In our pharmacogenomic analysis, we investigated in a step-wise, increasingly complex genetic fashion, the interactions between candidate polymorphisms and tamoxifen exposure among P-1 breast cancer cases. This case-only analysis revealed that only one of our genomic variants (CYP2D6_C1111T) was associated (marginal uncorrected statistical significance) with treatment arm at either the individual or the haplotype level. Thus, none of the polymorphisms or haplotypes, when tested individually or in pairs, showed a significant association with treatment arm. This limited association can be explained by: 1) In most cases, our candidate polymorphisms were selected from epidemiologic studies which involved estimates of population-based breast cancer risk, as opposed to the impact of pharmacogenomic interactions on risk. Our assumption was that genes shown to affect cancer risk would also influence the effect of gene-environment (e.g. drug) interactions on breast cancer risk; 2) The small P-1 case sample size (288), of which only 249 were available for analysis, also contributed to the lack of detection of statistically significant interaction at these levels of analysis; 3) Individually the tested polymorphisms were expected to have minor impact on phenotypic outcomes (risk or drug interaction), which likely contributed to inconsistencies in detecting risk associations in prior studies.
In contrast to the primary (SNP) and secondary (haplotype) analyses, pathway analysis is an additional approach which could potentially amplify the small risk associations occurring at the level of individual genetic variants and haplotypes by elevating the analysis to the level of a network. Multiple polymorphisms are expected to influence each other's effects and/or to show interaction with environmental factors (i.e.
, drug exposure) in relation to a specific shared outcome (breast cancer occurrence). The genetic polymorphisms are related to each other through a global “causal” network, or pathway. Since all the genes occupy specific places in a pathway, the biological effect of each genetic factor is not independent of the others; the genes are epistatically related to each other. An advantage of the pathway method is that it reduces the statistical complexity by limiting statistical measurements to gene-gene interactions specified by the network. Our application of pathway analysis, using SEMs [39
], incorporated constitutional genomic variations. These candidate genomic variants had previously been shown to have a strong positive, i.e.
“causal”, relationship with increased breast cancer risk, presumably via differences in the activity of their encoded enzyme products. One way to view these genotypic variants is as “perturbers” of the functioning of a metabolic pathway network [43
]. We investigated how these genomic variations perturbed responsiveness to tamoxifen at both the individual gene level and the pathway network, or systems, level [44
]. The pathway approach enabled us to analyze the simultaneous perturbation of tamoxifen response by all the co-existing genetic variants in the study. For example, tamoxifen was shown to reduce the risk of breast cancer in P-1:BCPT high-risk women exposed to this drug. However, it is possible for some individuals to carry minor alleles that perturb responsiveness to tamoxifen in a manner that prevents an effective response. These women could develop breast cancer despite receiving tamoxifen.
A priori, given that there was a preventive effect of tamoxifen in the overall P-1 trial, we would expect the cases in the P-arm to more closely resemble the overall P-1 population than the T-arm cases with regard to the allelic state of genes encoding tamoxifen-metabolizing enzymes. According to the results of our pathway analysis, the composite genotype dataset in the P-arm is consistent with the TAM pathway model, i.e. the observed data are not significantly different from the predictions of the model (chi-square p-value=0.4279). In contrast, the pathway is disrupted in women with breast cancer in the presence of tamoxifen such that the T-arm cases have different patterns of genotypes than the P-arm cases. Because P-1:BCPT was a randomized trial, we expect the P-arm and T-arm populations in the overall trial (i.e. cases and controls) to have the same distribution of genotypes associated with tamoxifen responsiveness and resistance. Thus, some of the alleles seen in P-arm cases would have conferred responsiveness to tamoxifen, had these women received the drug. Tamoxifen recipients who have such tamoxifen-responsive alleles, i.e. the common alleles that encode the normal metabolizing enzymes that successfully activate this pro-drug, were more likely to benefit from tamoxifen exposure, i.e., to have experienced prevention of breast cancer; they would have been selected out of the case population, leaving them under-represented among all P-1:BCPT participants who developed breast cancer, and thus, eliminated from the T-arm in this case-only study. The allelic combinations that appear in the T-arm cases would be enriched for genomic variants that encode enzymes that conjointly confer a decreased ability to metabolize tamoxifen; the T-arm cases represent a tamoxifen-resistant population. This could explain why the TAM pathway allelic observations in this ta-moxifen-resistant population do not fit the model described by the TAM pathway; in fact, there is a statistically significant difference at this network level between the observations and the predictions of the TAM pathway model for the tamoxifen-treated arm (chi-square p-value=0.0090).
