemoattractant cytokines CXCL7 and CXCL12 in focal lesion tumor cells as well as a significant depletion of macrophages in the focal lesion microenvironment. In contrast, a site-specific expansion of T-cell clones was not detected in focal lesions. In conclusion, our results demonstrate the relevance of considering spatial heterogeneity with potential implications for immunotherapies and molecular studies analyzing the role of gene signatures and MM-microenvironment interactions in disease progression.

  Study EGAS00001006090

• 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