Measures of genotype versus gene products: promise and pitfalls in cancer prevention

Habibul Ahsan1,3 and Andrew G. Rundle1,2

1 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA
2 Herbert Irving Comprehensive Cancer Center, Columbia University, New York, USA

3 To whom correspondence should be addressed Email: habibul.ahsan{at}columbia.edu


    Abstract
 Top
 Abstract
 Introduction
 Potential limitations of...
 Issues in directing research...
 Conclusion
 References
 
We present a critical assessment of the promise and pitfalls of genotype versus gene function measures in cancer epidemiology studies. While both measures have pros and cons and are complementary, in terms of potential for contributing knowledge that directly leads to prevention, we argue that attention should be given to research relating functional parameters (single protein expression, functional or phenotypic assays or patterns of gene/protein expression) to disease risk. We present the theoretical and conceptual basis of why studies focusing on polymorphisms in the low-penetrance genes may be logically less fruitful for making inroads to cancer prevention than appropriately designed studies using validated functional parameters. We then substantiate these arguments with some concrete examples based on the current literature. We also discuss the limitations of including functional parameters in epidemiological studies and technical improvements required before such studies can truly fulfil their promise. Finally, we offer some specific recommendations for future research directions in this area.

Abbreviations: GST, glutathione S-transferase; PAH, polycyclic aromatic hydrocarbon; SNPs, single nucleotide polymorphisms


    Introduction
 Top
 Abstract
 Introduction
 Potential limitations of...
 Issues in directing research...
 Conclusion
 References
 
Exploitation of variations in DNA base sequences across individuals is an attractive option for examining genetic etiology of cancer and other diseases (1,2). Of the range of possible variations, single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome (3). However, larger-scale variations such as deletions, variable tandem repeats and gene amplifications can also be exploited for genetic studies of cancer etiology (46). The recent completion of human genome project has opened the door for the identification of a large number of SNPs and other polymorphisms. On average there is one SNP for every 1000–1200 DNA bases (3). Several million SNPs have already been identified by various government and non-government initiatives (7). Molecular genetic techniques to identify these SNPs and computing capabilities to analyze a large amount of SNP data are becoming increasingly affordable and feasible (8). The combination of the abundance of SNPs, the ease in genotyping these SNPs and researchers' growing enthusiasm for finding genetic risk factors have led to an exponential increase in genetic association studies investigating SNPs in human diseases. Such studies fall within the rubric of molecular/genetic epidemiologic research and investigate whether the presence of a polymorphism alone or in combination with an exposure or another polymorphism are associated with disease status.

While some authors have emphasized efficient strategies for prudent and rational use of polymorphisms in genetic association studies (9) others have expressed caution about the utility of many of these polymorphism studies (10). A growing concern is the increasing number of contradictory reports on genetic associations and gene–gene and gene–environment interactions (11). In this commentary, as a contribution to the ongoing dialogue, we present a critical assessment of the promise and pitfalls of genotype versus gene function measures in epidemiologic studies investigating cancer etiology. While both measures have pros and cons and are complementary, in terms of contributing knowledge that may directly lead to cancer prevention, we argue that attention should be given to research relating functional parameters to disease risk. The functional parameters could encompass measurements of single gene–protein expression, the use of functional or phenotypic assays, or assessments of patterns of gene–protein expression through the use of array-based methods.

In the first part of this commentary, we describe the theoretical and conceptual rationales why studies using polymorphisms to understand disease risks may be logically less fruitful than appropriately designed studies using validated functional parameters. We then substantiate these logical arguments with some concrete examples based on the current literature. In the second part of the paper, we highlight the limitations of including functional parameters in epidemiological studies and discuss the technical improvements required before such studies can truly fulfil their promise. Finally, we offer some specific recommendations for future research directions in this area. The key points raised in this commentary are summarized in Table I.


View this table:
[in this window]
[in a new window]
 
Table I. Some key advantages and disadvantages of genotype versus gene function measures in cancer prevention research

 

    Potential limitations of polymorphism analyses
 Top
 Abstract
 Introduction
 Potential limitations of...
 Issues in directing research...
 Conclusion
 References
 
When phenotype is defined by a single gene
Why do we believe that most polymorphism studies, at least in the way currently employed and interpreted, have potential limitations in making real inroads to prevention? We begin simply by making the point that polymorphisms in genes are biologically more distal measures than the proteins encoded by those genes for the key disease-related events. Polymorphisms are often considered to be indicators of genetic risks across individuals. While this may be true for whole gene deletions such as for GSTM1, in truth, for most cases, a single nucleotide polymorphism (SNP) merely reflects a variation in the DNA sequence of a given gene in an individual. Whether this variation is related to disease risk depends on the correspondence between the SNP and the cascade of disease-related cellular events determined by the quantity and quality of the products of the gene. So, whether or not an SNP produces disease by itself or in combination with other genetic/non-genetic factors, functional parameters related to the gene (if measured accurately) should, at least theoretically, be more strongly associated with the disease than the SNP, making detection of associations more efficient.

