A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay

Jochen Einbeck*, Elizabeth A. Ainsbury, Rachel Sales, Stephen Barnard, Felix Kaestle, Manuel Higueras

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Over the last decade, the γ–H2AX focus assay, which exploits the phosphorylation of the H2AX histone following DNA double–strand–breaks, has made considerable progress towards acceptance as a reliable biomarker for exposure to ionizing radiation. While the existing literature has convincingly demonstrated a dose–response effect, and also presented approaches to dose estimation based on appropriately defined calibration curves, a more widespread practical use is still hampered by a certain lack of discussion and agreement on the specific dose–response modelling and uncertainty quantification strategies, as well as by the unavailability of implementations. This manuscript intends to fill these gaps, by stating explicitly the statistical models and techniques required for calibration curve estimation and subsequent dose estimation. Accompanying this article, a web applet has been produced which implements the discussed methods.

Original languageEnglish
Article numbere0207464
JournalPLoS ONE
Volume13
Issue number11
DOIs
Publication statusPublished - Nov 2018

Bibliographical note

Funding Information:
This work was supported by Grant Number: Action IC1408, Funder: European Cooperation in Science and Technology URL https://www.cost.eu/actions/IC1408/, Recipient: Jochen Einbeck; Grant Number: BERC 360 2014-2017, Funder: Basque Government, URL http:// www.euskadi.eus/gobierno-vasco/-/ayuda_subvencion/2014/berc/, Recipient: Manuel Higureas; Grant Number: SEV-2013-0323, Funder: Spanish Ministry of Economy and Competitiveness, URL https://rio.jrc.ec.europa.eu/ en/organisations/ministry-economy-and-competitiveness-mineco, Recipient: Manuel Higueras; Grant Number: EUMUC2017, Funder: European Union, URL: https://www.erasmusplus.org.uk/, Recipient: Felix Kaestle; Grant Number: NIHR-HPRU-Chemical&RadiationThreats&Hazards, Funder: UK NIHR, URL: https://www.nihr.ac.uk/about-us/how-we-are-managed/our-structure/research/health-protection-research-units.htm, Recipient: Liz Ainsbury and Stephen Barnard; and Grant Number: U19AI067773, Funder: Pilot Grant from the Opportunity Funds Management Core of the Centers for Medical Countermeasures against Radiation, National Institute of Allergy and Infectious Diseases, URL: https://www.niaid.nih.gov/research/radiation-nuclear-countermeasures-program, Recipient: Liz Ainsbury and Stephen Barnard. The authors wish to thank Yuqi Gao, Chenyu Liu and Chen Cheng for the help with preparing the data from Section 2 for analysis.

Publisher Copyright:
© 2018 Einbeck et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Fingerprint

Dive into the research topics of 'A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay'. Together they form a unique fingerprint.

Cite this