Lack of significant association between CYP1A1 T3801C polymorphism and breast cancer risk: a meta-analysis involving 25,087 subjects

Impact of CYP1A1 variants on the risk of acute lymphoblastic leukemia: evidence from an updated meta-analysis

OBJECTIVE

Our study aimed to investigate the association between cytochrome P450 1A1 (CYP1A1) polymorphisms (T3801C and A2455G) and acute lymphoblastic leukemia (ALL) risk, considering genetic models and ethnicity.

MATERIALS AND METHODS

PubMed, Embase, Web of Knowledge, Scopus, and the Cochrane electronic databases were searched using combinations of keywords related to CYP1A1 polymorphisms and the risk of ALL. Studies retrieved from the database searches underwent screening based on strict inclusion and exclusion criteria.

RESULTS

In total, 2822 cases and 4252 controls, as well as 1636 cases and 2674 controls of the C3801T and A2455G variants of CYP1A1, respectively, were included in this meta-analysis. The T3801C polymorphism of CYP1A1 significantly increases the risk of ALL, particularly those observed in Asian and Hispanic populations, independent of age. Similarly, the A2455G polymorphism of CYP1A1 plays a significant role in the susceptibility to ALL in all genetic models, except the heterozygous form. This association was observed mainly in mixed populations and in both children and adults (except in the heterozygous model).

CONCLUSION

Our comprehensive analysis indicates that the T3801 and A2455G polymorphisms of CYP1A1 may increase the risk of ALL depending on ethnicity. Therefore, both variants should be considered promising biomarkers for ALL risk. Further large-scale investigations are necessary to assess other factors, such as gene-gene or gene-environment interactions.

Associations of CYP1A1 gene polymorphisms and risk of breast cancer in Indian women: a meta-analysis

Reported associations of CYP1A1 polymorphisms with breast cancer have been inconsistent. In this meta-analysis examining breast cancer associations of three CYP1A1 polymorphisms (M1, M2 and M4) among Indian women may yield information that may be of clinical and epidemiological use for this particular demography. We searched MEDLINE using PubMed and Embase for association studies. From seven published case-control studies, we estimated overall associations and applied subgroup analysis to explore differential effects. All three polymorphisms exhibited overall increased risk, significant in M1 (OR 1.61–1.65, p = 0.04) and M4 (OR 2.02–3.92, p = 0.02–0.04). Differential effects were observed only in the M1 polymorphism where M1 effects were significant in South Indians (OR 2.20–4.34, p < 0.0001) but not the North population, who were at reduced risk (OR 0.64–0.77, p = 0.03–0.55). These populations were not materially different in regard to M2 and M4 as did the women stratified by menopausal status. In this meta-analysis, M1 and M4 effects may render Indian women susceptible, but may be limited by heterogeneity of the studies. Differential effects of the M1 polymorphism in breast cancer render South Indians susceptible compared to those in the North.

Evaluation of association studies and a systematic review and meta-analysis of CYP1A1 T3801C and A2455G polymorphisms in breast cancer risk

Background

Nine previous meta-analyses have been published to analyze the CYP1A1 T3801C and A2455G polymorphisms with BC risk. However, they did not assess the credibility of statistically significant associations. In addition, many new studies have been reported on the above themes. Hence, we conducted an updated systematic review and meta-analysis to further explore the above issues.

Objectives

To explore the association on the CYP1A1 T3801C and A2455G polymorphisms with BC risk.

Methods

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (The PRISMA) were used.

Results

In this study, there were 63 case–control studies from 56 publications on the CYP1A1 T3801C polymorphism (including 20,825 BC cases and 25,495 controls) and 51 case–control studies from 46 publications on the CYP1A1 A2455G polymorphism (including 20,124 BC cases and 29,183 controls). Overall, the CYP1A1 T3801C polymorphism was significantly increased BC risk in overall analysis, especially in Asians and Indians; the CYP1A1 A2455G polymorphism was associated with BC risk in overall analysis, Indians, and postmenopausal women. However, when we used BFDP correction, associations remained significant only in Indians (CC vs. TT + TC: BFDP < 0.001) for the CYP1A1 T3801C polymorphism with BC risk, but not in the CYP1A1 A2455G polymorphism. In addition, when we further performed sensitivity analysis, no significant association in overall analysis and any subgroup. Moreover, we found that all studies from Indians was low quality. Therefore, the results may be not credible.

Conclusion

This meta-analysis strongly indicates that there is no significant association between the CYP1A1 T3801C and A2455G polymorphisms and BC risk. The increased BC risk may most likely on account of false-positive results.

