- 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.
MetadatenAuthor: | Bruno A. Lopes, Caroline Pires PoubelORCiD, Cristiane Esteves TeixeiraORCiD, Aurélie Caye-EudeORCiD, Hélène CavéORCiD, Claus MeyerORCiDGND, Rolf MarschalekORCiDGND, Mariana BoroniORCiD, Mariana EmerencianoORCiD |
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URN: | urn:nbn:de:hebis:30:3-628925 |
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DOI: | https://doi.org/10.3389/fphar.2022.749472 |
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ISSN: | 1663-9812 |
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Parent Title (English): | Frontiers in pharmacology |
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Publisher: | Frontiers Media |
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Place of publication: | Lausanne |
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Document Type: | Article |
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Language: | English |
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Date of Publication (online): | 2022/06/06 |
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Date of first Publication: | 2022/06/06 |
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Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
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Release Date: | 2023/01/10 |
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Tag: | KMT2A; MLL; acute leukemia; biomarker; machine learning; therapy |
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Volume: | 13 |
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Issue: | art. 749472 |
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Article Number: | 749472 |
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Page Number: | 13 |
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First Page: | 1 |
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Last Page: | 13 |
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Note: | Data Availability Statement
Publicly available datasets were analyzed in this study. This data can be found here: https://github.com/bioinformatics-inca/KMT2Ar-prediction (codes for machine learning analysis); https://portal.gdc.cancer.gov/projects (TARGET and TCGA datasets: accession TARGET-AML, TARGET-ALL-P2, TARGET-ALL-P3, and TCGA-LAML). |
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Institutes: | Biochemie, Chemie und Pharmazie |
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Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
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Sammlungen: | Universitätspublikationen |
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Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |
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