Targeting lipid metabolism by the lipoprotein lipase inhibitor orlistat results in apoptosis of B-cell chronic lymphocytic leukemia cells.

Constitutively activated pathways contribute to apoptosis resistance in chronic lymphocytic leukemia (CLL). Little is known about the metabolism of lipids and function of lipases in CLL cells. Performing gene expression profiling including B-cell receptor (BCR) stimulation of CLL cells in comparison to healthy donor CD5+ B cells, we found significant overexpression of lipases and phospholipases in CLL cells. In addition, we observed that the recently defined prognostic factor lipoprotein lipase (LPL) is induced by stimulation of BCR in CLL cells but not in CD5+ normal B cells. CLL cellular lysates exhibited significantly higher lipase activity compared to healthy donor controls. Incubation of primary CLL cells (n=26) with the lipase inhibitor orlistat resulted in induction of apoptosis, with a half-maximal dose (IC(50)) of 2.35 microM. In healthy B cells a significantly higher mean IC(50) of 148.5 microM of orlistat was observed, while no apoptosis was induced in healthy peripheral blood mononuclear cells (PBMCs; P<0.001). Orlistat-mediated cytotoxicity was decreased by BCR stimulation. Finally, the cytotoxic effects of orlistat on primary CLL cells were enhanced by the simultaneous incubation with fludarabine (P=0.003). In summary, alterations of lipid metabolism are involved in CLL pathogenesis and might represent a novel therapeutic target in CLL.

Comparison of different isolation techniques prior gene expression profiling of blood derived cells: impact on physiological responses, on overall expression and the role of different cell types.

Owing to its clinical accessibility, peripheral blood is probably the best source for the assessment of differences or changes in gene expression associated with disease or drug response and therapy. Gene expression patterns in peripheral blood cells greatly depend on temporal and interindividual variations. However, technical aspects of blood sampling, isolation of cellular components, RNA isolation techniques and clinical aspects such as time to analysis and temperature during processing have been suggested to affect gene expression patterns. We therefore assessed gene expression patterns in peripheral blood from 29 healthy individuals by using Affymetrix microarrays. When RNA isolation was delayed for 20-24 h-a typical situation in clinical studies-gene signatures related to hypoxia were observed, and downregulation of genes associated with metabolism, cell cycle or apoptosis became dominant preventing the assessment of gene signatures of interindividual variation. Similarly, gene expression patterns were strongly dependent on choice of cell and RNA isolation and preparation techniques. We conclude that for large clinical studies, it is crucial to reduce maximally the time to RNA isolation. Furthermore, prior to study initiation, the cell type of interest should already be defined. Our data therefore will help to optimize clinical studies applying gene expression analysis of peripheral blood to exploit drug responses and to better understand changes associated with disease.