1
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Ma W, Tang W, Kwok JS, Tong AH, Lo CW, Chu AT, Chung BH. A review on trends in development and translation of omics signatures in cancer. Comput Struct Biotechnol J 2024; 23:954-971. [PMID: 38385061 PMCID: PMC10879706 DOI: 10.1016/j.csbj.2024.01.024] [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: 10/27/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/23/2024] Open
Abstract
The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.
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Affiliation(s)
- Wei Ma
- Hong Kong Genome Institute, Hong Kong, China
| | - Wenshu Tang
- Hong Kong Genome Institute, Hong Kong, China
| | | | | | | | | | - Brian H.Y. Chung
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hong Kong Genome Project
- Hong Kong Genome Institute, Hong Kong, China
- Department of Pediatrics and Adolescent Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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2
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Pan Y, Zeng W, Nie X, Chen H, Xie C, Guo S, Xu D, Chen Y. Immunotherapy-relevance of a candidate prognostic score for Acute Myeloid Leukemia. Heliyon 2024; 10:e32154. [PMID: 38961904 PMCID: PMC11219318 DOI: 10.1016/j.heliyon.2024.e32154] [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: 07/26/2023] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 07/05/2024] Open
Abstract
Background Acute Myeloid Leukemia (AML) exhibits a wide array of phenotypic manifestations, progression patterns, and heterogeneous responses to immunotherapies, suggesting involvement of complex immunobiological mechanisms. This investigation aimed to develop an integrated prognostic model for AML by incorporating cancer driver genes, along with clinical and phenotypic characteristics of the disease, and to assess its implications for immunotherapy responsiveness. Methods Critical oncogenic driver genes linked to survival were identified by screening primary effector and corresponding gene pairs using data from The Cancer Genome Atlas (TCGA), through univariate Cox proportional hazard regression analysis. This was independently verified using dataset GSE37642. Primary effector genes were further refined using LASSO regression. Transcriptomic profiling was quantified using multivariate Cox regression, and the derived prognostic score was subsequently validated. Finally, a multivariate Cox regression model was developed, incorporating the transcriptomic score along with clinical parameters such as age, gender, and French-American-British (FAB) classification subtype. The 'Accurate Prediction Model of AML Overall Survival Score' (APMAO) was developed and subsequently validated. Investigations were conducted into functional pathway enrichment, alterations in the gene mutational landscape, and the extent of immune cell infiltration associated with varying APMAO scores. To further investigate the potential of APMAO scores as a predictive biomarker for responsiveness to cancer immunotherapy, we conducted a series of analyses. These included examining the expression profiles of genes related to immune checkpoints, the interferon-gamma signaling pathway, and m6A regulation. Additionally, we explored the relationship between these gene expression patterns and the Tumor Immune Dysfunction and Exclusion (TIDE) dysfunction scores. Results Through the screening of 95 cancer genes associated with survival and 313 interacting gene pairs, seven genes (ACSL6, MAP3K1, CHIC2, HIP1, PTPN6, TFEB, and DAXX) were identified, leading to the derivation of a transcriptional score. Age and the transcriptional score were significant predictors in Cox regression analysis and were integral to the development of the final APMAO model, which exhibited an AUC greater than 0.75 and was successfully validated. Notable differences were observed in the distribution of the transcriptional score, age, cytogenetic risk categories, and French-American-British (FAB) classification between high and low APMAO groups. Samples with high APMAO scores demonstrated significantly higher mutation rates and pathway enrichments in NFKB, TNF, JAK-STAT, and NOTCH signaling. Additionally, variations in immune cell infiltration and immune checkpoint expression, activation of the interferon-γ pathway, and expression of m6A regulators were noted, including a negative correlation between CD160, m6A expression, and APMAO scores. Conclusion The combined APMAO score integrating transcriptional and clinical parameters demonstrated robust prognostic performance in predicting AML survival outcomes. It was linked to unique phenotypic characteristics, distinctive immune and mutational profiles, and patterns of expression for markers related to immunotherapy sensitivity. These observations suggest the potential for facilitating precision immunotherapy and advocate for its exploration in upcoming clinical trials.
