Abstract 654: Discovery and validation of a gene expression signature for recurrence prediction in high-risk diffuse-type gastric cancer
Authors: Inseob Lee, Divya Sahu, Hoon Hur, Jeong-Hwan Yook, Byung-Sik Kim, Ajay Goel
Journal: Cancer Research (2021)

Abstract Background: Diffuse type gastric cancer (DGC), represented by low sensitivity to 5-fluorouracil based chemotherapy and poor prognosis, is a heterogenous malignancy in which patient subsets exhibit diverse oncological risk-profiles. The current risk stratification criteria based on the TNM staging system are inadequate in predicting the prognosis and in guiding optimal treatment in patients with DGC. Our study aimed to develop molecular biomarkers for prognostic risk-stratification in patients with DGC. Methods: We undertook a systematic and comprehensive discovery and validation effort to identify recurrence prediction biomarkers by analyzing genome-wide transcriptomic expression profiling data from 157 patients with DGC (GSE62254, GSE13861, and TCGA-STAD). Rigorous bioinformatic approaches were used to identify biomarker panels, followed by evaluation of their performance in two independent clinical cohorts (a training cohort, n=180; a validation cohort, n=74) using qRT-PCR assays. Furthermore, using Cox proportional hazard and Kaplan-Meier analyses, the clinical significance of the biomarkers was evaluated for their ability to predict recurrence individually, and when analyzed in combination with key prognostic factors. Results: Genome-wide transcriptomic profiling identified a 7-gene panel for robust prediction of recurrence in DGC patients (AUC=0.91; 95% CI: 0.83-0.96), which was successfully validated in an independent dataset (AUC=0.86; 95% CI: 0.66-0.96). Examination of 180 tissue specimens from a training cohort allowed us to establish a risk prediction model (AUC=0.78; 95% CI: 0.71-0.84), which was subsequently validated in an independent cohort of 74 GC patients (AUC=0.83; 95% CI: 0.72-0.90). The Kaplan-Meier analyses exhibited a consistently superior performance of our 7-gene risk-prediction model in the identification of high and low-risk patient subgroups. Interestingly, the performance of our gene panel to predict recurrence was significantly improved when combined with the tumor stage, in both clinical cohorts (AUC=0.83; 95% CI: 0.76-0.88 - training cohort; AUC=0.89; 95% CI: 0.80-0.95 - validation cohort). Finally, for an easier clinical translation, we established a nomogram that comprised of the 7-gene panel and the tumor stage, which robustly predicted prognosis in patients with DGC. Conclusions: We developed a novel gene expression-based risk-stratification model for predicting recurrence and the identification of high-risk patients, which could facilitate individualized treatments and improving the survival outcomes in patients suffering from DGC. Citation Format: Inseob Lee, Divya Sahu, Hoon Hur, Jeong-Hwan Yook, Byung-Sik Kim, Ajay Goel. Discovery and validation of a gene expression signature for recurrence prediction in high-risk diffuse-type gastric 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 654.