A related issue is that environmental factors may induce or inhibit gene expression or influence protein processing, altering the association between genotype and phenotype. Thus, the functional effect of a polymorphism may be masked by an environmental exposure. For instance, grapefruit juice can inhibit several of the cytochrome P450 enzymes (12,13), smoking induces CYP1A2 (14) and there is evidence that alcohol inhibits GSTP1 (15). Alternately, alcohol induces certain hepatic microsomal enzymes and thus may cause disease by increasing the amount of gene product (16). Studies investigating associations between the SNPs in that microsomal gene and disease risk will not be able to detect the effects unless the SNPs themselves (which is unlikely) are related to the induction/inhibition effect of alcohol. In addition to causing induction or inhibition of gene expression, exposures may cause post-translational alterations in expressed gene products such as impacting protein stability, folding and targeting (17). Such events could also reduce the correspondence between genotype and phenotype and disease risk.

Another point we make relates to the fact that polymorphisms are only one of many types of genetic alterations that can cause human diseases, especially cancer. When disease-related cellular events are determined by non-structural genetic variations, e.g. epigenetic changes, polymorphisms are not going to be useful at all. An example would be diseases, including some specific cancers, that are considered to be related to genetic imprinting, which is characterized by uniparental gene expression due to hypermethylation of the promoter region of disease-related genes (1822). Another classical example would be the role of E-cadherin gene in epithelial cancers. While many epithelial cancers are associated with the loss of E-cadherin function this is not due to the changes in the genomic sequence of the E-cadherin gene, rather this is due to epigenetic silencing of the gene resulting from DNA methylation (23). Measurement of polymorphisms in the E-cadherin gene would not be expected to be informative once the gene is silenced.

When phenotype is defined by multiple genes
The preceding arguments pertain to structural versus functional variations in a single gene. A further conceptual issue in focusing on single gene polymorphisms as central to our constructs of genetically determined cancer susceptibility is the knowledge that often many enzymes are involved in determining a phenotype. For instance, a great deal of research has been conducted on the role of glutathione S-transferases (GSTs) in determining susceptibility to chemical carcinogenesis (5,24,25). The rationale is that GSTs play a role in the detoxification of numerous reactive epoxide metabolites of xenobiotics and may serve as a marker of poor metabolism. In this case the hypothesized disease-causing susceptibility (i.e. the underlying measure that is hypothesized to drive the causal relationship) is a metabolic phenotype that is associated with higher levels of reactive metabolites that can damage DNA. However, considering the example of benzo[a]pyrene, metabolism is a series of sequential steps catalyzed by numerous enzymes, coded for by genes that are commonly polymorphic. For instance, there are polymorphisms in the CYP1A1, CYP1B1 and epoxide hydrolase genes that have been hypothesized to impact PAH metabolism (26,27). Additionally, at many of the steps that yield a metabolite there are several phase II enzymes available to detoxify the metabolite. Furthermore, the various GST subfamilies have overlapping substrate specificity, which can be conceptualized as providing redundant protective capacity (28). For example, GST M1, P1 and A1 have all been shown to metabolize diol-epoxides of PAH, although with differing efficiencies depending on the exact PAH species tested (2931). A further complicating factor is the observation that expression of the various GSTs varies by organ site (28). Thus, it is arguable whether we should expect a polymorphism in any one GST gene to contribute considerably to inter-individual variability in the amount of reactive metabolite available to react with DNA (32).

An alternative example can be seen in the DNA repair gene XRCC1. Research interest has focused on polymorphisms in XRCC1 because this gene is thought to be a determinant of DNA repair capacity, variation in which is the hypothesized disease-causing characteristic in this example (33,34). XRCC1 acts as a molecular scaffold bringing other enzymes into functional proximity to repair DNA (15,34,35). If an SNP causes a distortion in this scaffold structure, such that the other enzymes cannot configure correctly, this may be sufficient to reduce DNA repair capacity. However, even in the presence of fully functional XRCC1, polymorphisms in genes that code for the other associated enzymes may impact DNA repair capacity. Again, the relationships between enzymes underlying the hypothesized disease-causing construct are sufficiently complex that polymorphisms in one gene may not explain a great deal of inter-individual variability in disease risks.

If the polymorphism does not correlate well with the phenotype, or the hypothesized disease-causing characteristic then studies examining GxE interactions using polymorphism will be prone to errors. A common analytical approach is to determine whether an exposure has a stronger effect in the presence or absence of a SNP. However, if the SNP does not correlate well with the functional protein product or phenotype, that is the hypothesized disease-causing characteristic, the SNP will have poor measurement validity and the stratification will include measurement error. The presence of such measurement error can obscure true interactions or can cause the appearance of spurious interactions (36). This may explain the abundance of conflicting SNP studies reported in the literature.

