Improved radiation expression profiling in blood by sequential application of sensitive and specific gene signatures

Abstract

Purpose

Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning (ML) based signatures (with 8–20% misclassification rates). These signatures can quantify therapeutically relevant as well as accidental radiation exposures. The prodromal symptoms of acute radiation syndrome (ARS) overlap those present in influenza and dengue fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy.

Methods

This study investigated recall by previous and novel radiation signatures independently derived from multiple Gene Expression Omnibus datasets on common and rare non-neoplastic blood disorders and blood-borne infections (thromboembolism, S. aureus bacteremia, malaria, sickle cell disease, polycythemia vera, and aplastic anemia). Normalized expression levels of signature genes are used as input to ML-based classifiers to predict radiation exposure in other hematological conditions.

Results

Except for aplastic anemia, these blood-borne disorders modify the normal baseline expression values of genes present in radiation signatures, leading to false-positive misclassification of radiation exposures in 8–54% of individuals. Shared changes, predominantly in DNA damage response and apoptosis-related gene transcripts in radiation and confounding hematological conditions, compromise the utility of these signatures for radiation assessment. These confounding conditions (sickle cell disease, thrombosis, S. aureus bacteremia, malaria) induce neutrophil extracellular traps, initiated by chromatin decondensation, DNA damage response and fragmentation followed by programmed cell death or extrusion of DNA fragments. Riboviral infections (e.g. influenza or dengue fever) have been proposed to bind and deplete host RNA binding proteins, inducing R-loops in chromatin. R-loops that collide with incoming replication forks can result in incompletely repaired DNA damage, inducing apoptosis and releasing mature virus. To mitigate the effects of confounders, we evaluated predicted radiation-positive samples with novel gene expression signatures derived from radiation-responsive transcripts encoding secreted blood plasma proteins whose expression levels are unperturbed by these conditions.

Conclusions

This approach identifies and eliminates misclassified samples with underlying hematological or infectious conditions, leaving only samples with true radiation exposures. Diagnostic accuracy is significantly improved by selecting genes that maximize both sensitivity and specificity in the appropriate tissue using combinations of the best signatures for each of these classes of signatures.

Keywords:

• Biodosimetry
• gene expression
• false positive reactions
• DNA damage response (DDR)
• radiation; hematological disease

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

Ben C. Shirley is an employee and Peter K. Rogan is a cofounder of CytoGnomix Inc. This work is patent pending.

Data availability statement

A Zenodo data repository has been created for this study (DOI: https://doi.org/10.5281/zenodo.5009007). This archive provides additional violin plots which illustrate the expression of genes in models M1–M4, KM3–KM7 and SM1–SM5 for patients with a bloodborne condition or RNA viral infection. This archive also provides each ML model utilized in this manuscript (M1-M20, KM1-KM7, SM1-SM5) as MATLAB formatted data (MAT files) with usage documentation.

Additional information

Funding

This work was supported by the University of Western Ontario and CytoGnomix Inc. The authors thank Drs. Ruth Wilkins and Joan Knoll for their constructive comments.

Notes on contributors

Eliseos J. Mucaki

Eliseos J. Mucaki, M.Sc., is a Technologist in the Department of Biochemistry, University of Western Ontario, Canada.

Ben C. Shirley

Ben C. Shirley, M.Sc., is the Chief Software Architect, CytoGnomix Inc. Canada.

Peter K. Rogan

Peter K. Rogan, Ph.D., is a Professor of Biochemistry and Oncology, Schulich School of Medicine and Dentistry, University of Western Ontario, Canada, and President, CytoGnomix Inc.