Case Comprehensive Cancer Center, Case Western Reserve University, 11100 Euclid Ave - Wearn 152, Cleveland, OH, 44106-5065, United States.
Inherited variability in genes that influence androgen metabolism has been associated with prostate cancer risk. The objective of this analysis was to evaluate interactions for prostate cancer risk using classification and regression trees (CART) and to evaluate whether these interactive effects add information about risk prediction beyond that of "traditional" risk factors.
We compared CART models to traditional logistic regression (LR) models for associations of factors with prostate cancer risk using 1084 prostate cancer cases and 941 controls. All analyses were stratified by race. We used unconditional LR to complement and compare to the race-stratified CART results using the area under curve (AUC) for the receiver operating characteristic (ROC) curves.
The CART modeling of prostate cancer risk showed different interaction profiles by race. For European Americans, interactions among CYP3A43 genotype, history of benign prostate hypertrophy, family history of prostate cancer and age at consent revealed a distinct hierarchy of gene-environment and gene-gene interactions. While for African Americans, interactions among family history of prostate cancer, European ancestry proportion, GGC AR repeats and CYP3A4/CYP3A5 haplotype revealed distinct interaction effects from those found in European Americans. For European Americans the CART model had the highest AUC, while for African Americans the LR model with the CART discovered factors had the largest AUC.
These results show novel gene-gene and gene-environment racial/ethnic specific interactions that would not have been found using traditional LR approaches.
These results provide new insight into underlying prostate cancer biology for European Americans and African Americans.
Written by:
Barnholtz-Sloan J, Guan X, Zeigler-Johnson C, Meropol NJ, Rebbeck TR. Are you the author?
Reference: Cancer Epidemiol Biomarkers Prev. 2011 Apr 14. Epub ahead of print.
doi: 10.1158/1055-9965.EPI-10-0996
PubMed Abstract
PMID: 21493872
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