DNA repair polymorphisms modify bladder cancer risk: a multi-factor analytic strategy

Abstract

Objectives: A number of common non-synonymous single nucleotide polymorphisms (SNPs) in DNA repair genes have been reported to modify bladder cancer risk. These include: APE1-Asn148Gln, XRCC1-Arg399Gln and XRCC1-Arg194Trp in the BER pathway, XPD-Gln751Lys in the NER pathway and XRCC3-Thr241Met in the DSB repair pathway.

Methods: To examine the independent and interacting effects of these SNPs in a large study group, we analyzed these genotypes in 1,029 cases and 1,281 controls enrolled in two case-control studies of incident bladder cancer, one conducted in New Hampshire, USA and the other in Turin, Italy.

Results: The odds ratio among current smokers with the variant XRCC3-241 (TT) genotype was 1.7 (95% CI 1.0-2.7) compared to wild-type. We evaluated gene-environment and gene-gene interactions using four analytic approaches: logistic regression, Multifactor Dimensionality Reduction (MDR), hierarchical interaction graphs, classification and regression trees (CART), and logic regression analyses. All five methods supported a gene-gene interaction between XRCC1-399/XRCC3-241 (p = 0.001) (adjusted OR for XRCC1-399 GG, XRCC3-241 TT vs. wild-type 2.0 (95% CI 1.4-3.0)). Three methods predicted an interaction between XRCC1-399/XPD-751 (p = 0.008) (adjusted OR for XRCC1-399 GA or AA, XRCC3-241 AA vs. wild-type 1.4 (95% CI 1.1-2.0)).

Conclusions: These results support the hypothesis that common polymorphisms in DNA repair genes modify bladder cancer risk and highlight the value of using multiple complementary analytic approaches to identify multi-factor interactions.

Fig. 1

Hierarchical interaction graph of genotypes. The percentage of entropy removed (i.e. information gain) by each variable is visualized for each node (box). The percentage of entropy removed for each pairwise Cartesian product of variables is visualized for each connection. A positive entropy (plotted in green) indicates interaction while a negative entropy (plotted in red) indicates redundancy. a Overall analysis of genotype. b Analysis of genotype and smoking status.

Fig. 2

Classification and regression tree (CART) model of genotypes. Splitting rules are used to stratify data into subsets of individuals, which are represented in the CART decision tree as nodes. Each ‘child node’ is selected considering only a subset of the population within a ‘parent node’ to explain class, thus, the results are conditioned on the first splitting variable. a Overall analysis of genotype. b Analysis of genotype within current smokers.

Fig. 3

Logic Regression model of genotype interactions. The algorithm constructs predictors from binary SNP data that are Boolean (logical) combinations of the original genotype data. Logic expressions are depicted as trees with AND/OR operators at each branch point.