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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.
Background: Alterations in the DNA methylation pattern are a hallmark of leukemias and lymphomas. However, most epigenetic studies in hematologic neoplasms (HNs) have focused either on the analysis of few candidate genes or many genes and few HN entities, and comprehensive studies are required. Methodology/Principal Findings: Here, we report for the first time a microarray-based DNA methylation study of 767 genes in 367 HNs diagnosed with 16 of the most representative B-cell (n = 203), T-cell (n = 30), and myeloid (n = 134) neoplasias, as well as 37 samples from different cell types of the hematopoietic system. Using appropriate controls of B-, T-, or myeloid cellular origin, we identified a total of 220 genes hypermethylated in at least one HN entity. In general, promoter hypermethylation was more frequent in lymphoid malignancies than in myeloid malignancies, being germinal center mature B-cell lymphomas as well as B and T precursor lymphoid neoplasias those entities with highest frequency of gene-associated DNA hypermethylation. We also observed a significant correlation between the number of hypermethylated and hypomethylated genes in several mature B-cell neoplasias, but not in precursor B- and T-cell leukemias. Most of the genes becoming hypermethylated contained promoters with high CpG content, and a significant fraction of them are targets of the polycomb repressor complex. Interestingly, T-cell prolymphocytic leukemias show low levels of DNA hypermethylation and a comparatively large number of hypomethylated genes, many of them showing an increased gene expression. Conclusions/Significance: We have characterized the DNA methylation profile of a wide range of different HNs entities. As well as identifying genes showing aberrant DNA methylation in certain HN subtypes, we also detected six genes—DBC1, DIO3, FZD9, HS3ST2, MOS, and MYOD1—that were significantly hypermethylated in B-cell, T-cell, and myeloid malignancies. These might therefore play an important role in the development of different HNs.
This study deals with 3D laser investigation on the border between the human lymph node T-zone and germinal centre. Only a few T-cells specific for antigen selected B-cells are allowed to enter germinal centres. This selection process is guided by sinus structures, chemokine gradients and inherent motility of the lymphoid cells. We measured gaps and wall-like structures manually, using IMARIS, a 3D image software for analysis and interpretation of microscopy datasets. In this paper, we describe alpha-actin positive and semipermeable walls and wall-like structures that may hinder T-cells and other cell types from entering germinal centres. Some clearly defined holes or gaps probably regulate lymphoid traffic between T- and B-cell areas. In lymphadenitis, the morphology of this border structure is clearly defined. However, in case of malignant lymphoma, the wall-like structure is disrupted. This has been demonstrated exemplarily in case of angioimmunoblastic T-cell lymphoma. We revealed significant differences of lengths of the wall-like structures in angioimmunoblastic T-cell lymphoma in comparison with wall-like structures in reactive tissue slices. The alterations of morphological structures lead to abnormal and less controlled T- and B-cell distributions probably preventing the immune defence against tumour cells and infectious agents by dysregulating immune homeostasis.
Aims: The examination of histological sections is still the gold standard in diagnostic pathology. Important histopathological diagnostic criteria are nuclear shapes and chromatin distribution as well as nucleus-cytoplasm relation and immunohistochemical properties of surface and intracellular proteins. The aim of this investigation was to evaluate the benefits and drawbacks of three-dimensional imaging of CD30+ cells in classical Hodgkin Lymphoma (cHL) in comparison to CD30+ lymphoid cells in reactive lymphoid tissues.
Materials and results: Using immunoflourescence confocal microscopy and computer-based analysis, we compared CD30+ neoplastic cells in Nodular Sclerosis cHL (NScCHL), Mixed Cellularity cHL (MCcHL), with reactive CD30+ cells in Adenoids (AD) and Lymphadenitis (LAD). We confirmed that the percentage of CD30+ cell volume can be calculated. The amount in lymphadenitis was approx. 1.5%, in adenoids around 2%, in MCcHL up to 4,5% whereas the values for NScHL rose to more than 8% of the total cell cytoplasm. In addition, CD30+ tumour cells (HRS-cells) in cHL had larger volumes, and more protrusions compared to CD30+ reactive cells. Furthermore, the formation of large cell networks turned out to be a typical characteristic of NScHL.
Conclusion: In contrast to 2D histology, 3D laser scanning offers a visualisation of complete cells, their network interaction and spatial distribution in the tissue. The possibility to differentiate cells in regards to volume, surface, shape, and cluster formation enables a new view on further diagnostic and biological questions. 3D includes an increased amount of information as a basis of bioinformatical calculations.
Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) can show variable histological growth patterns and present remarkable overlap with T-cell/histiocyte-rich large B-cell lymphoma (THRLBCL). Previous studies suggest that NLPHL histological variants represent progression forms of NLPHL and THRLBCL transformation in aggressive disease. Since molecular studies of both lymphomas are limited due to the low number of tumor cells, the present study aimed to learn if a better understanding of these lymphomas is possible via detailed measurements of nuclear and cell size features in 2D and 3D sections. Whereas no significant differences were visible in 2D analyses, a slightly increased nuclear volume and a significantly enlarged cell size were noted in 3D measurements of the tumor cells of THRLBCL in comparison to typical NLPHL cases. Interestingly, not only was the size of the tumor cells increased in THRLBCL but also the nuclear volume of concomitant T cells in the reactive infiltrate when compared with typical NLPHL. Particularly CD8+ T cells had frequent contacts to tumor cells of THRLBCL. However, the nuclear volume of B cells was comparable in all cases. These results clearly demonstrate that 3D tissue analyses are superior to conventional 2D analyses of histological sections. Furthermore, the results point to a strong activation of T cells in THRLBCL, representing a cytotoxic response against the tumor cells with unclear effectiveness, resulting in enhanced swelling of the tumor cell bodies and limiting proliferative potential. Further molecular studies combining 3D tissue analyses and molecular data will help to gain profound insight into these ill-defined cellular processes.