RENEB Inter-Laboratory Comparison 2021: Inter-Assay Comparison of Eight Dosimetry Assays

Port, Matthias; Barquinero, Jordi; Endesfelder, David; Moquet, Jayne; Oestreicher, Ursula; Terzoudi, Georgia I.; Trompier, F.; Vral, Anne; Abe, Yoshihiro; Ainsbury, Liz; Alkebsi, L; Amundson, Sally A.; Badie, Christophe; Baeyens, Ans; Balajee, Adayabalam S.; Balázs, Katalin; Barnard, Stephen; Bassinet, C.; Beaton-Green, Lindsay A.; Beinke, Christina; Bobyk, Laure; Brochard, Patricia; Brzóska, Kamil; Bucher, M.; Ciesielski, B.; Cuceu, Corina; Discher, Michael; C, Deu; Domı́nguez, I.; Doucha-Senf, Sven; Dumitrescu, Andreea; Duy, Pham Ngoc; Finot, F.; Garty, Guy; Ghandhi, Shanaz A.; Grégoire, Éric; Goh, Valerie Swee Ting; Güçlü, İnci; Hadjiiska, Ljubomira; Hargitai, Rita; Hristova, Rositsa; Ishii, Kenji; Kis, Enikő; Juniewicz, Małgorzata; Kriehuber, Ralf; Lacombe, Jérôme; Lee, Y.; Riego, Milagrosa López; Lumniczky, Katalin; Mai, Tesigawara; Maltar-Strmečki, Nadica; Marrale, Maurizio; Martinez, J.; Marciniak, Agnieszka; Maznyk, Nataliya; McKeever, S.W.S.; Meher, P-K; Milanova, M.; Miura, Tomisato; Gil, Octávia Monteiro; Montoro, Alegría; Domene, Mercedes Moreno; Mrozik, Anna; Nakayama, Ryo; O’Brien, Gráinne; Oskamp, Dominik; Ostheim, Patrick; Pajić, Jelena; Pastor, Nuria; Patrono, Clarice; Pujol-Canadell, Mònica; Rodríguez, María Jesús Prieto; Repin, Mikhail; Romanyukha, Alexander; Rößler, Ute; Sabatier, Laure; Sakai, Akira; Scherthan, Harry; Schüle, Simone; Seong, Ki Moon; Sevriukova, Olga; Sholom, S.; Sommer, Sylwester; Suto, Yumiko; Sypko, Tetiana; Szatmári, Tünde; Takahashi-Sugai, M.; Takebayashi, Katsushi; Testa, Antonella; Testard, Isabelle; Tichý, Aleš; Triantopoulou, Sotiria; Tsuyama, Naohiro; Unverricht-Yeboah, Marcus; Valente, Marco; Hoey, Olivier Van; Wilkins, Russell; Wójcik, Andrzej; Wojewódzka, Maria; Younghyun, Lee; Zafiropoulos, D.; Abend, Michael

doi:10.1667/rade-22-00207.1

Cited by 13 publications

(3 citation statements)

References 85 publications

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“…In patients, blood can serve as a surrogate tissue for determining the radiation sensitivity in organs-or tissues-at-risk, such as the bone marrow. This model also has the potential to become an important part of radiation accident preparedness and management after internal irradiation [34,35]. In future, it will be important to determine whether the results of this study can be transferred to other treatment modalities in nuclear medicine and further cell and tissue types.…”

Section: Discussionmentioning

confidence: 99%

Modelling the In Vivo and Ex Vivo DNA Damage Response after Internal Irradiation of Blood from Patients with Thyroid Cancer

Schumann,

Scherthan,

Hartrampf

et al. 2024

IJMS

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This work reports on a model that describes patient-specific absorbed dose-dependent DNA damage response in peripheral blood mononuclear cells of thyroid cancer patients during radioiodine therapy and compares the results with the ex vivo DNA damage response in these patients. Blood samples of 18 patients (nine time points up to 168 h post-administration) were analyzed for radiation-induced γ-H2AX + 53BP1 DNA double-strand break foci (RIF). A linear one-compartment model described the absorbed dose-dependent time course of RIF (Parameters: c characterizes DSB damage induction; k1 and k2 are rate constants describing fast and slow repair). The rate constants were compared to ex vivo repair rates. A total of 14 patient datasets could be analyzed; c ranged from 0.012 to 0.109 mGy−1, k2 from 0 to 0.04 h−1. On average, 96% of the damage is repaired quickly with k1 (range: 0.19–3.03 h−1). Two patient subgroups were distinguished by k1-values (n = 6, k1 > 1.1 h−1; n = 8, k1 < 0.6 h−1). A weak correlation with patient age was observed. While induction of RIF was similar among ex vivo and in vivo, the respective repair rates failed to correlate. The lack of correlation between in vivo and ex vivo repair rates and the applicability of the model to other therapies will be addressed in further studies.

