Showing papers by "Yon-Dschun Ko published in 2008"

Head and Neck Squamous-Cell Cancer and its Association with Polymorphic Enzymes of Xenobiotic Metabolism and Repair

Abstract: Tobacco smoking, alcohol drinking, and occupational exposures to polycyclic aromatic hydrocarbons are the major proven risk factors for human head and neck squamous-cell cancer (HNSCC) Major research focus on gene-environment interactions concerning HNSCC has been on genes encoding enzymes of metabolism for tobacco smoke constituents and repair enzymes To investigate the role of genetically determined individual predispositions in enzymes of xenobiotic metabolism and in repair enzymes under the exogenous risk factor tobacco smoke in the carcinogenesis of HNSCC, we conducted a case-control study on 312 cases and 300 noncancer controls We focused on the impact of 22 sequence variations in CYP1A1, CYP1B1, CYP2E1, ERCC2/XPD, GSTM1, GSTP1, GSTT1, NAT2, NQO1, and XRCC1 To assess relevant main and interactive effects of polymorphic genes on the susceptibility to HNSCC we used statistical models such as logic regression and a Bayesian version of logic regression In subgroup analysis of nonsmokers, main effects in ERCC2 (Lys751Gln) C/C genotype and combined ERCC2 (Arg156Arg) C/A and A/A genotypes were predominant When stratifying for smokers, the data revealed main effects on combined CYP1B1 (Leu432Val) C/G and G/G genotypes, followed by CYP1B1 (Leu432Val) G/G genotype and CYP2E1 (-70G>T) G/T genotype When fitting logistic regression models including relevant main effects and interactions in smokers, we found relevant associations of CYP1B1 (Leu432Val) C/G genotype and CYP2E1 (-70G>T) G/T genotype (OR, 1084; 95% CI, 164-7153) as well as CYP1B1 (Leu432Val) G/G genotype and GSTM1 null/null genotype (OR, 1179; 95% CI, 218-6377) with HNSCC The findings underline the relevance of genotypes of polymorphic CYP1B1 combined with exposures to tobacco smoke

Association of a Common AKAP9 Variant With Breast Cancer Risk: A Collaborative Analysis

Abstract: Data from several studies have suggested that polymorphisms in A-kinase anchoring proteins (AKAPs), which are key components of signal transduction, contribute to carcinogenesis. To evaluate the impact of AKAP variants on breast cancer risk, we genotyped six nonsynonymous single-nucleotide polymorphisms that were predicted to be deleterious and found two (M463I, 1389G>T and N2792S, 8375A>G) to be associated with an allele dose-dependent increase in risk of familial breast cancer in a German population. We extended the analysis of AKAP9 M463I, which is in strong linkage disequilibrium with AKAP9 N2792S, to 9523 breast cancer patients and 13770 healthy control subjects from seven independent European and Australian breast cancer studies. All statistical tests were two-sided. The collaborative analysis confirmed the association of M463I with increased breast cancer risk. Among all breast cancer patients, the combined adjusted odds ratio (OR) of breast cancer for individuals homozygous for the rare allele TT (frequency = 0.19) compared with GG homozygotes was 1.17 (95% confidence interval [CI] = 1.08 to 1.27, P = .0003), and the OR for TT homozygotes plus GT heterozygotes compared with GG homozygotes was 1.10 (95% CI = 1.04 to 1.17, P = .001). Among the combined subset of 2795 familial breast cancer patients, the respective ORs were 1.27 (95% CI = 1.12 to 1.45, P = .0003) and 1.16 (95% CI = 1.06 to 1.27, P = .001).

The CYP1B1_1358_GG genotype is associated with estrogen receptor-negative breast cancer.

Abstract: Cytochrome P450 1B1 (CYP1B1) is a major enzyme in the initial catabolic step of estradiol (E2) metabolism and belongs to the multitude of genes regulated by the estrogen receptor alpha (ERα). The common non-synonymous polymorphisms CYP1B1_1358_A>G and CYP1B1_1294_C>G increase CYP1B1 enzymatic activity. Given a relationship between CYP1B1 and breast tumor E2 level as well as E2 level and breast tumor ERα expression it is of interest to know whether CYP1B1 polymorphisms have an impact on the ERα status of breast cancer. We genotyped the GENICA population-based breast cancer case–control collection (1,021 cases, 1,015 controls) by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and investigated in cases the association between genotypes and tumor ERα status (739 ERα positive cases; 212 ERα negative cases) by logistic regression. We observed a significant association between the homozygous variant CYP1B1_1358_GG genotype and negative ERα status (P = 0.005; OR 2.82, 95% CI: 1.37–5.82) with a highly significant P trend for CYP1B1_1358_A>G and negative ERα status (P = 0.003). We also observed an association of CYP1B1_1358_GG and negative PR status (P = 0.015; OR 2.36, 95% CI: 1.18–4.70) and a P trend of 0.111 for CYP1B1_1358_A>G and negative progesterone receptor (PR) status. We conclude that the CYP1B1_1358_A>G polymorphism has an impact on ERα status in breast cancer in that the CYP1B1_1358_GG genotype known to encode higher CYP1B1 activity is associated with ERα negativity.

Statistical methods for detecting genetic interactions: a head and neck squamous-cell cancer study.

Abstract: Tobacco smoke and occupational exposures to chemicals such as polycyclic aromatic hydrocarbons (PAHs) are, aside from alcohol, the major risk factors for development of head and neck squamous-cell cancer (HNSCC). In this study, new statistical methods were applied. We employ new statistical methods to detect genetic interactions perhaps of higher order, that might play a role in developing HNSCC. The underlying study comprises 312 HNSCC cases and 300 controls. Single-nucleotide polymorphisms (SNPs) of PAH metabolizing and repair enzymes, somatic p53 mutations, and tobacco smoke were examined. Key statistical tools for our analysis are methods of unsupervised and supervised learning. In unsupervised learning, one performs cluster analyses based on well-known and new distance measures to find differences in the SNP patterns of cases and controls, and to understand the role of p53. Our main goal in supervised learning was to identify SNPs and SNP interactions that are likely to alter the susceptibility to HN...