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Affiliation(s)
- Yiyun Pan
- Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu, China
- Ganzhou Cancer Hospital, Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Wen Zeng
- Ganzhou Cancer Hospital, Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Xiaoming Nie
- Ganzhou Cancer Hospital, Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Hailong Chen
- Ganzhou Cancer Hospital, Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Chuanhua Xie
- Ganzhou Cancer Hospital, Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Shouju Guo
- Ganzhou Cancer Hospital, Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Dechang Xu
- Ganzhou Cancer Hospital, Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Yijian Chen
- Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu, China
- The First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, Jiangxi, China
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3
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Zhang Z, Huang J, Zhang Z, Shen H, Tang X, Wu D, Bao X, Xu G, Chen S. Application of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia. Biomark Res 2024; 12:60. [PMID: 38858750 PMCID: PMC11165883 DOI: 10.1186/s40364-024-00600-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 05/17/2024] [Indexed: 06/12/2024] Open
Abstract
Acute myeloid leukemia (AML) is the most frequent leukemia in adults with a high mortality rate. Current diagnostic criteria and selections of therapeutic strategies are generally based on gene mutations and cytogenetic abnormalities. Chemotherapy, targeted therapies, and hematopoietic stem cell transplantation (HSCT) are the major therapeutic strategies for AML. Two dilemmas in the clinical management of AML are related to its poor prognosis. One is the inaccurate risk stratification at diagnosis, leading to incorrect treatment selections. The other is the frequent resistance to chemotherapy and/or targeted therapies. Genomic features have been the focus of AML studies. However, the DNA-level aberrations do not always predict the expression levels of genes and proteins and the latter is more closely linked to disease phenotypes. With the development of high-throughput sequencing and mass spectrometry technologies, studying downstream effectors including RNA, proteins, and metabolites becomes possible. Transcriptomics can reveal gene expression and regulatory networks, proteomics can discover protein expression and signaling pathways intimately associated with the disease, and metabolomics can reflect precise changes in metabolites during disease progression. Moreover, omics profiling at the single-cell level enables studying cellular components and hierarchies of the AML microenvironment. The abundance of data from different omics layers enables the better risk stratification of AML by identifying prognosis-related biomarkers, and has the prospective application in identifying drug targets, therefore potentially discovering solutions to the two dilemmas. In this review, we summarize the existing AML studies using omics methods, both separately and combined, covering research fields of disease diagnosis, risk stratification, prognosis prediction, chemotherapy, as well as targeted therapy. Finally, we discuss the directions and challenges in the application of multi-omics in precision medicine of AML. Our review may inspire both omics researchers and clinical physicians to study AML from a different angle.
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Affiliation(s)
- Zhiyu Zhang
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, the First Affiliated Hospital of Soochow University, Suzhou, China
- Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Key Laboratory of Drug Research for Prevention and Treatment of Hyperlipidemic Diseases, Soochow University, Suzhou, 215123, Jiangsu, China
- Suzhou International Joint Laboratory for Diagnosis and Treatment of Brain Diseases, College of Pharmaceutical Sciences, Soochow University, Suzhou, 215123, Jiangsu, China
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu Province, China
| | - Jiayi Huang
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhibo Zhang
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Hongjie Shen
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaowen Tang
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Depei Wu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiebing Bao
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, the First Affiliated Hospital of Soochow University, Suzhou, China.
| | - Guoqiang Xu
- Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Key Laboratory of Drug Research for Prevention and Treatment of Hyperlipidemic Diseases, Soochow University, Suzhou, 215123, Jiangsu, China.
- Suzhou International Joint Laboratory for Diagnosis and Treatment of Brain Diseases, College of Pharmaceutical Sciences, Soochow University, Suzhou, 215123, Jiangsu, China.
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu Province, China.
| | - Suning Chen
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, the First Affiliated Hospital of Soochow University, Suzhou, China.
