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Bogatu A, Wysocka M, Wysocki O, Butterworth H, Pillai M, Allison J, Landers D, Kilgour E, Thistlethwaite F, Freitas A. Meta-analysis informed machine learning: Supporting cytokine storm detection during CAR-T cell Therapy. J Biomed Inform 2023; 142:104367. [PMID: 37105509 DOI: 10.1016/j.jbi.2023.104367] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/11/2023] [Accepted: 04/17/2023] [Indexed: 04/29/2023]
Abstract
Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown otherwise promising results in cancer treatment. When emerging, CRS could be identified by the analysis of specific cytokine and chemokine profiles that tend to exhibit similarities across patients. In this paper, we exploit these similarities using machine learning algorithms and set out to pioneer a meta-review informed method for the identification of CRS based on specific cytokine peak concentrations and evidence from previous clinical studies. To this end we also address a widespread challenge of the applicability of machine learning in general: reduced training data availability. We do so by augmenting available (but often insufficient) patient cytokine concentrations with statistical knowledge extracted from domain literature. We argue that such methods could support clinicians in analyzing suspect cytokine profiles by matching them against the said CRS knowledge from past clinical studies, with the ultimate aim of swift CRS diagnosis. We evaluate our proposed methods under several design choices, achieving performance of more than 90% in terms of CRS identification accuracy, and showing that many of our choices outperform a purely data-driven alternative. During evaluation with real-world CRS clinical data, we emphasize the potential of our proposed method of producing interpretable results, in addition to being effective in identifying the onset of cytokine storm.
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Affiliation(s)
- Alex Bogatu
- Department of Computer Science, The University of Manchester, United Kingdom; Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, United Kingdom.
| | - Magdalena Wysocka
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, United Kingdom
| | - Oskar Wysocki
- Department of Computer Science, The University of Manchester, United Kingdom; Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, United Kingdom
| | | | - Manon Pillai
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, UK
| | - Jennifer Allison
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, UK
| | - Dónal Landers
- Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, United Kingdom
| | - Elaine Kilgour
- Cancer Biomarker Centre, CRUK Manchester Institute, United Kingdom
| | - Fiona Thistlethwaite
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - André Freitas
- Department of Computer Science, The University of Manchester, United Kingdom; Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, United Kingdom; Idiap Research Institute, Switzerland
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Decherchi S, Pedrini E, Mordenti M, Cavalli A, Sangiorgi L. Opportunities and Challenges for Machine Learning in Rare Diseases. Front Med (Lausanne) 2021; 8:747612. [PMID: 34676229 PMCID: PMC8523988 DOI: 10.3389/fmed.2021.747612] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022] Open
Abstract
Rare diseases (RDs) are complicated health conditions that are difficult to be managed at several levels. The scarcity of available data chiefly determines an intricate scenario even for experts and specialized clinicians, which in turn leads to the so called “diagnostic odyssey” for the patient. This situation calls for innovative solutions to support the decision process via quantitative and automated tools. Machine learning brings to the stage a wealth of powerful inference methods; however, matching the health conditions with advanced statistical techniques raises methodological, technological, and even ethical issues. In this contribution, we critically point to the specificities of the dialog of rare diseases with machine learning techniques concentrating on the key steps and challenges that may hamper or create actionable knowledge and value for the patient together with some on-field methodological suggestions and considerations.
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Affiliation(s)
- Sergio Decherchi
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy
| | - Elena Pedrini
- Department of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Marina Mordenti
- Department of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Andrea Cavalli
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Luca Sangiorgi
- Department of Rare Skeletal Disorders, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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