When the expression of many genes determines a particular cellular function it will be almost impossible to accurately characterize the functional outcome by measuring the combinations of all relevant polymorphisms in the genes involved. If many different SNPs in multiple genes play a role in a given pathway to produce a specific hypothesized at risk biological response, a collective functional measure of this biological response would be much more effective in predicting the disease risks than the SNPs themselves.

Another problem that arises from multiple genes/SNPs defining a cellular function relates to the statistical efficiency issue. If SNPs are present in multiple genes that define a phenotype, the large possible combinations of genotypes will require an impractical study size to meaningfully examine their effects. Even some single genes are sufficiently polymorphic, i.e. multiple allelic variants exist for a given SNP locus that large numbers of combinations of genotypes exist.

This line of logic invokes the causal notions of necessary and sufficient causes. For multi-step pathways or complexes of enzymes we do not expect single gene polymorphisms to be necessary and sufficient to cause large amounts of inter-individual variability in many of the phenotypes hypothesized to be related to disease susceptibility. In some cases this may be true but we believe it to be the minority of cases, particularly where genetic polymorphisms are common. In fact, this point of view is the logical outcome of the frequently discussed dichotomy between single gene mutations such as BRCA1 and low-penetrance polymorphisms such as GSTM1 (37). Furthermore, as mentioned above, commonly occurring lifestyle factors such as smoking, drinking grapefruit juice or alcohol consumption can induce or inhibit genes and enzymes adding further non-polymorphism defined variability to phenotypes. Returning to the example of GSTM1, the deletion polymorphism does not account for all of the variability in protein expression, Cole and colleagues have demonstrated a 12.5-fold range of expression among non-null individuals (38). Furthermore, while GSTM1 genotype had been shown to be associated with PAH–DNA adduct levels, an interaction between alcohol consumption and GSTM1 genotype has also been observed (5,39). Together, these findings suggest that we should not expect GSTM1 genotype status alone to be particularly predictive of cancer risk.

Rather than genotyping each gene in a particular pathway or complex and then segregating study subjects into increasingly small SNP-defined strata, it may be useful to turn the attention to measurements of the phenotype of interest. An example of such an approach is the mutagen sensitivity assay, which is used as a measure of DNA repair capacity (40,41). In this assay peripheral lymphocytes are challenged in vitro with bleomycin or benzo[a]pyrene diol-epoxide and the number of resulting chromatid breaks are measured (40,42). The mutagen sensitivity assay has been shown to be related, in part, to the codon 280 polymorphism in the DNA repair-related gene XRCC1, but this polymorphism explains only a component of the variability in sensitivity (41). Another example is measurements of estrogen or its metabolite levels in blood or tissue samples, which reflect the sum total effect of many genes responsible for estrogen biosynthesis and metabolism (43). Despite substantial variability over the menstrual cycles, estrogen or its metabolite levels have been more consistently shown to be a predictor of breast cancer risk then the polymorphisms in the genes responsible for estrogen biosynthesis and metabolism (4449). Similarly, while plasma insulin-like growth factor I and II (IGF I and II) levels have been shown to be very consistently related to breast, colon, prostate and other cancer risks (5059) the polymorphisms in the IGF I and II genes have not (6062). Measures of functional parameters integrate the sum total of the influences of the various genes and the effects of potential gene/enzyme inhibiting or inducing factors. This is similar to the argument that measurements of biomarkers of biologically effective dose, such as carcinogen–DNA adducts, are a better measure of exposure, because they integrate the sum total of various exposure routes (63).


    Issues in directing research efforts towards functional phenotypic measures
 Top
 Abstract
 Introduction
 Potential limitations of...
 Issues in directing research...
 Conclusion
 References
 
Despite the disadvantages of polymorphism studies highlighted above, there are some advantages to such studies. In addition to the ease and low cost of polymorphism genotyping, these measurements are likely to have greater sensitivity and specificity than measurements of gene products (6467). We have argued above that polymorphism analyses suffer from measurement error because they may not reliably serve as markers of the disease-causing phenotype. However, polymorphism status can be determined with little laboratory error. The question of whether the analytical gain of using phenotype measurements to achieve better measurement construct validity outweighs the reduced reliability of the current laboratory analyses of phenotypes requires investigation.

This is especially the case for the array-based methods for high-throughput assessment of gene expressions and proteins. Array-based methods simultaneously examine the expression patterns of multiple biologically relevant genes/proteins and thus can detect disease related biological pathways. Because of the necessity to individually amplify each of the genomic regions containing an SNP with unique set of primers the array-based detection of SNPs is far more challenging than the array-based assessment of gene expressions (which does not involve individual amplification). However, the expression/protein arrays fundamentally comprise constellations of individual expressions of numerous genes/proteins. As a result, each of the units in an array is prone to measurement error and individually may or may not be reflective of a disease-causing phenotypic event. Thus, refocusing our research priorities on gene products, especially using array-based methods, may be associated with its own problems related to measurement error and the issue of multiple comparisons. However, we see these issues as being surmountable technological challenges rather than major conceptual roadblocks. That is, improved technology, biological understanding and statistical techniques coupled with careful hypothesis-based selection can reduce measurement errors and false positives in phenotypic and in the gene expression and proteomic analyses. While we see the inherent conceptual limitations of analyses of biologically distal polymorphism measures described above to be more difficult to surmount.

