In the current study, we investigated the potential impact of the confounders smoking and gender on the ability of WBC gene expression profiles to predict radiation exposure dose. Various classifiers built from the gene expression data correctly predicted samples as being exposed to 0, 0.1, 0.5 or 2 Gy with 97–99% accuracy, regardless of smoking status or gender.
We had previously identified a 74-gene signature (Paul and Amundson 2008
) that robustly distinguished radiation doses across a window of time (6 to 24 h) and dose (0, 0.5, 2, 5, and 8 Gy). In the present study, we focused on lower doses as we reasoned that if smoking constitutively altered transcription levels, confounding might be more likely among the controls and radiation exposures at the low end of the dose range. We found that classifiers built using the previously identified 74-gene signature performed similarly to those built using the gene expression data from the present study. Classifiers built from the 74-gene set could predict radiation doses of 0, 0.1, 0.5 or 2 Gy with 98% accuracy, the same performance found in the original study, despite the lower dose range used here, the use of independent donors, and the addition of smoking as a potential confounding variable. A trend has been emerging in predictive biomarker studies suggesting that application of a predictive signature to a new set of independent samples often results in a drastic loss of predictive power (Michiels et al. 2005
; Buyse et al. 2006
; Koscielny 2010
). The ability of the 74-gene set to accurately classify samples from independent donors by radiation dose, especially considering the lower dose range and addition of the smoking variable, is thus quite remarkable. We have also recently reported on the conservation of performance of the same ex vivo
derived 74-gene signature in patients undergoing total body irradiation. In that study, the ex vivo
signature predicted the in vivo
exposure dose correctly in 98% of samples (Paul et al. 2011
), supporting the usefulness of the ex vivo
model for developing clinically relevant gene expression signatures. Further work is obviously needed in this area, but the observed conservation of signature performance may be due, at least in part, to the robust and dose-dependent nature of the radiation response, as compared to the potentially more varied and subtle gene expression differences that may be involved in other complex physiological processes, such as tumor development or metastasis.
A number of genes that were radiation responsive in this or our previous peripheral blood study (Ras-related associated with diabetes (RRAD), calcium binding tyrosine-(γ)-phosphorylation regulated (CABYR), caveolin 1 (CAV1), apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3G (APOBEC3G), harakiri (HRK), like-glycosyltransferase (LARGE), methyltransferase like 7A (METTL7A), nuclear receptor subfamily 4, group A, member 3 (NR4A3), triggering receptor expressed on myeloid cells 2 (TREM2), tetratricopeptide repeat domain 21A (TTC21A), CDKN1A, DDB2 and MYC) have previously been reported to be differentially expressed in alveolar cells from smokers’ lungs compared to those from non-smokers (Chari et al. 2007
; Harvey et al. 2007
; Lodovici et al. 2007
; Landi et al. 2008
; Boelens et al. 2009
). Three of these genes, CDKN1A, DDB2 and MYC, were included in our previous ex vivo
74-gene signature (Paul and Amundson 2008
), and might have been of concern as potential confounders. However, we did not detect any significant differences in basal expression or radiation response in WBC of any of these genes by microarray, or of CDKN1A or DDB2 by qRT-PCR. Fewer studies have considered gene expression differences in peripheral WBC of smokers and non-smokers, and none of the genes from the 74-gene set have been reported to be differentially expressed in WBC. Among genes reported to be up regulated in the WBC of smokers, only superoxide dismutase 2, mitochondrial (SOD2) (van Leeuwen et al. 2007
), killer cell immunoglobulin-like receptor, two domains, short cytoplasmic tail, 4 (KIR2DS4) and thromboxane A synthase 1 (TBXAS1) (Dumeaux et al. 2010
) were also responsive to radiation in prior WBC studies. It seems likely that smoking-related altered expression of most genes may require direct exposure to cigarette smoke, such as would be experienced by alveolar cells.
