A comparison of models used to predict MLH1, MSH2 and MSH6 mutation carriers

Performance evaluation of predictive models for detecting MMR gene mutations associated with Lynch syndrome in cancer patients in a Chinese cohort in Taiwan

Identifying Lynch syndrome significantly impacts cancer risk management, treatment, and prognosis. Validation of mutation risk predictive models for mismatch repair (MMR) genes is crucial for guiding genetic counseling and testing, particularly in the understudied Asian population. We evaluated the performance of four MMR mutation risk predictive models in a Chinese cohort of 604 patients with colorectal cancer (CRC), endometrial cancer (EC), or ovarian cancer (OC) in Taiwan. All patients underwent germline genetic testing and 36 (6.0%) carried a mutation in the MMR genes (MLH1, MSH2, MSH6, and PMS2). All models demonstrated good performance, with area under the receiver operating characteristic curves comparable to Western cohorts: PREMM5 0.80 (95% confidence interval [CI], 0.73-0.88), MMRPro 0.88 (95% CI, 0.82-0.94), MMRPredict 0.82 (95% CI, 0.74-0.90), and Myriad 0.76 (95% CI, 0.67-0.84). Notably, MMRPro exhibited exceptional performance across all subgroups regardless of family history (FH+ 0.88, FH- 0.83), cancer type (CRC 0.84, EC 0.85, OC 1.00), or sex (male 0.83, female 0.90). PREMM5 and MMRPredict had good accuracy in the FH+ subgroup (0.85 and 0.82, respectively) and in CRC patients (0.76 and 0.82, respectively). Using the ratio of observed and predicted mutation rates, MMRPro and PREMM5 had good overall fit, while MMRPredict and Myriad overestimated mutation rates. Risk threshold settings in different models led to different positive predictive values. We suggest a lower threshold (5%) for recommending genetic testing when using MMRPro, and a higher threshold (20%) when using PREMM5 and MMRPredict. Our findings have important implications for personalized mutation risk assessment and counseling on genetic testing.

A validation of models for prediction of pathogenic variants in mismatch repair genes

PURPOSE

Models used to predict the probability of an individual having a pathogenic homozygous or heterozygous variant in a mismatch repair gene, such as MMRpro, are widely used. Recently, MMRpro was updated with new colorectal cancer penetrance estimates. The purpose of this study was to evaluate the predictive performance of MMRpro and other models for individuals with a family history of colorectal cancer.

METHODS

We performed a validation study of 4 models, Leiden, MMRpredict, PREMM5, and MMRpro, using 784 members of clinic-based families from the United States. Predicted probabilities were compared with germline testing results and evaluated for discrimination, calibration, and predictive accuracy. We analyzed several strategies to combine models and improve predictive performance.

RESULTS

MMRpro with additional tumor information (MMRpro+) and PREMM5 outperformed the other models in discrimination and predictive accuracy. MMRpro+ was the best calibrated with an observed to expected ratio of 0.98 (95% CI = 0.89-1.08). The combination models showed improvement over PREMM5 and performed similar to MMRpro+.

CONCLUSION

MMRpro+ and PREMM5 performed well in predicting the probability of having a pathogenic homozygous or heterozygous variant in a mismatch repair gene. They serve as useful clinical decision tools for identifying individuals who would benefit greatly from screening and prevention strategies.

Identification of Lynch Syndrome

Lynch syndrome (LS) is an autosomal dominant hereditary cancer syndrome caused by pathogenic germline variants (PGV) in any of the 4 DNA mismatch repair (MMR) genes, MLH1, MSH2, MSH6, and PMS2, or deletions in EPCAM. LS leads to an increased risk of intestinal and extraintestinal cancers, of which colorectal and endometrial cancers are the most common. Individuals at risk for LS can be identified by using clinical criteria, prediction models, and universal tumor testing. Understanding each of these tools, including limitations and mimics of LS, is essential to the early identification of at-risk individuals.

