Glutathione S-transferase polymorphisms have been widely studied in the literature, and thus there have been many attempts over time to synthesize the effect of genetic variants through meta-analyses. Compared to previous attempts of meta-analyzing data, we have used more rigorous and standardized criteria, as defined through the Venice initiative [4
]. These have been specifically developed for genetic data, and include an evaluation of the amount of evidence available, the degree of consistency/replication of findings, and of bias. With such strict criteria we have found that overall there is not strong evidence of an involvement of commonly studied GST polymorphisms in lung cancer.
Application of the Venice criteria indicates weak
evidence in support of an association with GSTM1
null, and GSTP1
Ile105Val polymorphisms and lung cancer, both overall and after analysis of ancestral subsets. The only exception is for the GSTP1
allele in East Asian populations, where the evidence for association with lung cancer is moderate
. This result may be attributable to differences in genotype frequencies in East Asian populations, more carefully designed studies, or local differences in exposure to relevant carcinogens, although bias cannot be fully excluded [131
The available a priori evidence on a role of GSTs in lung cancer is rather strong, i.e. a criterion of biological plausibility can be easily met. This is why, in fact, they have been studied extensively. Glutathione S-transferases catalyze the detoxification of electrophilic metabolites, including benzo[α]pyrene and other polycyclic aromatic hydrocarbons (PAHs) found in tobacco smoke, which are likely to play a key role in lung carcinogenesis. The GSTM1 and GSTT1 deletions result in null activity of their respective enzymes, and so a strong functional impact would be expected. However, the loss of function associated with these gene variants is likely compensated by the existence of alternative metabolic pathways, which may help to explain why the effect, if any, is weak.
Despite the fact that GST variants are considered to be among the most extensively studied genetic markers of susceptibility, and that GSTM1 deletion in particular exhibited statistically significant associations with lung cancer in previous meta-analyses, the application of the Venice criteria shows that when this standardized method for evaluating the cumulative evidence of association is applied, the previous meta-analyses would fall into the category of “weak evidence”. While the estimates of effect size changed over time for some associations, inconsistencies dominate and the evaluation of “weak” is mainly related to heterogeneity and/or lack of protection from bias.
In spite of the systematic approach outlined by the Venice interim criteria, the present exercise shows that there may be room for improvement in the standardization and performance of the Venice criteria [56
]. One issue that emerged from this analysis is the possibility that different weights should be assigned to the different criteria included in the Venice method. For example, the assessment of protection from bias
should probably have less weight than the consistent replication across studies and meta-analyses of a very strong association, with a large effect estimate (e.g. an odds ratio of 2.0). However, when small effects are observed, as in the case of the associations of GST variants and lung cancer, the presence of a modest bias could easily contribute to producing a spurious, small association of the magnitude reported in this re-analysis. In the area of genetic susceptibility, where small associations are expected, hints for bias cannot be discarded even though tests for bias are not perfect [132
One aspect emerging from our exercise is the good correspondence of the results obtained with the meta- and respective pooled (individual-level) analyses. These two approaches have different principles, strengths, and limitations and may not always agree in their results [134
]. For some analyses, such as those restricted to populations of African descent, the pooled analysis offered the appropriate data that were missing in the meta-analysis, thus allowing the drawing of a more complete picture. Another potential strength is that in several instances the replication
of the meta-analysis was classified with a grade of C due to large amounts of heterogeneity, while the pooled analysis constantly produces less heterogeneity. This could be attributable to the ability of pooled analyses to deliver adjusted estimates but also may reflect the smaller sample size relative to the meta-analyses. It should also be acknowledged that heterogeneity estimates have substantial uncertainty, as evidenced by the large 95% confidence intervals of heterogeneity metrics, even when a large number of studies are available [22
As expected from the role that metabolic genes have in carcinogenesis, the associations derived from individual studies and from summary estimates in meta- and pooled analysis confirm the definition of “low penetrance” genes, with increases in lung cancer risk ranging from 15%–21% at the most, but with many studies showing no significant associations. The original Venice criteria applied here weight the credibility that an association is present, but do not score the certainty that the association is absent. Very small effects (e.g. odds ratios of 1.05) cannot be excluded with certainty.
The effort to date regarding elucidation of the true effects of GSTM1
deletion, and GSTP1
Ile105Val polymorphism has been a resource-consuming, laborious task with a lot of uncertainties surrounding the evidence collected so far. In spite of the biologic plausibility due to involvement in xenobiotic detoxification, GST polymorphisms have not fulfilled their early promise as biomarkers of lung cancer susceptibility. Rather, the large body of literature either displays wide variability between studies, yields no overall significant summary effect, or both. This seems to be a common feature of studies on metabolic gene variants and cancer. Metabolic gene variants are known to produce very small odds ratios in both directions, either for a positive or negative association, and the significant heterogeneity and susceptibility to bias shown in most of the analyses conducted so far may have contributed to dilute the real effect, if any exist. It is certainly possible that poor replication – leading to a high degree of heterogeneity - is related to local differences in exposures affecting the populations from which samples are drawn, (for example differences in environmental pollution, smoking habits or dietary factors) which are difficult to incorporate in the score proposed by the Venice guidelines. It is possible that restricting the assessment to studies including a formal test for gene-environment interaction would yield more positive evaluations of the overall associations. One should also consider the degree of misclassification that is much higher for environmental or lifestyle exposures than for gene variants [135
A further aspect highlighted by this exercise is that certain ethnic groups, such as populations of African descent, are still highly understudied. No conclusion about a possible association between susceptibility genes and cancer can be drawn at present.
In light of the limitations and determination of weak cumulative evidence shown by this exercise that evaluated the credibility of the association between commonly studied GST polymorphisms and lung cancer, future studies may try to evaluate interaction of GST variants with other genes in adequately powered datasets, as well as include the assessment of gene-environment interaction. This may allow for identification of gene–disease associations and interactions where the cumulative evidence for association is strong, while evidence for main effects is conclusively uniformly weak. Application of criteria for assessment of the strength of cumulative evidence, such as the Venice criteria, to meta-analyses will help to clarify the validity of summary estimates for gene-disease associations, making it easier for researchers and clinicians to comprehend the true nature of the risk involved, if any.