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Górecki M, Kozioł I, Kopystecka A, Budzyńska J, Zawitkowska J, Lejman M. Updates in KMT2A Gene Rearrangement in Pediatric Acute Lymphoblastic Leukemia. Biomedicines 2023; 11:biomedicines11030821. [PMID: 36979800 PMCID: PMC10045821 DOI: 10.3390/biomedicines11030821] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 03/10/2023] Open
Abstract
The KMT2A (formerly MLL) encodes the histone lysine-specific N-methyltransferase 2A and is mapped on chromosome 11q23. KMT2A is a frequent target for recurrent translocations in acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), or mixed lineage (biphenotypic) leukemia (MLL). Over 90 KMT2A fusion partners have been identified until now, including the most recurring ones—AFF1, MLLT1, and MLLT3—which encode proteins regulating epigenetic mechanisms. The presence of distinct KMT2A rearrangements is an independent dismal prognostic factor, while very few KMT2A rearrangements display either a good or intermediate outcome. KMT2A-rearranged (KMT2A-r) ALL affects more than 70% of new ALL diagnoses in infants (<1 year of age), 5–6% of pediatric cases, and 15% of adult cases. KMT2A-rearranged (KMT2A-r) ALL is characterized by hyperleukocytosis, a relatively high incidence of central nervous system (CNS) involvement, an aggressive course with early relapse, and early relapses resulting in poor prognosis. The exact pathways of fusions and the effects on the final phenotypic activity of the disease are still subjects of much research. Future trials could consider the inclusion of targeted immunotherapeutic agents and prioritize the identification of prognostic factors, allowing for the less intensive treatment of some infants with KMT2A ALL. The aim of this review is to summarize our knowledge and present current insight into the mechanisms of KMT2A-r ALL, portray their characteristics, discuss the clinical outcome along with risk stratification, and present novel therapeutic strategies.
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Affiliation(s)
- Mateusz Górecki
- Student Scientific Society of Independent Laboratory of Genetic Diagnostics, Medical University of Lublin, 20-093 Lublin, Poland
| | - Ilona Kozioł
- Student Scientific Society of the Department of Pediatric Hematology, Oncology and Transplantology, Medical University of Lublin, 20-093 Lublin, Poland
| | - Agnieszka Kopystecka
- Student Scientific Society of the Department of Pediatric Hematology, Oncology and Transplantology, Medical University of Lublin, 20-093 Lublin, Poland
| | - Julia Budzyńska
- Student Scientific Society of the Department of Pediatric Hematology, Oncology and Transplantology, Medical University of Lublin, 20-093 Lublin, Poland
| | - Joanna Zawitkowska
- Department of Paediatric Haematology, Oncology and Transplantology, Medical University of Lublin, 20-093 Lublin, Poland
| | - Monika Lejman
- Independent Laboratory of Genetic Diagnostics, Medical University of Lublin, 20-093 Lublin, Poland
- Correspondence:
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Wen B, Njunge JM, Bourdon C, Gonzales GB, Gichuki BM, Lee D, Wishart DS, Ngari M, Chimwezi E, Thitiri J, Mwalekwa L, Voskuijl W, Berkley JA, Bandsma RHJ. Systemic inflammation and metabolic disturbances underlie inpatient mortality among ill children with severe malnutrition. SCIENCE ADVANCES 2022; 8:eabj6779. [PMID: 35171682 PMCID: PMC8849276 DOI: 10.1126/sciadv.abj6779] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
Children admitted to hospital with an acute illness and concurrent severe malnutrition [complicated severe malnutrition (CSM)] have a high risk of dying. The biological processes underlying their mortality are poorly understood. In this case-control study nested within a multicenter randomized controlled trial among children with CSM in Kenya and Malawi, we found that blood metabolomic and proteomic profiles robustly differentiated children who died (n = 92) from those who survived (n = 92). Fatalities were characterized by increased energetic substrates (tricarboxylic acid cycle metabolites), microbial metabolites (e.g., propionate and isobutyrate), acute phase proteins (e.g., calprotectin and C-reactive protein), and inflammatory markers (e.g., interleukin-8 and tumor necrosis factor-α). These perturbations indicated disruptions in mitochondria-related bioenergetic pathways and sepsis-like responses. This study identified specific biomolecular disturbances associated with CSM mortality, revealing that systemic inflammation and bioenergetic deficits are targetable pathophysiological processes for improving survival of this vulnerable population.
