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Emde-Rajaratnam M, Beck S, Benes V, Salwender H, Bertsch U, Scheid C, Hänel M, Weisel K, Hielscher T, Raab MS, Goldschmidt H, Jauch A, Maes K, De Bruyne E, Menu E, De Veirman K, Moreaux J, Vanderkerken K, Seckinger A, Hose D. RNA-sequencing based first choice of treatment and determination of risk in multiple myeloma. Front Immunol 2023; 14:1286700. [PMID: 38035078 PMCID: PMC10684778 DOI: 10.3389/fimmu.2023.1286700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
Background Immunotherapeutic targets in multiple myeloma (MM) have variable expression height and are partly expressed in subfractions of patients only. With increasing numbers of available compounds, strategies for appropriate choice of targets (combinations) are warranted. Simultaneously, risk assessment is advisable as patient's life expectancy varies between months and decades. Methods We first assess feasibility of RNA-sequencing in a multicenter trial (GMMG-MM5, n=604 patients). Next, we use a clinical routine cohort of untreated symptomatic myeloma patients undergoing autologous stem cell transplantation (n=535, median follow-up (FU) 64 months) to perform RNA-sequencing, gene expression profiling (GEP), and iFISH by ten-probe panel on CD138-purified malignant plasma cells. We subsequently compare target expression to plasma cell precursors, MGUS (n=59), asymptomatic (n=142) and relapsed (n=69) myeloma patients, myeloma cell lines (n=26), and between longitudinal samples (MM vs. relapsed MM). Data are validated using the independent MMRF CoMMpass-cohort (n=767, FU 31 months). Results RNA-sequencing is feasible in 90.8% of patients (GMMG-MM5). Actionable immune-oncological targets (n=19) can be divided in those expressed in all normal and >99% of MM-patients (CD38, SLAMF7, BCMA, GPRC5D, FCRH5, TACI, CD74, CD44, CD37, CD79B), those with expression loss in subfractions of MM-patients (BAFF-R [81.3%], CD19 [57.9%], CD20 [82.8%], CD22 [28.4%]), aberrantly expressed in MM (NY-ESO1/2 [12%], MUC1 [12.7%], CD30 [4.9%], mutated BRAF V600E/K [2.1%]), and resistance-conveying target-mutations e.g., against part but not all BCMA-directed treatments. Risk is assessable regarding proliferation, translated GEP- (UAMS70-, SKY92-, RS-score) and de novo (LfM-HRS) defined risk scores. LfM-HRS delineates three groups of 40%, 38%, and 22% of patients with 5-year and 12-year survival rates of 84% (49%), 67% (18%), and 32% (0%). R-ISS and RNA-sequencing identify partially overlapping patient populations, with R-ISS missing, e.g., 30% (22/72) of highly proliferative myeloma. Conclusion RNA-sequencing based assessment of risk and targets for first choice treatment is possible in clinical routine.
