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Tumor copy number alteration (CNA) burden as a prognostic factor for overall survival in Chinese gastric cancers

ASCO 2019

作者:Bo Han, Dandan Ren, Beibei Mao, Xue Song, Wanning Yang, Henghui Zhang, Feng Gao




Background: Gastric cancer (GC) is associated with high morbidity and mortality rates in the world with poor prognosis and limited treatment options. The level of copy number alteration (CNA), termed CNA burden, is reported as a pan-cancer prognostic factor associated with recurrence and death. The current study aims to investigate association between CNA burden in primary tumor tissue and overall survival (OS) of Chinese patients with GC after surgical resection. 


Methods: The present study included 78 patients who had received surgical resection and adjuvant chemotherapy with poorly differentiated GC. The primary outcome was OS. Tumor specimens were obtained from surgery and submitted for next generation sequencing (NGS) with matched normal tissue samples. A 1408-gene panel was used to identify genome profiles. Data were analyzed using Cox proportional hazards models and Kaplan-Meier survival analysis. 


Results: The most frequently altered genes were TP53 (47%), PIK3CA (10%), PTEN (9%), NOTCH1 (8%) and RNF43 (6%), and copy numbers of TRPS1 (65%), COL1A2 (50%), CSMD3 (45%), ZFHX4 (45%), NAV3 (36%) varied most frequency in current cohort. Greater tumor CNA burden correlated with an increase in death from disease compared to a lower tumor CNA burden (p= 0.0066). In addition, there were statistically significant differences in OS between different clinical staging (p= 0.0011). Moreover, the Cox proportional hazard model showed that CNA burden was an independent prognosis factor in GC. Finally, we performed an independent signature that includes CNA burden and clinical staging to predict survival of GC. 


Conclusions: This study indicates that tumor CNA burden is an independent predictive survival biomarker for Chinese gastric cancers. CNA burden combined with clinical staging is a better predictor for postoperative survival prediction of gastric cancer.

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