Evaluation of current prediction models for Lynch syndrome: updating the PREMM5 model to identify PMS2 mutation carriers

Until recently, no prediction models for Lynch syndrome (LS) had been validated for PMS2 mutation carriers. We aimed to evaluate MMRpredict and PREMM5 in a clinical cohort and for PMS2 mutation carriers specifically. In a retrospective, clinic-based cohort we calculated predictions for LS according to MMRpredict and PREMM5. The area under the operator receiving characteristic curve (AUC) was compared between MMRpredict and PREMM5 for LS patients in general and for different LS genes specifically. Of 734 index patients, 83 (11%) were diagnosed with LS; 23 MLH1, 17 MSH2, 31 MSH6 and 12 PMS2 mutation carriers. Both prediction models performed well for MLH1 and MSH2 (AUC 0.80 and 0.83 for PREMM5 and 0.79 for MMRpredict) and fair for MSH6 mutation carriers (0.69 for PREMM5 and 0.66 for MMRpredict). MMRpredict performed fair for PMS2 mutation carriers (AUC 0.72), while PREMM5 failed to discriminate PMS2 mutation carriers from non-mutation carriers (AUC 0.51). The only statistically significant difference between PMS2 mutation carriers and non-mutation carriers was proximal location of colorectal cancer (77 vs. 28%, p < 0.001). Adding location of colorectal cancer to PREMM5 considerably improved the models performance for PMS2 mutation carriers (AUC 0.77) and overall (AUC 0.81 vs. 0.72). We validated these results in an external cohort of 376 colorectal cancer patients, including 158 LS patients. MMRpredict and PREMM5 cannot adequately identify PMS2 mutation carriers. Adding location of colorectal cancer to PREMM5 may improve the performance of this model, which should be validated in larger cohorts.

Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression

Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the PREMM_1,2,6 and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67-0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51-0.82, P = 0.09) for PREMM_1,2,6. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis.

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.

Hereditary Syndromes Manifesting as Endometrial Carcinoma: How Can Pathological Features Aid Risk Assessment?

Endometrial carcinoma is the most common gynecological tumor worldwide. It can be the presenting malignancy, acting as the harbinger, of an undiagnosed hereditary syndrome. Up to 50% of females with Lynch syndrome present in this manner. Differentiation between Lynch, Muir-Torre, and Cowden syndromes can at times be challenging due to the overlapping features. Our review emphasizes on the strengths, pitfalls, and limitations of microscopic features as well as immunohistochemical and polymerase chain reaction- (PCR-) based tests used by laboratories to screen for DNA mismatch repair (MMR) and PTEN gene mutations in patients to enable a more targeted and cost effective approach in the use of confirmatory gene mutational analysis tests. This is crucial towards initiating timely and appropriate surveillance measures for the patient and affected family members. We also review the evidence postulating on the possible inclusion of uterine serous carcinoma as part of the spectrum of malignancies seen in hereditary breast and ovarian carcinoma syndrome, driven by mutations in BRCA1/2.

Validation of Prediction Models for Mismatch Repair Gene Mutations in Koreans

PURPOSE

Lynch syndrome, the commonest hereditary colorectal cancer syndrome, is caused by germline mutations in mismatch repair (MMR) genes. Three recently developed prediction models for MMR gene mutations based on family history and clinical features (MMRPredict, PREMM(1,2,6), and MMRPro) have been validated only in Western countries. In this study, we propose validating these prediction models in the Korean population.

MATERIALS AND METHODS

We collected MMR gene analysis data from 188 individuals in the Korean Hereditary Tumor Registry. The probability of gene mutation was calculated using three prediction models, and the overall diagnostic value of each model compared using receiver operator characteristic (ROC) curves and area under the ROC curve (AUC). Quantitative test characteristics were calculated at sensitivities of 90%, 95%, and 98%.

RESULTS

Of the individuals analyzed, 101 satisfied Amsterdam criteria II, and 87 were suspected hereditary nonpolyposis colorectal cancer. MMR mutations were identified in 62 of the 188 subjects (33.0%). All three prediction models showed a poor predictive value of AUC (MMRPredict, 0.683; PREMM(1,2,6), 0.709; MMRPro, 0.590). Within the range of acceptable sensitivity (> 90%), PREMM(1,2,6) demonstrated higher specificity than the other models.

