Abstract
For the detection of BCR-ABL1-like ALL cases, two methodologies, specifically Gene expression profiling (GEP) or Next-generation targeted sequencing (NGS) and TaqMan based low-density (TLDA) card, are being used. NGS is very costly and TLDA is not widely commercially available. In this study, we quantified the expression of 8 selected overexpressed genes in 536 B-ALL cases. We identified 26.67% (143/536) BCR-ABL1-like ALLs using hierarchical clustering and principal component analysis. BCR-ABL1-like ALL cases were significantly older at presentation (p = 0.036) and had male preponderance (p = 0.047) compared to BCR-ABL1-negative ALL cases. MRD-positivity and induction failure were more commonest in BCR-ABL1-like ALL cases (30.55 vs.19.35% in BCR-ABL1-negative ALL cases). Lastly, we built a PHi-RACE classifier (sensitivity = 95.2%, specificity= 83.7%, AUC= 0.927) using logistic regression to detect BCR-ABL1-like ALL cases promptly at diagnosis. This classifier is beneficial for hematologists in quick decision making at baseline to start tailored treatment regimes.
Acknowledgement
Special thanks to Mr. Ashish Kumar for its inputs and efforts in creating the PHi-RACE classifier.
Author contributions
D.G.G. and N.V. designed the research work, analyzed the whole data and wrote manuscript. D.G.G., J.B., P.B., M.G., P.S. and P.R. performed the experiments. P.M., S.V., A.K, and A.T. provided the samples from patients recruited in the study. D.G.G. and A.K. performed the statistical analysis of generated logistic regression classifier. S.N. and M.U.S. read the manuscript and provided intellectual inputs to the submitted manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.