Filter and export

Publications

3 Publications matching the given criteria: (Clear all filters)
Human disease: Burkitt lymphoma3
A modular transcriptome map of mature B cell lymphomas.
GLA - German Lymphoma Alliance, MMML-MYC-SYS, oBIG - omics Bioinformatics for Health

Abstract (Expand)

BACKGROUND: Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic … phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt's lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma. METHODS: We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics. RESULTS: We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt's lymphoma and particularly on 'double-hit' MYC and BCL2 transformed lymphomas. CONCLUSIONS: The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities.

Authors: H. Loeffler-Wirth, M. Kreuz, L. Hopp, A. Arakelyan, A. Haake, S. B. Cogliatti, A. C. Feller, M. L. Hansmann, D. Lenze, P. Moller, H. K. Muller-Hermelink, E. Fortenbacher, E. Willscher, G. Ott, A. Rosenwald, C. Pott, C. Schwaenen, H. Trautmann, S. Wessendorf, H. Stein, M. Szczepanowski, L. Trumper, M. Hummel, W. Klapper, R. Siebert, M. Loeffler, H. Binder

Date Published: 30th Apr 2019

Publication Type: Not specified

Human Diseases: B-cell lymphoma, diffuse large B-cell lymphoma, follicular lymphoma, Burkitt lymphoma

Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma.
MMML - Molecular mechanisms in malignant lymphoma

Abstract (Expand)

Burkitt lymphoma (BL) is the most common B-cell lymphoma in children. Within the International Cancer Genome Consortium (ICGC), we performed whole genome and transcriptome sequencing of 39 sporadic BL. … Here, we unravel interaction of structural, mutational, and transcriptional changes, which contribute to MYC oncogene dysregulation together with the pathognomonic IG-MYC translocation. Moreover, by mapping IGH translocation breakpoints, we provide evidence that the precursor of at least a subset of BL is a B-cell poised to express IGHA. We describe the landscape of mutations, structural variants, and mutational processes, and identified a series of driver genes in the pathogenesis of BL, which can be targeted by various mechanisms, including IG-non MYC translocations, germline and somatic mutations, fusion transcripts, and alternative splicing.

Authors: C. Lopez, K. Kleinheinz, S. M. Aukema, M. Rohde, S. H. Bernhart, D. Hubschmann, R. Wagener, U. H. Toprak, F. Raimondi, M. Kreuz, S. M. Waszak, Z. Huang, L. Sieverling, N. Paramasivam, J. Seufert, S. Sungalee, R. B. Russell, J. Bausinger, H. Kretzmer, O. Ammerpohl, A. K. Bergmann, H. Binder, A. Borkhardt, B. Brors, A. Claviez, G. Doose, L. Feuerbach, A. Haake, M. L. Hansmann, J. Hoell, M. Hummel, J. O. Korbel, C. Lawerenz, D. Lenze, B. Radlwimmer, J. Richter, P. Rosenstiel, A. Rosenwald, M. B. Schilhabel, H. Stein, S. Stilgenbauer, P. F. Stadler, M. Szczepanowski, M. A. Weniger, M. Zapatka, R. Eils, P. Lichter, M. Loeffler, P. Moller, L. Trumper, W. Klapper, S. Hoffmann, R. Kuppers, B. Burkhardt, M. Schlesner, R. Siebert

Date Published: 29th Mar 2019

Publication Type: Not specified

Human Diseases: lymphoma, Burkitt lymphoma

A biologic definition of Burkitt's lymphoma from transcriptional and genomic profiling.
MMML - Molecular mechanisms in malignant lymphoma

Abstract (Expand)

BACKGROUND: The distinction between Burkitt's lymphoma and diffuse large-B-cell lymphoma is unclear. We used transcriptional and genomic profiling to define Burkitt's lymphoma more precisely … and to distinguish subgroups in other types of mature aggressive B-cell lymphomas. METHODS: We performed gene-expression profiling using Affymetrix U133A GeneChips with RNA from 220 mature aggressive B-cell lymphomas, including a core group of 8 Burkitt's lymphomas that met all World Health Organization (WHO) criteria. A molecular signature for Burkitt's lymphoma was generated, and chromosomal abnormalities were detected with interphase fluorescence in situ hybridization and array-based comparative genomic hybridization. RESULTS: We used the molecular signature for Burkitt's lymphoma to identify 44 cases: 11 had the morphologic features of diffuse large-B-cell lymphomas, 4 were unclassifiable mature aggressive B-cell lymphomas, and 29 had a classic or atypical Burkitt's morphologic appearance. Also, five did not have a detectable IG-myc Burkitt's translocation, whereas the others contained an IG-myc fusion, mostly in simple karyotypes. Of the 176 lymphomas without the molecular signature for Burkitt's lymphoma, 155 were diffuse large-B-cell lymphomas. Of these 155 cases, 21 percent had a chromosomal breakpoint at the myc locus associated with complex chromosomal changes and an unfavorable clinical course. CONCLUSIONS: Our molecular definition of Burkitt's lymphoma clarifies and extends the spectrum of the WHO criteria for Burkitt's lymphoma. In mature aggressive B-cell lymphomas without a gene signature for Burkitt's lymphoma, chromosomal breakpoints at the myc locus were associated with an adverse clinical outcome.

Authors: M. Hummel, S. Bentink, H. Berger, W. Klapper, S. Wessendorf, T. F. Barth, H. W. Bernd, S. B. Cogliatti, J. Dierlamm, A. C. Feller, M. L. Hansmann, E. Haralambieva, L. Harder, D. Hasenclever, M. Kuhn, D. Lenze, P. Lichter, J. I. Martin-Subero, P. Moller, H. K. Muller-Hermelink, G. Ott, R. M. Parwaresch, C. Pott, A. Rosenwald, M. Rosolowski, C. Schwaenen, B. Sturzenhofecker, M. Szczepanowski, H. Trautmann, H. H. Wacker, R. Spang, M. Loeffler, L. Trumper, H. Stein, R. Siebert

Date Published: 8th Jun 2006

Publication Type: Not specified

Human Diseases: Burkitt lymphoma

Navigate

Health Atlas - Local Data Hub/Leipzig

The Health Atlas - Local Data Hub/Leipzig is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields.

Registered Repository

re3data.org Badge
Powered by
 (v.1.13.0-master)
Health Atlas | Funding and Programmes | Credits | Terms & Conditions | Privacy Policy | Imprint
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig

By continuing to use this site you agree to the use of cookies