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Cancer Epidemiology and Prevention$

David Schottenfeld and Joseph F. Fraumeni

Print publication date: 2006

Print ISBN-13: 9780195149616

Published to Oxford Scholarship Online: September 2009

DOI: 10.1093/acprof:oso/9780195149616.001.0001

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Genetic Modifiers of Cancer Risk

Genetic Modifiers of Cancer Risk

Chapter:
(p.577) 29 Genetic Modifiers of Cancer Risk
Source:
Cancer Epidemiology and Prevention
Author(s):

NEIL E. CAPORASO

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195149616.003.0029

Abstract and Keywords

This chapter discusses genetic modifiers of cancer risk. Topics covered include rationale for the study of low-penetrance genes, the role of low-penetrance genes in cancer susceptibility, methodological issues, gene selection in population studies, overview of candidate genes, overview of cancer-specific associations, and gene-environment interaction.

Keywords:   cancer susceptibility, low-penetrance genes, gene selection, candidate genes, gene-environment interaction

Despite decades of accumulated evidence suggesting important contributions of inherited susceptibility to most types of cancer, the precise genetic determinants and how they act in concert with environmental exposures and host factors remain to be described. A notable area of success is the identification of major genes that follow Mendelian patterns of inheritance for certain hereditary cancer syndromes as well as somatically altered genes universally present in tumors (Futreal, 2004). However, all the identified genes together only account for a few percent of all cancer (Peto and Houlston, 2001).

Understanding the contribution of genetic susceptibility to common disease and cancer outcomes was a key motivation for sequencing of the human genome (Green, 1991; Khoury, 1999) and emerging genome science (Gibson and Muse, 2002); however, establishing and verifying the precise relationships of genes, their variants, and disease phenotypes has proved to be challenging (Lander and Schork, 1994; Risch and Merikangas, 1996; Houlston and Tomlins, 2000). There is only weak to mixed evidence for verifiable associations of specific susceptibility genes with common human diseases, and neither of two recent reviews identified even one association considered “proven” for a common human cancer (Hirschhorn, 2002, Lohmueller, 2003).

Nevertheless, published metanalyses support at least a few specific associations, such as increased risk of bladder cancer in NAT2 “deficient” subjects (slow acetylators). Many other genes or pathways are mechanistically plausible modifiers of one or more cancers but to date have been insufficiently studied in human populations to establish causal associations. Understanding how gene–environment and gene–gene effects contribute to susceptibility also awaits larger studies. It is anticipated that a broader understanding of the contribution of genetic modifiers to human cancer will improve mechanistic understanding, shed light on new modes of chemoprevention (Lippman and Spitz, 2001) and therapy, identify subgroups at altered risk, and help identify heretofore unsuspected environmental agents that contribute to risk.

CONTEXT

The more frequent but less characterized genes that modify “sporadic” cancer, rather than the rare high-penetrance alleles of major genes that underlie familial cancer, is the topic of this chapter. Some of the distinctions between the two classes of inherited genes are depicted in Table 29–1 (Caporaso, 1995; Vineis, 2002). The high-penetrance genes that contribute to hereditary cancer syndromes involve only a few percent of all cancer. Potter (2001) observed that certain high-risk genes and their associated cancers (e.g., Rb and retinoblastoma) are minimally influenced by the environment, whereas others exhibit more variable penetrance (e.g., BRCA1 and breast cancer) as a result of other genes or environmental influences. In general, the role of the environment for the high-penetrance genes is less prominent, whereas for the common adult-onset tumors, putative susceptibility genes act in concert with exposures and other modifier genes to alter risk. The relative and absolute risks conferred by these genes are small to modest, and familial aggregation is not a conspicuous feature. The lower absolute risks (and lack of major implications for family members) make informed consent requirements somewhat less stringent for the study of susceptibility genes in the population (Beskow, 2001): however complex; psychological and social issuer are emerging with expanded genetic testing (Lerman and Shields, 2004). To detect these modestly augmented relative and absolute risks, substantial study sizes involving many hundreds to thousands of study subjects are required. The allelic variants are common; although rare genes might also have weak effects, they would be extremely difficult to detect reliably because of power considerations.

To date, these polymorphic variants (which affect more than 1% of the population) have been associated with small changes in absolute and relative risk, so their effects have been too small to justify population screening even for the more common tumors. However, as the pharmacogenetic literature documents (Evans, 2003; Weinshilboum, 2003), common gene variants are responsible for substantial differences in drug metabolism or in receptor function, so that the eventual identification of strong effects on “phenotypes” with clinical impact cannot be ruled out.

However, even modest effects cannot be dismissed. For example, the NAT2 deficient genotype showed an odds ratio of 1.4 in relation to bladder cancer (i.e., a 40% increased risk for the 50% of the population that are “slow acetylators”) (Marcus, 2000). Although this risk would not be considered clinically important, the slow acetylation mechanism might account for >15% of all bladder cancers.

RATIONALE FOR THE STUDY OF LOW-PENETRANCE GENES

The study of modifier or susceptibility genes can provide major insights not only into genetic susceptibility but also into interactions with other causal factors in the carcinogenic process. This general aim subsumes more specific goals including (1) implicating specific environmental (sometimes unsuspected) or genetic factors amenable to interventions, (2) understanding determinants of risk in individuals and the population, (3) suggesting specific individual, clinical, or population-based strategies for prevention, screening, or treatment, (4) refining clinical and etiologic subclassifications of disease, (5) identifying critical intermediate phenotypes (endophenotypes) that contribute to disease and may be more closely related to particular genes, and (6) identifying subgroups for special interventions. As the genetic architecture of cancer is increasingly revealed using new molecular technologies in the context of epidemiologic study designs, it will emerge that understanding cancer etiology will be incomplete without taking genetics into account. Broadly, population studies will be needed for gene discovery, population risk characterization, and evaluation of genetic information for diagnosis and prevention (Khoury, 2003).

THE ROLE OF LOW-PENETRANCE GENES IN CANCER SUSCEPTIBILITY

Although the precise contribution of genetic variation to cancer and the best models to measure it are matters of vigorous debate (Lichtenstein et al., 2000; Risch, 2001), a hereditary contribution is widely posited based on evidence that includes increased risk of specific cancers in relatives of cases compared to relatives of controls, twin studies, hereditary cancer syndromes, and animal models. Nonspecific types of evidence suggested to implicate genetic susceptibility include (p.578)

Table 29–1. Features of Genetic Modifiers Contrasted with HighPenetrance Genes

Cancer Modifier

Cancer Gene

Terms

Minor gene

Major gene

Low-penetrance gene

High-penetrance gene

Study design

Population-based

Family-based

(association)

(linkage)

Goal of study

Gene characterization

Gene discovery

Study population

General population

Families

Absolute risk

Low

High

Relative risk

Low

High

Role of environment

Major

Minor

Gene frequency

Generally high

Low to rare

Number of genes

Many

One (or few)

Clinical role

Generally minor

Increasingly well established

Attributable risk

Potentially high

Low

Familiality

Not prominent

Apparent

Type of gene alteration

Polymorphism, typically present in >1%

Mutation

Potential testing application

Susceptibility

Diagnostic

Ethical concerns

Less stringent

Highly stringent

early age of onset and multifocal development of cancer in a single organ. Although these clinical clues are often characteristic of family cancer syndromes (Lindor and Greene, 1998), it is not established whether these features are also associated with low-penetrance candidate genes.

In contrast to highly familial conditions due to a gene or a small number of genes segregating at particular genetic loci, “complex” diseases such as sporadic forms of cancer result from the combined effects of multiple genetic and environmental factors. Virtually all cancers exhibit some degree of aggregation among related individuals (Risch, 2001). Even for the cancers where a well-recognized gene accounts for some familial aggregations, additional genes are likely to be present (Peto and Houlston, 2001). Dite et al. (2003) concluded that BRCA1 and BRCA2 account for less than one-third of the excess breast cancers diagnosed before age 40 in relatives of cases attributable to familial factors. Using a population-based series of breast cancer, Pharoah et al. (2002) concluded that a log-normal distribution of genetic risk exists in which the half of the population at highest risk based on susceptibility genes accounts for 88% of the risk. These and similar results suggest that additional modifier loci remain to be discovered in the setting of highly familial as well as sporadic cancers (Mack and Peto, 2000).

For certain cancers, an environmental factor might account for familial aggregation, particularly when Mendelian expectations are violated. Despite examples where environmental carcinogens may cause familial cancer (e.g., spouses of asbestos-exposed workers) (Li et al., 1978), the origin of most familial aggregation is genetic. However, a more subtle sharing of behavioral risk factors (perhaps influenced by genetics) may contribute to familial clustering at tobacco- and alcohol-related cancer sites. A considerable component of familiality is yet to be explained, and there is substantial room for low-penetrance genes to play a role. The role of genes is further supported in that for many cancers, good evidence exists that polymorphic genes contribute to the metabolic activation or elimination of carcinogenic species implicated in their etiology. For example, known polymorphic genes contribute to the activation or elimination of aromatic amines (bladder cancer), aflatoxin (liver cancer), and polycyclic aromatic hydrocarbons, and nitrosamines (lung cancer). For cancers where specific environmental agents are poorly understood (e.g., leukemias and brain cancer), a potential gain from the identification of susceptibility genes could be the linked substrates or cofactors that may implicate as yet unrecognized etiologic factors.

Epidemiologic studies that evaluate whether relatives of cases have excess cancer compared to relatives of controls generally support a familial component of risk that persists after adjustment for known risk factors. Tokuhata and Lilienfeld (1963) first showed that lung cancer mortality was increased in both smoking and nonsmoking relatives of lung cancer cases. Since then, many other investigators have confirmed elevated risk of lung cancer in first-degree relatives of cases, taking into account potential confounders such as active and passive smoking, age, and gender (Samet et al., 1986; Shaw et al., 1991; Amos et al., 1992; Bromen et al., 2000; Etzel et al., 2003).

Studies in large populations have been conducted to evaluate sex-and age-specific risks of cancer in relatives of cancer probands such as the Family Cancer Database in Sweden, which includes 0.5 million primary adult cancers (Hemminki et al., 1999). Such large-scale databases can take gender and age into account, although this design cannot consider risk factors such as shared environmental exposures (e.g., smoking). In a recent study, age-specific familial risks in offspring were increased at 24 of 25 sites for concordant cancer in only the parent and in 20 of 21 sites for sibling probands (Hemminki, 2004). Similar findings were reported in the Utah Cancer Registry involving a Mormon population (Cannon-Albright et al., 1994; Goldgar et al., 1994). These studies establish that virtually all tumors exhibit some degree of excess risk in relatives. The increased risk of cancer in relatives tended to be restricted to cancers of the same site, suggesting that modifier genes may have site-specific effects.

