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Despite advances in allogeneic stem cell transplantation, BCR-ABL-positive acute lymphoblastic leukaemia (ALL) remains a high-risk disease, necessitating the development of novel treatment strategies. As the known oncomir, miR-17~92, is regulated by BCR-ABL fusion in chronic myeloid leukaemia, we investigated its role in BCR-ABL translocated ALL. miR-17~92-encoded miRNAs were significantly less abundant in BCR-ABL-positive as compared to -negative ALL-cells and overexpression of miR-17~19b triggered apoptosis in a BCR-ABL-dependent manner. Stable isotope labelling of amino acids in culture (SILAC) followed by liquid chromatography and mass spectroscopy (LC-MS) identified several apoptosis-related proteins including Bcl2 as potential targets of miR-17~19b. We validated Bcl2 as a direct target of this miRNA cluster in mice and humans, and, similar to miR-17~19b overexpression, Bcl2-specific RNAi strongly induced apoptosis in BCR-ABL-positive cells. Furthermore, BCR-ABL-positive human ALL cell lines were more sensitive to pharmacological BCL2 inhibition than negative ones. Finally, in a xenograft model using patient-derived leukaemic blasts, real-time, in vivo imaging confirmed pharmacological inhibition of BCL2 as a new therapeutic strategy in BCR-ABL-positive ALL. These data demonstrate the role of miR-17~92 in regulation of apoptosis, and identify BCL2 as a therapeutic target of particular relevance in BCR-ABL-positive ALL.
Wissenschaft im Wettbewerb
(2007)
The Time Projection Chamber (TPC) of the ALICE experiment at the CERN LHC was upgraded for Run 3 and Run 4. Readout chambers based on Gas Electron Multiplier (GEM) technology and a new readout scheme allow continuous data taking at the highest interaction rates expected in Pb-Pb collisions. Due to the absence of a gating grid system, a significant amount of ions created in the multiplication region is expected to enter the TPC drift volume and distort the uniform electric field that guides the electrons to the readout pads. Analytical calculations were considered to correct for space-charge distortion fluctuations but they proved to be too slow for the calibration and reconstruction workflow in Run 3. In this paper, we discuss a novel strategy developed by the ALICE Collaboration to perform distortion-fluctuation corrections with machine learning and convolutional neural network techniques. The results of preliminary studies are shown and the prospects for further development and optimization are also discussed.