MATERIALS AND METHODS: BCR-ABL positive CML cells resistant to imatinib (K562-R) were developed by overexposure of K562 cell lines to the drug. Cytotoxicity was determined by MTS assays and IC50 values calculated. Apoptosis assays were performed using annexin V-FITC binding assays and analyzed by flow cytometry. Methylation profiles were investigated using methylation specific PCR and sequencing analysis of SOCS-1 and SOCS-3 genes. Gene expression was assessed by quantitative real-time PCR, and protein expression and phosphorylation of STAT1, 2 and 3 were examined by Western blotting.
RESULTS: The IC50 for imatinib on K562 was 362 nM compared to 3,952 nM for K562-R (p=0.001). Percentage of apoptotic cells in K562 increased upto 50% by increasing the concentration of imatinib, in contrast to only 20% in K562-R (p<0.001). A change from non-methylation of the SOCS-3 gene in K562 to complete methylation in K562-R was observed. Gene expression revealed down- regulation of both SOCS-1 and SOCS-3 genes in resistant cells. STAT3 was phosphorylated in K562-R but not K562.
CONCLUSIONS: Development of cells resistant to imatinib is feasible by overexposure of the drug to the cells. Activation of STAT3 protein leads to uncontrolled cell proliferation in imatinib resistant BCR-ABL due to DNA methylation of the SOCS-3 gene. Thus SOCS-3 provides a suitable candidate for mechanisms underlying the development of imatinib resistant in CML patients.
MATERIALS AND METHODS: We evaluated simple statistics and published model-based approaches. Multiplex-qPCR was conducted to determine the expression of 24 candidate RG in AMLs (N=9). Singleplex-qPCR was carried out on selected RG (SRP14, B2M and ATP5B) and genes of interest in AML (N=15) and healthy controls, HC (N=12).
RESULTS: RG expression levels in AML samples were highly variable and coefficient of variance (CV) ranged from 0.37% to 10.17%. Analysis using GeNorm and Normfinder listed different orders of most stable genes but the top seven (ACTB, UBE2D2, B2M, NF45, RPL37A, GK, QARS) were the same. In singleplex-qPCR, SRP14 maintained the lowest CV in AML samples. B2M, one of most stable reference genes in AML, was expressed near significantly different in AML and HC. GeNorm selected ATP5B+SRP14 while Normfinder chose SRP14+B2M as the best two RG in combination. The median expressions of combined RG genes in AML compared to HC were less significantly different than individually implying smaller expression variation after combination. Genes of interest normalised with RG in combination or individually, displayed significantly different expression patterns.
CONCLUSIONS: The selection of best reference gene in qPCR must consider all sample sets. Model-based approaches are important in large candidate gene analysis. This study showed combination of RG SRP14+B2M was the most suitable normalisation factor for qPCR analysis of AML and healthy individuals.
METHODS: A total of 127 patients with acute leukaemia (myeloid and lymphoid), of both genders, aged between 13 and 77 years, were examined by an ophthalmologist for retinal changes using direct/indirect ophthalmoscopy within 2 days of diagnosis before starting chemotherapy.
RESULTS: Retinal lesions were seen in 62 cases (49%), with intraretinal haemorrhages being the most common lesion (42%). A high white blood cell count was significantly associated with intraretinal haemorrhages (p = 0.04) and white-centred haemorrhages (p = 0.001), while a low platelet count was significantly associated with intraretinal haemorrhages (p = 0.03) in acute myeloid leukaemia patients.
CONCLUSIONS: A high white blood cell count may be considered as important as a low platelet count in the pathogenesis of leukaemic retinopathy.