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Binder M, Szalat RE, Talluri S, Fulciniti M, Avet-Loiseau H, Parmigiani G, Samur MK, Munshi NC. Bone marrow stromal cells induce chromatin remodeling in multiple myeloma cells leading to transcriptional changes. Nat Commun 2024; 15:4139. [PMID: 38755155 PMCID: PMC11098817 DOI: 10.1038/s41467-024-47793-5] [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: 01/06/2023] [Accepted: 04/12/2024] [Indexed: 05/18/2024] Open
Abstract
The natural history of multiple myeloma is characterized by its localization to the bone marrow and its interaction with bone marrow stromal cells. The bone marrow stromal cells provide growth and survival signals, thereby promoting the development of drug resistance. Here, we show that the interaction between bone marrow stromal cells and myeloma cells (using human cell lines) induces chromatin remodeling of cis-regulatory elements and is associated with changes in the expression of genes involved in the cell migration and cytokine signaling. The expression of genes involved in these stromal interactions are observed in extramedullary disease in patients with myeloma and provides the rationale for survival of myeloma cells outside of the bone marrow microenvironment. Expression of these stromal interaction genes is also observed in a subset of patients with newly diagnosed myeloma and are akin to the transcriptional program of extramedullary disease. The presence of such adverse stromal interactions in newly diagnosed myeloma is associated with accelerated disease dissemination, predicts the early development of therapeutic resistance, and is of independent prognostic significance. These stromal cell induced transcriptomic and epigenomic changes both predict long-term outcomes and identify therapeutic targets in the tumor microenvironment for the development of novel therapeutic approaches.
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Affiliation(s)
- Moritz Binder
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Raphael E Szalat
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA, USA
| | - Srikanth Talluri
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
| | | | - Hervé Avet-Loiseau
- University Cancer Center of Toulouse, Institut National de la Santé, Toulouse, France
| | - Giovanni Parmigiani
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mehmet K Samur
- Department of Data Science, Dana Farber Cancer Institute, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Nikhil C Munshi
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA.
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Hadiloo K, Taremi S, Safa SH, Amidifar S, Esmaeilzadeh A. The new era of immunological treatment, last updated and future consideration of CAR T cell-based drugs. Pharmacol Res 2024; 203:107158. [PMID: 38599467 DOI: 10.1016/j.phrs.2024.107158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/11/2024] [Accepted: 03/24/2024] [Indexed: 04/12/2024]
Abstract
Cancer treatment is one of the fundamental challenges in clinical setting, especially in relapsed/refractory malignancies. The novel immunotherapy-based treatments bring new hope in cancer therapy and achieve various treatment successes. One of the distinguished ways of cancer immunotherapy is adoptive cell therapy, which utilizes genetically modified immune cells against cancer cells. Between different methods in ACT, the chimeric antigen receptor T cells have more investigation and introduced a promising way to treat cancer patients. This technology progressed until it introduced six US Food and Drug Administration-approved CAR T cell-based drugs. These drugs act against hematological malignancies appropriately and achieve exciting results, so they have been utilized widely in cell therapy clinics. In this review, we introduce all CAR T cells-approved drugs based on their last data and investigate them from all aspects of pharmacology, side effects, and compressional. Also, the efficacy of drugs, pre- and post-treatment steps, and expected side effects are introduced, and the challenges and new solutions in CAR T cell therapy are in the last speech.
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Affiliation(s)
- Kaveh Hadiloo
- Department of immunology, Zanjan University of Medical Sciences, Zanjan, the Islamic Republic of Iran; School of Medicine, Zanjan University of Medical Sciences, Zanjan, the Islamic Republic of Iran
| | - Siavash Taremi
- Department of immunology, Zanjan University of Medical Sciences, Zanjan, the Islamic Republic of Iran; School of Medicine, Zanjan University of Medical Sciences, Zanjan, the Islamic Republic of Iran
| | - Salar Hozhabri Safa
- School of Medicine, Zanjan University of Medical Sciences, Zanjan, the Islamic Republic of Iran
| | - Sima Amidifar
- School of Medicine, Zanjan University of Medical Sciences, Zanjan, the Islamic Republic of Iran
| | - Abdolreza Esmaeilzadeh
- Department of Immunology, Zanjan University of Medical Sciences, Zanjan, the Islamic Republic of Iran; Cancer Gene Therapy Research Center (CGRC), Zanjan University of Medical Sciences, Zanjan, the Islamic Republic of Iran.