CONCLUSION

In the Korean population, overall predictive values of the three models (MMRPredict, PREMM(1,2,6), MMRPro) for MMR gene mutations are poor, compared with their performance in Western populations. A new prediction model is therefore required for the Korean population to detect MMR mutation carriers, reflecting ethnic differences in genotype-phenotype associations.

Prediction models in Lynch syndrome

Prediction models for the identification of Lynch syndrome have been developed to quantify an individual's risk of carrying a mismatch repair gene mutation and help clinicians decide for whom further risk assessment and genetic testing is necessary. There are diverse clinical settings in which a healthcare provider has the opportunity to assess an individual for Lynch syndrome. Prediction models offer a potentially feasible and useful strategy to systematically identify at-risk individuals, whether they are affected with colorectal cancer or not, and to help with management of the implications of molecular and germline test results. Given the complexity of diagnostic information currently available to clinicians involved in identifying and caring for patients with Lynch syndrome, prediction models provide a useful and complementary aid in medical decision-making. Systematic implementation of prediction models estimates should be considered in routine clinical care and at various stages of cancer risk assessment and prevention. In this manuscript, we review the main prediction models developed for Lynch syndrome, focus on their specific features and performance assessed in several validation studies, compare the models with other clinical and molecular strategies for the diagnosis of Lynch syndrome, and discuss their potential uses in clinical practice.

Criteria and prediction models for mismatch repair gene mutations: a review

One of the strongest predictors of colorectal cancer risk is carrying a germline mutation in a DNA mismatch repair (MMR) gene. Once identified, mutation carriers can be recommended for intensive screening that will substantially reduce their high colorectal cancer risk. Conversely, the relatives of carriers identified as non-carriers can be relieved of the burden of intensive screening. Criteria and prediction models that identify likely mutation carriers are needed for cost-effective, targeted, germline testing for MMR gene mutation. We reviewed 12 criteria/guidelines and 8 prediction models (Leiden, Amsterdam-plus, Amsterdam-alternative, MMRpro, PREMM1,2,6, MMRpredict, Associazione Italiana per lo studio della Familiarità ed Ereditarietà dei tumori Gastrointestinali (AIFEG) and the Myriad Genetics Prevalence table) for identifying mutation carriers. While criteria are only used to identify individuals with colorectal cancer (yes/no for screening followed by germline testing), all prediction models except MMRpredict and Myriad tables can predict the probability of carrying mutations for individuals with or without colorectal cancer. We conducted a meta-analysis of the discrimination performance of 17 studies that validated the prediction models. The pooled estimate for the area under curve was 0.80 (95% CI 0.72 to 0.88) for MMRpro, 0.81 (95% CI 0.73 to 0.88) for MMRpredict, 0.84 (95% CI 0.81 to 0.88) for PREMM, and 0.85 (95% CI 0.78 to 0.91) for Leiden model. Given the high degree of overlap in the CIs, we cannot state that one model has a higher discrimination than any of the others. Overall, the existing statistical models have been shown to be sensitive and specific (at a 5% cut-off) in predicting MMR gene mutation carriers. Future models may need to: provide prediction of PMS2 mutations, take into account a wider range of Lynch syndrome-associated cancers when assessing family history, and be applicable to all people irrespective of any cancer diagnosis.

Inherited colorectal cancer syndromes

Colorectal cancer is the most common gastrointestinal malignancy and the second leading cause of cancer death in both men and women in the United States. Most colorectal cancer cases diagnosed annually are due to sporadic events, but up to 5% are attributed to known monogenic disorders including Lynch syndrome, familial adenomatous polyposis, MYH-associated polyposis, and the rare hamartomatous polyposis syndromes. These inherited colorectal cancer syndromes confer a markedly increased risk for the development of multiple cancers, and predictive genetic testing is available to identify mutation carriers and at-risk family members. Through personalized strategies for diagnosis and management, a substantial reduction in morbidity and mortality has been appreciated among patients at highest risk for the development of colorectal cancer.