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4
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Bruserud Ø, Selheim F, Hernandez-Valladares M, Reikvam H. Monocytic Differentiation in Acute Myeloid Leukemia Cells: Diagnostic Criteria, Biological Heterogeneity, Mitochondrial Metabolism, Resistance to and Induction by Targeted Therapies. Int J Mol Sci 2024; 25:6356. [PMID: 38928061 PMCID: PMC11203697 DOI: 10.3390/ijms25126356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
We review the importance of monocytic differentiation and differentiation induction in non-APL (acute promyelocytic leukemia) variants of acute myeloid leukemia (AML), a malignancy characterized by proliferation of immature myeloid cells. Even though the cellular differentiation block is a fundamental characteristic, the AML cells can show limited signs of differentiation. According to the French-American-British (FAB-M4/M5 subset) and the World Health Organization (WHO) 2016 classifications, monocytic differentiation is characterized by morphological signs and the expression of specific molecular markers involved in cellular communication and adhesion. Furthermore, monocytic FAB-M4/M5 patients are heterogeneous with regards to cytogenetic and molecular genetic abnormalities, and monocytic differentiation does not have any major prognostic impact for these patients when receiving conventional intensive cytotoxic therapy. In contrast, FAB-M4/M5 patients have decreased susceptibility to the Bcl-2 inhibitor venetoclax, and this seems to be due to common molecular characteristics involving mitochondrial regulation of the cellular metabolism and survival, including decreased dependency on Bcl-2 compared to other AML patients. Thus, the susceptibility to Bcl-2 inhibition does not only depend on general resistance/susceptibility mechanisms known from conventional AML therapy but also specific mechanisms involving the molecular target itself or the molecular context of the target. AML cell differentiation status is also associated with susceptibility to other targeted therapies (e.g., CDK2/4/6 and bromodomain inhibition), and differentiation induction seems to be a part of the antileukemic effect for several targeted anti-AML therapies. Differentiation-associated molecular mechanisms may thus become important in the future implementation of targeted therapies in human AML.
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MESH Headings
- Humans
- Cell Differentiation
- Leukemia, Myeloid, Acute/metabolism
- Leukemia, Myeloid, Acute/pathology
- Leukemia, Myeloid, Acute/diagnosis
- Leukemia, Myeloid, Acute/drug therapy
- Leukemia, Myeloid, Acute/genetics
- Mitochondria/metabolism
- Monocytes/metabolism
- Monocytes/pathology
- Drug Resistance, Neoplasm/genetics
- Molecular Targeted Therapy
- Antineoplastic Agents/pharmacology
- Antineoplastic Agents/therapeutic use
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Affiliation(s)
- Øystein Bruserud
- Acute Leukemia Research Group, Department of Clinical Science, University of Bergen, 5007 Bergen, Norway; (M.H.-V.); (H.R.)
- Section for Hematology, Department of Medicine, Haukeland University Hospital, 5009 Bergen, Norway
| | - Frode Selheim
- Proteomics Unit of University of Bergen (PROBE), University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway;
| | - Maria Hernandez-Valladares
- Acute Leukemia Research Group, Department of Clinical Science, University of Bergen, 5007 Bergen, Norway; (M.H.-V.); (H.R.)
- Department of Physical Chemistry, University of Granada, Avenida de la Fuente Nueva S/N, 18071 Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| | - Håkon Reikvam
- Acute Leukemia Research Group, Department of Clinical Science, University of Bergen, 5007 Bergen, Norway; (M.H.-V.); (H.R.)
- Section for Hematology, Department of Medicine, Haukeland University Hospital, 5009 Bergen, Norway
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5
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Severens JF, Karakaslar EO, van der Reijden BA, Sánchez-López E, van den Berg RR, Halkes CJM, van Balen P, Veelken H, Reinders MJT, Griffioen M, van den Akker EB. Mapping AML heterogeneity - multi-cohort transcriptomic analysis identifies novel clusters and divergent ex-vivo drug responses. Leukemia 2024; 38:751-761. [PMID: 38360865 DOI: 10.1038/s41375-024-02137-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 12/28/2023] [Accepted: 01/04/2024] [Indexed: 02/17/2024]
Abstract
Subtyping of acute myeloid leukaemia (AML) is predominantly based on recurrent genetic abnormalities, but recent literature indicates that transcriptomic phenotyping holds immense potential to further refine AML classification. Here we integrated five AML transcriptomic datasets with corresponding genetic information to provide an overview (n = 1224) of the transcriptomic AML landscape. Consensus clustering identified 17 robust patient clusters which improved identification of CEBPA-mutated patients with favourable outcomes, and uncovered transcriptomic subtypes for KMT2A rearrangements (2), NPM1 mutations (5), and AML with myelodysplasia-related changes (AML-MRC) (5). Transcriptomic subtypes of KMT2A, NPM1 and AML-MRC showed distinct mutational profiles, cell type differentiation arrests and immune properties, suggesting differences in underlying disease biology. Moreover, our transcriptomic clusters show differences in ex-vivo drug responses, even when corrected for differentiation arrest and superiorly capture differences in drug response compared to genetic classification. In conclusion, our findings underscore the importance of transcriptomics in AML subtyping and offer a basis for future research and personalised treatment strategies. Our transcriptomic compendium is publicly available and we supply an R package to project clusters to new transcriptomic studies.
