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Liu Y, Li T, Zhang H, Wang L, Cao R, Zhang J, Liu J, Liu L. Establishment and validation of a gene mutation-based risk model for predicting prognosis and therapy response in acute myeloid leukemia. Heliyon 2024; 10:e31249. [PMID: 38831838 PMCID: PMC11145431 DOI: 10.1016/j.heliyon.2024.e31249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 04/23/2024] [Accepted: 05/13/2024] [Indexed: 06/05/2024] Open
Abstract
Background Acute myeloid leukemia (AML) is a malignant clonal proliferative disease of hematopoietic system. Despite tremendous progress in uncovering the AML genome, only a small number of mutations have been incorporated into risk stratification and used as therapeutic targets. In this research, we performed to construct a predictive prognosis risk model for AML patients according to gene mutations. Methods Next-generation sequencing (NGS) technology was utilized to detect gene mutation from 118 patients. mRNA expression profiles and related clinical information were mined from TCGA and GEO databases. Consensus cluster analysis was applied to obtain molecular subtypes, and differences in clinicopathological features, prognosis, and immune microenvironment of different clusters were systematically compared. According to the differentially expressed genes (DEGs) between clusters, univariate and LASSO regression analysis were applied to identify gene signatures to build a prognostic risk model. Patients were classified into high-risk (HR) and low-risk (LR) groups according to the median risk score (RS). Differences in prognosis, immune profile, and therapeutic sensitivity between two groups were analyzed. The independent predictive value of RS was assessed and a nomogram was developed. Results NGS detected 24 mutated genes, with higher mutation frequencies in CBL (63 %) and SETBP1 (49 %). Two clusters exhibited different immune microenvironments and survival probability (p = 0.0056) were identified. A total of 444 DEGs were screened in two clusters, and a mutation-associated risk model was constructed, including MPO, HGF, SH2B3, SETBP1, HLA-DRB1, LGALS1, and KDM5B. Patients in LR had a superior survival time compared to HR. Predictive performance of this model was confirmed and the developed nomogram further improved the applicability of the risk model with the AUCs for predicting 1-, 3-, 5-year survival rate were 0.829, 0.81 and 0.811, respectively. HR cases were more sensitive to erlotinib, CI-1040, and AZD6244. Conclusion These findings supplemented the understanding of gene mutations in AML, and constructed models had good application prospect to provide effective information for predicting prognosis and treatment response of AML.
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Affiliation(s)
- Yun Liu
- Department of Hematology, The People's Hospital of Weifang, Weifang, Shandong, 261041, China
| | - Teng Li
- Department of Interventional Radiology, The People's Hospital of Weifang, Weifang, Shandong, 261041, China
| | - Hongling Zhang
- Department of Hematology, The People's Hospital of Weifang, Weifang, Shandong, 261041, China
| | - Lijuan Wang
- Department of Hematology, The People's Hospital of Weifang, Weifang, Shandong, 261041, China
| | - Rongxuan Cao
- Department of Hematology, The People's Hospital of Weifang, Weifang, Shandong, 261041, China
| | - Junying Zhang
- Department of Hematology, The People's Hospital of Weifang, Weifang, Shandong, 261041, China
| | - Jing Liu
- Department of Hematology, The People's Hospital of Weifang, Weifang, Shandong, 261041, China
| | - Liping Liu
- Department of Hematology, The People's Hospital of Weifang, Weifang, Shandong, 261041, China
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Zhou J, Chng WJ. Unveiling novel insights in acute myeloid leukemia through single-cell RNA sequencing. Front Oncol 2024; 14:1365330. [PMID: 38711849 PMCID: PMC11070491 DOI: 10.3389/fonc.2024.1365330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/09/2024] [Indexed: 05/08/2024] Open
Abstract
Acute myeloid leukemia (AML) is a complex and heterogeneous group of aggressive hematopoietic stem cell disease. The presence of diverse and functionally distinct populations of leukemia cells within the same patient's bone marrow or blood poses a significant challenge in diagnosing and treating AML. A substantial proportion of AML patients demonstrate resistance to induction chemotherapy and a grim prognosis upon relapse. The rapid advance in next generation sequencing technologies, such as single-cell RNA-sequencing (scRNA-seq), has revolutionized our understanding of AML pathogenesis by enabling high-resolution interrogation of the cellular heterogeneity in the AML ecosystem, and their transcriptional signatures at a single-cell level. New studies have successfully characterized the inextricably intertwined interactions among AML cells, immune cells and bone marrow microenvironment and their contributions to the AML development, therapeutic resistance and relapse. These findings have deepened and broadened our understanding the complexity and heterogeneity of AML, which are difficult to detect with bulk RNA-seq. This review encapsulates the burgeoning body of knowledge generated through scRNA-seq, providing the novel insights and discoveries it has unveiled in AML biology. Furthermore, we discuss the potential implications of scRNA-seq in therapeutic opportunities, focusing on immunotherapy. Finally, we highlight the current limitations and future direction of scRNA-seq in the field.
