Abstract 650: Transcriptomic profiling identifies an enhancer RNA signature for recurrence prediction in colorectal cancer

Abstract Background: Colorectal cancer (CRC) is one of the most fatal malignancies worldwide, and this is in part due to high rates of tumor recurrence in these patients. Currently, TNM staging remains the gold standard for predicting prognosis and recurrence in CRC patients; however, this approach is inadequate for the identification of high-risk patients who have the highest likelihood of disease recurrence. Recent evidence has revealed that enhancer RNAs (eRNAs) represent a higher level of cellular regulation and their expression is frequently dysregulated in several cancers including CRC. However, the clinical significance of eRNAs as recurrence predictor biomarkers in CRC remains unexplored; which was the primary aim of the present study. Methods: We performed a systematic analysis of eRNA expression profiles in a cohort of 379 colon cancer (CC) and 140 rectal cancer (RC) patients, from the TCGA dataset. By using rigorous biomarker discovery approaches, by splitting the entire dataset into a training and the testing cohort, we identified an eRNA signature for recurrence prediction in CRC. Kaplan-Meier analysis, multivariate and LASSO Cox regression models were used to evaluate the associations of discovered biomarkers with patient cancer recurrence. The performance of the prioritized biomarker panel was first trained and compared against key clinicopathological factors using receiver operating characteristics (ROC) curves analysis, followed by independent validation in the testing cohort. Results: The genome-wide eRNA profiling resulted in the identification of a 22-eRNA in patients with CC, and a 19-eRNA panel in RC. These biomarker panels performed robustly in stratifying low and high-risk CC (P= 7.29e-05) and RC (P = 6.81e-03) patients with recurrence. Multivariate Cox regression analysis demonstrated that both biomarker panels were independent predictors of recurrence, and significantly superior than TNM staging in CC (HR = 11.89, P = 9.54e-04) and RC (HR = 3.91, P = 3.52e-02). Importantly, ROC analysis revealed that both panels exhibited excellent recurrence prediction accuracy in CC (AUC = 0.83; 95% CI: 0.74-0.93) and RC (AUC = 0.81; 95% CI: 0.72-0.92) patients. Subsequently, a combination signature which included the eRNA panels along with TNM staging achieved an even greater predictive accuracy in patients with CC (AUC=0.85) and RC (AUC=0.83). Conclusions: Herein, we report a novel eRNA signature for predicting recurrence in patients with CRC. Pending validation in independent clinical cohorts, these biomarkers have the potential to improve current risk stratification approaches for guiding precision oncology treatments in patients suffering from this lethal malignancy. Citation Format: Divya Sahu, Ajay Goel. Transcriptomic profiling identifies an enhancer RNA signature for recurrence prediction in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 650.