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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.

PHi-RACE: PGIMER in-house rapid & cost effective classifier for the detection of BCR-ABL1 -like acute lymphoblastic leukaemia in Indian patients