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Salem A, Little RA, Latif A, Featherstone AK, Babur M, Peset I, Cheung S, Watson Y, Tessyman V, Mistry H, Ashton G, Behan C, Matthews JC, Asselin MC, Bristow RG, Jackson A, Parker GJM, Faivre-Finn C, Williams KJ, O'Connor JPB. Oxygen-enhanced MRI Is Feasible, Repeatable, and Detects Radiotherapy-induced Change in Hypoxia in Xenograft Models and in Patients with Non-small Cell Lung Cancer. Clin Cancer Res 2019; 25:3818-3829. [PMID: 31053599 DOI: 10.1158/1078-0432.ccr-18-3932] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 02/04/2019] [Accepted: 03/14/2019] [Indexed: 11/16/2022]
Abstract
PURPOSE Hypoxia is associated with poor prognosis and is predictive of poor response to cancer treatments, including radiotherapy. Developing noninvasive biomarkers that both detect hypoxia prior to treatment and track change in tumor hypoxia following treatment is required urgently. EXPERIMENTAL DESIGN We evaluated the ability of oxygen-enhanced MRI (OE-MRI) to map and quantify therapy-induced changes in tumor hypoxia by measuring oxygen-refractory signals in perfused tissue (perfused Oxy-R). Clinical first-in-human study in patients with non-small cell lung cancer (NSCLC) was performed alongside preclinical experiments in two xenograft tumors (Calu6 NSCLC model and U87 glioma model). RESULTS MRI perfused Oxy-R tumor fraction measurement of hypoxia was validated with ex vivo tissue pathology in both xenograft models. Calu6 and U87 experiments showed that MRI perfused Oxy-R tumor volume was reduced relative to control following single fraction 10-Gy radiation and fractionated chemoradiotherapy (P < 0.001) due to both improved perfusion and reduced oxygen consumption rate. Next, evaluation of 23 patients with NSCLC showed that OE-MRI was clinically feasible and that tumor perfused Oxy-R volume is repeatable [interclass correlation coefficient: 0.961 (95% CI, 0.858-0.990); coefficient of variation: 25.880%]. Group-wise perfused Oxy-R volume was reduced at 14 days following start of radiotherapy (P = 0.015). OE-MRI detected between-subject variation in hypoxia modification in both xenograft and patient tumors. CONCLUSIONS These findings support applying OE-MRI biomarkers to monitor hypoxia modification, to stratify patients in clinical trials of hypoxia-modifying therapies, to identify patients with hypoxic tumors that may fail treatment with immunotherapy, and to guide adaptive radiotherapy by mapping regional hypoxia.
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Affiliation(s)
- Ahmed Salem
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom
- Department of Clinical Oncology, The Christie Hospital NHS Trust, Manchester, United Kingdom
| | - Ross A Little
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Ayşe Latif
- Division of Pharmacy, University of Manchester, Manchester, United Kingdom
| | - Adam K Featherstone
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Muhammad Babur
- Division of Pharmacy, University of Manchester, Manchester, United Kingdom
| | - Isabel Peset
- Imaging and Flow Cytometry, Cancer Research UK Manchester Institute, Manchester, United Kingdom
| | - Susan Cheung
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Yvonne Watson
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Victoria Tessyman
- Division of Pharmacy, University of Manchester, Manchester, United Kingdom
| | - Hitesh Mistry
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
- Division of Pharmacy, University of Manchester, Manchester, United Kingdom
| | - Garry Ashton
- Histology, Cancer Research UK Manchester Institute, Manchester, United Kingdom
| | - Caron Behan
- Histology, Cancer Research UK Manchester Institute, Manchester, United Kingdom
| | - Julian C Matthews
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
| | - Marie-Claude Asselin
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Robert G Bristow
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
- Department of Clinical Oncology, The Christie Hospital NHS Trust, Manchester, United Kingdom
| | - Alan Jackson
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Geoff J M Parker
- Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom
- Bioxydyn Limited, Manchester, United Kingdom
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
- Department of Clinical Oncology, The Christie Hospital NHS Trust, Manchester, United Kingdom
| | - Kaye J Williams
- Division of Pharmacy, University of Manchester, Manchester, United Kingdom
| | - James P B O'Connor
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom. James.O'
- Department of Radiology, The Christie Hospital NHS Trust, Manchester, United Kingdom
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Featherstone AK, O'Connor JP, Little RA, Watson Y, Cheung S, Babur M, Williams KJ, Matthews JC, Parker GJ. Data-driven mapping of hypoxia-related tumor heterogeneity using DCE-MRI and OE-MRI. Magn Reson Med 2018; 79:2236-2245. [PMID: 28856728 PMCID: PMC5836865 DOI: 10.1002/mrm.26860] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 07/13/2017] [Accepted: 07/13/2017] [Indexed: 01/06/2023]
Abstract
PURPOSE Previous work has shown that combining dynamic contrast-enhanced (DCE)-MRI and oxygen-enhanced (OE)-MRI binary enhancement maps can identify tumor hypoxia. The current work proposes a novel, data-driven method for mapping tissue oxygenation and perfusion heterogeneity, based on clustering DCE/OE-MRI data. METHODS DCE-MRI and OE-MRI were performed on nine U87 (glioblastoma) and seven Calu6 (non-small cell lung cancer) murine xenograft tumors. Area under the curve and principal component analysis features were calculated and clustered separately using Gaussian mixture modelling. Evaluation metrics were calculated to determine the optimum feature set and cluster number. Outputs were quantitatively compared with a previous non data-driven approach. RESULTS The optimum method located six robustly identifiable clusters in the data, yielding tumor region maps with spatially contiguous regions in a rim-core structure, suggesting a biological basis. Mean within-cluster enhancement curves showed physiologically distinct, intuitive kinetics of enhancement. Regions of DCE/OE-MRI enhancement mismatch were located, and voxel categorization agreed well with the previous non data-driven approach (Cohen's kappa = 0.61, proportional agreement = 0.75). CONCLUSION The proposed method locates similar regions to the previous published method of binarization of DCE/OE-MRI enhancement, but renders a finer segmentation of intra-tumoral oxygenation and perfusion. This could aid in understanding the tumor microenvironment and its heterogeneity. Magn Reson Med 79:2236-2245, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Adam K. Featherstone
- Division of Informatics, Imaging & Data SciencesThe University of ManchesterManchesterUK
- CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and ManchesterUK
| | - James P.B. O'Connor
- CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and ManchesterUK
- Division of Cancer StudiesThe University of ManchesterManchesterUK
- Department of RadiologyChristie NHS Foundation TrustManchesterUK
| | - Ross A. Little
- Division of Informatics, Imaging & Data SciencesThe University of ManchesterManchesterUK
| | - Yvonne Watson
- Division of Informatics, Imaging & Data SciencesThe University of ManchesterManchesterUK
| | - Sue Cheung
- Division of Informatics, Imaging & Data SciencesThe University of ManchesterManchesterUK
| | - Muhammad Babur
- Division of Pharmacy & OptometryThe University of ManchesterManchesterUK
| | - Kaye J. Williams
- CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and ManchesterUK
- Division of Pharmacy & OptometryThe University of ManchesterManchesterUK
| | - Julian C. Matthews
- Division of Informatics, Imaging & Data SciencesThe University of ManchesterManchesterUK
- CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and ManchesterUK
| | - Geoff J.M. Parker
- Division of Informatics, Imaging & Data SciencesThe University of ManchesterManchesterUK
- CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and ManchesterUK
- Bioxydyn LtdManchesterUK
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