Therefore, it appears the longer time to diagnosis in the FOTS data set did not appear to substantially affect the mutation prevalence estimate.
However, the main concern here is whether a longer time to diagnosis led to overrepresentation of mutation-positive cases in the FOTS data set, thereby biasing our mutation prevalence estimate.
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.
We have presented estimates of mutation prevalence, and described infections as harbouring two, three or more mutations.
In addition, some studies have targeted LR testing to very high-risk patients already known to be negative for mutations detected with sequencing, without providing prevalence estimates of sequencing versus LR mutations overall.
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.