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Affiliation(s)
- Bijun Wen
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Translational medicine, Hospital for Sick Children, Toronto, Canada
| | - James M. Njunge
- The Childhood Acute Illness & Nutrition Network, Nairobi, Kenya
- KEMRI/Wellcome Trust Research Programme, Kilifi, Kenya
| | - Celine Bourdon
- Department of Translational medicine, Hospital for Sick Children, Toronto, Canada
- The Childhood Acute Illness & Nutrition Network, Nairobi, Kenya
| | - Gerard Bryan Gonzales
- Nutrition, Metabolism and Genomics Group, Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, Netherlands
| | - Bonface M. Gichuki
- The Childhood Acute Illness & Nutrition Network, Nairobi, Kenya
- KEMRI/Wellcome Trust Research Programme, Kilifi, Kenya
| | - Dorothy Lee
- Department of Translational medicine, Hospital for Sick Children, Toronto, Canada
| | | | - Moses Ngari
- The Childhood Acute Illness & Nutrition Network, Nairobi, Kenya
- KEMRI/Wellcome Trust Research Programme, Kilifi, Kenya
| | | | - Johnstone Thitiri
- The Childhood Acute Illness & Nutrition Network, Nairobi, Kenya
- KEMRI/Wellcome Trust Research Programme, Kilifi, Kenya
| | - Laura Mwalekwa
- KEMRI/Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Paediatrics, Coast General Hospital, Mombasa, Kenya
| | - Wieger Voskuijl
- The Childhood Acute Illness & Nutrition Network, Nairobi, Kenya
- Department of Global Health, Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centres, Amsterdam, Netherlands
- Department of Pediatrics, the College of Medicine, University of Malawi, Blantyre, Malawi
| | - James A. Berkley
- The Childhood Acute Illness & Nutrition Network, Nairobi, Kenya
- KEMRI/Wellcome Trust Research Programme, Kilifi, Kenya
- Centre for Tropical Medicine & Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Robert HJ Bandsma
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Translational medicine, Hospital for Sick Children, Toronto, Canada
- The Childhood Acute Illness & Nutrition Network, Nairobi, Kenya
- Department of Pediatrics, the College of Medicine, University of Malawi, Blantyre, Malawi
- Department of Biomedical Sciences, the College of Medicine, University of Malawi, Blantyre, Malawi
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Abstract
DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.In this chapter we provide an overview of the methods and tools used to create networks from microarray data and describe multiple methods on how to analyze a single network or a group of networks. The described methods range from topological metrics, functional group identification to data integration strategies, topological pathway analysis as well as graphical models.
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Affiliation(s)
- Alisa Pavel
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- BioMediTech Institute, Tampere University, Tampere, Finland.
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere University, Tampere, Finland.
- Institute of Biotechnology , University of Helsinki, Helsinki, Finland.
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LncRNA VPS9D1-AS1 promotes cell proliferation in acute lymphoblastic leukemia through modulating GPX1 expression by miR-491-5p and miR-214-3p evasion. Biosci Rep 2021; 40:226132. [PMID: 32808668 PMCID: PMC7536331 DOI: 10.1042/bsr20193461] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 08/13/2020] [Accepted: 08/17/2020] [Indexed: 12/16/2022] Open
Abstract
Alterations in messenger RNAs (mRNAs) of protein-coding genes can influence the malignant behaviors of acute lymphoblastic leukemia (ALL) cells. According to the prediction from The Cancer Genome Atlas (TCGA) database, we discovered that glutathione peroxidase 1 (GPX1) was up-regulated in acute myeloid leukemia (LAML) tissues, which pushed us to explore the feasible role and its related modulatory mechanism of GPX1 in ALL. In this research, we first proved the high expression of GPX1 in ALL cells compared with normal cells. Functional assays further revealed that the proliferation was obstructed and the apoptosis was facilitated in ALL cells with silenced GPX1. Then, both miR-491-5p and miR-214-3p that were down-regulated in ALL cells were affirmed to target GPX1. Subsequently, VPS9D1 antisense RNA 1 (VPS9D1-AS1) was recognized as the upstream regulator of miR-491-5p-miR-214-3p/GPX1 axis in a competing endogenous RNA (ceRNA) model. Importantly, we proved that VPS9D1-AS1 served as a tumor promoter in ALL through elevating GPX1. In conclusion, VPS9D1-AS1 contributed to ALL cell proliferation through miR-491-5p-miR-214-3p/GPX1 axis, hinting an optional choice for the treatment of ALL.
