CYP1A1 Ile462Val is a risk factor for ovarian cancer development

Targeting Cytochrome P450 Enzymes in Ovarian Cancers: New Approaches to Tumor-Selective Intervention

Over the past decade, there have been significant developments in treatment for ovarian cancer, yet the lack of targeted therapy with few side effects still represents a major issue. The cytochrome P450 (CYP) enzyme family plays a vital role in the tumorigenesis process and metabolism of drugs and has a negative impact on therapy outcomes. Gaining more insight into CYP expression is crucial to understanding the pathophysiology of ovarian cancer since many isoforms are essential to the metabolism of xenobiotics and steroid hormones, which drive the disease's development. To the best of our knowledge, no review articles have documented the intratumoral expression of CYPs and their implications in ovarian cancer. Therefore, the purpose of this review is to provide a clear understanding of differential CYP expression in ovarian cancer and its implications for the prognosis of ovarian cancer patients, together with the effects of CYP polymorphisms on chemotherapy metabolism. Finally, we discuss opportunities to exploit metabolic CYP expression for the development of novel therapeutic methods to treat ovarian cancer.

Discovery of breast cancer risk genes and establishment of a prediction model based on estrogen metabolism regulation

Background

Multiple common variants identified by genome-wide association studies have shown limited evidence of the risk of breast cancer in Chinese individuals. In this study, we aimed to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes in breast cancer (BC) and establish a risk prediction model composed of estrogen-metabolizing enzyme genes and GWAS-identified breast cancer-related genes based on a polygenic risk score.

Methods

Unrelated BC patients and healthy subjects were recruited for analysis of estrogen levels and single nucleotide polymorphisms (SNPs) in genes encoding estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes, which was calculated using a Bayesian approach. An independent sample t-test was used to evaluate the differences between PRS scores of BC and healthy subjects. The discriminatory accuracy of the models was compared using the area under the receiver operating characteristic (ROC) curve.

Results

The estrogen homeostasis profile was disturbed in BC patients, with parent estrogens (E1, E2) and carcinogenic catechol estrogens (2/4-OHE1, 2-OHE2, 4-OHE2) significantly accumulating in the serum of BC patients. We then established a PRS model to evaluate the role of SNPs in multiple genes. PRS model 1 (M1) was established from SNPs in 6 GWAS-identified high risk genes. On the basis of M1, we added SNPs from 7 estrogen metabolism enzyme genes to establish PRS model 2 (M2). The independent sample t-test results showed that there was no difference between BC and healthy subjects in M1 (P = 0.17); however, there was a significant difference between BC and healthy subjects in M2 (P = 4.9*10^− 5). The ROC curve results showed that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than that of M1 (AUC = 54.56%).

Conclusion

Estrogen and related metabolic enzyme gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme gene SNPs has a good predictive ability for breast cancer risk, and the accuracy is greatly improved compared with that of the PRS model that only includes GWAS-identified gene SNPs.

Impaired dacarbazine activation and 7-ethoxyresorufin deethylation in vitro by polymorphic variants of CYP1A1 and CYP1A2: implications for cancer therapy

OBJECTIVES

To extend our understanding of how interindividual variability mediates the efficacy of cancer treatment.

MATERIALS AND METHODS

The kinetics of dacarbazine (DTIC) N-demethylation by the most frequent polymorphic variants of CYP1A1 (T461N, I462V) and CYP1A2 (F186L, D348N, I386F, R431W, R456H) were characterized, along with kinetic parameters for the O-deethylation of the prototypic CYP1A substrate 7-ethoxyresorufin, using recombinant protein expression and high-performance liquid chromatographic techniques.

RESULTS

A reduction of ∼30% in the catalytic efficiencies (measured as in-vitro intrinsic clearance, CLint) was observed for DTIC N-demethylation by the two CYP1A1 variants relative to wild type. Although a modest increase in the CLint value for DTIC N-demethylation was observed for the CYP1A2 D348N variant relative to the wild type, the CLint for the F186L variant was reduced and the I386F, R431W, and R456H variants all showed loss of catalytic function.

