Chromosome aberration dynamics in breast cancer patients treated with radiotherapy: Implications for radiation biodosimetry

CXCL10 upregulation in radiation-exposed human peripheral blood mononuclear cells as a candidate biomarker for rapid triage after radiation exposure

PURPOSE

In case of a nuclear accident, individuals with high-dose radiation exposure (>1-2 Gy) should be rapidly identified. While ferredoxin reductase (FDXR) was recently suggested as a radiation-responsive gene, the use of a single gene biomarker limits radiation dose assessment. To overcome this limitation, we sought to identify reliable radiation-responsive gene biomarkers.

MATERIALS AND METHODS

Peripheral blood mononuclear cells (PBMCs) were isolated from mice after total body irradiation, and gene expression was analyzed using a microarray approach to identify radiation-responsive genes.

RESULTS

In light of the essential role of the immune response following radiation exposure, we selected several immune-related candidate genes upregulated by radiation exposure in both mouse and human PBMCs. In particular, the expression of ACOD1 and CXCL10 increased in a radiation dose-dependent manner, while remaining unchanged following lipopolysaccharide (LPS) stimulation in human PBMCs. The expression of both genes was further evaluated in the blood of cancer patients before and after radiotherapy. CXCL10 expression exhibited a distinct increase after radiotherapy and was positively correlated with FDXR expression.

CONCLUSIONS

CXCL10 expression in irradiated PBMCs represents a potential biomarker for radiation exposure.

Preleukemic Fusion Genes Induced via Ionizing Radiation

Although the prevalence of leukemia is increasing, the agents responsible for this increase are not definitely known. While ionizing radiation (IR) was classified as a group one carcinogen by the IARC, the IR-induced cancers, including leukemia, are indistinguishable from those that are caused by other factors, so the risk estimation relies on epidemiological data. Several epidemiological studies on atomic bomb survivors and persons undergoing IR exposure during medical investigations or radiotherapy showed an association between radiation and leukemia. IR is also known to induce chromosomal translocations. Specific chromosomal translocations resulting in preleukemic fusion genes (PFGs) are generally accepted to be the first hit in the onset of many leukemias. Several studies indicated that incidence of PFGs in healthy newborns is up to 100-times higher than childhood leukemia with the same chromosomal aberrations. Because of this fact, it has been suggested that PFGs are not able to induce leukemia alone, but secondary mutations are necessary. PFGs also have to occur in specific cell populations of hematopoetic stem cells with higher leukemogenic potential. In this review, we describe the connection between IR, PFGs, and cancer, focusing on recurrent PFGs where an association with IR has been established.

Machine learning approach for quantitative biodosimetry of partial-body or total-body radiation exposures by combining radiation-responsive biomarkers

During a large-scale radiological event such as an improvised nuclear device detonation, many survivors will be shielded from radiation by environmental objects, and experience only partial-body irradiation (PBI), which has different consequences, compared with total-body irradiation (TBI). In this study, we tested the hypothesis that applying machine learning to a combination of radiation-responsive biomarkers (ACTN1, DDB2, FDXR) and B and T cell counts will quantify and distinguish between PBI and TBI exposures. Adult C57BL/6 mice of both sexes were exposed to 0, 2.0-2.5 or 5.0 Gy of half-body PBI or TBI. The random forest (RF) algorithm trained on ½ of the data reconstructed the radiation dose on the remaining testing portion of the data with mean absolute error of 0.749 Gy and reconstructed the product of dose and exposure status (defined as 1.0 × Dose for TBI and 0.5 × Dose for PBI) with MAE of 0.472 Gy. Among irradiated samples, PBI could be distinguished from TBI: ROC curve AUC = 0.944 (95% CI: 0.844-1.0). Mouse sex did not significantly affect dose reconstruction. These results support the hypothesis that combinations of protein biomarkers and blood cell counts can complement existing methods for biodosimetry of PBI and TBI exposures.