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Affiliation(s)
- Jeppe F Severens
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, The Netherlands
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - E Onur Karakaslar
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, The Netherlands
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Bert A van der Reijden
- Laboratory of Hematology, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Elena Sánchez-López
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Redmar R van den Berg
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Peter van Balen
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hendrik Veelken
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | - Marcel J T Reinders
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, The Netherlands
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Marieke Griffioen
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | - Erik B van den Akker
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
- Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, The Netherlands.
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands.
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6
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Han DJ, Kim S, Lee SY, Kang SJ, Moon Y, Kim HS, Kim M, Kim TM. Cellular abundance-based prognostic model associated with deregulated gene expression of leukemic stem cells in acute myeloid leukemia. Front Cell Dev Biol 2024; 12:1345660. [PMID: 38523628 PMCID: PMC10958127 DOI: 10.3389/fcell.2024.1345660] [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: 11/28/2023] [Accepted: 02/06/2024] [Indexed: 03/26/2024] Open
Abstract
Background: Previous studies have reported that genes highly expressed in leukemic stem cells (LSC) may dictate the survival probability of patients and expression-based cellular deconvolution may be informative in forecasting prognosis. However, whether the prognosis of acute myeloid leukemia (AML) can be predicted using gene expression and deconvoluted cellular abundances is debatable. Methods: Nine different cell-type abundances of a training set composed of the AML samples of 422 patients, were used to build a model for predicting prognosis by least absolute shrinkage and selection operator Cox regression. This model was validated in two different validation sets, TCGA-LAML and Beat AML (n = 179 and 451, respectively). Results: We introduce a new prognosis predicting model for AML called the LSC activity (LSCA) score, which incorporates the abundance of 5 cell types, granulocyte-monocyte progenitors, common myeloid progenitors, CD45RA + cells, megakaryocyte-erythrocyte progenitors, and multipotent progenitors. Overall survival probabilities between the high and low LSCA score groups were significantly different in TCGA-LAML and Beat AML cohorts (log-rank p-value = 3.3 × 10 - 4 and 4.3 × 10 - 3 , respectively). Also, multivariate Cox regression analysis on these two validation sets shows that LSCA score is independent prognostic factor when considering age, sex, and cytogenetic risk (hazard ratio, HR = 2.17; 95% CI 1.40-3.34; p < 0.001 and HR = 1.20; 95% CI 1.02-1.43; p < 0.03, respectively). The performance of the LSCA score was comparable to other prognostic models, LSC17, APS, and CTC scores, as indicated by the area under the curve. Gene set variation analysis with six LSC-related functional gene sets indicated that high and low LSCA scores are associated with upregulated and downregulated genes in LSCs. Conclusion: We have developed a new prognosis prediction scoring system for AML patients, the LSCA score, which uses deconvoluted cell-type abundance only.
