Derivation of genetic biomarkers for cancer risk stratification in XXXX : a prospective cohort study.

@entity1 @entity355 @entity986 @entity517 @entity876 @entity1141 @entity420 @entity1722
OBJECTIVE: The risk of developing @entity420 in @entity355 is low and difficult to predict. Accurate tools for risk stratification are needed to increase the efficiency of surveillance. We aimed to develop a prediction model for progression using clinical variables and genetic markers. METHODS: In a prospective cohort of @entity1 with @entity355 , we evaluated six molecular markers: @entity986 , @entity876 , @entity1722 , 20q, @entity517 and aneusomy by DNA fluorescence in situ hybridisation on brush cytology specimens. Primary study outcomes were the development of high-grade @entity1141 or @entity420 . The most predictive clinical variables and markers were determined using Cox proportional-hazards models, receiver operating characteristic curves and a leave-one-out analysis. RESULTS: A total of 428 @entity1 participated (345 @entity1 ; median age 60   years) with a cumulative follow-up of 2019 @entity1 -years (median 45   months per @entity1 ). Of these @entity1 , 22 progressed; nine developed high-grade @entity1141 and 13 @entity420 . The clinical variables, age and circumferential Barrett's length, and the markers, @entity986 loss, @entity517 gain and aneusomy, were significantly associated with progression on univariate analysis. We defined an 'Abnormal Marker Count' that counted abnormalities in @entity986 , @entity517 and aneusomy, which significantly improved risk prediction beyond using just age and Barrett's length. In multivariate analysis, these three factors identified a high-risk group with an 8.7-fold (95% CI 2.6 to 29.8) increased HR when compared with the low-risk group, with an area under the curve of 0.76 (95% CI 0.66 to 0.86). CONCLUSIONS: A prediction model based on age, Barrett's length and the markers @entity986 , @entity517 and aneusomy determines progression risk in @entity355 .
