Literature DB >> 19653273

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

Jose G Monzon1, Carol Cremin, Linlea Armstrong, Jennifer Nuk, Sean Young, Doug E Horsman, Kristy Garbutt, Chris D Bajdik, Sharlene Gill.   

Abstract

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.

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Year:  2010        PMID: 19653273     DOI: 10.1002/ijc.24808

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  16 in total

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

Authors:  Fay Kastrinos; Ewout W Steyerberg; Rowena Mercado; Judith Balmaña; Spring Holter; Steven Gallinger; Kimberly D Siegmund; James M Church; Mark A Jenkins; Noralane M Lindor; Stephen N Thibodeau; Lynn Anne Burbidge; Richard J Wenstrup; Sapna Syngal
Journal:  Gastroenterology       Date:  2010-08-19       Impact factor: 22.682

Review 2.  Traditional statistical methods for evaluating prediction models are uninformative as to clinical value: towards a decision analytic framework.

Authors:  Andrew J Vickers; Angel M Cronin
Journal:  Semin Oncol       Date:  2010-02       Impact factor: 4.929

3.  Comparison of the clinical prediction model PREMM(1,2,6) and molecular testing for the systematic identification of Lynch syndrome in colorectal cancer.

Authors:  Fay Kastrinos; Ewout W Steyerberg; Judith Balmaña; Rowena Mercado; Steven Gallinger; Robert Haile; Graham Casey; John L Hopper; Loic LeMarchand; Noralane M Lindor; Polly A Newcomb; Stephen N Thibodeau; Sapna Syngal
Journal:  Gut       Date:  2012-02-16       Impact factor: 23.059

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

Authors:  Omar Khan; Amie Blanco; Peggy Conrad; Cassandra Gulden; Tovah Z Moss; Olufunmilayo I Olopade; Sonia S Kupfer; Jonathan Terdiman
Journal:  Am J Gastroenterol       Date:  2011-07-12       Impact factor: 10.864

Review 5.  Inherited colorectal cancer syndromes.

Authors:  Fay Kastrinos; Sapna Syngal
Journal:  Cancer J       Date:  2011 Nov-Dec       Impact factor: 3.360

Review 6.  Prediction models in Lynch syndrome.

Authors:  Fay Kastrinos; Judith Balmaña; Sapna Syngal
Journal:  Fam Cancer       Date:  2013-06       Impact factor: 2.375

7.  Universal Versus Targeted Screening for Lynch Syndrome: Comparing Ascertainment and Costs Based on Clinical Experience.

Authors:  Mujde Z Erten; Luca P Fernandez; Hank K Ng; Wendy C McKinnon; Brandie Heald; Christopher J Koliba; Marc S Greenblatt
Journal:  Dig Dis Sci       Date:  2016-07-06       Impact factor: 3.199

8.  Comparison of Prediction Models for Lynch Syndrome Among Individuals With Colorectal Cancer.

Authors:  Fay Kastrinos; Rohit P Ojha; Celine Leenen; Carmelita Alvero; Rowena C Mercado; Judith Balmaña; Irene Valenzuela; Francesc Balaguer; Roger Green; Noralane M Lindor; Stephen N Thibodeau; Polly Newcomb; Aung Ko Win; Mark Jenkins; Daniel D Buchanan; Lucio Bertario; Paola Sala; Heather Hampel; Sapna Syngal; Ewout W Steyerberg
Journal:  J Natl Cancer Inst       Date:  2015-11-18       Impact factor: 13.506

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

Authors:  Rowena C Mercado; Heather Hampel; Fay Kastrinos; Ewout Steyerberg; Judith Balmana; Elena Stoffel; David E Cohn; Floor J Backes; John L Hopper; Mark A Jenkins; Noralane M Lindor; Graham Casey; Robert Haile; Subha Madhavan; Albert de la Chapelle; Sapna Syngal
Journal:  Genet Med       Date:  2012-03-08       Impact factor: 8.822

10.  Contribution of large genomic rearrangements in Italian Lynch syndrome patients: characterization of a novel alu-mediated deletion.

Authors:  Francesca Duraturo; Angela Cavallo; Raffaella Liccardo; Bianca Cudia; Marina De Rosa; Giuseppe Diana; Paola Izzo
Journal:  Biomed Res Int       Date:  2012-12-30       Impact factor: 3.411

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