Performance of PREMM(1,2,6), MMRpredict, and MMRpro in detecting Lynch syndrome among endometrial cancer cases

PURPOSE

Lynch syndrome accounts for 2-5% of endometrial cancer cases. Lynch syndrome prediction models have not been evaluated among endometrial cancer cases.

METHODS

Area under the receiver operating curve (AUC), sensitivity and specificity of PREMM(1,2,6), MMRpredict, and MMRpro scores were assessed among 563 population-based and 129 clinic-based endometrial cancer cases.

RESULTS

A total of 14 (3%) population-based and 80 (62%) clinic-based subjects had pathogenic mutations. PREMM(1,2,6), MMRpredict, and MMRpro were able to distinguish mutation carriers from noncarriers (AUC of 0.77, 0.76, and 0.77, respectively), among population-based cases. All three models had lower discrimination for the clinic-based cohort, with AUCs of 0.67, 0.64, and 0.54, respectively. Using a 5% cutoff, sensitivity and specificity were as follows: PREMM(1,2,6), 93% and 5% among population-based cases and 99% and 2% among clinic-based cases; MMRpredict, 71% and 64% for the population-based cohort and 91% and 0% for the clinic-based cohort; and MMRpro, 57% and 85% among population-based cases and 95% and 10% among clinic-based cases.

CONCLUSION

Currently available prediction models have limited clinical utility in determining which patients with endometrial cancer should undergo genetic testing for Lynch syndrome. Immunohistochemical analysis and microsatellite instability testing may be the best currently available tools to screen for Lynch syndrome in endometrial cancer patients.

Predictive models for mutations in mismatch repair genes: implication for genetic counseling in developing countries

BACKGROUND

Lynch syndrome (LS) is the most common form of inherited predisposition to colorectal cancer (CRC), accounting for 2-5% of all CRC. LS is an autosomal dominant disease characterized by mutations in the mismatch repair genes mutL homolog 1 (MLH1), mutS homolog 2 (MSH2), postmeiotic segregation increased 1 (PMS1), post-meiotic segregation increased 2 (PMS2) and mutS homolog 6 (MSH6). Mutation risk prediction models can be incorporated into clinical practice, facilitating the decision-making process and identifying individuals for molecular investigation. This is extremely important in countries with limited economic resources. This study aims to evaluate sensitivity and specificity of five predictive models for germline mutations in repair genes in a sample of individuals with suspected Lynch syndrome.

METHODS

Blood samples from 88 patients were analyzed through sequencing MLH1, MSH2 and MSH6 genes. The probability of detecting a mutation was calculated using the PREMM, Barnetson, MMRpro, Wijnen and Myriad models. To evaluate the sensitivity and specificity of the models, receiver operating characteristic curves were constructed.

RESULTS

Of the 88 patients included in this analysis, 31 mutations were identified: 16 were found in the MSH2 gene, 15 in the MLH1 gene and no pathogenic mutations were identified in the MSH6 gene. It was observed that the AUC for the PREMM (0.846), Barnetson (0.850), MMRpro (0.821) and Wijnen (0.807) models did not present significant statistical difference. The Myriad model presented lower AUC (0.704) than the four other models evaluated. Considering thresholds of ≥ 5%, the models sensitivity varied between 1 (Myriad) and 0.87 (Wijnen) and specificity ranged from 0 (Myriad) to 0.38 (Barnetson).

CONCLUSIONS

The Barnetson, PREMM, MMRpro and Wijnen models present similar AUC. The AUC of the Myriad model is statistically inferior to the four other models.

Risk prediction models for colorectal cancer: a review

Risk prediction models are important to identify individuals at high risk of developing the disease who can then be offered individually tailored clinical management, targeted screening and interventions to reduce the burden of disease. They are also useful for research purposes when attempting to identify new risk factors for the disease. In this article, we review the risk prediction models that have been developed for colorectal cancer and appraise their applicability, strengths, and weaknesses. We also discuss the factors to be considered for future development and improvement of models for colorectal cancer risk prediction. We conclude that there is no model that sufficiently covers the known risk factors for colorectal cancer that is suitable for assessment of people from across the full range of risk and that a new comprehensive model is needed.

