Advertisement

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

  • Cathy Shyr
    Affiliations
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA

    Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
    Search for articles by this author
  • Amanda L. Blackford
    Affiliations
    Division of Biostatistics and Bioinformatics, Johns Hopkins School of Medicine, Baltimore, MD
    Search for articles by this author
  • Theodore Huang
    Affiliations
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA

    Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
    Search for articles by this author
  • Jianfeng Ke
    Affiliations
    Department of Data Science, Dana-Farber Cancer Institute, Boston, MA

    Department of Mathematical Sciences, Tsinghua University, Beijing, China
    Search for articles by this author
  • Nofal Ouardaoui
    Affiliations
    Department of Computer Science, School of Engineering, Tufts University, Medford, MA
    Search for articles by this author
  • Lorenzo Trippa
    Affiliations
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA

    Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
    Search for articles by this author
  • Sapna Syngal
    Affiliations
    Cancer Genetics and Prevention Division, Dana-Farber Cancer Institute, Boston, MA

    Division of Gastroenterology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA
    Search for articles by this author
  • Chinedu Ukaegbu
    Affiliations
    Cancer Genetics and Prevention Division, Dana-Farber Cancer Institute, Boston, MA
    Search for articles by this author
  • Hajime Uno
    Affiliations
    Department of Data Science, Dana-Farber Cancer Institute, Boston, MA

    McGraw/Patterson Center for Population Sciences, Dana-Farber Cancer Institute, Boston, MA
    Search for articles by this author
  • Khedoudja Nafa
    Affiliations
    Department of Pathology and Laboratory Medicine, Molecular Diagnostic Service, Memorial Sloan Kettering Cancer Center, New York, NY
    Search for articles by this author
  • Zsofia K. Stadler
    Affiliations
    Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Comprehensive Cancer Center, New York, NY

    Niehaus Center for Inherited Cancer Genomics, Memorial Sloan Kettering Cancer Center, New York, NY
    Search for articles by this author
  • Kenneth Offit
    Affiliations
    Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Comprehensive Cancer Center, New York, NY

    Niehaus Center for Inherited Cancer Genomics, Memorial Sloan Kettering Cancer Center, New York, NY
    Search for articles by this author
  • Christopher I. Amos
    Affiliations
    Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX

    Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX

    Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX
    Search for articles by this author
  • Patrick M. Lynch
    Affiliations
    Gastroenterology, Hepatology and Nutrition, University of Texas MD Anderson Cancer Center, Houston, TX
    Search for articles by this author
  • Sining Chen
    Affiliations
    Nokia Bell Labs, Murray Hill, NJ
    Search for articles by this author
  • Francis M. Giardiello
    Affiliations
    Department of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD
    Search for articles by this author
  • Daniel D. Buchanan
    Affiliations
    Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia

    University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia

    Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
    Search for articles by this author
  • John L. Hopper
    Affiliations
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
    Search for articles by this author
  • Mark A. Jenkins
    Affiliations
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

    University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
    Search for articles by this author
  • Melissa C. Southey
    Affiliations
    Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia

    Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia

    Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
    Search for articles by this author
  • Aung Ko Win
    Affiliations
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
    Search for articles by this author
  • Jane C. Figueiredo
    Affiliations
    Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
    Search for articles by this author
  • Danielle Braun
    Correspondence
    Correspondence and requests for materials should be addressed to Danielle Braun, Harvard T.H. Chan School of Public Health, Dana-Farber Cancer Institute, 677 Huntington Avenue, Boston, MA, 02115
    Affiliations
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA

    Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
    Search for articles by this author
  • Giovanni Parmigiani
    Affiliations
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA

    Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
    Search for articles by this author
Published:August 23, 2022DOI:https://doi.org/10.1016/j.gim.2022.07.004

      Abstract

      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.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      ACMG Member Login

      Are you an ACMG Member? Sign in for online access.