In addition to the higher sensitivity and specificity of the available laboratory techniques, polymorphism measurements represent a stable construct while, due to possible inhibition and induction, there is likely to be temporal intra-individual variability in an individual's functional parameters (68). However, for most polymorphisms, this advantage is of limited practical use given that a person's genetic status, by itself, may not be deterministic of the hypothesized disease-causing phenotype. In many cases there are enormously complex intermediate cellular processes involved between the sequence of a gene and disease manifestation. Nevertheless, for studies examining functional measures in disease etiology, especially for cancer, the uncertainty in the temporal relationship between cancer susceptibility and functional parameters measured at diagnosis or after disease initiation remains a major limitation. Clearly, the measurement of functional parameters is more appropriate in the context of nested case-control or case-cohort studies than in case-control studies in which biological samples are collected at or after diagnosis.

Another issue that concerns phenotypic assays, such as the mutagen sensitivity assays, is the determination of phenotype in an in vitro environment. It is not clear whether the in vitro environment magnifies or minimizes apparent inter-individual variability. That is, whether the magnitude of the response to the challenge is the same in the in vitro environment as it is in the in vivo environment. While such assays may maintain the rank ordering of individuals, increases or decreases in the variance may cause us to under- or over-estimate the magnitude of the effect.

A further issue is that focusing on phenotypes as disease-causing constructs moves us away from notions of heritability of disease, in that we would not identify in individuals the gene(s) underlying the construct. On one hand this may obscure potential avenues of prevention, such as genetic testing in newborns. However, our premise is that the majority of the single gene polymorphisms, which have not been linked to hereditary conditions already, are unlikely to be particularly informative and so this is not of a major concern. On the other hand given the growing and difficult ethical issues related to genetic analyses, focusing on phenotypes may relieve some of the growing tension between the need for research and the concerns/rights of study subjects and advocates. This is because even if a phenotypic measure closely predicts disease risk the measure is not related (unlike a polymorphism) to family members in a predictable fashion. Furthermore, it may often be possible to alter an individual's phenotype through lifestyle modification, such as avoiding grapefruit juice or alcohol. Nevertheless, from the methodological perspective, the predictable transmission pattern of genes within family provides a unique advantage for genotype- and haplotype-based measures for examining their associations with disease using powerful family-based studies (69,70). This advantage needs to be exploited efficiently for identifying the small subsets of disease-related genetic sequence variants from the large number of polymorphisms available to the researchers. Recently, however, large-scale well-designed population-based approaches using genomic controls have been proposed for examining genetic associations (71).

A final, rather, philosophical issue is that this proposal amounts to closing the ‘black box’, in that we advocate measuring the hypothesized disease-causing characteristic, not the component causes, i.e. the genes that underlie the phenotype. In part our position rests on the idea that analyses of the underlying genes are too complex for currently available analytic approaches and that they fail to acknowledge the role of inhibition and induction of gene and enzyme systems. Furthermore, we expect associations between phenotypes and disease will be stronger and thus more easily identifiable, allowing for faster public health applications. In a sense our proposal to close the black box and focus on phenotypes is consistent with the philosophy that epidemiology is a prevention-oriented (rather than solely mechanism-oriented) discipline (72).


    Conclusion
 Top
 Abstract
 Introduction
 Potential limitations of...
 Issues in directing research...
 Conclusion
 References
 
In conclusion, despite logical and conceptual advantages of functional measures, it is true that defining and measuring gene functions are not easy tasks. Unlike a person's genotype status, the status in relation to gene expression or protein function is very dynamic. It is difficult to define the status of an individual or an organ or even a single cell in relation to a protein or enzymatic function given the constant dynamic nature of these elements' functions and structures. So, although logically gene products are closer to the disease events their measurement may be less definitive. The point of this commentary is that efforts should be directed towards ways in which functional assays can be improved, validated and investigated in relation to disease risk, rather than investing heavily on a more easily measurable but less informative and interpretable measure, polymorphisms. The recently conceptualized field of proteomics, i.e. a global analysis of the levels and variations of a large number of proteins in a parallel manner, although at infancy currently, has the potential to make an inroad to cancer prevention once its technological limitations are overcome. Although in the long run a combination of genotype and phenotype measures would be the ideal approach to investigate disease etiology we believe that measures of functional parameters or phenotypes including measurement of gene expression and proteins need to be improved and integrated before genotype–phenotype studies can be beneficial.


