Gene expression analysis of pretreatment biopsies predicts the pathological response of esophageal squamous cell carcinomas to neo-chemoradiotherapy.

BACKGROUND

Neoadjuvant chemoradiotherapy (neo-CRT) followed by surgery has been shown to improve esophageal squamous cell carcinoma (ESCC) patients' survival compared with surgery alone. However, the outcomes of CRT are heterogeneous, and no clinical or pathological method can currently predict CRT response. In this study, we aim to identify mRNA markers useful for ESCC CRT-response prediction.

PATIENTS AND METHODS

Gene expression analyses were carried out on pretreated cancer biopsies from 28 ESCCs who received neo-CRT and surgery. Surgical specimens were assessed for pathological response to CRT. The differentially expressed genes identified by expression profiling were validated by real-time quantitative polymerase chain reaction (qPCR), and a classifying model was built from qPCR data using Fisher's linear discriminant analysis. The predictive power of this model was further assessed in a second set of 32 ESCCs.

RESULTS

The profiling of the 28 ESCCs identified 10 differentially expressed genes with more than a twofold change between patients with pathological complete response (pCR) and less than pCR (<pCR). A prediction model based on the qPCR values of three genes was generated, which provided a predictive accuracy of 86% upon leave-one-out cross-validation. Furthermore, the predictive power of this model was validated in another cohort of 32 ESCCs, among which a predictive accuracy of 81% was achieved. Importantly, the discriminant score was found to be the only independent factor that affected neo-CRT response in both the training (P = 0.015) and validation (P = 0.017) sets, respectively.

CONCLUSION

The expression levels of three genes determined by qPCR provide a possible model for ESCC CRT prediction, which will facilitate the individualization of ESCC treatment. Further prospective validation in larger independent cohorts is necessary to fully assess its predictive power.