      Subscribe:

      Subscribe to Genetics in Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Jass J.R.
        Hereditary non-polyposis colorectal cancer: the rise and fall of a confusing term.
        World J Gastroenterol. 2006; 12: 4943-4950https://doi.org/10.3748/wjg.v12.i31.4943
        • Rustgi A.K.
        The genetics of hereditary colon cancer.
        Genes Dev. 2007; 21: 2525-2538https://doi.org/10.1101/gad.1593107
        • Jasperson K.W.
        • Tuohy T.M.
        • Neklason D.W.
        • Burt R.W.
        Hereditary and familial colon cancer.
        Gastroenterology. 2010; 138: 2044-2058https://doi.org/10.1053/j.gastro.2010.01.054
        • Umar A.
        • Boland C.R.
        • al Terdiman J.P.
        • et al.
        Revised Bethesda Guidelines for hereditary nonpolyposis colorectal cancer (Lynch syndrome) and microsatellite instability.
        J Natl Cancer Inst. 2004; 96: 261-268https://doi.org/10.1093/jnci/djh034
        • Giardiello F.M.
        • Allen J.I.
        • Axilbund J.E.
        • et al.
        Guidelines on genetic evaluation and management of Lynch syndrome: a consensus statement by the US Multi-Society Task Force on colorectal cancer.
        Gastroenterology. 2014; 147: 502-526https://doi.org/10.1053/j.gastro.2014.04.001
        • Syngal S.
        • Brand R.E.
        • Church J.M.
        • et al.
        ACG clinical guideline: genetic testing and management of hereditary gastrointestinal cancer syndromes.
        Am J Gastroenterol. 2015; 110 (quiz 263. https://doi.org/10.1038/ajg.2014.435): 223-262
        • Lynch H.T.
        • de la Chapelle A.
        Hereditary colorectal cancer.
        N Engl J Med. 2003; 348: 919-932https://doi.org/10.1056/NEJMra012242
        • Jenkins M.A.
        • Baglietto L.
        • Dowty J.G.
        • et al.
        Cancer risks for mismatch repair gene mutation carriers: a population-based early onset case-family study.
        Clin Gastroenterol Hepatol. 2006; 4: 489-498https://doi.org/10.1016/j.cgh.2006.01.002
        • Stoffel E.
        • Mukherjee B.
        • Raymond V.M.
        • et al.
        Calculation of risk of colorectal and endometrial cancer among patients with Lynch syndrome.
        Gastroenterology. 2009; 137: 1621-1627https://doi.org/10.1053/j.gastro.2009.07.039
        • Engel C.
        • Loeffler M.
        • Steinke V.
        • et al.
        Risks of less common cancers in proven mutation carriers with lynch syndrome.
        J Clin Oncol. 2012; 30: 4409-4415https://doi.org/10.1200/JCO.2012.43.2278
        • Win A.K.
        • Young J.P.
        • Lindor N.M.
        • et al.
        Colorectal and other cancer risks for carriers and noncarriers from families with a DNA mismatch repair gene mutation: a prospective cohort study.
        J Clin Oncol. 2012; 30: 958-964https://doi.org/10.1200/JCO.2011.39.5590
        • Vasen H.F.
        • Watson P.
        • Mecklin J.P.
        • Lynch H.T.
        New clinical criteria for hereditary nonpolyposis colorectal cancer (HNPCC, Lynch syndrome) proposed by the International Collaborative group on HNPCC.
        Gastroenterology. 1999; 116: 1453-1456https://doi.org/10.1016/s0016-5085(99)70510-x
        • Provenzale D.
        • Gupta S.
        • Ahnen D.J.
        • et al.
        Genetic/familial high-risk assessment: colorectal version 1.2016, NCCN clinical practice guidelines in oncology.
        J Natl Compr Canc Netw. 2016; 14: 1010-1030https://doi.org/10.6004/jnccn.2016.0108
        • Kastrinos F.
        • Idos G.
        • Parmigiani G.
        Prediction models for Lynch syndrome.
        in: Valle L. Gruber S.B. Capella G. Hereditary Colorectal Cancer: Genetic Basis and Clinical Implications. Springer, Cham2018: 281-303
