A Multigene Model for Predicting Tumor Responsiveness After Preoperative Chemoradiotherapy for Rectal Cancer

Abstract

Although preoperative chemoradiotherapy (PCRT) is regarded as a standard treatment for locally advanced rectal cancer, there is no reliable biomarker for predicting responsiveness to PCRT. We aimed to develop a biomarker model for predicting response to PCRT. We included 184 patients who received PCRT followed by surgical resection and categorized them as good responders (complete or near-complete regression) or poor responders (all other patients). Candidate gene mRNAs were isolated from formalin-fixed paraffin-embedded tumor specimens and analyzed using the NanoString nCounter gene expression assay. Stepwise logistic regression analysis was used to select genes in discovery and training phases. A quantitative radio-responsiveness prediction model was developed and validated using internal cross-validation groups, and the model's predictive value was assessed based on the area under the receiver operating characteristic curve (AUC). By comparing the gene expressions between good and poor responders, we created a multigene mRNA model using FZD9, HRAS, ITGA7, MECOM, MMP3, NKD1, PIK3CD, and PRKCB. This panel showed good ability to predict treatment response (AUC: 0.846 for the whole data set). Internal cross-validation was performed to evaluate the model's predictive stability among 3 cohorts, which provided AUC values of 0.808-0.909. The satisfactory diagnostic performance of the radio-response prediction index persisted regardless of other clinicopathologic features such as clinical T or N stage, interval between radiation and surgery, and pretreatment carcinoembryonic antigen levels (P = .001, 95% CI, 0.686-0.905). We developed a multigene mRNA-based biomarker model that allows prediction of rectal cancer response to PCRT, which may help identify patients who will benefit most from PCRT.