Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment

Abstract

Purpose

Benchmark dose (BMD) modeling is used to determine the dose of a stressor at which a predefined increase in any biological effect above background occurs (e.g. 10% increase from control values). BMD analytical tools have the capacity to model transcriptional dose-response data to derive BMDs for genes, pathways and gene ontologies. We recently demonstrated the value of this approach to support various areas of radiation research using predominately ‘in-house’ generated datasets.

Materials and methods

As a continuation of this work, transcriptomic studies of relevance to ionizing radiation were retrieved through the Gene Expression Omnibus (GEO). The datasets were compiled and filtered, then analyzed using BMDExpress. The objective was to determine the reproducibility of BMD values in relation to pathways and genes across different exposure scenarios and compare to those derived using cytogenetic endpoints. A number of graphic visualization approaches were used to determine if BMD outputs could be correlated to parameters such as dose-rate, radiation quality and cell type.

Results

Curated studies were diverse and derived from experiments with varied design and intent. Despite this, common genes and pathways were identified with low and high dose thresholds. The higher BMD values were associated with immune response and cell death, while transcripts with lower BMD values were generally related to the classic DNA damage response/repair processes, centered on TP53 signaling. Analysis of datasets with relatively similar dose-ranges under comparable experimental conditions showed a bi-modal distribution with a high degree of consistency in BMD values across shared genes and pathways, particularly for those below the 25th percentile of total distribution by dose. The median BMD values were noted to be approximately 0.5 Gy for genes/pathways that comprised mode 1. Furthermore, transcriptional BMD values derived from a subset of genes using in vivo and in vitro datasets were in accord to those using cytogenetic endpoints.

Conclusion

Overall, the results from this work highlight the value of the BMD methodology to derive meaningful outputs that are consistent across different models, provided the studies are conducted using a similar dose-range.

Keywords:

• Point of departure
• radiation
• risk assessment
• benchmark dose

Acknowledgments

The authors would like to acknowledge Lindsay Beaton and Ngoc Vuong for insightful comments and edits to the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Vinita Chauhan

Vinita Chauhan, Ruth Wilkins and Carole Yauk are research scientist at Health Canada.

Nadine Adam

Nadine Adam is a research assistant at Health Canada.

Andrew Williams

Byron Ko and Andrew Williams are Bioinformatic/Statisticians at Health Canada

Robert Stainforth

Robert Stainforth, is a post-doctoral fellow at Health Canada.