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Background: Philadelphia chromosome (Ph)–like B‐acute lymphoblastic leukemia (B‐ALL) is a clinically significant, high‐risk genetic subtype of B‐ALL cases. There are few data on the incidence, characterization, and treatment outcomes of Ph‐like ALL cases from low‐ and middle‐income countries. There is a pressing need to establish a well‐organized/cost‐effective approach for identifying Ph‐like ALL instances. Methods: Multiplex reverse transcriptase polymerase chain reaction, nCounter NanoString, and fluorescence in situ hybridization were used to detect and characterize Ph‐like ALL cases among recurrent genetic abnormalities (RGA)neg B‐ALL cases. At the end of induction therapy, flow cytometry‐minimal residual disease (MRD) assay was used to quantify MRD positivity in Ph‐like ALL cases. Results: Of 130 newly diagnosed B‐ALL cases, 25% (BCR::ABL1), 4% (ETV6::RUNX1), 5% (TCF3::PBX1), 2% (KM2TA::AFF1), and 65% RGAneg B‐ALL cases were revealed by multiplex reverse transcriptase polymerase chain reaction. Among RGAneg B‐ALL cases, 24% Ph‐like ALL cases using nCounter NanoString were identified, with 48% CRLF2high cases with 45% CRLF2::P2RY8 and 18% CRLF2::IGH rearrangements(∼r) revealed by fluorescence in situ hybridization. In 52% of CRLF2low cases, 17% ABL1 and JAK2∼r 8% EPOR::IGH & PDGRFB∼r were identified. Ph‐like ALL cases had higher total leukocyte count (p <.05), male preponderance (p <.05), and high MRD‐positivity/induction failure compared with RGAneg B‐ALL cases. Furthermore, in Ph‐like ALL cases, 11 significant genes using quantitative polymerase chain reaction were identified and validated. CRLF2, IGJ, CEACAM6, MUC4, SPATS2L and NRXN3 genes were overexpressed and show statistical significance (p <.05) in Ph‐like ALL cases. Conclusions: This study showed the high incidence of Ph‐like ALL cases with kinase activating alterations and treatment outcomes from low‐ and middle‐income region. Furthermore, a surrogate cost‐effective multiplex panel of 11 overexpressed genes for the prompt detection of Ph‐like ALL cases is proposed. Plain Language Summary: Identification of recurrent gene abnormalities (RGA)neg B‐acute lymphoblastic leukemia (B‐ALL) cases using multiplex‐reverse transcriptase polymerase chain reaction.Identification and characterization of Philadelphia (Ph)–like ALL cases using nCounter NanoString gene expression profiling and fluorescence in situ hybridization.Furthermore, Ph‐like ALL cases were characterized according to CRLF2 expression and kinase‐activating genomic alterations. Minimal residual disease of Ph‐like ALL cases were quantified using flow cytometry‐minimal residual disease assay.A surrogate molecular approach was established to detect Ph‐like ALL cases from low‐ and middle‐income countries. A surrogate approach for the rapid baseline detection of Philadelphia chromosome (Ph)–like acute lymphoblastic leukemia cases, followed by its kinase gene alterations. Early detection of Ph–like acute lymphoblastic leukemia cases is extremely important to start personalized treatments to improve the overall survival/outcomes of these cases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Early detection of BCR::ABL1-like ALL could impact treatment management and improve the overall survival/outcome. BCR::ABL1-like ALL cases are characterised by diverse genetic alterations activating cytokine receptors and kinase signalling. Its detection is still an unmet need in low–middle-income countries due to the unavailability of a patented TLDA assay. Methods: This study's rationale is to identify BCR::ABL1-like ALLs using the PHi-RACE classifier, followed by the characterisation of underlying adverse genetic alterations in recurrent gene abnormalities negative (RGAneg) B-ALLs (n = 108). Results: We identified 34.25% (37/108) BCR::ABL1-like ALLs using PHi-RACE classifier, characterised by TSLPR/CRLF2 expression (11.58%), IKZF1 (Δ4–7) deletion (18.9%) and chimeric gene fusions (34.61%). In overexpressed TSLPR/CRLF2 BCR::ABL1-like ALLs, we identified 33.33% (1/3) CRLF2::IGH and 33.33% (1/3) EPOR::IGH rearrangements with concomitant JAK2 mutation R683S (50%). We identified 18.91% CD13 (P = 0.02) and 27.02% CD33 (P = 0.05) aberrant myeloid markers positivity, which was significantly higher in BCR::ABL1-like ALLs compared to non-BCR::ABL1-like ALLs. MRD positivity was considerably higher (40% in BCR::ABL1-like vs. 19.29% in non-BCR::ABL1-like ALLs). Conclusions: With this practical approach, we reported a high incidence of BCR::ABL1-like ALLs, and a lower frequency of CRLF2 alteration & associated CGFs. Recognising this entity, early at diagnosis is crucial to optimise personalised treatment strategies. [ABSTRACT FROM AUTHOR]