The World Health Organization (WHO) classification system defines recurrent chromosomal translocations as the sole diagnostic and prognostic criteria for acute leukemia (AL). These fusion transcripts are pivotal in the pathogenesis of AL. Clinical laboratories universally employ conventional karyotype/FISH to detect these chromosomal translocations, which is complex, labour intensive and lacks multiplexing capacity. Hence, it is imperative to explore and evaluate some newer automated, cost-efficient multiplexed technologies to accommodate the expanding genetic landscape in AL.
“nCounter® Leukemia fusion gene expression assay” by NanoString was employed to detect various fusion transcripts in a large set samples (n = 94) utilizing RNA from formalin fixed paraffin embedded (FFPE) diagnostic bone marrow biopsy specimens. This series included AL patients with various recurrent translocations (n = 49), normal karyotype (n = 19), or complex karyotype (n = 21), as well as normal bone marrow samples (n = 5). Fusion gene expression data were compared with results obtained by conventional karyotype and FISH technology to determine sensitivity/specificity, as well as positive /negative predictive values.
Junction probes for PML/RARA; RUNX1-RUNX1T1; BCR/ABL1 showed 100 % sensitivity/specificity. A high degree of correlation was noted for MLL/AF4 (85 sensitivity/100 specificity) and TCF3-PBX1 (75 % sensitivity/100 % specificity) probes. CBFB-MYH11 fusion probes showed moderate sensitivity (57 %) but high specificity (100 %). ETV6/RUNX1 displayed discordance between fusion transcript assay and FISH results as well as rare non-specific binding in AL samples with normal or complex cytogenetics.
Our study presents preliminary data with high correlation between fusion transcript detection by a throughput automated multiplexed platform, compared to conventional karyotype/FISH technique for detection of chromosomal translocations in AL patients. Our preliminary observations, mandates further vast validation studies to explore automated molecular platforms in diagnostic pathology.