According to a recent study, p53 mutation prevalence estimates were 45, 5 and 1%, respectively, for invasive, LMP and benign ovarian tumours (Reles et al, 2001; Kmet et al, 2003).
This is a result of mutation prevalence being estimated to be 2.5% in their model.
Previous studies have estimated mutation prevalence in clinical referrals for primary ovarian insufficiency, but these are likely to be biased as compared with cases in the general population.
As expected, the lower estimated mutation prevalence in this 10 patient cohort yielded a slightly lower prediction power per sampling site.
Additionally, it was recently proposed that BRCA testing all women with TN cancers diagnosed below 50 years would be cost-effective with respect to overall health spending at a national level (Kwon et al, 2010), based on an estimated mutation prevalence of 10 25%.
Based on the mutations' prevalence we estimated that three different tumor regions should be sampled to detect mutations in PBRM1, SETD2, BAP1, and/or KDM5C with 90% certainty.
Although the output obtained from genotyping this cohort of subjects could not be used in genotype phenotype correlation studies or to estimate SOS1 mutation prevalence in the NS population, the mutation data were utilized to provide a more detailed picture about the molecular spectrum of disease-causing mutations affecting the SOS1 gene.
We have presented estimates of mutation prevalence, and described infections as harbouring two, three or more mutations.
We then used binomial distribution and the estimated intratumor mutation prevalence for each gene to calculate probability of detecting a mutation in that gene as a function of total number of biopsies.
