Comparison of Prediction Models for Lynch Syndrome among Individuals with Colorectal Cancer

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

Research output: Contribution to journalArticle

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Abstract

Background: Recent guidelines recommend the Lynch Syndrome prediction models MMRPredict, MMRPro, and PREMM1,2,6 for the identification of MMR gene mutation carriers. We compared the predictive performance and clinical usefulness of these prediction models to identify mutation carriers. Methods: Pedigree data from CRC patients in 11 North American, European, and Australian cohorts (6 clinic- and 5 population-based sites) were used to calculate predicted probabilities of pathogenic MLH1, MSH2, or MSH6 gene mutations by each model and gene-specific predictions by MMRPro and PREMM1,2,6. We examined discrimination with area under the receiver operating characteristic curve (AUC), calibration with observed to expected (O/E) ratio, and clinical usefulness using decision curve analysis to select patients for further evaluation. All statistical tests were two-sided. Results: Mutations were detected in 539 of 2304 (23%) individuals from the clinic-based cohorts (237 MLH1, 251 MSH2, 51 MSH6) and 150 of 3451 (4.4%) individuals from the population-based cohorts (47 MLH1, 71 MSH2, 32 MSH6). Discrimination was similar for clinic- and population-based cohorts: AUCs of 0.76 vs 0.77 for MMRPredict, 0.82 vs 0.85 for MMRPro, and 0.85 vs 0.88 for PREMM1,2,6. For clinic- and population-based cohorts, O/E deviated from 1 for MMRPredict (0.38 and 0.31, respectively) and MMRPro (0.62 and 0.36) but were more satisfactory for PREMM1,2,6 (1.0 and 0.70). MMRPro or PREMM1,2,6 predictions were clinically useful at thresholds of 5% or greater and in particular at greater than 15%. Conclusions: MMRPro and PREMM1,2,6 can well be used to select CRC patients from genetics clinics or population-based settings for tumor and/or germline testing at a 5% or higher risk. If no MMR deficiency is detected and risk exceeds 15%, we suggest considering additional genetic etiologies for the cause of cancer in the family.

Original languageEnglish
Article numberdjv308
JournalJournal of the National Cancer Institute
Volume108
Issue number2
DOIs
Publication statusPublished - Feb 1 2016

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Hereditary Nonpolyposis Colorectal Neoplasms
Colorectal Neoplasms
Mutation
Population
Area Under Curve
Genes
Decision Support Techniques
Pedigree
ROC Curve
Calibration
Neoplasms
Guidelines

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

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Kastrinos, F., Ojha, R. P., Leenen, C., Alvero, C., Mercado, R. C., Balmaña, J., ... Steyerberg, E. W. (2016). Comparison of Prediction Models for Lynch Syndrome among Individuals with Colorectal Cancer. Journal of the National Cancer Institute, 108(2), [djv308]. https://doi.org/10.1093/jnci/djv308

Comparison of Prediction Models for Lynch Syndrome among Individuals with Colorectal Cancer. / Kastrinos, Fay; Ojha, Rohit P.; Leenen, Celine; Alvero, Carmelita; Mercado, Rowena C.; Balmaña, Judith; Valenzuela, Irene; Balaguer, Francesc; Green, Roger; Lindor, Noralane M.; Thibodeau, Stephen N.; Newcomb, Polly; Win, Aung Ko; Jenkins, Mark; Buchanan, Daniel D.; Bertario, Lucio; Sala, Paola; Hampel, Heather; Syngal, Sapna; Steyerberg, Ewout W.

In: Journal of the National Cancer Institute, Vol. 108, No. 2, djv308, 01.02.2016.

