Predicting recurrence and progression of noninvasive XXXX at initial presentation based on quantitative gene expression profiles.

@entity1 @entity16262 @entity7 @entity5 @entity23249 @entity189 @entity319 @entity150 @entity18923 @entity8498
BACKGROUND: Currently, @entity5 grade is the best predictor of outcome at first presentation of noninvasive @entity150 . However, reliable predictors of @entity5 recurrence and progression for individual @entity1 , which could optimize treatment and follow-up schedules based on specific @entity5 biology, are yet to be identified. OBJECTIVE: To identify genes predictive for recurrence and progression in Ta @entity150 at first presentation using a quantitative, pathway-specific approach. DESIGN, SETTING, AND @entity1 : Retrospective study of @entity1 with Ta G2/3 @entity150 at initial presentation with three distinct clinical outcomes: absence of recurrence (n=16), recurrence without progression (n=16), and progression to @entity189 in situ or invasive disease (n=16). MEASUREMENTS: Expressions of 24 genes that feature in relevant pathways that are deregulated in @entity150 were quantified by real-time polymerase chain reaction on @entity5 biopsies from the @entity1 at initial presentation. RESULTS AND LIMITATIONS: @entity18923 (p=0.003) and @entity8498 (p=0.01) were predictive for recurrence by univariate analysis. In a multivariable model based on @entity18923 expression, sensitivity and specificity for recurrence were 97% and 63%, respectively. @entity8498 (p<0.001), @entity16262 (p=0.017), @entity23249 /Survivin (p=0.038), and @entity319 (p=0.047) were predictive for progression by univariate analysis. Multivariable analysis based on @entity8498 , @entity319 , and @entity7 identified progression with 81% sensitivity and 94% specificity. Since this is a small retrospective study using medium-throughput profiling, larger confirmatory studies are needed. CONCLUSIONS: Gene expression profiling across relevant @entity5 pathways appears to be a promising approach for Ta @entity150 outcome prediction at initial diagnosis. These results could help differentiate between @entity1 who need aggressive versus expectant management.
