Obesity-associated inflammation and prostate cancer risk
Obesity is well known as a chronic inflammatory disease with increased levels of inflammatory cytokines/chemokines such as IL-6, TNFalpha, IL-1beta, CXCL1, CCL20, etc, many of which are IL-17 downstream target genes. IL-17 level is increased in obese mice and humans. IL-17 has been shown to promote development of many cancer types including prostate, colon, skin, and breast cancers. It is unknown if IL-17 and its downstream target genes play any role in obesity-associated prostate cancer aggressiveness and mortality. A recent study at NCI has reported measurable serum levels of IL-6, IL-7, IL-13, IL-8, and IL-10 among the 13-member panel of cytokines screened (Hofmann, et al., 2011, Cytokine, 56:145-148). Personal communications with Dr. Hofmann found that another group at NCI was conducting further analysis, however, the results, if any, have not been seen from any publications. It is speculated that the data are available at PLCO.
Understanding the role of obesity in prostate cancer aggressiveness and mortality is critical for the prevention and treatment of prostate cancer.
Our hypothesis is that obesity-associated inflammatory chemokines/cytokines are associated with the aggressiveness and mortality of prostate cancer. We propose five specific aims: 1) To determine if obesity is associated with the risk of developing aggressive prostate cancer (Gleason score 8 or above and/or stage T3); 2) To determine if obesity is associated with poor survival of prostate cancer patients; 3) To determine if inflammatory chemokine/cytokine level is associated with obesity; 4) To determine if inflammatory chemokine/cytokine level is associated with the aggressiveness of prostate cancer; 5) To determine if inflammatory chemokine/cytokine level is associated with the survival of prostate cancer patients. We request access to PLCO data of prostate cancer patients and non-cancer men. The requested data set include: age, body mass index (BMI), prostate cancer stage, Gleason score, PSA level, survival time, serum levels of chemokines and cytokines. Cases with these data are included. Cases with incomplete data are excluded. BMI will be categorized into < 25.0 (normal weight, because there are only a few cases of underweight patients whose BMI < 18.5), 25.0 to < 30.0 (overweight), and more than 30.0 (obese). Gleason scores will be categorized into low (6 or below), intermediate (7), and high (8 or above). Chemokine/cytokine levels will be dichotomized into low (less than median level) or high (more than median level). For association analysis for categorical variables, the Kruskal-Wallis test will be used to test whether there are significant differences in BMI and chemokine/cytokine levels between groups (Gleason score, stages, cancer vs non-cancer). For association analysis of continuous variables, the Spearman correlation coefficients will be assessed between chemokine/cytokine levels and Gleason scores or stages. The Kaplan-Meier curves of the groups will be analyzed using the log-rank test. A multivariate Cox proportional hazards model will be used to determine whether any of the variables are independent predictors of survival after adjustment for other factors. In regard to sample size, because we do not have any preliminary data to perform power analysis, we will request access to at least 50 cases per variable listed above. Based on our previous experience in association analysis (Dai, et al, 2010, BMC Cancer 10:220; Ma, et al, 2010, BMC Cancer 10:112, see biosketch), 50 cases per variable will give us decent power to demonstrate a significant difference. If no significance is achieved, we will use the acquired data to estimate the sample size needed. Ideally we want to analyze all cases with the required data set. All data analysis will be carried out using SAS, R or S-plus statistical tools, assisted by Dr. Leann Myers (Professor of Biostatistics and Bioinformatics). This project will provide clinical relevance to our ongoing in vitro and animal in vivo studies addressing our hypothesis. We are open to any chance to collaborate with the NCI group who is working on this subject. In case the data on chemokine/cytokine levels are not available, we will request serum samples as an alternative approach.
Dr. Leann Myers, Tulane University