Rossi et al. The univariate analysis comparing the overall mean CA frequency (SE) in cases and controls [1.98 (0.14) vs. 1.38 (0.14)] revealed a highly significant difference (t-test, p = 0.006). At the national level, cancer cases had a sig- nificantly higher mean CA frequency in the Danish and Italian databases (data not shown). The results of the multivariate Bayesian model linking CA tertile and cancer risk are reported in Table 4. No effect of sex or age were observed; smoking habit increased the overall risk of cancer but did not confound or modify the effect of CA level on cancer risk. The overall analysis showed a borderline risk increase for subjects in the medium tertile of CA distri- bution (OR = 1.5; 95% CrI, 0.88–2.50) and an increase for those in the high tertile (OR = 2.8; 95% CrI, 1.6–4.6) when compared with the lowest tertile. Increased cancer risks for the medium and high tertiles were found in all national data sets, although only in the high- est tertile of the Italian subset did the CrI not include 1 (OR = 9.4; 95% CrI, 2.6–28.0). The possible role of GSTM1 and GSTT1 genetic polymorphisms as effect modifiers of the association between CA frequency and cancer risk was tested in the Bayesian mod- els (model M3 vs. model M2). The analysis, based on the BIC index, favored the model without interaction terms (M2), ruling against the hypothesis that the cancer risk predictivity of CA frequency could be modified by these polymorphisms, i.e., BIC(M3) = 481.9 > BIC(M2) = 473.1 for GSTM1 and BIC(M3) = 365.5 > BIC(M2) = 357.7 for GSTT1. A similar multivariate model was used to estimate if the GSTM1 and GSTT1 genotypes were a risk factor for all cancers (model M2 vs. model M1). Again, the BIC index did not show any increase in the genotypes, as anticipated by the results of the univariate analysis, i.e., BIC(M2) = 473.1 > BIC(M1) = 467.3 for GSTM1 and BIC(M2) = 357.7 > BIC(M1) = 352.6 for GSTT1. Finally, we fitted multivariate Bayesian models to major cancer sites to test the hypoth- esis that CA frequency could more specifi- cally predict the risk by cancer type (Table 5). Credible associations (see statistical methods for a definition of credibility intervals) were found with the high tertile of the CA distribu- tion for cancers of the genitourinary tract (OR = 4.0; 95% CrI, 1.4–10.0); respiratory tract (OR = 6.2; 95% CrI, 1.5–20.0), and digestive tract (OR = 2.8; 95% CrI, 1.2–5.8). Discussion The results of this case–control study provide new information for the validation of CA as a biomarker of cancer risk. Although the study groups were rather small, our findings sug- gest that the association between CA and can- cer is not modified by GSTM1 and GSTT1 polymorphisms, that the association is higher when the evaluation is based on uniform cyto- genetic data rather than pooled historical data, and that the use of Bayesian modeling is a credible approach for risk estimation in case of sparse data, a common condition in biomarker validation studies. Our study offered a rare opportunity to assess the impact of genetic polymorphisms on the relationship between chromosomal dam- age and cancer risk with a longitudinal design. We were able to evaluate only two genotypes, that is GSTM1 and GSTT1, because of the limited amount of DNA available for each subject. Previous data have suggested that the genotypes of both GSTM1 and GSTT1 are associated with a slightly increased risk of some forms of cancer, although this does not appear to concern all cancers (Bolufer et al. 2006; Bolt and Thier 2006; Carlsten et al. 2008; Hiyama et al. 2008; Parl 2005; Shi et al. 2008; Vineis et al. 2007; White et al. 2008). There is also evidence in favor of GSTM1 and GSTT1 genotypes affecting the level of CA in lymphocytes especially in smokers, although most studies have suggested no clear effects of either polymorphism on the baseline level of CA (Norppa 2004; Tuimala et al. 2004; Vodicka et al. 2004). Furthermore, the pos- sible presence of heterogeneity in the genetic background of national populations, as well as the activation of carcinogens occasionally caused by polymorphic GSTs (Hu et al. 2006; Kligerman and Hu 2007), may have weak- ened the associations studied. Thus, our nega- tive findings on the modifying effect of the two GST polymorphisms on the association between CA and cancer risk seem to agree with the existing information about their negligible influence on total cancer risk and baseline CA level. Our conclusions are, however, limited by the small size of the study groups, which did not allow assessing possible interactions between genotype and smoking. Moreover, the genotype data were obtained using dif- ferent sources of DNA, and thereby different methods, for DNA isolation and genotyping in the participating laboratories. In general, the present findings confirmed, largely with new data, the results published by the ESCH in 2000 (Bonassi et al. 2000)— that cancer prediction based on CA frequency is independent of smoking and exposure to carcinogens. The polymorphisms of other genes, such as those involved in DNA repair, might have been relevant as well (Tuimala et al. 2004; Vodicka et al. 2004). However, polymor- phisms in the DNA repair genes OGG1 (8-oxoguanine DNA glycosylase; U96710, GeneBank), XRCC1 (X-ray repair comple- menting defective repair in Chinese ham- ster cells 1; M36089, GeneBank), XRCC3 (X-ray repair complementing defective repair in Chinese hamster cells 3; AF035586, GeneBank), ERCC2 (exicision repair cross- complementing rodent repair deficiency, complementation group 2; HGNC:3434, GeneBank), and the folate metabolism gene MTHFR (5,10-methylenetetrahydrofolate reductase; BC053509, GeneBank) were eval- uated in the Norwegian data set (Skjelbred et al. 2006) and explained only 4–10% of the variance in CA, suggesting that these polymor- phisms would not have strong effects on the association between CA frequency and cancer risk. Although many genetic polymorphisms have been observed to affect the level of chro- mosome damage (Norppa 2004; Skjelbred et al. 2006; Tuimala et al. 2004; Vodicka et al. 2004), single or a few genes may be expected to have only a small effect on CA frequency. Most studies of CA and cancer risk have been multicentric, and the cytogenetic data generated during many years in a number of laboratories are subject to methodologic vari- ability due to multiple scorers and differences in cell culture, slide preparation, staining, and analysis. A unique feature of the current Italian data was that the cytogenetic slides of the can- cer cases and selected controls, originating from surveillance studies performed in Pisa in the early 1990s (Barale et al. 1998a, 1998b; Landi et al. 1999; Milillo et al. 1996), were retrieved from the freezer, stained, and consecutively scored for CA by a single microscopist spe- cifically for the present study. Most probably, such procedure reduced technical variability, and this is the most likely explanation for the very high risk estimates in Pisa, previously reported only by another case–control study from Taiwan (Liou et al. 1999). This observa- tion raises an interesting point concerning the CA level Low Medium High Total 206 aEstimates based on 20,000 (MCMC) updates. Table 4. Multivariate Bayesian estimates of cancer risk by tertiles of CA frequency by country.a Case/control 4/40 8/19 17/12 29/71 Italy OR (95% CrI) 1.00 2.9 (0.8–8.3) 9.4 (2.6–28.0) Norway Denmark Total Case/control 22/73 22/58 22/40 66/171 OR (95% CrI) 1.00 1.3 (0.7–2.3) 1.9 (1.0– 3.4) Case/control 6/27 1/10 5/12 12 /49 OR (95% CrI) 1.00 1.0 (0.2–2.7) 2.0 (0.6–5.1) Case/Control 32/140 31/87 44/64 107/291 OR (95% CrI) 1.00 1.5 (0.9–2.5) 2.8 (1.6–4.6) ovlume 117 | number 2 | February 2009 • Environmental Health Perspectives