Genomic alterations in advanced gastric cancer endoscopic biopsy samples using targeted next-generation sequencing

Abstract

Gastric cancer (GC) remains the second tumor caused death threat worldwide, and personalized medicine for GC is far from expectation. Finding novel, recurrently mutated genes through next-generation sequencing (NGS) is a powerful and productive approach. However, previous genomic data for GC are based on surgical resected samples while a large proportion of advanced gastric cancer (AGC) patients have already missed the chance for operation. The aim of this study is to assess frequent genomic alteration in AGC via biopsy samples. Here we performed targeted genomic sequencing of 78 AGC patients' tumor biopsies along with matched lymphocyte samples based on a 118 cancer related gene panel. In total, we observed 301 somatic nonsynonymous genomic alterations in 92 different genes, as well as 37 copy number gain events among 15 different genes (fold change 2-12), and validated the fold changes of ERBB2 copy number gains with IHC and FISH test showed an accuracy of 81.8%. Previously reported driver genes for gastric cancer (TP53, KMT2D, KMT2B, EGFR, PIK3CA, GNAQ, and ARID1A), and several unreported mutations (TGFBR2, RNF213, NF1, NSD1, and LRP2) showed high non-silent mutation prevalence (7.7%-34.6%). When comparing intestinal-type gastric cancer (IGC) with diffuse-type gastric cancer (DGC), TP53 and GNAQ appear to be more frequently mutated in IGC (P=0.028 and P=0.023, respectively), whereas LRP2, BRCA2 and FGFR3 mutations are not observed in IGC, but have 12.8%, 7.7% and 7.7% mutation rates, respectively, in DGC patients. Patients with one or more mutations in adherens junction pathway (CREBBP, EP300, CDH1, CTNNB1, EGFR, MET, TGFBR2 and ERBB2) or TGF-β signaling pathway (CREBBP, EP300, MYST4, KRAS and TGFBR2) showed significantly better overall survival (P=0.007 and P=0.014, respectively), consistent with The Cancer Genome Atlas (TCGA) cohort data. Importantly, 57 (73.1%) patients harbored at least one genomic alteration with potential treatments, making NGS-based drug target screening a viable option for AGC patients. Our study established a comprehensive genomic portrait of AGC, and identified several mutation signatures highly associated with clinical features, survival outcomes, which may be used to design future personalized treatments.

Keywords: Gastric cancer; next-generation sequencing; prognosis; targetable alterations.

Figure 1

Summary of the somatic mutations and Indels of all 78 patients. Tumors are divided into three groups: intestinal-type gastric cancer, diffuse-type gastric cancer and mixed-type gastric cancer and arranged from left to right by the number of non-silent mutations per sample in each group, shown in the top track. Clinical features are shown in the middle panel, primary tumor location are labeled as Up (upper 1/3 of stomach), Mid (middle 1/3 of stomach) and Low (lower 1/3 of stomach). Bottom panel shows the summary of somatic mutation of 38 significant mutated genes. Right bar chart shows the prevalence of each mutation category in each gene. (WT: wild type; FS indel: frameshift indel; AA del: amino acid deletion; IF indel: in-frame indel; OE: overexpression; NA: not available).

Figure 2

Comparing hot spots between PUCH and TCGA cohorts of selected genes. Lollipop plots showing the type and location of mutations in TP53, GNAQ, LRP2, FGFR3, BRCA2 and TGFBR2. We compared our study (bottom plot; PUCH) with TCGA 286 gastric cancer patients (top plot; TCGA), percentage means mutation rate in each cohort.

Figure 3

Altered genes in adherens junction and TGF-β pathways (PUCH and TCGA cohort), and association with prognosis. A. Oncomaps and schematics of genetic alterations in adherens junction pathway and TGF-β pathway, the frequencies (%) in PUCH cohort (yellow) and TCGA cohort (blue) are shown. B. Patients with one or more mutations in both pathways showed significantly better overall survival in PUCH cohort, and the same trend in disease-free survival data in TCGA cohort.

Figure 4

Distribution of potential druggable alterations observed in this study, based on DGIdb.

Figure 5

IHC and FISH validation of HER2 expression in Sample S884. A. H&E stain result; B. Strong membrane immunostaining for the HER2 protein, IHC score 3+; C and D. FISH test showed HER2 overexpression; E. NGS method detected HER2 amplification.