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Affiliation(s)
- Dong-Jin Han
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sunmin Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seo-Young Lee
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Su Jung Kang
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Youngbeen Moon
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hoon Seok Kim
- Department of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Catholic Genetic Laboratory Center, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Myungshin Kim
- Department of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Catholic Genetic Laboratory Center, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Tae-Min Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
- CMC Institute for Basic Medical Science, The Catholic Medical Center of The Catholic University of Korea, Seoul, Republic of Korea
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7
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Li JF, Cheng WY, Lin XJ, Wen LJ, Wang K, Zhu YM, Zhu HM, Chen XJ, Zhang YL, Yin W, Zhang JN, Yi X, Zhang F, Weng XQ, Wang SY, Jiang L, Wu HY, Ren JQ, Lin XJ, Qiao N, Dai YT, Fang H, Tan Y, Sun XJ, Lv G, Yan XY, Chen SN, Chen Z, Jin J, Wu DP, Ren RB, Chen SJ, Shen Y. Aging and comprehensive molecular profiling in acute myeloid leukemia. Proc Natl Acad Sci U S A 2024; 121:e2319366121. [PMID: 38422020 DOI: 10.1073/pnas.2319366121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/19/2024] [Indexed: 03/02/2024] Open
Abstract
Acute myeloid leukemia (AML) is an aging-related and heterogeneous hematopoietic malignancy. In this study, a total of 1,474 newly diagnosed AML patients with RNA sequencing data were enrolled, and targeted or whole exome sequencing data were obtained in 94% cases. The correlation of aging-related factors including age and clonal hematopoiesis (CH), gender, and genomic/transcriptomic profiles (gene fusions, genetic mutations, and gene expression networks or pathways) was systematically analyzed. Overall, AML patients aged 60 y and older showed an apparently dismal prognosis. Alongside age, the frequency of gene fusions defined in the World Health Organization classification decreased, while the positive rate of gene mutations, especially CH-related ones, increased. Additionally, the number of genetic mutations was higher in gene fusion-negative (GF-) patients than those with GF. Based on the status of CH- and myelodysplastic syndromes (MDS)-related mutations, three mutant subgroups were identified among the GF- AML cohort, namely, CH-AML, CH-MDS-AML, and other GF- AML. Notably, CH-MDS-AML demonstrated a predominance of elderly and male cases, cytopenia, and significantly adverse clinical outcomes. Besides, gene expression networks including HOXA/B, platelet factors, and inflammatory responses were most striking features associated with aging and poor prognosis in AML. Our work has thus unraveled the intricate regulatory circuitry of interactions among different age, gender, and molecular groups of AML.
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Affiliation(s)
- Jian-Feng Li
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Wen-Yan Cheng
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiang-Jie Lin
- Department of Hematology, The First Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou, Zhejiang 310003, China
- Key Laboratory of Hematologic Malignancies, Diagnosis and Treatment, Hangzhou, Zhejiang 310003, China
| | - Li-Jun Wen
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou 215006, China
| | - Kai Wang
- International Center for Aging and Cancer, Department of Hematology of The First Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Yong-Mei Zhu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hong-Ming Zhu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xin-Jie Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yu-Liang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Wei Yin
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jia-Nan Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiao Yi
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Fan Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiang-Qin Weng
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Sheng-Yue Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Lu Jiang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hui-Yi Wu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jia-Qi Ren
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiao-Jing Lin
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Niu Qiao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yu-Ting Dai
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yun Tan
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiao-Jian Sun
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Gang Lv
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiao-Yu Yan
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Su-Ning Chen
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou 215006, China
| | - Zhu Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jie Jin
- Department of Hematology, The First Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou, Zhejiang 310003, China
- Key Laboratory of Hematologic Malignancies, Diagnosis and Treatment, Hangzhou, Zhejiang 310003, China
- Zhejiang University Cancer Center, Hangzhou, Zhejiang 310003, China
| | - De-Pei Wu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou 215006, China
| | - Rui-Bao Ren
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- International Center for Aging and Cancer, Department of Hematology of The First Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Sai-Juan Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yang Shen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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8
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Yang S, Xu J, Dai Y, Jin S, Sun Y, Li J, Liu C, Ma X, Chen Z, Chen L, Hou J, Mi JQ, Chen SJ. Neutrophil activation and clonal CAR-T re-expansion underpinning cytokine release syndrome during ciltacabtagene autoleucel therapy in multiple myeloma. Nat Commun 2024; 15:360. [PMID: 38191582 PMCID: PMC10774397 DOI: 10.1038/s41467-023-44648-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 12/21/2023] [Indexed: 01/10/2024] Open
Abstract
Cytokine release syndrome (CRS) is the most common complication of chimeric antigen receptor redirected T cells (CAR-T) therapy. CAR-T toxicity management has been greatly improved, but CRS remains a prime safety concern. Here we follow serum cytokine levels and circulating immune cell transcriptomes longitudinally in 26 relapsed/refractory multiple myeloma patients receiving the CAR-T product, ciltacabtagene autoleucel, to understand the immunological kinetics of CRS. We find that although T lymphocytes and monocytes/macrophages are the major overall cytokine source in manifest CRS, neutrophil activation peaks earlier, before the onset of severe symptoms. Intracellularly, signaling activation dominated by JAK/STAT pathway occurred prior to cytokine cascade and displayed regular kinetic changes. CRS severity is accurately described and potentially predicted by temporal cytokine secretion signatures. Notably, CAR-T re-expansion is found in three patients, including a fatal case characterized by somatic TET2-mutation, clonal expanded cytotoxic CAR-T, broadened cytokine profiles and irreversible hepatic toxicity. Together, our findings show that a latent phase with distinct immunological changes precedes manifest CRS, providing an optimal window and potential targets for CRS therapeutic intervention and that CAR-T re-expansion warrants close clinical attention and laboratory investigation to mitigate the lethal risk.
