Win AK, Jenkins MA, Dowty JG, Antoniou AC, Lee A, Giles GG, et al. Prevalence and penetrance of major genes and polygenes for colorectal cancer. Cancer Epidemiol Biomark Prev. 2017;26:404–12.
Article
CAS
Google Scholar
Haraldsdottir S, Rafnar T, Frankel WL, Einarsdottir S, Sigurdsson A, Hampel H, et al. Comprehensive population-wide analysis of Lynch syndrome in Iceland reveals founder mutations in MSH6 and PMS2. Nat Commun. 2017;8:14755.
Article
Google Scholar
Jasperson KW, Tuohy TM, Neklason DW, Burt RW. Hereditary and familial colon cancer. Gastroenterology. 2010;138:2044–58.
Article
CAS
Google Scholar
Moreira L, Balaguer F, Lindor N, de la Chapelle A, Hampel H, Aaltonen LA, et al. Identification of Lynch syndrome among patients with colorectal cancer. JAMA. 2012;308:1555–65.
Article
CAS
Google Scholar
Dominguez-Valentin M, Sampson JR, Seppälä TT, ten Broeke SW, Plazzer J-P, Nakken S, et al. Cancer risks by gene, age, and gender in 6350 carriers of pathogenic mismatch repair variants: findings from the Prospective Lynch Syndrome Database. Genet Med. 2019;22:15–25.
Article
Google Scholar
Hampel H, de la Chapelle A. The search for unaffected individuals with Lynch syndrome: do the ends justify the means? Cancer Prev Res. 2011;4:1–5.
Article
Google Scholar
Sjursen W, Haukanes BI, Grindedal EM, Aarset H, Stormorken A, Engebretsen LF, et al. Current clinical criteria for Lynch syndrome are not sensitive enough to identify MSH6 mutation carriers. J Med Genet. 2010;47:579–85.
Article
CAS
Google Scholar
LaDuca H, Polley EC, Yussuf A, Hoang L, Gutierrez S, Hart SN, et al. A clinical guide to hereditary cancer panel testing: evaluation of gene-specific cancer associations and sensitivity of genetic testing criteria in a cohort of 165,000 high-risk patients. Genet Med. 2020;22:407–15.
Article
CAS
Google Scholar
Sijmons RH, Greenblatt MS, Genuardi M. Gene variants of unknown clinical significance in Lynch syndrome. An introduction for clinicians. Familial Cancer. 2013;12:181–7.
Article
CAS
Google Scholar
Tricarico R, Kasela M, Mareni C, Thompson BA, Drouet A, Staderini L, et al. Assessment of the InSiGHT Interpretation Criteria for the Clinical Classification of 24 MLH1 and MSH2 Gene Variants. Hum Mutat. 2017;38:64–77.
Article
CAS
Google Scholar
Mersch J, Brown N, Pirzadeh-Miller S, Mundt E, Cox HC, Brown K, et al. Prevalence of variant reclassification following hereditary cancer genetic testing. JAMA. 2018;320:1266–74.
Article
Google Scholar
Welsh JL, Hoskin TL, Day CN, Thomas AS, Cogswell JA, Couch FJ, et al. Clinical decision-making in patients with variant of uncertain significance in BRCA1 or BRCA2 Genes. Ann Surg Oncol. 2017;24:3067–72.
Article
Google Scholar
Hampel H, Yurgelun MB. Point/counterpoint: is it time for universal germline genetic testing for all GI cancers? J Clin Oncol. 2022:JCO2102764.
Backwell L, Marsh JA. Diverse molecular mechanisms underlying pathogenic protein mutations: beyond the loss-of-function paradigm. Annu Rev Genomics Hum Genet. 2022. https://doi.org/10.1146/annurev-genom-111221-103208.
Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46:D1062–7.
Article
CAS
Google Scholar
Hechtman JF, Rana S, Middha S, Stadler ZK, Latham A, Benayed R, et al. Retained mismatch repair protein expression occurs in approximately 6% of microsatellite instability-high cancers and is associated with missense mutations in mismatch repair genes. Mod Pathol. 2020;33:871–9.
Article
CAS
Google Scholar
Weile J, Roth FP. Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas. Hum Genet. 2018;137:665–78.
