Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute Leukemias

被引:8
|
作者
Lopes, Bruno A. [1 ]
Poubel, Caroline Pires [1 ,2 ]
Teixeira, Cristiane Esteves [2 ]
Caye-Eude, Aurelie [3 ,4 ]
Cave, Helene [3 ,4 ]
Meyer, Claus [5 ]
Marschalek, Rolf [5 ]
Boroni, Mariana [2 ]
Emerenciano, Mariana [1 ]
机构
[1] Inst Nacl Canc Jose Alencar Gomes Silva INCA, Div Clin Res & Technol Dev, Acute Leukemia RioSearch Grp, Rio De Janeiro, Brazil
[2] Inst Nacl Canc Jose Alencar Gomes Silva INCA, Bioinformat & Computat Biol Lab, Rio De Janeiro, Brazil
[3] Hop Robert Debre, Assistance Publ Hop Paris AP HP, Dept Genet, UF Genet Mol, Paris, France
[4] Univ Paris Cite, Inst Rech St Louis, INSERM UMR S1131, Paris, France
[5] Goethe Univ Frankfurt, Inst Pharmaceut Biol, DCAL, Frankfurt Am Main, Germany
关键词
KMT2A; MLL; acute leukemia; biomarker; machine learning; therapy; ACUTE MYELOID-LEUKEMIA; CHONDROITIN SULFATE PROTEOGLYCAN; DIFFERENTIAL EXPRESSION ANALYSIS; ACUTE LYMPHOBLASTIC-LEUKEMIA; HUMAN HOMOLOG; MLL REARRANGEMENTS; NG2; ANTIGEN; RAT NG2; ABNORMALITIES; BIOCONDUCTOR;
D O I
10.3389/fphar.2022.749472
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
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 (PI3K beta 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.
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页数:13
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