Gene expression profiles predict survival and progression of XXXX .

@entity1 @entity2615 @entity3620 @entity1274 @entity37622
PURPOSE: Clinical outcomes for @entity3620 ( @entity3620 ) @entity1 having surgery are imprecisely predicted by histopathology and intraoperative staging. We hypothesized that gene expression profiles could predict time to progression and survival in surgically cytoreduced @entity2615 of all stages. EXPERIMENTAL DESIGN: Gene expression analyses from 21 @entity3620 @entity1 having cytoreductions and identical postoperative adjuvant therapy were performed using the U95 Affymetrix gene @entity37622 . Using both @entity37622 and SAM, @entity1274 constructed a common 27 gene classifier, which was associated with either the high-risk and low-risk group of @entity1 . Data were validated using real-time PCR and immunohistochemical staining. The 27 gene classifier was also used for validation in a separate set of 17 @entity3620 @entity1 from another institution. RESULTS: The groups predicted by the gene classifier recapitulated the actual time to progression and survival of the test set with 95.2% accuracy using 10-fold cross-validation. Clinical outcomes were independent of histology, and heterogeneity of progression and survival in early stage @entity1 was defined by the classifier. The gene classifier had a 76% accuracy in the separate validation set of MPMs. CONCLUSIONS: These data suggest that pretherapy gene expression analysis of @entity2615 biopsies may predict which @entity1 may benefit from a surgical approach.
