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Estimating CDKN2A mutation carrier probability among global familial melanoma cases using GenoMELPREDICT

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@article{20aba26c7bac499a89da6873be8ccf50,
title = "Estimating CDKN2A mutation carrier probability among global familial melanoma cases using GenoMELPREDICT",
abstract = "BACKGROUND: Although rare in the general population, highly penetrant germline mutations in CDKN2A are responsible for 5{\%}-40{\%} of melanoma cases reported in melanoma-prone families. We sought to determine whether MELPREDICT was generalizable to a global series of families with melanoma and whether performance improvements can be achieved.METHODS: In total, 2116 familial melanoma cases were ascertained by the international GenoMEL Consortium. We recapitulated the MELPREDICT model within our data (GenoMELPREDICT) to assess performance improvements by adding phenotypic risk factors and history of pancreatic cancer. We report areas under the curve (AUC) with 95{\%} confidence intervals (CIs) along with net reclassification indices (NRIs) as performance metrics.RESULTS: MELPREDICT performed well (AUC 0.752, 95{\%} CI 0.730-0.775), and GenoMELPREDICT performance was similar (AUC 0.748, 95{\%} CI 0.726-0.771). Adding a reported history of pancreatic cancer yielded discriminatory improvement (P < .0001) in GenoMELPREDICT (AUC 0.772, 95{\%} CI 0.750-0.793, NRI 0.40). Including phenotypic risk factors did not improve performance.CONCLUSION: The MELPREDICT model functioned well in a global data set of familial melanoma cases. Adding pancreatic cancer history improved model prediction. GenoMELPREDICT is a simple tool for predicting CDKN2A mutational status among melanoma patients from melanoma-prone families and can aid in directing these patients to receive genetic testing or cancer risk counseling.",
author = "Taylor, {Nicholas J} and Nandita Mitra and Lu Qian and Marie-Fran{\cc}oise Avril and Bishop, {D Timothy} and {Bressac-de Paillerets}, Brigitte and William Bruno and Donato Calista and Francisco Cuellar and Cust, {Anne E} and Florence Demenais and Elder, {David E} and Anne-Marie Gerdes and Paola Ghiorzo and Goldstein, {Alisa M} and Grazziotin, {Thais C} and Gruis, {Nelleke A} and Johan Hansson and Mark Harland and Hayward, {Nicholas K} and Marko Hocevar and Veronica H{\"o}iom and Holland, {Elizabeth A} and Christian Ingvar and Landi, {Maria Teresa} and Gilles Landman and Alejandra Larre-Borges and Mann, {Graham J} and Eduardo Nagore and H{\aa}kan Olsson and Palmer, {Jane M} and Barbara Perić and Dace Pjanova and Pritchard, {Antonia L} and Susana Puig and Helen Schmid and {van der Stoep}, Nienke and Tucker, {Margaret A} and Wadt, {Karin A W} and Yang, {Xiaohong R} and Newton-Bishop, {Julia A} and Kanetsky, {Peter A} and {GenoMEL Study Group}",
note = "Copyright {\circledC} 2019 American Academy of Dermatology, Inc. All rights reserved.",
year = "2019",
month = "8",
doi = "10.1016/j.jaad.2019.01.079",
language = "English",
volume = "81",
pages = "386--394",
journal = "American Academy of Dermatology. Journal",
issn = "0190-9622",
publisher = "Mosby, Inc",
number = "2",

}

RIS

TY - JOUR

T1 - Estimating CDKN2A mutation carrier probability among global familial melanoma cases using GenoMELPREDICT