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Jung SH, Park SS, Lim JY, Sohn SY, Kim NY, Kim D, Lee SH, Chung YJ, Min CK. Single-cell analysis of multiple myelomas refines the molecular features of bortezomib treatment responsiveness. Exp Mol Med 2022; 54:1967-1978. [PMID: 36380017 PMCID: PMC9723182 DOI: 10.1038/s12276-022-00884-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/25/2022] [Accepted: 09/01/2022] [Indexed: 11/16/2022] Open
Abstract
Both the tumor and tumor microenvironment (TME) are crucial for pathogenesis and chemotherapy resistance in multiple myeloma (MM). Bortezomib, commonly used for MM treatment, works on both MM and TME cells, but innate and acquired resistance easily develop. By single-cell RNA sequencing (scRNA-seq), we investigated bone marrow aspirates of 18 treatment-naïve MM patients who later received bortezomib-based treatments. Twelve plasma and TME cell types and their subsets were identified. Suboptimal responders (SORs) to bortezomib exhibited higher copy number alteration burdens than optimal responders (ORs). Forty-four differentially expressed genes for SORs based on scRNA-seq data were further analyzed in an independent cohort of 90 treatment-naïve MMs, where 24 genes were validated. A combined model of three clinical variables (older age, low absolute lymphocyte count, and no autologous stem cell transplantation) and 24 genes was associated with bortezomib responsiveness and poor prognosis. In T cells, cytotoxic memory, proliferating, and dysfunctional subsets were significantly enriched in SORs. Moreover, we identified three monocyte subsets associated with bortezomib responsiveness and an MM-specific NK cell trajectory that ended with an MM-specific subset. scRNA-seq predicted the interaction of the GAS6-MERTK, ALCAM-CD6, and BAG6-NCR gene networks. Of note, tumor cells from ORs and SORs were the most prominent sources of ALCAM on effector T cells and BAG6 on NK cells, respectively. Our results indicate that the complicated compositional and molecular changes of both tumor and immune cells in the bone marrow (BM) milieu are important in the development and acquisition of resistance to bortezomib-based treatment of MM.
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Affiliation(s)
- Seung-Hyun Jung
- grid.411947.e0000 0004 0470 4224Department of Biochemistry, College of Medicine, The Catholic University of Korea, Seoul, South Korea ,grid.411947.e0000 0004 0470 4224Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Sung-Soo Park
- Department of Hematology, Seoul St. Mary’s Hematology Hospital, Seoul, South Korea ,grid.411947.e0000 0004 0470 4224Leukemia Research Institute, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Ji-Young Lim
- Department of Hematology, Seoul St. Mary’s Hematology Hospital, Seoul, South Korea
| | - Seon Yong Sohn
- grid.411947.e0000 0004 0470 4224Department of Biochemistry, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Na Yung Kim
- grid.411947.e0000 0004 0470 4224Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Dokyeong Kim
- grid.411947.e0000 0004 0470 4224Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea ,grid.411947.e0000 0004 0470 4224Precision Medicine Research Center/IRCGP, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Sug Hyung Lee
- grid.411947.e0000 0004 0470 4224Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea ,grid.411947.e0000 0004 0470 4224Cancer Evolution Research Center, College of Medicine, The Catholic University of Korea, Seoul, South Korea ,grid.411947.e0000 0004 0470 4224Department of Pathology, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Yeun-Jun Chung
- grid.411947.e0000 0004 0470 4224Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea ,grid.411947.e0000 0004 0470 4224Precision Medicine Research Center/IRCGP, College of Medicine, The Catholic University of Korea, Seoul, South Korea ,grid.411947.e0000 0004 0470 4224Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Chang-Ki Min
- Department of Hematology, Seoul St. Mary’s Hematology Hospital, Seoul, South Korea ,grid.411947.e0000 0004 0470 4224Leukemia Research Institute, College of Medicine, The Catholic University of Korea, Seoul, South Korea
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Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection. Cancers (Basel) 2022; 14:cancers14030606. [PMID: 35158874 PMCID: PMC8833500 DOI: 10.3390/cancers14030606] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/20/2022] [Accepted: 01/24/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Multiple myeloma is a malignant neoplasm of plasma cells with complex pathogenesis. With major progresses in multiple myeloma research, it is essential that we reconsider our methods for diagnosing and monitoring multiple myeloma disease. This fact needs the integration of serology, histology, radiology, and genetic data; therefore, multiple myeloma study has generated massive quantities of granular high-dimensional data exceeding human understanding. With improved computational techniques, artificial intelligence tools for data processing and analysis are becoming more and more relevant. Artificial intelligence represents a wide set of algorithms for which machine learning and deep learning are presently among the most impactful. This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multiple myeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment. Abstract Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.
