4.7 Article

Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute Leukemias

Journal

FRONTIERS IN PHARMACOLOGY
Volume 13, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fphar.2022.749472

Keywords

KMT2A; MLL; acute leukemia; biomarker; machine learning; therapy

Funding

  1. Brazilian Ministry of Health
  2. Alexander von Humboldt Foundation
  3. Brazilian National Counsel of Technological and Scientific Development-CNPq [PQ-311220/2020-7]
  4. Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro-FAPERJ [E_26/203.214/2017, E-26-010.101072-2018, E-26/010.002187//2019]

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Using a machine learning model, we accurately predicted KMT2A-r leukemia and identified 20 genes that can estimate KMT2A-r. The overexpression of SKIDA1 and LAMP5 were found to be better markers associated with KMT2A-r compared to CSPG4. LAMP5 overexpression was also associated with all KMT2A-USP2 fusions. Additionally, drug sensitivity analysis revealed potential treatments for KMT2A-r leukemia, including Foretinib for AML patients with FLT3 activating mutations.
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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