MDM2 promotor polymorphism and disease characteristics in chronic lymphocytic leukemia: results of an individual patient data-based meta-analysis.
Benner A., Mansouri L., Rossi D., Majid A., Willander K., Parker A., Bond G., Pavlova S., Nückel H., Merkel O., Ghia P., Montserrat E., Kaderi MA., Rosenquist R., Gaidano G., Dyer MJS., Söderkvist P., Linderholm M., Oscier D., Tvaruzkova Z., Pospisilova S., Dührsen U., Greil R., Döhner H., Stilgenbauer S., Zenz T., European Research Initiative on CLL (ERIC) None.
A number of single nucleotide polymorphisms have been associated with disease predisposition in chronic lymphocytic leukemia. A single nucleotide polymorphism in the MDM2 promotor region, MDM2SNP309, was shown to soothe the p53 pathway. In the current study, we aimed to clarify the effect of the MDM2SNP309 on chronic lymphocytic leukemia characteristics and outcome. We performed a meta-analysis of data from 2598 individual patients from 10 different cohorts. Patients' data and genetic analysis for MDM2SNP309 genotype, immunoglobulin heavy chain variable region mutation status and fluorescence in situ hybridization results were collected. There were no differences in overall survival based on the polymorphism (log rank test, stratified by study cohort; P=0.76; GG genotype: cohort-adjusted median overall survival of 151 months; TG: 153 months; TT: 149 months). In a multivariable Cox proportional hazards regression analysis, advanced age, male sex and unmutated immunoglobulin heavy chain variable region genes were associated with inferior survival, but not the MDM2 genotype. The MDM2SNP309 is unlikely to influence disease characteristics and prognosis in chronic lymphocytic leukemia. Studies investigating the impact of individual single nucleotide polymorphisms on prognosis are often controversial. This may be due to selection bias and small sample size. A meta-analysis based on individual patient data provides a reasonable strategy for prognostic factor analyses in the case of small individual studies. Individual patient data-based meta-analysis can, therefore, be a powerful tool to assess genetic risk factors in the absence of large studies.