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Gene expression profiling is the criterion standard for recognizing Ph-like ALL signatures among B-ALLs. The prerequisite of GEP is the accurate normalization of target genes with stable expression of housekeeping genes in a quantitative PCR. HKGs exhibit differential expression in the different experimental conditions and affect the target genes' expression, leading to imprecise qPCR results. The selection of stable HKGs is crucial in GEP experiments, especially in identifying high-risk Ph-like ALL cases. We have evaluated the expression stability of nine HKGs (GAPDH, ACTB, GUSB, RNA18S, EEF2, PGK1, B2M, TBP and ABL1) in identified Ph-like ALLs and Ph-negative (n = 23 each) using six algorithms, 4 traditional softwares; geNorm, BestKeeper, NormFinder, Delta Cq value method, and two algorithms, RefFinderTM and ComprFinder. Further, we have validated the expression of 8 overexpressed normalized genes in Ph-like ALL cases (JCHAIN, CA6, MUC4, SPATS2L, BMPR1B, CRLF2, ADGRF1 and NRXN3). GeNorm, BestKeeper, NormFinder, Delta Cq value method, RefFinderTM and ComprFinder algorithm analysis revealed that EEF2, GAPDH, and PGK1 form the best representative HKGs in Ph-like ALL cases, while RNA18s, ß-actin, and ABL1 in Ph-negative ALLs. Lastly, we performed a correlation analysis and found that the combination of EEF2, GAPDH, and PGK1 represents the best combination with a very high correlation in Ph-like ALL cases. This is the first report that shows EEF2, GAPDH, and PGK1 are the best HKG genes and can be used in the diagnostic panel of Ph-like ALL cases using qPCR at baseline diagnosis., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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. [ABSTRACT FROM AUTHOR]