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Affiliation(s)
- Martina Emde-Rajaratnam
- Department of Hematology and Immunology, Myeloma Center Brussels & Labor für Myelomforschung, Vrije Universiteit Brussel (VUB), Jette, Belgium
| | - Susanne Beck
- Department of Hematology and Immunology, Myeloma Center Brussels & Labor für Myelomforschung, Vrije Universiteit Brussel (VUB), Jette, Belgium
- Universitätsklinikum Heidelberg, Molekularpathologisches Zentrum, Heidelberg, Germany
| | - Vladimir Benes
- Europäisches Laboratorium für Molekularbiologie, GeneCore, Heidelberg, Germany
| | - Hans Salwender
- Asklepios Tumorzentrum Hamburg, AK Altona and St. Georg, Hamburg, Germany
| | - Uta Bertsch
- Universitätsklinikum Heidelberg, Medizinische Klinik V, Heidelberg, Germany
| | - Christoph Scheid
- Department I of Internal Medicine, University of Cologne, Cologne, Germany
| | - Mathias Hänel
- Department of Internal Medicine III, Klinikum Chemnitz GmbH, Chemnitz, Germany
| | - Katja Weisel
- Department of Oncology, Hematology and Bone Marrow Transplantation with Section of Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thomas Hielscher
- Deutsches Krebsforschungszentrum, Abteilung für Biostatistik, Heidelberg, Germany
| | - Marc S. Raab
- Universitätsklinikum Heidelberg, Medizinische Klinik V, Heidelberg, Germany
| | - Hartmut Goldschmidt
- Universitätsklinikum Heidelberg, Medizinische Klinik V, Heidelberg, Germany
- Nationales Centrum für Tumorerkrankungen, Heidelberg, Germany
| | - Anna Jauch
- Universität Heidelberg, Institut für Humangenetik, Heidelberg, Germany
| | - Ken Maes
- Department of Hematology and Immunology, Myeloma Center Brussels & Labor für Myelomforschung, Vrije Universiteit Brussel (VUB), Jette, Belgium
| | - Elke De Bruyne
- Department of Hematology and Immunology, Myeloma Center Brussels & Labor für Myelomforschung, Vrije Universiteit Brussel (VUB), Jette, Belgium
| | - Eline Menu
- Department of Hematology and Immunology, Myeloma Center Brussels & Labor für Myelomforschung, Vrije Universiteit Brussel (VUB), Jette, Belgium
| | - Kim De Veirman
- Department of Hematology and Immunology, Myeloma Center Brussels & Labor für Myelomforschung, Vrije Universiteit Brussel (VUB), Jette, Belgium
| | - Jérôme Moreaux
- Institute of Human Genetics, UMR 9002 CNRS-UM, Montpellier, France
| | - Karin Vanderkerken
- Department of Hematology and Immunology, Myeloma Center Brussels & Labor für Myelomforschung, Vrije Universiteit Brussel (VUB), Jette, Belgium
| | - Anja Seckinger
- Department of Hematology and Immunology, Myeloma Center Brussels & Labor für Myelomforschung, Vrije Universiteit Brussel (VUB), Jette, Belgium
| | - Dirk Hose
- Department of Hematology and Immunology, Myeloma Center Brussels & Labor für Myelomforschung, Vrije Universiteit Brussel (VUB), Jette, Belgium
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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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Ovejero S, Moreaux J. Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2021. [DOI: 10.37349/etat.2020.00034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.
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Affiliation(s)
- Sara Ovejero
- Department of Biological Hematology, CHU Montpellier, 34295 Montpellier, France 2Institute of Human Genetics, UMR 9002 CNRS-UM, 34000 Montpellier, France
| | - Jerome Moreaux
- Department of Biological Hematology, CHU Montpellier, 34295 Montpellier, France 2Institute of Human Genetics, UMR 9002 CNRS-UM, 34000 Montpellier, France 3University of Montpellier, UFR Medicine, 34093 Montpellier, France 4 Institut Universitaire de France (IUF), 75000 Paris France
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4
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Ovejero S, Moreaux J. Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2021; 2:65-106. [PMID: 36046090 PMCID: PMC9400753 DOI: 10.37349/etat.2021.00034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 01/06/2021] [Indexed: 11/19/2022] Open
Abstract
Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.