Research output: Contribution to journalArticle

Kastrinos, F, Ojha, RP, Leenen, C, Alvero, C, Mercado, RC, Balmaña, J, Valenzuela, I, Balaguer, F, Green, R, Lindor, NM, Thibodeau, SN, Newcomb, P, Win, AK, Jenkins, M, Buchanan, DD, Bertario, L, Sala, P, Hampel, H, Syngal, S & Steyerberg, EW 2016, 'Comparison of Prediction Models for Lynch Syndrome among Individuals with Colorectal Cancer', Journal of the National Cancer Institute, vol. 108, no. 2, djv308. https://doi.org/10.1093/jnci/djv308
Kastrinos, Fay ; Ojha, Rohit P. ; Leenen, Celine ; Alvero, Carmelita ; Mercado, Rowena C. ; Balmaña, Judith ; Valenzuela, Irene ; Balaguer, Francesc ; Green, Roger ; Lindor, Noralane M. ; Thibodeau, Stephen N. ; Newcomb, Polly ; Win, Aung Ko ; Jenkins, Mark ; Buchanan, Daniel D. ; Bertario, Lucio ; Sala, Paola ; Hampel, Heather ; Syngal, Sapna ; Steyerberg, Ewout W. / Comparison of Prediction Models for Lynch Syndrome among Individuals with Colorectal Cancer. In: Journal of the National Cancer Institute. 2016 ; Vol. 108, No. 2.
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title = "Comparison of Prediction Models for Lynch Syndrome among Individuals with Colorectal Cancer",
abstract = "Background: Recent guidelines recommend the Lynch Syndrome prediction models MMRPredict, MMRPro, and PREMM1,2,6 for the identification of MMR gene mutation carriers. We compared the predictive performance and clinical usefulness of these prediction models to identify mutation carriers. Methods: Pedigree data from CRC patients in 11 North American, European, and Australian cohorts (6 clinic- and 5 population-based sites) were used to calculate predicted probabilities of pathogenic MLH1, MSH2, or MSH6 gene mutations by each model and gene-specific predictions by MMRPro and PREMM1,2,6. We examined discrimination with area under the receiver operating characteristic curve (AUC), calibration with observed to expected (O/E) ratio, and clinical usefulness using decision curve analysis to select patients for further evaluation. All statistical tests were two-sided. Results: Mutations were detected in 539 of 2304 (23{\%}) individuals from the clinic-based cohorts (237 MLH1, 251 MSH2, 51 MSH6) and 150 of 3451 (4.4{\%}) individuals from the population-based cohorts (47 MLH1, 71 MSH2, 32 MSH6). Discrimination was similar for clinic- and population-based cohorts: AUCs of 0.76 vs 0.77 for MMRPredict, 0.82 vs 0.85 for MMRPro, and 0.85 vs 0.88 for PREMM1,2,6. For clinic- and population-based cohorts, O/E deviated from 1 for MMRPredict (0.38 and 0.31, respectively) and MMRPro (0.62 and 0.36) but were more satisfactory for PREMM1,2,6 (1.0 and 0.70). MMRPro or PREMM1,2,6 predictions were clinically useful at thresholds of 5{\%} or greater and in particular at greater than 15{\%}. Conclusions: MMRPro and PREMM1,2,6 can well be used to select CRC patients from genetics clinics or population-based settings for tumor and/or germline testing at a 5{\%} or higher risk. If no MMR deficiency is detected and risk exceeds 15{\%}, we suggest considering additional genetic etiologies for the cause of cancer in the family.",
author = "Fay Kastrinos and Ojha, {Rohit P.} and Celine Leenen and Carmelita Alvero and Mercado, {Rowena C.} and Judith Balma{\~n}a and Irene Valenzuela and Francesc Balaguer and Roger Green and Lindor, {Noralane M.} and Thibodeau, {Stephen N.} and Polly Newcomb and Win, {Aung Ko} and Mark Jenkins and Buchanan, {Daniel D.} and Lucio Bertario and Paola Sala and Heather Hampel and Sapna Syngal and Steyerberg, {Ewout W.}",
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T1 - Comparison of Prediction Models for Lynch Syndrome among Individuals with Colorectal Cancer

AU - Kastrinos, Fay

AU - Ojha, Rohit P.

AU - Leenen, Celine

AU - Alvero, Carmelita

AU - Mercado, Rowena C.

AU - Balmaña, Judith

AU - Valenzuela, Irene

AU - Balaguer, Francesc

AU - Green, Roger

AU - Lindor, Noralane M.

AU - Thibodeau, Stephen N.

AU - Newcomb, Polly

AU - Win, Aung Ko

AU - Jenkins, Mark

AU - Buchanan, Daniel D.

