Classification of genetic variants in genes associated with Lynch syndrome using a clinical history weighting algorithm. Issue 1 (December 2016) - Morris, Brian; Hughes, Elisha; Rosenthal, Eric; Gutin, Alexander; Bowles, Karla
Abstract Background Lynch syndrome is a hereditary cancer syndrome associated with high risks of colorectal and endometrial cancer that is caused by pathogenic variants in the mismatch repair genes (MLH1, MSH2, MSH6, PMS2, EPCAM ). Accurate classification of variants identified in these genes as pathogenic or benign enables informed medical management decisions. Previously, we developed a clinical History Weighting Algorithm (HWA) for the classification of variants of uncertain significance (VUSs) inBRCA1 andBRCA2 . TheBRCA1/2 HWA is based on the premise that pathogenic variants in these genes will be identified more often in individuals with strong personal and/or family histories of breast and/or ovarian cancer, while the identification of benign variants should be independent of cancer history. Here we report the development of a similar HWA to allow for classification of VUSs in genes associated with Lynch syndrome using data collected through both syndrome-specific and pan-cancer panel testing. Methods Upon completion of algorithm development, the HWA was tested using simulated variants constructed from 79, 214 probands, as well as 379 true variants. Positive (PPV) and negative predictive values (NPV) were calculated on a per gene basis. Results 25, 500 pathogenic and 50, 500 benign simulated variants were analyzed using the HWA and the PPVs and NPVs for each gene were greater than 0.997 and 0.999, respectively. The HWA was also evaluated using 100 trials for each of the 379 true variants. PPVs of >0.998 and NPVs of >0.999 were obtained for all genes. Conclusions We have developed and implemented a HWA to aid in the classification of VUSs in genes associated with Lynch syndrome. The work presented here demonstrates that this HWA is able to classifyMLH1, MSH2, andMSH6 VUSs as either benign or pathogenic with high accuracy.