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Affiliation(s)
- Jianbiao Zhou
- Cancer Science Institute of Singapore, Center for Translational Medicine, National University of Singapore, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Center for Cancer Research, Center for Translational Medicine, Singapore, Singapore
| | - Wee-Joo Chng
- Cancer Science Institute of Singapore, Center for Translational Medicine, National University of Singapore, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Center for Cancer Research, Center for Translational Medicine, Singapore, Singapore
- Department of Hematology-Oncology, National University Cancer Institute of Singapore (NCIS), The National University Health System (NUHS), Singapore, Singapore
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Gil JV, Miralles A, de las Heras S, Such E, Avetisyan G, Díaz-González Á, Santiago M, Fuentes C, Fernández JM, Lloret P, Navarro I, Montesinos P, Llop M, Barragán E. Comprehensive detection of CRLF2 alterations in acute lymphoblastic leukemia: a rapid and accurate novel approach. Front Mol Biosci 2024; 11:1362081. [PMID: 38370004 PMCID: PMC10869515 DOI: 10.3389/fmolb.2024.1362081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 01/22/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction: Acute lymphoblastic leukemia (ALL) is a prevalent childhood cancer with high cure rate, but poses a significant medical challenge in adults and relapsed patients. Philadelphia-like acute lymphoblastic leukemia (Ph-like ALL) is a high-risk subtype, with approximately half of cases characterized by CRLF2 overexpression and frequent concomitant IKZF1 deletions. Methods: To address the need for efficient, rapid, and cost-effective detection of CRLF2 alterations, we developed a novel RT-qPCR technique combining SYBR Green and highresolution melting analysis on a single plate. Results: The method successfully identified CRLF2 expression, P2RY8::CRLF2 fusions, and CRLF2 and JAK2 variants, achieving a 100% sensitivity and specificity. Application of this method across 61 samples revealed that 24.59% exhibited CRLF2 overexpression, predominantly driven by IGH::CRLF2 (73.33%). High Resolution Melting analysis unveiled concurrent CRLF2 or JAK2 variants in 8.19% of samples, as well as a dynamic nature of CRLF2 alterations during disease progression. Discussion: Overall, this approach provides an accurate identification of CRLF2 alterations, enabling improved diagnostic and facilitating therapeutic decision-making.