Article
CAS
Google Scholar
Starita LM, Ahituv N, Dunham MJ, Kitzman JO, Roth FP, Seelig G, et al. Variant interpretation: functional assays to the rescue. Am J Hum Genet. 2017;101:315–25.
Article
CAS
Google Scholar
Livesey BJ, Marsh JA. Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations. Mol Syst Biol. 2020;16:e9380.
Article
CAS
Google Scholar
Cubuk C, Garrett A, Choi S, King L, Loveday C, Torr B, et al. Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes. Genet Med. 2021. https://doi.org/10.1038/s41436-021-01265-z.
Fayer S, Horton C, Dines JN, Rubin AF, Richardson ME, McGoldrick K, et al. Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN. Am J Hum Genet. 2021;108:2248–58.
Article
CAS
Google Scholar
Gelman H, Dines JN, Berg J, Berger AH, Brnich S, Hisama FM, et al. Recommendations for the collection and use of multiplexed functional data for clinical variant interpretation. Genome Med. 2019;11:85.
Article
Google Scholar
Brnich SE, Rivera-Muñoz EA, Berg JS. Quantifying the potential of functional evidence to reclassify variants of uncertain significance in the categorical and Bayesian interpretation frameworks. Hum Mutat. 2018;39:1531–41.
Article
Google Scholar
Findlay GM, Daza RM, Martin B, Zhang MD, Leith AP, Gasperini M, et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature. 2018;562:217–22.
Article
CAS
Google Scholar
Schiabor Barrett KM, Masnick M, Hatchell KE, Savatt JM, Banet N, Buchanan A, et al. Clinical validation of genomic functional screen data: analysis of observed BRCA1 variants in an unselected population cohort. HGG Adv. 2022;3:100086.
CAS
Google Scholar
Jia X, Burugula BB, Chen V, Lemons RM, Jayakody S, Maksutova M, et al. Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk. Am J Hum Genet. 2021;108:163–75.
Article
CAS
Google Scholar
Brnich SE, Abou Tayoun AN, Couch FJ, Cutting GR, Greenblatt MS, Heinen CD, et al. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med. 2019;12:3.
Article
Google Scholar
Tavtigian SV, Greenblatt MS, Harrison SM, Nussbaum RL, Prabhu SA, Boucher KM, et al. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet Med. 2018;20:1054–60.
Article
Google Scholar
Heinen CD, Wilson T, Mazurek A, Berardini M, Butz C, Fishel R. HNPCC mutations in hMSH2 result in reduced hMSH2-hMSH6 molecular switch functions. Cancer Cell. 2002;1:469–78.
Article
CAS
Google Scholar
Chao EC, Velasquez JL, Witherspoon MS, Rozek LS, Peel D, Ng P, Gruber SB, Watson P, Rennert G, Anton-Culver H, Lynch H, Lipkin SM. Accurate classification of MLH1/MSH2 missense variants with multivariate analysis of protein polymorphisms-mismatch repair (MAPP-MMR). Hum Mutat. 2008;29(6):852-60. https://doi.org/10.1002/humu.20735.
Møller P, Seppälä TT, Bernstein I, Holinski-Feder E, Sala P, Gareth Evans D, et al. Cancer risk and survival in path_MMR carriers by gene and gender up to 75 years of age: a report from the Prospective Lynch Syndrome Database. Gut. 2018;67:1306–16.
Article
Google Scholar
Niessen RC, Hofstra RMW, Westers H, Ligtenberg MJL, Kooi K, Jager POJ, et al. Germline hypermethylation of MLH1 and EPCAM deletions are a frequent cause of Lynch syndrome. Genes Chromosom Cancer. 2009;48:737–44.
Article
CAS
Google Scholar
Ligtenberg MJL, Kuiper RP, Chan TL, Goossens M, Hebeda KM, Voorendt M, et al. Heritable somatic methylation and inactivation of MSH2 in families with Lynch syndrome due to deletion of the 3’ exons of TACSTD1. Nat Genet. 2009;41:112–7.
Article
CAS
Google Scholar
Boland CR, Thibodeau SN, Hamilton SR, Sidransky D, Eshleman JR, Burt RW, et al. A National Cancer Institute Workshop on Microsatellite Instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer. Cancer Res. 1998;58:5248–57.
CAS
Google Scholar
Shirts BH, Konnick EQ, Upham S, Walsh T, Ranola JMO, Jacobson AL, et al. Using somatic mutations from tumors to classify variants in mismatch repair genes. Am J Hum Genet. 2018;103:19–29.
