Guidelines on Chemotherapy in Advanced Stage Gynecological Malignancies: An Evaluation of 224 Professional Societies and Organizations

BACKGROUND: Clinical practice guidelines are important for guiding practice, but it is unclear if they are commensurate with the available evidence. METHODS: We examined guidelines produced by cancer and gynecological societies and organizations and evaluated their coverage of and stance towards chemotherapy for advanced stage disease among 4 gynecological malignancies (breast, ovarian, cervical, endometrial cancer) where the evidence for the use of chemotherapy is very different (substantial and conclusive for breast and ovarian cancer, limited and suggesting no major benefit for cervical and endometrial cancer). Eligible societies and organizations were identified through systematic internet searches (last update June 2009). Pertinent websites were scrutinized for presence of clinical practice guidelines, and relative guidelines were analyzed. RESULTS: Among 224 identified eligible societies and organizations, 69 (31%) provided any sort of guidelines, while recommendations for chemotherapy on advanced stage gynecological malignancies were available in 20 of them. Only 14 had developed their own guideline, and only 5 had developed guidelines for all 4 malignancies. Use of levels of evidence and grades of recommendations, and aspects of the production, implementation, and timeliness of the guidelines did not differ significantly across malignancies. Guidelines on breast and ovarian cancer utilized significantly more randomized trials and meta-analyses. Guidelines differed across malignancies on their coverage of disease-free survival (p = 0.033), response rates (p = 0.024), symptoms relief (p = 0.005), quality of life (p = 0.001) and toxicity (p = 0.039), with breast and ovarian cancer guidelines typically covering more frequently these outcomes. All guidelines explicitly or implicitly endorsed the use of chemotherapy. CONCLUSIONS: Clinical practice guidelines are provided by the minority of professional societies and organizations. Available guidelines tend to recommend chemotherapy even for diseases where the effect of chemotherapy is controversial and recommendations are based on scant evidence

Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data

Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the goal was to detect true causal SNPs from among the measured variants in these genes. Examining all available SNPs in the known causal genes, BNSL produced a Bayesian network from which subsets of SNPs connected to the Affected outcome were identified and measured for statistical significance using the hypergeometric distribution. The exploratory phase of analysis for pooled replicates sometimes identified a set of involved SNPs that contained more true causal SNPs than expected by chance in the Asian population. Analyses of single replicates gave inconsistent results. No nominally significant results were found in analyses of African or European populations. Overall, the method was not able to identify sets of involved SNPs that included a higher proportion of true causal SNPs than expected by chance alone. We conclude that this method, as currently applied, is not effective for identifying causal SNPs that follow the simulation model for the GAW17 data set, which includes many rare causal SNPs