Detection of genomic aberrations in molecularly defined Burkitt’s lymphoma by array-based, high resolution, single nucleotide polymorphism analysis

The present findings suggest that uniparental disomies do not play a major role in the pathogenesis of Burkitt's lymphoma, whereas some genes may contribute to the development of this lymphoma through gene dosage effects. Amplifications of the polymerase iota gene may be functionally linked with increased genomic alterations in Burkitt's lymphoma. The pattern and rarity of chromosomal changes detectable, even at the high resolution employed here, together with aberrations of genes regulating MYC activity, support the hypothesis that deregulation of the MYC pathway is the major force driving the pathogenesis of Burkitt's lymphoma, but show that this deregulation is more complex than previously known

High resolution copy number analysis of IRF4 translocation-positive diffuse large B-cell and follicular lymphomas

Translocations affecting chromosome subband 6p25.3 containing the IRF4 gene have been recently described as characteristic alterations in a molecularly distinct subset of germinal center B-cell-derived lymphomas. Secondary changes have yet only been described in few of these lymphomas. Here, we performed array-comparative genomic hybridization and molecular inversion probe microarray analyses on DNA from 12 formalin-fixed paraffin-embedded and two fresh-frozen IRF4 translocation-positive lymphomas, which together with the previously published data on nine cases allowed the extension of copy number analyses to a total of 23 of these lymphomas. All except one case carried chromosomal imbalances, most frequently gains in Xq28, 11q22.3-qter, and 7q32.1-qter and losses in 6q13-16.1, 15q14-22.31, and 17p. No recurrent copy-neutral losses of heterozygosity were observed. TP53 point mutations were detected in three of six cases with loss of 17p. Overall this study unravels a recurrent pattern of secondary genetic alterations in IRF4 translocation-positive lymphomas

A modular transcriptome map of mature B cell lymphomas

Abstract 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