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

, ; Taylor, Nicholas J; Mitra, Nandita; Qian, Lu; Avril, Marie-Françoise; Bishop, D Timothy; Paillerets, Brigitte Bressac-de; Bruno, William; Calista, Donato and Cuellar, Francisco, et al. (2019) In Journal of the American Academy of Dermatology 81(2). p.386-394
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 melanoma families and whether performance improvements can be achieved.

METHODS: 2,116 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 (CI) along with net reclassification indices (NRI) as performance... (More)

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 melanoma families and whether performance improvements can be achieved.

METHODS: 2,116 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 (CI) along with net reclassification indices (NRI) 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<0.0001) 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 dataset 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 counselling these patients towards genetic testing or cancer risk counselling.

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Journal of the American Academy of Dermatology
volume
81
issue
2
pages
386 - 394
publisher
Elsevier
external identifiers
  • scopus:85068769924
ISSN
0190-9622
DOI
10.1016/j.jaad.2019.01.079
language
English
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yes
id
10c123b0-f236-4bff-a830-f8308a594272
date added to LUP
2019-02-27 12:06:05
date last changed
2019-08-14 04:33:03
@article{10c123b0-f236-4bff-a830-f8308a594272,
  abstract     = {<p>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 melanoma families and whether performance improvements can be achieved.</p><p>METHODS: 2,116 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 (CI) along with net reclassification indices (NRI) as performance metrics.</p><p>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&lt;0.0001) in GenoMELPREDICT (AUC=0.772; 95%CI: 0.750, 0.793; NRI=0.40). Including phenotypic risk factors did not improve performance.</p><p>CONCLUSION: The MELPREDICT model functioned well in a global dataset 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 counselling these patients towards genetic testing or cancer risk counselling.</p>},
  author       = {,  and Taylor, Nicholas J and Mitra, Nandita and Qian, Lu and Avril, Marie-Françoise and Bishop, D Timothy and Paillerets, Brigitte Bressac-de and Bruno, William and Calista, Donato and Cuellar, Francisco and Cust, Anne E and Demenais, Florence and Elder, David E and Gerdes, Anne-Marie and Ghiorzo, Paola and Goldstein, Alisa M and Grazziotin, Thais C and Gruis, Nelleke A and Hansson, Johan and Harland, Mark and Hayward, Nicholas K and Hocevar, Marko and Höiom, Veronica and Holland, Elizabeth A and Ingvar, Christian and Landi, Maria Teresa and Landman, Gilles and Larre-Borges, Alejandra and Mann, Graham J and Nagore, Eduardo and Olsson, Håkan and Palmer, Jane M and Perić, Barbara and Pjanova, Dace and Pritchard, Antonia L and Puig, Susana and Schmid, Helen 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},
  issn         = {0190-9622},
  language     = {eng},
  number       = {2},
  pages        = {386--394},
  publisher    = {Elsevier},
  series       = {Journal of the American Academy of Dermatology},
  title        = {Estimating CDKN2A mutation carrier probability among global familial melanoma cases using GenoMELPREDICT},
  url          = {http://dx.doi.org/10.1016/j.jaad.2019.01.079},
  volume       = {81},
  year         = {2019},
}