PHi-RACE: PGIMER in-house rapid & cost effective classifier for the detection of <i>BCR-ABL1</i> -like acute lymphoblastic leukaemia in Indian patients
Dikshat Gopal Gupta, Neelam Varma, Ashish Kumar, Shano Naseem, Man Updesh Singh Sachdeva, Jogeshwar Binota, Parveen Bose, Minakshi Gupta, Preeti Sonam, Palak Rana, Pankaj Malhotra, Alka Khadwal, Amita Trehan, Subhash Varma
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.