The TAM pathway gene that has emerged as encoding the most important single enzyme involved in tamoxifen activation is CYP2D6
, a highly polymorphic gene. An extensive pharmacogenetic literature documents the many CYP2D6
genotypes encoding isozymes with reduced activity [30
]. Decreased activity results in poor conversion of tamoxifen to its most active metabolite, endoxifen (4-hydroxy-N
-desmethyl tamoxifen) [45
]. Women with low or absent CYP2D6 activity (due to either perturbation by an inherited CYP2D6
variant or concomitant treatment with a drug that suppresses CYP2D6 activity [32
] exhibit lower levels of endoxifen which, in turn, might be expected to impact clinical outcomes. Such an effect of CYP2D6
has been observed in several studies [4
]. For example, in these studies homozygosity for poor metabolizing CYP2D6
genotypes was shown to correlate with worse outcomes such as higher risk of relapse, shorter time to recurrence, and worse relapse-free survival in women with breast cancer treated with adjuvant tamoxifen [4
]. As a result of such accumulating data supporting associations between CYP2D6 activity and clinical outcome, CYP2D6
genotype is beginning to be considered by some clinicians in making therapeutic decisions regarding tamoxifen use in the clinical treatment setting. However, in contrast to the findings of positive associations, a number of other studies have shown no correlation or even lower recurrence risk in tamoxifen-treated patients with poor metabolizing CYP2D6
variant status [53
]. Among these, the updated results of an ongoing multi-center study conducted by the International Tamoxifen Pharmacogenomics Consortium, presented at the 2009 San Antonio Breast Cancer Symposium, showed no difference in clinical outcome in relation to CYP2D6 metabolizer phenotype [58
]. The clinical significance of CYP2D6
variants is therefore still a topic of debate among oncologists who prescribe tamoxifen in the treatment setting [8
Given these contradictory findings about the impact, if any, of CYP2D6
allele status on clinical outcome, the role for factoring CYP2D6
genotype into therapeutic decision-making regarding tamoxifen requires further clarification in large prospective randomized clinical trials [9
]. The NSABP P-1:BCPT offers a cohort of cases nested within such a large prospective randomized trial (13,388 participants), in this case among high-risk women. Furthermore, the inconsistency of the data regarding the impact of polymorphisms in CYP2D6
analyzed as a single gene on clinical outcomes in breast cancer patients [8
] provides a rationale for evaluating alternative analytic strategies. Our systems approach, by incorporating multiple relevant genes taken together as a network, offers a meaningful alternative to the pharmacogenomic analysis of tamoxifen in relation to breast cancer at the single gene level. The inconsistency among study outcomes is undoubtedly due to multiple factors, both exogenous and endogenous, particularly genetic factors, that obscure the effect of variation in the single gene, CYP2D6
. The systems approach to genomic analysis that we present in this paper, in providing an alternative to single gene pharmacogenetic analysis, has the potential to override the limitations inherent in focusing on one gene, even when that gene, like CYP2D6
, encodes the dominant enzyme in the tamoxifen pathway. Pathway analysis offers a global analytic approach that recruits the small but additive effects of variants in multiple contributory genes that metabolize tamoxifen.
Our search for relevant CYP2D6
variants predated the current pharmacogenetic literature and was based on the then-available reports that focused on association of polymorphisms with breast cancer risk. In our candidate gene approach, five such CYP2D6
polymorphisms () emerged from this literature search, and we tested all of them in our MALDI-TOF assay. One of the tested variants, CYP2D6
_EX4, was not observed in our P-1G3 case population and was excluded from our statistical analyses. Of the four CYP2D6
variants that were subjected to statistical analyses, CYP2D6
_C1111T was the only one found to exhibit significant associations with study arm. These associations were apparent when CYP2D6
_C1111T was tested as a single variant or within a CYP2D6
haplotype defined for all four tested polymorphic sites (). The C1111T variant is found in haplotypes with decreased enzymatic activity and thus may be associated with resistance to tamoxifen through its failure to convert tamoxifen efficiently to endoxifen (http://www.cypalleles.ki.se/cyp2d6.htm
, last accessed June 22, 2010). This mechanism could explain our statistical observations regarding CYP2D6
_C1111T. Thus, the three observed C1111T alleles all occurred in the T-arm subjects, where their negative impact on endoxifen production would be expected to allow breast carcinogenesis despite the presence of the pro-drug tamoxifen.
