Pretreatment transcriptional profiling for predicting response to neoadjuvant chemoradiotherapy in XXXX .

@entity1 @entity41 @entity5 @entity3 @entity420
PURPOSE: @entity1 presenting with locally advanced @entity5 currently receive preoperative radiotherapy with or without chemotherapy. Although pathologic complete response is achieved for approximately 10% to 30% of @entity1 , a proportion of @entity1 derive no benefit from this therapy while being exposed to toxic side effects of treatment. Therefore, there is a strong need to identify @entity1 who are unlikely to benefit from neoadjuvant therapy to help direct them toward alternate and ultimately more successful treatment options. EXPERIMENTAL DESIGN: In this study, we obtained expression profiles from pretreatment biopsies for 51 @entity5 @entity1 . All @entity1 underwent preoperative chemoradiotherapy, followed by resection of the @entity5 6 to 8 weeks posttreatment. Gene expression and response to treatment were correlated, and a supervised learning algorithm was used to generate an original predictive classifier and validate previously published classifiers. RESULTS: Novel predictive classifiers based on Mandard's @entity5 regression grade, metabolic response, TNM ( @entity5 node @entity3 ) downstaging, and normal tissue expression profiles were generated. Because there were only 7 @entity1 who had minimal treatment response (>80% @entity41 ), expression profiles were used to predict @entity5 response and outcome. These classifiers peaked at 82% sensitivity and 89% specificity; however, classifiers with the highest sensitivity had poor specificity, and vice versa. Validation of predictive classifiers from previously published reports was attempted using this cohort; however, sensitivity and specificity ranged from 21% to 70%. CONCLUSIONS: These results show that the clinical utility of microarrays in predictive medicine is not yet within reach for @entity5 and alternatives to microarrays should be considered for predictive studies in @entity420 .
