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, compl...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2022.749472/full |
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| author | Bruno A. Lopes Caroline Pires Poubel Caroline Pires Poubel Cristiane Esteves Teixeira Aurélie Caye-Eude Aurélie Caye-Eude Hélène Cavé Hélène Cavé Claus Meyer Rolf Marschalek Mariana Boroni Mariana Emerenciano |
| author_facet | Bruno A. Lopes Caroline Pires Poubel Caroline Pires Poubel Cristiane Esteves Teixeira Aurélie Caye-Eude Aurélie Caye-Eude Hélène Cavé Hélène Cavé Claus Meyer Rolf Marschalek Mariana Boroni Mariana Emerenciano |
| author_sort | Bruno A. Lopes |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T08:11:49Z |
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| id | doaj.art-6598dfb31ad047d2baac2bc909938f13 |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-12-12T08:11:49Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Pharmacology |
| spelling | doaj.art-6598dfb31ad047d2baac2bc909938f132022-12-22T00:31:44ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122022-06-011310.3389/fphar.2022.749472749472Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute LeukemiasBruno A. Lopes0Caroline Pires Poubel1Caroline Pires Poubel2Cristiane Esteves Teixeira3Aurélie Caye-Eude4Aurélie Caye-Eude5Hélène Cavé6Hélène Cavé7Claus Meyer8Rolf Marschalek9Mariana Boroni10Mariana Emerenciano11Acute Leukemia RioSearch Group, Division of Clinical Research and Technological Development, Instituto Nacional de Câncer José Alencar Gomes da Silva (INCA), Rio de Janeiro, BrazilAcute Leukemia RioSearch Group, Division of Clinical Research and Technological Development, Instituto Nacional de Câncer José Alencar Gomes da Silva (INCA), Rio de Janeiro, BrazilBioinformatics and Computational Biology Laboratory, Instituto Nacional de Câncer José Alencar Gomes da Silva (INCA), Rio de Janeiro, BrazilBioinformatics and Computational Biology Laboratory, Instituto Nacional de Câncer José Alencar Gomes da Silva (INCA), Rio de Janeiro, BrazilDépartement de Génétique, UF de Génétique moléculaire, Assistance Publique des Hópitaux de Paris (AP-HP), Hópital Robert Debré, Paris, FranceINSERM UMR_S1131, Institut de Recherche Saint-Louis, Université de Paris-Cité, Paris, FranceDépartement de Génétique, UF de Génétique moléculaire, Assistance Publique des Hópitaux de Paris (AP-HP), Hópital Robert Debré, Paris, FranceINSERM UMR_S1131, Institut de Recherche Saint-Louis, Université de Paris-Cité, Paris, FranceDCAL/Institute of Pharmaceutical Biology, Goethe-University Frankfurt, Frankfurt am Main, GermanyDCAL/Institute of Pharmaceutical Biology, Goethe-University Frankfurt, Frankfurt am Main, GermanyBioinformatics and Computational Biology Laboratory, Instituto Nacional de Câncer José Alencar Gomes da Silva (INCA), Rio de Janeiro, BrazilAcute Leukemia RioSearch Group, Division of Clinical Research and Technological Development, Instituto Nacional de Câncer José Alencar Gomes da Silva (INCA), Rio de Janeiro, BrazilThe 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.https://www.frontiersin.org/articles/10.3389/fphar.2022.749472/fullKMT2AMLLacute leukemiabiomarkermachine learningtherapy |
| spellingShingle | Bruno A. Lopes Caroline Pires Poubel Caroline Pires Poubel Cristiane Esteves Teixeira Aurélie Caye-Eude Aurélie Caye-Eude Hélène Cavé Hélène Cavé Claus Meyer Rolf Marschalek Mariana Boroni Mariana Emerenciano Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute Leukemias Frontiers in Pharmacology KMT2A MLL acute leukemia biomarker machine learning therapy |
| title | Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute Leukemias |
| title_full | Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute Leukemias |
| title_fullStr | Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute Leukemias |
| title_full_unstemmed | Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute Leukemias |
| title_short | Novel Diagnostic and Therapeutic Options for KMT2A-Rearranged Acute Leukemias |
| title_sort | novel diagnostic and therapeutic options for kmt2a rearranged acute leukemias |
| topic | KMT2A MLL acute leukemia biomarker machine learning therapy |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2022.749472/full |
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