Article
CAS
Google Scholar
Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, et al. Predicting splicing from primary sequence with deep learning. Cell. 2019;176:535–548.e24.
Article
CAS
Google Scholar
Dominguez-Valentin M, Plazzer J-P, Sampson JR, Engel C, Aretz S, Jenkins MA, et al. No difference in penetrance between truncating and missense/aberrant splicing pathogenic variants in MLH1 and MSH2: a Prospective Lynch Syndrome Database Study. J Clin Med Res. 2021:10. https://doi.org/10.3390/jcm10132856.
Fahed AC, Wang M, Homburger JR, Patel AP, Bick AG, Neben CL, et al. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions. Nat Commun. 2020;11:3635.
Article
CAS
Google Scholar
Knudson AG Jr. Mutation and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci U S A. 1971;68:820–3.
Article
Google Scholar
Rhees J, Arnold M, Boland CR. Inversion of exons 1-7 of the MSH2 gene is a frequent cause of unexplained Lynch syndrome in one local population. Familial Cancer. 2014;13:219–25.
Article
CAS
Google Scholar
Pritchard CC, Morrissey C, Kumar A, Zhang X, Smith C, Coleman I, et al. Complex MSH2 and MSH6 mutations in hypermutated microsatellite unstable advanced prostate cancer. Nat Commun. 2014;5:4988.
Article
CAS
Google Scholar
Rhine CL, Cygan KJ, Soemedi R, Maguire S, Murray MF, Monaghan SF, et al. Hereditary cancer genes are highly susceptible to splicing mutations. PLoS Genet. 2018;14:e1007231.
Article
Google Scholar
Morak M, Pineda M, Martins A, Gaildrat P, Tubeuf H, Drouet A, et al. Splicing analyses for variants in MMR genes: best practice recommendations from the European Mismatch Repair Working Group. Eur J Hum Genet. 2022:1–9.
Erwood S, Bily TMI, Lequyer J, Yan J, Gulati N, Brewer RA, et al. Saturation variant interpretation using CRISPR prime editing. Nat Biotechnol. 2022;40:885–95.
Article
CAS
Google Scholar
Gergics P, Smith C, Bando H, Jorge AAL, Rockstroh-Lippold D, Vishnopolska SA, et al. High-throughput splicing assays identify missense and silent splice-disruptive POU1F1 variants underlying pituitary hormone deficiency. Am J Hum Genet. 2021;108:1526–39.
Article
CAS
Google Scholar
Adamson SI, Zhan L, Graveley BR. Vex-seq: high-throughput identification of the impact of genetic variation on pre-mRNA splicing efficiency. Genome Biol. 2018;19:71.
Article
Google Scholar
Soemedi R, Cygan KJ, Rhine CL, Wang J, Bulacan C, Yang J, et al. Pathogenic variants that alter protein code often disrupt splicing. Nat Genet. 2017;49:848–55.
Article
CAS
Google Scholar
Rentzsch P, Schubach M, Shendure J, Kircher M. CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 2021;13:31.
Article
CAS
Google Scholar
Pesaran T, Karam R, Huether R, Li S, Farber-Katz S, Chamberlin A, et al. Beyond DNA: an integrated and functional approach for classifying germline variants in breast cancer genes. Int J Breast Cancer. 2016;2016:2469523.
Article
CAS
Google Scholar
Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405–24.
Article
Google Scholar
Martin S, Chamberlin A, Shinde DN, Hempel M, Strom TM, Schreiber A, et al. De novo variants in GRIA4 lead to intellectual disability with or without seizures and gait abnormalities. Am J Hum Genet. 2017;101:1013–20.
Article
CAS
Google Scholar
Sherrill JD, Kc K, Wang X, Wen T, Chamberlin A, Stucke EM, et al. Whole-exome sequencing uncovers oxidoreductases DHTKD1 and OGDHL as linkers between mitochondrial dysfunction and eosinophilic esophagitis. JCI Insight. 2018:3. https://doi.org/10.1172/jci.insight.99922.
Seabold S, Perktold J. Statsmodels: Econometric and statistical modeling with python. Proceedings of the 9th Python in Science Conference [Internet]. SciPy; 2010. Available from: https://conference.scipy.org/proceedings/scipy2010/seabold.html.