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Annals of Oncology Advance Access originally published online on January 22, 2009
Annals of Oncology 2009 20(4):681-688; doi:10.1093/annonc/mdn686
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© The Author 2009. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org

gastrointestinal tumors

A comparison of models used to predict MLH1, MSH2 and MSH6 mutation carriers

C. J. Pouchet1,2,*, N. Wong1,3, G. Chong4, M. J. Sheehan5, G. Schneider2, B. Rosen-Sheidley2, W. Foulkes1,6,7 and M. Tischkowitz8,9

1 Department of Medical Genetics, SMBD-Jewish General Hospital, Montreal, Canada
2 Genetic Counseling Program, Brandeis University, Waltham, USA
3 Department of Human Genetics, McGill University, Montreal, Canada
4 Molecular Pathology, SMBD-Jewish General Hospital, Montreal, Canada
5 Department of Psychology, Brandeis University, Waltham, USA
6 Departments of Medicine, Human Genetics and Oncology, McGill University
7 Department of Medical Genetics, McGill University Health Centre
8 Departments of Medicine, Human Genetics and Oncology, McGill University
9 Department of Medical Genetics, Oncology and Medicine, SMBD-Jewish General Hospital, Montreal, Canada

* Correspondence to: Ms. C. J. Pouchet MS, Cancer Prevention Centre, SMBD-Jewish General Hospital, E-758, 3755 ch. de la Côte Ste-Catherine, Montreal, Quebec H3T 1E2, Canada. Tel: +1-514-340-8222 ext 4315; Fax: +1-514-340-8600; E-mail: cpouchet{at}jgh.mcgill.ca

Background: MMRpro, prediction of mutations in MLH1 and MLH2 (PREMM1,2) and MMRpredict are models which were developed to predict the probability that an individual carries a Lynch syndrome-causing mutation. Each model utilizes data from personal and family histories of cancer. To date, no studies have compared these models in a cancer genetics clinic. The purpose of this study was to determine each model's ability to predict the probability of carrying a Lynch syndrome-causing mutation in individuals with a family history of colorectal cancer and to determine their clinical applicability.

Methods: We obtained family pedigrees from 81 individuals who presented for Lynch syndrome testing due to a personal and/or family history of cancer. Data from each pedigree were entered into the models and analyzed using SPSS.

Results: We found that MMRpredict, PREMM1,2 and MMRpro showed similar performances with areas under the receiver-operating characteristic curve of 0.731, 0.765 and 0.732, respectively. MMRpro showed the least dispersion of mutation probability estimates with a P value of 0.205, compared with 0.034 for PREMM1,2 and 0.001 for MMRpredict.

Conclusion: We found all three carried out well in a cancer genetics setting, with PREMM1,2 giving slightly better estimates. There were some significant discrepancies between the models in cases where the proband had endometrial cancer.

Key words: colorectal cancer, genetic testing, Lynch syndrome, mismatch repair, prediction models

Received for publication August 18, 2008. Revision received September 25, 2008. Accepted for publication September 26, 2008.


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