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. 2020 Jan;10(1):353-362.
doi: 10.1002/ctm2.32.

Classification of gastric cancer by EBV status combined with molecular profiling predicts patient prognosis

Cai-Yun He  1   2 , Miao-Zhen Qiu  1   3 , Xin-Hua Yang  1   2 , Da-Lei Zhou  1   2 , Jiang-Jun Ma  1   2 , Ya-Kang Long  1   2 , Zu-Lu Ye  1   2 , Bo-Heng Xu  1   2 , Qi Zhao  1 , Ying Jin  1   3 , Shi-Xun Lu  1   4 , Zhi-Qiang Wang  1   3 , Wen-Long Guan  1   3 , Bai-Wei Zhao  1   5 , Zhi-Wei Zhou  1   5 , Jian-Yong Shao  1   2 , Rui-Hua Xu  1   3
Affiliations

Classification of gastric cancer by EBV status combined with molecular profiling predicts patient prognosis

Cai-Yun He et al. Clin Transl Med. 2020 Jan.

Abstract

Purpose: To identify how Epstein-Barr virus (EBV) status combined with molecular profiling predicts the prognosis of gastric cancer patients and their associated clinical actionable biomarkers.

Experimental design: A next-generation sequencing assay targeting 295 cancer-related genes was performed in 73 EBV-associated gastric cancer (EBVaGC) and 75 EBV-negative gastric cancer (EBVnGC) specimens and these results were compared with overall survival (OS).

Results: PIK3CA, ARID1A, SMAD4, and PIK3R1 mutated significantly more frequently in EBVaGC compared with their corresponding mutation rate in EBVnGC. As the most frequently mutated gene in EBVnGC (62.7%), TP53 also displayed a mutation rate of 15.1% in EBVaGC. PIK3R1 was revealed as a novel mutated gene (11.0%) associated almost exclusively with EBVaGC. PIK3CA, SMAD4, PIK3R1, and BCOR were revealed to be unique driver genes in EBVaGC. ARID1A displayed a significantly large proportion of inactivated variants in EBVaGC. A notable finding was that integrating the EBV status with tumor mutation burden (TMB) and large genomic instability (LGI) categorized the tumors into four distinct molecular subtypes and optimally predicted patient prognosis. The corresponding median OSs for the EBV+/TMB-high, EBV+/TMB-low, EBV-/LGI-, and EBV-/LGI+ subtypes were 96.2, 75.3, 44.4, and 20.2 months, respectively. The different subtypes were significantly segregated according to distinct mutational profiles and pathways.

Conclusions: Novel mutations in PIK3R1 and TP53 genes, driver genes such as PIK3CA, SMAD4, PIK3R1, BCOR, and ARID1A, and distinguished genomic profiles from EBVnGC were identified in EBVaGC tumors. The classification of gastric cancer by EBV, TMB, and LGI could be a good prognostic indicator, and provides distinguishing, targetable markers for treatment.

Keywords: EBV-associated gastric cancer; copy number variation; prognosis; tumor mutation burden.

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Figures

FIGURE 1
FIGURE 1
Driver genes determined by MutSig
FIGURE 2
FIGURE 2
The types and locations of the mutations in the TP53 and ARID1A genes. A, TP53 mutations in EBVaGC. B, TP53 mutations in EBVnGC. C, ARID1A mutations in EBVaGC. D, ARID1A mutations in EBVnGC
FIGURE 3
FIGURE 3
The four‐subtype classification system of gastric cancer by EBV status combined with TMB and LGI. A, Kaplan‐Meier survival curve for the four‐subtype classification system and overall survival. B, Venny plot for the four‐subtype classification system. C, Pathways in each subtype. D, Top 50 genes in each subtype
See this image and copyright information in PMC

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