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Affiliation(s)
- José Vicente Gil
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
| | - Alberto Miralles
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
| | - Sandra de las Heras
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
| | - Esperanza Such
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
- Hematology Service, Hospital Universitario y Politécnico la Fe, Valencia, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC CB16/12/00284, Instituto de Salud Carlos III, Madrid, Spain
| | - Gayane Avetisyan
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
| | - Álvaro Díaz-González
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
| | - Marta Santiago
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
| | - Carolina Fuentes
- Accredited Research Group on Clinical and Translational Cancer Research, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
- Onco-Hematology Unit, Pediatrics Service, Hospital Universitario y Politécnico la Fe, Valencia, Spain
| | - José María Fernández
- Accredited Research Group on Clinical and Translational Cancer Research, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
- Onco-Hematology Unit, Pediatrics Service, Hospital Universitario y Politécnico la Fe, Valencia, Spain
| | - Pilar Lloret
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
- Hematology Service, Hospital Universitario y Politécnico la Fe, Valencia, Spain
| | - Irene Navarro
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
- Hematology Service, Hospital Universitario y Politécnico la Fe, Valencia, Spain
| | - Pau Montesinos
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
- Hematology Service, Hospital Universitario y Politécnico la Fe, Valencia, Spain
| | - Marta Llop
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC CB16/12/00284, Instituto de Salud Carlos III, Madrid, Spain
- Molecular Biology Unit, Clinical Analysis Service, Hospital Universitario y Politécnico la Fe, Valencia, Spain
| | - Eva Barragán
- Accredited Research Group on Hematology, Instituto de Investigación Sanitaria la Fe, Valencia, Spain
- Centro de Investigación Biomédica en Red de Cáncer, CIBERONC CB16/12/00284, Instituto de Salud Carlos III, Madrid, Spain
- Molecular Biology Unit, Clinical Analysis Service, Hospital Universitario y Politécnico la Fe, Valencia, Spain
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Sargas C, Ayala R, Larráyoz MJ, Chillón MC, Rodriguez-Arboli E, Bilbao C, Prados de la Torre E, Martínez-Cuadrón D, Rodríguez-Veiga R, Boluda B, Gil C, Bernal T, Bergua J, Algarra L, Tormo M, Martínez-Sánchez P, Soria E, Serrano J, Alonso-Dominguez JM, García R, Amigo ML, Herrera-Puente P, Sayas MJ, Lavilla-Rubira E, Martínez-López J, Calasanz MJ, García-Sanz R, Pérez-Simón JA, Gómez Casares MT, Sánchez-García J, Barragán E, Montesinos P. Comparison of the 2022 and 2017 European LeukemiaNet risk classifications in a real-life cohort of the PETHEMA group. Blood Cancer J 2023; 13:77. [PMID: 37173322 PMCID: PMC10182047 DOI: 10.1038/s41408-023-00835-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 03/24/2023] [Accepted: 04/11/2023] [Indexed: 05/15/2023] Open
Abstract
Next-Generation Sequencing is needed for the accurate genetic risk stratification of acute myeloid leukemia according to European LeukemiaNet (ELN) guidelines. We validated and compared the 2022 ELN risk classification in a real-life cohort of 546 intensively and 379 non-intensively treated patients. Among fit patients, those aged ≥65 years old showed worse OS than younger regardless risk classification. Compared with the 2017 classification, 14.5% of fit patients changed the risk with the 2022 classification, increasing the high-risk group from 44.3% to 51.8%. 3.7% and 0.9% FLT3-ITD mutated patients were removed from the favorable and adverse 2017 categories respectively to 2022 intermediate risk group. We suggest that midostaurin therapy could be a predictor for 3 years OS (85.2% with vs. 54.8% without midostaurin, P = 0.04). Forty-seven (8.6%) patients from the 2017 intermediate group were assigned to the 2022 adverse-risk group as they harbored myelodysplasia (MDS)-related mutations. Patients with one MDS-related mutation did not reach median OS, while patients with ≥2 mutations had 13.6 months median OS (P = 0.002). Patients with TP53 ± complex karyotype or inv(3) had a dismal prognosis (7.1 months median OS). We validate the prognostic utility of the 2022 ELN classification in a real-life setting providing supportive evidences to improve risk stratification guidelines.
