Monitoring BCR-ABL in the treatment of chronic myeloid leukemia by polymerase chain reaction

Magnetic Nanoparticles PCR Enzyme-Linked Gene Assay for Quantitative Detection of BCR/ABL Fusion Gene in Chronic Myelogenous Leukemia

BACKGROUND

Magnetic nanoparticles (MNPs) have been widely used in medical diagnostic research. In this work, two technologies, MNPs and polymerase chain reaction (PCR), were combined to increase detection sensitivity and specificity. A novel technique based on the MNPs-PCR enzyme-linked gene assay (MELGA) was developed for detection of the BCR/ABL abnormal gene in chronic myelogenous leukemia (CML) patients.

METHODS

An MNPs-labeled BCR forward primer and a biotin-labeled ABL reverse primer were used to specifically amplify the target gene. After magnetic separation, the PCR product bound to MNPs labeled with streptavidin-conjugated horseradish peroxidase was incubated with the peroxidase substrate and hydrogen peroxide to generate the colorimetric signal.

RESULTS

When compared with real-time quantitative-PCR (RQ-PCR), the MELGA technique exhibited an increased sensitivity of <1 fg with high specificity for the BCR/ABL fusion gene in CML patients. In addition, MELGA colorimetric results correlated well with the number of copies obtained from RQ-PCR.

CONCLUSION

This simple and cost-effective technique is suitable for monitoring CML patients during targeted therapy (tyrosine kinase inhibitors) especially in rural hospitals.

Blood and tissue neuroendocrine tumor gene cluster analysis correlate, define hallmarks and predict disease status

A multianalyte algorithmic assay (MAAA) identifies circulating neuroendocrine tumor (NET) transcripts (n=51) with a sensitivity/specificity of 98%/97%. We evaluated whether blood measurements correlated with tumor tissue transcript analysis. The latter were segregated into gene clusters (GC) that defined clinical 'hallmarks' of neoplasia. A MAAA/cluster integrated algorithm (CIA) was developed as a predictive activity index to define tumor behavior and outcome. We evaluated three groups. Group 1: publically available NET transcriptome databases (n=15; GeneProfiler). Group 2: prospectively collected tumors and matched blood samples (n=22; qRT-PCR). Group 3: prospective clinical blood samples, n=159: stable disease (SD): n=111 and progressive disease (PD): n=48. Regulatory network analysis, linear modeling, principal component analysis (PCA), and receiver operating characteristic analyses were used to delineate neoplasia 'hallmarks' and assess GC predictive utility. Our results demonstrated: group 1: NET transcriptomes identified (92%) genes elevated. Group 2: 98% genes elevated by qPCR (fold change >2, P<0.05). Correlation analysis of matched blood/tumor was highly significant (R(2)=0.7, P<0.0001), and 58% of genes defined nine omic clusters (SSTRome, proliferome, signalome, metabolome, secretome, epigenome, plurome, and apoptome). Group 3: six clusters (SSTRome, proliferome, metabolome, secretome, epigenome, and plurome) differentiated SD from PD (area under the curve (AUC)=0.81). Integration with blood-algorithm amplified the AUC to 0.92±0.02 for differentiating PD and SD. The CIA defined a significantly lower SD score (34.1±2.6%) than in PD (84±2.8%, P<0.0001). In conclusion, circulating transcripts measurements reflect NET tissue values. Integration of biologically relevant GC differentiate SD from PD. Combination of GC data with the blood-algorithm predicted disease status in >92%. Blood transcript measurement predicts NET activity.