Twin studies also support a hereditary contribution to a variety of cancers. Comparisons of concordance between monozygotic (MZ) and dizygotic (DZ) twin pairs are used to derive a heritability estimate, suggesting whether an observed pattern is due to hereditary and/or environmental influences. This estimate is subject to a variety of assumptions, for example, that the shared environment is the same for MZ twins and DZ twins. Generally, no gene–environment interaction is modeled. For cancer, the precise proportion of variance explained by heredity is uncertain (Braun et al., 1995; Ahlbom et al., 1997; Lichtenstein et al., 2000; Risch, 2001). However, a recent population-based twin study with complete incidence data on 90,000 twins in Scandinavia revealed higher concordance rates in MZ pairs than in DZ pairs, and estimates of the proportion of susceptibility to cancer due to heritable effects ranged from 26% to 42% for cancer at five common sites (Hoover, 2000; Lichtenstein et al., 2000).

Although the proportion of cancer attributable to genetic influences is unclear, surveys of the literature (Vineis, 1999, 2002; Garte and Taioli, 2000) focusing on low-penetrance genotypes generally estimate odds ratios for single genes that are less than 2 and suggest attributable risks for specific genotypes that range from 0% to 30%. However, the available data are too limited for drawing stable conclusions, but suggest (in agreement with twin studies) that attributable risks due to genetic susceptibility may be substantial but lower than those due to lifestyle and other environmental factors. It is likely, however, that the contributions of as yet unrecognized genes, as well as gene–environment and gene–gene interactions, make this range conservative (Caporaso, 2002).

An early rationale for gene-cancer associations was based on the appreciation that carcinogens require metabolic activation to form active DNA-binding species. This drove the first pharmacogenetic investigations into genes that modify cancer risk in the population. The four candidate genes studied were GSTM1 (Seidegard et al., 1986), CYP2D6 (Ayesh et al., 1984), NAT2 (Lower et al., 1979), and CYP1A1 (Kellermann et al., 1973). Metabolic phenotyping (administration of a probe drug and measurement of metabolites or related approach) was used to infer the genetic variant a subject possessed in cancer cases and controls. The studies were relatively small and subject to various forms of bias (i.e., cancer in the host might alter metabolism irrespective of the underlying gene) as well as deficiencies in epidemiologic design (i.e., inadequate power, poor control selection, failure to collect appropriate exposure data). However, the studies had the advantage that the “metabolic phenotype” reflected a physiological integration of gene action on a target substrate and thus represented a biologically realistic assessment (Caporaso et al., 1991). By the late 1980s this approach was supplanted by direct genotype determinations, and in the late 1990s high-throughput genotyping made possible simultaneous evaluation of a few and then many candidate genes. The technological capacity currently exists to study (p.579) substantial numbers of variants, although this development has been accompanied by formidable methodologic challenges (Table 29–2).

METHODOLOGICAL ISSUES

Table 29–2 highlights methodological issues of particular importance in gene-based population studies, including confounding, power, misclassification, gene–environment misspecification, multiple comparisons, and the appropriate inclusion of biomarkers into study design. The validity of genotyping and systematic approaches to data evaluation are also key concerns (Rothman et al., 1995; Little et al., 2002; Haddow and Palomaki, 2004).

All epidemiologic study designs used to evaluate effects of genes must take into account various forms of potential bias (selection, recall, misclassification and confounding) (Khoury et al., 1993; Rothman and Greenland, 1998; Garcia-Closas et al., 2004). Also fundamental are provisions for an appropriate analytic approach and proper control selection. Population-based controls are the gold standard, but high response rates are extremely difficult to achieve in studies that include biospecimens. Hospital controls can be more convenient and feasible but entail more opportunities for bias (Wacholder, 1992). Population association studies are commonly used for low-penetrance genes because of the requirement for substantial numbers of subjects (statistical power) and the need to take environmental exposures into account. Family-based approaches using linkage analysis have been successful in identifying the genes that account for many Mendelian traits including familial cancer syndromes but require DNA from well-characterized members of high-risk kindreds, making this strategy unsuitable for the detection of low-penetrance genes (Risch, 1996). Characteristics of the various population design options, including case-control (Caporaso et al., 1999), cohort (Hunter, 1997; Langholz et al., 1999) and case-case (case-only) approaches (Khoury and Flanders, 1996; Yang et al., 1997) have been described.

Specific issues pertinent to gene studies in the population are summarized in Table 29–3. In particular, virtually all genetic polymorphisms exhibit ethnic, racial, and geographic variation. If both the gene and the disease vary by ethnicity, the conditions are met for uncontrolled confounding (population stratification). Population stratification may be dealt with by usual epidemiologic approaches to control confounding (Wacholder et al., 2000, 2004). Genomic control or the use of panels of genetic markers that establish ethnicity may have a role in selected settings (i.e., studies involving mixed ethnic groups that cannot be reliably distinguished by self-report) (Hoggart et al., 2003; Freedman et al., 2004; Marchini et al., 2004).

A methodologic defect that has received much emphasis in critical reviews is inadequate study size. Only a handful of cancer–gene associations have achieved some degree of verification (Hirschorn, 2002); it is clear in retrospect that most of the hundreds of studies conducted during the genotyping era have been underpowered, if the most commonly reported effects from metanalyses are realistic. The situation with regard to power is actually worse, considering the multiple comparison issues, the need to take gene–environment and gene–gene effects into account, and the expected low prior probabilities of an increasing number of gene variants that will be tested. In general, study sizes in the thousands are required to address these issues adequately.

GENE SELECTION IN POPULATION STUDIES

Given the thousands of candidate genes and the appreciation that prioritizing candidates offers statistical advantages, there is clearly a need to identify efficient approaches to gene and mutation/polymorphism selection when conducting population studies. Considerations that frame studies of genetic markers include (1) the number of genes to be studied, (2) the method of gene selection, and (3) the number and type of polymorphic variants or mutations to be studied.

Regarding the number of genes to be studied, at one extreme is testing a single sequence variant of one gene. This approach has the advantage of providing a straightforward framework for hypothesis testing. The early pharmacogenetic studies of cancer employed this approach using a “metabolic phenotype” (i.e., metabolism of a drug probe dependent upon the gene of interest), but given the resources required to design, field, and assemble epidemiologic studies including DNA collection, the testing of single gene variants in population settings is wasteful.

At the other extreme is a whole genome search, in which anonymous markers (e.g., microsatellite markers spaced at every 5–10cMs or single nucleotide polymorphisms [SNPs] at much closer intervals) are selected to cover the entire genome (Uhl, 2001). This approach has become increasingly more feasible with the availability of the sequence of the human genome, improved SNP databases, and high-throughput genotyping platforms (Taylor, 2001). Currently available chips contain 500,000 SNPs and those expected within a few years will contain millions. There are two distinct modes of whole genome study. Linkage analysis (evaluating the cosegregation of genetic markers and the pattern of disease transmission in order to identify chromosome regions likely to harbor disease-related genes) is appropriate when multiple-case families suggesting a Mendelian pattern of inheritance are available or when the familial risk is substantial (Risch and Merikangas, 1996). This approach has led to the successful positional cloning of numerous genes involved in hereditary cancer syndromes. However, for evaluating low-penetrance genes for common malignancies, linkage disequilibrium or whole genome mapping approaches are looming as an option (Bonnen, 2002; Weiss, 2002). There is widespread expectation that improvements in the quality and density of the emerging genetic and physical map, relatively inexpensive and reliable high-throughput SNP genotyping, improved data storage, and computational approaches will enable linkage disequilibrium mapping to discover candidate genes in population (association) studies. The population study designs will involve the testing of thousands to millions of SNPs in whole genome scans to identify loci that are in strong linkage disequilibrium (LD) with putative susceptibility genes. Pursuing this approach in complex diseases poses difficulties including the assessment of environmental risk factors, as well as informatics and technological challenges. Technologies to pool DNA that will reduce the number of genotypes required by a factor of 100 or more may be helpful (Barcellos, 1997; Hellard, 2002), although haplotyping and exposure assessment will be sacrificed. There is also uncertainty concerning proper sample size, extent of linkage disequilibrium likely to be present, number of markers required (Hellard, 2002; Gibson and Muse, 2002; Pfeiffer, 2002), potential advantages of conducting such studies in population isolates, such as Iceland (Peltonen, 2000), and the benefits of using haplotypes (Weiss and Clark, 2002). The use of LD-based methods are likely to work best if there is a single susceptibility allele, while substantial allelic heterogeneity poses difficulties (Pritchard, 2002). The current international HapMap project is designed to characterize “haplotype blocks” permitting more efficient SNP selection capturing an estimated 75% of common SNP variants (Kruglyak, 2005), thereby improving the chances to detect associations (Cardon and Abecasis, 2003). The low prior probability of the thousands of SNPs tested means a stringent p-value will be required to reduce the chance of false positives and follow-up of promising candidates in other studies will be required (Wacholder, 2004). To date, only a few such studies have been conducted involving cancer but the results are promising. A study of 254 breast cancer cases and >25,000 SNPs identified a 20kb region (19p13.2) including the intercellular adhesion molecules (ICAM 1,4,5) with replication in two independent collections (Kammerer, 2004), although a more recent report was null (Cox, 2006).

The majority of current studies, however, fall between these extremes. Most investigate a few to thousands of SNPs or other markers from a selected group of candidate genes, gene families, or regions of interest. Special strategies used to select genes for study include prior functional data, targeted gene families or pathways, or specific disease associations (Table 29–3). How does an investigator decide among the available approaches for gene selection? This complex challenge grows as facile genotyping expands the available choices for genomic information. Among the key considerations are (p.580)

Table 29–2. Overview of Methodological Issues

Topic

Selected Points and Problems

Comment

Candidate gene

Selection of specific candidates includes a

The literature provides examples of mechanistically plausible genes based on

selection (see

spectrum of options from single genes

metabolism of putative carcinogens or key roles in carcinogenic mechanisms

Table 29–3)

(hypothesis driven) to whole-genome

such as DNA repair.

searches (anonymous).

With the increasing availability of high throughput genotyping, proposals to

Many technologies can nominate candidate genes including expression profiling, somatic mutations, loss of heterozygosity

conduct “genome searches” on population samples are increasingly feasible, although cost, DNA pooling, use of haplotypes, variability of LD structure, informatics limitations, and other problems remain challenges.

studies, mouse or other models, etc.