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Paradzik T, Bandini C, Mereu E, Labrador M, Taiana E, Amodio N, Neri A, Piva R. The Landscape of Signaling Pathways and Proteasome Inhibitors Combinations in Multiple Myeloma. Cancers (Basel) 2021; 13:1235. [PMID: 33799793 PMCID: PMC8000754 DOI: 10.3390/cancers13061235] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/04/2021] [Accepted: 03/06/2021] [Indexed: 12/14/2022] Open
Abstract
Multiple myeloma is a malignancy of terminally differentiated plasma cells, characterized by an extreme genetic heterogeneity that poses great challenges for its successful treatment. Due to antibody overproduction, MM cells depend on the precise regulation of the protein degradation systems. Despite the success of PIs in MM treatment, resistance and adverse toxic effects such as peripheral neuropathy and cardiotoxicity could arise. To this end, the use of rational combinatorial treatments might allow lowering the dose of inhibitors and therefore, minimize their side-effects. Even though the suppression of different cellular pathways in combination with proteasome inhibitors have shown remarkable anti-myeloma activities in preclinical models, many of these promising combinations often failed in clinical trials. Substantial progress has been made by the simultaneous targeting of proteasome and different aspects of MM-associated immune dysfunctions. Moreover, targeting deranged metabolic hubs could represent a new avenue to identify effective therapeutic combinations with PIs. Finally, epigenetic drugs targeting either DNA methylation, histone modifiers/readers, or chromatin remodelers are showing pleiotropic anti-myeloma effects alone and in combination with PIs. We envisage that the positive outcome of patients will probably depend on the availability of more effective drug combinations and treatment of early MM stages. Therefore, the identification of sensitive targets and aberrant signaling pathways is instrumental for the development of new personalized therapies for MM patients.
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Affiliation(s)
- Tina Paradzik
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (T.P.); (C.B.); (E.M.); (M.L.)
| | - Cecilia Bandini
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (T.P.); (C.B.); (E.M.); (M.L.)
| | - Elisabetta Mereu
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (T.P.); (C.B.); (E.M.); (M.L.)
| | - Maria Labrador
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (T.P.); (C.B.); (E.M.); (M.L.)
| | - Elisa Taiana
- Department of Oncology and Hemato-oncology, University of Milano, 20122 Milano, Italy; (E.T.); (A.N.)
- Hematology Unit, Fondazione Cà Granda IRCCS, Ospedale Maggiore Policlinico, 20122 Milano, Italy
| | - Nicola Amodio
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy;
| | - Antonino Neri
- Department of Oncology and Hemato-oncology, University of Milano, 20122 Milano, Italy; (E.T.); (A.N.)
- Hematology Unit, Fondazione Cà Granda IRCCS, Ospedale Maggiore Policlinico, 20122 Milano, Italy
| | - Roberto Piva
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (T.P.); (C.B.); (E.M.); (M.L.)
- Città Della Salute e della Scienza Hospital, 10126 Torino, Italy
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Novel molecular subgroups within the context of receptor tyrosine kinase and adhesion signalling in multiple myeloma. Blood Cancer J 2021; 11:51. [PMID: 33664224 PMCID: PMC7933144 DOI: 10.1038/s41408-021-00442-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 02/03/2021] [Accepted: 02/09/2021] [Indexed: 12/17/2022] Open
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