Drug-Drug Interactions Involving Intestinal and Hepatic CYP1A Enzymes

Cytochrome P450 (CYP) 1A enzymes are considerably expressed in the human intestine and liver and involved in the biotransformation of about 10% of marketed drugs. Despite this doubtless clinical relevance, CYP1A1 and CYP1A2 are still somewhat underestimated in terms of unwanted side effects and drug–drug interactions of their respective substrates. In contrast to this, many frequently prescribed drugs that are subjected to extensive CYP1A-mediated metabolism show a narrow therapeutic index and serious adverse drug reactions. Consequently, those drugs are vulnerable to any kind of inhibition or induction in the expression and function of CYP1A. However, available in vitro data are not necessarily predictive for the occurrence of clinically relevant drug–drug interactions. Thus, this review aims to provide an up-to-date summary on the expression, regulation, function, and drug–drug interactions of CYP1A enzymes in humans.

A meta-analysis and in silico analysis of polymorphic variants conferring breast cancer risk in the Indian subcontinent

Background: Genetic association studies on breast cancer on the Indian subcontinent have yielded conflicting results, and the precise effect of these variants on breast cancer pathogenesis is not known. Methods: Genomic variants, as obtained from selected studies from the Indian subcontinent, were subjected to random-effects and fixed-effect meta-analysis. Functional annotation of the relevant variants was done through a tried and tested in silico pipeline. Results: We found rs4646903/CYP1A1, rs1799814/CYP1A1, rs61886492/GCPII, del2/GSTM1, rs4680/COMT and rs1801394/MTRR to be associated with breast cancer. The del2/GSTM1 holds the association in premenopausal women. Conclusions: This is the first study of its kind from the Indian subcontinent analysing the extent of association of variants across populations followed by their functional annotation in the disease pathway.

Ethnic background and genetic variation in the evaluation of cancer risk: a systematic review

The clinical use of genetic variation in the evaluation of cancer risk is expanding, and thus understanding how determinants of cancer susceptibility identified in one population can be applied to another is of growing importance. However there is considerable debate on the relevance of ethnic background in clinical genetics, reflecting both the significance and complexity of genetic heritage. We address this via a systematic review of reported associations with cancer risk for 82 markers in 68 studies across six different cancer types, comparing association results between ethnic groups and examining linkage disequilibrium between risk alleles and nearby genetic loci. We find that the relevance of ethnic background depends on the question. If asked whether the association of variants with disease risk is conserved across ethnic boundaries, we find that the answer is yes, the majority of markers show insignificant variability in association with cancer risk across ethnic groups. However if the question is whether a significant association between a variant and cancer risk is likely to reproduce, the answer is no, most markers do not validate in an ethnic group other than the discovery cohort's ancestry. This lack of reproducibility is not attributable to studies being inadequately populated due to low allele frequency in other ethnic groups. Instead, differences in local genomic structure between ethnic groups are associated with the strength of association with cancer risk and therefore confound interpretation of the implied physiologic association tracked by the disease allele. This suggest that a biological association for cancer risk alleles may be broadly consistent across ethnic boundaries, but reproduction of a clinical study in another ethnic group is uncommon, in part due to confounding genomic architecture. As clinical studies are increasingly performed globally this has important implications for how cancer risk stratifiers should be studied and employed.

Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation

Cytochromes P450 (CYP) are a major source of variability in drug pharmacokinetics and response. Of 57 putatively functional human CYPs only about a dozen enzymes, belonging to the CYP1, 2, and 3 families, are responsible for the biotransformation of most foreign substances including 70-80% of all drugs in clinical use. The highest expressed forms in liver are CYPs 3A4, 2C9, 2C8, 2E1, and 1A2, while 2A6, 2D6, 2B6, 2C19 and 3A5 are less abundant and CYPs 2J2, 1A1, and 1B1 are mainly expressed extrahepatically. Expression of each CYP is influenced by a unique combination of mechanisms and factors including genetic polymorphisms, induction by xenobiotics, regulation by cytokines, hormones and during disease states, as well as sex, age, and others. Multiallelic genetic polymorphisms, which strongly depend on ethnicity, play a major role for the function of CYPs 2D6, 2C19, 2C9, 2B6, 3A5 and 2A6, and lead to distinct pharmacogenetic phenotypes termed as poor, intermediate, extensive, and ultrarapid metabolizers. For these CYPs, the evidence for clinical significance regarding adverse drug reactions (ADRs), drug efficacy and dose requirement is rapidly growing. Polymorphisms in CYPs 1A1, 1A2, 2C8, 2E1, 2J2, and 3A4 are generally less predictive, but new data on CYP3A4 show that predictive variants exist and that additional variants in regulatory genes or in NADPH:cytochrome P450 oxidoreductase (POR) can have an influence. Here we review the recent progress on drug metabolism activity profiles, interindividual variability and regulation of expression, and the functional and clinical impact of genetic variation in drug metabolizing P450s.