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Affiliation(s)
- Shuangshuang Yang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jie Xu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yuting Dai
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shiwei Jin
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yan Sun
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jianfeng Li
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Chenglin Liu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiaolin Ma
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhu Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lijuan Chen
- Department of Hematology, First affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
| | - Jian Hou
- Department of Hematology, Ren Ji Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jian-Qing Mi
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Sai-Juan Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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9
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Wang M, Cao HY, Tan KW, Qiu QC, Huang YH, Ge SS, Wang ZH, Chen J, Tang XW, Wu DP, Xue SL, Li Z, Dai HP. Venetoclax plus hypomethylating agents in newly diagnosed acute myeloid leukemia patients with RUNX1::RUNX1T1: a retrospective propensity score matching study. Blood Cancer J 2023; 13:173. [PMID: 38012154 PMCID: PMC10682468 DOI: 10.1038/s41408-023-00948-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/29/2023] Open
Affiliation(s)
- Miao Wang
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Han-Yu Cao
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Kai-Wen Tan
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Qiao-Cheng Qiu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Yuan-Hong Huang
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Shuai-Shuai Ge
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Zi-Hao Wang
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Jia Chen
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Xiao-Wen Tang
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - De-Pei Wu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China
| | - Sheng-Li Xue
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China.
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China.
| | - Zheng Li
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China.
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China.
| | - Hai-Ping Dai
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China.
- Institute of Blood and Marrow Transplantation, Collaborative Innovation Center of Hematology, Soochow University, Suzhou, China.
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10
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More P, Ngaffo JAM, Goedtel-Armbrust U, Hähnel PS, Hartwig UF, Kindler T, Wojnowski L. Transcriptional Response to Standard AML Drugs Identifies Synergistic Combinations. Int J Mol Sci 2023; 24:12926. [PMID: 37629110 PMCID: PMC10455220 DOI: 10.3390/ijms241612926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/07/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Unlike genomic alterations, gene expression profiles have not been widely used to refine cancer therapies. We analyzed transcriptional changes in acute myeloid leukemia (AML) cell lines in response to standard first-line AML drugs cytarabine and daunorubicin by means of RNA sequencing. Those changes were highly cell- and treatment-specific. By comparing the changes unique to treatment-sensitive and treatment-resistant AML cells, we enriched for treatment-relevant genes. Those genes were associated with drug response-specific pathways, including calcium ion-dependent exocytosis and chromatin remodeling. Pharmacological mimicking of those changes using EGFR and MEK inhibitors enhanced the response to daunorubicin with minimum standalone cytotoxicity. The synergistic response was observed even in the cell lines beyond those used for the discovery, including a primary AML sample. Additionally, publicly available cytotoxicity data confirmed the synergistic effect of EGFR inhibitors in combination with daunorubicin in all 60 investigated cancer cell lines. In conclusion, we demonstrate the utility of treatment-evoked gene expression changes to formulate rational drug combinations. This approach could improve the standard AML therapy, especially in older patients.
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Affiliation(s)
- Piyush More
- Department of Pharmacology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany; (J.A.M.N.); (U.G.-A.); (L.W.)
| | - Joëlle Aurelie Mekontso Ngaffo
- Department of Pharmacology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany; (J.A.M.N.); (U.G.-A.); (L.W.)
- Leibniz Institute for New Materials, 66123 Saarbrücken, Germany
| | - Ute Goedtel-Armbrust
- Department of Pharmacology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany; (J.A.M.N.); (U.G.-A.); (L.W.)
| | - Patricia S. Hähnel
- University Cancer Center (UCT) Mainz, Johannes Gutenberg-University, 55131 Mainz, Germany; (P.S.H.); (T.K.)