AU - Taylor, Nicholas J

AU - Mitra, Nandita

AU - Qian, Lu

AU - Avril, Marie-Françoise

AU - Bishop, D Timothy

AU - Bressac-de Paillerets, Brigitte

AU - Bruno, William

AU - Calista, Donato

AU - Cuellar, Francisco

AU - Cust, Anne E

AU - Demenais, Florence

AU - Elder, David E

AU - Gerdes, Anne-Marie

AU - Ghiorzo, Paola

AU - Goldstein, Alisa M

AU - Grazziotin, Thais C

AU - Gruis, Nelleke A

AU - Hansson, Johan

AU - Harland, Mark

AU - Hayward, Nicholas K

AU - Hocevar, Marko

AU - Höiom, Veronica

AU - Holland, Elizabeth A

AU - Ingvar, Christian

AU - Landi, Maria Teresa

AU - Landman, Gilles

AU - Larre-Borges, Alejandra

AU - Mann, Graham J

AU - Nagore, Eduardo

AU - Olsson, Håkan

AU - Palmer, Jane M

AU - Perić, Barbara

AU - Pjanova, Dace

AU - Pritchard, Antonia L

AU - Puig, Susana

AU - Schmid, Helen

AU - van der Stoep, Nienke

AU - Tucker, Margaret A

AU - Wadt, Karin A W

AU - Yang, Xiaohong R

AU - Newton-Bishop, Julia A

AU - Kanetsky, Peter A

AU - GenoMEL Study Group

N1 - Copyright © 2019 American Academy of Dermatology, Inc. All rights reserved.

PY - 2019/8

Y1 - 2019/8

N2 - BACKGROUND: Although rare in the general population, highly penetrant germline mutations in CDKN2A are responsible for 5%-40% of melanoma cases reported in melanoma-prone families. We sought to determine whether MELPREDICT was generalizable to a global series of families with melanoma and whether performance improvements can be achieved.METHODS: In total, 2116 familial melanoma cases were ascertained by the international GenoMEL Consortium. We recapitulated the MELPREDICT model within our data (GenoMELPREDICT) to assess performance improvements by adding phenotypic risk factors and history of pancreatic cancer. We report areas under the curve (AUC) with 95% confidence intervals (CIs) along with net reclassification indices (NRIs) as performance metrics.RESULTS: MELPREDICT performed well (AUC 0.752, 95% CI 0.730-0.775), and GenoMELPREDICT performance was similar (AUC 0.748, 95% CI 0.726-0.771). Adding a reported history of pancreatic cancer yielded discriminatory improvement (P < .0001) in GenoMELPREDICT (AUC 0.772, 95% CI 0.750-0.793, NRI 0.40). Including phenotypic risk factors did not improve performance.CONCLUSION: The MELPREDICT model functioned well in a global data set of familial melanoma cases. Adding pancreatic cancer history improved model prediction. GenoMELPREDICT is a simple tool for predicting CDKN2A mutational status among melanoma patients from melanoma-prone families and can aid in directing these patients to receive genetic testing or cancer risk counseling.

AB - BACKGROUND: Although rare in the general population, highly penetrant germline mutations in CDKN2A are responsible for 5%-40% of melanoma cases reported in melanoma-prone families. We sought to determine whether MELPREDICT was generalizable to a global series of families with melanoma and whether performance improvements can be achieved.METHODS: In total, 2116 familial melanoma cases were ascertained by the international GenoMEL Consortium. We recapitulated the MELPREDICT model within our data (GenoMELPREDICT) to assess performance improvements by adding phenotypic risk factors and history of pancreatic cancer. We report areas under the curve (AUC) with 95% confidence intervals (CIs) along with net reclassification indices (NRIs) as performance metrics.RESULTS: MELPREDICT performed well (AUC 0.752, 95% CI 0.730-0.775), and GenoMELPREDICT performance was similar (AUC 0.748, 95% CI 0.726-0.771). Adding a reported history of pancreatic cancer yielded discriminatory improvement (P < .0001) in GenoMELPREDICT (AUC 0.772, 95% CI 0.750-0.793, NRI 0.40). Including phenotypic risk factors did not improve performance.CONCLUSION: The MELPREDICT model functioned well in a global data set of familial melanoma cases. Adding pancreatic cancer history improved model prediction. GenoMELPREDICT is a simple tool for predicting CDKN2A mutational status among melanoma patients from melanoma-prone families and can aid in directing these patients to receive genetic testing or cancer risk counseling.

U2 - 10.1016/j.jaad.2019.01.079

DO - 10.1016/j.jaad.2019.01.079

M3 - Journal article

VL - 81

SP - 386

EP - 394

JO - American Academy of Dermatology. Journal

JF - American Academy of Dermatology. Journal

SN - 0190-9622

IS - 2

ER -

ID: 58278756