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Katsenou A, O’Farrell R, Dowling P, Heckman CA, O’Gorman P, Bazou D. Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach. Int J Mol Sci 2023; 24:15570. [PMID: 37958554 PMCID: PMC10650823 DOI: 10.3390/ijms242115570] [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: 09/20/2023] [Revised: 10/11/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
This paper describes a machine learning (ML) decision support system to provide a list of chemotherapeutics that individual multiple myeloma (MM) patients are sensitive/resistant to, based on their proteomic profile. The methodology used in this study involved understanding the parameter space and selecting the dominant features (proteomics data), identifying patterns of proteomic profiles and their association to the recommended treatments, and defining the decision support system of personalized treatment as a classification problem. During the data analysis, we compared several ML algorithms, such as linear regression, Random Forest, and support vector machines, to classify patients as sensitive/resistant to therapeutics. A further analysis examined data-balancing techniques that emerged due to the small cohort size. The results suggest that utilizing proteomics data is a promising approach for identifying effective treatment options for patients with MM (reaching on average an accuracy of 81%). Although this pilot study was limited by the small patient cohort (39 patients), which restricted the training and validation of the explored ML solutions to identify complex associations between proteins, it holds great promise for developing personalized anti-MM treatments using ML approaches.
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Affiliation(s)
- Angeliki Katsenou
- Department of Electronics and Electrical Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland;
- School of Computer Science, University of Bristol, Bristol BS1 8UB, UK
| | - Roisin O’Farrell
- Department of Electronics and Electrical Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland;
| | - Paul Dowling
- Department of Biology, Maynooth University, W23 F2K8 Kildare, Ireland;
| | - Caroline A. Heckman
- Institute for Molecular Medicine Finland-FIMM, HiLIFE-Helsinki Institute of Life Science, iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, 00290 Helsinki, Finland;
| | - Peter O’Gorman
- Department of Haematology, Mater Misericordiae University Hospital, D07 R2WY Dublin, Ireland;
| | - Despina Bazou
- School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
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Kraeber-Bodéré F, Jamet B, Bezzi D, Zamagni E, Moreau P, Nanni C. New Developments in Myeloma Treatment and Response Assessment. J Nucl Med 2023; 64:1331-1343. [PMID: 37591548 PMCID: PMC10478822 DOI: 10.2967/jnumed.122.264972] [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: 02/16/2023] [Revised: 07/06/2023] [Indexed: 08/19/2023] Open
Abstract
Recent innovative strategies have dramatically redefined the therapeutic landscape for treating multiple myeloma patients. In particular, the development and application of immunotherapy and high-dose therapy have demonstrated high response rates and have prolonged remission duration. Over the past decade, new morphologic or hybrid imaging techniques have gradually replaced conventional skeletal surveys. PET/CT using 18F-FDG is a powerful imaging tool for the workup at diagnosis and for therapeutic evaluation allowing medullary and extramedullary assessment. The independent negative prognostic value for progression-free and overall survival derived from baseline PET-derived parameters such as the presence of extramedullary disease or paramedullary disease, as well as the number of focal bone lesions and SUVmax, has been reported in several large prospective studies. During therapeutic evaluation, 18F-FDG PET/CT is considered the reference imaging technique because it can be performed much earlier than MRI, which lacks specificity. Persistence of significant abnormal 18F-FDG uptake after therapy is an independent negative prognostic factor, and 18F-FDG PET/CT and medullary flow cytometry are complementary tools for detecting minimal residual disease before maintenance therapy. The definition of a PET metabolic complete response has recently been standardized and the interpretation criteria harmonized. The development of advanced PET analysis and radiomics using machine learning, as well as hybrid imaging with PET/MRI, offers new perspectives for multiple myeloma imaging. Most recently, innovative radiopharmaceuticals such as C-X-C chemokine receptor type 4-targeted small molecules and anti-CD38 radiolabeled antibodies have shown promising results for tumor phenotype imaging and as potential theranostics.
