Comprehensive Analysis of DNA Methylation and Prediction of Response to NeoadjuvantTherapy in Locally Advanced Rectal Cancer.

Abstract

Simple SummaryPatients with locally advanced rectal cancer have been treated with chemoradiotherapy followed by surgery, which results in variable therapy response. To date, there is a lack of established predictive biomarkers to distinguish responsive from non-responsive patients. Therefore, patients can be overtreated resulting in unnecessary toxicity, or therapy changes can occur later when the cancer is more aggressive. We used pre-treatment biopsies to evaluate changes in DNA methylation that could predict the chemoradiotherapy response. Data from an external dataset were used to confirm our findings. We identified and validated a classifier, composed of three candidates, that was able to distinguish responders from non-responders. The genomic context of our biomarkers was explored, giving evidence that they play a role in regulating gene expression. The biomarkers herein described can be easily evaluated in the clinical practice and can help to guide rectal cancer patients’ treatment by identifying responders and non-responders.The treatment for locally advanced rectal carcinomas (LARC) is based on neoadjuvant chemoradiotherapy (nCRT) and surgery, which results in pathological complete response (pCR) in up to 30% of patients. Since epigenetic changes may influence response to therapy, we aimed to identify DNA methylation markers predictive of pCR in LARC patients treated with nCRT. We used high-throughput DNA methylation analysis of 32 treatment-naïve LARC biopsies and five normal rectal tissues to explore the predictive value of differentially methylated (DM) CpGs. External validation was carried out with The Cancer Genome Atlas-Rectal Adenocarcinoma (TCGA-READ 99 cases). A classifier based on three-CpGs DM (linked to OBSL1, GPR1, and INSIG1 genes) was able to discriminate pCR from incomplete responders with high sensitivity and specificity. The methylation levels of the selected CpGs confirmed the predictive value of our classifier in 77 LARCs evaluated by bisulfite pyrosequencing. Evaluation of external datasets (TCGA-READ, GSE81006, GSE75546, and GSE39958) reproduced our results. As the three CpGs were mapped near to regulatory elements, we performed an integrative analysis in regions associated with predicted cis-regulatory elements. A positive and inverse correlation between DNA methylation and gene expression was found in two CpGs. We propose a novel predictive tool based on three CpGs potentially useful for pretreatment screening of LARC patients and guide the selection of treatment modality.