    Acknowledgments
 
This research was supported by US Department of Defense Grants DAMD 17-00-1-0213 and DAMD 17-02-1-0354, AVON Breast Cancer Research Program Grant CU-1470301 and US National Cancer Institute Grants KO7CA92348 and RFACA95-003 (Breast Cancer Family Registry).


    References
 Top
 Abstract
 Introduction
 Potential limitations of...
 Issues in directing research...
 Conclusion
 References
 

  1. Lander,E.S., Linton,L.M., Birren,B. et al. (2001) Initial sequencing and analysis of the human genome. Nature, 409, 860–921.[CrossRef][ISI][Medline]
  2. Venter,J.C., Adams,M.D., Myers,E.W. et al. (2001) The sequence of the human genome. Science, 291, 1304–1351.[Abstract/Free Full Text]
  3. Sachidanandam,R., Weissman,D., Schmidt,S.C. et al. (2001) A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature, 409, 928–933.[CrossRef][ISI][Medline]
  4. Bernal,M.L., Sinues,B., Johansson,I., McLellan,R.A., Wennerholm,A., Dahl,M.L., Ingelman-Sundberg,M. and Bertilsson,L. (1999) Ten percent of North Spanish individuals carry duplicated or triplicated CYP2D6 genes associated with ultrarapid metabolism of debrisoquine. Pharmacogenetics, 9, 657–660.[ISI][Medline]
  5. Rundle,A., Tang,D., Zhou,J., Cho,S. and Perera,F. (2000) The association between glutathione S-transferase M1 genotype and polycyclic aromatic hydrocarbon-DNA adducts in breast tissue. Cancer Epidemiol. Biomarkers Prev., 9, 1079–1085.[Abstract/Free Full Text]
  6. Weitzel,J.N., Ding,S., Larson,G.P., Nelson,R.A., Goodman,A., Grendys,E.C., Ball,H.G. and Krontiris,T.G. (2000) The HRAS1 minisatellite locus and risk of ovarian cancer. Cancer Res., 60, 259–261.[Abstract/Free Full Text]
  7. Holden,A.L. (2002) The SNP consortium: summary of a private consortium effort to develop an applied map of the human genome. Biotechniques, 22–4 (suppl.), 26.
  8. Roses,A.D. (2002) Genome-based pharmacogenetics and the pharmaceutical industry. Nature Rev. Drug Discov., 1, 541–549.[CrossRef][ISI][Medline]
  9. Glatt,C.E., DeYoung,J.A., Delgado,S., Service,S.K., Giacomini,K.M., Edwards,R.H., Risch,N. and Freimer,N.B. (2001) Screening a large reference sample to identify very low frequency sequence variants: comparisons between two genes. Nature Genet., 27, 435–438.[CrossRef][ISI][Medline]
  10. Hemminki,K. and Forsti,A. (2002) Proper controls for SNP studies? Carcinogenesis, 23, 1405.[Free Full Text]
  11. Taioli,E. and Garte,S. (2002) Covariates and confounding in epidemiologic studies using metabolic gene polymorphisms. Int. J. Cancer, 100, 97–100.[CrossRef][ISI][Medline]
  12. Feldman,E.B. (1997) How grapefruit juice potentiates drug bioavailability. Nutr. Rev., 55, 398–400.[ISI][Medline]
  13. Bailey,D.G., Malcolm,J., Arnold,O. and Spence,J.D. (1998) Grapefruit juice--drug interactions. Br. J. Clin. Pharmacol., 46, 101–110.[CrossRef][ISI][Medline]
  14. Schrenk,D., Brockmeier,D., Morike,K., Bock,K.W. and Eichelbaum,M. (1998) A distribution study of CYP1A2 phenotypes among smokers and non-smokers in a cohort of healthy Caucasian volunteers. Eur. J. Clin. Pharmacol., 53, 361–367.[CrossRef][ISI][Medline]
  15. Barnes,S.L., Singletary,K.W. and Frey,R. (2000) Ethanol and acetaldehyde enhance benzo[a]pyrene-DNA adduct formation in human mammary epithelial cells. Carcinogenesis, 21, 2123–2128.[Free Full Text]
  16. Ingelman-Sundberg,M., Johansson,I., Yin,H., Terelius,Y., Eliasson,E., Clot,P. and Albano,E. (1993) Ethanol-inducible cytochrome P4502E1: genetic polymorphism, regulation, and possible role in the etiology of alcohol-induced liver disease. Alcohol, 10, 447–452.[CrossRef][ISI][Medline]
  17. Minamoto,T., Mai,M. and Ronai,Z. (1999) Environmental factors as regulators and effectors of multistep carcinogenesis. Carcinogenesis, 20, 519–527.[Abstract/Free Full Text]
  18. Feinberg,A., Cui,H. and Ohlsson,R. (2002) DNA methylation and genomic imprinting: insights from cancer into epigenetic mechanisms. Semin. Cancer Biol., 12, 389.[CrossRef][ISI][Medline]
