Towards molecular diagnosis and targeted therapy of lymphoid malignancies
Section snippets
Interpretation of gene expression data
Gene expression profiling rapidly generates an abundance of information that presents a daunting interpretive challenge to researchers and statisticians alike. New analytical methods attempt to group samples based on their gene expression; these methods generally fall into one of two classes. “Unsupervised” algorithms use gene expression data to detect relationships among samples, without any reference to external clinical data. One unsupervised algorithm in widespread use is hierarchical
CLL: a disease of antigen-experienced B cells with two biologically and prognostically distinct subtypes
The clinical course of CLL is quite variable. Some patients have an indolent disease that may never require treatment. In others a relentlessly progressive clinical course is rapidly fatal. Insights into this clinical variability have come from analysis of the immunoglobulin heavy chain variable region (IgVH) genes of leukemic cells in CLL patients.12, 20 A specialized molecular machinery, termed somatic hypermutation, can mutate the IgVH regions of B cells at specific stages of maturation in
MCL: quantitative measurement of proliferation in prognosis and pathogenesis
MCL, which accounts for approximately 8% of non-Hodgkin’s lymphomas, is a challenging disease to manage clinically and is, at present, incurable. Most MCLs have a characteristic t(11;14) translocation that leads to overexpression of cyclin D1.50, 54, 70 This D-type cyclin regulates the transition from the G1 to S phase of the cell cycle and is not normally expressed at appreciable levels in lymphoid cells. Current WHO guidelines for the diagnosis of MCL rely on morphologic assessment
Dissecting the heterogeneity of DLBCL by gene expression profiling
DLBCL, the largest diagnostic category of non-Hodgkin’s lymphoma, is markedly heterogeneous in morphology and in clinical behavior. Attempts to subclassify the DLBCLs based on morphology are susceptible to considerable interobserver variability and consequently the current WHO classification groups all of these lymphomas together.34 DLBCL can be cured by standard CHOP chemotherapy in only about 40% of cases13 and several modifications of this chemotherapeutic regimen have failed to improve
Conclusions: using gene expression profiling to advance clinical science
As the examples in this review indicate, gene expression profiling has proven effective in providing accurate diagnoses and prognoses for patients with lymphoid malignancies. For CLL, two clinically distinct subtypes can be defined by the expression of a single gene, ZAP-70. In MCL, gene expression profiling has defined a new cyclin D1-negative subtype, and expression of proliferation signature genes can provide strong prognostic information. In DLBCL, two subgroups defined by gene expression
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2004, BloodCitation Excerpt :Oligonucleotide microarrays and gene chips offer powerful tools to measure global gene expression profiles and to describe the “transcriptome” of minimally manipulated healthy and abnormal cells.37,44,46 For human cells, such analyses have been applied to describe differences between normal and malignant cells,35 to delineate stages of malignant evolution,47 to discriminate subsets of disease by genetic rather than morphologic criteria,48 and to develop prognostic “cassettes” of gene activities in order to predict outcomes and drug toxicities.48-50 MDS is an attractive candidate for a genomics strategy for several reasons.