Purpose: Clinical risk assessment models can identify patients with hereditary cancer susceptibility, but it is unknown how multigene cancer syndrome prediction models compare with syndrome-specific models in assessing risk for individual syndromes such as Lynch syndrome (LS). Our aim was to compare PREMMplus (a 19-gene cancer risk prediction model) with PREMM5 (a LS gene-specific model) for LS identification.
Methods: We analyzed data from two cohorts of patients undergoing germline testing from a commercial laboratory (n = 12,020) and genetics clinic (n = 6,232) with personal and/or family histories of LS-associated cancer. Individual PREMMplus and PREMM5 scores were calculated for all patients. Using a score cutoff of 2.5%, we calculated the sensitivity, specificity, positive predictive value, and negative predictive value (NPV) for identifying LS with each model. Overall ability to discriminate LS carriers from noncarriers was measured using receiver operating characteristic (ROC)-AUC.
Results: PREMMplus had higher sensitivity than PREMM5 in the laboratory- (63.7% [95% CI, 57.0 to 70.0] v 89.2% [95% CI, 84.4 to 93.0]) and clinic-based cohorts (60.8% [95% CI, 52.7 to 68.4] v 90.5% [95% CI, 84.8 to 94.6]). NPV was ≥98.8% for both models in both cohorts. PREMM5 had superior discriminatory capacity to PREMMplus in the laboratory- (ROC-AUC, 0.81 [95% CI, 0.77 to 0.84] v 0.71 [95% CI, 0.67 to 0.75]) and clinic-based cohorts (ROC-AUC, 0.79 [95% CI, 0.75 to 0.84] v 0.68 [95% CI, 0.64 to 0.73]).
Conclusion: Both PREMM5 and PREMMplus demonstrated high NPVs (>98%) in LS discrimination across all patient cohorts, and both models may be used to identify individuals at risk of LS. The choice of which model to use can be based on the goals of risk assessment and patient population.