  19. Baylin,S. and Bestor,T.H. (2002) Altered methylation patterns in cancer cell genomes: cause or consequence? Cancer Cell, 1, 299–305.[CrossRef][ISI][Medline]
  20. Reeve,A.E., Becroft,D.M., Morison,I.M. and Fukuzawa,R. (2002) Insulin-like growth factor-II imprinting in cancer. Lancet, 359, 2050–2051.[CrossRef][ISI][Medline]
  21. Kim,S.J., Park,S.E., Lee,C., Lee,S.Y., Jo,J.H., Kim,J.M. and Oh,Y.K. (2002) Alterations in promoter usage and expression levels of insulin-like growth factor-II and H19 genes in cervical carcinoma exhibiting biallelic expression of IGF-II. Biochim. Biophys. Acta, 1586, 307–315.[ISI][Medline]
  22. Plass,C. and Soloway,P.D. (2002) DNA methylation, imprinting and cancer. Eur. J. Hum. Genet., 10, 6–16.[CrossRef][ISI][Medline]
  23. Strathdee,G. (2002) Epigenetic versus genetic alterations in the inactivation of E-cadherin. Semin. Cancer Biol., 12, 373–379.[CrossRef][ISI][Medline]
  24. Engel,L.S., Taioli,E., Pfeiffer,R. et al. (2002) Pooled analysis and meta-analysis of glutathione S-transferase M1 and bladder cancer: a HuGE review. Am. J. Epidemiol., 156, 95–109.[Abstract/Free Full Text]
  25. Tang,D.L., Rundle,A., Warburton,D., Santella,R.M., Tsai,W.Y., Chiamprasert,S., Hsu,Y.Z. and Perera,F.P. (1998) Associations between both genetic and environmental biomarkers and lung cancer: evidence of a greater risk of lung cancer in women smokers. Carcinogenesis, 19, 1949–1953.[Abstract]
  26. Pastorelli,R., Guanci,M., Cerri,A. et al. (1998) Impact of inherited polymorphisms in glutathione S-transferase M1, microsomal epoxide hydrolase, cytochrome P450 enzymes on DNA, and blood protein adducts of benzo(a)pyrene-diolepoxide. Cancer Epidemiol. Biomarkers Prev., 7, 703–709.[Abstract]
  27. Thier,R., Bruning,T., Roos,P.H. and Bolt,H.M. (2002) Cytochrome P450 1B1, a new keystone in gene–environment interactions related to human head and neck cancer? Arch. Toxicol., 76, 249–256.[CrossRef][ISI][Medline]
  28. Hayes,J.D. and Strange,R.C. (2000) Glutathione S-transferase polymorphisms and their biological consequences. Pharmacology, 61, 154–166.[CrossRef][ISI][Medline]
  29. Sundberg,K., Dreij,K., Seidel,A. and Jernstrom,B. (2002) Glutathione conjugation and DNA adduct formation of dibenzo[a,l]pyrene and benzo[a]pyrene diol epoxides in V79 cells stably expressing different human glutathione transferases. Chem. Res. Toxicol., 15, 170–179.[CrossRef][ISI][Medline]
  30. Sundberg,K., Widersten,M., Seidel,A., Mannervik,B. and Jernstrom,B. (1997) Glutathione conjugation of bay- and fjord-region diol epoxides of polycyclic aromatic hydrocarbons by glutathione transferases M1-1 and P1-1. Chem. Res. Toxicol., 10, 1221–1227.[CrossRef][ISI][Medline]
  31. Jernstrom,B., Funk,M., Frank,H., Mannervik,B. and Seidel,A. (1996) Glutathione S-transferase A1-1-catalysed conjugation of bay and fjord region diol epoxides or polycyclic aromatic hydrocarbons with glutathione. Carcinogenesis, 17, 1491–1498.[Abstract]
  32. Bartsch,H. and Hietanen,E. (1996) The role of individual susceptibility in cancer burden related to environmental exposure. Environ. Health Perspect., 104 (suppl. 3), 569–577.[ISI][Medline]
  33. Duell,E.J., Millikan,R.C., Pittman,G.S., Winkel,S., Lunn,R.M., Tse,C.K., Eaton,A., Mohrenweiser,H.W., Newman,B. and Bell,D.A. (2001) Polymorphisms in the DNA repair gene XRCC1 and breast cancer. Cancer Epidemiol. Biomarkers Prev., 10, 217–222.[Abstract/Free Full Text]
  34. Vidal,A.E., Boiteux,S., Hickson,I.D. and Radicella,J.P. (2001) XRCC1 coordinates the initial and late stages of DNA abasic site repair through protein–protein interactions. EMBO J., 20, 6530–6539.[Abstract/Free Full Text]
  35. Caldecott,K.W., McKeown,C.K., Tucker,J.D., Ljungquist,S. and Thompson,L.H. (1994) An interaction between the mammalian DNA repair protein XRCC1 and DNA ligase III. Mol. Cell Biol., 14, 68–76.[Abstract]
