Transcriptomic Profiling Identifies a Risk Stratification Signature for Predicting Peritoneal Recurrence and Micrometastasis in Gastric Cancer

Abstract

Purpose: Gastric cancer peritoneal carcinomatosis is fatal. Delay in detection of peritoneal metastases contributes to high mortality, highlighting the need to develop biomarkers that can help identify patients at high risk for peritoneal recurrence or metastasis.

Experimental design: We performed a systematic discovery and validation for the identification of peritoneal recurrence prediction and peritoneal metastasis detection biomarkers by analyzing expression profiling datasets from 249 patients with gastric cancer, followed by analysis of 426 patients from three cohorts for clinical validation.

Results: Genome-wide expression profiling identified a 12-gene panel for robust prediction of peritoneal recurrence in patients with gastric cancer (AUC = 0.95), which was successfully validated in a second dataset (AUC = 0.86). Examination of 216 specimens from a training cohort allowed us to establish a six gene-based risk-prediction model [AUC = 0.72; 95% confidence interval (CI): 0.66-0.78], which was subsequently validated in an independent cohort of 111 patients with gastric cancer (AUC = 0.76; 95% CI: 0.67-0.83). In both cohorts, combining tumor morphology and depth of invasion further improved the predictive accuracy of the prediction model (AUC = 0.84). Thereafter, we evaluated the performance of the identical six-gene panel for its ability to detect peritoneal metastasis by analyzing 210 gastric cancer specimens (prior 111 patients plus additional 99 cases), which discriminated patients with and without peritoneal metastasis (AUC = 0.72). Finally, our biomarker panel was also remarkably effective for identifying peritoneal micrometastasis (AUC = 0.72), and its diagnostic accuracy was significantly enhanced when depth of invasion was included in the model (AUC = 0.85).

Conclusions: Our novel transcriptomic signature for risk stratification and identification of high-risk patients with peritoneal carcinomatosis might serve as an important clinical decision making in patients with gastric cancer.

Figure 1.

Performance of a 12-gene panel to predict peritoneal recurrence in a genomewide expression profiling dataset (GSE62254). (A) An ROC curve illustrating the performance of the gene panel, (B) A multivariate analysis, depicting that the gene panel is an independent prognostic factor for peritoneal recurrence free survival along with pathologic tumor stage (pStage), (C) Improvement in the performance of the biomarker panel to predict peritoneal recurrence when combined with pStage, and (D) The Kaplan-Meier curve showing the significant survival difference between two risk groups derived from the biomarker.

Figure 2.

Performance of the biomarker to predict peritoneal recurrence in clinical cohorts. In the training cohort, (A) a risk prediction model that included the 12-gene biomarker panel along with clinical factors (T stage and Borrmann type) was established; (B) The performance of a reduced 6-gene biomarker panel along with the same clinical variables. (C) A scatter plot illustrating the performance of the 6-gene biomarker to discriminate cases with peritoneal recurrence (PR) from non-recurrence group (NR). (D) The identical risk prediction formula derived from the training phase was validated in an independent cohort, and the ROC curves for the 6-gene biomarker and combined gene panel were produced. The biomarker consistently stratified the prognosis in (E) the training cohort and (F) the validation cohort in the Kaplan-Meier curves.

Figure 3.

The 6-gene biomarker’s performance to detect peritoneal metastasis in the peritoneal metastasis evaluation cohort. (A) A forest plot demonstrating that the gene panel was a significant predictor for peritoneal metastasis (PM) of gastric cancer along with depth of tumor invasion (T stage) in multivariate logistic regression. (B) the 6-gene biomarker discriminated patients with PM from those without metastasis. The combination of T stage improved the performance of the gene panel (arrows indicate grossly disseminated tumor deposits on peritoneum). (C) The biomarker successfully identified peritoneal micro-metastasis of gastric cancer detected by washing cytology as well (no visible or minimal cancer is seen). (D) A violin plot exhibited a significant difference in gene expression of ZBTB1.