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Section: Discussionmentioning

confidence: 99%

Modelling the In Vivo and Ex Vivo DNA Damage Response after Internal Irradiation of Blood from Patients with Thyroid Cancer

Schumann,

Scherthan,

Hartrampf

et al. 2024

IJMS

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“…A standardized measurement protocol (Bassinet et al, 2014a) was applied in this study. This protocol called "OSL with preheat" was developed using OSL of resistors extracted from the circuit board of mobile phones in the framework of the EU-FP7 MULTIBIODOSE project and tested in several interlaboratory comparisons for retrospective dosimetry and field tests in the framework of the EU-FP7 CATO project (Discher et al, 2021), the "European Radiation Dosimetry" (EURADOS) WG 10 and "Running the European Network for Biological Dosimetry and Retrospective Physical Dosimetry" (RENEB) network (Waldner et al, 2021)), the "Korea retrospective dosimetry network" (KREDOS) group (Kim et al, 2022) and, more recently, again the RENEB network (Port et al, 2023). The OSL signals were measured at 100 • C for 30 s after a preheat up to 120 • C for 10 s. The calibration dose was approx.…”

Section: Measurement Protocols and Data Analysismentioning

confidence: 99%

Feasibility Study of Using Earbuds and Wireless Headphones for Retrospective Dosimetry

Discher

Bassinet

Kim

2023

Preprint

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“…It supports the medical countermeasures and estimation of the long-term health effects of radiation exposure (Sproull et al 2017). Apart from gold-standard cytogenetic methods used by biodosimetry laboratories, complementary approaches are emerging with gene-expression assays being one of the most promising (Kultova et al 2020;Port et al 2023;).…”

Section: Introductionmentioning

confidence: 98%

Calibration curve for radiation dose estimation using FDXR gene expression biodosimetry – premises and pitfalls

Brzóska,

Abend,

O’Brien

et al. 2024

International Journal of Radiation Biology

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Radiation-induced alterations in gene expression show great promise for dose reconstruction and for severity prediction of acute health effects. Among several genes explored as potential biomarkers, FDXR is widely used due to high upregulation in white blood cells following radiation exposure. Nonetheless, the absence of a standardized protocols for gene expression-based biodosimetry is a notable gap that warrants attention to enhance the accuracy, reproducibility and reliability of such methods. The primary objective of this study was to evaluate the sensitivity of transcriptional biodosimetry to differences in protocols used by different laboratories and establish guidelines for the calculation of calibration curve using FDXR expression data. Additionally, we aimed to investigate the feasibility of extrapolating a calibration curve derived from samples irradiated with doses exceeding 0.1 Gy to doses below this threshold. Material and MethodsTwo sets of irradiated blood samples generated during RENEB exercise were used.The first included samples irradiated with known doses including: 0, 0.25, 0.5, 1, 2, 3 and 4 Gy. The second set consisted of three 'blind' samples irradiated with 1.8 Gy, 0.4 Gy and a sham-irradiated sample. After irradiation, samples were incubated at 37 °C over 24 h. Subsequently, aliquots were filled into PAXgene tubes and sent to participating laboratories where RNA isolation and FDXR expression analysis by qPCR were performed. ResultsCalibration curves were generated using non-linear and linear regression. Dose estimates for nonirradiated sample and sample irradiated with 0.4 Gy showed remarkable consistency across all laboratories, closely approximating the true doses regardless methodological variations. For sample irradiated with 1.8 Gy the differences were more pronounced but remained within an acceptable margin for triage within the context of high dose range. ConclusionMethodological differences in reference genes and primers/probes used for FDXR expression measurement do not have a significant impact on the dose estimates generated, provided that all reference genes performed as expected and the primers/probes target a similar set of transcript variants. The preferred method for constructing a calibration curve based on FDXR expression data involves employing linear regression to establish a function that describes the relationship between the logarithm of absorbed dose and FDXR Ct values. However, one should be careful with using non-irradiated sample data as these cannot be accurately represented on a logarithmic scale. A standard curve generated using this approach can give reliable dose estimations in a dose range from 50 mGy to 4 Gy at least.

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RENEB Inter-Laboratory Comparison 2021: Inter-Assay Comparison of Eight Dosimetry Assays

Cited by 13 publications

References 85 publications

Modelling the In Vivo and Ex Vivo DNA Damage Response after Internal Irradiation of Blood from Patients with Thyroid Cancer

Modelling the In Vivo and Ex Vivo DNA Damage Response after Internal Irradiation of Blood from Patients with Thyroid Cancer

Feasibility Study of Using Earbuds and Wireless Headphones for Retrospective Dosimetry

Calibration curve for radiation dose estimation using FDXR gene expression biodosimetry – premises and pitfalls

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