The recent literature on CYP2D6 genotyping in relation to enzymatic function and clinical outcome reports numerous additional genomic variants that were not in the epidemiologic literature used to plan this study. Based on these updated reports we attempted to obtain additional P-1:BCPT case DNA for more complete CYP2D6 genotyping. This proved impossible because the quantity of DNA remaining in the original aliquots after our genotyping was insufficient for additional testing, and the sample anonymization required for human research subject protection prevented our matching new samples to the residual DNA.
In our analysis of the P-arm in relation to the TAM pathway, we observed that allelic variation in CYP2D6
had nonrandom associations with alleles in multiple TAM pathway genes. The CYP2D6
gene was a hub at the center of strong associations between genotypic variations only in this arm (Figure 2A
). The tight correlations between allelic variation in CYP2D6
and allelic variation in adjacent pathway genes (CYP3A4, CYP2C9
, and SULT1A2
) are depicted as red numbers in . However, this cluster of nonrandom associations centered on CYP2D6
is only seen in TAM pathway analysis of the P-arm. This hub of associations disappears in the T-arm, implying a random association of CYP2D6
with specific variants in its neighboring genes (). This difference between the two arms with respect to the central “hub” nature of CYP2D6
suggests that the tight allelic correlations seen only in the P-arm are a major contributor to the goodness-of-fit, i.e.
agreement, that the P-arm shows with the TAM pathway model. The emergence of CYP2D6
as a hub of activity in the P-arm but not the T-arm in our TAM pathway model is consistent with the key role played by this gene in tamoxifen metabolism. In this respect, our findings at the pathway, but not the individual gene, level concur with data from studies supporting an impact of CYP2D6
genotype on clinical outcomes [4
]. The ability of pathway analysis to override the null associations seen at the individual gene level offers a possible reconciliation of the contradictory findings from other studies, all of which incorporated CYP2D6
variation in a single-gene approach.
Beyond the CYP2D6 hub, path analysis has elucidated other key foci of genetic variation. The correlations between variation in three additional pairs of adjacent genes, CYP1A1-CYP3A4, CYP3A4-CYP2C9, and CYP2C9-SULT1A2, were also significant. None of these associations involves CYP2D6, suggesting that these other pathway enzymes are meaningful contributors to tamoxifen activation. Again, these non-CYP2D6 correlations may partially explain the negative results in some of the studies that focused solely on the effect of slow-metabolizing variants of CYP2D6 on clinical outcome.
In conclusion, we have evaluated correlations between variants in multiple genes simultaneously, viewing them as a composite system. We have shown that by treating the entire system as a marker of drug response, the limitations inherent in evaluating individual genetic variants, each conferring a minor effect on tamoxifen response, may be overcome. Our underlying candidate gene approach was critical to this process, with variants identified from preexisting biological literature offering a qualitative framework on which to quantitatively model actual data from the P-1 cases. Our application of pathway analysis to the genetic network underlying tamoxifen metabolism (TAM pathway) has shown the ability to discern differences between the cases exposed to the two drugs (tamoxifen versus placebo), despite the evidence for non-significant or only marginally significant distinctions based on less complex statistical genetic comparisons. We have also demonstrated that CYP2D6 plays a key role in tamoxifen activation and clinical response at the level of pathway analysis, observations that fit with the extensive recent literature documenting its importance in metabolizing tamoxifen to its active form. Beyond reinforcing the centra I ity of CYP2D6 to tamoxifen response, our pathway analysis strongly suggests that specific combinations of allelic variants in other genes, including CYP1A1 with CYP3A4, CYP3A4 with CYP2C9, and CYP2C9 with SULT1A2, also make major contributions to the tamoxifen-resistance phe-notype.
Our observations suggest directions for pursuing more detailed analysis of pharmacogenomic interactions involving the TAM pathway. Thus, four critical foci in the TAM pathway (at CYP2D6, CYP1A1-CYP3A4, CYP3A4-CYP2C9, and CYP2C9-SULT1A2) offer a starting point for future investigations to dissect apart the specific allelic interactions that underlie these correlations. This should allow us to build more reliable genetic classifiers of tamoxifen response which could be used to offer an individualized approach to tamoxifen chemoprevention by identifying and treating only those women who are most likely to benefit from its administration.