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Rees MJ, Kumar S. High-risk multiple myeloma: Redefining genetic, clinical, and functional high-risk disease in the era of molecular medicine and immunotherapy. Am J Hematol 2024; 99:1560-1575. [PMID: 38613829 DOI: 10.1002/ajh.27327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/22/2024] [Accepted: 04/02/2024] [Indexed: 04/15/2024]
Abstract
Multiple myeloma (MM) exhibits significant heterogeneity in its presentation, genetics, and treatment response. Despite therapeutic advances, some patients continue to relapse early (ER, <18-months) and rapidly cycle through therapies. Myriad prognostic factors have been identified and incorporated into risk stratification models; however, these produce discordant, often three-tiered outputs that fail to identify many patients destined for ER. Treatment strategies are increasingly focused on disease biology and trials enriched for high-risk (HR)MM, but consensus on the minimum required testing and a succinct, specific, and clinically meaningful definition for HRMM remains elusive. We review the risk-factors, definitions, and future directions for HRMM.
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Affiliation(s)
- Matthew J Rees
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA
| | - Shaji Kumar
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA
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Murie C, Turkarslan S, Patel A, Coffey DG, Becker PS, Baliga NS. Individualized dynamic risk assessment for multiple myeloma. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.01.24305024. [PMID: 38633807 PMCID: PMC11023676 DOI: 10.1101/2024.04.01.24305024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Background Individualized treatment decisions for patients with multiple myeloma (MM) requires accurate risk stratification that takes into account patient-specific consequences of genetic abnormalities and tumor microenvironment on disease outcome and therapy responsiveness. Methods Previously, SYstems Genetic Network AnaLysis (SYGNAL) of multi-omics tumor profiles from 881 MM patients generated the mmSYGNAL network, which uncovered different causal and mechanistic drivers of genetic programs associated with disease progression across MM subtypes. Here, we have trained a machine learning (ML) algorithm on activities of mmSYGNAL programs within individual patient tumor samples to develop a risk classification scheme for MM that significantly outperformed cytogenetics, International Staging System, and multi-gene biomarker panels in predicting risk of PFS across four independent patient cohorts. Results We demonstrate that, unlike other tests, mmSYGNAL can accurately predict disease progression risk at primary diagnosis, pre- and post-transplant and even after multiple relapses, making it useful for individualized dynamic risk assessment throughout the disease trajectory. Conclusion mmSYGNAL provides improved individualized risk stratification that accounts for a patient's distinct set of genetic abnormalities and can monitor risk longitudinally as each patient's disease characteristics change.
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Martello M, Solli V, Termini R, Kanapari A, Remondini D, Borsi E, Poletti A, Armuzzi S, Taurisano B, Vigliotta I, Mazzocchetti G, Zamagni E, Merlotti A, Tacchetti P, Pantani L, Rocchi S, Rizzello I, Mancuso K, Cavo M, Terragna C. Identification of a Maturation Plasma Cell Index through a Highly Sensitive Droplet Digital PCR Assay Gene Expression Signature Validation in Newly Diagnosed Multiple Myeloma Patients. Int J Mol Sci 2022; 23:12450. [PMID: 36293315 PMCID: PMC9604171 DOI: 10.3390/ijms232012450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/30/2022] [Accepted: 10/11/2022] [Indexed: 02/17/2024] Open
Abstract
DNA microarrays and RNA-based sequencing approaches are considered important discovery tools in clinical medicine. However, cross-platform reproducibility studies undertaken so far have highlighted that microarrays are not able to accurately measure gene expression, particularly when they are expressed at low levels. Here, we consider the employment of a digital PCR assay (ddPCR) to validate a gene signature previously identified by gene expression profile. This signature included ten Hedgehog (HH) pathways' genes able to stratify multiple myeloma (MM) patients according to their self-renewal status. Results show that the designed assay is able to validate gene expression data, both in a retrospective as well as in a prospective cohort. In addition, the plasma cells' differentiation status determined by ddPCR was further confirmed by other techniques, such as flow cytometry, allowing the identification of patients with immature plasma cells' phenotype (i.e., expressing CD19+/CD81+ markers) upregulating HH genes, as compared to others, whose plasma cells lose the expression of these markers and were more differentiated. To our knowledge, this is the first technical report of gene expression data validation by ddPCR instead of classical qPCR. This approach permitted the identification of a Maturation Index through the integration of molecular and phenotypic data, able to possibly define upfront the differentiation status of MM patients that would be clinically relevant in the future.
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Affiliation(s)
- Marina Martello
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Vincenza Solli
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Rosalinda Termini
- Clinica Universidad de Navarra, Centro de Investigación Médica Aplicada (CIMA), Instituto de Investigacion Sanitaria de Navarra (IDISNA), CCUN, CIBER-ONC Numbers CB16/12/00369, CB16/12/00489, 31001 Pamplona, Spain
| | - Ajsi Kanapari
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy, DIFA—University of Bologna, 40126 Bologna, Italy
| | - Enrica Borsi
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Andrea Poletti
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Silvia Armuzzi
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Barbara Taurisano
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Ilaria Vigliotta
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Gaia Mazzocchetti
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Elena Zamagni
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Alessandra Merlotti
- Department of Physics and Astronomy, DIFA—University of Bologna, 40126 Bologna, Italy
| | - Paola Tacchetti
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
| | - Lucia Pantani
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
| | - Serena Rocchi
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Ilaria Rizzello
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Katia Mancuso
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Michele Cavo
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
| | - Carolina Terragna
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, 40138 Bologna, Italy
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de Lima MF, Lisboa MDO, Terceiro LEL, Rangel-Pozzo A, Mai S. Chromosome Territories in Hematological Malignancies. Cells 2022; 11:1368. [PMID: 35456046 PMCID: PMC9028803 DOI: 10.3390/cells11081368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/12/2022] [Accepted: 04/15/2022] [Indexed: 11/21/2022] Open
Abstract
Chromosomes are organized in distinct nuclear areas designated as chromosome territories (CT). The structural formation of CT is a consequence of chromatin packaging and organization that ultimately affects cell function. Chromosome positioning can identify structural signatures of genomic organization, especially for diseases where changes in gene expression contribute to a given phenotype. The study of CT in hematological diseases revealed chromosome position as an important factor for specific chromosome translocations. In this review, we highlight the history of CT theory, current knowledge on possible clinical applications of CT analysis, and the impact of CT in the development of hematological neoplasia such as multiple myeloma, leukemia, and lymphomas. Accumulating data on nuclear architecture in cancer allow one to propose the three-dimensional nuclear genomic landscape as a novel cancer biomarker for the future.
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Affiliation(s)
- Matheus Fabiao de Lima
- Department of Physiology and Pathophysiology, CancerCare Manitoba Research Institute, University of Manitoba, Winnipeg, MB R3E 0V9, Canada;
| | - Mateus de Oliveira Lisboa
- Core for Cell Technology, School of Medicine, Pontifícia Universidade Católica do Paraná—PUCPR, Curitiba 80215-901, Brazil;
| | - Lucas E. L. Terceiro
- Department of Pathology, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB R3E 3P5, Canada;
| | - Aline Rangel-Pozzo
- Department of Physiology and Pathophysiology, CancerCare Manitoba Research Institute, University of Manitoba, Winnipeg, MB R3E 0V9, Canada;
| | - Sabine Mai
- Department of Physiology and Pathophysiology, CancerCare Manitoba Research Institute, University of Manitoba, Winnipeg, MB R3E 0V9, Canada;
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