Abstract 2413: Genomewide expression profiling identifies a novel gene-expression signature for recurrence prediction in patients with early-onset colorectal cancer (EOCRC)
Authors: Kensuke Yamamura, Lina Zhu, Raju Kandimalla, Takatoshi Matsuyama, Yusuke Kinugasa, Francesc Balaguer, Hideo Baba, Xin Wang, Ajay Goel
Journal: Cancer Research (2019)

Abstract Purpose: Although the incidence of CRC has declined over the last decade, recent evidence indicates that the incidence of early onset CRC (EOCRC; patients younger than age 50) is actually on the rise. The disease presentation in patients with EOCRC is quite distinct, with often the disease diagnosis at more advanced stages, poorer histological grade, and higher local recurrence rates, compared to late onset CRC (LOCRC). Hence, availability of biomarkers that can predict recurrence in high-risk EOCRC patients is much needed as it will lead to improved prognosis, and tailoring personalized treatment regimens in this population. Herein, we have conducted a genomewide biomarker discovery approach, followed by robust bioinformatic analysis to establish a novel gene-based recurrence prediction classifier in patients with EOCRC. Methods: We undertook a comprehensive and systematic biomarker discovery effort by first analyzing six independent, genomewide expression datasets (GSE14333, GSE17538, GSE3311, GSE37892, GSE39084 and GSE39582) of EOCRC patients to identify (discovery dataset; n=81) and validate (validation dataset; n=75) a gene signature for predicting recurrence in EOCRC. Subsequently, the robustness of this signature was analytically evaluated in an in-house clinical training cohort (n=54). Using recursive feature elimination approach based on random forest classification, the signature was further refined and the gene signature was validated in another independent clinical cohort (n=54). Results: We initially identified a 25-gene signature from a total of 3591 mRNAs, which showed significant potential for predicting recurrence free survival (RFS) in both the in-silico discovery (HR, 32.83, 95% confidential interval (CI), 10.73 - 100.51, P&amp;lt;0.001) and validation datasets in the discovery phase (HR, 4.11, 95% CI, 1.77 - 9.56, P &amp;lt;0.001). Subsequently, based on our clinical training cohort, we developed a reduced and optimized 5-gene signature, which demonstrated robust prognostic power in both the clinical training cohort (HR, 3.67; 95% CI, 1.48-9.52; P=0.003) and an independent validation cohort (HR, 7.53; 95% CI, 1.73-51.3; P=0.007). In univariate and multivariate cox regression analysis, our 5-gene signature emerged as an independent predictor of worse RFS. Furthermore, a combination signature comprising of lymph node metastasis and our 5-gene signature demonstrated an even superior predictive performance compared to individual factors in both cohorts (training cohort: HR 4.56; 95% CI, 0.65-0.88; validation cohort: HR 8.25, 0.86; 95% CI, 0.74-0.94). Conclusion: Our novel 5-gene signature provides a robust prognostic tool for patients with EOCRC; which can be clinically transformative in guiding post-surgical decision-making to improve survival and management of young patients suffering from this malignancy. Citation Format: Kensuke Yamamura, Lina Zhu, Raju Kandimalla, Takatoshi Matsuyama, Yusuke Kinugasa, Francesc Balaguer, Hideo Baba, Xin Wang, Ajay Goel. Genomewide expression profiling identifies a novel gene-expression signature for recurrence prediction in patients with early-onset colorectal cancer (EOCRC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2413.