- Department of Hematology & Medical Oncology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany;
| | - Udo F. Hartwig
- Department of Hematology & Medical Oncology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany;
- Research Center of Immunotherapy, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany
| | - Thomas Kindler
- University Cancer Center (UCT) Mainz, Johannes Gutenberg-University, 55131 Mainz, Germany; (P.S.H.); (T.K.)
- Department of Hematology & Medical Oncology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany;
| | - Leszek Wojnowski
- Department of Pharmacology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany; (J.A.M.N.); (U.G.-A.); (L.W.)
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11
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Testa U, Castelli G, Pelosi E. TP53-Mutated Myelodysplasia and Acute Myeloid Leukemia. Mediterr J Hematol Infect Dis 2023; 15:e2023038. [PMID: 37435040 PMCID: PMC10332352 DOI: 10.4084/mjhid.2023.038] [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: 05/08/2023] [Accepted: 06/01/2023] [Indexed: 07/13/2023] Open
Abstract
TP53-mutated myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) form a distinct and heterogeneous group of myeloid malignancies associated with poor outcomes. Studies carried out in the last years have in part elucidated the complex role played by TP53 mutations in the pathogenesis of these myeloid disorders and in the mechanisms of drug resistance. A consistent number of studies has shown that some molecular parameters, such as the presence of a single or multiple TP53 mutations, the presence of concomitant TP53 deletions, the association with co-occurring mutations, the clonal size of TP53 mutations, the involvement of a single (monoallelic) or of both TP53 alleles (biallelic) and the cytogenetic architecture of concomitant chromosome abnormalities are major determinants of outcomes of patients. The limited response of these patients to standard treatments, including induction chemotherapy, hypomethylating agents and venetoclax-based therapies and the discovery of an immune dysregulation have induced a shift to new emerging therapies, some of which being associated with promising efficacy. The main aim of these novel immune and nonimmune strategies consists in improving survival and in increasing the number of TP53-mutated MDS/AML patients in remission amenable to allogeneic stem cell transplantation.
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Affiliation(s)
- Ugo Testa
- Department of Oncology, Istituto Superiore di Sanità, Rome Italy
| | - Germana Castelli
- Department of Oncology, Istituto Superiore di Sanità, Rome Italy
| | - Elvira Pelosi
- Department of Oncology, Istituto Superiore di Sanità, Rome Italy
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12
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Testa U, Pelosi E, Castelli G. Genetic, Phenotypic, and Clinical Heterogeneity of NPM1-Mutant Acute Myeloid Leukemias. Biomedicines 2023; 11:1805. [PMID: 37509445 PMCID: PMC10376179 DOI: 10.3390/biomedicines11071805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/13/2023] [Accepted: 06/21/2023] [Indexed: 07/30/2023] Open
Abstract
The current classification of acute myeloid leukemia (AML) relies largely on genomic alterations. AML with mutated nucleophosmin 1 (NPM1-mut) is the largest of the genetically defined groups, involving about 30% of adult AMLs and is currently recognized as a distinct entity in the actual AML classifications. NPM1-mut AML usually occurs in de novo AML and is associated predominantly with a normal karyotype and relatively favorable prognosis. However, NPM1-mut AMLs are genetically, transcriptionally, and phenotypically heterogeneous. Furthermore, NPM1-mut is a clinically heterogenous group. Recent studies have in part clarified the consistent heterogeneities of these AMLs and have strongly supported the need for an additional stratification aiming to improve the therapeutic response of the different subgroups of NPM1-mut AML patients.
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Affiliation(s)
- Ugo Testa
- Department of Oncology, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
| | - Elvira Pelosi
- Department of Oncology, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
| | - Germana Castelli
- Department of Oncology, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
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13
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Multi-Omic Approaches to Classify, Predict, and Treat Acute Leukemias. Cancers (Basel) 2023; 15:cancers15041049. [PMID: 36831391 PMCID: PMC9954455 DOI: 10.3390/cancers15041049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/11/2023] Open
Abstract
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, in which nearly 5% of the cases are diagnosed before the first year of age [...].
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