Although we observed some differences in peripheral WBC baseline gene expression between smokers and non-smokers, and between males and females, very few of these genes were also significantly responsive to radiation exposure. The radiation response of only one gene (FHL2) was found to be altered by smoking. Another gene (APOBEC3H) seemed to respond to radiation differently in males and females based on microarray measurements, but the difference was not significant by pRT-PCR. Since very few of the radiation response genes were affected by these variables, such potential confounding genes could easily be excluded from biodosimetric signatures. This also suggests that confounding by smoking or gender would not be expected to negatively affect classification of radiation dose using gene expression, as indeed it did not in this study. Because our study looked at only a small number of individuals (24), however, and considered only 1–1.5 pack-a-day current smokers, it is still possible that other aspects of the smoking variable, such as previous smokers, lighter or heavier smokers than those included here, or total number of pack-years smoked could affect the gene expression response to radiation. It is also possible that smoking could affect the radiation signature in only a subset of individuals with a specific genetic make-up or other interacting lifestyle or health factor. Large-scale population studies will ultimately be necessary to determine if such effects might impact radiation dosimetry signatures.
Gender-related variation in WBC gene expression in healthy human populations has also been previously reported (Whitney et al. 2003
) and could potentially limit the accuracy of WBC-based gene expression radiation biodosimetry. In a murine radiation biodosimetry study, Meadows et al.
, (Meadows et al. 2008
) found a strong gender specificity for radiation responses, with only 2 genes in common between the male and female WBC signatures of a 50 cGy exposure. Although their different male- and female-specific signatures did perform relatively well in classifying samples from the opposite gender, they concluded that gender-related differences could be a concern for distinguishing low dose exposures from unirradiated controls. Studies of other endpoints, including DNA strand breaks, DNA methylation (Pogribny et al. 2004
), and expression of micro-RNA (Ilnytskyy et al. 2008
) have also reported very different responses to radiation in male and female mice. In contrast, in human peripheral WBC, we have found that gene expression can accurately predict exposure to 0, 0.1, 0.5 or 2 Gy doses irrespective of gender. Of 289 radiation dose responsive genes identified in the present study, only five genes (LAMP3, TP53I3, THC2695815, THC2518594 and APOBEC3H) were also differentially expressed between males and females, but with high FDR. Since the Meadows study indicated little similarity between the radiation responsive genes in male and female mice, but did show a fair ability of the very distinct signatures to classify samples from the opposite gender, we also re-analyzed our data to identify radiation dose classifiers using data only from males or only from females in order to see if our human classifiers could show a similar gender specificity. We found that classifiers derived using data from a single gender performed equally well when applied to unknown samples from the same or opposite gender ( and Supplementary Tables 5
). We also found an 88% overlap in the genes contained in the two signatures. Thus it appears that gender may have less influence on the radiation responsiveness of gene expression in humans than in mice. It is also possible that the different modes of irradiation (ex vivo
versus in vivo
) and different analyses applied in the two studies may have contributed to the differing results.
Accuracy of classifiers derived from only one gender.
Previous studies have indicated that inter-individual variation in gene expression in peripheral blood generally occurs within populations of healthy individuals (Cheung et al. 2003
; Whitney et al. 2003
; Radich et al. 2004
; Dumeaux et al. 2010
) and may therefore limit the practical use of gene expression for radiation biodosimetry. Further biodosimetry studies are still needed to understand potential variations in larger populations, and should include people with diverse clinical conditions, different lifestyle factors, ethnic backgrounds, and so on. Conditions like extreme exercise, infection or septic shock, have also been shown to alter gene expression in peripheral blood (Zieker et al. 2005
; Ramilo et al. 2007
; Wong et al. 2009
; Nakamura et al. 2010
), and should also be considered for their potential to confound dosimetric estimates based on gene expression. In addition, the potential effect of combined injuries (involving radiation exposure and physical trauma, such as lacerations, broken bones or burns) should also be accounted for in the development of gene expression biodosimetry, especially as such injuries might be expected in a large-scale radiological disaster.
Although there is clearly further work to be done, our results demonstrate that a large number of genes responded to radiation exposure in a dose-dependent manner in a group of donors comprising two potential confounding variables. Most importantly, our previously defined 74-gene signature performed dose classification of the samples in the present study equally as well as did the gene set selected here. The 74-gene signature was based on the responses of non-smokers, who were different individuals from those included in the present study. Those genes were also selected from responses to a higher range of doses, including 5 and 8 Gy exposures not used in the present study, and not including the 0.1 Gy dose, which was nonetheless effectively classified here. These findings support the continued development of a gene expression approach for radiation biodosimetry in large heterogeneous populations, and suggest that the effects of gender or smoking status are not likely to meaningfully confound dose estimates.