Evaluating a 4-marker signature of XXXX using time-dependent AUC.

@entity1 @entity263 @entity66 @entity283 @entity5 @entity4910 @entity983 @entity1096 @entity1594 @entity3
BACKGROUND: We previously identified a @entity5 signature of @entity983 , @entity1594 , @entity1096 , and @entity4910 that, together with clinical features, was associated with lethal outcomes among @entity263 @entity1 . In the current study, we sought to validate the molecular model using time-dependent measures of AUC and predictive values for discriminating lethal from non-lethal @entity263 . METHODS: Using data from the initial study, we fit survival models for @entity1 with @entity263 who were @entity1 in the Physicians' Health Study (PHS; n   =   276). Based on these models, we generated prognostic risk scores in an independent population, the @entity283 ( @entity283 ; n   =   347) to evaluate external validity. In each cohort, @entity1 were followed prospectively from @entity5 diagnosis through 2011 for development of distant @entity3 or @entity5 mortality. We measured @entity5 expression of @entity983 , @entity1594 , @entity1096 , and @entity4910 on tissue microarrays. RESULTS: During a median of 11.9 and 14.3 years follow-up in the PHS and @entity283 cohorts, 24 and 32 @entity1 (9%) developed @entity66 . When used as a prognostic factor in a new population, addition of the four markers to clinical variables did not improve discriminatory accuracy through 15 years of follow-up. CONCLUSIONS: Although the four markers have been identified as key biological mediators in metastatic progression, they do not provide independent, long-term prognostic information beyond clinical factors when measured at diagnosis. This finding may underscore the broad heterogeneity in @entity263 and highlight the challenges that may result from overfitting in discovery-based research.
