Validation of Gene Expression-Based Predictive Biomarkers for Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

Simple Summary There is a clinical need for predictive biomarkers that can identify patients with rectal cancer who do not respond to preoperative neoadjuvant chemoradiotherapy. In this study, we assembled multiple independent microarray datasets of biopsy specimens obtained from patients with rectal cancer before neoadjuvant treatment, including 237 non-responders and 152 responders. These datasets were utilized as the discovery cohorts or the validation cohorts, to develop and validate gene expression signatures predictive of treatment response. Using an in silico meta-analysis approach, here we tested not only our 4-gene signature built in this study but also nine different single-gene and multi-gene predictive signatures that were previously reported in the literature. Nevertheless, in the validation cohorts, none of the tested signatures were consistently differentially expressed between tumor specimens from non-responders and responders, and the meta-analyses revealed that those signatures had limited predictive values in clinical practice. Background: Neoadjuvant chemoradiotherapy (nCRT) followed by surgery is widely used for patients with locally advanced rectal cancer. However, response to nCRT varies substantially among patients, highlighting the need for predictive biomarkers that can distinguish non-responsive from responsive patients before nCRT. This study aimed to build novel multi-gene assays for predicting nCRT response, and to validate our signature and previously-reported signatures in multiple independent cohorts. Methods: Three microarray datasets of pre-therapeutic biopsies containing a total of 61 non-responders and 53 responders were used as the discovery cohorts to screen for genes that were consistently associated with nCRT response. The predictive values of signatures were tested in a meta-analysis using six independent datasets as the validation cohorts, consisted of a total of 176 non-responders and 99 responders. Results: We identified four genes, including BRCA1, GPR110, TNIK, and WDR4 in the discovery cohorts. Although our 4-gene signature and nine published signatures were evaluated, they were unable to predict nCRT response in the validation cohorts. Conclusions: Although this is one of the largest studies addressing the validity of gene expression-based classifiers using pre-treatment biopsies from patients with rectal cancer, our findings do not support their clinically meaningful values to be predictive of nCRT response.