title: MELPREDICT: a logistic regression model to estimate CDKN2A carrier probability.
abstract: BACKGROUND
  Heritable alterations in CDKN2A account for a subset of familial melanoma cases although no robust method exists to identify those at risk of being a mutation carrier.
  
  
  METHODS
  We set out to construct a model for estimating CDKN2A mutation carrier probability using a cohort of 116 consecutive familial cutaneous melanoma patients evaluated at Massachusetts General Hospital Pigmented Lesion Center between April 2001 and September 2004. Germline CDKN2A and CDK4 status on the familial melanoma cases and clinical features associated with mutational status were then used to build a multiple logistic regression model to predict carrier probability and performance of model on external validation.
  
  
  RESULTS
  From the 116 kindreds prone to melanoma in the Boston area, 13 CDKN2A mutation carriers were identified and 12 were subsequently used in the modeling. Proband age at diagnosis, number of proband primaries, and number of additional family primaries were most closely associated with germline mutations. The estimated probability of the proband being a mutation carrier based on the logistic regression model (MELPREDICT) is given by e(L)/(1 + e(L) where L = 1.99+[0.92x(no. of proband primaries)]+[0.74x(no. of additional family primaries)]-[2.11xln(age)]. The mean estimated probabilities for subjects in the Boston dataset were 55.4% and 5.1% for the mutation carriers and non-carriers respectively. In a receiver operator characteristic analysis, the area under the curve was 0.881 (95% confidence interval 0.739 to 1.000) for the Boston model set (n = 116) and 0.803 (0.729 to 0.877) for an external Toronto hereditary melanoma cohort (n = 143).
  
  
  CONCLUSIONS
  These results represent the first-iteration logistic regression model to approximate CDKN2A carrier probability. Validation of this model with an external dataset revealed relatively robust performance.
authors.affiliation: Center for Cancer Risk Analysis, Massachusetts General Hospital, Boston, MA 02114, USA.
authors.forename: K B
authors.initials: KB
authors.lastname: Niendorf
authors.forename: W
authors.initials: W
authors.lastname: Goggins
authors.forename: G
authors.initials: G
authors.lastname: Yang
authors.forename: K Y
authors.initials: KY
authors.lastname: Tsai
authors.forename: M
authors.initials: M
authors.lastname: Shennan
authors.forename: D W
authors.initials: DW
authors.lastname: Bell
authors.forename: A J
authors.initials: AJ
authors.lastname: Sober
authors.forename: D
authors.initials: D
authors.lastname: Hogg
authors.forename: H
authors.initials: H
authors.lastname: Tsao
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001900:Boston; D002648:Child; D015331:Cohort Studies; D019295:Computational Biology; D004252:DNA Mutational Analysis / Q000379:methods*; D005260:Female; D019942:Genes, p16; D006580:Genetic Carrier Screening / Q000379:methods*; D005838:Genotype; D018095:Germ-Line Mutation; D006801:Humans; D016015:Logistic Models; D008297:Male; D008545:Melanoma / Q000175:diagnosis* / Q000235:genetics; D008875:Middle Aged; D009864:Ontario; D018570:Risk Assessment