Pathway activation patterns in diffuse large B-cell lymphomas.

Deregulation of cell signaling pathways controlling cell growth and cell survival is a common feature of all cancers. Although a core repertoire of oncogenic mechanisms is widely conserved between various malignancies, the constellation of pathway activities can vary even in patients with the same malignant disease. Modern molecularly targeted cancer drugs intervene in cell signaling compensating for pathway deregulation. Hence characterizing tumors with respect to pathway activation will become crucial for treatment decisions. Here we have used semi-supervised machine learning methodology to generate signatures of eight oncogene-inducible pathways, which are conserved across epithelial and lymphoid tissues. We combined them to patterns of pathway activity called PAPs for pathway activation patterns and searched for them in 220 morphologically, immunohistochemically and genetically well-characterized mature aggressive B-cell lymphomas including 134 cases with clinical data available. Besides Burkitt lymphoma, which was characterized by a unique pattern, the PAPs identified four distinct groups of mature aggressive B-cell lymphomas across independent gene expression studies with distinct biological characteristics, genetic aberrations and prognosis. We confirmed our findings through cross-platform analysis in an independent data set of 303 mature aggressive B-cell lymphomas.

Development and implementation of an analysis tool for array-based comparative genomic hybridization.

OBJECTIVES: Array-comparative genomic hybridization (aCGH) is a high-throughput method to detect and map copy number aberrations in the genome. Multi-step analysis of high-dimensional data requires an integrated suite of bioinformatic tools. In this paper we detail an analysis pipeline for array CGH data. METHODS: We developed an analysis tool for array CGH data which supports single and multi-chip analyses as well as combined analyses with paired mRNA gene expression data. The functions supporting relevant steps of analysis were implemented using the open source software R and combined as package aCGHPipeline. Analysis methods were illustrated using 189 CGH arrays of aggressive B-cell lymphomas. RESULTS: The package covers data input, quality control, normalization, segmentation and classification. For multi-chip analysis aCGHPipeline offers an algorithm for automatic delineation of recurrent regions. This task was performed manually up to now. The package also supports combined analysis with mRNA gene expression data. Outputs consist of HTML documents to facilitate communication with clinical partners. CONCLUSIONS: The R package aCGHPipeline supports basic tasks of single and multi-chip analysis of array CGH data.

Further delineation of chromosomal consensus regions in primary mediastinal B-cell lymphomas: an analysis of 37 tumor samples using high-resolution genomic profiling (array-CGH).

Primary mediastinal B-cell lymphoma (PMBL) is an aggressive extranodal B-cell non-Hodgkin's lymphoma with specific clinical, histopathological and genomic features. To characterize further the genotype of PMBL, we analyzed 37 tumor samples and PMBL cell lines Med-B1 and Karpas1106P using array-based comparative genomic hybridization (matrix- or array-CGH) to a 2.8k genomic microarray. Due to a higher genomic resolution, we identified altered chromosomal regions in much higher frequencies compared with standard CGH: for example, +9p24 (68%), +2p15 (51%), +7q22 (32%), +9q34 (32%), +11q23 (18%), +12q (30%) and +18q21 (24%). Moreover, previously unknown small interstitial chromosomal low copy number alterations (for example, -6p21, -11q13.3) and a total of 19 DNA amplifications were identified by array-CGH. For 17 chromosomal localizations (10 gains and 7 losses), which were altered in more than 10% of the analyzed cases, we delineated minimal consensus regions based on genomic base pair positions. These regions and selected immunohistochemistries point to candidate genes that are discussed in the context of NF-kappaB transcription activation, human leukocyte antigen class I/II defects, impaired apoptosis and Janus kinase/signal transducer and activator of transcription (JAK/STAT) activation. Our data confirm the genomic uniqueness of this tumor and provide physically mapped genomic regions of interest for focused candidate gene analysis.

DNA microarray analysis in malignant lymphomas.

Recently, DNA microarray technology has opened new avenues for the understanding of lymphomas. By hybridization of cDNA to arrays containing >10,000 different DNA fragments, this approach allows the simultaneous evaluation of the mRNA expression of thousands of genes in a single experiment. Using sophisticated bioinformatic tools, the huge amount of raw data can be clustered resulting in (1) tumor subclassification, (2) identification of pathogenetically relevant genes, or (3) biological predictors for the clinical course. This approach already has provided novel insights into different entities of B-cell non-Hodgkin's lymphomas. Genomic DNA chip hybridization (matrix-CGH) is a complementary approach focussing on genomic aberrations. In this review, we discuss the impact of this new technology both with regard to methodological aspects as well as to novel findings influencing our understanding of lymphomas.

[A new dimension of DNA analysis: genomic profiling by matrix CGH]. / Genomanalysen menschlicher Tumoren mittels Matrix-CGH.

Analysis of genetic alterations in tumor cells represent a first step to understand the molecular mechanism of cancer etiology and development. Due to the progress in genome research, it is feasible to assess the complexity of genomic changes on a large scale. Protocols for gene expression profiling using cDNA arrays have been developed allowing to test the activity of almost all human genes in tumor cells. Another important approach is matrix-CGH which was recently developed to assess gains and losses on the genomic level with high resolution. This method not only allows to narrow down the position of novel oncogenes or tumor suppressor-genes but also contributes to a refinement of tumor classifications. Since matrix-CGH can be performed under highly standardized conditions in a fully automatized way, it is suited for diagnostics in clinical laboratories.