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Affiliation(s)
- Sara Ovejero
- Department of Biological Hematology, CHU Montpellier, 34295 Montpellier, France 2Institute of Human Genetics, UMR 9002 CNRS-UM, 34000 Montpellier, France
| | - Jerome Moreaux
- Department of Biological Hematology, CHU Montpellier, 34295 Montpellier, France 2Institute of Human Genetics, UMR 9002 CNRS-UM, 34000 Montpellier, France 3UFR Medicine, University of Montpellier, 34093 Montpellier, France 4Institut Universitaire de France (IUF), 75000 Paris, France
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Malvisi M, Curti N, Remondini D, De Iorio MG, Palazzo F, Gandini G, Vitali S, Polli M, Williams JL, Minozzi G. Combinatorial Discriminant Analysis Applied to RNAseq Data Reveals a Set of 10 Transcripts as Signatures of Exposure of Cattle to Mycobacterium avium subsp. paratuberculosis. Animals (Basel) 2020; 10:E253. [PMID: 32033399 PMCID: PMC7070263 DOI: 10.3390/ani10020253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 01/28/2020] [Indexed: 12/16/2022] Open
Abstract
Paratuberculosis or Johne's disease in cattle is a chronic granulomatous gastroenteritis caused by infection with Mycobacterium avium subspecies paratuberculosis (MAP). Paratuberculosis is not treatable; therefore, the early identification and isolation of infected animals is a key point to reduce its incidence. In this paper, we analyse RNAseq experimental data of 5 ELISA-negative cattle exposed to MAP in a positive herd, compared to 5 negative-unexposed controls. The purpose was to find a small set of differentially expressed genes able to discriminate between exposed animals in a preclinical phase from non-exposed controls. Our results identified 10 transcripts that differentiate between ELISA-negative, clinically healthy, and exposed animals belonging to paratuberculosis-positive herds and negative-unexposed animals. Of the 10 transcripts, five (TRPV4, RIC8B, IL5RA, ERF, CDC40) showed significant differential expression between the three groups while the remaining 5 (RDM1, EPHX1, STAU1, TLE1, ASB8) did not show a significant difference in at least one of the pairwise comparisons. When tested in a larger cohort, these findings may contribute to the development of a new diagnostic test for paratuberculosis based on a gene expression signature. Such a diagnostic tool could allow early interventions to reduce the risk of the infection spreading.
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Affiliation(s)
- Michela Malvisi
- Parco Tecnologico Padano, 26900 Lodi, Italy;
- Department of Veterinary Medicine DIMEVET, University of Milan, 20133 Milan, Italy; (M.G.D.I.); (G.G.); (M.P.)
| | - Nico Curti
- Department of Physics and Astronomy, University di Bologna, 40126 Bologna, Italy; (N.C.); (S.V.)
| | - Daniel Remondini
- Department of Physics and Astronomy, University di Bologna, 40126 Bologna, Italy; (N.C.); (S.V.)
| | - Maria Grazia De Iorio
- Department of Veterinary Medicine DIMEVET, University of Milan, 20133 Milan, Italy; (M.G.D.I.); (G.G.); (M.P.)
| | - Fiorentina Palazzo
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, 64100 Teramo, Italy;
| | - Gustavo Gandini
- Department of Veterinary Medicine DIMEVET, University of Milan, 20133 Milan, Italy; (M.G.D.I.); (G.G.); (M.P.)
| | - Silvia Vitali
- Department of Physics and Astronomy, University di Bologna, 40126 Bologna, Italy; (N.C.); (S.V.)
| | - Michele Polli
- Department of Veterinary Medicine DIMEVET, University of Milan, 20133 Milan, Italy; (M.G.D.I.); (G.G.); (M.P.)
| | - John L. Williams
- Davies Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, South Australia 5005, Australia;
| | - Giulietta Minozzi
- Department of Veterinary Medicine DIMEVET, University of Milan, 20133 Milan, Italy; (M.G.D.I.); (G.G.); (M.P.)
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Lê GN, Bones J, Coyne M, Bazou D, Dowling P, O'Gorman P, Larkin AM. Current and future biomarkers for risk-stratification and treatment personalisation in multiple myeloma. Mol Omics 2019; 15:7-20. [PMID: 30652172 DOI: 10.1039/c8mo00193f] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Multiple myeloma, an incurable malignancy of the plasma cells in the bone marrow, has a complex pathogenesis due to clonal heterogeneity. Over the years, many clinical trials and researches have led to the development of effective myeloma treatments, resulting in survival prolongation. Molecular prognostic markers for risk-stratification to predict survival, and predictive markers for treatment response are being extensively explored. This review discusses the current risk-adaptive strategies based on genetic and molecular risk signatures that are in practice to predict survival and describes the future prognostic and predictive biomarkers across the fields of genomics, proteomics, and glycomics in myeloma. Gene expression profiling and next generation sequencing are coming to the forefront of risk-stratification and therapeutic-response prediction. Similarly, proteomic and glycomic-based platforms are gaining momentum in biomarker discovery to predict drug resistance and disease progression.
