Comprehensive evaluation of the effectiveness of gene expression signatures to predict complete response to neoadjuvant chemoradiotherapy and guide surgical intervention in XXXX .

@entity5 @entity1
UNASSIGNED: Neoadjuvant chemoradiotherapy (nCRT) may lead to complete @entity5 regression in @entity5 @entity1 . Prediction of complete response to nCRT may allow a personalized management of @entity5 and spare @entity1 from unnecessary radical total mesorectal excision with or without sphincter preservation. To identify a gene expression signature capable of predicting complete pathological response (pCR) to nCRT, we performed a gene expression analysis in 25 pretreatment biopsies from @entity1 who underwent 5FU-based nCRT using RNA-Seq. A supervised learning algorithm was used to identify expression signatures capable of predicting pCR, and the predictive value of these signatures was validated using independent samples. We also evaluated the utility of previously published signatures in predicting complete response in our cohort. We identified 27 differentially expressed genes between @entity1 with pCR and @entity1 with incomplete responses to nCRT. Predictive gene signatures using subsets of these 27 differentially expressed genes peaked at 81.8% accuracy. However, signatures with the highest sensitivity showed poor specificity, and vice-versa, when applied in an independent set of @entity1 . Testing previously published signatures on our cohort also showed poor predictive value. Our results indicate that currently available predictive signatures are highly dependent on the sample set from which they are derived, and their accuracy is not superior to current imaging and clinical parameters used to assess response to nCRT and guide surgical intervention.
