use of CA as a biomarker of cancer risk. The cancer risk associated with a high frequency of CA, although consistently detected in all studies thus far published (Boffetta et al. 2007; Bonassi et al. 1995, 2000, 2004; Brøgger et al. 1990; Hagmar et al. 1994, 1998, 2004; Liou et al. 1999; Rossner et al. 2005), has generally been low and variable. Therefore, it has been considered that CA can hardly be applied for individual risk assessment. If the findings of this study are confirmed, and a stronger asso- ciation between CA frequency and cancer risk can be obtained by reducing technical variabil- ity, the perspective for the use of this biomarker in cancer prevention may have to be rethought. Uniform CA analysis is a routine practice in cytogenetic surveillance studies, which are mostly interpreted at the group level, with the exception of radiation biodosimetry. For group-level evaluation, the present practice of scoring 100–200 cells per person is adequate, but it is likely not sufficient for reliable indi- vidual CA assessment. However, if the level of CA had, in reality, a stronger association with cancer risk than has thus far been assumed, rea- sonable individual cancer risk estimation might become feasible with extended CA analysis. At any rate, our findings lend further support to earlier considerations (Bonassi et al. 2005; Norppa et al. 2006) that the CA assay could be used more widely in estimating cancer risk at the group level. Another innovative feature of the present study was the potential provided by the Bayesian approach for improving the reli- ability of the estimates based on data from small groups, as often happens with biomarker validation studies. Most recent results from large CA cohort studies, such as the Czech cohort (Rossner et al. 2005) and the new cohort of Central and Eastern European coun- tries (Boffetta et al. 2007), described stronger associations with specific cancers. In smaller cohorts and in case–control studies, risk esti- mates based on classic frequentist modeling are likely to produce unreliable estimates in small strata, and any likelihood-based analysis for Table 5. Multivariate Bayesian risk estimates of cancer risk by tertiles of CA frequency and by cancer site. Case/control OR (95% CrI) Cancer site (ICD–9 code) CA tertile Oral cavity–digestive tract (140–159) Low 9/140 Medium 8/87 High 13/64 Larynx/lung (160–169) Low 1/140 Medium 4/87 High 8/64 Genitourinary organs/bladder (179–189) Low 4/140 Medium 5/87 High 10/64 1.00 1.4 (0.6–2.8) 2.8 (1.2–5.8) 1.00 2.2 (0.6–6.1) 6.2 (1.5–20.0) 1.00 1.6 (0.5–3.8) 4.0 (1.4–10.0) small data, or even worse, missing data, usually involves computationally intensive methods or ad hoc adjustments. Even without epidemio- logic biases, the presence of small counts indi- cates that large statistical biases may affect the point estimates (Greenland et al. 2000). In contrast, the Bayesian analysis allows one to efficiently manage extra information, sparse data, and missing covariates. In the present study, MCMC sampling enabled us to make inferences for any sample size without resorting to asymptotic calculations. In many cases, a frequentist inference can be obtained as a special case of the Bayesian inference, and when vague prior information is used, point and interval estimates are similar to the fre- quentist counterpart. However, the fundamen- tal difference is the interpretation of intervals of variability associated with risk estimates. This is the best possible description of risk vari- ability within the study context. If the study is correctly designed and the prior information is correctly selected, this risk range provides the most credible assessment of risk variability. Interestingly, the present results showed the strongest associations of CA with the same cancer types reported by the cohort studies mentioned above, that is, cancers of the diges- tive (Boffetta et al. 2007; Rossner et al. 2005) and respiratory tract (Bonassi et al. 1995), although the numbers of specific cancer cases in the present evaluation were small. In conclusion, this nested case–control study provided relevant new information for interpreting the role of chromosomal dam- age in carcinogenesis. In particular, it sug- gested that reducing technical variability in the cytogenetic analysis may increase the strength of the association between CA and cancer risk, with possible implications for the results provided by cohort studies published so far, which were affected by large discrepan- cies in laboratory protocols and scoring. The negative result concerning the influence of GSTM1 and GSTT1 polymorphisms, despite the intrinsic limitation due to small numbers, is in agreement with the idea that individual polymorphisms are not expected to have a dra- matic influence on baseline CA level or overall cancer risk. Our findings support the hypoth- esis that CA frequency, although indirectly measured in surrogate tissues, can predict the risk of cancer by itself as a phenotypic mani- festation of multiple carcinogenic processes or as an intermediate step of a causal process. This conclusion is of great value for the use of CA as biomarker in cancer prevention policies. Ref Rencs Barale R, Chelotti L, Davini T, Del Ry S, Andreassi MG, Ballardin M, et al. 1998a. Sister chromatid exchange and micronu- cleus frequency in human lymphocytes of 1,650 subjects in an Italian population. II. Contribution of sex, age, and lifestyle. Environ Mol Mutagen 31:228–242. Environmental Health Perspectives • volume 117 | number 2 | February 2009 CA, cancer risk, and genetic polymorphism Barale R, Marrazzini A, Bacci E, Di Sibio A, Tessa A, Cocchi L, et al. 1998b. Sister chromatid exchange and micronucleus frequency in human lymphocytes of 1,650 subjects in an Italian population. I. 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