Circulating miRNA Signature Predicts Response to Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer. (2021)

• Record Type: Journal Article
• Authors: Wada, Yuma; Shimada, Mitsuo; Morine, Yuji; Ikemoto, Tetsuya; Saito, Yu; Zhu, Zhongxu; Wang, Xin; Etxart, Ane; Park, Yangsoon; Bujanda, Luis; Park, In Ja; Goel, Ajay
• Is Part Of: JCO precision oncology. Volume 5(2021)
• Publication Date: 2021
• Subjects: Precision Medicine; Neoplasms; Pharmacogenetics; Molecular Targeted Therapy; Personalized medicine; Oncology; Pharmacogenomics; Periodical; Periodicals; 616.994
• DOI: 10.1200/PO.21.00015
• Languages: English
• ISSNs: 2473-4284

Abstract

Abstract : PURPOSE: Patients with locally advanced rectal cancer (LARC) are recommended to receive preoperative chemoradiotherapy (PCRT) followed by surgery. Response to PCRT varies widely: 60%-70% of patients with LARC do not derive therapeutic benefit from PCRT, whereas 15%-20% of patients achieve pathologic complete response (pCR). We sought to develop a liquid biopsy assay for identifying response to PCRT in patients with LARC. MATERIALS AND METHODS: We analyzed two genome-wide microRNA (miRNA) expression profiling data sets from tumor tissue samples for in silico discovery (GSE68204) and validation (GSE29298). We prioritized biomarkers in pretreatment plasma specimens from clinical training (n = 41; 15 responders and 26 nonresponders) and validation (n = 65; 29 responders and 36 nonresponders) cohorts of patients with LARC. We developed an integrated miRNA panel and established a risk assessment model, which was combined with the miRNA panel and carcinoembryonic antigen levels. RESULTS: Our comprehensive discovery effort identified an 8-miRNA panel that robustly predicted response to PCRT, with an excellent accuracy in the discovery (area under the curve [AUC] = 0.95) and validation (AUC = 0.92) cohorts. We successfully established a circulating miRNA panel with remarkable diagnostic accuracy in the clinical training (AUC = 0.82) and validation (AUC = 0.81) cohorts. Moreover, the predictive accuracy of the panel was significantly superior to conventional clinical factors in both cohorts ( P < .01) and the risk assessment model was superior (AUC = 0.83). Finally, we applied our model to detect patients with pathologic complete response and showed that it was dramatically superior to currently used pathologic features (AUC = 0.92). CONCLUSION: Our novel risk assessment signature for predicting response to PCRT has a potential for clinical translation as a liquid biopsy assay in patients with LARC.