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Quantitative methylation profiling was performed using the Illumina GoldenGate Assay in untreated follicular lymphoma (FL) (164), paired pre- and post-transformation FL (20), benign haematopoietic (24) samples and purified B and T cells from two FL cases. Methylation values allowed separation of untreated FL samples from controls with one exception, based primarily on tumour-specific gains of methylation typically occurring within CpG islands. Genes that are targets for epigenetic repression in stem cells by Polycomb Repressor Complex 2 were significantly over-represented among hypermethylated genes. Methylation profiles were conserved in sequential FL and t-FL biopsies, suggesting that widespread methylation represents an early event in lymphomagenesis and may not contribute substantially to transformation. A significant (P<0.05) correlation between FL methylation values and reduced gene expression was shown for up to 28% of loci. Methylation changes occurred predominantly in B cells with variability in the amount of non-malignant tissue between samples preventing conclusive correlation with survival. This represents an important caveat in attributing prognostic relevance to methylation and future studies in cancer will optimally require purified tumour populations to address the impact of methylation on clinical outcome.

Background: The addition of rituximab to combination chemotherapy with cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP), or R-CHOP, has significantly improved the survival of patients with diffuse large-B-cell lymphoma. Whether gene-expression signatures correlate with survival after treatment of diffuse large-B-cell lymphoma is unclear., Methods: We profiled gene expression in pretreatment biopsy specimens from 181 patients with diffuse large-B-cell lymphoma who received CHOP and 233 patients with this disease who received R-CHOP. A multivariate gene-expression-based survival-predictor model derived from a training group was tested in a validation group., Results: A multivariate model created from three gene-expression signatures--termed "germinal-center B-cell," "stromal-1," and "stromal-2"--predicted survival both in patients who received CHOP and patients who received R-CHOP. The prognostically favorable stromal-1 signature reflected extracellular-matrix deposition and histiocytic infiltration. By contrast, the prognostically unfavorable stromal-2 signature reflected tumor blood-vessel density., Conclusions: Survival after treatment of diffuse large-B-cell lymphoma is influenced by differences in immune cells, fibrosis, and angiogenesis in the tumor microenvironment., (2008 Massachusetts Medical Society)

Background: Microarrays for the analysis of gene expression are of three different types: short oligonucleotide (25-30 base), long oligonucleotide (50-80 base), and cDNA (highly variable in length). The short oligonucleotide and cDNA arrays have been the mainstay of expression analysis to date, but long oligonucleotide platforms are gaining in popularity and will probably replace cDNA arrays. As part of a validation study for the long oligonucleotide arrays, we compared and contrasted expression profiles from the three formats, testing RNA from six different cell lines against a universal reference standard., Results: The three platforms had 6430 genes in common. In general, correlation of gene expression levels across the platforms was good when defined by concordance in the direction of expression difference (upregulation or downregulation), scatter plot analysis, principal component analysis, cell line correlation or quantitative RT-PCR. The overall correlations (r values) between platforms were in the range 0.7 to 0.8, as determined by analysis of scatter plots. When concordance was measured for expression ratios significant at p-values of <0.05 and at expression threshold levels of 1.5 and 2-fold, the agreement among the platforms was very high, ranging from 93% to 100%., Conclusion: Our results indicate that the long oligonucleotide platform is highly suitable for expression analysis and compares favorably with the cDNA and short oligonucleotide varieties. All three platforms can give similar and reproducible results if the criterion is the direction of change in gene expression and minimal emphasis is placed on the magnitude of change.

Adult T-cell leukemia (ATL) is a severe chemotherapy-resistant malignancy associated with prolonged infection by the human T cell-lymphotropic virus 1 (HTLV-1) retrovirus. Although the Tax viral transactivator is clearly an oncogene, the role of its continuous expression in the maintenance of the transformed phenotype is controversial. Because arsenic trioxide (As) and interferon alpha (IFN) synergize to induce cell cycle arrest and apoptosis of ATL cells both ex vivo and in vitro, we investigated the effects of As alone and As/IFN combination on gene networks in HTLV-1-infected leukemic cells. The As/IFN combination reduced Tax expression and, accordingly, reversed the Tax-induced constitutive nuclear factor kappaB (NF-kappaB) activation. Using DNA microarray analyses, we demonstrated that As rapidly and selectively blocks the transcription of NF-kappaB-dependent genes in HTLV-1-infected cells only. Reversal of NF-kappaB activation by As alone resulted from dramatic stabilization of IkappaB-alpha and IkappaB-epsilon, independently of IkappaB kinase (IKK) activity modulation or Tax degradation. In contrast, only the As/IFN combination induced late and massive down-regulation of cell cycle-regulated genes, concomitantly with Tax degradation by the proteasome and cell death induction, indicating the importance of continuous Tax expression for ATL cell survival. These 2 successive events likely account for the potent and specific effects of the As/IFN combination in ATL.

Autoimmune diseases such as multiple sclerosis are characterized by complex genetic traits and pathomechanisms that translate into clinical heterogeneity. This wide heterogeneity of multiple sclerosis as well as different biological responses to immunomodulatory drugs can be expected to contribute to differential treatment responses. Strategies that dissect the relationship between the treatment response and the biological characteristics in individual patients are valuable not only as a clinical tool, but also in leading to a better understanding of the disease. Here we address the in vitro and ex vivo RNA expression profile under one approved therapy of multiple sclerosis, interferon-beta (IFN-beta, Betaseron), by cDNA microarrays and demonstrate that non-responder and responder phenotypes to IFN-beta as assessed by longitudinal gadolinium-enhanced MRI scans and clinical disease activity differ in their ex vivo gene expression profile. These findings will help to better elucidate the mechanism of action of IFN-beta in relation to different disease patterns and eventually lead to optimized therapy.

Gene expression variation is critical for the normal development and physiology of immune cells. Using cDNA microarrays, a systematic, genomic-scale view of gene expression in immune cells at many stages of differentiation and activation can be obtained. From the high vantagepoint provided by this technology, the gene expression physiology of immune cells appears remarkably ordered and logical. Each stage of lymphocyte differentiation can be defined by a characteristic gene expression signature. Genes that are co-regulated over hundreds of experimental conditions often encode functionally related proteins. Gene expression profiles also provide unprecedented ability to define the molecular and functional relationships between normal and malignant lymphocyte cell populations.

Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin's lymphoma, is clinically heterogeneous: 40% of patients respond well to current therapy and have prolonged survival, whereas the remainder succumb to the disease. We proposed that this variability in natural history reflects unrecognized molecular heterogeneity in the tumours. Using DNA microarrays, we have conducted a systematic characterization of gene expression in B-cell malignancies. Here we show that there is diversity in gene expression among the tumours of DLBCL patients, apparently reflecting the variation in tumour proliferation rate, host response and differentiation state of the tumour. We identified two molecularly distinct forms of DLBCL which had gene expression patterns indicative of different stages of B-cell differentiation. One type expressed genes characteristic of germinal centre B cells ('germinal centre B-like DLBCL'); the second type expressed genes normally induced during in vitro activation of peripheral blood B cells ('activated B-like DLBCL'). Patients with germinal centre B-like DLBCL had a significantly better overall survival than those with activated B-like DLBCL. The molecular classification of tumours on the basis of gene expression can thus identify previously undetected and clinically significant subtypes of cancer.

Background: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings., Results: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival., Conclusions: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.