Dataset Information

Ena-DATASET-UQDI-06-07-2018-03:19:47:723-1903 - samples

ABSTRACT: This dataset contains raw sequencing reads for matched MGUS/SMM to MM patient samples, including normal germline controls. FASTQ files were generated on Illumina NextSeq 500 and HiSeq 4000 machines following exome capture using the Agilent Clinical Research Exome kit. DNA was extracted from CD138+CD38++ cells (representing MGUS/SMM/MM cells) and CD138-CD38- (representing normal cells) isolated from bone marrow. 10 patients are included with 3 samples each representing normal, MGUS/SMM, MM stages.

PROVIDER: EGAD00001004190 | EGA |

REPOSITORIES: EGA

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Publications

Cutting edge genomics reveal new insights into tumour development, disease progression and therapeutic impacts in multiple myeloma.

Dutta Ankit K AK Hewett Duncan R DR Fink J Lynn JL Grady John P JP Zannettino Andrew C W ACW

British journal of haematology 20170503 2

Multiple Myeloma (MM) is a haematological malignancy characterised by the clonal expansion of plasma cells (PCs) within the bone marrow. Despite advances in therapy, MM remains a largely incurable disease with a median survival of 6 years. In almost all cases, the development of MM is preceded by the benign PC condition Monoclonal Gammopathy of Undetermined Significance (MGUS). Recent studies show that the transformation of MGUS to MM is associated with complex genetic changes. Understanding how ...[more]

PMID: 28466550

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Project description:Smoldering myeloma (SMM) is a pre-malignant monoclonal gammopathy with a 10% annual risk to progress to active multiple myeloma (MM). SMM diagnostic criteria, as well of those of others monoclonal gammopathies, have been updated by the International Myeloma Working Group (IMWG) in 2014. In particular, the previously defined “ultra high-risk” SMM (characterized by the presence of specific biomarkers associated with a ≥ 80% risk of progression to symptomatic MM within 2 years) has been included among patients with active MM. SMM is biologically heterogeneous, including a subset of patients with biological pre-malignancy and a subset with biological malignancy who have not yet developed organ damage, defined as onset of the classical CRAB criteria or Myeloma Defining Events (MDE). Thus, SMM encompassed patients with a very low rate of progression to symptomatic MM, similar to patients with monoclonal gammopathy of uncertain significance (MGUS), as well as patients who acquired organ damage and progress to active MM within the first year from diagnosis. The molecular mechanisms involved in the SMM to MM progression are still far to be fully understood. Genomic studies indicate that the genetic alterations that characterize MM patients are already present in SMM ones, who present similar mutational and copy number alteration load. However, few data are available on the transcriptional profiles of SMM patients in relationship to the progression to active MM, overall indicating minimal differential expression either in coding or non-coding RNA fraction. To date, robust data are still lacking which describe the transcriptional profiles of plasma cells (PCs) from paired samples obtained at the time of SMM and at MM onset. Herein, we compared the transcriptome of purified CD138+ PCs from paired samples of SMM patients progressed to active MM (P-SMM), aimed at describing any possible common transcriptional discrepancy that may help to understand the intra-patient disease evolution; at the same time, we investigated the transcriptional differences between P-SMM and a subset of non-progressed SMM (NP-SMM) at a minimum of 36 months.

Project description:To gain further insights into the role of the transcriptome deregulation in the transition from a normal plasma cell (NPC) to a clonal PC and from an indolent clonal PC to a malignant PC, we performed gene expression profiling in 20 patients with MGUS, 33 with high-risk SMM and 41 with MM. The analysis showed that 126 genes were differentially expressed in MGUS, SMM and MM as compared to NPC. Interestingly, 17 and 9 out of the 126 significant differentially expressed genes were small nucleolar RNA molecules (snoRNA) and zinc finger proteins. GADD45A was the most significant up-regulated gene in clonal PC compared to NPC. Several proapoptotic genes (AKT1 and AKT2) were downregulated and antiapoptotic genes (APAF1 and BCL2L1) were upregulated in MM, both symptomatic and asymptomatic, compared to MGUS. Myc mediated apoptosis signaling is one of the top canonical pathways differentiating the asymptomatic and symptomatic myeloma. When we looked for those genes progressively modulated through the evolving stages of monoclonal gammopathies, eight snoRNA showed a progressive increase while APAF1, VCAN and MEGF9 exhibited a progressive downregulation in the transition from MGUS to SMM and to MM. In conclusion, our data show that although MGUS, SMM and MM are not clearly distinguishable groups according to their GEP, several signaling pathways and genes were significant deregulated in the different steps of transformation process. Bone marrow (BM) samples were obtained from 20 patients with MGUS, 33 with high-risk SMM and 41 with MM. All samples corresponded to newly diagnosed untreated patients. To avoid misclassification or overlapping entities we decided to focus on MGUS patients with more than two years of stable follow-up. Samples were classified according to the International Myeloma Working Group criteria. The criteria for defining high-risk SMM has been described previously. In addition, five healthy donors were also included in order to relate the deregulation of gene expression profiling of clonal populations to normal condition. The study was approved by the research ethic committees of all participating centers and written informed consent was obtained from all patients in accordance with the Helsinki Declaration. The main clinical and laboratory characteristics of these patients are shown in Supplementary Table 1.

Dataset Information

Ena-DATASET-UQDI-06-07-2018-03:19:47:723-1903 - samples

Publications

Cutting edge genomics reveal new insights into tumour development, disease progression and therapeutic impacts in multiple myeloma.

Similar Datasets

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