Prioritizing polymorphisms based on functional changes is advocated (Pfeiffer, 2002; Tabor, 2002; Rebbeck, 2004; Zhu, 2004).

Ethnicity, race

Population stratification (PS) is addressed by

Qualitative and quantitative differences in genes across racial/geographic groups

and population

taking ethnicity into account in study

exist and must be taken into account in design.

stratification

design and control of confounding by

PS’ is not a major cause of bias and can be dealt with using standard

restriction, matching, or adjustment.

epidemiological approaches (Wacholder, 2000, 2002) although some argue

Genomic control involves using genetic information to classify individuals

“Ethnic” labels are inadequate to represent geographic variation in population genetic structure (Wilson, 2001).

(Hoggart, 2003; Pritchard, 2002).

Genomic control, i.e., using a panel of highly polymorphic markers to assign

Ethnic, racial and geographic variation (see review by Risch et al., 2003).

ethnicity, may play a role in control of population stratification, but self-reported ancestry is less intrusive and correlates better with unknown

Population isolates offer certain advantages for genetic studies (Peletonen, 2000).

environmental risks; genetic ancestry may have advantages under certain circumstances, i.e. recently admixed populations (Rosenberg, 2002).

Statistical issues

Inadequate study size (power)

Studies have often been too small to achieve sufficient statistical power to

Multiple comparisons

identify associations with genes that have modest effects. Subgroup analysis is

False positives due to low prior

severely limited in small studies. Interaction (gene–environment, gene–gene, or

probabilities (Wacholder, 2004) Selection of risk models (Khoury, 1988)

higher order interactions) requires large study size (Foppa, 1997; Garcia-Closas, 1999). Testing numerous genes with low a priori expectations of effects will inevitably result in false positive associations unless alpha values or priors are adjusted (Bonferroni type or other) for the multiple tests conducted. Calculation of the False Probability Report Probability allows different stringencies for more likely genes than for less likely ones; no penalty for multiple testing; possible trade-offs of power against false positives (Wacholder, 2004).

Claiming “subgroup” effects in the absence of overall findings is a very common flaw; it can be correct but probably not often.

General failure to

General challenges:

Metanalyses generally have demonstrated low confirmation rates for candidate

replicate

Phenotype definition

genes studies in human disease (not just cancer; see Ioannidis, 2001;

Involvement of diverse biological pathways

Hirschhorn, 2002; Lohmueller, 2003).

Polygenic mechanisms (implying small contributions of individual genes) First study and publication bias

For overview of sources of bias and implications, see Wacholder (2002, 2004), Tabor (2002), Little (2002), Cardon and Bell (2001), Botstein (2003), Rebbeck (2004).

Multiple comparison issues and high false

See Dragani (1996) and Comings (1998) for views on polygenic mechanism.

positive rate due to low priors (related to inadequate study size)

See Chanock (2002) for implications of pleiotropic effects of genes.

Failure to study enough genetic variants within candidates (Neale and Sham, 2004)

Disease effect(s)

Prospective studies; Mendelian “randomization”

Disease does not influence germline polymorphisms and therefore the

Prevalent/incident cases

case-control design is not problematic to test polymorphic genes in relation to disease, however, disease in the host or its treatment may alter assays of gene function, exposures, and metabolism, investigating those assays may require a prospective design. Including prevalent cases will introduce bias if for example, genes under investigation are directly or indirectly related to survival; see Shu (2004) or Yokomizo (2002).

“Phenotype definition”

Variation within disease subgroups by histology

Cancer has traditionally posed less of a problem with phenotype definition than

or other disease characteristics; genetic

“behavioral phenotypes” such as alcoholism. Nevertheless, molecular

heterogeneity

tools are increasingly demonstrating molecular heterogeneity within cancer that will likely have genetic correlates, e.g., by prognosis (CLL and heavy-chain mutations) and lung cancer (adenocarcinoma has distinct expression and possible gene associations) (Yanagitani, 2003).

Subgrouping disease may increase power in presence of etiologic heterogeneity.

Bioinformatics

Tracking and using the vast amount of data

Enhanced data processing, management and statistical approaches will be required

generated by new technologies in the context

(Elkin, 2003). The web provides vast new resources for investigating the

of population studies will be a key challenge.

genetics of complex disease (see Table 29–4) (Pevsner, 2003).

Gene–environment

Gene–environment interaction in the context of

Various models have been described (Ottman, 1990); study size (Foppa, 1997;

interaction

cancer requires larger study sizes. See references for specific methods issues.

Garcia-Closas, 1998, 1999), misclassification (Garcia-Closas, 1998), case-only designs (Khoury, 1996).

Gene–gene

How do genes act in concert to influence

Study size issues are nominally similar to those required for detecting gene–

interaction

common cancers?

environment interaction but there are important biological, molecular, and

Epistasis (Moore, 2003).

mechanistic differences. Modeling new approaches to combining genes in and across pathways will be a future direction, i.e. hierarchical (Hung, 2004), oligogenic (Sellers, 2005) models.

Quality control

Quality issues related to general epidemiological

Include blinded duplicate and replicate samples in runs. Include QC (standard

practice are treated elsewhere. Issues related to biospecimen and genotype determination.

operating procedures) at every stage of DNA processing from: collection, labeling, shipping, processing, DNA extraction, aliquoting, storage (freezer monitoring), DNA integrity, amount and assay testing, and shipping.

Bias results if reasons for suboptimum biospecimen collection or assay variability are differential.

Increasing capacity to obtain DNA from noninvasive sources and whole genome amplification.

(p.581)

Table 29–3. Selection of Candidate Genes

Strategy to Select Genes

Advantages (+) and Challenges (-) of the Particular Approach

Based on functional variants

+

from available SNPs

Certain codon sequences or SNP typologies more likely alter function (Risch, 2000; Tabor, 2002; Zhu, 2004), i.e., cSNPS that alter the amino acid sequence of the encoded protein (Cargill, 1999).

-

High-throughput screening may be more efficient (cost-effective) than selecting best candidates from known variants.

Information from nonfunctional variants may contribute to understanding haplotypes or be in LD with important variants.

Based on areas identified in

+

linkage studies

Regions identified may include major genes. Finer mapping may proceed using conserved haplotypes, linkage disequilibrium, or based on chromosome translocations, deletions, etc. LOH, CGH or multiple strategies (Gao, 2000; Dahia, 2005).

Variants of major genes likely to play a role in nonfamilial case (see examples Table 29–3).

-

Linkage signals are too weak to detect odds ratios in the range most common for cancer modifier genes; the environment is not taken into account.

Generally there is a solid mechanistic basis for studying genes already implicated in families.

Based on cytogenetic

+

evidence

Successful identification of genes important in leukemias and some solid tumors. Cytogenetic regions implicated, e.g., 3p21 in lung (Protopopov, 2003; Wei, 1996), NPC (Xiong, 2004), and renal cancers (Sukosd, 2003) and in the origin or development of multiple solid tumors likely harbor tumor suppressor genes (Protopopov, 2003).

-

May be less relevant to modifier genes with low penetrance.

Cytogenetic abnormalities are more numerous and variable in solid tumors.

Chromosome alterations in lymphocytes may independently predict cancer susceptibility (Bonassi, 2000) but have little localizing information.

Based on animal models

+

Increasing number of model systems and ability to understand homologies suggests new pathways and specific candidates.

Easier to demonstrate organ specific effects and obtain tissue.

Controlled matings can elucidate genetics.

Animal models (knockout and transgenic) may implicate susceptibility and resistance genes (Gonzalez, 1999; Kimura, 2000; Wang, 2005) and describe gene–gene interactions (Tripodis, 2001; Samuelson, 2005).

For example, 60 loci contribute to lung cancer susceptibility in the mouse genome, including many with interactive effects (Dragani, 2000; Manenti, 2000).

Can study both temporal and spatial events.

Improved databases exist, i.e., http://tumor.informatics.jax.org (Naf, 2002).

-

Animals don’t duplicate the complex exposures that underlie human cancer and are distinct from humans in clinical, histologic, and other aspects.

Animal findings must be verified in human population studies.

Based on somatic mutations

+

observed in tumors, LOH,

“Two hit” model established for at least a few high-penetrance genes.

CGH, etc.

Patterns of mutations may suggest gene pathways (Kimura, 2003) or specific genes.

Certain molecular finding in tumors suggest oncogenes or TSGs.

Somatic mutations may be more related to prognosis, disease histology, or clinical characteristics then etiologic factors (Coe, 2006).

See Web sites that catalog somatic mutations (Bamford, 2004).

Based on role in a related

+

phenotype, disease or

Genes involved in, e.g., one tobacco- or alcohol-related cancer are candidates for investigation in another. Genes involved in

exposure

other conditions related to a particular exposure (i.e., insulin resistance may be related to abdominal obesity, heart disease, and prostate cancer) may be worth investigating in all those conditions. Genes involved in emphysema may mediate lung cancer risk as well (Minematsu, 2005)

Based on expression studies

+

Expression data provide a rationale for gene’s role in tissue of interest and suggest tumor mechanisms, candidate genes, and families (Crawford, 2000; Williams, 2003; Amatschek, 2004).

Pathways may be suggested for tumors where current understanding of etiology and therapy is inadequate (Godard, 2003).

Tumor differences by anatomic location, i.e., left and right colon may be elucidated (Glebov, 2003).

Expression profiles may suggest or even define tumor subtypes and suggest critical discriminators (Tay, 2003).

Organ specific genes, mechanisms, and pathways important for specific tumors are indicated (Sanchez-Carbayo, 2002).

Provide insight into molecular pharmacology of new and established therapies (Clarke, 2003).

-

Numerous factors unrelated to disease may influence expression in tissue.

Obtaining a well-characterized sample of tumors from a generalizable sample of subjects with a cancer is challenging.

There is enormous variability in expression; studies to date have been modest in size.

Statistical problems include “dimensionality,” overfitting, poor power, and informatics limitations.

Patient specific transcription profiles differ due to genetic instability inherent to tumors as well as technical factors.

Based on protein studies

+

Protein is “effector arm” of genome and therefore must be involved in action of altered genes involved in carcinogenesis.

-

Current techniques do not always allow facile identification of protein/peptides involved in cancer to their genetic components.

Proteomics is less mature technology.

Validation and reproducibility are challenges.

Based on known etiologic

+

exposures

Early mechanistic “pharmacogenetics” approach has historical precedent; processing compounds known to influence cancer risk has biological plausibility (Zhang, 2003).

Epidemiologic (and clinical) observations suggest important molecular mechanism for investigation.

-

Many categories of genes known to be involved in carcinogenesis do not fall in these categories and new genes may implicate novel mechanisms

Based on previous

+

population studies

Candidates with known gene frequencies, established prior hypotheses.