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Affiliation(s)
- Françoise Kraeber-Bodéré
- Médecine nucléaire, CHU Nantes, Nantes Université, Université Angers, INSERM, CNRS, CRCI2NA, F-44000, Nantes, France
| | - Bastien Jamet
- Médecine nucléaire, CHU Nantes, F-44000, Nantes, France
| | - Davide Bezzi
- Department of Nuclear Medicine, Alma Mater Studiorum, University of Bologna, Bologna. Italy
| | - Elena Zamagni
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli," Bologna, Italy
- Dipartimento di Scienze Mediche e Chirurgiche, Università di Bologna, Bologna, Italy
| | - Philippe Moreau
- Hématologie, CHU Nantes, Nantes Université, Université Angers, INSERM, CNRS, CRCI2NA, F-44000, Nantes, France; and
| | - Cristina Nanni
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
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Park SS, Lee JC, Byun JM, Choi G, Kim KH, Lim S, Dingli D, Jeon YW, Yahng SA, Shin SH, Min CK, Koo J. ML-based sequential analysis to assist selection between VMP and RD for newly diagnosed multiple myeloma. NPJ Precis Oncol 2023; 7:46. [PMID: 37210456 DOI: 10.1038/s41698-023-00385-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 05/03/2023] [Indexed: 05/22/2023] Open
Abstract
Optimal first-line treatment that enables deeper and longer remission is crucially important for newly diagnosed multiple myeloma (NDMM). In this study, we developed the machine learning (ML) models predicting overall survival (OS) or response of the transplant-ineligible NDMM patients when treated by one of the two regimens-bortezomib plus melphalan plus prednisone (VMP) or lenalidomide plus dexamethasone (RD). Demographic and clinical characteristics obtained during diagnosis were used to train the ML models, which enabled treatment-specific risk stratification. Survival was superior when the patients were treated with the regimen to which they were low risk. The largest difference in OS was observed in the VMP-low risk & RD-high risk group, who recorded a hazard ratio of 0.15 (95% CI: 0.04-0.55) when treated with VMP vs. RD regimen. Retrospective analysis showed that the use of the ML models might have helped to improve the survival and/or response of up to 202 (39%) patients among the entire cohort (N = 514). In this manner, we believe that the ML models trained on clinical data available at diagnosis can assist the individualized selection of optimal first-line treatment for transplant-ineligible NDMM patients.
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Affiliation(s)
- Sung-Soo Park
- Catholic Research Network for Multiple Myeloma, Catholic Hematology Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea
- Department of Hematology, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, 06591, Republic of Korea
| | - Jong Cheol Lee
- Department of Otorhinolaryngology, GangNeung Asan Hospital, University of Ulsan College of Medicine, Gangneung-si, Gangwon-do, 25440, Republic of Korea
| | - Ja Min Byun
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Gyucheol Choi
- ImpriMedKorea, Inc., Seoul, 08507, Republic of Korea
| | - Kwan Hyun Kim
- ImpriMedKorea, Inc., Seoul, 08507, Republic of Korea
| | - Sungwon Lim
- ImpriMedKorea, Inc., Seoul, 08507, Republic of Korea
- ImpriMed, Inc., Palo Alto, CA, 94303, USA
| | - David Dingli
- Division of Hematology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Young-Woo Jeon
- Catholic Research Network for Multiple Myeloma, Catholic Hematology Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea
- Department of Hematology, Yeoido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 07345, Republic of Korea
| | - Seung-Ah Yahng
- Catholic Research Network for Multiple Myeloma, Catholic Hematology Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea
- Department of Hematology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, 22711, Republic of Korea
| | - Seung-Hwan Shin
- Catholic Research Network for Multiple Myeloma, Catholic Hematology Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea
- Department of Hematology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 03312, Republic of Korea
| | - Chang-Ki Min
- Catholic Research Network for Multiple Myeloma, Catholic Hematology Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea.
- Department of Hematology, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, 06591, Republic of Korea.
| | - Jamin Koo
- ImpriMedKorea, Inc., Seoul, 08507, Republic of Korea.
- ImpriMed, Inc., Palo Alto, CA, 94303, USA.