        • Chen S.
        • Wang W.
        • Lee S.
        • et al.
        Prediction of germline mutations and cancer risk in the Lynch syndrome.
        JAMA. 2006; 296: 1479-1487https://doi.org/10.1001/jama.296.12.1479
        • Kastrinos F.
        • Uno H.
        • Ukaegbu C.
        • et al.
        Development and validation of the PREMM5 model for comprehensive risk assessment of Lynch syndrome.
        J Clin Oncol. 2017; 35: 2165-2172https://doi.org/10.1200/JCO.2016.69.6120
        • Barnetson R.A.
        • Tenesa A.
        • Farrington S.M.
        • et al.
        Identification and survival of carriers of mutations in DNA mismatch-repair genes in colon cancer.
        N Engl J Med. 2006; 354: 2751-2763https://doi.org/10.1056/NEJMoa053493
        • Wijnen J.T.
        • Vasen H.F.
        • Khan P.M.
        • et al.
        Clinical findings with implications for genetic testing in families with clustering of colorectal cancer.
        N Engl J Med. 1998; 339: 511-518https://doi.org/10.1056/NEJM199808203390804
        • Benson A.B.
        • Venook A.P.
        • Al-Hawary M.M.
        • et al.
        Colon cancer. version 2.2021, NCCN clinical practice guidelines in oncology.
        J Natl Compr Canc Netw. 2021; 19: 329-359https://doi.org/10.6004/jnccn.2021.0012
        • Monzon J.G.
        • Cremin C.
        • Armstrong L.
        • et al.
        Validation of predictive models for germline mutations in DNA mismatch repair genes in colorectal cancer.
        Int J Cancer. 2010; 126: 930-939https://doi.org/10.1002/ijc.24808
        • Balaguer F.
        • Balmaña J.
        • Castellví-Bel S.
        • et al.
        Validation and extension of the PREMM1,2 model in a population-based cohort of colorectal cancer patients.
        Gastroenterology. 2008; 134: 39-46https://doi.org/10.1053/j.gastro.2007.10.042
        • Balmaña J.
        • Stockwell D.H.
        • Steyerberg E.W.
        • et al.
        Prediction of MLH1 and MSH2 mutations in the Lynch syndrome.
        JAMA. 2006; 296: 1469-1478https://doi.org/10.1001/jama.296.12.1469
        • Green R.C.
        • Parfrey P.S.
        • Woods M.O.
        • Younghusband H.B.
        Prediction of Lynch syndrome in consecutive patients with colorectal cancer.
        J Natl Cancer Inst. 2009; 101: 331-340https://doi.org/10.1093/jnci/djn499
        • Pouchet C.J.
        • Wong N.
        • Chong G.
        • et al.
        A comparison of models used to predict MLH1, MSH2 and MSH6 mutation carriers.
        Ann Oncol. 2009; 20: 681-688https://doi.org/10.1093/annonc/mdn686
        • Ramsoekh D.
        • van Leerdam M.E.
        • Wagner A.
        • Kuipers E.J.
        • Steyerberg E.W.
        Mutation prediction models in Lynch syndrome: evaluation in a clinical genetic setting.
        J Med Genet. 2009; 46: 745-751https://doi.org/10.1136/jmg.2009.066589
        • Mercado R.C.
        • Hampel H.
        • Kastrinos F.
        • et al.
        Performance of PREMM(1,2,6), MMRpredict, and MMRpro in detecting Lynch syndrome among endometrial cancer cases.
        Genet Med. 2012; 14: 670-680https://doi.org/10.1038/gim.2012.18
        • Wang C.
        • Wang Y.
        • Hughes K.S.
        • Parmigiani G.
        • Braun D.
        Penetrance of colorectal cancer among mismatch repair gene mutation carriers: a meta-analysis.
        JNCI Cancer Spectr. 2020; 4: pkaa027https://doi.org/10.1093/jncics/pkaa027
        • Jasperson K.W.
        • Lowstuter K.
        • Weitzel J.N.
        Assessing the predictive accuracy of hMLH1 and hMSH2 mutation probability models.
        J Genet Couns. 2006; 15: 339-347https://doi.org/10.1007/s10897-006-9035-6
