Identification of microRNA Biomarkers of Response to Neoadjuvant Chemoradiotherapy in Esophageal Adenocarcinoma Using Next Generation Sequencing
- 264 Downloads
Clinical trials report improved overall survival following neoadjuvant chemoradiotherapy in patients undergoing surgery for esophageal adenocarcinoma, with a 10–15% survival improvement. MicroRNAs (miRNAs) are small noncoding RNAs that are known to direct the behavior of cancers, including response to treatment. We investigated the ability of miRNAs to predict outcomes after neoadjuvant chemoradiotherapy.
Endoscopic biopsies from esophageal adenocarcinomas were obtained before neoadjuvant chemoradiotherapy and esophagectomy. miRNA levels were measured in the biopsies using next generation sequencing and compared with pathological response in the surgical resection, and subsequent survival. miRNA ratios that predicted pathological response were identified by Lasso regression and leave-one-out cross-validation. Association between miRNA ratio candidates and relapse-free survival was assessed using Kaplan–Meier analysis. Cox regression and Harrell’s C analyses were performed to assess the predictive performance of the miRNAs.
Two miRNA ratios (miR-4521/miR-340-5p and miR-101-3p/miR-451a) that predicted the pathological response to neoadjuvant chemoradiotherapy were found to be associated with relapse-free survival. Pretreatment expression of these two miRNA ratios, pretreatment tumor differentiation, posttreatment AJCC histopathological tumor regression grading, and posttreatment tumor clearance/margins were significant factors associated with survival in Cox regression analysis. Multivariate analysis of the two ratios together with pretherapy factors resulted in a risk prediction accuracy of 85% (Harrell’s C), which was comparable with the prediction accuracy of the AJCC treatment response grading (77%).
miRNA-ratio biomarkers identified using next generation sequencing can be used to predict disease free survival following neoadjuvant chemoradiotherapy and esophagectomy in patients with esophageal adenocarcinoma.
The authors thank Peter Devitt for assistance with sample identification, and members of the ACRF Cancer Genomics Facility including Joel Geoghegan, David Lawrence, Andreas Schreiber, and Anna Tsykin. Funding for this study was from NHMRC Project Grant APP595964, and a project grant awarded by Tour de Cure Australia.
- 3.Donahue JM, Nichols FC, Li Z, et al. Complete pathologic response after neoadjuvant chemoradiotherapy for esophageal cancer is associated with enhanced survival. Ann Thorac Surg. 2009;87(2):392–8; discussion 398–9.Google Scholar
- 10.Sakai NS, Samia-Aly E, Barbera M, Fitzgerald RC. A review of the current understanding and clinical utility of miRNAs in esophageal cancer. Sem Cancer Biol. 2013;23(6 Pt B):512–21.Google Scholar
- 14.Lynam-Lennon N, Bibby BA, Mongan AM, et al. Low miR-187 expression promotes resistance to chemoradiation therapy in vitro and correlates with treatment failure in patients with esophageal adenocarcinoma. Mol Med. 2016;22.Google Scholar
- 15.Ko MA, Zehong G, Virtanen C, et al. MicroRNA expression profiling of esophageal cancer before and after induction chemoradiotherapy. Ann Thorac Surg. 2012;94(4):1094–102; discussion 1102–3.Google Scholar
- 16.Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17(6):1471–4.Google Scholar
- 17.Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics (Oxford, England). 2009;25(14):1754–60.Google Scholar
- 24.Li Z, Sillanpaa MJ. Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection. TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik. 2012;125(3):419–35.Google Scholar
- 27.Blum Murphy M, Xiao L, Patel VR, et al. Pathological complete response in patients with esophageal cancer after the trimodality approach: the association with baseline variables and survival-The University of Texas MD Anderson Cancer Center experience. Cancer. 2017;123(21):4106–13.CrossRefPubMedGoogle Scholar
- 40.Ogutu JO, Schulz-Streeck T, Piepho HP. Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proc. 2012;6 Suppl 2:S10.Google Scholar
- 41.Pritchard CC, Kroh E, Wood B, et al. Blood cell origin of circulating microRNAs: a cautionary note for cancer biomarker studies. Cancer Prev Res (Philadelphia, Pa.). 2012;5(3):492–97.Google Scholar