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Novel diagnostic and therapeutic options for KMT2A-rearranged acute leukemias

  • 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.

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Author:Bruno A. Lopes, Caroline Pires PoubelORCiD, Cristiane Esteves TeixeiraORCiD, Aurélie Caye-Eude, Hélène CavéORCiD, Claus MeyerGND, Rolf MarschalekORCiDGND, Mariana BoroniORCiD, Mariana EmerencianoORCiD
URN:urn:nbn:de:hebis:30:3-628925
DOI:https://doi.org/10.3389/fphar.2022.749472
ISSN:1663-9812
Parent Title (English):Frontiers in pharmacology
Publisher:Frontiers Media
Place of publication:Lausanne
Document Type:Article
Language:English
Date of Publication (online):2022/06/06
Date of first Publication:2022/06/06
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2023/01/10
Tag:KMT2A; MLL; acute leukemia; biomarker; machine learning; therapy
Volume:13
Issue:art. 749472
Article Number:749472
Page Number:13
First Page:1
Last Page:13
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).
Institutes:Biochemie, Chemie und Pharmazie
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International