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Lambrou GI, Adamaki M, Hatziagapiou K, Vlahopoulos S. Gene Expression and Resistance to Glucocorticoid-Induced Apoptosis in Acute Lymphoblastic Leukemia: A Brief Review and Update. Curr Drug Res Rev 2021; 12:131-149. [PMID: 32077838 DOI: 10.2174/2589977512666200220122650] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 12/29/2019] [Accepted: 01/23/2020] [Indexed: 01/18/2023]
Abstract
BACKGROUND Resistance to glucocorticoid (GC)-induced apoptosis in Acute Lymphoblastic Leukemia (ALL), is considered one of the major prognostic factors for the disease. Prednisolone is a corticosteroid and one of the most important agents in the treatment of acute lymphoblastic leukemia. The mechanics of GC resistance are largely unknown and intense ongoing research focuses on this topic. AIM The aim of the present study is to review some aspects of GC resistance in ALL, and in particular of Prednisolone, with emphasis on previous and present knowledge on gene expression and signaling pathways playing a role in the phenomenon. METHODS An electronic literature search was conducted by the authors from 1994 to June 2019. Original articles and systematic reviews selected, and the titles and abstracts of papers screened to determine whether they met the eligibility criteria, and full texts of the selected articles were retrieved. RESULTS Identification of gene targets responsible for glucocorticoid resistance may allow discovery of drugs, which in combination with glucocorticoids may increase the effectiveness of anti-leukemia therapies. The inherent plasticity of clinically evolving cancer justifies approaches to characterize and prevent undesirable activation of early oncogenic pathways. CONCLUSION Study of the pattern of intracellular signal pathway activation by anticancer drugs can lead to development of efficient treatment strategies by reducing detrimental secondary effects.
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Affiliation(s)
- George I Lambrou
- First Department of Pediatrics, National and Kapodistrian University of Athens, Choremeio Research Laboratory, Athens, Greece
| | - Maria Adamaki
- First Department of Pediatrics, National and Kapodistrian University of Athens, Choremeio Research Laboratory, Athens, Greece
| | - Kyriaki Hatziagapiou
- First Department of Pediatrics, National and Kapodistrian University of Athens, Choremeio Research Laboratory, Athens, Greece
| | - Spiros Vlahopoulos
- First Department of Pediatrics, National and Kapodistrian University of Athens, Choremeio Research Laboratory, Athens, Greece
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G L, Adamaki M, Hatziagapiou K, Geronikolou SA, Tsartsalis AN, Vlahopoulos S. Early and Very Early GRIM19 and MCL1 Expression Are Correlated to Late Acquired Prednisolone Effects in a T-Cell Acute Leukemia Cell Line. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1339:147-160. [DOI: 10.1007/978-3-030-78787-5_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Hua X, Zhang H, Jia J, Chen S, Sun Y, Zhu X. Roles of S100 family members in drug resistance in tumors: Status and prospects. Biomed Pharmacother 2020; 127:110156. [PMID: 32335300 DOI: 10.1016/j.biopha.2020.110156] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 04/06/2020] [Accepted: 04/08/2020] [Indexed: 02/06/2023] Open
Abstract
Chemotherapy and targeted therapy can significantly improve survival rates in cancer, but multiple drug resistance (MDR) limits the efficacy of these approaches. Understanding the molecular mechanisms underlying MDR is crucial for improving drug efficacy and clinical outcomes of patients with cancer. S100 proteins belong to a family of calcium-binding proteins and have various functions in tumor development. Increasing evidence demonstrates that the dysregulation of various S100 proteins contributes to the development of drug resistance in tumors, providing a basis for the development of predictive and prognostic biomarkers in cancer. Therefore, a combination of biological inhibitors or sensitizers of dysregulated S100 proteins could enhance therapeutic responses. In this review, we provide a detailed overview of the mechanisms by which S100 family members influence resistance of tumors to cancer treatment, with a focus on the development of effective strategies for overcoming MDR.
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Affiliation(s)
- Xin Hua
- Southeast University Medical College, Nanjing, 210009, China.
| | - Hongming Zhang
- Department of Respiratory Medicine, Yancheng Third People's Hospital, Southeast University Medical College, Yancheng, 224000, China.
| | - Jinfang Jia
- Southeast University Medical College, Nanjing, 210009, China.
| | - Shanshan Chen
- Southeast University Medical College, Nanjing, 210009, China.
| | - Yue Sun
- Southeast University Medical College, Nanjing, 210009, China.
| | - Xiaoli Zhu
- Southeast University Medical College, Nanjing, 210009, China; Department of Respiratory Medicine, Zhongda Hospital of Southeast University Medical College, Nanjing, 210009, China.
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