Challenges in the quantification approach to a radiation relevant adverse outcome pathway for lung cancer

Abstract

Purpose

Adverse outcome pathways (AOPs) provide a modular framework for describing sequences of biological key events (KEs) and key event relationships (KERs) across levels of biological organization. Empirical evidence across KERs can support construction of quantified AOPs (qAOPs). Using an example AOP of energy deposition from ionizing radiation onto DNA leading to lung cancer incidence, we investigate the feasibility of quantifying data from KERs supported by all types of stressors. The merits and challenges of this process in the context of AOP construction are discussed.

Materials and methods

Empirical evidence across studies of dose-response from four KERs of the AOP were compiled independently for quantification. Three upstream KERs comprised of evidence from various radiation types in line with AOP guidelines. For these three KERs, a focused analysis of data from alpha-particle studies was undertaken to better characterize the process to the adverse outcome (AO) for a radon gas stressor. Numerical information was extracted from tables and graphs to plot and tabulate the response of KEs. To complement areas of the AOP quantification process, Monte Carlo (MC) simulations in TOPAS-nBio were performed to model exposure conditions relevant to the AO for an example bronchial compartment of the lung with secretory cell nuclei targets.

Results

Quantification of AOP KERs highlighted the relevance of radiation types under the stressor-agnostic intent of AOP design, motivating a focus on specific types. For a given type, significant differences of KE response indicate meaningful data to derive linkages from the MIE to the AO is lacking and that better response-response focused studies are required. The MC study estimates the linear energy transfer (LET) of alpha-particles emitted by radon-222 and its progeny in the secretory cell nuclei of the example lung compartment to range from ${94}_{-5}^{+5}$ to ${192}_{-18}^{+15}$ keV/µm.

Conclusion

Quantifying AOP components provides a means to assemble empirical evidence across different studies. This highlights challenges in the context of studies examining similar endpoints using different radiation types. Data linking KERs to a MIE of ‘deposition of energy’ is shown to be non-compatible with the stressor-agnostic principles of AOP design. Limiting data to that describing response-response relationships between adjacent KERs may better delineate studies relevant to the damage that drives a pathway to the next KE and still support an ‘all hazards’ approach. Such data remains limited and future investigations in the radiation field may consider this approach when designing experiments and reporting their results and outcomes.

Keywords:

• Quantified adverse outcome pathway
• key events
• key event relationships
• DNA strand break
• Monte Carlo

Acknowledgment

The authors acknowledge Sami Qutob for critical review and final editing of the AOP as well as Trevor Stocki for additional feedback.

Disclosure statement

The authors are aware of no potential conflict of interests as a result of the work discussed here.

Additional information

Funding

This work has been in part supported by the NIH/NCI [grant no. R01CA187003] (TOPAS-nBio: a Monte Carlo tool for radiation biology research).

Notes on contributors

Robert Stainforth

Robert Stainforth, PhD, is a physical scientist at Health Canada.

Jan Schuemann

Jan Schuemann, PhD, is an Associate Professor and Head of the Multi-Scale Monte Carlo Modeling Lab in the department of Radiation Oncology at the Massachusetts General Hospital and the Harvard Medical School.

Aimee L. McNamara

Aimee L. McNamara, PhD, is an Instructor in the department of Radiation Oncology at the Massachusetts General Hospital and the Harvard Medical School.

Ruth C. Wilkins

Ruth C. Wilkins, PhD, is a research scientist at Health Canada.

Vinita Chauhan

Vinita Chauhan, PhD, is a research scientist at Health Canada.