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Shi L, Yan W, Xu J, Li L, Cui J, Liu Y, Du C, Yu T, Zhang S, Sui W, Deng S, Xu Y, Zou D, Wang H, Qiu L, An G. Immunophenotypic profile defines cytogenetic stability and unveils distinct prognoses in patients with newly-diagnosed multiple myeloma (NDMM). Ann Hematol 2024; 103:1305-1315. [PMID: 38049586 DOI: 10.1007/s00277-023-05573-z] [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: 10/12/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023]
Abstract
Prognostic significance of multiple immune antigens in multiple myeloma has been well established. However, a level of uncertainty remains regarding the intrinsic relationship between immunophenotypes and cytogenetic stability and precise risk stratification. To address these unresolved issues, we conducted a study involving 1389 patients enrolled in the National Longitudinal Cohort of Hematological Diseases in China (NCT04645199). Our results revealed that the correlation between antigen expression and cytogenetics is more prominent than cytopenia or organ dysfunction. Most immune antigens, apart from CD38, CD138, and CD81, exhibit significant associations with the incidence of at least one cytogenetic abnormality. In turn, we identified CD138-low/CD27-neg as specific adverse immunophenotypic profile, which remaining independent impact on progression-free survival (HR, 1.49; P = 0.007) and overall survival (HR, 1.77; P < 0.001) even in the context of cytogenetics. Importantly, CD138-low/CD27-neg profile was also associated with inferior survival after first relapse (P < 0.001). Moreover, the antigen expression profiles were not strictly similar when comparing diagnosis and relapse; in particular, the CD138-low/CD27-neg pattern was notably increased after disease progression (19.1 to 29.1%; P = 0.005). Overall, our study demonstrates that diverse immune profiles are strongly associated with cytogenetic stability, and a specific immunophenotype (CD138-low/CD27-neg) could effectively predict prognoses across different disease stages.
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Affiliation(s)
- Lihui Shi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Wenqiang Yan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Jingyu Xu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Lingna Li
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Jian Cui
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Yuntong Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Chenxing Du
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Tengteng Yu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Shuaishuai Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Weiwei Sui
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Shuhui Deng
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Yan Xu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Dehui Zou
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Huijun Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
- Tianjin Institutes of Health Science, Tianjin, 301600, China
| | - Lugui Qiu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Tianjin Institutes of Health Science, Tianjin, 301600, China.
| | - Gang An
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Tianjin Institutes of Health Science, Tianjin, 301600, China.
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Song Y, Zhang HJ, Song X, Geng J, Li HY, Zhang LZ, Yang B, Lu XC. Gene signatures to therapeutics: Assessing the potential of ivermectin against t(4;14) multiple myeloma. World J Clin Oncol 2024; 15:115-129. [PMID: 38292661 PMCID: PMC10823940 DOI: 10.5306/wjco.v15.i1.115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/13/2023] [Accepted: 01/02/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Multiple myeloma (MM) is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow. The translocation, (t)(4;14), results in high-risk MM with limited treatment alternatives. Thus, there is an urgent need for identification and validation of potential treatments for this MM subtype. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets. AIM To elucidate the molecular basis and search for potential effective drugs of t(4;14) MM subtype by employing a comprehensive approach. METHODS The transcriptional signature of t(4;14) MM was sourced from the Gene Expression Omnibus. Two datasets, GSE16558 and GSE116294, which included 17 and 15 t(4;14) MM bone marrow samples, and five and four normal bone marrow samples, respectively. After the differentially expressed genes were identified, the Cytohubba tool was used to screen for hub genes. Then, the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Using the STRING database and Cytoscape, protein-protein interaction networks and core targets were identified. Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis, respectively. RESULTS In this study, a total of 258 differentially expressed genes with enriched functions in cancer pathways, namely cytokine receptor interactions, nuclear factor (NF)-κB signaling pathway, lipid metabolism, atherosclerosis, and Hippo signaling pathway, were identified. Ten hub genes (cd45, vcam1, ccl3, cd56, app, cd48, btk, ccr2, cybb, and cxcl12) were identified. Nine drugs, including ivermectin, deforolimus, and isoliquiritigenin, were predicted by the Connectivity Map database to have potential therapeutic effects on t (4;14) MM. In molecular docking, ivermectin showed strong binding affinity to all 10 identified targets, especially cd45 and cybb. Ivermectin inhibited t(4;14) MM cell growth via the NF-κB pathway and induced MM cell apoptosis in vitro. Furthermore, ivermectin increased reactive oxygen species accumulation and altered the mitochondrial membrane potential in t(4;14) MM cells. CONCLUSION Collectively, the findings offer valuable molecular insights for biomarker validation and potential drug development in t(4;14) MM diagnosis and treatment, with ivermectin emerging as a potential therapeutic alternative.
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Affiliation(s)
- Yang Song
- School of Basic Medicine, Medical School of Chinese PLA, Beijing 100853, China
| | - Hao-Jun Zhang
- School of Basic Medicine, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
| | - Xia Song
- School of Basic Medicine, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
| | - Jie Geng
- School of Basic Medicine, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
| | - Hong-Yi Li
- School of Basic Medicine, Medical School of Chinese PLA, Beijing 100853, China
| | - Li-Zhong Zhang
- School of Basic Medicine, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
| | - Bo Yang
- Department of Hematology, The Second Medical Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Xue-Chun Lu
- Department of Hematology, The Second Medical Centre, Chinese PLA General Hospital, Beijing 100853, China
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