The molecular biology knowledge in β-thalassaemia is limited due to the involvement of various erythropoeitic processes where the genetic information is lack due to nucleus ejection throughout the maturation of red blood cell activities concurrence with the accumulation of haemoglobin. Nucleated red blood cells (NRBCs) are typically found in peripheral blood (PB) of β-thalassaemia transfusion dependent patients and abundant in post splenectomy (Fig. 1A) [1]. The presence of NRBCs will provide further understanding on the molecular aspect of ineffective erythropoiesis in β-thalassaemia patients. Therefore, the objectives of this study were to isolate the NRBCs using CD71 magnetic beads from PB of β-thalassaemia patients and to compare the quantity of NRBCs enriched between non-splenectomised transfusion dependent and post-splenectomised transfusion dependent β-thalassaemia patients.
Blood cancer is an umbrella term for cancers that affect the blood, bone marrow and lymphatic system. There are three main groups of blood cancer: leukemia, lymphoma and myeloma. Some types are more common than others. In this paper, a new image transform based on geometric mean properties of integral values in both horizontal and vertical image directions is proposed for leukemia cancer cell classification. Available classification methods using the classical feature extraction methods which are sensitive to rotation and deformation of the blood cells. The new transform is based on geometric mean projection, which —unlike other image transforms, such as Radon transform— is not considered all signals in an image with the same signal acquisition rate. Instead, it is general and thus applicable to all capturing signal functions to achieve sufficient invariant features. The geometric mean projection transforms (GMPT) guarantees that the detector only extracts the highly informative information from the object to achieve an invariant feature vector for an accurate classification process. This method has been used as cancer cell identification using microscopic Imagery analysis in this study. Dissimilarity metric calculation and shape analysis by using image transform has been used to extract the feature vectors of the imagery. Then, the accumulated feature vectors have been classified to different classes by using artificial neural network (ANN). The proposed technique has been evaluated in the standard images sourced from USIM, Malaysia. The evaluation results indicate the robustness of the technique in different types of images available in the dataset.
The introduction and success of imatinib mesylate (IM) has become a paradigm shift in chronic myeloid leukemia (CML) treatment. However, the high efficacy of IM has been hampered by the issue of clinical resistance that might due to pharmacogenetic variability. In the current study, the contribution of three common single nucleotide polymorphisms (SNPs) of ABCB1 (T1236C, G2677T/A and C3435T) and two SNPs of ABCG2 (G34A and C421A) genes in mediating resistance and/or good response among 215 CML patients on IM therapy were investigated. Among these patients, the frequency distribution of ABCG2 421 CC, CA and AA genotypes were significantly different between IM good response and resistant groups (P=0.01). Resistance was significantly associated with patients who had homozygous ABCB1 1236 CC genotype with OR 2.79 (95%CI: 1.217-6.374, P=0.01). For ABCB1 G2677T/A polymorphism, a better complete cytogenetic remission was observed for patients with variant TT/AT/AA genotype, compared to other genotype groups (OR=0.48, 95%CI: 0.239-0.957, P=0.03). Haplotype analysis revealed that ABCB1 haplotypes (C1236G2677C3435) was statistically linked to higher risk to IM resistance (25.8% vs. 17.4%, P=0.04), while ABCG2 diplotype A34A421 was significantly correlated with IM good response (9.1% vs. 3.9%, P=0.03). In addition, genotypic variant in ABCG2 421C>A was associated with a major molecular response (MMR) (OR=2.20, 95%CI: 1.273-3.811, P=0.004), whereas ABCB1 2677G>T/A variant was associated with a significantly lower molecular response (OR=0.49, 95%CI: 0.248-0.974, P=0.04). However, there was no significant correlation of these SNPs with IM intolerance and IM induced hepatotoxicity. Our results suggest the usefulness of genotyping of these single nucleotide polymorphisms in predicting IM response among CML patients.
Discovery of imatinib mesylate (IM) as the targeted BCR-ABL protein tyrosine kinase inhibitor (TKI) has resulted in its use as the frontline therapy for chronic myeloid leukemia (CML) across the world. Although high response rates are observed in CML patients who receive IM treatment, a significant number of patients develop resistance to IM. Resistance to IM in patients has been associated with a heterogeneous array of mechanisms of which point mutations within the ABL tyrosine kinase domain (TKD) are the frequently documented. The types and frequencies of mutations reported in different population studies have shown wide variability. We screened 125 Malaysian CML patients on IM therapy who showed either TKI refractory or resistance to IM to investigate the frequency and pattern of BCR-ABL kinase domain mutations among Malaysian CML patients undergoing IM therapy and to determine the clinical significance. Mutational screening using denaturing high performance liquid chromatography (dHPLC) followed by DNA sequencing was performed on 125 IM resistant Malaysian CML patients. Mutations were detected in 28 patients (22.4%). Fifteen different types of mutations (T315I, E255K, G250E, M351T, F359C, G251E, Y253H, V289F, E355G, N368S, L387M, H369R, A397P, E355A, D276G), including 2 novel mutations were identified, with T315I as the predominant type of mutation. The data generated from clinical and molecular parameters studied were correlated with the survival of CML patients. Patients with Y253H, M351T and E355G TKD mutations showed poorer prognosis compared to those without mutation. Interestingly, when the prognostic impact of the observed mutations was compared inter-individually, E355G and Y253H mutations were associated with more adverse prognosis and shorter survival (P=0.025 and 0.005 respectively) than T315I mutation. Results suggest that apart from those mutations occurring in the three crucial regions (catalytic domain, P-loop and activation-loop), other rare mutations also may have high impact in the development of resistance and adverse prognosis. Presence of mutations in different regions of BCR-ABL TKD leads to different levels of resistance and early detection of emerging mutant clones may help in decision making for alternative treatment. Serial monitoring of BCR-ABL1 transcripts in CML patients allows appropriate selection of CML patients for BCR-ABL1 KD mutation analysis associated with acquired TKI resistance. Identification of these KD mutations is essential in order to direct alternative treatments in such CML patients.