Eprobe mediated RT-qPCR for the detection of leukemia-associated fusion genes.
Koji TsuchiyaYoko TabeTomohiko AiTakahiro OhkawaKengo UsuiMaiko YuriShigeki MisawaSoji MorishitaTomoiku TakakuAtsushi KakimotoHaeun YangHiromichi MatsushitaTakeshi HanamiYasunari YamanakaAtsushi OkuzawaTakashi HoriiYoshihide HayashizakiAkimichi OhsakaPublished in: PloS one (2018)
The detection and quantification of leukemia-associated fusion gene transcripts play important roles in the diagnosis and follow-up of leukemias. To establish a standardized method without interlaboratory discrepancies, we developed a novel one-step reverse transcription quantitative PCR (RT-qPCR) assay, called "the Eprobe leukemia assay," for major and minor BCR-ABL1, RUNX1-RUNX1T1, and various isoforms of PML-RARA. This assay is comprised of Eprobes that are exciton-controlled hybridization-sensitive fluorescent oligonucleotides. Melting curve analyses were performed on synthetic quantitative standard RNAs with strict quality control. Quantification capacity was evaluated by comparison with TaqMan RT-qPCR using 67 primary leukemia patient samples. The lower limit of detection and the limit of quantification of this assay were less than 31.3 copies/reaction and 62.5 copies/reaction, respectively. This assay correctly detected the fusion genes in samples with 100% sensitivity and specificity. The specificity of the reactions was confirmed by melting curve analyses. The assay detected low-level expression of minor BCR-ABL1 co-expressed with major BCR-ABL1. These results illustrate the feasibility and high accuracy of the Eprobe leukemia assay, even for minimal residual disease monitoring.
Keyphrases
- high throughput
- tyrosine kinase
- acute myeloid leukemia
- chronic myeloid leukemia
- bone marrow
- high resolution
- acute lymphoblastic leukemia
- real time pcr
- label free
- transcription factor
- quality control
- genome wide
- poor prognosis
- loop mediated isothermal amplification
- genome wide identification
- quantum dots
- single molecule
- single cell
- bioinformatics analysis
- living cells