  36. Armstrong,B.K., White,E. and Saracci,R. (1994) Principles of Exposure Measurement in Epidemiology. Oxford University Press, New York.
  37. Rebbeck,T.R. (1999) Inherited genetic predisposition in breast cancer. A population-based perspective. Cancer, 86, 2493–2501.[CrossRef][ISI][Medline]
  38. Coles,B.F., Anderson,K.E., Doerge,D.R., Churchwell,M.I., Lang,N.P. and Kadlubar,F.F. (2000) Quantitative analysis of interindividual variation of glutathione S-transferase expression in human pancreas and the ambiguity of correlating genotype with phenotype. Cancer Res., 60, 573–579.[Abstract/Free Full Text]
  39. Rundle,A., Tang,D., Mooney,L., Grumet,S. and Perera,F. (2003) The interaction between alcohol consumption and GSTM1 genotype on PAH–DNA adduct levels in breast tissue. Cancer Epidemiol. Biomarkers Prev., accepted for publication.
  40. Spitz,M.R., Fueger,J.J., Halabi,S., Schantz,S.P., Sample,D. and Hsu,T.C. (1993) Mutagen sensitivity in upper aerodigestive tract cancer: a case-control analysis. Cancer Epidemiol. Biomarkers Prev., 2, 329–333.[Abstract]
  41. Tuimala,J., Szekely,G., Gundy,S., Hirvonen,A. and Norppa,H. (2002) Genetic polymorphisms of DNA repair and xenobiotic-metabolizing enzymes: role in mutagen sensitivity. Carcinogenesis, 23, 1003–1008.[Abstract/Free Full Text]
  42. Wei,Q., Gu,J., Cheng,L., Bondy,M.L., Jiang,H., Hong,W.K. and Spitz,M.R. (1996) Benzo(a)pyrene diol epoxide-induced chromosomal aberrations and risk of lung cancer. Cancer Res., 56, 3975–3979.[Abstract]
  43. Willett,W.C. (2002) Balancing life-style and genomics research for disease prevention. Science, 296, 695–698.[Abstract/Free Full Text]
  44. Rogan,E.G., Badawi,A.F., Devanesan,P.D., Meza,J.L., Edney,J.A., West,W.W., Higginbotham,S.M. and Cavalieri,E.L. (2003) Relative imbalances in estrogen metabolism and conjugation in breast tissue of women with carcinoma: potential biomarkers of susceptibility to cancer. Carcinogenesis, 24, 697–702.[Abstract/Free Full Text]
  45. Kabuto,M., Akiba,S., Stevens,R.G., Neriishi,K. and Land,C.E. (2000) A prospective study of estradiol and breast cancer in Japanese women. Cancer Epidemiol. Biomarkers Prev., 9, 575–579.[Abstract/Free Full Text]
  46. Thomas,H.V., Reeves,G.K. and Key,T.J. (1997) Endogenous estrogen and postmenopausal breast cancer: a quantitative review. Cancer Causes Control, 8, 922–928.[CrossRef][ISI][Medline]
  47. Thompson,P.A. and Ambrosone,C. (2000) Molecular epidemiology of genetic polymorphisms in estrogen metabolizing enzymes in human breast cancer. J. Natl Cancer Inst. Monogr., 125–134.
  48. Haiman,C.A., Hankinson,S.E., Spiegelman,D., Colditz,G.A., Willett,W.C., Speizer,F.E., Kelsey,K.T. and Hunter,D.J. (1999) The relationship between a polymorphism in CYP17 with plasma hormone levels and breast cancer. Cancer Res., 59, 1015–1020.[Abstract/Free Full Text]
  49. Ahsan,H., Chen,Y., Whittmore,A.S. et al. (2003) Variants in estrogen-biosynthesis genes CYP17 and CYP19 and breast cancer risk: A family-based genetic association study. Cancer Epidemiol. Biomarkers Prev., in press.
  50. Sandhu,M.S., Dunger,D.B. and Giovannucci,E.L. (2002) Insulin, insulin-like growth factor-I (IGF-I), IGF binding proteins, their biologic interactions, and colorectal cancer. J. Natl Cancer Inst., 94, 972–980.[Abstract/Free Full Text]
  51. Furstenberger,G. and Senn,H.J. (2002) Insulin-like growth factors and cancer. Lancet Oncol., 3, 298–302.[CrossRef][ISI][Medline]
  52. Giovannucci,E. (2001) Insulin, insulin-like growth factors and colon cancer: a review of the evidence. J. Nutr., 131, 3109S–3120S.[Abstract/Free Full Text]
  53. Djavan,B., Waldert,M., Seitz,C. and Marberger,M. (2001) Insulin-like growth factors and prostate cancer. World J. Urol., 19, 225–233.[CrossRef][ISI][Medline]
  54. Yu,H. and Rohan,T. (2000) Role of the insulin-like growth factor family in cancer development and progression. J. Natl Cancer Inst., 92, 1472– 1489.[Abstract/Free Full Text]