AU - Bertario, Lucio

AU - Sala, Paola

AU - Hampel, Heather

AU - Syngal, Sapna

AU - Steyerberg, Ewout W.

PY - 2016/2/1

Y1 - 2016/2/1

N2 - Background: Recent guidelines recommend the Lynch Syndrome prediction models MMRPredict, MMRPro, and PREMM1,2,6 for the identification of MMR gene mutation carriers. We compared the predictive performance and clinical usefulness of these prediction models to identify mutation carriers. Methods: Pedigree data from CRC patients in 11 North American, European, and Australian cohorts (6 clinic- and 5 population-based sites) were used to calculate predicted probabilities of pathogenic MLH1, MSH2, or MSH6 gene mutations by each model and gene-specific predictions by MMRPro and PREMM1,2,6. We examined discrimination with area under the receiver operating characteristic curve (AUC), calibration with observed to expected (O/E) ratio, and clinical usefulness using decision curve analysis to select patients for further evaluation. All statistical tests were two-sided. Results: Mutations were detected in 539 of 2304 (23%) individuals from the clinic-based cohorts (237 MLH1, 251 MSH2, 51 MSH6) and 150 of 3451 (4.4%) individuals from the population-based cohorts (47 MLH1, 71 MSH2, 32 MSH6). Discrimination was similar for clinic- and population-based cohorts: AUCs of 0.76 vs 0.77 for MMRPredict, 0.82 vs 0.85 for MMRPro, and 0.85 vs 0.88 for PREMM1,2,6. For clinic- and population-based cohorts, O/E deviated from 1 for MMRPredict (0.38 and 0.31, respectively) and MMRPro (0.62 and 0.36) but were more satisfactory for PREMM1,2,6 (1.0 and 0.70). MMRPro or PREMM1,2,6 predictions were clinically useful at thresholds of 5% or greater and in particular at greater than 15%. Conclusions: MMRPro and PREMM1,2,6 can well be used to select CRC patients from genetics clinics or population-based settings for tumor and/or germline testing at a 5% or higher risk. If no MMR deficiency is detected and risk exceeds 15%, we suggest considering additional genetic etiologies for the cause of cancer in the family.

AB - Background: Recent guidelines recommend the Lynch Syndrome prediction models MMRPredict, MMRPro, and PREMM1,2,6 for the identification of MMR gene mutation carriers. We compared the predictive performance and clinical usefulness of these prediction models to identify mutation carriers. Methods: Pedigree data from CRC patients in 11 North American, European, and Australian cohorts (6 clinic- and 5 population-based sites) were used to calculate predicted probabilities of pathogenic MLH1, MSH2, or MSH6 gene mutations by each model and gene-specific predictions by MMRPro and PREMM1,2,6. We examined discrimination with area under the receiver operating characteristic curve (AUC), calibration with observed to expected (O/E) ratio, and clinical usefulness using decision curve analysis to select patients for further evaluation. All statistical tests were two-sided. Results: Mutations were detected in 539 of 2304 (23%) individuals from the clinic-based cohorts (237 MLH1, 251 MSH2, 51 MSH6) and 150 of 3451 (4.4%) individuals from the population-based cohorts (47 MLH1, 71 MSH2, 32 MSH6). Discrimination was similar for clinic- and population-based cohorts: AUCs of 0.76 vs 0.77 for MMRPredict, 0.82 vs 0.85 for MMRPro, and 0.85 vs 0.88 for PREMM1,2,6. For clinic- and population-based cohorts, O/E deviated from 1 for MMRPredict (0.38 and 0.31, respectively) and MMRPro (0.62 and 0.36) but were more satisfactory for PREMM1,2,6 (1.0 and 0.70). MMRPro or PREMM1,2,6 predictions were clinically useful at thresholds of 5% or greater and in particular at greater than 15%. Conclusions: MMRPro and PREMM1,2,6 can well be used to select CRC patients from genetics clinics or population-based settings for tumor and/or germline testing at a 5% or higher risk. If no MMR deficiency is detected and risk exceeds 15%, we suggest considering additional genetic etiologies for the cause of cancer in the family.

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