Performance of Lynch syndrome predictive models in a multi-center US referral population

OBJECTIVES

Lynch syndrome is the most common cause of inherited colorectal cancer (CRC) and is due to germline mutations in mismatch repair (MMR) genes. Early Lynch syndrome diagnosis and appropriate CRC surveillance improves mortality. Traditional qualitative clinical criteria including Amsterdam and Bethesda guidelines may miss mutation carriers. Recently, quantitative predictive models including MMRPredict, PREMM(1,2,6), and MMRPro were developed to facilitate diagnosis. However, these models remain to be externally validated in the United States. Therefore, we evaluated the test characteristics of Lynch syndrome predictive models in a tertiary referral group at two US academic centers.

METHODS

We retrospectively collected data on 230 consecutive individuals who underwent genetic testing for MMR gene mutations at the University of Chicago and University of California at San Francisco's Cancer Risk Clinics. Each individual's risk of mutation was examined using MMRPredict, PREMM(1,2,6), and MMRPro. Amsterdam and Bethesda criteria were also determined. Testing characteristics were calculated for each of the models.

RESULTS

We included 230 individuals in the combined cohort. In all, 113 (49%) probands were MMR mutation carriers. Areas under the receiver operator characteristic curves were 0.76, 0.78, and 0.82 for MMRPredict, PREMM(1,2,6), and MMRPro, respectively. While similar in overall performance, our study highlights unique test characteristics of these three quantitative models including comparisons of sensitivity and specificity. Moreover, we identify characteristics of mutation carriers who were missed by each model.

CONCLUSIONS

Overall, all three Lynch syndrome predictive models performed comparably in our multi-center US referral population. These results suggest that Lynch syndrome predictive models can be used to screen for MMR mutation carriers and can provide improved test characteristics compared with traditional clinical criteria. Identification of MMR mutation carriers is paramount as appropriate screening can prevent CRC mortality in this high-risk group.

Microsatellite instability in the management of colorectal cancer

Microsatellite instability (MSI) is a form of genetic instability caused by alterations in the DNA mismatch repair system. Approximately 15% of colorectal cancers display MSI due to a germline mutation in one of the mismatch repair genes (MLH1, MSH2, MSH6 and PMS2) or to epigenetic silencing of MLH1. Colorectal cancers with MSI have distinctive features, including a tendency to arise in the proximal colon, poor differentiation, lymphocytic infiltration and mucinous or signet-ring histology. Patients with MSI tumors appear to have a better prognosis than those with microsatellite stable tumors, but curiously the responses to 5-fluorouracil-based chemotherapy regimens are poorer with MSI tumors. Preliminary data suggest possible advantages of irinotecan-based regimens, but these findings need validation in well-designed clinical trials.

The PREMM(1,2,6) model predicts risk of MLH1, MSH2, and MSH6 germline mutations based on cancer history

BACKGROUND & AIMS

We developed and validated a model to estimate the risks of mutations in the mismatch repair (MMR) genes MLH1, MSH2, and MSH6 based on personal and family history of cancer.

METHODS

Data were analyzed from 4539 probands tested for mutations in MLH1, MSH2, and MSH6. A multivariable polytomous logistic regression model (PREMM(1,2,6)) was developed to predict the overall risk of MMR gene mutations and the risk of mutation in each of the 3 genes. The discriminative ability of the model was validated in 1827 population-based colorectal cancer (CRC) cases.

RESULTS

Twelve percent of the original cohort carried pathogenic mutations (204 in MLH1, 250 in MSH2, and 71 in MSH6). The PREMM(1,2,6) model incorporated the following factors from the probands and first- and second-degree relatives (odds ratio; 95% confidence intervals [CIs]): male sex (1.9; 1.5-2.4), a CRC (4.3; 3.3-5.6), multiple CRCs (13.7; 8.5-22), endometrial cancer (6.1; 4.6-8.2), and extracolonic cancers (3.3; 2.4-4.6). The areas under the receiver operating characteristic curves were 0.86 (95% CI, 0.82-0.91) for MLH1 mutation carriers, 0.87 (95% CI, 0.83-0.92) for MSH2, and 0.81 (95% CI, 0.69-0.93) for MSH6; in validation, they were 0.88 for the overall cohort (95% CI, 0.86-0.90) and the population-based cases (95% CI, 0.83-0.92).