Necessary to assess candidates in the population to verify their role, gauge public health impact, further explore subgroup and covariate relations.

-

Such an approach will miss new or understudied genes or variants.

Previous studies may have been flawed.

Based on evolutionarily

+

conserved regions

Conserved regions likely to harbor areas that involve critical functions that when altered perturb important biological processes.

-

May miss some genes, for example, those involved in gene–environment effects, those that uniquely involve humans, recent mutations, etc.

Selection may not conserve regions important to common cancers that affect individuals well beyond reproductive age.

Based on regions determined

+

from whole genome

Identify previously unknown genes and families.

searches/haplotype linkage disequilibrium

Use haplotype blocks to guide selection of polymorphisms (Bonnen, 2002; Cardon, 2003), identify blocks of genes in linkage disequilibrium.

Fine-gene localization (Risch and Merikangas, 1996; Jorde, 2000).

-

Locus heterogeneity complicates complex disease analysis (Weiss, 2002).

Power may be limiting.

Markers studied may not correspond to functional variants that account for associations.

Frequency mismatch of marker gene and functional genes will reduce power.

Variation of LD across populations, genome regions and between markers in close proximity requires further study (Shifman, 2003).

Role of haplotypes in SNP selection still controversial (Belmont and Gibbs, 2004).

Based on epigenetic changes

+

May account for plausible candidate genes that fail to show mutations or functional polymorphisms. Epigenetic silencing of tumor suppressor genes by promoter hypermethylation operates in some cancers (Baylin, 2000; Chang, 2003).

-

Methylation profiles vary according to previous exposure (Anttila, 2003), histology, geographic origin (Toyooka, 2003), as well as germ line mutations (Paz, 2002).

(p.582) the size and design of the study (case-control, cohort, sib pair), whether the study is primarily designed to test a specific hypothesis or generate new hypotheses (i.e., gene discovery or characterization), the degree to which tissue studies will be integrated, genotyping approach, laboratory resources, and the available budget. Generally, an investigator will amass evidence favoring candidate genes or genes families from the categories indicated in Table 29–2 and then rank them. Selection should balance the desire to include diverse categories of modifier genes and the opportunity to comprehensively cover many or all the genes in key pathways. The investigator must also decide how many polymorphisms/SNPs per gene should be investigated, the type of polymorphism (i.e., SNPs, microsatellite, functional or not, etc.), and whether selection will be such that haplotypes can be reconstructed. How many variants to investigate within a candidate gene is a controversial point. Neale and Sham (2004) argue for extensive study of risk-conferring variations within candidate genes as a gold standard, although this would not be necessary if functional variants were well understood and would be impractical for large numbers of genes. Within a particular haplotype block, SNP selection can be limited, as flanking SNPs will be in linkage disequilibrium and additional SNPs will not provide much further information. Common SNPs (>5–20%) are often favored because of power considerations.

Given a limit on how many genes to test, the genes with the highest prior probabilities of disease are favored, as candidates with low priors will have high false positive rates. For presumptively positive findings involving randomly selected SNPs (given that there are potentially millions of SNPs that can be tested but the number of true associations are likely to be far fewer for any given cancer), replication will be mandatory. Underpowered studies will have reduced probability of detecting a true association between a gene and disease and will report a higher proportion of false positive findings than larger studies with adequate study power (Garcia-Closas et al., 2004; Wacholder et al., 2004).

Evidence that can contribute to candidate gene selection can be loosely divided into mechanistic considerations and findings from population studies. Table 29–4 provides a semiquantitative summary of the mechanistic and population evidence for several candidate gene families. In the table, both categories are given an estimated score (i.e., 0 = no data or null findings, 1 = some suggestive evidence that is short of confirmatory, 2 = strong evidence). The summed scores for the two categories could serve as one guide to setting prior expectations. For example, a summed score of 0–1 indicates a low prior expectation (i.e., the probability of a true association is quite low, e.g., p ≤ 0.001). A score of 2–3 indicates a medium prior of 0.01. A high score of 4 indicates the highest prior (i.e., p = 0.10) (Wacholder, 2004).

Assessing the mechanistic component involves considering the action of the gene in relation to physiological, cellular, or biochemical functions potentially relevant to cancer. This can include metabolism/activation/inactivation of exogenous or endogenous factors related to cancer, whether the gene action is relevant to cancer in animals or model systems, and whether the gene is active in the tissue of interest (consistent with direct carcinogenic action). Also related to mechanism is the specific effect of the genetic mutation or polymorphism. Coding for a nonsynonymous mutation, a mutation in an evolutionarily conserved region, a mutation in an exon or regulatory region, or a mutation known to influence function increases relevance to cancer and should be given priority (Tabor, 2002; Zhu, 2004). Actual SNP selection can proceed using increasingly comprehensive databases that are searchable by gene, chromosome, and pathway, e.g., http://snp500cancer.nci.nih.gov). See Table 29–4 for other relevant web based resources. The population component of the evidence derives from epidemiologic studies. Genes that have exhibited associations with cancer, associated conditions, or precursors in high-quality studies will be more attractive for investigation.

The case to investigate unselected “random” SNPs is based on the availability of improving technologies that lower the cost of SNP testing, the time and difficulty in establishing functional evidence, certain exceptions to the usual rules (e.g., synonymous mutations found to be important in selected genes) (Duan et al., 2003), the possibility that associations may reflect linkage disequilibrium with the real functional markers (Chapman et al., 2003), and the certainty that many important genes remain undiscovered. It is possible that as yet unrecognized genes acting via novel mechanisms will require a systematic (p.583)

Table 29–4. Web Sites Useful in Susceptibility Gene Investigations

Locus Link

http://www.ncbi.nlm.nih.gov/LocusLink

Descriptive information about genetic loci

Nomenclature

http://www.gene.ucl.ac.uk/nomenclature/

Human Gene Nomenclature

Committee (HUGO) assigns

official names to genes and proteins

Human Genome Informatics

http://www-l.lanl.gov/HGhotlist.htlm

Informatics related to the genome

National Center for Biotechnology Information (NCBI)

http://www.ncbi.nlm.nih.gov

Comprehensive genetics information

Online Mendelian Inheritance in Man (OMIM)

PubMed

http://www.ncbi.nlm.nih.gov/PubMed/

National Library of Medicine including Medline

GeneCards

http://bioinfo.weizmann.ac.il/cards

Database of human genes, their products, and involvement in human disease

SNP-related

dbSNP

www.ncbi.nlm.nih.gov/SNP

SNP Consortium

HGBASE

HapMap

Genetic Association

Database

http://snp.cshl.org

http://hgbase.cgr.ki.se/

http://www.hapmap.org/

http://geneticassociationdb.

Archive of human genetic association studies

A comprehensive review of bioinformatics and material on web sites covering sequencing, DNA, RNA, and protein, model organisms, genome analysis and the Human Genome Project and web resources related to the study of human disease, see Bioinformatics and Functional Genomics, Jonathan Pevsner, Wiley-Liss, 2003.

search in currently unexplored areas of the genome, through the use of random markers in linkage disequilibrium studies.

OVERVIEW OF CANDIDATE GENES

Table 29–5 lists the candidate cancer susceptibility genes, as well as representative examples of genes and key references. Historically important reports, meta-analyses (boldface type in the table), and reviews are selected where available.

The first candidate pharmacogenetic studies investigated genes thought to activate (i.e., Phase 1, genes such as CYP1A1 and CYP2D6) (Kellerman, 1973; Ayesh, 1984) or eliminate carcinogens (i.e., Phase 2 genes, such as NAT2 and GSTM1) (Lower, 1979; Siedegard, 1986) in relation to tobacco-related cancers. These Phase 1 and Phase 2 genes have been the most extensively studied, based on mechanistically plausible pharmacogenetic relationships of key exposures with known phenotypes. Mechanistic studies have documented the relation of these genes to exposure, intermediate markers, subtypes of disease, and prognosis (Bartsch, 2000). The best established Phase 1 association is CYP1A1 and lung cancer. There is also good evidence that two Phase 2 genes are associated with bladder cancer (NAT2, the slow acetylator phenotype or deficient genotype, and the GSTM1 null genotype). The evidence for other Phase 1 and 2 genes is inconclusive, although based on the associations observed and the known mechanisms involved, it would not be surprising if weak to modest effects are eventually established for combinations of genes from both of these classes.

The gene families categories in Tables 29–5 and 29–6 are somewhat arbitrary, as genes commonly influence multiple processes (pleiotropy). For example, MTHFR may be classified in the nutrient category based on its involvement in folate metabolism, but it also influences oxidative metabolism, DNA repair, and chromosome integrity. Certain genes may both activate and detoxify (pro)carcinogens (i.e., have both Phase 1 and Phase 2 activity, such as mEH and SULT1A1).

Gene polymorphisms involving hormonal pathways are another well-studied group. Despite plausible mechanisms and some suggestive findings (Medeiros, 2003) for the role of androgen-pathway genes in prostate cancer and estrogen-related genes in breast and endometrial cancer (Zheng, 2004), consistent evidence for associations is not yet evident.

DNA repair genes are a major focus of recent study with suggestive findings across a broad spectrum of malignancies. The variety of mechanisms of DNA repair and many mutations known to influence these complex processes suggest the need for further comprehensive study and emphasize the need to prioritize their study based on biological function and relevance to cancer (Berwick and Vineis, 2000).

Immunologic and inflammatory mechanisms appear to be affected by susceptibility as indicated by the association of HLA types with selected diseases. The oldest known association (ABO group and gastric cancer; Aird, 1953) falls in this category, and a variety of processes involving cytokine, chemokine, and immunoglobulin gene processes are under active investigation and will likely be established to have importance in cancer susceptibility. Recently reported are associations linking aggressive NHL to TNF-alpha, and gastric cancer to proinflammatory cytokine polymorphisms (El-Omar et al., 2003).

Because various oncogenes and tumor suppressor genes determine susceptibility to specific cancers in the setting of high-risk kindreds, polymorphic variants of these genes that influence but do not ablate function deserve further study as candidates that modify susceptibility. For example, the specific founder mutation in the NBS1 gene responsible for Nijmegan breakage syndrome is present in 9% of familial prostate cancer in a Polish population but also in 2.2% of non-familial prostate cancer (compared to 0.6% of controls) (Cybulski, 2004). Various polymorphisms of tumor suppressor genes and mismatch repair genes implicated in familial cancer are under study in the sporadic forms of the disease.

Several other molecular mechanisms known to influence later stages of cancer including apoptosis, angiogenesis, cell-cycle control, telomere integrity, and many others have been investigated but only in a fragmentary manner.