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Affiliation(s)
- Giao N Lê
- NIBRT - The National Institute for Bioprocessing Research and Training, Foster Avenue, Mount Merion, Blackrock Co., Dublin A94 X099, Ireland.
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Szalat R, Avet-Loiseau H, Munshi NC. Gene Expression Profiles in Myeloma: Ready for the Real World? Clin Cancer Res 2016; 22:5434-5442. [PMID: 28151711 PMCID: PMC5546147 DOI: 10.1158/1078-0432.ccr-16-0867] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 09/19/2016] [Accepted: 09/20/2016] [Indexed: 12/16/2022]
Abstract
Multiple myeloma is a plasma cell malignancy characterized by molecular and clinical heterogeneity. The outcome of the disease has been dramatically improved with the advent of new drugs in the past few years. However, even in this context of increasing therapeutic options, important challenges remain, such as accurately evaluating patients' prognosis and predicting sensitivity to specific treatments and drug combinations. Transcriptomic studies have largely contributed to help decipher multiple myeloma complexity, characterizing multiple myeloma subgroups distinguished by different outcomes. Microarrays and, more recently, RNA sequencing allow evaluation of expression of coding and noncoding genes, alternate splicing events, mutations, and novel transcriptome modifiers, providing new information regarding myeloma biology, prognostication, and therapy. In this review, we discuss the role and impact of gene expression profiling studies in myeloma. Clin Cancer Res; 22(22); 5434-42. ©2016 AACR SEE ALL ARTICLES IN THIS CCR FOCUS SECTION, "MULTIPLE MYELOMA MULTIPLYING THERAPIES".
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Affiliation(s)
- Raphael Szalat
- Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Herve Avet-Loiseau
- Centre de Recherche en Cancerologie de Toulouse, Institut National de la Sante et de la Recherche Medicale, Toulouse, France.
| | - Nikhil C Munshi
- Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.
- Boston Veterans Administration Healthcare System, Boston, Massachusetts
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Identifying Professional Education Gaps and Barriers in Multiple Myeloma Patient Care: Findings of the Managing Myeloma Continuing Educational Initiative Advisory Committee. CLINICAL LYMPHOMA MYELOMA & LEUKEMIA 2014; 14:356-69. [DOI: 10.1016/j.clml.2014.04.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2014] [Revised: 03/26/2014] [Accepted: 04/03/2014] [Indexed: 12/31/2022]
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Preeshagul IR, Van Besien K, Mark TM. Controversies in multiple myeloma: to transplant or not? Curr Hematol Malig Rep 2014; 9:360-7. [PMID: 25145553 DOI: 10.1007/s11899-014-0230-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The treatment of multiple myeloma (MM) has dramatically changed in the last decade due to the introduction of the immunomodulatory drugs (IMIDs) and proteasome inhibitors, otherwise known as the novel agents. Prior to the advent of the novel agents, the gold standard of treatment had been high-dose chemotherapy with autologous stem cell transplantation (HDT/ASCT) for eligible candidates. Given the remarkable activity of the novel agents, and the significant morbidity of HDT/ASCT, the role of stem cell transplantation has now come into question. In this review, we explore the benefits and drawbacks to HDT/ASCT in the era of the novel therapies.
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Affiliation(s)
- Isabel Ruth Preeshagul
- Department of Medicine, Division of Hematology and Oncology, Mount Sinai Beth Israel, 16th street and 1st avenue, New York, NY, 10003, USA,
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