CONCLUSIONS

We developed the PREMM(1,2,6) model, which incorporates information on cancer history from probands and their relatives to estimate an individual's risk of mutations in the MMR genes MLH1, MSH2, and MSH6. This Web-based decision making tool can be used to assess risk of hereditary CRC and guide clinical management.

Underdiagnosis of Lynch syndrome involves more than family history criteria

BACKGROUND & AIMS

Physicians' cancer-related family history assessment for Lynch syndrome is often inadequate. Furthermore, the extent to which clinicians recognize non-family history-related clues for Lynch syndrome is unclear. We reviewed an integrated electronic health record (EHR) to determine diagnostic evaluation for Lynch syndrome in patients diagnosed with colorectal cancer (CRC).

METHODS

We conducted a retrospective cohort study of consecutive patients with CRC, newly diagnosed at a tertiary care Veterans Affairs facility, between 1999 and 2007. A detailed review of the EHR was conducted to evaluate the presence of family history-related and non-family history-related criteria of the Bethesda guidelines. Patient outcomes (identification in clinical practice and referral for genetic testing) were also determined.

RESULTS

We identified a total of 499 patients (mean age, 65.4 years; 98.6% male; 51.1% non-Hispanic white). At least 1 of the Bethesda criteria was met for 57 patients (11.4%), none was met for 198 (39.7%), and there was uncertainty for 244 (48.9%) because of inadequate family history documentation and/or the patient was unsure about their family history. Forty-nine patients met criteria unrelated to family history. Only 4 of 57 patients (7%) who met the Bethesda guidelines had documentation of counseling. Among 244 patients with uncertainty, a suspicion for Lynch syndrome was documented in the EHR of 6 patients (2.5%); 3 received counseling.

CONCLUSIONS

Lynch syndrome is under-recognized, even when patients have clear criteria unrelated to family history. Multifaceted strategies focused on reducing providers' cognitive errors and harnessing EHR capabilities to improve recognition of Lynch syndrome are needed.

Validation of predictive models for germline mutations in DNA mismatch repair genes in colorectal cancer

Lynch syndrome is defined by the presence of germline mutations in mismatch repair (MMR) genes. Several models have been recently devised that predict mutation carrier status (Myriad Genetics, Wijnen, Barnetson, PREMM and MMRpro models). Families at moderate-high risk for harboring a Lynch-associated mutation, referred to the BC Cancer Agency (BCCA) Hereditary Cancer Program (HCP), underwent mutation analysis, immunohistochemistry and/or microsatellite testing. Seventy-two tested cases were included. Twenty-five patients were mutation positive (34.7%) and 47 were mutation negative (65.3%). Nineteen of 43 patients who were both microsatellite stable and normal on immunohistochemistry for MLH1 and MSH2 were also genotyped for mutations in these genes; all 19 were negative for MMR gene mutations. Model-derived probabilities of harboring a MMR gene mutation in the proband were calculated and compared to observed results. The area under the ROC curves were 0.75 (95%CI; 0.63-0.87), 0.86 (0.7-0.96), 0.89 (0.82-0.97), 0.89 (0.81-0.98) and 0.93 (0.86-0.99) for the Myriad, Barnetson, Wijnen, MMRpro and PREMM models, respectively. The Amsterdam II criteria had a sensitivity and specificity of 0.76 and 0.74, respectively, in this cohort. The PREMM model demonstrated the best performance for predicting carrier status based on the positive likelihood ratios at the >10%, >20% and >30% probability thresholds. In this referred cohort, the PREMM model had the most favorable concordance index and predictive performance for carrier status based on the positive LR. These prediction models (PREMM, MMRPro and Wijnen) may soon replace the Amsterdam II and revised Bethesda criteria as a prescreening tool for Lynch mutations.