Dietary nutrients and energy balance including obesity all alter the risk of cancer and are under various degrees of genetic control. Only a handful of genes have been well studied, such as MTHFR. Because these genes act in pathways and in concert with environmental, metabolic, and genetic factors (Ulrich, 1999), large-scale studies will be needed to sort out their effects.

Behavioral genes are a new category of genes. Although their primary role may be in influencing exposures such as alcohol, tobacco, or food intake that are major determinants of cancer in the population, (p.584)

Table 29–5. Candidate Cancer Susceptibility Genes

Gene Family

Gene Examples

Relevant Cancers

Mech

Pop

G-E or G-G

Reference

Alcohol

ADH3

Oral, Head and Neck, Esophagus, Colorectal

2

1

GE = alcohol GG = ALDH2

Monzoni, 2001 (mechanism); Tiemersma, 2003 (colorectal adenomas) Brennan, 2003 (oropharyngeal)

Alcohol

ALDH2

Oral, Esophagus

2

1

GE = alcohol GG-ADH3

Yokoyama, 2003a, 2003b (Oro Yang, 2005 (esophagus))

Alcohol

CYP2E1

Oral

1

1

GE = alcohol, HCV GG-ADH2

Kato, 2003

Angiogenesis

Endostatin

Prostate

1

1

Inghetti, 2001

Angiogenesis

ecNOS

Prostate, Lung

1

1

Medeiros, 2002, 2003b (prostate)

Angiogeneiss

ecNOS

Lung

1

1

Cheon, 2000 (lung)

Angiogenesis (?)

ACE

Breast

1

1

Koh, 2003

Apoptosis

FAS; BCL6

Bladder

1

0

Hazra, 2003 (bladder)

AML

Sibley, 2003 (AML)

Lymphoma

Lossos, 2001, Zhang, 2005 (lymphoma)

Behavior

OPRM1, many others

All tobacco, alcohol, drug, obesity related cancers

1

0

GE = alcohol, tobacco, obesity

McGue and Bouchard, 1998; Han, 1999, Caspi, 2003; Uhl, 2002; Crowley, 2003 (mechanism)

Behavior

COMT

Same

1

0

GE = same

Hariri, 2003

Blood type

ABO

Gastric

1

2

Aird, 1953; Boren, 1993

Cell cycle

CHEK2

Breast

1

1

Zhang, 2003

Colorectal

Kilpivaara, 2003 (colorectal)

Prostate

Cybulski, 2004a (prostate)

Cell cycle

CCND1

Lung

1

1

Qiuling, 2003 (lung)

Colorectal

Lewis, 2003 (colorectal adenomas)

Cell cycle

TGFB

Breast

1

1

Shu, 2004

Chemokine receptors

CCR2 and CCR5

NHL

1

1

GE = HIV infection

Smith, 1997

Circadian

PER1

Breast

1

1

Zhu, 2005

Cytokines

IL-10

NHL

1

1

Cunningham, 2003

DNA repair

XRCC1, XRCC3 XPC, XPD, RAD51, OGG1, MGMT, and others.

Esophagus, Lung, Colorectal, Prostate, Melanoma, Glioma, Bladder, Breast, Gastric, Pancreas, NPC, HCC, Skin

2

1

GG = other DNA repair genes; With BRCA1/2 (breast)

Friedberg, 2000, Savas, 2004 Mohrenweiser, 2003 (mechanistic) Butkiewicz, 2001; David-Beabes, 2001 Wang, 2003 (lung), Kirk, 2005 (HCC) Han, 2004 (breast) Stern, 2002; Kelsey, 2004 (bladder)

GE

Heather, 2002 (skin, XRCC1) Duell, 2002 (pancreas) Lee, 2002 (gastric), Han, 2005 (skin) Elahi, 2002 (oropharyngeal) Cho, 2003 (NPC) Rybicki, 2004 (prostate) Tomescu, 2001; Baccarelli, 2004 (melanoma) Goode, 2002 (review)

Drug transport

MDR1

Leukemia, Lung

1

1

Sinues, 2003 (lung) Jamroziak, 2004 (childhood ALL)

Environmental Contaminant

NQO1

Bladder Esophagus

1

1

GE-benzene

Bauer, 2003; Moran, 1999 (benzene toxicity)

Breast

Park, 2003 (mechanism) Zhang, 2003b (esophagus) Menzel, 2004 (breast)

Environmental contaminant

PON1

?

1

0

Battuello, 2004 Kelada, 2003 (general review)

Growth Factor

IGFBP3, IGF1

Breast

2

1

Ren, 2004 (breast) Fletcher, 2005

Hormone

Estrogen-related: CYP11A, COMT, CYP17, CYP19, CYP1B1, EDH17B2

Breast, (Prostate, Ovary, Endometrial, Liver)

2

1

Mitrunen, 2003; Jefcoate, 2000 (mechanism) Cai, 2003 (breast); Zheng, 2004 (breast); Mitrunen, 2003 (breast) Thompson, 1998 (COMT)

ESR1 PR

Tworoger, 2004 (genes and hormones), Gold, 2004 (ESR1) Hachey, 2003 (CYP1B1) Madigan, 2003 (prostate, CYP17); Berstein, 2002 (endometrial, CYP17) Zheng, 2004 (breast, CYP11A) Rossi, 2003 (HCC); Goodman, 2001 (ovary) Ntiais, 2003c (CYP17 and prostate)

Hormone

Other steroid hormones: i.e., progesterone oxytocin, prolactin

Breast, (Prostate, Endometrial), Liver

1

1

GE = HCV (liver)

Maloney, 2003 (general), Zheng, 2004 (CYP11A and breast) DeVivo, 2003 (progesterone) Vondehaar, 1999 Rossi, 2003 (liver)

Hormones

Androgen receptor, SRD5A2

Prostate, Breast, Esophagus (squamous)

2

1

GE = mammographic density, estrogen therapy

Dietzsch, 2003 (esophagus). Liede, 2003; Lillie, 2004; Suter, 2003 (breast) van Gils, 2003 (SRD5A2, breast) Li, 2003; Soderstrom, 2002 (SRD5A2, prostate) Mononen, 2000 (prostate) Chang, 2003c; Medeiros, 2003a (prostate, ER and others)

Hormones

Pepsinogen C

Gastric

1

1

Liu, 2003

Immune

HLA region genes

Lung, Breast, NPC, Lymphoma

2

1

de Jong, 2003 (breast), Butsch Kovacic, 2005 (NPC) Snoek, 2000 (lung) de Jong, 2003 (breast) Lu, 2003 (NPC); Hirunsatit, 2000 (NPC) Howell, 2002 (lymphoma)

Immune

IFNGR1

Gastric

2

1

Thye 2003

Inflammation

COX2 and others

Colorectal, Lung, Breast

2

1

DuBois, 2003; Thun, 2002 (mechanism) Landi, 2003 (colorectal) Campa, 2004 (lung)

Inflammation

Multiple

Prostate

1

1

GG

Xu, 2005

Inflammation

ODC

Colorectal

2

1

GE-aspirin

Martinez, 2003

Interleukins

IL-1, IL-8

Gastric, Lung

2

1

GE-H. pylori

Thye, 2003 (mechanism) El-Omar, 2002, 2003; Taguchi, 2005 (gastric) Zienolddiny, 2004 (lung)

Interleukins

TNFalpha

Leukemia, Lymphoma, Cervix

1

1

GE-infection, (CIN in cervix)

Tsukasaki, 2001 (leukemia) Kirkpatrick, 2004 (cervix)

Lipid metabolism

APOE

Colorectal

1

1

GE-gender

Watson, 2003

Matrix metalloprotein ases

MMP-1, MMP-3, MMP-2

Lung, Breast, Gastric

1

1

Egeblad, 2002; Ghilardi, 2002 (breast) Yu, 2002; Su, 2005; Fang, 2005 (lung) Miao, 2003 (gastric)

Major gene modifier

ATM

Breast

1

1

Sommer, 2003; Bretsky, 2003; Angele, 2003; Thompson, 2005; Lee, 2005

Major gene modifier

HPC

Prostate

2

1

Meitz, 2002

Major gene modifier

BRCA1

Breast

2

0

Dunning, 1997

Major gene modifier

APC

Colorectal

1

1

GG = NAT1/2

Crabtree, 2004 Hahnloser, 2003

Major gene modifier

MSR1

Prostate

1

1

Miller, 2003

Major gene modifier

RET

Thyroid

1

1

Robledo, 2003

Major gene modifier

CDKN1 *

Prostate

1

0

Kibel, 2003

Metalloprotease family (MMPs)

ADAM33, MM1

Tobacco-related cancers and related conditions

1

0

Zhao, 2004 (mechanism) Van Eerdewegh, 2002 (asthma) Zhu, 2001 (lung) Egeblad and Werb, 2002 (cancer progression)

Miscellaneous

HFE

Breast

1

1

Kallianpur, 2004

Miscellaneous

MDR-1

Lung

1

0

Sinues, 2003

Nutrient

VDR

Prostate, Colon

1

0

GE-calcium, Vitamin D GG-IDGF

Ma, 1998 (prostate); Chokkalingam, 2001 Ntais, 2003b (prostate); John, 2005 (prostate)

Nutrient

MTHFR (also related, MTR, MS)

Colorectal, NHL, Gastric, Breast Endometrum; Prostate Leukemia (ALL)

2

1

GE-folate, homocyst., Cardiovascular, diet, alcohol GG = one-carbon pathway

Curtin, 2004; Ulrich, 1999 Goode, 2004, Ma, 1997 (colorectal) Giovannucci, 2003 (colorectal adenomas, alcohol); Cicek, 2004 (prostate)

Bladder

Jacques, 1996; Spotilla, 2003 (homocysteine) Bailey, 2003; Heijmans 2003 Gao, 2002 (gastric) Hung, 2004 (bladder) Lin, 2004 (bladder) Ergul, 2003; Chen 2005; Shrubsole, 2004 (lowfolate) (breast) Krajinovic, 2004 (ALL) Infante-Rivard, 2003 (mechanism leukemia) Esteller, 1997 (endometrium) Spotila, 2003 Robien 2003 (leukemia)

Nutrients

Vitamin E

Prostate

1

0

GE-weight, nutrition

Heinonen, 1998

Obesity (energy balance)

Leptin (LEP), LEPR, PPAG

Endometrial, Kidney, Prostate, Colon, Esophagus, Breast (postmenopausal)

1

1

GE-insulin resistance, energy balance

Calle, 2003; Thompson, 2004 (mechanism)

