Lynch syndrome (LS) is a hereditary cancer syndrome responsible for 3% of all
endometrial cancer and 5% in those aged under 70 years. It is unclear whether universal testing for
LS in endometrial cancer patients would be cost-effective. The Manchester approach to identifying
LS in endometrial cancer patients uses immunohistochemistry ...
Lynch syndrome (LS) is a hereditary cancer syndrome responsible for 3% of all
endometrial cancer and 5% in those aged under 70 years. It is unclear whether universal testing for
LS in endometrial cancer patients would be cost-effective. The Manchester approach to identifying
LS in endometrial cancer patients uses immunohistochemistry (IHC) to detect mismatch repair
(MMR) deficiency, incorporates testing for MLH1 promoter hypermethylation, and genetic testing
for pathogenic MMR variants. We aimed to assess the cost-effectiveness of the Manchester approach
based on primary research data from clinical practice in Manchester. The PETALS study informed
estimates of diagnostic performances for a number of different strategies. A recent microcosting
study was adapted and used to estimate diagnostic costs. A Markov model was used to predict
long-term costs and health outcomes (measured in quality-adjusted life years, QALYs) for
individuals and their relatives. Bootstrapping and probabilistic sensitivity analysis were used to
estimate the uncertainty in cost-effectiveness. The Manchester approach dominated other reflex
testing strategies when considering diagnostic costs and Lynch syndrome cases identified. When
considering long-term costs and QALYs the Manchester approach was the optimal strategy, costing
£5459 per QALY gained (compared to thresholds of £20,000 to £30,000 per QALY commonly used
in the NHS). Cost-effectiveness is not an argument for restricting testing to younger patients or those
with a strong family history. Universal testing for Lynch syndrome in endometrial cancer patients
is expected to be cost-effective in the UK NHS, and the Manchester approach is expected to be the
optimal testing strategy.