Meta-analysis of the association of CYP1A1 polymorphisms with gastric cancer susceptibility and interaction with tobacco smoking

The association of two cytochrome P4501A1 (CYP1A1) polymorphisms, m1 (T6235C transition) and m2 (A4889G transition), with gastric cancer risk is inconclusive. We conducted a meta-analysis of all available studies to evaluate the potential role of the polymorphisms and their interactions with tobacco smoking in gastric cancer susceptibility. Published literature from PubMed was retrieved by two investigators independently. Fourteen case-control studies with 2,032 gastric cancer cases and 5,099 controls were selected. A fixed effects model or a random-effects model was used to estimate the odds ratio (OR) for the CYP1A1 polymorphisms and the occurrence of gastric cancer. Significant associations between CYP1A1 m1 and m2 polymorphisms and gastric cancer susceptibility were not observed in all genetic models in the overall analyses. Subgroup analyses by ethnicity and source of controls did not reveal significant associations with gastric cancer risk. Stratification analysis by smoking status found that carriers of the heterozygous and homozygous m1 genotypes decreased the susceptibility of gastric cancer among ever-smokers (pooled OR = 0.56, 95 % CI 0.36-0.89, fixed effects). In contrast, the m2 genotypes (G/G and A/G) did not show any relevance to gastric cancer risk among the smoking population (pooled OR = 1.30, 95 % CI 0.84-2.00, fixed effects). Overall, we found that the CYP1A1 polymorphism itself, either m1 or m2, did not represent an independent genetic risk factor influencing gastric cancer. However, subgroup analyses suggest that carriers of the heterozygous and homozygous m1 genotype who are exposed to tobacco smoke have a significantly lower risk of developing gastric cancer. To explain the observed reduction of gastric cancer risk, we proposed a novel hypothesis of "observation bias". This hypothesis is also applicable to explain the combined effects of other genetic polymorphisms and environmental factors on the risk of developing cancers, and the rationality of the hypothesis needs to be further investigated.

Breast cancer risk, fungicide exposure and CYP1A1*2A gene-environment interactions in a province-wide case control study in Prince Edward Island, Canada

Scientific certainty regarding environmental toxin-related etiologies of breast cancer, particularly among women with genetic polymorphisms in estrogen metabolizing enzymes, is lacking. Fungicides have been recognized for their carcinogenic potential, yet there is a paucity of epidemiological studies examining the health risks of these agents. The association between agricultural fungicide exposure and breast cancer risk was examined in a secondary analysis of a province-wide breast cancer case-control study in Prince Edward Island (PEI) Canada. Specific objectives were: (1) to derive and examine the level of association between estimated fungicide exposures, and breast cancer risk among women in PEI; and (2) to assess the potential for gene-environment interactions between fungicide exposure and a CYP1A1 polymorphism in cases versus controls. After 1:3 matching of 207 cases to 621 controls by age, family history of breast cancer and menopausal status, fungicide exposure was not significantly associated with an increased risk of breast cancer (OR = 0.74; 95% CI: 0.46-1.17). Moreover, no statistically significant interactions between fungicide exposure and CYP1A1*2A were observed. Gene-environment interactions were identified. Though interpretations of findings are challenged by uncertainty of exposure assignment and small sample sizes, this study does provide grounds for further research.

Genome-based prediction of breast cancer risk in the general population: a modeling study based on meta-analyses of genetic associations

BACKGROUND

Genome-wide association studies identified novel breast cancer susceptibility variants that could be used to predict breast cancer in asymptomatic women. This review and modeling study aimed to investigate the current and potential predictive performance of genetic risk models.

METHODS

Genotypes and disease status were simulated for a population of 10,000 women. Genetic risk models were constructed from polymorphisms from meta-analysis including, in separate scenarios, all polymorphisms or statistically significant polymorphisms only. We additionally investigated the magnitude of the odds ratios (OR) for 1 to 100 hypothetical polymorphisms that would be needed to achieve similar discriminative accuracy as available prediction models [modeled range of area under the receiver operating characteristic curve (AUC) 0.70-0.80].

RESULTS

Of the 96 polymorphisms that had been investigated in meta-analyses, 41 showed significant associations. AUC was 0.68 for the genetic risk model based on all 96 polymorphisms and 0.67 for the 41 significant polymorphisms. Addition of 50 additional variants, each with risk allele frequencies of 0.30, requires per-allele ORs of 1.2 to increase this AUC to 0.70, 1.3 to increase AUC to 0.75, and 1.5 to increase AUC to 0.80. To achieve AUC of 0.80, even 100 additional variants would need per-allele ORs of 1.3 to 1.7, depending on risk allele frequencies.

CONCLUSION

The predictive ability of genetic risk models in breast cancer has the potential to become comparable to that of current breast cancer risk models.

IMPACT

Risk prediction based on low susceptibility variants becomes a realistic tool in prevention of nonfamilial breast cancer.