Phase 1

CYP1A1 (Related

Lung, Prostate, Head

2

2

GE-tobacco;

Kellermann, 1973 (lung)

CYP1A2)

and Neck, Renal,

Endometrium,

Acute Leukemia

Colorectal

GG-GSTM1

Kihara, 1995 (lung)

Sugawara, 2003 (female tumors)

Chang, 2003a (prostate); Longuemaux,

1999 (renal)

D’Alo, 2004 (AML)

Esteller, 1997 (endometrium); Landi,

2005 (colorectal)

Hung, 2003 (nonsmokers)

LeMarchaud 2003

Vineis 2003

Phase 1

MPO

Lung

2

1

GE = H. pylori

Roe, 2002; Caporaso, 2002; Chevier,

2003; Kiyohara, 2005

Kantarci, 2002; Schabath, 2000 (lung)

Roe, 2002 (gastric)

Phase 1

CYP1B1

Breast, Prostate,

Endometrium

1

1

Rylander-Rudqvist, 2003 (breast); Landi,

2005 (colorectal)

Colorectal

Sasaki, 2003 (endometrium)

Tanaka, 2002 (prostate)

Chang, 2003b (prostate)

Phase 1

CYP2A6 (CYP2A13)

Lung, Esophagus

2

1

GE-tobacco, nicotine

Hecht, 2000; Wang, 2003; Minematsu,

2003 (mechanism)

Tan, 2001 (lung and esophagus) Loriot, 2001 (lung) Oscarson, 2001 (nicotine)

Phase 1

CYP2D6

Lung, Bladder, Breast,

others

1

1

Ayesh, 1984 (first study)

Phase 1

CYP3A4 CYP3A5

Prostate, Lung,

Various

1

1

Lamba, 2002 (mechanism)

Plummer, 2003 (prostate);

Tayeb, 2003; Dally, 2003 (lung)

Phase 1

CYP2E1

Gastric, Lung, NPC,

Prostate

1

1

Ferreira, 2003 (prostate)

Kongruttanachok, 2001 (NPC)

Park, 2003 (gastric)

Phase 1

CYP2C9

Lung

0

1

Garcia-Martin, 2002

Phase 1/2

MEH

Lung

2

1

Cajas-Salazar, 2003; Gsur, 2003

Lee, 2002 (review)

Phase 2

NAT2 (NAT1)

Bladder

2

2

GE-tobacco

Lower, 1979 (bladder, first study)

Lung, Breast

HAA

Muckel, 2002 (mechanism)

Wikman, 2001 (lung)

Weber, 1987 (review)

Phase 2

NAT2 (NAT1)

Colorectal, Gastric,

HCC

2

1

GE-meat

consumption

Lan, 2003 (gastric)

LeMarchaud, 2002; Landi, 2005 (colorectal)

Huang, 2003 (HCC, red meat)

Brockton, 2000 (colorectal)

Phase 2

NQO1

Lung, Bladder,

Colorectal

1

1

GE-benzene

Moran, 1999; Chen, 1999 (lung)

Park, 2003 (bladder); van der Logt, 2005

Phase 2

GSTfamily

Lung, Bladder, Head

and Neck, Breast,

Thyroid, NHL,

Esophagus, Skin

2

1

GG-CYP1A1

GE-smoking, young

age (lung);

GSTT1 and HPV

Seidegard, 1986 (first study)

Egan, 2004 (breast), Coughlin, 2002

Miller, 2002 (diverse GSTs)

Evans, 2004 (head and neck)

(Malignant

(cervix), GST1/

Hung, 2004 (bladder)

Melanoma, Basal

Cell Carcinoma),

Larynx, Renal,

Ovary, Testicular

M1-cruciferous

vegetables (lung)

Lee, 2004 (cervix)

Rollinson, 2000; d D’Alo, 2004

(acute leukemia); Kirk, 2005 (HCC)

Sweeney, 2000 (renal)

Brain, Leukemia,

Cervix, HCC

DeRoos, 2003; Ezer, 2002 (brain);

Brennan, 2005 (lung)

McWilliams, 1995 (lung)

Benhamou, 2002; Cotton, 2000

(GST and colorectal)

Houlston, 1999 (GST and lung)

Taioli, 2003 (GSTM1 and lung, age

<45)

Engel, 2002 (GSTM1 and bladder)

Geisler, 2001 (GSTs and sq head and

neck)

Phase 2

UGT1A1 (UGT1A7)

Lung, Oropharyngeal,

Head and Neck,

Pancreas, HCC

2

1

Wiener, 2004; Burchell, 2003 (mech)

Fang, 2002; Tseng, 2005 (HCC)

Elahi, 2003 (oropharynx)

Ockenga, 2003; Dugay, 2004 (pancreas)

Phase 2

SULT1A1

Prostate, Lung

2

1

Muckel, 2002 (mechanism)

Wang, 2002 (lung)

Nowell, 2004 (prostate)

Pigmentation

MC1R

Melanoma

2

1

GE-sun, freckling,

mole count

Sturm, 2003 (mechanism)

Hayward, 2003; Hearle, 2003;

Vajdic, 2003 (ocular melanoma)

Pigmentation

MC1R

Skin Cancer

2

1

Same

Sturm, 2003

Proto-oncogene

HRAS1, HER2

Lung, Breast, others

1

1

Rutter, 2004; Tamimi, 2003 (breast)

Lindstedt, 1999 (lung)

Krontiris, 1993 (review)

Senescence

MnSOD

General

1

0

Lundberg, 2000

Signal transduction

ICAM

Breast, Prostate

2

1

Kammerer, 2004 (breast)

TSG

PTEN

HCC

1

0

GE-hepatitis

Chung, 2003

TSG

P53

Lung, HCC, Cervix,

Endometrial,

Colorectal

2

0

Jackson, 2003 (HCC)

Koushik, 2004 (cervix)

Roh, 2004 (endometrial)

Esophagus

Gemignani, 2004 (colorectal)

Hong, 2005 (esophagus)

TSG

FHIT

Cervix

1

0

Jee, 2003

TSG

CDH1

Breast, Lung, Gastric

1

1

Toyooka, 2001

Humar, 2002 (gastric)

TSG

KLK10

Prostate

1

1

Bharaj, 2002

NKX3.1

Prostate

1

1

Gelmann, 2002

Mechanism: 0 = no plausible mechanism related to cancer is established; 1 = possible cancer mechanism is associated with the gene but evidence weak or mechanism only weakly linked to cancer; 2 = cancer mechanism is well-established for the gene.

Population evidence: 0 = no population data or null; 1 = some suggestive population data exists but too sparse to draw firm conclusions; 2 = association with cancer in population studies is reasonably well-established.

Boldface type indicates data derive from a review, or meta- or pooled analysis.

(p.585) (p.586) (p.587)

Table 29–6. Specific Cancers and Associated Gene Families

Cancer

Key Families and Genes

Comment

Cancer

Key Families and Genes

Comment

Bladder

Phase 1 and Phase 2,

DNA repair,

Apoptosis,

Nutrient

(MTHFR/folate)

Park, 2003 (NQO1)

Hazra, 2003; Sanchez-Carbayo,

2002

Stern, 2002 (XPD)

Hung, 2004 (MTHFR, Phase 2s);

Lin, 2004 (MTHFR)

Marcus, 2000a (NAT2)

Marcus, 2000b (NAT2 and

tobacco)

Vineis, 2001 (NAT2)

Engel, 2002 (GSTM1)

modifiers,

Alcohol,

Cell cycle,

TSG

Lewis, 2003 (CCND1)

Goode, 2004 (environmental risks)

Hahnloser, 2003 (APC)

Cotton, 2000 (review GSTM1)

Brockton, 2000 (review

NAT1/NAT2)

LeMarchand, 2001 (colorectal)

Tiemersma, 2003 (alcohol)

Gemignani, 2004 (p53)

Giovannucci, 2003

(MTHFR/ADH3/alcohol)

Breast

Steroid hormone

metabolism genes

(estrogen,

progesterone and

androgen),

carcinogen

metabolism genes,

DNA repair,

Nutrients (especially

folate pathways),

Major gene modifiers

(BRCAx, ATM),

Immunologic (HLA),

Phase 1 and Phase 2,

Proto-oncogene and

TSG, Nutrient, Signal

transduction, Growth

Factor, Circadian

Zheng, 2004 (sex hormone); Gold,

2004 (estrogen receptor)

Angele, 2003; Lee, 2005 (ATM);

Zheng, 2004 (CYP11A)

Egan, 2004 (Phase 2 review)

Zhang, 2003

Han, 2004 (DNA repair genes)

Rutter, 2003 (HER)

Setiawan, 2004 (HSD17B1)

Suter, 2003 (androgen repeats)

Tamimi, 2003 (H-ras)

Dunning, 1999 (review), Zhu,

2005 (PER1)

Menzel, 2004 (NQO1 and p53)

Chen, 2005 (MTHFR)

Kammerer, 2004 (ICAM)

Ren, 2004 (IGFBP3)

Endometrial

Esophagus

Gastric

Estrogen/hormone;

Phase 1;

Nutrient;

TSG

Phase 1 and Phase 2,

Alcohol Metabolizing

Genes, Hormones

Hormone

polymorphisms

(pepsinogen);

Phase 1,

DNA repair,

Nutrient,

Dugay, 2004 (estrogen)

Berstein, 2002 (CYP17)

Sasaki, 2003 (CYP1B1)

Esteller, 1997 (Phase 1 and

MTHFR); Sugawara, 2003

(CYP1A1)

Roh, 2004 (p53 and p21); Jee, 2003

(FHIT)

Hamajima, 2001; Yang, 2005

(ALDH2)

Zhang, 2003 (cyclin D)

Tan, 2001 (CYP2A6)

Liu, 2003 (pepsinogen C)

Humar, 2002 (CDH1)

Park, 2003; Wu, 2002 (CYP2E1)

Roe, 2002 (MPO)

Gao, 2002 (MTHFR)

Ming, 2003 (MMP2)

Brain

Phase 1 and Phase 2

GSTs (De Roos, 2003; Ezer, 2002)

CYP2E1 (DeRoos, 2003) require

further study

EGRF dysregulation, Weiss (2003)

Head and

Phase 2, Immune

Alcohol metabolizing

El-Omar, 2002, 2003; Taguchi, 2005

(interleukin genes)

Lan, 2001; Lan, 2003 (Phase 2)

Hardy, 1997 (ADH3)

Cervix

TSG, Phase 2,

Nutrients

Koushik, 2004 (p53)

Lee, 2004 (GSTM1)

Sharma, 2004 (GSTs)

Neck

genes;