CONCLUSION

Comparison of the kinetic data for DTIC N-demethylation and 7-ethoxyresorufin O-deethylation indicated that alterations in the kinetic parameters (Km, Vmax, CLint) observed with each of the CYP1A1 and CYP1A2 polymorphic variants were substrate dependent. These data indicate that cancer patients treated with DTIC who possess any of the CYP1A1-T461N and I462V variants or the CYP1A2-F186L, D348N, I386F, R431W, and R456H variants are likely to have decreased prodrug activation, and hence may respond less favorably to DTIC treatment compared with individuals with wild-type CYP1A alleles.

Three Polymorphisms of Vascular Endothelial Growth Factor (+936C > T, -460C > T, and -2578C > A) and Their Susceptibility to Ovarian Cancer: A Meta-Analysis

OBJECTIVE

Vascular endothelial growth factor (VEGF) is a potent endothelial cell mitogen that plays a vital role in angiogenesis, tumor growth, and metastasis. The associations between 3 polymorphisms of VEGF (+936 C > T, -2578 C > A, and -460 C > T) and ovarian cancer (OC) risk have been extensively investigated, but the currently available results are inconsistent. To obtain a more accurate estimation of the association, a meta-analysis was conducted in this study.

METHODS

PubMed, Cochrane Library, Embase, and Chinese National Knowledge Infrastructure were searched for all relevant studies published before November 30, 2014. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for the VEGF polymorphisms to assess the strength of the association.

RESULTS

With regard to the +936 C > T polymorphism, 5 articles were available for analysis (882 cases and 1155 controls), whereas for -2578 C > A (559 cases and 632 controls) and -460 C > T (350 cases and 409 controls), only 2 articles were eligible for analysis, respectively. A significant association between the VEGF +936 C/T polymorphism and OC was demonstrated in white populations (CT vs CC: OR, 0.638 [95% CI, 0.437-0.932; P = 0.020]; TT + CT vs CC: OR, 0.694 [95% CI, 0.483-0.995; P = 0.047]). No relationship was found between -2578 C > A and -460 C > T and susceptibility to develop OC.

CONCLUSIONS

This meta-analysis provides supportive evidence that the VEGF +936 C/T polymorphism may influence the risk for the development of OC in a protective model among whites.

Lack of association between CYP1A1 T6235C polymorphism and coronary artery disease: evidence from a meta-analysis

A number of studies have evaluated the correlation between the cytochrome P450 1A1 (CYP1A1) T6235C polymorphism and coronary artery disease (CAD) risk, however, at present the results remain inconclusive. To provide a more robust investigation of this correlation, a meta-analysis was performed. In the present study, a systematic search of PubMed, Embase and CBM databases for studies published prior to June 6, 2012 was performed. The correlation between the CYP1A1 T6235C polymorphism and CAD risk was assessed by calculating pooled odds ratios (ORs) and 95% confidence intervals (95% CIs). Seven studies with a total of 2,903 cases and 2,304 controls were included in the meta-analysis. Overall, the CYP1A1 T6235C polymorphism was not correlated with CAD risk (C vs. T: OR=1.03; 95% CI, 0.87-1.22; P=0.728; CC vs. TT: OR=1.04; 95% CI, 0.84‑1.19; P=0.699; CC+TC vs. TT: OR=1.04; 95% CI, 0.93-1.18; P=0.478; CC vs. TC+TT: OR=1.04; 95% CI, 0.85‑1.28; P=0.704). A meta-analysis of five high-quality studies demonstrated that the CYP1A1 T6235C polymorphism is not correlated with risk of CAD in 4 genetic models. Ethnic subgroup analyses identified no significant correlation in Caucasian, Asian and African populations. The present meta-analysis study indicates that the CYP1A1 T6235C polymorphism is not correlated with CAD risk. Additional studies with a larger sample size and consistent design must be performed to confirm the present hypothesis.