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Multi-centre technical evaluation of the radiation-induced lymphocyte apoptosis assay as a predictive test for radiotherapy toxicity.

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posted on 2019-09-05, 12:46 authored by CJ Talbot, MR Veldwijk, D Azria, C Batini, M Bierbaum, M Brengues, J Chang-Claude, K Johnson, A Keller, S Smith, E Sperk, RP Symonds, F Wenz, CML West, C Herskind, C Bourgier
Predicting which patients will develop adverse reactions to radiotherapy is important for personalised treatment. Prediction will require an algorithm or nomogram combining clinical and biological data. The radiation-induced lymphocyte apoptosis (RILA) assay is the leading candidate as a biological predictor of radiotherapy toxicity. In this study we tested the potential of the assay for standardisation and use in multiple testing laboratories. The assay was standardised and reproducibility determined using samples from healthy volunteers assayed concurrently in three laboratories in Leicester (UK), Mannheim (Germany) and Montpellier (France). RILA assays were performed on samples taken prior to radiotherapy from 1319 cancer patients enrolled in the REQUITE project at multiple centres. The patients were being treated for breast (n = 753), prostate (n = 506) or lung (n = 60) cancer. Inter-laboratory comparisons identified several factors affecting results: storage time, incubation periods and type of foetal calf serum. Following standardisation, there was no significant difference in results between the centres. Significant differences were seen in RILA scores between cancer types (prostate > breast > lung), by smoking status (non-smokers > smokers) and co-morbidity with rheumatoid arthritis (arthritics > non-arthritics). An analysis of acute radiotherapy toxicity showed as expected that RILA assay does not predict most end-points, but unexpectedly did predict acute breast pain. This result may elucidate the mechanism by which the RILA assay predicts late radiotherapy toxicity. The work shows clinical trials involving multiple laboratory measurement of the RILA assay are feasible and the need to account for tumour type and other variables when applying to predictive models.

Funding

This work was supported by funding from the European Union Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 601826 (REQUITE). We gratefully acknowledge the contribution of the physicians, study nurses and patients enrolled into the REQUITE study. These include in the UK: Teresa Beaver, Kiran Kancherla, Sarah Nicholson and Tim Rattay; Germany: Elena Sperk, Anke Keller, Annette Kipke, Katharina Fleckenstein, Martina Ottstadt, Michael Ehmann, Gritt Welzel, Daniel Bürgy, Yasser Aboumadian, Chrisian Neumaier, Katharina Heim, Mario Grimm, Anna Simeonova, Frank Giordano, Elisabeth Heesch, Georg Lars Hildenbrand, Benjamin Gauter-Fleckenstein, Tina Reis, Sabine Mai, Christiane Zimmermann, Stefanie Kolb and Markus Mihalko, in France: Jean-Pierre Bleuse, Laura Bourillon, Roxana Draghici, Anne Fenoglietto and Julie Grataloup. The FACS machine in Leicester was supported by Medical Research Council (UK) grant G0802524. Catharine West is supported by Cancer Research UK funding for the Manchester Cancer Research Centre and the NIHR Manchester Biomedical Research Centre (both UK).

History

Citation

Clinical and Translational Radiation Oncology, 2019, 18, pp. 1-8

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciences

Version

  • VoR (Version of Record)

Published in

Clinical and Translational Radiation Oncology

Publisher

Elsevier

eissn

2405-6308

Acceptance date

2019-06-03

Copyright date

2019

Available date

2019-09-05

Publisher version

https://www.sciencedirect.com/science/article/pii/S2405630819300278?via=ihub

Notes

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ctro.2019.06.001.

Language

en

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