- Department of Chemical Engineering, Hongik University, Seoul, 04066, Republic of Korea.
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Sinha K, Ghosh J, Sil PC. Machine Learning in Drug Metabolism Study. Curr Drug Metab 2022; 23:CDM-EPUB-128463. [PMID: 36578255 DOI: 10.2174/1389200224666221227094144] [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: 06/28/2022] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 12/30/2022]
Abstract
Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug's reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug's metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resource-demanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-to-activity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.
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Affiliation(s)
| | | | - Parames C Sil
- Division of Molecular Medicine, Bose Institute, Kolkata-700054
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Barreto IV, Machado CB, Almeida DB, Pessoa FMCDP, Gadelha RB, Pantoja LDC, Oliveira DDS, Ribeiro RM, Lopes GS, de Moraes Filho MO, de Moraes MEA, Khayat AS, de Oliveira EHC, Moreira-Nunes CA. Kinase Inhibition in Multiple Myeloma: Current Scenario and Clinical Perspectives. Pharmaceutics 2022; 14:pharmaceutics14091784. [PMID: 36145532 PMCID: PMC9506264 DOI: 10.3390/pharmaceutics14091784] [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: 07/21/2022] [Revised: 08/16/2022] [Accepted: 08/23/2022] [Indexed: 11/16/2022] Open
Abstract
Multiple myeloma (MM) is a blood cell neoplasm characterized by excessive production of malignant monoclonal plasma cells (activated B lymphocytes) by the bone marrow, which end up synthesizing antibodies or antibody fragments, called M proteins, in excess. The accumulation of this production, both cells themselves and of the immunoglobulins, causes a series of problems for the patient, of a systemic and local nature, such as blood hyperviscosity, renal failure, anemia, bone lesions, and infections due to compromised immunity. MM is the third most common hematological neoplasm, constituting 1% of all cancer cases, and is a disease that is difficult to treat, still being considered an incurable disease. The treatments currently available cannot cure the patient, but only extend their lifespan, and the main and most effective alternative is autologous hematopoietic stem cell transplantation, but not every patient is eligible, often due to age and pre-existing comorbidities. In this context, the search for new therapies that can bring better results to patients is of utmost importance. Protein tyrosine kinases (PTKs) are involved in several biological processes, such as cell growth regulation and proliferation, thus, mutations that affect their functionality can have a great impact on crucial molecular pathways in the cells, leading to tumorigenesis. In the past couple of decades, the use of small-molecule inhibitors, which include tyrosine kinase inhibitors (TKIs), has been a hallmark in the treatment of hematological malignancies, and MM patients may also benefit from TKI-based treatment strategies. In this review, we seek to understand the applicability of TKIs used in MM clinical trials in the last 10 years.
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Affiliation(s)
- Igor Valentim Barreto
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | - Caio Bezerra Machado
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | | | - Flávia Melo Cunha de Pinho Pessoa
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | - Renan Brito Gadelha
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | - Laudreísa da Costa Pantoja
- Department of Biological Sciences, Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil
| | | | | | - Germison Silva Lopes
- Department of Hematology, César Cals General Hospital, Fortaleza 60015-152, CE, Brazil
| | - Manoel Odorico de Moraes Filho
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | - Maria Elisabete Amaral de Moraes
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | - André Salim Khayat
- Department of Biological Sciences, Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil
| | - Edivaldo Herculano Correa de Oliveira
- Faculty of Natural Sciences, Institute of Exact and Natural Sciences, Federal University of Pará (UFPA), Rua Augusto Correa, 01, Belém 66075-990, PA, Brazil
- Laboratory of Cytogenomics and Environmental Mutagenesis, Environment Section (SAMAM), Evandro Chagas Institute (IEC), BR 316, KM 7, s/n, Levilândia, Ananindeua 67030-000, PA, Brazil
| | - Caroline Aquino Moreira-Nunes
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
- Department of Biological Sciences, Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil
- Northeast Biotechnology Network (RENORBIO), Itaperi Campus, Ceará State University, Fortaleza 60740-903, CE, Brazil
- Correspondence:
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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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