        • Murphy E.A.
        • Mutalik G.S.
        The application of Bayesian methods in genetic counselling.
        Hum Hered. 1969; 19: 126-151https://doi.org/10.1159/000152210
        • Chen S.
        • Wang W.
        • Broman K.W.
        • Katki H.A.
        • Parmigiani G.
        BayesMendel: an R environment for Mendelian risk prediction.
        Stat Appl Genet Mol Biol. 2004; 3 (Article21.)
        • Steyerberg E.W.
        Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating.
        Springer, 2019
        • Steyerberg E.W.
        • Vickers A.J.
        • Cook N.R.
        • et al.
        Assessing the performance of prediction models: a framework for some traditional and novel measures.
        Epidemiology. 2010; 21: 128-138https://doi.org/10.1097/EDE.0b013e3181c30fb2
        • Van Calster B.
        • McLernon D.J.
        • van Smeden M.
        • Wynants L.
        • Steyerberg E.W.
        Calibration: the Achilles heel of predictive analytics.
        BMC Med. 2019; 17: 2301https://doi.org/10.1186/s12916-019-1466-7
        • Huang T.
        • Gorfine M.
        • Hsu L.
        • Parmigiani G.
        • Braun D.
        Practical implementation of frailty models in Mendelian risk prediction.
        Genet Epidemiol. 2020; 44: 564-578https://doi.org/10.1002/gepi.22323
        • Huang T.
        • Idos G.
        • Hong C.
        • Gruber S.B.
        • Gio- vanni Parmigiani
        • Braun Danielle
        Extending models via gradient boosting: an application to Mendelian models.
        Ann Appl Stat. 2021; 15: 1126-1146https://doi.org/10.1214/21-AOAS1482
        • Pencina Michael J.
        • D’Agostino Sr Ralph B.
        • D’Agostino Jr.,
        • Ralph B.
        • Vasan Ramachandran S.
        Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.
        Stat Med. 2008; 27 (discussion 207-12.): 157-172
        • Murphy K.P.
        Machine Learning: A Probabilistic Perspective.
        MIT Press, 2012
        • Biswas S.
        • Atienza P.
        • Chipman J.
        • et al.
        Simplifying clinical use of the genetic risk prediction model BRCAPRO.
        Breast Cancer Res Treat. 2013; 139: 571-579https://doi.org/10.1007/s10549-013-2564-4
        • Biswas S.
        • Atienza P.
        • Chipman J.
        • et al.
        A two-stage approach to genetic risk assessment in primary care.
        Breast Cancer Res Treat. 2016; 155: 375-383https://doi.org/10.1007/s10549-016-3686-2
        • Wang C.
        • Gallo R.E.
        • Fleisher L.
        • Miller S.M.
        Literacy assessment of family health history tools for public health prevention.
        Public Health Genomics. 2011; 14: 222-237https://doi.org/10.1159/000273689
        • Wang C.
        • Paasche-Orlow M.K.
        • Bowen D.J.
        • et al.
        Utility of a virtual counselor (VICKY) to collect family health histories among vulnerable patient populations: a randomized controlled trial.
        Patient Educ Couns. 2021; 104: 979-988https://doi.org/10.1016/j.pec.2021.02.034
        • Lu J.
        • Knapp S.
        • Seymour G.G.
        • et al.
        Evaluation of Lynch syndrome risk models in a multicenter diverse population.
        J Clin Oncol. 2022; 4010597
        • Lee G.
        • Liang J.W.
        • Zhang Q.
        • et al.
        Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO.
        Elife. 2021; 10e68699https://doi.org/10.7554/eLife.68699
        • Liang J.W.
        • Idos G.E.
        • Hong C.
        • Gruber S.B.
        • Parmigiani G.
        • Braun D.
        Statistical methods for Mendelian models with multiple genes and cancers.
        Genetic Epidemiol. 2022;