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The presence of IKZF1 deletions has been associated with an increased relapse rate in B-cell precursor acute lymphoblastic leukaemia (BCP-ALL). There is a particular subset of IKZF1del cases called IKZF1plus (defined by the co-occurrence of IKZF1del and deletions in CDKN2A/B, PAX5, or the PAR1 region, in the absence of ERG deletions), which is also associated with worse prognosis, but some recent studies have not found major differences between the IKZF1del and IKZF1plus groups. Therefore, the IKZF1plus group still needs further comprehension and our study aims to characterise the molecular heterogeneity and identify molecular markers exclusively associated with IKZF1plus. Two independent series of cases (TARGET, n = 125 and GenLAb, n = 60) were evaluated by segregating patients into 3 groups: IKZF1plus, IKZF1del, and IKZF1wild. Differential expression analyses showed that the membrane protein-coding genes most associated with the IKZF1plus group were: KCNA5, GREB1, EPOR, SDK1, and PTPRB. Notably, KCNA5 and GREB1 differential expression levels were validated in the GenLAb validation series. Regarding copy number alterations, we observed a high frequency of VPREB1 deletions in the IKZF1plus group, as well as additional exclusive deletions in the CD200 and BTLA genes. Recent research suggests that the importance of the IKZF1plus profile varies depending on the genetic subgroup. In this scenario, we found associations between IKZF1plus and certain genes in BCP-ALL, being KCNA5 and GREB1 the most promising biomarkers for predicting IKZF1plus. A deeper understanding of these genetic profiles will allow a better risk assessment and offer precise rationale for therapeutic strategies in BCP-ALL.

The KMT2A (MLL) gene rearrangements (KMT2A-r) are associated with a diverse spectrum of acute leukemias. Although most KMT2A-r are restricted to nine partner genes, we have recently revealed that KMT2A-USP2 fusions are often missed during FISH screening of these genetic alterations. Therefore, complementary methods are important for appropriate detection of any KMT2A-r. Here we use a machine learning model to unravel the most appropriate markers for prediction of KMT2A-r in various types of acute leukemia. A Random Forest and LightGBM classifier was trained to predict KMT2A-r in patients with acute leukemia. Our results revealed a set of 20 genes capable of accurately estimating KMT2A-r. The SKIDA1 (AUC: 0.839; CI: 0.799–0.879) and LAMP5 (AUC: 0.746; CI: 0.685–0.806) overexpression were the better markers associated with KMT2A-r compared to CSPG4 (also named NG2; AUC: 0.722; CI: 0.659–0.784), regardless of the type of acute leukemia. Of importance, high expression levels of LAMP5 estimated the occurrence of all KMT2A-USP2 fusions. Also, we performed drug sensitivity analysis using IC50 data from 345 drugs available in the GDSC database to identify which ones could be used to treat KMT2A-r leukemia. We observed that KMT2A-r cell lines were more sensitive to 5-Fluorouracil (5FU), Gemcitabine (both antimetabolite chemotherapy drugs), WHI-P97 (JAK-3 inhibitor), Foretinib (MET/VEGFR inhibitor), SNX-2112 (Hsp90 inhibitor), AZD6482 (PI3Kβ inhibitor), KU-60019 (ATM kinase inhibitor), and Pevonedistat (NEDD8-activating enzyme (NAE) inhibitor). Moreover, IC50 data from analyses of ex-vivo drug sensitivity to small-molecule inhibitors reveals that Foretinib is a promising drug option for AML patients carrying FLT3 activating mutations. Thus, we provide novel and accurate options for the diagnostic screening and therapy of KMT2A-r leukemia, regardless of leukemia subtype.