TY  - JOUR
A1  - Lopes, Bruno A.
A1  - Poubel, Caroline Pires
A1  - Esteves Teixeira, Cristiane
A1  - Caye-Eude, Aurélie
A1  - Cavé, Hélène
A1  - Meyer, Claus
A1  - Marschalek, Rolf
A1  - Boroni, Mariana
A1  - Emerenciano, Mariana
T1  - Novel diagnostic and therapeutic options for KMT2A-rearranged acute leukemias
T2  - Frontiers in pharmacology
N2  - 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.
KW  - KMT2A
KW  - MLL
KW  - acute leukemia
KW  - biomarker
KW  - machine learning
KW  - therapy
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/62892
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-628925
SN  - 1663-9812
N1  - 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).
VL  - 13
IS  - art. 749472
SP  - 1
EP  - 13
PB  - Frontiers Media
CY  - Lausanne
ER  -