  55. Chan,J.M., Stampfer,M.J., Giovannucci,E., Ma,J. and Pollak,M. (2000) Insulin-like growth factor I (IGF-I), IGF-binding protein-3 and prostate cancer risk: epidemiological studies. Growth Horm. IGF. Res., 10 (suppl. A), S32–S33.[ISI][Medline]
  56. Pollak,M. (2000) The question of a link between insulin-like growth factor physiology and neoplasia. Growth Horm. IGF. Res., 10 (suppl. B), S21–S24.[Medline]
  57. Pollak,M. (2000) Insulin-like growth factor physiology and cancer risk. Eur. J. Cancer, 36, 1224–1228.[CrossRef][ISI][Medline]
  58. Giovannucci,E. (1999) Insulin-like growth factor-I and binding protein-3 and risk of cancer. Horm. Res., 51 (suppl. 3), 34–41.[CrossRef][ISI][Medline]
  59. Pollak,M., Beamer,W. and Zhang,J.C. (1998) Insulin-like growth factors and prostate cancer. Cancer Metastasis Rev., 17, 383–390.[CrossRef][ISI][Medline]
  60. Figer,A., Karasik,Y.P., Baruch,R.G., Chetrit,A., Papa,M.Z., Sade,R.B., Riezel,S. and Friedman,E. (2002) Insulin-like growth factor I polymorphism and breast cancer risk in Jewish women. Isr. Med. Assoc. J., 4, 759–762.[ISI][Medline]
  61. Missmer,S.A., Haiman,C.A., Hunter,D.J., Willett,W.C., Colditz,G.A., Speizer,F.E., Pollak,M.N. and Hankinson,S.E. (2002) A sequence repeat in the insulin-like growth factor-1 gene and risk of breast cancer. Int. J. Cancer, 100, 332–336.[CrossRef][ISI][Medline]
  62. Yu,H., Li,B.D., Smith,M., Shi,R., Berkel,H.J. and Kato,I. (2001) Polymorphic CA repeats in the IGF-I gene and breast cancer. Breast Cancer Res. Treat., 70, 117–122.[CrossRef][ISI][Medline]
  63. Perera,F.P. and Weinstein,I.B. (1982) Molecular epidemiology and carcinogen–DNA adduct detection: new approaches to studies of human cancer causation. J. Chronic Dis., 35, 581–600.[ISI][Medline]
  64. Lee,J.J., Trizna,Z., Hsu,T.C., Spitz,M.R. and Hong,W.K. (1996) A statistical analysis of the reliability and classification error in application of the mutagen sensitivity assay. Cancer Epidemiol. Biomarkers Prev., 5, 191–197.[Abstract]
  65. Prince,J.A., Feuk,L., Howell,W.M., Jobs,M., Emahazion,T., Blennow,K. and Brookes,A.J. (2001) Robust and accurate single nucleotide polymorphism genotyping by dynamic allele-specific hybridization (DASH): design criteria and assay validation. Genome Res., 11, 152–162.[Abstract/Free Full Text]
  66. Orlow,I., Roy,P., Barz,A., Canchola,R., Song,Y. and Berwick,M. (2001) Validation of denaturing high performance liquid chromatography as a rapid detection method for the identification of human INK4A gene mutations. J. Mol. Diagn., 3, 158–163.[Abstract/Free Full Text]
  67. Rinaldi,S., Dechaud,H., Biessy,C. et al. (2001) Reliability and validity of commercially available, direct radioimmunoassays for measurement of blood androgens and estrogens in postmenopausal women. Cancer Epidemiol. Biomarkers Prev., 10, 757–765.[Abstract/Free Full Text]
  68. Hardy,B.G., Lemieux,C., Walker,S.E. and Bartle,W.R. (1988) Interindividual and intraindividual variability in acetylation: characterization with caffeine. Clin. Pharmacol. Ther., 44, 152–157.[ISI][Medline]
  69. Zhang,K., Calabrese,P., Nordborg,M. and Sun,F. (2002) Haplotype block structure and its applications to association studies: power and study designs. Am. J. Hum. Genet., 71, 1386–1394.[CrossRef][ISI][Medline]
  70. Thomas,D.C. and Witte,J.S. (2002) Point: population stratification: a problem for case-control studies of candidate-gene associations? Cancer Epidemiol. Biomarkers Prev., 11, 505–512.[Free Full Text]
  71. Cardon,L.R. and Palmer,L.J. (2003) Population stratification and spurious allelic association. Lancet, 361, 598–604.[CrossRef][ISI][Medline]
  72. Ng,S.K. (1991) Does epidemiology need a new philosophy? A case study of logical inquiry in the acquired immunodeficiency syndrome epidemic. Am. J. Epidemiol., 133, 1073–1077.[Abstract]
Received March 13, 2003; revised May 14, 2003; accepted June 14, 2003.