Phase 1 and 2;

DNA Repair

Geisler and Olshan, 2001 (GST

family)

Elahi, 2003 (UGT1A1)

Colorectal

(including

adenomas)

Inflammation

pathways; DNA

repair, Nutrients,

Cytokines;

Phase 1;

Phase 2;

Major gene

Landi, 2003 (inflammation)

Martinez, 2003 (ODC and adenoma)

LeMarchaud, 2001 (NATs)

Herman, 1998; Ulrich, 1999;

Giovannucci, 2000

Bailey, 2003; Curtin, 2004

(MTHFR)

Lee, 2002 (XRCC)

Elahi, 2002 (OGG1)

Evans, 2004 (GSTT1)

Hasibe, 2003 (Phase 1 and 2)

Brennan, 2003 (ADH3)

Geisler, 2003 (GSTs)

HCC

Alcohol metabolizing

genes;

Phase 1 and 2

Steroid hormone

metabolizing genes

DNA Repair

Yu, 2002; Sakamoto, 2005 (ALDH2)

Kato, 2003 (CYP2E1); Rossi, 2003

(hormones); Tseng, 2005

(UGP1A7)

Munaka, 2003 (alcohol-related and

Phase 1)

Kirk, 2005 (GSTM1, XRCC1)

Huang, 2003 (NAT2 and red meat)

Ovary

Phase 1,

TSG

Phase 2;

Galactose;

Estrogen, Phase 1

Cho, 2003 (DNA repair)

Kongruttanachok, 2001 (CYP2E1)

Cho, 2003 (Phase 1)

Tiwawch, 2003 (p53)

Jalbout, 2003 (HSP71-2)

Coughlin, 2002 (GST)

Fung, 2003 (GALT)

Goodman, 2001 (CYP1B1)

Leukemia

(acute)

Phase 1 and 2,

Apoptosis;

Nutrition (folate

metabolism);

DNA repair;

Drug transport

Sbley, 2003 (FAS)

Krajinovic, 2004 (ALL and

MTHFR)

Infante-Rivard, 2003 (DNA repair)

Bowen, 2003 (Phase 1)

D’Alo, 2004 (AML and CYP1A1

and GSTT1)

Jamroziak, 2004 (ALL and MDR1)

Rollinson, 2000 (Phase 2); van der

Logt, 2005 (NQO1)

Robien, 2003 (leukemia)

Pancreas

Prostate

DNA repair;

Phase 1 and 2

Steroid hormone

genes;

Growth factors;

Cell cycle;

Obesity/energy

regulation;

Nutrient

TSG

Duell, 2002a and 2002b (DNA

repair)

Ockenga, 2003 (UGT1A7)

Soderstrom, 2002; Chang, 2003

(SRD5A2)

Margiotti, 2002 (HSD)

Allen, 2003; Li, 2003 (testosterone)

Mononen, 2000 (androgen)

Madigan, 2003 (CYP17)

John, 2005 (VDR)

Gelmann, 2003 (TSG)

Lung

Phase 1 and 2;

DNA repair;

Nutrition,

Inflammation/

Immune

Gsur, 2003 (mEH)

Snoek, 2000 (HLA)

Xing, 2003 (XPD)

Cajas-Salazar, 2003 (EH)

Loriot, 2001; Tan, 2001 (CYP2A6)

Wang, 2003 (CYP2A13)

Chevier, 2003 (MTHFR)

Chen, 1999 (NADPH)

Sinuues, 2003 (MDR1)

Quiling, 2003 (CCND1)

Zienolddiny, 2004 (IL1B)

Wikman, 2001 (NATs)

Miller, 2002; McWilliams, 1995

(GSTM1)

Benhamou, 2002 (GSTM1);

Houlston, 1999 (GSTM1)

LeMarchaud, 2003 (CYP1A1,

exon 7)

Rostami-Hodjegan, 1998

(CYP2D6)

Taioli, 2003 (CYP1A1 and GST,

<age 45)

Vineis, 2003 (CYP1A1)

Phase 1 and 2

TSG;

Major gene variant;

Nutrients;

DNA repair,

Angiogenesis

Nowell, 2004 (SULT1)

Ho (insulin)

Medeiros, 2002, 2003 (NOS)

Fang, 1992 (misc. genes)

Xu, 2002 (MSR1)

Chang, 2003; Tanaka, 2002

(CYP1B1)

Miller, 2003 (macrophage

scavenger)

Kibel, 2003 (CDKN1*)

Cybulski, 2004b (NBS1)

Plummer (CYP3A4)

Tayeb, 2003 (CYP3A4)

Wu, 2002; Ferreira, 2003; Chang,

2003 (Phase 1 and 2)

Miller, 2003; Adler, 2003; Stanford,

2003; Severi, 2003; Meitz, 2002

(familial prostate genes)

Chokkalingam, 2001 (vitamin D)

Rybicki, 2004 (DNA repair)

Simard, 2003 (overview); Ntais,

2003a (SRD5A2);

Ntais, 2003b (vitamin D); Ntais,

2003c (CYP17)

Lymphoma

Immune response

(HLA, cytokines),

Phase 2,

Folate metabolism,

DNA repair,

Apoptosis

Howell, 2002 (mechanism)

Hishida, 2003 (thymidylate

synthase), Zhang, 2005

(apoptosis)

Cerosaletti, 2002 (DNA repair)

Kerridge, 2002; Battuello, 2004

Cunningham, 2003 (inflammatory

cytokines)

Robien and Ulrich, 2002

Skin

Renal

Thyroid

DNA repair;

Pigmentation

Phase 1,

Phase 2,

Angiogenesis

Tyrosine kinase

Nelson, 2002; Han, 2004 and 2005

(DNA repair)

Sturm, 2003 (MC1R)

Longuemaux, 1999 (CYP1A1)

Sweeney, 2000 (GSTT1)

Sweeney, 2000 (GSTs)

Abe, 2002 (VEGF)

Kimura, 2003 (RET/PTC-RAS-

Melanoma

DNA repair,

Pigmentation,

Major gene variants

Garcia-Borron, 2003; Millikan,

2005 (DNA repair)

Hayward, 2003 (MC1R)

Tomescu, 2001 (XPD)

Bertram, 2002 (major genes)

Testicular

receptors

Phase 2

BRAF pathway)

Youngren, 2003 (mechanism)

Harries, 1997 (GSTP1)

NPC

Immune (HLA,

PIGR),

DNA repair,

Lu, 2003; Jalbout, 2003 (HSP)

Hirunsatit, 2003; Butschkovacic,

2005 (HLA)

(*) Boldface type indicates data derive from a pooled or meta-analysis.

(p.588) understanding their role and mechanisms of action will offer new perspectives for prevention.

OVERVIEW OF CANCER-SPECIFIC ASSOCIATIONS

Details of studies organized by tumor type are summarized in Table 29–5. The tobacco-related cancers currently provide the best examples of reasonably well-established associations, notably the NAT2 “slow acetylator” polymorphism and the GSTM1 “null” genotype with bladder cancer. Lung cancer exhibits associations with a Phase 1 gene (e.g., CYP1A1) and a Phase 2 gene (e.g., GSTM1), but the precise role that these genes play in concert and the degree of gene–environment and gene–gene interaction await larger studies. Other tobacco-related neoplasms such as pancreas cancer have not been as extensively studied, and because the degree of association with tobacco is less than with lung cancer, associations with genes involved in processing the exposures may be correspondingly weaker.

Next most studied are the hormone-related malignancies such as breast, endometrial, and prostate cancers. In general, consistent asso- (p.589) ciations with specific mechanistically plausible genes have not been demonstrated. Much larger and more comprehensive studies are planned in a coalition of cohort studies involving replication and pooling strategies.

Colorectal cancer and adenomas have been widely studied, and nutrient-related genes (e.g., MTHFR) and inflammation-related genes (e.g., COX2) are the most plausible candidates. Genes involved in immune-related and inflammatory processes are the best current candidates in gastric cancer. There are some suggestive findings in melanoma involving genes that influence skin pigmentation (e. g., MC1R) and in NHL in genes involved in immune processes, consistent with the known factors that influence their etiology. For the cancers where etiologic factors are poorly understood, such as testicular and brain cancers, and leukemia, susceptibility genes are not yet established.

As the relation of cancer to susceptibility genes is investigated in larger population samples with greater numbers of genes, it is likely that strong relationships of genes with intermediate phenotypes or “endophenotypes” will be found. Genes involved in myriad precursor states and mechanisms relevant to cancer such as Helicobacter pylori infection, mammographic density (Lillie, 2004), addiction to alcohol or nicotine, enhanced inflammatory response to hepatitis C virus, chronic obstructive pulmonary disease, or poor DNA repair after sun damage may reveal many strong associations with these intermediate phenotypes that are stronger than with cancer.

Another emerging trend is the investigation of histological and molecular subclassifications of cancer. Associations of polymorphic gene variants with specific disease histologies are illustrated by lung cancer, e.g., small cell lung cancer (Daly, 2003), adencarcinoma (Wang, 2003), or tendecy to early metastasis (Rostami-Hodjegan, 2003). Selected histologic variants of esophageal (Dally, 2003; Wang, 2003) or gastric cancer (Meireles, 2004), and folliclular center lymphoma (Lossos, 2001) are further examples. Related to this will be investigations of the genetics of cancer precursors such as atypical adenomatous hyperplasia (lung) (Marcus and Travis, 2002) or B-cell monoclonal lymphocytosis (B-cell malignancies) (Marti et al., 2003).

It seems likely that certain genes and their families will be shown to alter risk for a spectrum of cancer types. Tobacco carcinogen-metabolizing genes may affect a number of smoking-related cancers, although the precise tobacco constituents, mechanisms, and levels of risk may vary by tumor type. Similarly, genes that affect energy balance, inflammation, DNA repair, and other critical pathways may have an effect common to several types of tumors as well as other chronic conditions. Cohort studies (where multiple tumor end-points may be studied) will be useful to address these questions.

A complementary consideration is that a complex disease such as cancer will likely be influenced by many genes and pathways (i.e., a polygenic mechanism). Theoretical constructs (multistage theories of carcinogenesis) or simple consideration of the independent steps necessary for a cancer to take hold suggest that genes can operate on multiple targets (i.e., different carcinogens), directions (to accelerate, block, or dysregulate a process), and biological levels (i.e., DNA, cell, tissue, organ, etc.) compatible with the action of many genes.

