title: Performance of PREMM(1,2,6), MMRpredict, and MMRpro in detecting Lynch syndrome among endometrial cancer cases.
abstract: OBJECTIVE
  Lynch syndrome accounts for 2-5% of endometrial cancer cases. Lynch syndrome prediction models have not been evaluated among endometrial cancer cases.
  
  
  METHODS
  Area under the receiver operating curve (AUC), sensitivity and specificity of PREMM(1,2,6), MMRpredict, and MMRpro scores were assessed among 563 population-based and 129 clinic-based endometrial cancer cases.
  
  
  RESULTS
  A total of 14 (3%) population-based and 80 (62%) clinic-based subjects had pathogenic mutations. PREMM(1,2,6), MMRpredict, and MMRpro were able to distinguish mutation carriers from noncarriers (AUC of 0.77, 0.76, and 0.77, respectively), among population-based cases. All three models had lower discrimination for the clinic-based cohort, with AUCs of 0.67, 0.64, and 0.54, respectively. Using a 5% cutoff, sensitivity and specificity were as follows: PREMM(1,2,6), 93% and 5% among population-based cases and 99% and 2% among clinic-based cases; MMRpredict, 71% and 64% for the population-based cohort and 91% and 0% for the clinic-based cohort; and MMRpro, 57% and 85% among population-based cases and 95% and 10% among clinic-based cases.
  
  
  CONCLUSIONS
  Currently available prediction models have limited clinical utility in determining which patients with endometrial cancer should undergo genetic testing for Lynch syndrome. Immunohistochemical analysis and microsatellite instability testing may be the best currently available tools to screen for Lynch syndrome in endometrial cancer patients.
authors.affiliation: Population Sciences Division, Dana-Farber Cancer Institute, Boston, MA, USA.
authors.forename: Rowena C
authors.initials: RC
authors.lastname: Mercado
authors.forename: Heather
authors.initials: H
authors.lastname: Hampel
authors.forename: Fay
authors.initials: F
authors.lastname: Kastrinos
authors.forename: Ewout
authors.initials: E
authors.lastname: Steyerberg
authors.forename: Judith
authors.initials: J
authors.lastname: Balmana
authors.forename: Elena
authors.initials: E
authors.lastname: Stoffel
authors.forename: David E
authors.initials: DE
authors.lastname: Cohn
authors.forename: Floor J
authors.initials: FJ
authors.lastname: Backes
authors.forename: John L
authors.initials: JL
authors.lastname: Hopper
authors.forename: Mark A
authors.initials: MA
authors.lastname: Jenkins
authors.forename: Noralane M
authors.initials: NM
authors.lastname: Lindor
authors.forename: Graham
authors.initials: G
authors.lastname: Casey
authors.forename: Robert
authors.initials: R
authors.lastname: Haile
authors.forename: Subha
authors.initials: S
authors.lastname: Madhavan
authors.forename: Albert
authors.initials: A
authors.lastname: de la Chapelle
authors.forename: Sapna
authors.initials: S
authors.lastname: Syngal
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D015331:Cohort Studies; D003123:Colorectal Neoplasms, Hereditary Nonpolyposis / Q000175:diagnosis* / Q000235:genetics*; D016889:Endometrial Neoplasms / Q000235:genetics*; D005260:Female; D005820:Genetic Testing; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D009154:Mutation; D012680:Sensitivity and Specificity