Phenotype comparison of MLH1 and MSH2 mutation carriers in a cohort of 1,914 individuals undergoing clinical genetic testing in the United States

Background and Aims: Lynch syndrome is caused by germ-line mismatch repair gene mutations. We examined the phenotypic differences between MLH1 and MSH2 gene mutation carriers and whether mutation type (point versus large rearrangement) affected phenotypic expression. Methods: This is a cross-sectional prevalence study of 1,914 unrelated probands undergoing clinical genetic testing for MLH1 and MSH2 mutations at a commercial laboratory. Results: Fifteen percent (285 of 1,914) of subjects had pathogenic mutations (112 MLH1, 173 MSH2). MLH1 carriers had a higher prevalence of colorectal cancer (79% versus 69%, P = 0.08) and younger mean age at diagnosis (42.2 versus 44.8 years, P = 0.03) than MSH2 carriers. Forty-one percent of female carriers had endometrial cancer and prevalence was similar in both groups. Other cancers were more frequent in MSH2 carriers (24% versus 9%, P = 0.001) and their families (P < 0.001). Multivariable analyses confirmed these associations. Of the 1,016 subjects who underwent Southern blot analysis, 42 had large rearrangements (7 MLH1, 35 MSH2). There were no phenotypic differences between carriers with large rearrangements and point mutations. Conclusions: In this large study of mismatch repair gene mutation carriers from the United States, MLH1 carriers had more colorectal cancer than MSH2 carriers whereas endometrial cancer prevalence was similar. Large genomic rearrangements were more frequent in the MSH2 gene. MSH2 carriers and their relatives have more extracolonic nonendometrial Lynch syndrome-associated cancers and may benefit from additional screening. Copyrigh

Comparison of the clinical prediction model PREMM1,2,6 and molecular testing for the systematic identification of Lynch syndrome in colorectal cancer

Background Lynch syndrome is caused by germline mismatch repair (MMR) gene mutations. The PREMM1,2,6 model predicts the likelihood of a MMR gene mutation based on personal and family cancer history. Objective To compare strategies using PREMM1,2,6 and tumour testing (microsatellite instability (MSI) and/or immunohistochemistry (IHC) staining) to identify mutation carriers. Design Data from population-based or clinic-based patients with colorectal cancers enrolled through the Colon Cancer Family Registry were analysed. Evaluation included MSI, IHC and germline mutation analysis for MLH1, MSH2, MSH6 and PMS2. Personal and family cancer histories were used to calculate PREMM1,2,6 predictions. Discriminative ability to identify carriers from non-carriers using the area under the receiver operating characteristic curve (AUC) was assessed. Predictions were based on logistic regression models for (1) cancer assessment using PREMM1,2,6, (2) MSI, (3) IHC for loss of any MMR protein expression, (4) MSI+IHC, (5) PREMM1,2,6+MSI, (6) PREMM1,2,6+IHC, (7) PREMM1,2,6+IHC+MSI. Results Among 1651 subjects, 239 (14%) had mutations (90 MLH1, 125 MSH2, 24 MSH6). PREMM1,2,6 discriminated well with AUC 0.90 (95% CI 0.88 to 0.92). MSI alone, IHC alone, or MSI+IHC each had lower AUCs: 0.77, 0.82 and 0.82, respectively. The added value of IHC+PREMM1,2,6 was slightly greater than PREMM1,2,6+MSI (AUC 0.94 vs 0.93). Adding MSI to PREMM1,2,6+IHC did not improve discrimination. Conclusion PREMM1,2,6 and IHC showed excellent performance in distinguishing mutation carriers from noncarriers and performed best when combined. MSI may have a greater role in distinguishing Lynch syndrome from other familial colorectal cancer subtypes among cases with high PREMM1,2,6 scores where genetic evaluation does not disclose a MMR mutation

Comparison of Prediction Models for Lynch Syndrome among Individuals with Colorectal Cancer

BACKGROUND: Recent guidelines recommend the Lynch Syndrome prediction models MMRPredict, MMRPro, and PREMM1,2,6 for the identification of MMR gene mutation carriers. We compared the predictive performance and clinical usefulness of these prediction models to identify mutation carriers. METHODS: Pedigree data from CRC patients in 11 North American, European, and Australian cohorts (6 clinic- and 5 population-based sites) were used to calculate predicted probabilities of pathogenic MLH1, MSH2, or MSH6 gene mutations by each model and gene-specific predictions by MMRPro and PREMM1,2,6. We examined discrimination with area under the receiver operating characteristic curve (AUC), calibration with observed to expected (O/E) ratio, and clinical usefulness using decision curve analysis to select patients for further evaluation. All statistical tests were two-sided. RESULTS: Mutations were detected in 539 of 2304 (23%) individuals from the clinic-based cohorts (237 MLH1, 251 MSH2, 51 MSH6) and 150 of 3451 (4.4%) individuals from the population-based cohorts (47 MLH1, 71 MSH2, 32 MSH6). Discrimination was similar for clinic- and population-based cohorts: AUCs of 0.76 vs 0.77 for MMRPredict, 0.82 vs 0.85 for MMRPro, and 0.85 vs 0.88 for PREMM1,2,6. For clinic- and population-based cohorts, O/E deviated from 1 for MMRPredict (0.38 and 0.31, respectively) and MMRPro (0.62 and 0.36) but were more satisfactory for PREMM1,2,6 (1.0 and 0.70). MMRPro or PREMM1,2,6 predictions were clinically useful at thresholds of 5% or greater and in particular at greater than 15%. CONCLUSIONS: MMRPro and PREMM1,2,6 can well be used to select CRC patients from genetics clinics or population-based settings for tumor and/or germline testing at a 5% or higher risk. If no MMR deficiency is detected and risk exceeds 15%, we suggest considering additional genetic etiologies for the cause of cancer in the family