META-ANALYSES

Table 29–7 summarizes published pooled and meta-analyses exhibiting suggestive findings for well-studied genes and specific cancers. Although subject to well-known limitations as well as the rapid acceleration of molecular technologies that render older approaches archaic, these studies provide the current best assessments of reported gene–cancer associations. The much larger and more comprehensive studies expected in the next few years should provide a clearer picture for the role of these genes in cancer. Overall, we are on the early part of an exponential growth curve with regard to genetic investigations of complex diseases, so that the status of individual gene–cancer relationships and the pattern of overall associations that will emerge are difficult to predict based on the available data. Nevertheless, given the mechanistic data and the body of work represented by the meta-analyses, evidence-to date supports a role for metabolic genes in altering risk of tobacco-related cancers. Most convincing is the increased risk associated with GSTM1 null (lung and bladder cancer), CYP1A1 variant (lung cancer), and NAT2 “slow acetylators” (bladder cancer). Even with their limitations, pooled analyses also provide the only substantial data on gene–environment effects and combinations of genes. For example, increased risk of lung cancer due to at-risk variants of CYP1A1 and GSTM1 was observed in nonsmokers (Hung, 2003), those with both at-risk gene variants and in those below age 45 (Taioli, 2003). The available evidence also suggests that NAT2 slow acetylators have a more pronounced risk of bladder cancer among tobacco smokers (Marcus, 2000a, 2000b).

GENE–ENVIRONMENT INTERACTION

All genes act in the context of the environment, and early models for gene–environment interaction were described by Ottman (1990). Gene–environment interaction may be defined as a differential effect of the environment on disease risk based on genotype, or a differential effect of a genotype based on environmental exposures. Smith and Day (1984) showed that detecting interactions of the same magnitude as main effects in 1:1 unmatched case-control studies required at least a fourfold increase in study size. Depending on whether an additive or multiplicative scale as a statistical model is defined, the presence or absence of interaction may entail some ambiguity. When genes are rare or environmental factors uncommon, a countermatching design may provide a way to improve efficiency (Andrieu et al., 2001).

A likely place to observe combined effects in cancer risk involve genes that metabolize extrinsic carcinogens such as bladder cancer associated with the interaction of NAT2 genotype and exposure to aromatic amines in tobacco smoke (Marcus, 2000b). In addition, the MTHFR C677T polymorphism increases the rate of colorectal adenomas when folate levels are low (Ulrich, 1999), and individuals homozygous for the ODC A-allele who use aspirin are less likely to experience adenoma recurrence than those homozygous for the major G-allele (Martinez, 2003). Other gene–environment relationships are plausible based on preliminary study, for example, increased risk of colorectal cancer in rapid NAT2/CYP1A2 phenotypes in those who consume well-done red meat (LeMarchand, 2001), a protective effect of cruciferous vegetables limited to GSTM1/T1 null subjects (Brennon, 2005) and an increased risk of squamous esophageal cancer associated with alcohol drinking in ALDH2 “inactive” subjects (Yokoyama, 2003). Individuals with inactive ALDH2 generally consume less alcohol because this genotype results in slow elimination of acetaldehyde and causes “flushing” reactions. However, when these individuals consume alcohol, risks of esophageal and possibly other cancers are increased (Yokoyama, 2003).

Because of limited efforts to consider genes and the environment in concert, little is known about the contribution of specific agents in complex mixtures as well as causal pathways in cancer risk. For example, there are many reported studies of CYP1A1 polymorphisms based on their putative role in activation of PAH carcinogens in tobacco-related cancer. Another group of studies assessed GSTM1, involved in the elimination of PAHs. However, only a few studies take environmental cofactors and both genes into account (Smith, 2001; Alexandrie, 2004), with some studies finding a combined effect of the genes that is more substantial. However, no studies have evaluated a comprehensive group of the genes involved in PAH metabolism or all the genes involved in the activation or detoxification of carcinogens present in tobacco smoke (e.g., nitrosamines, aryl amines, nicotine).

The available designs for study of gene–environment interaction (Goldstein, 1999) and the impact of disease and exposure misclassification and sample size in population-based designs have been reviewed (Garcia-Closas et al., 1998, 1999, 2003, 2004). Case-only designs can be used when certain conditions are met (i.e., exposure is independent of genotype) (Yang, 1997) and main effects or additive models cannot be directly assessed. It has been suggested that the population attributable fraction due to interaction be considered in power calculations in order to gauge feasibility and public health impact (Yang, 2003). Whatever the design, it is important to obtain detailed (p.590)

Table 29–7. Selected Meta-analyses and Pooled Analyses of Candidate Genes in Relation to Specific Cancers

Cancer

Gene

No. Studies

Summary OR and 95% CI

Author and Year

Lung

GSTM1

23

2.12 (1.43–3.13)

Houlston, 1999

Lung

GSTM1

21

1.34 (1.21–1.48)1

D’Errico, 1999

Lung

MPO

?

0.52 (0.39–0.90)

Schabath, 2000

(G > A)

Lung

CYP2D6

16

1.28 (1.01–1.58)

D’errico, 1999

Lung

CYP1A1 and GSTM1

14

CYP1A1 ile(462)Val

Hung, 2003

(Caucasian

2.99 (1.51–5.91)

nonsmokers)

GSTM1 1.20 (0.89–1.63)

Lung

CYP1A1

4

1.73 (1.30–2.31)

D’errico, 1999

(MspI-Asians)2

Lung

CYP1A1

10

1.04 (0.85–1.27)

D’errico, 1999

(MspI-Caucasians)

Lung

CYP1A1, “exon 7”

11

1.15 (0.95–1.39) het

Le Marchaud, 2003

1.54 (0.97–1.46) homo

p for gene-dosage 0.03

Lung

CYP1A1

22

2.36 (1.16–4.81)

Vineis, 2003

T3801C

homozygous variant

Lung

GSTM1

43

1.17 (1.07–1.27)

Benhamou, 2002

Lung

GSTM1

12

1.41 (1.23–1.61)

McWilliams, 1995

Lung

EPHX1 Exon 3 his/his

8

0.70 (0.51–0.96)

Lee, 2002

Lung

EPHX1 (his/his)4

8

0.65 (0.44–0.96)

Kiyohara, 2005

Lung

NQ01 (pro187ser)5

3

0.70 (0.56–0.88)

Kiyohara, 2005

Lung

MP0 (G-463A)6

22

0.70 (0.47–1.04)

Kiyohara, 2005

Bladder

NAT2

16

1.37 (1.20–1.57)

D’errico, 1999

Bladder

NAT2

22

1.40 (1.2–1.6)

Marcus, 2000a

Bladder

NAT2* ever smoking3

16

1.30 (1.0–1.6)

Marcus, 2000b

Bladder

NAT2

7

1.42 (1.14–1.77)

Vineis, 2002

with increase in risk limited to current smokers (OR = 1.74, 0.96–3.15)

Bladder

GSTM1

11

1.57 (1.36–1.81)

D’errico, 1999

Bladder

GSTM1

17

1.44 (1.23–1.68)

Engels, 2002

Colorectal

NAT2

12

1.11 (1.02–1.39)

D’errico, 1999

Head and Neck

GSTM1

28

1.23 (1.06–1.42)

Hashibe, 2003

Head and Neck

GSTT1

18

1.17 (0.98–1.40)

Hashibe, 2003

Head and Neck

CYP1A1 (Val462)

1.35 (0.95–1.82)

Hashibe, 2003

“Cancer”

HRAS1 “rare”

23

1.93 (1.63–2.30)

Krontiris, 1993

(1) Caucasians only, 13 studies, OR = 1.21 (1.06–1.39).

(2) CYP1A1, Asians exon 7, 3 studies, OR = 2.25 (1.37–3.69); Caucasians 4 studies, OR = 1.30 (0.89–1.90).

(3) An interaction is this context means that smokers who are slow acetylators have a higher risk of bladder cancer than smokers who are rapid acetylators, and that slow acetylators who do not smoke are at similar risk to nonsmoking rapid acetylators.

(4) Whites only.

(5) Japanese.

(6) Caucasians only.

data on clinical status, exposure, and related biological information derived from specimen evaluation to provide the best context for interpretation.

GENE–GENE EFFECTS

Genes also act in concert with other genes, and animal models suggest that epistatic relationships between genes may be common (Moore, 2003). Epistasis was originally defined as “masking” of one gene’s effect by another but more broadly refers to differential phenotypic expression of a genotype at one locus dependent on a genotype at another. An example of genes thought to exhibit this type of effect among modifier loci in humans is the combination of the Phase 1 CYP1A1 minor allele and the Phase 2 GSTM1 null genotype (Kihara et al., 1995; Taioli et al., 2003; Vineis et al., 2004). Both of these genes seem to exhibit an independent effect on some tobacco-related cancers (see Table 29–6), but there is suggestive evidence that their combined effects may be supra-additive. Sample size and design considerations for population-based studies involving gene–gene interactions are described by Wang and Zhao (2003). The ability to recognize gene– gene effects is complicated by the degree to which genes act in pathways or flexible networks. For example, if a mutation in one gene alters the genetic pathway, it becomes difficult to detect combined effects (Greenspan, 2001). A data mining approach, multifactor dimensionality reduction, has been used to identify putative higher-order gene–gene interactions in the inflammation pathway in prostate cancer (Xu, 2005).

It is important to appreciate that genes operate in pathways and that feedback, homeostatic, and compensatory mechanisms are involved, so that it would be unusual for polymorphism in one particular gene to have a critical effect on a common cancer. In order to dissect the intrinsically weak effects of modifier genes, it is more realistic to evaluate genes comprehensively, including multiple SNPs and the joint effects of related genes in a particular pathway or family.

PROSPECTS

The investigation of modifying genes in cancer induction is a dominant theme in molecular and genomic epidemiology at the beginning of the new millennium (Ioannidis, 2006). The integration of genomics and epidemiology in large population studies should allow application of advanced technologies and participation of scientists from diverse disciplines to advance our understanding of the role of genes across the cancer spectrum. A primary goal of epidemiology is to understand the role of genes in cancer causation and progression in large-scale epidemiologic platforms. Future studies will clarify their interactions with environmental exposures, their role at various stages of carcinogenesis including precursor states, their effects on molecular effectors in tissue, and their influence on outcome and response to therapy.

(p.591) While progress in identifying the precise hereditary determinants that contribute to common cancers has not been as rapid as some may have anticipated, meta-analyses support a number of associations of common genes with cancer and other complex diseases, and it is likely that we are at a very early stage of discovery. As high-throughput genotyping is combined with well-designed large-scale population studies supported by efficient biospecimen collection and informatics capability, specific genes and pathways that are critical to cancer should emerge. These insights will fulfill the promise of the genomics revolution and galvanize new approaches to cancer prevention, diagnosis, and treatment.

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