We have developed and validated a clinical model to predict mismatch repair gene mutations in the MLH1, MSH2, and MSH6 genes: the PREMM1,2,6 model. This model can estimate the probability of carrying an MMR gene mutation, as well as provide such risk estimates for each particular MMR gene based on an individual patient’s personal and family cancer history phenotype and performs equally well among patients with CRC recruited through population-based cancer registries.
model is derived from genotype and phenotypic data from the largest series of unrelated mutation carriers to date and makes it possible to determine the relative importance of predictors for each specific gene. The model discriminated well for each gene, with areas under the ROC curve over 0.8. The discriminatory capacity for predicting a mutation in the MSH6
gene was lowest, which may be explained by the later onset of CRC among probands and their relatives, similar to other studies of MSH6
The model performed equally well among population-based CRC cases ascertained through the CCFR, providing sound external validation of its discriminative ability to predict MMR gene mutation carriers and the individual likelihood of finding a mutation in each of the three genes.
We found some differences in this larger cohort of patients used to develop PREMM1,2,6
compared to that analyzed for the previously developed PREMM1,2
model. First, we observed sex-specific differences in the frequency of mutations detected: 190/1214 (16%) for men and 335/3325 (10%) for women (OR:1.6; 95% CI: 1.3–1.9). Men were two times more likely to have a MMR gene mutation than women. Based on an autosomal dominant mode of inheritance, one would not expect a deviation of mutation carriage based on gender. We suspect this may be due in part to selection bias where men presenting for genetic testing may have had a higher pretest probability of having Lynch Syndrome than women. Another plausible explanation may be related to sex-linked modifier genes and/or underlying environmental factors which may contribute to the gender-related differences also seen for CRC in the general population. Similar gender-specific penetrance effects in Lynch Syndrome have been described and deserve further study.22–24
A second variation of the current model from PREMM1,2
is that the presence of colonic adenomas in the proband was no longer a relevant predictor of mutation status. We suspect this effect is related to the reporting of polyps, which may not be as consistent as that for known cancer diagnoses.
A number of prediction models have recently been developed to identify individuals at risk for Lynch Syndrome, in addition to PREMM.22,25
These models have differed in the methodology used to predict carrier status as well as the patient populations from which they were derived and validated. Two other algorithms that have been developed are MMRpro25
: a comparison of these models to the PREMM models is provided in . A limitation of MMRpro is that it does not include information on Lynch Syndrome related cancers other than CRC or endometrial cancer. In the current study, such cancers were highly predictive of MSH2
gene mutations. Genotype-phenotype differences have been previously described based on the altered gene type, which support the recommendation for collecting all types of Lynch Syndrome-associated tumors when assessing a family history and incorporating these neoplasms into current clinical prediction models. This is particularly important in designing a gene-specific prediction model. While MMRpro can provide gene-specific risk estimates, the accuracy with which this can be done has not been reported. The MMRpredict model can only be used in patients affected with CRC and does not offer gene-specific estimates but does, along with MMRpro, include tumor analysis data. Nevertheless, recent studies validating the performance of these prediction models have shown them to all outperform the existing clinical criteria (Amsterdam II and revised Bethesda criteria) in predicting MMR gene mutation carriers.26–28
Further studies and continued use of these prediction models may ultimately lead to them replacing the Amsterdam and revised Bethesda guidelines as prescreening tools for Lynch Syndrome.
Comparison of Prediction Models for Identifying Mismatch Repair Gene Mutation Carriers
Our study has potential limitations. For the identification of high-risk individuals we relied on the patients’ self-report of personal and family history of CRC, extracolonic cancers, and polyps. Although the information was provided by healthcare providers who filled out the data-collection form, there may have been some inaccuracies. However, we used similar methodology in developing the first version, PREMM1,2
, which performed well in an external European population.14
In addition, the external validation of the PREMM1,2,6
model among cases provided by the CCFR lends credence to the model’s discriminative performance as proband information is obtained through detailed, systematic methods to assure reliable data collection. While the development cohort represents patients at heightened risk based on cancer history, the external validation cohort allowed for study on the impact of population-based ascertainment on prediction. We did not note a difference in the model’s discriminatory ability based on population-based recruitment. We did not have data on MSI and IHC testing in the development cohort, but the CCFR validation cohort provides such data. Molecular tumor testing was conducted for all population-based cases and genetic testing was performed when tumors displayed microsatellite instability. In contrast, genetic testing was performed on all clinic-based cases regardless of MSI/IHC results. The excellent performance in the validation cohort indicates that the model performs well regardless of MSI/IHC status. Further studies are underway to assess the value of combinations of model predictions and tumor test results in refining predictions.29
How can prediction models help patients and clinicians? There is a clear demand for easy risk assessment tools as evidenced by the fact that the current PREMM1,2
website has over 1000 users per month. In contrast to breast cancer, awareness of hereditary CRC and genetic counseling referral and predictive testing is still extremely low. Part of this is due to the myriad of diagnostic criteria which are cumbersome to use and do not provide quantitative estimates for the individual patient. While tumor analyses with MSI and IHC is recommended to screen for Lynch Syndrome, the pool of at-risk individuals is far greater than those affected with cancer and includes unaffected individuals in whom close monitoring and aggressive intervention may have benefit. Therefore the PREMM1,2,6
model may be used as an initial screening tool in practice by healthcare providers to determine who needs further genetic assessment. Based on data from this study and a prior population-based cohort, we have shown that a score of >5% may be a reasonable threshold over which referral for further genetic risk assessment should be recommended.14
Once referral for genetic evaluation is made, the specialist can use the PREMM1,2,6
model to obtain gene-specific risk estimates which may be most useful in cases where molecular tumor analyses are unavailable or inconclusive. The gene-specific estimates provided by PREMM1,2,6
can be used with conclusive tumor data to further support the decision to pursue DNA mutation analysis on the single, specific gene. Additionally, in cases where a gene mutation in MLH1
, or MSH6
is not detected by clinical genetic testing, a high overall PREMM1,2,6
score (≥20%) may warrant consideration for PMS2
, or TACSTD1 testing, in the appropriate clinical scenario. These individuals will merit further study as new gene discoveries related to familial CRC are reported.
In conclusion, the PREMM1,2,6 model provides gene-specific risk estimates for the probability of a mutation in either the MLH1, MSH2 or MSH6 genes and should now be used in place of PREMM1,2. The PREMM1,2,6 model can help individualize cancer risk assessment and help decide for whom germline DNA sequencing is most appropriate. Estimating pretest gene mutation probability is an important initial step in assessing Lynch Syndrome, as the decision to undergo germline testing is complex. The PREMM1,2,6 model can help providers not only identify those patients at-risk but quantify the individual’s gene-specific risk. This information can influence the genetic testing strategy on an individual basis and facilitate the communication process between healthcare provider and patient, allowing for a tailored, personal, and possibly more cost-effective approach in managing genetic evaluation and cancer surveillance.