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Affiliation(s)
- Claudia Sargas
- Grupo Acreditado de Investigación en Hematología, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Rosa Ayala
- Hospital Universitario 12 de Octubre, CNIO, Complutense University, Madrid, Spain
| | | | - María C Chillón
- Servicio de Hematología, Hospital Universitario de Salamanca (HUS/IBSAL), CIBERONC, Centro de Investigación del Cáncer-IBMCC (USAL-CSIC), Salamanca, Spain
| | - Eduardo Rodriguez-Arboli
- Hospital Universitario Virgen del Rocío, Instituto de Biomedicina (IBIS/CSIC), Universidad de Sevilla, Sevilla, Spain
| | - Cristina Bilbao
- Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria, Spain
| | | | - David Martínez-Cuadrón
- Servicio de Hematología, Grupo Acreditado de Investigación en Hematología, Hospital Universitario y Politécnico La Fe, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Rebeca Rodríguez-Veiga
- Servicio de Hematología, Grupo Acreditado de Investigación en Hematología, Hospital Universitario y Politécnico La Fe, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Blanca Boluda
- Servicio de Hematología, Grupo Acreditado de Investigación en Hematología, Hospital Universitario y Politécnico La Fe, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Cristina Gil
- Hospital General Universitario de Alicante, Alicante, Spain
| | - Teresa Bernal
- Hospital Universitario Central de Asturias, Instituto Universitario (IUOPA), Instituto de investigación del Principado de Asturias (ISPA), Oviedo, Spain
| | - Juan Bergua
- Hospital Universitario San Pedro de Alcántara, Cáceres, Spain
| | | | - Mar Tormo
- Hospital Clínico Universitario-INCLIVA, Valencia, Spain
| | | | - Elena Soria
- Hospital Universitario Virgen del Rocío, Instituto de Biomedicina (IBIS/CSIC), Universidad de Sevilla, Sevilla, Spain
| | - Josefina Serrano
- IMIBIC, Hematology, Hospital Universitario Reina Sofía, UCO, Córdoba, Spain
| | | | | | | | | | | | | | | | | | - Ramón García-Sanz
- Servicio de Hematología, Hospital Universitario de Salamanca (HUS/IBSAL), CIBERONC, Centro de Investigación del Cáncer-IBMCC (USAL-CSIC), Salamanca, Spain
| | - José A Pérez-Simón
- Hospital Universitario Virgen del Rocío, Instituto de Biomedicina (IBIS/CSIC), Universidad de Sevilla, Sevilla, Spain
| | - María T Gómez Casares
- Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria, Spain
| | | | - Eva Barragán
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
- Servicio Análisis Clínicos, Grupo Acreditado de Investigación en Hematología, Hospital Universitario y Politécnico La Fe, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Pau Montesinos
- Servicio de Hematología, Grupo Acreditado de Investigación en Hematología, Hospital Universitario y Politécnico La Fe, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain.
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain.
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Design and Validation of a Custom Next-Generation Sequencing Panel in Pediatric Acute Lymphoblastic Leukemia. Int J Mol Sci 2023; 24:ijms24054440. [PMID: 36901871 PMCID: PMC10002321 DOI: 10.3390/ijms24054440] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/15/2023] [Accepted: 02/21/2023] [Indexed: 03/05/2023] Open
Abstract
The molecular landscape of acute lymphoblastic leukemia (ALL) is highly heterogeneous, and genetic lesions are clinically relevant for diagnosis, risk stratification, and treatment guidance. Next-generation sequencing (NGS) has become an essential tool for clinical laboratories, where disease-targeted panels are able to capture the most relevant alterations in a cost-effective and fast way. However, comprehensive ALL panels assessing all relevant alterations are scarce. Here, we design and validate an NGS panel including single-nucleotide variants (SNVs), insertion-deletions (indels), copy number variations (CNVs), fusions, and gene expression (ALLseq). ALLseq sequencing metrics were acceptable for clinical use and showed 100% sensitivity and specificity for virtually all types of alterations. The limit of detection was established at a 2% variant allele frequency for SNVs and indels, and at a 0.5 copy number ratio for CNVs. Overall, ALLseq is able to provide clinically relevant information to more than 83% of pediatric patients, making it an attractive tool for the molecular characterization of ALL in clinical settings.
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