Identifying Persons with Lynch Syndrome: Why and How?

The promise of personalized medicine rests on individualized management with emphasis on the twin goals of prevention when possible, and treatment when necessary. In the case of Lynch syndrome (LS), the paradigm of identifying persons at high risk for serious disease, and intervening to improve outcomes, can be realized. LS, one of the most common inherited cancer predisposition syndromes, is caused by germline mutations that affect DNA mismatch repair (MMR) genes [1]. Persons with LS are at much higher risk for colorectal, endometrial, gastric, ovarian and other cancers than is the general population. Since most people with LS are unaware of their condition, the initial diagnosis is often based on analysis of preventable cancers. In persons diagnosed with LS, close colonoscopic surveillance [2] and prophylactic hysterectomy–oophorectomy once child-bearing is completed [3] can substantially decrease cancer incidence and mortality; furthermore, chemoprevention with aspirin may reduce the overall risk of cancer [4]. When an individual with LS is identified, testing at-risk relatives can determine who also carries the family’s LS-causing mutation and should be offered intensive management. Unaffected relatives can follow average-risk screening recommendations. In this issue of Digestive Diseases and Sciences, GouldSuarez et al. [5] use decision analytic modeling to compare the cost-effectiveness of several strategies to diagnose LS among patients presenting with colorectal cancer. The strategies considered rely on first applying the revised Bethesda guidelines (RBG), or first testing colorectal cancers for features suggestive of LS [abnormal immunohistochemistry (IHC) or microsatellite instability (MSI)], or proceeding directly to germline testing for mutations in the DNA MMR genes. The primary clinical effectiveness measure in the study was the number of true LS cases detected. The primary economic outcome was the mean cost to detect one case of LS with each strategy. Decision analytic modeling can address questions that cannot be answered directly with a definitive, controlled clinical trial—although a prospective randomized controlled trial of competing strategies to identify LS, with long enough follow-up to ascertain relevant clinical outcomes, is unlikely to ever be performed, such a trial can be simulated based on best estimates for probabilities and costs derived from the literature. Key features of a decision analysis include the time horizon and the degree to which downstream clinical and economic consequences are considered. The analysis by Gould-Suarez et al. focuses on the number of LS cases detected; it does not attempt to estimate the downstream consequences of detecting these cases. This short-term analysis has the advantage of needing a simpler model that requires fewer assumptions and model inputs than would be needed for a longer-term analysis. It has the limitation that the measure of cost-effectiveness (cost per case detected) can be difficult to interpret. Deciding what is ‘‘cost-effective’’ usually requires a judgment about willingness to pay for an incremental improvement in clinical outcome. When one strategy is more effective and less costly than another (that is, it is dominant), then the choice between strategies is straightforward. But when one strategy is more effective and more costly than another one, then the acceptability of the more U. Ladabaum (&) Gastrointestinal Cancer Prevention Program, and Clinical Cancer Genetics Program, Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Alway Bldg Rm M211, Stanford, CA 94305-5187, USA e-mail: uri.ladabaum@stanford.edu